WO2017143152A1

WO2017143152A1 - Nasal biomarkers of asthma

Info

Publication number: WO2017143152A1
Application number: PCT/US2017/018318
Authority: WO
Inventors: Supinda BUNYAVANICH; Gaurav Pandey; Eric S. SCHADT
Original assignee: Icahn School Of Medicine At Mount Sinai
Priority date: 2016-02-17
Filing date: 2017-02-17
Publication date: 2017-08-24
Also published as: CA3017582A1; EP3417079A1; US20200216900A1; EP3417079A4

Abstract

Asthma is a common, under-diagnosed disease affecting all ages. Mild to moderate asthma is particularly difficult to diagnose given currently available tools. A nasal biomarker of asthma is of high interest given the accessibility of the nose and shared airway biology between the upper and lower respiratory tract. A machine learning pipeline identified an asthma gene panel of 275 unique nasally-expressed genes interpreted via different classification models. This asthma gene panel can be utilized to reliably diagnose asthma in patients, including mild to moderate asthma, in a non-invasive manner and to distinguish asthma from other respiratory disorders, allowing appropriate treatment of the patient's asthma.

Description

NASAL BIOMARKERS OF ASTHMA

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Nos. 62/296,291, filed on 17 February 2016 and 62/296,915, filed on 18 February 2016, the disclosures of each of which are herein incorporated by reference in their entirety.

GOVERNMENT SPONSORSHIP

This invention was made with government support under Grant Nos. R01GM114434, K08AI093538 and R01 All 18833, all awarded by the National Institutes of Health (NIH). The government has certain rights in the invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the present invention relate generally to methods for diagnosis and monitoring of asthma, including but not limited to mild to moderate asthma, and its differentiation from other respiratory disorders by determining the expression profiles of asthma- specific genes in nasal brushing samples.

2. Background

Asthma is a chronic respiratory disease that affects 8.6% of children and 7.4% of adults in the United States¹. The true prevalence of asthma may be higher than these estimates. In one study of US middle school children, 11% reported physician-diagnosed asthma with current symptoms, while an additional 17% reported active asthma-like symptoms without a diagnosis of asthma². Undiagnosed asthma leads to missed school and work, restricted activity, emergency department visits, and hospitalizations²' ³. Mild to moderate asthma in particular can be difficult to diagnose, as it intrinsically involves fluctuating symptoms and signs⁴. The airflow obstruction, bronchial hyper-responsiveness and airway inflammation that characterize asthma are challenging to assess routinely and easily⁴. Given the high prevalence of asthma, there is high potential impact of improved diagnostic tools on reducing morbidity and mortality from asthma. Biomarkers could improve the identification of mild/moderate asthma so that appropriate management can be pursued.

National and international guidelines recommend that the diagnosis of asthma should be based on a history of typical symptoms and objective findings of variable expiratory airflow limitation⁶'⁷. However, obtaining such objective findings is challenging given currently available tools. Pulmonary function tests (PFTs) require equipment, expertise, and experience to execute well⁸' ⁹. Many individuals have difficulty with PFTs (e.g., spirometry) because they require coordinated breaths into a device. Results are unreliable if the procedure is done with poor technique⁸. Large epidemiologic studies of both children and adults substantiate that despite guidelines recommending objective tests such as PFTs to assess possible asthma, PFTs are not done in over half of patients suspected of having asthma⁸. Induced sputum and exhaled nitric oxide have been explored as asthma biomarkers, but their implementation requires technical expertise and does not yield better clinical results than physician-guided management alone¹⁰. Given the above, the reality is that most asthma is still clinically diagnosed and managed in children and adults based on self-report⁸' ⁹. This is suboptimal for mild/moderate asthma given its waxing/waning nature, and because self-reported symptoms and medicationuse are biased¹¹. There is need to improve asthma diagnosis, and an accurate biomarker of mild/moderate asthma could help meet that need. The ideal biomarker of mild/moderate asthma would be (1) obtainable noninvasively, (2) obtainable quickly, (3) interpretable without substantial expertise or infrastructure.

A nasal biomarker of asthma is of high interest given the accessibility of the nose and shared airway biology between the upper and lower respiratory tracts¹²' ¹³' ¹⁴' ¹⁵. The easily accessible nasal passages are directly connected to the lungs and exposed to common environmental and microbial factors. An accurate nasal biomarker of asthma that could be quickly obtained by a simple nasal brush could improve asthma diagnosis in adult and pediatric populations.

An asthma-specific gene panel has high potential to be used as a non-invasive biomarker to aid in asthma diagnosis, as it can be quickly obtained by simple nasal brush, does not require machinery for collection, and is easily interpreted. As discussed herein, objective findings of asthma are often not obtainable. Patients with mild/moderate asthma may not be asymptomatic at the time of the clinical encounter, so they may have no detectable wheezing or cough on exam. In many cases, then, a clinician may diagnose asthma on the basis of history alone, and this contributes to the under-diagnosis and misclassification of asthma. Studies have shown that patients with active asthma under-perceive their symptoms and do not tell their primary care physician. An objective diagnostic tool that is easy and quick to obtain and interpret with minimal effort required by the provider and patient could improve asthma diagnosis so that appropriate management can be pursued. A nasal brush-based asthma gene panel meets these biomarker criteria and capitalizes on the common biology of the upper and lower airway, a concept supported by clinical practice and previous findings.

In finding nasal biomarkers of mild/moderate asthma (Figure 1), the inventors used next- generation RNA sequencing and data analysis to comprehensively profile nasal epithelial gene expression from nasal brushings collected from a well-characterized cohort of subjects with mild/moderate asthma and non-asthmatic controls. These technologies have contributed to advances in several areas of biomedicine, such as disease biomarker identification¹⁶, personalized medicine and treatment¹⁷. Specifically, the inventors used RNA sequencing to comprehensively profile gene expression from nasal brushings collected from subjects with mild to moderate asthma and controls. Using a robust machine learning-based pipeline comprised of

18 19 20 feature selection , classification and statistical analyses of performance , the inventors identified a gene panel with 275 unique genes, and subsets specific for different classification analyses, that can accurately differentiate subjects with and without mild-moderate asthma. This asthma gene panel was validated on eight test sets of independent subjects with asthma and other respiratory conditions, finding that it performed with high accuracy, sensitivity, and specificity.. As used herein, the term "asthma gene panel" refers to these 275 genes collectively (see Table 4 for the list of genes and subsets). A subset of the asthma gene panel, the LR-RFE & Logistic asthma gene panel, was tested on three additional, independent cohorts of asthmatics and controls, and this panel consistently performed with accuracy. Further testing of the LR-RFE & Logistic asthma gene panel on five cohorts with non-asthma respiratory diseases validated the specificity of this nasal biomarker panel to asthma. The asthma gene panel currently identified through machine learning can be applied as a nasal brush-based biomarker tool for the clinical diagnosis of asthma, including mild/moderate asthma, and for distinguishing asthma from other respiratory disorders. Both diagnosis and differentiation with the invented methods enable the accurate diagnosis and treatment of asthma, including mild to moderate asthma, in the patient.

What is needed, therefore, is a noninvasive, quick and simple method for reliably diagnosing and/or classifying asthma, including but not limited to mild to moderate asthma, as well as distinguishing asthma from other respiratory disorders, and subsequently treating the patient appropriately. It is to such a method that embodiments of the present invention are primarily directed.

BRIEF SUMMARY OF THE INVENTION

As specified in the Background Section, there is a great need in the art to identify technologies for reliable, consistent, simple and non-invasive diagnosis of asthma, including but not limited to mild to moderate asthma, and use this understanding to develop novel diagnostic methods. The present invention satisfies this and other needs. Embodiments of the present invention relate generally to methods for diagnosis, classification and monitoring of asthma, including but not limited to mild to moderate asthma, and its differentiation from other respiratory disorders by determining the expression profiles of asthma-specific genes in nasal swab/ scraping/brushing/wash/ sponge samples .

In one aspect, the present invention provides a method for diagnosing asthma in a subject, comprising the steps of:

a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject;

b) performing classification analysis on the gene counts obtained from the gene expression profile(s);

c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and

d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.

In another aspect, the present invention provides a method for detection of asthma in a subject, comprising the steps of:

c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.

In one aspect, the present invention provides a method for differentially diagnosing asthma from other respiratory disorders in a subject, comprising the steps of:

In one aspect, the present invention provides a method for classifying a subject as having asthma or not having asthma, comprising the steps of:

In another aspect, the present invention provides a method for monitoring asthma in a subject, comprising the steps of:

b) performing classification analysis on the gene counts obtained from the gene expression profile(s); c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and

In one aspect, the present invention provides a method for selecting a subject for a clinical trial for asthma therapeutic compositions and/or methods, comprising the steps of:

In one aspect, the present invention provides a method for treating asthma in a subject, comprising the steps of:

c) comparing the probability output obtained from the classification analysis to the optimal classification threshold;

d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold; and

e) utilizing appropriate therapeutic compositions and/or methods if the subject has asthma.

In one aspect, the present invention provides a kit for diagnosing and/or detecting asthma in a subject, said kit comprising probes directed towards one or more of the genes in the asthma gene panel, as described in more detail herein, wherein the probes can be used to determine the expression levels of one or more of the genes in the asthma gene panel. The kit can also comprise (i) a detection means and/or (ii) an amplification means. The kit may further optionally include control probe sets for detection of control RNA in order to provide a control level as described herein.

In another aspect, the present invention provides a kit for diagnosing and/or detecting asthma in a subject, said kit comprising pairs of oligonucleotides directed towards one or more of the genes in the asthma gene panel, as described in more detail herein, wherein the pairs of oligonucleotides can be used to determine the expression levels of one or more of the genes in the asthma gene panel. The kit can also comprise (i) a detection means and/or (ii) an amplification means. The kit may further optionally include control primer/oligonucleotide sets for detection of control RNA in order to provide a control level as described herein.

In any of the above embodiments, step (a) further comprises the steps of (i) brushing, swabbing, scraping, washing or sponging the patient's nose, (ii) obtaining and appropriately preserving the nasal brushing/swab/scraping/wash/sponge sample, and (iii) assaying the gene expression profile of the cells and tissue contained in the sample, whether by isolating RNA as described herein or by use of a RNA profiling system that does not require a separate isolation step (such as, for example and not limitation, nanoString).

In any of the above embodiments, steps (b) and/or (c) and/or (d) are performed by a computer.

In any of the above embodiments, the classification analysis can comprise the Logistic Regression-Recursive Feature Elimination (LR-RFE) algorithm in combination with the Logistic algorithm as described in more detail below, with the gene expression profiles analyzed by this LR-RFE & Logistic model being the expression profiles of the genes in the LR-RFE & Logistic asthma gene panel. In this embodiment, the optimal classification threshold is about 0.76.

In any of the above embodiments, the classification analysis can alternatively comprise the LR-RFE & SVM-Linear combination model as described in more detail below, with the gene expression profiles analyzed by this model being the expression profiles of the genes in the LR- RFE & SVM-Linear asthma gene panel. The optimal classification threshold for this model is about 0.52. In any of the above embodiments, the classification analysis can alternatively comprise the SVM-RFE & SVM-Linear model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the SVM-RFE & SVM-Linear asthma gene panel, and the optimal classification threshold for this model is about 0.64.

In any of the above embodiments, the classification analysis can alternatively comprise the SVM-RFE & Logistic model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the SVM-RFE & Logistic asthma gene panel, and the optimal classification threshold for this model is about 0.69.

In any of the above embodiments, the classification analysis can alternatively comprise the LR-RFE & AdaBoost model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the LR-RFE & AdaBoost asthma gene panel, and the optimal classification threshold for this model is about 0.49.

In any of the above embodiments, the classification analysis can alternatively comprise the LR-RFE & RandomForest model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the LR-RFE & RandomForest asthma gene panel, and the optimal classification threshold for this model is about 0.60.

In any of the above embodiments, the classification analysis can alternatively comprise the SVM-RFE & RandomForest model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the SVM-RFE & RandomForest asthma gene panel, and the optimal classification threshold for this model is about 0.50.

In any of the above embodiments, the classification analysis can alternatively comprise the SVM-RFE & AdaBoost model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the SVM-RFE & AdaBoost asthma gene panel, and the optimal classification threshold for this model is about 0.55.

In any of the above embodiments, the patient is a mammal. In any of the above embodiments, the patient is a human. These and other objects, features and advantages of the present invention will become more apparent upon reading the following specification in conjunction with the accompanying description, claims and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying Figures, which are incorporated in and constitute a part of this specification, illustrate several aspects described below.

Figure 1 depicts the study flow for the identification of a nasal biomarker of asthma by machine learning analysis of next-generation transcriptomic data. Subjects with mild/moderate asthma and nonasthmatic controls were recruited for phenotyping, nasal brushing, and RNA sequencing of nasal epithelium. The RNAseq data generated were then a priori split into a development and test set. The development set was used for differential expression analysis and machine learning (involving feature selection, classification, and statistical analyses of classification performance) to identify an asthma gene panel that can accurately classify asthma from no asthma. Several classification models, including LR-RFE & Logistic, LR-RFE & SVM- Linear, SVM-RFE & Logistic, SVM-RFE & SVM-Linear, LR-RFE & AdaBoost, LR-RFE & RandomForest, SVM-RFE & RandomForest, and SVM-RFE & AdaBoost, were used to identify member genes of the asthma gene panel. The asthma gene panel identified was then tested on eight validation test sets, including (1) the RNAseq test set of subjects with and without asthma, (2) two test sets of subjects with and without asthma with nasal gene expression profiled by microarray, and (3) five test sets of subjects with non-asthma respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, and smoking) and nasal gene expression profiled by microarray. The strong precision and recall of the asthma gene panel across all test sets, reflected in the combined strong F-measure values, support its high potential to translate into a nasal brush-based biomarker for asthma diagnosis.

Figure 2 shows the receiver operating characteristic (ROC) curve of the predictions generated by applying the asthma gene panel to the samples in the RNAseq test set of independent subjects (n=40). The ROC curve for a random model is shown for reference. The curve and its corresponding AUC score show that the panel performs well for both asthma and no asthma (control) samples in this test set.

Figure 3 shows the validation of the asthma gene panel on test sets of independent subjects with asthma. Performance of the asthma panel in classifying asthma and no asthma in terms of Fmeasure, a conservative mean of precision and sensitivity . F-measure ranges from 0 to 1, with higher values indicating superior classification performance. The panel was applied to an RNAseq test set of independent subjects with and without asthma, and two external microarray data sets from subjects with and without asthma (Asthmal and Asthma2).

Figure 4 shows the comparative performance in the RNAseq test set of the LR-RFE &

Logistic asthma gene panel and other classification models processed through the inventors' machine learning pipeline. Performances of the LR-RFE & Logistic asthma gene panel and other classification models in classifying asthma (left panel) and no asthma (right panel) are shown in terms of F-measure, with individual measures shown in the bars. The number of genes in each model is shown in parentheses within the bars. The LR-RFE & Logistic classification model is listed first, followed by the other classification models. These other classification models were combinations of two feature selection algorithms (LR-RFE and SVM-RFE) and four global classification algorithms (Logistic Regression, SVM-Linear, AdaBoost and Random Forest). For context, alternative classification models are also shown and include: (1) a model derived from an alternative, single-step classification approach (sparse classification model learned using the Ll-Logistic regression algorithm), and (2) models substituting feature selection with each of the following preselected gene sets - all genes, all differentially expressed genes, and known asthma genes²⁹ - with their respective best performing global classification algorithms. These results show the performance of the LR-RFE & Logistic asthma gene panel compared to all other models, in terms of classification performance and/or model parsimony (number of genes included). LR = Logistic Regression. SVM = Support Vector Machine. RFE = Recursive Feature Elimination. RF = Random Forest.

Figure 5 shows the validation of the LR-RFE & Logistic asthma gene panel on test sets of independent subjects with non-asthma respiratory conditions. Performance statistics of the panel when applied to external microarray-generated data sets of nasal gene expression derived from case/control cohorts with non-asthma respiratory conditions. The LR-RFE & Logistic panel had a low to zero rate of misclassifying other respiratory conditions as asthma, supporting that the LR-RFE & Logistic panel is specific to asthma and would not misclassify other respiratory conditions as asthma.

Figure 6 shows a heatmap showing expression profiles of the 90 gene members of the

LR-RFE & Logistic asthma gene panel. Columns shaded dark grey (right-hand side) at the top denote asthma samples, while samples from subjects without asthma are denoted by columns shaded light grey (left-hand side). 22 and 24 of these genes were over- and under-expressed in asthma samples (DESeq2 FDR < 0.05), denoted by medium grey (uppermost group) and dark grey (middle group) groups of rows, respectively. The four genes in this set that have been previously associated with asthma²⁹ are C3, DEFB1, CYFIP2, and GSTT1. The LR-RFE & Logistic panel's inclusion of genes not previously known to be associated with asthma as well as genes not differentially expressed in asthma (light grey lowermost group of rows) demonstrates the ability of the inventors' machine learning methodology to move beyond traditional analyses of differential expression and current domain knowledge.

Figure 7 shows variancePartition analysis of the RNAseq development set. Gene expression variation across RNA samples due to age, race, and sex was assessed by variancePartition and found to be minimal.

Figure 8 shows a visual description of the machine learning pipeline used to select predictive features (genes) and develop classification models based on them from the RNAseq development set. By considering 100 splits of the development set into training and holdout sets (dotted box), many such models were evaluated for classification performance and then compared statistically using Friedman and Nemenyi tests. From this comparison, a highly precise combination of predictive genes and outer classification algorithms with good recall was determined, namely the LR-RFE & Logistic (Regression) model. This combination was in turn executed on the development set to train the LR-RFE & Logistic asthma gene panel. This LR- RFE & Logistic model was applied to several independent RNAseq and external microarray- derived cohorts with asthma and other respiratory conditions for final evaluation.

Figure 9 shows a visual description of the feature (gene) selection component of the invented machine learning pipeline. Given a training set, this component used a 5x5 nested (outer and inner) cross-validation (CV) setup to select sets of predictive features (genes). The inner CV round was used to determine the optimal number of features to be selected, and the outer one was used to select the set of predictive genes based on this number, thus reducing the cumulative effect of these potential sources of overfitting. The selection of features itself was performed using the Recursive Feature Elimination (RFE) algorithm in combination with wrapper Logistic Regression and SVM with Linear kernel classification algorithms. Figure 10A-10B shows Critical Difference plots demonstrating the statistical comparison of the performance of 100 asthma classification models obtained by various combinations of feature selection and outer classification algorithms. To emphasize the need for parsimony (small feature/gene sets) in these models, an adapted performance measure defined as the F-measure for each model divided by the number of genes in that model is used for this comparison. The Friedman followed by Nemenyi tests were used to statistically compare these adapted measures and obtain the p-values constituting the above plot. Each combination is represented individually by vertical+horizontal lines on the (10A) asthma and (10B) no asthma classes constituting the RNASeq development set. Combinations with improving performance are laid out from the left to right in terms of the average rank obtained by each of their 100 models, and the combinations connected by thick black lines perform statistically equivalently. The LR-RFE & Logistic model, which identified 90 genes (listed in Table 4 below) is a highly performing combination since, on average, it achieves good performance with the fewest selected genes. Other models that performed well, along with the identified genes, are listed in Table 4 below and discussed in more detail below. Across all eight of the models, 275 unique genes were identified as listed in Table 4

Figure 11 shows evaluation measures for classification models. The relationships between F-measure, sensitivity, precision, recall, positive predictive value, and negative predictive value are summarized. F-measure, which is a harmonic (conservative) mean of precision and recall that is computed separately for each class, provides a more comprehensive and reliable assessment of model performance when classes are imbalanced, as is frequently the case in biomedical scenarios.

Figure 12 shows the performance of permutation-based random classification models in test sets of independent subjects with asthma and controls. To determine the extent to which the classification performance of the LR-RFE & Logistic asthma gene panel could have been due to chance, 100 permutation-based random models were obtained by randomly permuting the labels of the samples in the development set and executing each of the feature selection-global classification combinations on these randomized data sets in the same way as described above for the real development set. These random models were then applied to each of the asthma test sets considered in our study, and their performances were also evaluated in terms of the F- measure. Figure 13 shows the performance of permutation-based random classification models in test sets of independent subjects with non-asthma respiratory conditions and controls. 100 permutation-based random models were obtained by randomly permuting the labels of the samples in the development set and executing each of the feature selection-global classification combinations on these randomized data sets in the same way as described above for the real development set. These random models were then applied to these test sets, and their performances were also evaluated in terms of the F-measure.

Figure 14 shows the distribution of DESeq2 FDR values of differentially expressed genes in the LR-RFE & Logistic asthma gene panel (dark grey bars) vs. other genes in the RNAseq development set (white bars), with overlaps between the bars shown in light grey. The Y-axis shows the probability of a gene having a -loglO(FDR) value in the corresponding bin. This plot shows that the genes in the LR-RFE & Logistic asthma panel were likely to be more differentially expressed, i.e., higher -loglO(FDR) or lower differential expression FDRs, than other genes in the development set.

DETAILED DESCRIPTION OF THE INVENTION

As specified in the Background Section, there is a great need in the art to identify technologies for reliable, consistent, simple and non-invasive diagnosis of asthma, including but not limited to mild to moderate asthma and use this understanding to develop novel diagnostic methods. The present invention satisfies this and other needs. Embodiments of the present invention relate generally to methods for diagnosis, classification and monitoring of asthma, including but not limited to mild to moderate asthma, and its differentiation from other respiratory disorders by determining the expression profiles of asthma-specific genes in nasal swab/scraping/brushing samples.

To facilitate an understanding of the principles and features of the various embodiments of the invention, various illustrative embodiments are explained below. Although exemplary embodiments of the invention are explained in detail, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the invention is limited in its scope to the details of construction and arrangement of components set forth in the following description or examples. The invention is capable of other embodiments and of being practiced or carried out in various ways. Also, in describing the exemplary embodiments, specific terminology will be resorted to for the sake of clarity. It must also be noted that, as used in the specification and the appended claims, the singular forms "a," "an" and "the" include plural references unless the context clearly dictates otherwise. For example, reference to a component is intended also to include composition of a plurality of components. References to a composition containing "a" constituent is intended to include other constituents in addition to the one named. In other words, the terms "a," "an," and "the" do not denote a limitation of quantity, but rather denote the presence of "at least one" of the referenced item.

Also, in describing the exemplary embodiments, terminology will be resorted to for the sake of clarity. It is intended that each term contemplates its broadest meaning as understood by those skilled in the art and includes all technical equivalents which operate in a similar manner to accomplish a similar purpose.

Ranges may be expressed herein as from "about" or "approximately" or "substantially" one particular value and/or to "about" or "approximately" or "substantially" another particular value. When such a range is expressed, other exemplary embodiments include from the one particular value and/or to the other particular value. Further, the term "about" means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, "about" can mean within an acceptable standard deviation, per the practice in the art. Alternatively, "about" can mean a range of up to ±20%, preferably up to ±10%, more preferably up to ±5%, and more preferably still up to ±1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 2-fold, of a value. Where particular values are described in the application and claims, unless otherwise stated, the term "about" is implicit and in this context means within an acceptable error range for the particular value.

By "comprising" or "containing" or "including" is meant that at least the named compound, element, particle, or method step is present in the composition or article or method, but does not exclude the presence of other compounds, materials, particles, method steps, even if the other such compounds, material, particles, method steps have the same function as what is named.

Throughout this description, various components may be identified having specific values or parameters, however, these items are provided as exemplary embodiments. Indeed, the exemplary embodiments do not limit the various aspects and concepts of the present invention as many comparable parameters, sizes, ranges, and/or values may be implemented. The terms "first," "second," and the like, "primary," "secondary," and the like, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

It is noted that terms like "specifically," "preferably," "typically," "generally," and

"often" are not utilized herein to limit the scope of the claimed invention or to imply that certain features are critical, essential, or even important to the structure or function of the claimed invention. Rather, these terms are merely intended to highlight alternative or additional features that may or may not be utilized in a particular embodiment of the present invention. It is also noted that terms like "substantially" and "about" are utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation.

The dimensions and values disclosed herein are not to be understood as being strictly limited to the exact numerical values recited. Instead, unless otherwise specified, each such dimension is intended to mean both the recited value and a functionally equivalent range surrounding that value. For example, a dimension disclosed as "50 mm" is intended to mean "about 50 mm."

It is also to be understood that the mention of one or more method steps does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Similarly, it is also to be understood that the mention of one or more components in a composition does not preclude the presence of additional components than those expressly identified.

As used herein, the term "subject" or "patient" refers to mammals and includes, without limitation, human and veterinary animals. In a preferred embodiment, the subject is human.

In the context of the present invention insofar as it relates to asthma, the terms "treat",

"treatment", and the like mean to relieve or alleviate at least one symptom associated with such condition, or to slow or reverse the progression of such condition. Within the meaning of the present invention, the term "treat" also denotes to arrest, delay the onset (i.e., the period prior to clinical manifestation of a disease) and/or reduce the risk of developing or worsening a disease. The terms "treat", "treatment", and the like regarding a state, disorder or condition may also include (1) preventing or delaying the appearance of at least one clinical or sub-clinical symptom of the state, disorder or condition developing in a subject that may be afflicted with or predisposed to the state, disorder or condition but does not yet experience or display clinical or subclinical symptoms of the state, disorder or condition; or (2) inhibiting the state, disorder or condition, i.e., arresting, reducing or delaying the development of the disease or a relapse thereof (in case of maintenance treatment) or at least one clinical or sub-clinical symptom thereof; or (3) relieving the disease, i.e., causing regression of the state, disorder or condition or at least one of its clinical or sub-clinical symptoms.

The term "a control level" as used herein encompasses predetermined standards (e.g., a published value in a reference) as well as levels determined experimentally in similarly processed samples from control subjects (e.g., BMI-, age-, and gender-matched subjects without asthma as determined by standard examination and diagnostic methods). The control level is included in the classification analyses as described herein.

RNA can be extracted from the collected tissue and/or cells (e.g., from nasal epithelial cells obtained from a nasal brushing, scraping, wash, sponge or swab) by any known method. For example, RNA may be purified from cells using a variety of standard procedures as described, for example, in RNA Methodologies, A Laboratory Guide for Isolation and Characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press. In addition, various commercial products are available for RNA isolation. As would be understood by those skilled in the art, total RNA or polyA+ RNA may be used for preparing gene expression profiles.

The expression levels (or expression profile) can be then determined using any of various techniques known in the art and described in detail elsewhere. Such methods generally include, for example and not limitation, polymerase-based assays such as RT-PCR (e.g., TAQMAN), hybridization-based assays such as DNA microarray analysis, flap-endonuclease-based assays (e.g., INVADER), direct mRNA capture (QUANTIGENE or HYBRID CAPTURE (Digene)), RNA sequencing (e.g., Illumina RNA sequencing platforms), and by the nanoString platform. See, for example, US 2010/0190173 for descriptions of representative methods that can be used to determine expression levels.

As used herein, the term "gene" refers to a DNA sequence expressed in a sample as an RNA transcript. As used herein, "differentially expressed" or "differential expression" means that the level or abundance of an RNA transcripts (or abundance of an RNA population sharing a common target sequence (e.g., splice variant RNAs)) is higher or lower by at least a certain value in a test sample as compared to a control level.

As used herein, the term "asthma gene panel" refers to the unique set of 275 genes identified by all of the models and listed in Table 4 as the unique set of genes. Preferred subsets of the asthma gene panel that may be analyzed by different classifiers are also described in Table 4. Specifically, as used herein, the term "LR-RFE & Logistic asthma gene panel" refers to those 90 genes identified by the LR-RFE & Logistic models. The term "LR-RFE & SVM-Linear asthma gene panel" refers to those 90 genes identified by the LR-RFE & SVM-Linear models. The term "SVM-RFE & SVM-Linear asthma gene panel" refers to those 119 genes identified by the SVM-RFE & SVM-Linear models. The term "SVM-RFE & Logistic asthma gene panel" refers to those 119 genes identified by the SVM-RFE & Logistic models. The term "LR-RFE & AdaBoost asthma gene panel" refers to those 90 genes identified by the LR-RFE & AdaBoost models. The term "LR-RFE & RandomForest asthma gene panel" refers to those 90 genes identified by the LR-RFE & RandomForest models. The term "SVM-RFE & RandomForest asthma gene panel" refers to those 123 genes identified by the SVM-RFE & RandomForest models. The term "SVM-RFE & AdaBoost asthma gene panel" refers to those 212 genes identified by the SVM-RFE & AdaBoost models.

In various embodiments disclosed herein, the expression levels of different combinations of genes can be used to glean different information. For example, increased expression levels of certain genes such as C3 in an individual as compared to a control are associated with a diagnosis of mild/moderate asthma. Decreased expression levels of other genes such as DEFB1 in an individual as compared to a control are associated with a diagnosis of mild/moderate asthma. Expression of ORMDL3 in an individual as compared to a control is associated with a differential diagnosis of mild/moderate asthma relative to other respiratory disorders such as, for example and not limitation, rhinitis, respiratory infection, and cystic fibrosis.

In various embodiments, RNA expression profiling systems are utilized to quantify the gene expression profiles from the patient's nasal brushing/swab/scraping/washing/sponge, such as for example and not limitation, the nanoString profiling system. The output from such systems will provide a count of genes in the asthma gene panel, and such output is analyzed in an automated manner, such as by a computer, via the classifier and classification threshold as described herein. The results obtained from the classifier enable a clinician to diagnose the patient as having asthma or not.

After determining and analyzing the expression levels of the appropriate combination of genes in a patient's nasal brushing/swab/scraping/washing/sponge, the patient can be classified as having asthma or not having asthma. The classification may be determined computationally based upon known methods as described herein. Particularly preferred computational methods include the classifiers and optimal classification thresholds as described herein. The result of the computation may be displayed on a computer screen or presented in a tangible form, for example, as a probability (e.g., from 0 to 100%) of the patient having asthma and/or a certain severity of asthma. The report will aid a physician in diagnosis or treatment of the patient. For example, in certain embodiments, the patient's expression levels will be diagnostic of asthma or enable a differential diagnosis of asthma from other respiratory disorders such as rhinitis, irritation resulting from smoking, respiratory infection and cystic fibrosis, and the patient will subsequently be treated as appropriate. In other embodiments, the patient's expression levels of the appropriate combination of genes will not support a diagnosis of asthma, thereby allowing the physician to exclude asthma and/or mild to moderate asthma as a diagnosis. In some embodiments, the patient may be selected to participate in clinical trials involving treatment of asthma and/or related conditions based on the patient's gene expression profile.

In some embodiments, the classifier used is the LR-RFE & Logistic model, the gene expression profiles analyzed are the expression profiles of the genes in the LR-RFE & Logistic asthma gene panel, and the optimal classification threshold for this model is about 0.76.

In other embodiments, the classifier used is the LR-RFE & SVM-Linear model, the gene expression profiles analyzed are the expression profiles of the genes in the LR-RFE & SVM- Linear asthma gene panel, and the optimal classification threshold for this model is about 0.52.

In other embodiments, the classifier used is the SVM-RFE & SVM-Linear model, the gene expression profiles analyzed are the expression profiles of the genes in the SVM-RFE & SVM-Linear asthma gene panel, and the optimal classification threshold for this model is about 0.64. In other embodiments, the classifier used is the SVM-RFE & Logistic model, the gene expression profiles analyzed are the expression profiles of the genes in the SVM-RFE & Logistic asthma gene panel, and the optimal classification threshold for this model is about 0.69.

In other embodiments, the classifier used is the LR-RFE & AdaBoost model, the gene expression profiles analyzed are the expression profiles of the genes in the LR-RFE & AdaBoost asthma gene panel, and the optimal classification threshold for this model is about 0.49.

In other embodiments, the classifier used is the LR-RFE & RandomForest model, the gene expression profiles analyzed are the expression profiles of the genes in the LR-RFE & RandomForest asthma gene panel, and the optimal classification threshold for this model is about 0.60.

In other embodiments, the classifier used is the SVM-RFE & RandomForest model, the gene expression profiles analyzed are the expression profiles of the genes in the SVM-RFE & RandomForest asthma gene panel, and the optimal classification threshold for this model is about 0.50.

In other embodiments, the classifier used is the SVM-RFE & AdaBoost model, the gene expression profiles analyzed are the expression profiles of the genes in the SVM-RFE & AdaBoost asthma gene panel, and the optimal classification threshold for this model is about 0.55.

In some embodiments, RNAs are purified prior to gene expression profile analysis. RNAs can be isolated and purified from nasal brushing/swab/scraping/wash/sponge by various methods, including the use of commercial kits (e.g., Qiagen RNeasy Mini Kit as described in Example 1 below). In some embodiments, RNA degradation in brushing/swab/scraping/wash/sponge samples and/or during RNA purification is reduced or eliminated. Useful methods for storing nasal brushing/swab/scraping/wash/sponge samples include, without limitation, use of RNALater as described herein. Useful methods for reducing or eliminating RNA degradation include, without limitation, adding RNase inhibitors (e.g., RNasin Plus [Promega], SUPERase-In [ABI], etc.), use of guanidine chloride, guanidine isothiocyanate, N-lauroylsarcosine, sodium dodecyl sulphate (SDS), or a combination thereof. Reducing RNA degradation in nasal brushing/swab/scraping/wash/sponge samples is particularly important when sample storage and transportation is required prior to RNA purification. In other embodiments, RNA is not purified prior to gene expression profile analysis. In such embodiments, RNA expression profiling platforms that can directly assay tissue and cells without a separate RNA isolation step are utilized (for example and not limitation, the nanoString system).

Examples of useful methods for measuring RNA level in nasal epithelial cells contained in nasal brushing/swab/scraping/wash/sponge include hybridization with selective probes (e.g., using Northern blotting, bead-based flow-cytometry, oligonucleotide microchip [microarray], or solution hybridization assays), polymerase chain reaction (PCR)-based detection (e.g., stem-loop reverse transcription-polymerase chain reaction [RT-PCR], quantitative RT-PCR based array method [qPCR-array]), direct sequencing, such as for example and not limitation, by RNA sequencing technologies (e.g., Illumina HiSeq 2500 platform, Helicos small RNA sequencing, miRNA BeadArray (Illumina), Roche 454 (FLX-Titanium), and ABI SOLiD), and the nanoString system. For review of additional applicable techniques see, e.g., Chen et al., BMC Genomics, 2009, 10:407; Kong et al., J Cell Physiol. 2009; 218:22-25.

In conjunction with the above diagnostic and screening methods, the present invention provides various kits comprising one or more primer and/or probe sets specific for the detection of target RNA. Such kits can further include primer and/or probe sets specific for the detection of other RNA that can aid in diagnosing, differentiating, and/or classifying asthma. In some embodiments, such kits can contain nucleic acid oligonucleotides for determining the level of expression of a particular combination of genes in a patient's nasal brushing/swab/scraping/wash/sponge sample. The kit may include one or more oligonucleotides that are complementary to one or more transcripts identified herein as being associated with asthma, and also may include oligonucleotides related to necessary or meaningful assay controls. A kit for evaluating an individual for asthma may include pairs of oligonucleotides (e.g., 4, 6, 8, 10, 12, 14 or more oligonucleotides). The oligonucleotides may be designed to detect expression levels in accordance with any assay format, including but not limited to those described herein. The kit may further optionally include control primer and/or probe sets for detection of control RNA in order to provide a control level as described herein.

A kit of the invention can also provide reagents for primer extension and amplification reactions. For example, in some embodiments, the kit may further include one or more of the following components: a reverse transcriptase enzyme, a DNA polymerase enzyme (such as, e.g., a thermostable DNA polymerase), a polymerase chain reaction buffer, a reverse transcription buffer, and deoxynucleoside triphosphates (dNTPs). Alternatively (or in addition), a kit can include reagents for performing a hybridization assay. The detecting agents can include nucleotide analogs and/or a labeling moiety, e.g., directly detectable moiety such as a fluorophore (fluorochrome) or a radioactive isotope, or indirectly detectable moiety, such as a member of a binding pair, such as biotin, or an enzyme capable of catalyzing a non-soluble colorimetric or luminometric reaction. In addition, the kit may further include at least one container containing reagents for detection of electrophoresed nucleic acids. Such reagents include those which directly detect nucleic acids, such as fluorescent intercalating agent or silver staining reagents, or those reagents directed at detecting labeled nucleic acids, such as, but not limited to, ECL reagents. A kit can further include RNA isolation or purification means as well as positive and negative controls. A kit can also include a notice associated therewith in a form prescribed by a governmental agency regulating the manufacture, use or sale of diagnostic kits. Detailed instructions for use, storage and trouble-shooting may also be provided with the kit. A kit can also be optionally provided in a suitable housing that is preferably useful for robotic handling in a high throughput setting.

The components of the kit may be provided as dried powder(s). When reagents and/or components are provided as a dry powder, the powder can be reconstituted by the addition of a suitable solvent. It is envisioned that the solvent may also be provided in another container. The container will generally include at least one vial, test tube, flask, bottle, syringe, and/or other container means, into which the solvent is placed, optionally aliquoted. The kits may also comprise a second container means for containing a sterile, pharmaceutically acceptable buffer and/or other solvent.

Where there is more than one component in the kit, the kit also will generally contain a second, third, or other additional container into which the additional components may be separately placed. However, various combinations of components may be comprised in a container.

Such kits may also include components that preserve or maintain DNA or RNA, such as reagents that protect against nucleic acid degradation. Such components may be nuclease or RNase-free or protect against RNases, for example. Any of the compositions or reagents described herein may be components in a kit. In accordance with the present invention there may be employed conventional molecular biology, microbiology, and recombinant DNA techniques within the skill of the art. Such techniques are explained fully in the literature. See, e.g., Sambrook, Fritsch & Maniatis, Molecular Cloning: A Laboratory Manual, Second Edition (1989) Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York (herein "Sambrook et al, 1989"); DNA Cloning: A Practical Approach, Volumes I and II (D.N. Glover ed. 1985); Oligonucleotide Synthesis (M.J. Gait ed. 1984); Nucleic Acid Hybridization (B.D. Hames & S.J. Higgins eds. (1985); Transcription and Translation (B.D. Hames & S.J. Higgins, eds. (1984); Animal Cell Culture (R.I. Freshney, ed. (1986); Immobilized Cells and Enzymes (IRL Press, (1986); B. Perbal, A Practical Guide To Molecular Cloning (1984); F.M. Ausubel et al. (eds.), Current Protocols in Molecular Biology, John Wiley & Sons, Inc. (1994); among others.

EXAMPLES

The present invention is also described and demonstrated by way of the following examples. However, the use of these and other examples anywhere in the specification is illustrative only and in no way limits the scope and meaning of the invention or of any exemplified term. Likewise, the invention is not limited to any particular preferred embodiments described here. Indeed, many modifications and variations of the invention may be apparent to those skilled in the art upon reading this specification, and such variations can be made without departing from the invention in spirit or in scope. The invention is therefore to be limited only by the terms of the appended claims along with the full scope of equivalents to which those claims are entitled.

Example 1. Development of the nasal biomarker panel

Materials and Methods

Experimental design and subjects

Subjects with mild/moderate asthma were a subset of participants of the Childhood

Asthma Management Program (CAMP), a multicenter North American clinical trial of 1041 subjects that took place between 1991 and 2012²¹'²². Findings from the CAMP cohort have defined current practice and guidelines for asthma care and research²². Participating subjects had asthma defined by symptoms greater than or equal to 2 times per week, use of an inhaled bronchodilator at least twice weekly or use of daily medication for asthma, and increased airway responsiveness to methacholine (PC₂o≤ 12.5 mg/ml). The subset of subjects included in this study were CAMP participants who presented for a visit between July 2011 and June 2012 at Brigham and Women's Hospital, one of eight study centers for this multicenter study.

Subjects without asthma or "no asthma" were recruited during the same time period (2011-2012) by advertisement at Brigham & Women's Hospital. Selection criteria were no personal history of asthma, no family history of asthma in first degree relatives, and self- described non-Hispanic white ethnicity. The rationale for limiting participation to non-Hispanic white individuals was to allow for optimal comparison to 968 CAMP subjects of Caucasian background who participated in the CAMP Genetics Ancillary study, which was focused on this population.⁵⁵ Subjects underwent pre and post-bronchodilator spirometry according to ATS guidelines, and only those meeting selection criteria and without lung function abnormality or bronchodilator response were considered nonasthmatic or "no asthma".

The institutional review boards of Brigham & Women's Hospital and the Icahn School of Medicine at Mount Sinai approved the study protocols.

Nasal sample collection and RNA sequencing

A standard cytology brush was applied to the right nare of each subject and rotated three times with circumferential pressure for nasal epithelial cell collection. The brush was immediately placed in RNALater and then stored at 4°C until RNA extraction. RNA extraction was performed with Qiagen RNeasy Mini Kit (Valencia, CA). Samples were assessed for yield and quality using the 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) and Qubit (Thermo Fisher Scientific, Grand Island, NY).

Of the 190 subjects who underwent nasal brushing (66 with mild/moderate asthma, 124 with no asthma), a random selection of 150 nasal brushes from subjects with asthma and nonasthmatic controls were a priori assigned as the development set, and the remaining 40 subjects were a priori assigned as the test set of independent subjects (for testing the classification model). To minimize potential bias due to batch effects, the inventors submitted all samples (training and test set samples) to the Mount Sinai Genomics Core for library preparation and RNA sequencing at the same time to allow for sequencing of all samples in a single run. Staff at the Mount Sinai Genomics Core were blinded to the assignment of samples as development or test set.

The sequencing library was prepared with the standard TruSeq RNA Sample Prep Kit v2 protocol (Illumina). The mRNA sequencing was performed on the Illumina HiSeq 2500 platform using 40-50 million 100 bp paired-end reads. The data were put through the inventors' standard mapping pipeline⁵⁶ (using Bowtie⁵⁷ and TopHat⁵⁸, and assembled into gene- and transcription- level summaries using Cufflinks⁵⁹). Mapped data were subjected to quality control with FastQC and RNA-SeQC.⁶⁰ Data were normalized separately for the development and test sets. Genes with fewer than 100 counts in at least half the samples were dropped to reduce the potentially adverse effects of noise. DESeq2²⁵ was used to normalize the data sets using its variance stabilizing transformation method.

VariancePartition Analysis of Potential Confounders

Given differences in age, race, and sex distributions between the asthma and "no asthma" classes, the inventors used variancePartition²⁴ to assess the degree to which these variables influenced gene expression. The total variance in gene expression was partitioned into the variance attributable to age, race, and sex using a linear mixed model implemented in variancePartition vl .0.0²⁴. Age (continuous variable) was modeled as a fixed effect while race and sex (categorical variables) were modeled as random effects. The results showed that age, race, and sex accounted for minimal contributions to total gene expression variance (Figure 7). Downstream analyses were therefore performed with unadjusted gene expression data.

Differential gene expression and pathway enrichment analysis

DESeq2²⁵ was used to identify differentially expressed genes in the development set. Genes with FDR < 0.05 were deemed differentially expressed, with fold change <1 implying under-expression and vice versa. Pathway enrichment analysis was performed using Gene SetEnrichment Analysis²⁶.

Statistical and Machine Learning Analyses of RNAseq Data Sets

To discover gene expression biomarkers that are capable of predicting the asthma status of a patient, the inventors used a rigorous machine learning pipeline in Python using the scikit- learn package⁶¹. This pipeline combined feature (gene) selection¹⁸, (outer) classification¹⁹ and statistical analyses of classification performance²⁰ to the development set (Figure 8). The first two components, feature selection and classification, were applied to a training set constituted of 120 randomly selected samples from the development set (n=150) to learn classification models. These models were evaluated on the corresponding remaining 30 samples (holdout set). This process (feature selection and classification) was repeated 100 times on 100 random splits of the development set into training and holdout sets. Feature (Gene) selection: Given a training set, a 5x5 nested (outer and inner) cross- validation (CV) setup²⁷ was used to select sets of predictive genes (Figure 9). The inner CV round was used to determine the optimal number of genes to be selected, and the outer CV round was used to select the set of predictive genes based on this number, thus reducing the cumulative effect of these potential sources of overfitting.

The Recursive Feature Elimination (RFE) algorithm⁶² was executed on the inner CV training split to determine the optimal number of features. The use of RFE within this setting enabled the inventors to identify groups of features that are collectively, but not necessarily individually, predictive. This reflects the systems biology-based expectation that many genes, even ones with marginal effects, can play a role in classifying diseases/phenotypes (here asthma) in combination with other more strongly predictive genes⁶³. Specifically, the inventors used the L2-regularized Logistic Regression (LR or Logistic)⁶⁴ and SVM-Linear(kernel)⁶⁵ classification algorithms in conjunction with RFE (conjunctions henceforth referred to as LR-RFE and SVM- RFE respectively). For this, for a given inner CV training split, all the features (genes) were ranked using the absolute values of the weights assigned to them by an inner classification model, trained using the LR or SVM algorithm, over this split. Next, for each of the conjunctions, the set of top-k ranked features, with k starting with 11587 (all filtered genes) and being reduced by 10% in each iteration until k=l, was considered. The discriminative strength of feature sets consisting of the top k features as per this ranking was assessed by evaluating the performance of the LR or SVM classifier based on them over all the inner CV training-test splits. The optimal number of features to be selected was determined as the value of k that produces the best performance. Next, a ranking of features was derived from the outer CV training split using exactly the same procedure as applied to the inner CV training split. The optimal number of features determined above was selected from the top of this ranking to determine the optimal set of predictive features for this outer CV training split. Executing this process over all the five outer CV training splits created from the development set identified five such sets. Finally, the set of features (genes) that was common to all these sets (i.e., in their intersection/overlap) was selected as the predictive gene set for this training set. One such set was identified for LR-RFE and SVM-RFE respectively.

(Outer) classification: Once respective predictive gene sets had been selected using LR-

RFE and SVM-RFE, four outer classification algorithms, namely L2-regularized Logistic Regression (LR or Logistic) , SVM-Linear , AdaBoost and Random Forest (RF) , were used to learn intermediate classification models over the training set. These intermediate models were applied to the corresponding holdout set to generate probabilistic asthma predictions for the constituent samples. An optimal threshold for converting these probabilistic predictions into binary ones was then computed from the holdout set. This optimization resulted in the proposed classification models. This optimization resulted in proposed classification models.

To obtain a comprehensive view of the performance of these proposed models, the above two components were executed on 100 random training-holdout splits of the development set. To determine the best performing combination of feature selection and outer classification algorithms, a statistical analysis of the classification performance of all the models resulting from all the considered combinations was conducted using the Friedman followed by the Nemenyi test 20,68 T ggg t_est_Sj which account for multiple hypothesis testing, assessed the statistical significance of the relative difference of performance of the combinations in terms of their relative ranks across the 100 splits, and allow the ordering of the overall performance of each combination in terms of the significance of their pairwise comparison. This statistical comparison was a novel aspect of the present pipeline, as this task, generally referred to as "model selection," is typically based on a single training-holdout split. Even if multiple such splits are employed, models are generally selected based on absolute performance scores, and not based on the statistical significance of performance comparisons, as was done in the present Examples.

Optimization for parsimony: For biomarker optimization, it is essential to consider parsimony (i.e., minimize number of features or genes for accurate classification) In these models, an adapted performance measure, defined as the absolute performance measure for each model divided by the number of genes in that model, was used for this statistical comparison. In terms of this measure, a model that does not obtain the best absolute performance measure among all models, but uses much fewer genes than the other, may be judged to be the best model. The result of this statistical analysis, visualized as a Critical Difference plot ²⁸ (Figure 10A-10B), enabled identification of the good-performing combination of feature selection and outer classification methods in terms of both performance and parsimony.

Final model development and evaluation: The final step in the pipeline was to determine the representative model from the 100 iterations of the most statistically superior combination of feature selection and classification method identified from the above steps. In case of ties among the models of the best performing combination, the gene set that produced the best asthma classification F-measure (Figure 11) across all four global classification algorithms was chosen as the gene set constituting the representative model for that combination. The result of this process was the asthma gene panel-based model that consisted of this representative gene set for each of eight models, a global classification algorithm and each model's optimized threshold for classifying samples with and without asthma. This optimized threshold was determined for this model as the one that produced the highest F-measure for the asthma class on the holdout set from which it was identified. The gene sets for each of the eight models are shown in Table 4 below, as well as the 275 unique genes in the asthma gene panel are also shown.

Validation of the LR-RFE & Logistic Asthma Gene Panel in an RNAseq test set of independent subjects

The LR-RFE & Logistic asthma gene panel identified by the machine learning pipeline was then tested on the RNAseq test set (n=40) to assess its performance in independent subjects. F-measure was used to measure performance. For comparison, the same machine learning methodology was used to train and evaluate models from all combinations of feature selection and classification methods considered in the pipeline.

LR-RFE & Logistic Performance Comparison to Alternative Classification Models

To evaluate the relative performance of the LR-RFE & Logistic asthma gene panel, the inventors also applied the machine learning pipeline with replacement of the feature (gene) selection step with these pre-determined gene sets: (1) all filtered RNAseq genes, (2) all differentially expressed genes, and (3) known asthma genes from a recent review of asthma genetics²⁹. These were each used as a predetermined gene set that was run through our machine learning pipeline (Figure 8 with the feature selection component turned off) to identify the best performing global classification algorithm and the optimal asthma classification threshold for this predetermined set of features. The algorithm and threshold were used to train this gene set's representative classification model over the entire development set, and the optimal model for each of these gene sets was then evaluated on the RNAseq test set in terms of the F-measures for the asthma and no asthma classes. Finally, as a baseline representative of sparse classification algorithms, which represent a one-step option for doing feature selection and classification simultaneously, the inventors also trained an LI -regularized logistic regression model (Ll- Logistic)⁶⁹ on the development set and evaluated it on the RNAseq test set.

Performance Comparison to Permutation-based Random Models

To determine the extent to which the performance of all the above classification models could have been due to chance, the inventors compared their performance with that of random counterpart models (Figures 12, 13). These models were obtained by randomly permuting the labels of the samples in the development set and executing each of the feature selection-global classification combinations on these randomized data sets in the same way as described above for the real development set. These random models were then applied to each of the test sets considered in our study, and their performances were also evaluated in terms of the F-measure. For each of real models trained using the combinations, 100 corresponding random models were learned and evaluated as above, and the performance of the real model was compared with the average performance of the corresponding random models.

Validation of the asthma gene panel in external asthma cohorts

To assess the generalizability of the asthma gene panel, microarray-profiled data sets of nasal gene expression from two external asthma cohorts— Asthmal (GSE19187)³⁰ and Asthma2 (GSE46171)³¹ (Table 5)- were obtained from NCBI Gene Expression Omnibus (GEO)⁷⁰. The asthma gene panel was evaluated on these external asthma test sets with performance measured by F-measures for the asthma and no asthma classes.

Validation of the asthma gene panel in external cohorts with other respiratory conditions

To assess the panel's ability to distinguish asthma from respiratory conditions that can have overlapping symptoms with asthma, microarray-profiled data sets of nasal gene expression were also obtained for five external cohorts with allergic rhinitis (GSE43523)³⁶, upper respiratory infection (GSE46171)³¹, cystic fibrosis (GSE40445)³⁷, and smoking (GSE8987)¹² (Table 6). The asthma gene panel was evaluated on these external test sets of non-asthma respiratory conditions with performance measured by F- measures for the asthma and no asthma classes.

Results

Study population and baseline characteristics

A total of 190 subjects underwent nasal brushing for this study, including 66 subjects with well-defined mild-moderate asthma (based on symptoms, medication use, and demonstrated airway hyperresponsiveness by methacholine challenge response) and 124 subjects without asthma (based on no personal or family history of asthma, normal spirometry, and no bronchodilator response). The definitional criteria we used for mild-moderate asthma were consistent with US National Heart Lung Blood Institute guidelines for the diagnosis of asthma⁷, and are the same criteria used in the longest NIH-sponsored study of mild-moderate asthma²¹'²²

From these 190 subjects, a random selection of 150 subjects were a priori assigned as the development set (to be used for classification model development and biomarker identification), and the remaining 40 subjects were a priori assigned as the RNAseq test set (to be used as one of 8 validation test sets for testing of the classification model and biomarker genes identified with the development set). Assignment of subjects to the development and test sets was done at this early juncture in the study to enable RNA sequencing from all subjects in a single run (to reduce potential bias from sequencing batch effects) with then immediate allocation of the sequence data to the development or test sets prior to any pre-processing and analysis. The test set was then set aside to preserve its independence.

The baseline characteristics of the subjects in the development set (n=150) are shown in the left section of Table 1. The mean age of subjects with and without asthma was comparable, with slightly more male subjects with asthma and more female subjects without asthma. Caucasians were more prevalent in subjects without asthma, which was expected based on the inclusion criteria. Consistent with the reversible airway obstruction that characterizes asthma⁴, subjects with asthma had significantly greater bronchodilator response than control subjects (P = 1.4 x 10-5). Allergic rhinitis was more prevalent in subjects with asthma (P = 0.005), consistent with known comorbidity between allergic rhinitis and asthma²³. Rates of smoking between subjects with and without asthma were not significantly different.

RNA isolated from nasal brushings from the subjects was of good quality with mean RIN 7.8 (±1.1). The median number of paired-end reads per sample from RNA sequencing was 36.3 million. Following normalization and filtering, 11,587 genes were used for analysis. VariancePartition analysis²⁴ showed that age, race, and sex minimally contributed to total gene expression variance (Figure 7).

Table 1: Baseline characteristics of subjects in the RNAseq development and test sets

value^B

All Asthma No All (n=40) Asthma No Asthma

(n=150) (n=53) Asthma (n=13) (n=27)

(n=97)

Age (years) 26.9 (5.4) 25.7 (2.0) 27.6 (6.5) 26.2 (5.1) 25.3 (2.1) 26.6 (6.1) 0.47

Sex - female 89 24 65 21 2 (15.3%) 19 (70.4%) 0.40

(59.3%) (45.3%) (67.0%) (52.5%)

Race 0.60

Caucasian 116 21 96 32 5 (38.5%) 27

(77.3%) (40.4%) (99.0%) (80.0%)

(100.0%)

African 24 23 1 (1.0%) 32 5 (38.5%) 0 (0.0%)

American (16.0%) (43.4%) (80.0%)

Latino 5 (3.3%) 5 (9.4%) 0 (0.0%) 5 (12.5%) 5 (38.5%) 0 (0.0%)

Other 5 (3.3%) 4 (7.5%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)

FEV1^A (% 94.7% 94.6% 94.8% 94.5% 94.4% 94.6 0.90 predicted) (10.0%) (10.9%) (9.7%)

(11.4%) (12.0%) (11.3%)

FEV1/FVC^A 82.5% 81.5% 83.1% 82.7% 84.8% 81.6% 0.91 (% (6.4%) (6.7%) (6.3%)

(5.5%) (4.4%) (5.8%) predicted)

Bronchodilat 5.6% 8.7% 3.9% 4.5% 7.0% 3.3% 0.29 or response (6.0%) (6.4%) (5.1%)

(5.4%) (6.1%) (4.7%)

(%)

Age asthma 3.2 (2.7) n/a 3.4 (2.0) 0.78 onset: years

Allergic 60 29 31 7 (17.5%) 7 (53.8%) 0 (0%) 0.009 rhinitis (40.0%) (54.7%) (32.0%)

Nasal 14 (9.3%) 9 (170.%) 5 (5.2%) 0 0 0 0.07 steroids

Smoking 7 (4.7%) 1 (1.9%) 6 (6.2%) 1 (2.5%) 0 1 (3.7%) 1.0 ^Apre-bronchodilator measures. FEV1 = forced expiratory flow volume in 1 second, FVC = forced vital capacity. Mean (SD) or Number (%) provided. ^B Fisher's Exact test for categorical variables and t-test for continuous variables.

Differential gene expression analysis by DeSeq2²⁵, showed that 1613 and 1259 genes were respectively over- and under-expressed in asthma cases versus controls (false discovery rate (FDR) <0.05) (Table 2A-2B). These genes were enriched for disease-relevant pathways²⁶ including immune system (fold change=3.6, FDR=1.07 x 10-22), adaptive immune system (fold change=3.91, FDR=1.46 x 10-15), and innate immune system (fold change=4.1, FDR=4.47 x 10- 9) (Table 2A-2B)

Identification of the asthma gene panel by machine learning analyses of RNAseq development set

To identify gene expression biomarkers that accurately predict asthma status, the inventors developed a nested machine learning pipeline that combines feature (gene) selection ¹⁸ and classification ¹⁹ techniques (Figure 8). The first component of the pipeline used a nested (inner and outer) cross-validation protocol ²⁷ for selecting predictive sets of features (Figure 8). For this, the inventors used the Recursive Feature Elimination (RFE) algorithm ¹⁸ combined with L2-regularized Logistic Regression (LR or Logistic) and Support Vector Machine (SVM (with Linear kernel)) ¹⁹ classification algorithms (the combinations are referred to as LR-RFE and SVM-RFE respectively). Asthma classification models were then learned by applying four global classification algorithms (SVM-Linear, AdaBoost, Random Forest, and Logistic) to the expression profiles of the selected genes. This learning and evaluation process was run over 100 training-holdout splits of the development set. All resulting models were statistically compared²⁰ in terms of their performance and parsimony (i.e., number of feature/gene sets included in the model) (Figure 10A-10B). Performance was measured in terms of F-measure²⁸, a conservative mean of precision and sensitivity. F-measure ranges from 0 to 1, with higher values indicating superior classification performance. A value of 0.5 for F-measure does not represent a random model. To estimate random performance, the inventors trained and evaluated permutation-based random models as described herein. Given the central role that F-measure plays in the interpretation of these results, a detailed explanation of F-measure and its relation to more common performance measures is provided below and in Figure 11.

Evaluation measures for predictive models The most commonly used evaluation measures for predictive models in medicine are the positive and negative predictive values (PPV and NPV respectively). As shown in Figure 11, PPV and NPV are equivalent to precisions²⁸ for the positive and negative classes (asthma and no asthma in our study) respectively. However, relying solely on predictive values (i.e., precisions) ignores the critical dimension of the sensitivity or recall²⁸ (also defined in Figure 11) of the test. For instance, the test may predict perfectly for only one asthma sample in a cohort and make no predictions for all other asthma samples. This will yield a PPV of 1, but poor sensitivity/recall. Thus, for all tasks involving evaluation of asthma classification models in our study, F-measure (Figure 11) was used as the main performance measure. This measure, which is a harmonic (conservative) mean of precision and recall that is computed separately for each class, provides a more comprehensive and reliable assessment of model performance. Furthermore, unlike area under the receiver operating characteristic (ROC) curve (AUC), F-measure is the preferred metric for classification performance when case and control groups are not balanced (i.e., 1 : 1)²⁸, which is frequently the case in clinical studies and medical practice. Like AUC, F-measure ranges from 0 to 1, with higher values indicating superior classification performance. However, unlike AUC, a value of 0.5 for F-measure does not represent a random model and could in some cases indicate superior performance over random. F-measures for random performance for specific datasets and models can be estimated using permutation-based random models as described herein.

A combination with good precision and recall determined from this comparison was LR-

RFE & Logistic (Figure 10A, 10B), as the models learned using this feature selection and classification model were able to obtain the best performance with the fewest number of selected genes. This combination used the logistic regression algorithm¹⁹ as both the feature selection algorithm and global classification algorithm. The model learned using this combination, built upon an optimal set of 90 predictive genes, had perfect F-measures (F=1.00) in classifying asthma and no asthma in its corresponding holdout set. This model also significantly outperformed permutation-based random models The other seven classification models listed in Table 4 also had good precision and recall with the asthma gene panel.

Forty six of the 90 genes included in the LR-RFE & Logistic model were differentially expressed genes, with 22 and 24 genes over- and under-expressed in asthma, respectively (Figure 6 and Table 2A-2B). The remaining 44 genes were not differentially expressed. These results support that the machine learning pipeline was able to extract information beyond differentially expressed genes, allowing for the identification of a parsimonious panel of genes that together allowed for accurate asthma classification. Among these 90 genes, only four (C3, DEFBI, CYFIP2 and GSTT1) are known asthma genes³⁷. This demonstrates that the invented methodology effectively mines data to discover predictive genes that would not have been found by relying exclusively on current domain knowledge.

The LR-RFE & Logistic model of 90 genes is a subset of the 275 unique genes identified in all eight models, which 275 genes are defined as the "asthma gene paneF . Preferably, the 90 genes in this LR-RFE & Logistic asthma gene panel are used in combination with the LR-RFE & Logistic classifier and the model's optimal classification threshold (classify as asthma if probability output > about 0.76, else no asthma) to be effectively used for asthma classification, diagnosis or detection. Similarly, the genes in the model-specific asthma gene panels (Table 4) are used in combination with their model-specific classifiers and the model-specific optimal classification threshold to classify, diagnose or detect asthma effectively.

Validation of the asthma gene panel in an RNAseq test set of independent subjects

The inventors tested the asthma gene panel identified from the above-described machine learning pipeline on an independent RNAseq test set. For this step, the inventors used the test set (n=40) of nasal RNAseq data from independent subjects that was set aside and remained untouched by the development set analysis. The baseline characteristics of the subjects in the test set (n=40) are shown in the right section of Table 1. The baseline characteristics were similar between the development and test sets, except for a lower prevalence of allergic rhinitis among those without asthma in the test set.

The LR-RFE & Logistic Model asthma gene panel performed with high accuracy in the RNAseq test set of independent subjects, achieving AUC = 0.994 (Figure 2). The panel achieved high positive predictive value (PPV) of 1.00 and negative predictive value (NPV) of 0.96. Given imbalances in the case and control groups, F-measure is the preferred and more conservative metric for classification performance (Figure 1). The asthma gene panel achieved F = 0.98 and 0.96 for classifying asthma and no asthma respectively (Figure 3, left set of bars). For comparison, the much lower performance of permutation-based random models is shown in Figure 12 As context for comparison to other models possible from the machine learning pipeline and other methods, Figure 4 shows the performance of the 90-gene LR-RFE & Logistic model in the test set relative to those of classification models built using (1) other combinations tested in the machine learning pipeline, (2) all genes after filtering (11587 genes), (3) differentially expressed genes (Table 2A-2B), (4) 70 known asthma genes²⁹ (Table 3) and (5) a commonly used one-step classification model (Ll-Logistic, 243 genes). All these models performed significantly better than their random counterparts. The LR-RFE & Logistic Model asthma gene panel performed consistently among all the models derived from the machine learning pipeline, as had been expected based on the extensive training and analysis on the development set. The LR-RFE & Logistic Model asthma gene panel also outperformed the model learned using the one-step Ll-Logistic method. By separating the feature/gene selection and (outer) classification components, the machine learning pipeline was able to learn a more accurate and more parsimonious classification model, both of which are valuable qualities for disease classification, than Ll-Logistic. Overall, these results confirmed that the performance of the LR-RFE & Logistic Model asthma gene panel translated to an independent RNAseq test set, more so than other models, thus lending confidence to this LR-RFE & Logistic Model panel's ability to classify asthma accurately.

Similarly, the other seven classification models and corresponding asthma gene panels performed well in terms of precision and recall, and also beat random performance, such that these models also classify asthma accurately.

Validation of the LR-RFE & Logistic Model asthma gene panel in external asthma cohorts

To test the generalizability of the LR-RFE & Logistic Model asthma gene panel for asthma classification, the inventors applied this model to gene expression array data sets generated from two independent cohorts by other investigators with and without asthma (AsthmalGEO GSE19187)³⁰ and Asthma2 (GEO GSE46171)²¹.). Table 5 summarizes the characteristics of these external independent test sets. These datasets were generated from nasal samples collected by independent investigators from subjects with and without asthma from distinct populations, which were then profiled on gene expression microarray platforms. In general, RNA-seq based predictive models are not expected to translate to microarray profiled samples. ^{32 33} Gene mappings do not perfectly correspond between RNAseq and microarray due to disparities between array annotations and RNAseq gene models . The goal was to assess the performance of the LR-RFE & Logistic Model asthma gene panel despite the discordance of study designs, sample collections, and gene expression profiling platforms.

The inventors found that the LR-RFE & Logistic Model asthma gene panel performed relatively well given the above handicaps, and better than expected in classifying both asthma and no asthma (Figure 3, middle and right set of bars) and with significantly better performance than permutation-based random models (Figure 12). In particular, the LR-RFE & Logistic Model asthma gene panel markedly outperformed random models in classifying no asthma in both the Asthmal and Asthma2 test sets. While classification of asthma in Asthma2 achieved an F-measure of 0.74, its random counterpart also performed well (Figure 12). Asthma2 included many more asthma cases than controls (23 vs. 5). In such a skewed data set, it is possible for a random model to yield an artificially high F-measure for the majority class (here asthma) by predicting every sample to belong to that class. The inventors verified that this occurred with this random model. These results show that the LR-RFE & Logistic Model asthma gene panel performed reasonably well in these microarray test sets, supporting a degree of generalizability of the panel across platforms and cohorts. Such a translatable result has not been observed very frequently in translational genomic medicine research³⁴'³⁵.

The LR-RFE & Logistic Model asthma gene panel is specific to asthma: validation in external cohorts with non-asthma respiratory conditions

Because symptoms of asthma often overlap with those of other respiratory diseases, the inventors next sought to test the specificity of the LR-RFE & Logistic Model gene panel to asthma classification. For this, the inventors evaluated the performance of this LR-RFE & Logistic Model panel on nasal gene expression data derived from case control cohorts with allergic rhinitis (GSE43523)³⁶, upper respiratory infection (GSE46171)³¹, cystic fibrosis (GSE40445)³⁷, and smoking (GSE8987)¹². Table 6 details the characteristics for these external cohorts with non-asthma respiratory conditions. In four of the five non-asthma data sets, the LR- RFE & Logistic Model asthma gene panel appropriately produced one-sided classifications, i.e., all samples were classified as "no asthma" or healthy, the term for the control class (Figure 5). Specifically, the positive predictive value of the LR-RFE & Logistic Model panel across these test sets was exactly and appropriately zero for these test sets of non-asthma respiratory conditions (Table 7). The one exception to this was upper respiratory infection (URI2) profiled on day 2 of the illness, where the LR-RFE & Logistic Model panel classified some samples as asthma (F=0.25). This may have been influenced by common inflammatory pathways underlying early viral inflammation and asthma³⁸. Nonetheless, consistent with the other non-asthma test sets, the panel's misclassification of URI2 as asthma was substantially less than its random counterparts (Figure 13). These results show that the invented method is specific for classifying asthma and would not misclassify other respiratory diseases as asthma.

Examination of Genes in the LR-RFE & Logistic Model Asthma Gene Panel

Forty-six of the 90 genes included in the LR-RFE & Logistic Model panel were differentially expressed (FDR <0.05), with 22 and 24 genes over- and under-expressed in asthma respectively (Figure 6, Table 2A-2B). More generally, the genes in LR-RFE & Logistic Model panel had lower differential expression FDR values than other genes (Kolmogorov-Smirnov statistic=0.289, P-value=2.73x10-37) (Figure 14). Pathway enrichment analysis of these 90 genes was statistically limited by the small number of genes, yielding enrichment for pathways including defense response (fold change=2.86, FDR=0.006) and response to external stimulus (fold change=2.50, FDR=0.012). Only four (C3, DEFB1, CYFIP2 and GSTT1) of the 90 genes are known asthma genes and are functionally involved in complement activation, microbicidal activity, T-cell differentiation, and oxidative stress, respectively²⁹. These results suggest that the machine learning pipeline was able to extract information beyond individually differentially expressed or previously known asthma genes, allowing for the identification of a parsimonious panel of genes, including the LR-RFE & Logistic Model panel, that collectively enabled accurate asthma classification.

Discussion

The inventors have identified a panel of genes, as well as subsets of these genes for use with specific classifiers, expressed in nasal epithelium that accurately classifies subjects with mild/moderate asthma from healthy controls. This asthma gene panel, consisting of 275 unique genes interpreted via eight logistic regression classification models, performed with good precision and sensitivity. Specifically, the LR-RFE & Logistic model and associated asthma gene panel performed with high precision (PPV=1.00 and NPV=0.96) and sensitivity (0.92 and 1.00 for asthma and no asthma respectively) for classifying asthma. The performance of the LR- RFE & Logistic Model asthma gene panel across independent asthma test sets supports the generalizability of this panel across different study populations and two major modalities of gene expression profiling (RNA sequencing and microarray), as well as the specificity of this LR-RFE & Logistic Model panel as a diagnostic tool for asthma in particular, as well as the gene panels identified by the other seven models as discussed herein.

The asthma gene panel has high potential to be used as a minimally invasive biomarker to aid in asthma diagnosis in children and adults, as it can be quickly obtained by simple nasal brush, does not require machinery for collection, and is easily interpreted. According to the Global Initiative for Asthma and US National Heart Lung Blood Institute, the diagnosis of asthma should be based on a history of typical symptoms and objective findings of variable expiratory airflow limitation by PFT⁶'⁷. Practically, however, objective findings are often not obtainable. Patients with mild/moderate asthma are frequently asymptomatic at the time of the clinical encounter, so they may have no detectable wheezing or cough on exam. Pulmonary function testing (PFT) is often not done for patients, as was keenly demonstrated by a study showing that over half of 465,866 patients age 7 years and older with newly diagnosed with asthma had no PFTs performed within a 3.5 year time period surrounding the time of diagnosis.⁸ Clinicians may defer PFTs due to lack of equipment, time, and/or expertise to perform and interpret results⁸' ⁹. Diagnosing asthma based on history alone contributes to its under-diagnosis, as patients with asthma under-perceive and under-report their symptoms¹¹. Misdiagnosis of asthma also occurs frequently given overlapping symptoms between asthma and other conditions³⁹. Even if PFTs are obtained, spirometric abnormalities in mild/moderate asthmatics are not always present. An objective, accurate diagnostic tool that is easy and quick to obtain and interpret with minimal effort required by the provider and patient could improve asthma diagnosis so that appropriate management can be pursued. The nasal brush-based asthma gene panel meets these biomarker criteria.

Implementation of the asthma gene panel could involve clinicians brushing a patient's nose, placing the brush in a prepackaged tube, and submitting the sample for gene expression profiling targeted to the panel. Some platforms allow for direct transcriptional profiling of tissue without an RNA isolation step, avoiding inconveniences associated with direct RNA work⁴⁰' ⁴¹ and yielding comparable results to RNAseq⁴². Bioinformatic interpretation of the output via the LR-RFE & Logistic model and classification threshold could be automated, resulting in a determination of asthma or no asthma for the clinician to consider. Biomarkers based on gene expression profiling are being successfully used in other disease areas (e.g., MammaPrint and Oncotype DX⁴⁴ for diagnosing/predicting breast cancer phenotypes).

Because it takes seconds for nasal brushing, the panel may be attractive to time- strapped clinicians, particularly primary care providers at the frontlines of asthma diagnosis. Asthma is frequently diagnosed and treated in the primary care setting⁴⁵ where access to PFTs is often not immediately available. Although PFTs yield results without specimen handling, these advantages do not seem to overcome its logistical limitations as evidenced by their low rate of real-life implementation⁸' ⁹ but low cost⁴⁶. However, gene expression profiling costs are likely to decrease47, and implementation of the LR-RFE & Logistic Model asthma gene panel could result in cost savings if it reduces the under-diagnosis and misdiagnosis of asthma³. Undiagnosed asthma leads to costly healthcare utilization worldwide³, including in the United States, where asthma accounts for $56 billion in medical costs, lost school and work days, and early deaths⁴⁸. Clinical implementation of the asthma gene panel could identify undiagnosed asthma, leading to its appropriate management before high healthcare costs from unrecognized asthma are incurred. Given the the LR-RFE & Logistic Model panel's demonstrated specificity, use of the LR-RFE & Logistic Model asthma gene panel could also reduce asthma misdiagnosis by correctly providing a determination of "no asthma" in non-asthmatic subjects with conditions often confused with asthma. Clinical benefit from gene-expression based biomarkers has already been seen in the breast cancer field, where use of the 70-gene panel test MammaPrint to guide chemotherapy in a clinical trial leads to a lower 5-year rate of survival without metastasis compared to standard management 43.

The nasal brush-based asthma gene panel capitalizes on the common biology of the upper and lower airway, a concept supported by clinical practice and previous findings. ^12-15 Clinically, clinicians rely on the united airway by screening for lower airway infections (without limitation, influenza, methicillin-resistant Staphylococcus aureus) with nasal swabs. ⁴⁹ Sridhar et al. found that gene expression consequences of tobacco smoking in bronchial epithelial cells were reflected in nasal epithelium. ¹² Wagener et al. compared gene expression in nasal and bronchial epithelium from 17 subjects, finding that 99.9% of 33,000 genes tested exhibited no differential expression between nasal and bronchial epithelium in those with airway disease. ¹³ In a study of 30 children, Guajardo et al. identified gene clusters with differential expression in exacerbated asthma vs. controls. ¹⁴ The above studies were done with small sample sizes and microarray technology, although more recently, Poole et al. compared RNA-seq profiles of nasal brushings from 10 asthmatic and 10 control subjects to publically available bronchial transcriptional data, finding strong correlation (p = 0.87) between nasal and bronchial transcripts, and strong correlation (p = 0.77) between nasal differential expression and previously observed bronchial differential expression in asthmatics. ¹⁵

Although based on only 90 genes, the LR-RFE & Logistic Model asthma gene panel classified asthma with greater accuracy than models using all differentially expressed genes in the sample (n = 2187), all known asthma genes from genetic studies of asthma (n = 70), as well as models based on information from all sequenced genes (n = 11587 after filtering) (Figure 4). Its superior performance supports that the machine learning pipeline described herein successfully selected a parsimonious set of informative genes that (1) captures more actionable knowledge than those identified by traditional differential expression and genetic analyses, and (2) cuts through the noise of genes that are irrelevant to asthma. The genes selected by the other seven models listed in Table 4 are also highly precise and have good recall. About half the genes in the LR-RFE & Logistic Model asthma gene panel were not differentially expressed at FDR < 0.05, and as such would not have been examined with greater interest if the inventors had performed only differential expression analysis, which is the main analytic approach of virtually all studies of gene expression in asthma. ^12"15' ⁵⁰' ⁵¹ The differential expression FDRs of the 90 genes in the LR-RFE & Logistic Model panel were skewed toward lower values as compared to the rest of the genes in our development set (Figure 14). This demonstrated that the LR-RFE & Logistic Model asthma gene panel captures signal from differential expression as well as genes below traditional significance thresholds that may still have a contributory role in asthma classification. Only four of the 90 genes in the LR-RFE & Logistic Model gene panel (complement component 3 (C3), defensing beta-1 (DEFB1), cytoplasmic FMR1 interacting protein (CYFIP2) and glutathione S-transferase theta 1 (GSTT1) were genes previously identified by genetic association studies. ²⁹In this study, the inventors were able to use the machine learning pipeline to identify this LR-RFE & Logistic Model panel of 90 genes - comprised of both differentially expressed and non-differentially expressed genes, and of genes largely without known genetic associations with asthma— whose gene expression levels can be jointly interpreted via a logistic regression algorithm to accurately predict asthma status. The asthma gene panel did not perform quite as well in the asthma microarray test sets, and this was to be expected due to differences in study design between the RNAseq and and microarray test sets. First, the baseline characteristics and phenotyping of the subjects differed. Subjects in the RNAseq test set were adults who were classified as mild/moderate asthmatic or healthy using the same strict criteria as the development set (see Materials and Methods above), which required subjects with asthma to have an objective measure of obstructive airway disease (i.e., positive methacholine challenge response). In contrast, subjects in the Asthmal microarray test set were all children (i.e., not adults) with underlying allergic rhinitis and dust mite allergen 358 sensitivity, whose asthma status was then determined clinically³⁰ (Table 5). Subjects from the Asthma2 cohort were adults who were classified as having asthma or as healthy based on history. As mentioned, the diagnosis of asthma based on history alone without objective lung function testing can be inaccurate⁵². The phenotypic differences between these test sets alone could explain the differences in performance of the LR-RFE & Logistic Model asthma gene panel in the microarray test sets. Second, the differential performance may be due to the difference in gene expression profiling approach. Gene mappings do not perfectly correspond between RNAseq and microarray due to disparities between array annotations and RNAseq gene models.³³ Compared to microarrays, RNAseq quantifies more RNA species and captures a wider range of signal. ⁵⁰ Prior studies have shown that microarray-derived models can reliably predict phenotypes based on samples' RNAseq profiles, but the converse does not often hold.³³ Despite the above limitations, the asthma gene panel (identified using the RNAseq-derived development set) performed with reasonable accuracy in classifying asthma in the independent microarray test sets. These results support the generalizability of the asthma gene panel to asthma populations that may be phenotyped or profiled differently. An effective biomarker for clinical use should have good positive and negative predictive value. ⁵³ In the present method, if an individual has asthma, the ideal biomarker would confirm this most of the time so that an accurate diagnosis is made, and if an individual does not have asthma, the ideal biomarker would confirm this (indicating "no asthma") so that misdiagnosis does not occur. This is indeed the case with the LR-RFE & Logistic Model asthma gene panel, which achieved high positive and negative predictive values of 1.00 and 0.96 respectively on the RNAseq test set. The inventors tested the LR-RFE & Logistic Model asthma gene panel on independent tests sets of subjects with upper respiratory infection, cystic fibrosis, allergic rhinitis, and smoking, showing that the panel had a low to zero rate of misclassifying subjects with these other respiratory conditions as having asthma (Figure 5). These results were particularly notable for allergic rhinitis, a predominantly nasal condition. Although the asthma gene panel is based on nasal gene expression, and asthma and allergic rhinitis frequently co- occur²³, the LR-RFE & Logistic Model panel did not misdiagnose allergic rhinitis as asthma. These results support the specificity of the LR-RFE & Logistic Model asthma gene panel, as well as the gene panels identified in the other models, as a diagnostic tool for asthma in particular.

Even though the development set was from a single center and its baseline characteristics do not characterize all populations, variancePartition analysis demonstrated minimal contribution of age, race, and gender to gene expression variance in these data (Figure 7). Further, the LR- RFE & Logistic Model panel performed well in multiple external data sets spanning children and adults of varied racial distributions, and with asthma and other respiratory conditions defined by heterogeneous criteria. Subjects with asthma in the development cohort were not all symptomatic at the time of sampling. The fact that the performance of the LR-RFE & Logistic Model asthma gene panel does not rely on symptomatic asthma is a strength, as many mild/moderate asthmatics are only sporadically symptomatic given the fluctuating nature of the disease.

As with any disease, the first step is to accurately identify affected patients. The asthma gene panel described in this study provides an accurate path to this critical diagnostic step. With a correct diagnosis, an array of existing asthma treatment options can be considered⁶. A next phase of research will be to develop a nasal biomarker to predict endotypes and treatment response, so that asthma treatment can be targeted, and even personalized, with greater efficiency and effectiveness⁵⁴.

In summary, the inventors applied a machine learning pipeline to identify a panel of genes expressed in nasal epithelium that accurately classifies subjects with mild/moderate asthma from healthy controls. This asthma gene panel, comprised of 275 genes and/or its subsets used in combination with model-specific classifiers and model-specific optimal classification thresholds, performed with accuracy across 8 independent test sets, demonstrating generalizability across study populations and gene expression profiling modality, as well as specificity to asthma. The asthma gene panel has high potential to be used as a minimally invasive biomarker to aid in asthma diagnosis, as it can be quickly obtained by simple nasal brush, does not require machinery for collection, and is easily interpreted. There are currently many limitations in asthma diagnostics. If applied to clinical practice, this asthma gene panel could improve asthma diagnosis and classification, reduce incorrect diagnoses, and prompt appropriate therapeutic management.

Table 2. Lists of over-expressed (A) and under-expressed (B) genes and pathways in asthma cases as compared to controls. Differentially expressed genes were identified using DESeq2²⁵ and enriched pathways were identified from the Molecular Signature Database²⁶.

Table 2A. Over-expressed Genes and Pathways

ENDOG 1.97993156 1.71162E-13 SLC25A29 1.30866247 0.000882489

IRX3 1.83337486 2.01018E-13 APOD 1.86608903 0.000889037

CAPS 4.06302266 2.40086E-13 LOC728743 1.75169318 0.00089053

LPHN1 2.10407317 2.68055E-13 ZNF628 1.42007237 0.000892028

C2orf55 2.27283672 3.17873E-13 COBL 1.40319221 0.000896699

SYNGAP1 2.13301423 4.22489E-13 TTC30A 1.67935463 0.000904764

CCDC24 1.96494776 4.42276E-13 RAB40C 1.32476452 0.000914679

SLC16A11 2.0521962 4.51489E-13 WDR92 1.46789585 0.000918523

UCKL1.AS1 3.82462625 6.69507E-13 BBS12 1.49170368 0.000920472

RRAD 3.39266415 6.69507E-13 SCAF1 1.27078484 0.000920472

NHLRC4 4.55169722 7.65957E-13 EXD3 1.63736942 0.000922835

PRR7 2.91887265 7.94092E-13 C16orf42 1.26458944 0.000924002

RAB3B 4.24372545 8.15138E-13 CBX7 1.30724875 0.000931098

CCDC17 4.24211711 8.23826E-13 KLHL29 1.52045452 0.000934632

ANKRD54 2.03165888 9.41636E-13 MTA1 1.28935596 0.000934937

TCTEX1D4 4.30165643 9.81969E-13 ZNF496 1.38327158 0.000955848

PPP1R16A 1.78187416 1.01874E-12 ANKRD45 1.70738389 0.000963023

NAT 14 3.06261532 1.03487E-12 LOC388564 1.93649556 0.000967111

CTXN1 4.61823126 1.03958E-12 HAGH 1.32213624 0.000998155

ANKK1 2.06364461 1.03958E-12 PDGFA 1.42863088 0.001019324

MAPK15 4.61083061 1.07813E-12 ZFP3 1.42226786 0.001019324

TEKT2 4.78797511 1.13157E-12 ST5 1.34063535 0.001032342

CCDC96 2.89251884 1.13157E-12 SLC39A13 1.36833179 0.001039645

CXCR7 2.57340048 1.18772E-12 XYLT2 1.32074435 0.001043171

SPEF1 4.04138282 1.28995E-12 OGFOD2 1.37705326 0.001063251

C2orf81 3.88312294 1.62387E-12 CCDC106 1.38920751 0.001077622

TPPP3 4.1122218 1.95083E-12 C10orf57 1.39625227 0.00108256

TP73 3.73216045 2.05602E-12 TYSND1 1.32704457 0.00108435

C17orf72 4.12597857 2.42931E-12 ZNF428 1.25531565 0.001085719

KIF19 4.04831578 2.42931E-12 ZBTB7A 1.27318182 0.001101095

CRNDE 1.90266433 2.42931E-12 FLJ90757 1.41213053 0.001112519

FDXR 1.75411331 2.42931E-12 TMEM120B 1.35883101 0.001112519

TNFAIP8L1 3.66812001 2.52964E-12 KIAA1456 1.49996729 0.001115207

IFT140 2.56011824 2.52964E-12 FAM125B 1.40872274 0.001117603

FBXW9 2.0309423 3.71669E-12 CLSTN1 1.3290101 0.001119504

ESPN 1.78254716 4.12128E-12 SF3A2 1.28509238 0.001134443 DFNB31 1.8555535 4.1682E-12 DYNC2LI1 1.43389873 0.00114729

TTLL10 3.97446989 4.96622E-12 SIGIRR 1.28806752 0.00114729

FAM116B 2.76115746 5.75046E-12 ABHD14B 1.32342281 0.001156608

CCDC19 3.97176187 5.83187E-12 OSBPL5 1.35005294 0.001181561

C6orf27 3.15382185 6.10565E-12 GCDH 1.32866052 0.001181561

C16orf48 2.28318997 6.26965E-12 GLTSCR1 1.31492951 0.001183371

GAS8 1.96553042 6.26965E-12 TMEM175 1.31373498 0.001185533

CD164L2 3.21331723 6.36707E-12 TRAPPC6A 1.3224038 0.001185954

CCDC78 4.79072783 6.85549E-12 HSD11B2 1.48148593 0.001191262

CCDC40 4.02185553 7.85218E-12 DEXI 1.28219144 0.001199474

CCDC157 2.50320674 1.03363E-11 TCF7 1.40542673 0.001215045

UBXN11 2.67485867 1.12753E-11 B4GALT7 1.28277814 0.001225929

C9orf24 4.24049927 1.13692E-11 MYBBP1A 1.34519608 0.00122885

B9D1 2.93782564 1.3303E-11 ATXN7L1 1.41659202 0.001242233

LRRC56 2.57381093 1.60583E-11 PIN1 1.30404482 0.001254241

PKIG 2.47239105 1.60583E-11 MT2A 2.04000703 0.001255227

ADSSL1 1.963967 1.70739E-11 DNAJB2 1.28234552 0.001261961

PASK 2.00442189 1.93192E-11 EPN1 1.26463544 0.001280015

C5orf49 3.85710623 1.95595E-11 TMEM61 1.50446719 0.001281574

TUBB2C 2.04908703 2.17307E-11 C7orf47 1.27854479 0.001321603

HSPBP1 1.8050605 2.17307E-11 IDUA 1.37272518 0.001349843

DLEC1 4.80156726 2.39955E-11 MACROD1 1.33230567 0.001350085

AN MY1 2.5681388 2.39955E-11 SERPINBIO 1.94661954 0.001361514

RUVBL2 1.8875842 2.41852E-11 ADCK3 1.28015615 0.001363257

WDR54 3.54079973 2.48129E-11 CD99L2 1.37191778 0.001364491

CCDC108 4.40594345 2.82076E-11 SIVA1 1.26797988 0.001374975

USP2 2.61579764 2.82076E-11 ST6GALNAC6 1.31105149 0.001381949

WDR90 2.25341462 3.47445E-11 KIAA0284 1.30334689 0.001396666

SLC1A4 1.7743007 3.60414E-11 DNASE1L1 1.29767606 0.001422038

ISYNA1 1.78188864 3.90247E-11 BPHL 1.35364961 0.001457025

LRRC48 4.23655785 4.33546E-11 KCTD17 1.41885194 0.001460503

SLC27A2 1.77294486 4.33546E-11 REXOl 1.27951422 0.001466253

Cl lorfl6 4.16123887 4.35926E-11 PLEKHA4 1.5120144 0.001477764

BBS5 2.05305886 4.96429E-11 LOC202781 1.39766879 0.001490088

C14orf79 1.9431267 4.96429E-11 ZCWPW1 1.4170765 0.001527816

DNAAF2 1.82683937 5.32802E-11 BPIFB1 1.57081973 0.001561587 IQCD 2.99396253 5.9179E-11 LRRC68 1.31705305 0.00159354

PPOX 2.466844 5.9179E-11 PITPNM3 1.30084505 0.00159354

ZNF703 1.80994279 6.27934E-11 TTC22 1.29235387 0.00159354

IGFBP2 2.12208723 6.3397E-11 IRF2BP1 1.28392082 0.00159354

KCNH3 3.74731532 6.67127E-11 Cl lorf 2 1.50310038 0.001602954

RHPN1 2.11269443 6.74204E-11 PPP2R3B 1.33531577 0.001643944

KNDC1 4.27320927 8.33894E-11 GALNTL4 1.32355512 0.001671166

TRAF3IP1 1.80219185 8.80362E-11 NFIC 1.31815493 0.001671166

FAM92B 3.96288061 8.91087E-11 SELO 1.29376914 0.001682582

C5orf4 2.02530771 9.38443E-11 GPX4 1.30577473 0.001695128

MAP6 4.48787026 9.67629E-11 CYP2J2 1.3244996 0.001696726

IQCE 1.88795828 9.71132E-11 LHPP 1.2977942 0.001696726

INPP5E 1.8396103 9.71132E-11 DNLZ 1.45201735 0.001710038

NWD1 3.99394282 1.13238E-10 DGCR6L 1.28160338 0.00171044

DNAH9 4.39061797 1.16455E-10 GATS 1.34306522 0.001752534

LTBP3 1.62487623 1.3309E-10 NAF1 1.46514246 0.001758144

CDK20 2.3240984 1.54953E-10 PAK4 1.32518993 0.001765767

CCNO 2.32391131 1.55262E-10 TMEM138 1.3805845 0.001773926

RAB36 3.80755493 1.59581E-10 D2HGDH 1.31785815 0.001788379

WDR34 1.87639055 1.87132E-10 NR2F2 1.33842839 0.001803287

DNAIl 4.84949642 2.12635E-10 EPB49 1.32650369 0.001819396

DNAAF1 3.83746993 2.14037E-10 POFUT2 1.31411257 0.001820415

CCDC164 4.2557065 2.20169E-10 B3GAT3 1.35107174 0.001832824

ASCL2 2.04147055 2.26234E-10 GLI4 1.44684606 0.001837393

FHAD1 3.13964638 2.37682E-10 FGF11 1.39446213 0.001840765

FAM179A 4.66078913 2.37965E-10 RHBDD2 1.26141125 0.001840765

TEKT1 4.13606595 2.48284E-10 ZNF444 1.3510369 0.001852547

DALRD3 1.75343551 2.48284E-10 PEBP1 1.30689705 0.001854974

TMCC2 1.90615943 2.60427E-10 ZCCHC3 1.34025699 0.001863781

CCDC114 4.09401076 2.95477E-10 LRRC37A4 1.4519284 0.001865

LRWD1 1.98021375 3.02767E-10 TUBGCP6 1.30193887 0.001904076

NCRNA00094 2.12505456 3.12538E-10 XRCC3 1.3864244 0.001922788

WDR38 4.23621789 3.26822E-10 RNF187 1.29592471 0.001936892

ALDH3B1 1.6813904 3.28037E-10 NCRNA00265 1.3750193 0.001948591

TMEM190 4.8685534 3.30569E-10 WRB 1.40277381 0.001971203

ULK4 2.32420099 3.48495E-10 CHST14 1.38178684 0.001993182 DMRT2 1.82662574 3.48718E-10 PIK3R2 1.30114605 0.002023385

C9orfl71 3.97704489 3.72441E-10 UBTD1 1.28646654 0.002023385

FUZ 2.72661607 3.81064E-10 SEC14L5 1.76950735 0.00203473

VWA3A 4.21877596 4.49516E-10 SFI1 1.34394937 0.002037678

CDHR4 5.12021012 4.57757E-10 DPY30 1.32184041 0.002046145

METRN 2.25309804 4.57757E-10 HSF1 1.31711734 0.002053899

LOCI 13230 1.81478964 4.57757E-10 NME4 1.30387104 0.002071504

DNAI2 4.03796529 4.76126E-10 RBM43 1.40951659 0.002083034

TCTN2 2.40490432 4.95937E-10 FAM98C 1.274507 0.002089047

FAM166B 3.90791018 5.63709E-10 EML2 1.32629448 0.002117113

ZMYND10 3.69143549 6.00928E-10 ZNF219 1.29662551 0.002118188

MZF1 1.76527865 6.58326E-10 C20orfl94 1.37210455 0.002121672

ROPN1L 3.43290481 6.64612E-10 B4GALNT3 1.30834896 0.002163609

APBB1 2.62366455 6.64612E-10 OBSL1 1.305937 0.00217526

PLEKHB1 3.4214872 6.72995E-10 C18orfl0 1.32144956 0.002179978

LRRC23 3.23420407 7.30088E-10 NAGLU 1.27039068 0.002183662

SLC4A8 3.06635647 8.20469E-10 MUC2 2.27000647 0.002193863

WNT9A 1.97501893 8.98004E-10 MGLL 1.27904425 0.002205765

CCDC103 3.21531173 9.17894E-10 FAM173A 1.38467098 0.002209168

C20orf85 3.7643551 9.37355E-10 PSIP1 1.34684146 0.002212642

TSNAXIP1 3.67477124 9.47472E-10 TSPANl 1.27665824 0.002224043

DNAH2 3.69841798 9.84984E-10 TUSC2 1.29490502 0.002232434

ZNF474 3.52004876 1.11372E-09 PROM1 1.46799121 0.002239807

TPPP 2.28275479 1.11372E-09 POLD2 1.31983997 0.002243731

TMEM231 3.16472296 1.12292E-09 SCRIB 1.29183479 0.002243731

TTC12 1.91008892 1.13249E-09 JMJD8 1.24988195 0.002286644

LDLRADl 3.56956748 1.15526E-09 RBP1 1.29553455 0.002297925

CHCHD10 1.87337748 1.18307E-09 UTRN 1.35691111 0.002362252

RFX2 2.66731378 1.23139E-09 PARP3 1.34735994 0.002369225

UBXN10 3.25532613 1.26161E-09 RASSF6 1.39490614 0.002390815

IFT172 2.64104339 1.3631E-09 LOC92249 1.40466136 0.002391912

BAIAP3 3.63613461 1.411E-09 OVCA2 1.3163436 0.002404409

EFCAB2 2.69292361 1.42619E-09 TRIM56 1.29535959 0.002427233

Cl lorf88 3.52355279 1.4444E-09 TREXl 1.26637345 0.002431847

SLC13A3 2.20805923 1.4444E-09 PECR 1.38681797 0.002480649

IFT122 2.04426301 1.48429E-09 FBXL14 1.33944092 0.002480649 NPHP4 1.89172058 1.51209E-09 TCN2 1.28764878 0.002480649

TXNDC5 1.86619199 1.515E-09 THOC3 1.35544993 0.002495975

C17orf 7 2.35986311 1.62066E-09 MRPL41 1.4462408 0.002497021

WDR16 4.36651228 1.62402E-09 WNT3A 1.56505668 0.002502772

DNALI1 3.46070328 1.63511E-09 MAP1LC3A 1.35719631 0.002502772

NUDT3 1.73970966 1.64286E-09 TOP1MT 1.4172985 0.00251409

SMYD2 2.10344741 1.70609E-09 KREMEN1 1.24654847 0.00251866

TTC25 3.71446639 2.05596E-09 LOC729013 1.39863494 0.002528217

RBM38 1.61948356 2.1203E-09 TTLL1 1.43077672 0.002625335

GGT7 1.66897144 2.14547E-09 DMPK 1.32867357 0.002625335

CES1 3.00060938 2.23456E-09 ODF2L 1.34583296 0.002626872

C21orf59 1.72965503 2.26356E-09 RBM20 1.43070108 0.00266198

CCDC65 3.41519122 2.38892E-09 CDC42EP5 1.49582876 0.002673583

WDR60 1.90360794 2.48798E-09 ZNF608 1.40853604 0.002676791

UNC119B 1.68295738 2.7675E-09 EYA1 1.3918948 0.002677512

EML1 3.14662458 2.86572E-09 SLFN11 1.6901633 0.002694402

ODF2 1.77285642 2.88517E-09 TMEM129 1.29584257 0.002694402

C20orf 6 3.28661501 2.92408E-09 PEX14 1.32225002 0.002740151

C21orf2 1.59981088 2.95269E-09 MAPK8IP3 1.26167122 0.002782515

LRRC45 1.73562887 2.9555E-09 CDC20B 2.92979203 0.002783456

LOC100506668 2.17031169 3.52531E-09 ROGDI 1.30155263 0.00278416

GLB1L 2.06829337 3.65952E-09 ABCB6 1.28553394 0.002829302

CCDC74A 3.2798251 3.94098E-09 NEK1 1.48582987 0.002837851

ABCA2 1.64595295 3.94098E-09 TIGD5 1.32981321 0.002841309

MAP1A 3.30677387 4.49644E-09 PNMA1 1.34478941 0.002879762

C9orf9 3.3529991 4.60478E-09 MLXIP 1.29784865 0.002879762

CHST9 1.75966672 4.8617E-09 SHANK3 1.49177371 0.002905903

MAPRE3 2.07180681 5.32347E-09 STEAP3 1.30957029 0.002908485

R D2 2.18107852 5.44526E-09 CUTA 1.27360936 0.002926573

DGCR6 1.8288164 5.45688E-09 FOXK1 1.28002126 0.002930286

SNED1 1.88272394 5.83476E-09 MFSD7 1.25269625 0.002962728

LRRC46 4.00288588 5.87568E-09 LONRF2 1.51428834 0.003024428

C16orf71 3.78067833 5.87568E-09 TRIT1 1.41931182 0.003031643

FBX036 1.97697195 5.87808E-09 MFI2 1.33497681 0.003031643

STK33 3.32049025 5.97395E-09 CYP4B1 1.5268612 0.003087739

FANK1 3.09673143 6.34411E-09 CIT 1.29305217 0.003090804 IRF2BPL 1.5943287 6.45821E-09 C8orf82 1.31308077 0.00315658

MEX3D 1.59132125 6.57088E-09 PTPMTl 1.28651139 0.003168897

TTC29 3.77710968 7.14688E-09 SPHK2 1.30201644 0.003181927

SPAG17 4.10266721 7.18248E-09 TTC7A 1.28286232 0.003226858

DNAH10 4.05401954 7.37766E-09 CLCN4 1.36981571 0.003255752

C19orf55 1.81580403 7.5128E-09 MSI2 1.35012032 0.003301438

GNA14 2.3089692 7.76554E-09 ING5 1.41166882 0.003322367

GPR162 3.42624459 7.78437E-09 PFN2 1.3345102 0.003361105

KIF24 2.6517961 8.23367E-09 SGSM1 1.48304522 0.00338494

C6orf 7 3.05579163 8.66959E-09 DUSP28 1.40424776 0.003417564

ATP2C2 1.60268251 8.79826E-09 MGMT 1.28389471 0.003429868

EFHC1 3.13154257 1.00071E-08 TP63 1.59679744 0.003467929

C9orfl l6 2.98680162 1.02805E-08 BTBD9 1.31826402 0.003467929

TUBA4B 3.44329925 1.10115E-08 IL17RC 1.24675615 0.003467929

TUB 3.28725084 1.10581E-08 0DZ4 1.36904786 0.003524126

IGFBP5 3.42171001 1.12425E-08 ZNF395 1.29186035 0.003586842

GOLGA2B 1.87746797 1.15371E-08 YDJC 1.33057894 0.003598986

RAGE 2.48773652 1.16413E-08 APOO 1.34408585 0.003608735

UCP2 1.52039355 1.17729E-08 SVEP1 1.40836202 0.003638829

KIAA1407 2.63617454 1.18646E-08 RAB11FIP3 1.3058731 0.003671701

TTC21A 2.5095734 1.20361E-08 TEF 1.3271192 0.003677553

Clorfl73 3.85335748 1.24014E-08 PIGQ 1.2693317 0.003740448

PSENEN 1.74442606 1.26734E-08 LGALS9B 1.36354436 0.003783693

MAPK8IP1 2.43031719 1.31409E-08 MAOB 1.66197193 0.003808831

WDR52 2.7867767 1.3227E-08 EID2 1.27884537 0.003835751

RCAN3 1.67977331 1.32982E-08 BAD 1.25388842 0.003897732

REC8 2.71104704 1.35783E-08 BTBD2 1.3199268 0.003913864

KCTD1 1.63948363 1.35783E-08 WNT5B 1.43246867 0.003931223

ZNF579 1.56261805 1.43116E-08 SLC25A10 1.24603921 0.004010737

NCALD 2.31903784 1.48365E-08 PLK4 1.81340223 0.004056611

IFT43 1.8372634 1.6037E-08 CEP97 1.41538101 0.004071998

GALNS 1.69455658 1.60813E-08 FAM53B 1.26253686 0.00411007

RABL5 2.20299003 1.6314E-08 CTSF 1.3223521 0.004131025

SLC22A4 2.22553299 1.66879E-08 C9orf86 1.2153444 0.004156197

CC2D2A 3.16499889 1.70886E-08 MAST2 1.32022199 0.004165643

C12orf75 2.65337293 1.74645E-08 TSKU 1.29264907 0.004165643 MS4A8B 4.57793875 1.78335E-08 CTBP1 1.2796825 0.004188226

DNAH5 3.74507278 1.82168E-08 CES2 1.2809789 0.00419032

LRTOMT 2.78785677 1.91101E-08 ZNF747 1.35584614 0.004211769

C18orfl 1.87715316 1.91101E-08 LOC100129034 1.27756324 0.004253091

TRADD 1.56913276 1.97067E-08 HIST3H2A 1.37492639 0.0043908

Clorfl94 3.88158651 1.98158E-08 C16orfl3 1.2824815 0.00441089

STOX1 2.81737017 2.04397E-08 ITGB4 1.28611762 0.004452134

SPAG6 3.38226503 2.05137E-08 MED24 1.28423462 0.004500601

EFCAB6 3.13972956 2.0547E-08 IYD 1.44205522 0.004540332

CDHR3 4.50496815 2.09665E-08 C2orf54 1.30578019 0.004584237

Clorfl92 3.27606806 2.13713E-08 PRRC2B 1.28521665 0.004638924

ST6GALNAC2 1.69322433 2.13713E-08 PHF7 1.38040111 0.004645863

CEP250 1.63128892 2.13713E-08 MFSD3 1.25286479 0.004724472

RSPH9 3.5289842 2.2596E-08 PARD6G 1.35223208 0.004755624

RFX3 2.64245161 2.28181E-08 POC1A 1.58918583 0.00476711

DMRTA2 1.55534501 2.28181E-08 LAMC2 1.33269517 0.004830864

CCDC113 3.00709138 2.33952E-08 RABEP2 1.23103314 0.004830864

TCTN1 2.57027348 2.43901E-08 HSPB 11 1.30028439 0.004881315

ZNHIT2 1.68919209 2.59867E-08 LOC642361 1.32431188 0.004908329

NELL2 4.27702275 2.62282E-08 LIME1 1.30504035 0.0049123

DNAH3 3.76161641 2.68229E-08 FLYWCH1 1.28311096 0.004926395

RSPH1 3.9078246 2.79364E-08 ANG 1.30320826 0.005082111

IP04 1.62195554 2.83731E-08 QTRT1 1.29616636 0.005082111

OSBPL6 2.51046395 2.86967E-08 CMTM4 1.31610931 0.005122846

NPHP1 3.03497793 2.87686E-08 TMEM125 1.26660312 0.005185303

NPEPL1 1.80587307 2.93319E-08 SLC22A18 1.25291574 0.005205062

PCDP1 3.86414265 3.03499E-08 KIAA1549 1.32573653 0.005215326

HES6 2.83951527 3.03499E-08 PRR5L 1.28471689 0.0052441

OSCP1 2.46419674 3.16173E-08 MOCS1 1.41983774 0.00527108

C6orf225 2.88981515 3.16232E-08 LIG3 1.36586625 0.005275193

RDH14 1.85367299 3.20457E-08 CEP85 1.34134846 0.005281836

WDR31 1.86799234 3.3187E-08 NGFR 2.00940868 0.005299414

NRSN2 1.72859689 3.33598E-08 FBX027 1.30963588 0.005345999

CYB5D1 2.01628245 3.53966E-08 B4GALT2 1.27095263 0.005369313

FAAH 1.64399385 3.56421E-08 GRINA 1.22714784 0.005469662

LRRC27 1.81134305 3.62992E-08 HMGN3 1.30614416 0.005501463 CIB1 1.51834252 3.65446E-08 SLC38A10 1.23802809 0.005603169

SPPL2B 1.52835317 3.68019E-08 PTPRF 1.26953871 0.005666966

CROCCP2 1.60146337 3.69799E-08 GBP6 1.48338148 0.005693169

NFIX 1.57340231 3.71894E-08 BMP7 1.28713632 0.005693169

RIBCl 3.0954211 3.73058E-08 SAMDl 1.33223945 0.005760574

ARMC2 2.45822891 3.73058E-08 GLTPD2 1.38603298 0.005780154

KIF9 2.3180051 3.79512E-08 WDPCP 1.43105126 0.005868184

COQ4 1.56458854 3.96258E-08 ZNF764 1.32764703 0.005880763

WDR66 3.18527022 4.13597E-08 SLC7A4 1.38094904 0.005896344

KLHL6 3.05051676 4.13597E-08 GRB10 1.24234552 0.005898053

A KRD9 1.68315489 4.18769E-08 PRICKLE3 1.3269405 0.005899727

PPIL6 3.49881233 4.5818E-08 CCDC61 1.31458986 0.005914279

CELSR1 1.5798801 4.61481E-08 LTK 1.32450408 0.005930841

ECT2L 3.92659277 4.67195E-08 ITM2C 1.25343875 0.005945917

TMEM107 2.25606657 4.72838E-08 TABl 1.3138026 0.005986003

IL5RA 3.38598476 4.91414E-08 WDR5B 1.39199432 0.006027191

SPATA18 3.04142002 5.0583E-08 EVC 1.36532048 0.006041191

ZNF865 1.55350931 5.11875E-08 SLC39A3 1.2652111 0.006058887

MKS1 1.72625587 5.31129E-08 NAA40 1.31875635 0.006126576

DNAH12 4.07123221 5.46701E-08 ZNF696 1.34935807 0.006126723

SNTN 3.41828613 5.48011E-08 CCDC57 1.37984887 0.006169795

SNAPC4 1.55079316 5.48488E-08 B3GNT1 1.34790314 0.006464002

KLHDC9 2.21375808 5.68972E-08 SCNN1B 1.24287546 0.006510517

MTSS1 1.59589799 5.76209E-08 SAP30 1.37835625 0.00653315

PTRH1 1.64149801 5.78872E-08 FAM3A 1.21815206 0.006541067

C16orf55 2.03868071 5.8729E-08 CYP27A1 1.39178134 0.006574926

C7orf57 3.24294862 6.00827E-08 GMPPB 1.26122262 0.006743861

NUDC 1.54151756 6.10697E-08 POLI 1.37956907 0.006792284

TNFRSF19 2.20738343 6.27622E-08 ALDH16A1 1.22035177 0.006837667

IQCG 2.95680296 6.2973E-08 MSLN 1.33518432 0.006865695

VWA3B 3.70172326 6.30683E-08 WDTC1 1.24564439 0.006879974

KALI 2.86964004 6.30683E-08 RAB11B 1.23317496 0.006954255

WRAP53 1.93108611 6.30683E-08 HRASLS2 1.44393323 0.006995945

CLUAPl 1.88649708 6.34659E-08 DAGLA 1.31649105 0.006995945

PACRG 3.25262251 6.37979E-08 DCXR 1.23902542 0.007010789

CCDC81 3.4942349 6.42368E-08 PLEKHHl 1.29761579 0.007058065 AKR7A2 1.57742473 6.47208E-08 NUDT16L1 1.24681519 0.007069306

KCNE1 3.35236141 6.58782E-08 KLHL26 1.35470062 0.007102702

INHBB 3.2633604 6.79537E-08 NPIPL3 1.26640845 0.007118708

PRDX5 1.55465969 6.79537E-08 DUOX1 1.28208189 0.007150069

MYB 1.84122844 6.81621E-08 LTBP2 1.28195811 0.007190191

NEK11 2.74190303 6.81892E-08 TCTA 1.30149363 0.007212297

RUVBL1 2.00081999 6.99548E-08 SPR 1.28479279 0.007287193

SYNE1 2.93233229 7.1936E-08 ZFYVE28 1.39878951 0.007333848

C17orf79 1.59608063 7.31685E-08 AGPAT4 1.37723985 0.007347907

JAG2 2.00848549 7.85574E-08 SLC39A11 1.27733497 0.007353196

ACOT2 1.61704514 8.52356E-08 TMEM150C 1.35301424 0.007388326

PRSS12 1.60068977 8.62009E-08 CDC42BPG 1.26124605 0.007488491

PHGDH 2.07652258 8.78686E-08 SLC7A1 1.28202511 0.007507941

AK8 2.99751993 8.85495E-08 COL4A5 1.32559521 0.007512488

Cl lorf49 1.65594025 8.87426E-08 PAX7 1.3155991 0.007535441

SYT5 3.23619723 9.00219E-08 ISOC2 1.23948495 0.007577305

C3orfl5 3.55197982 9.33003E-08 AGPAT3 1.26745455 0.007585223

PAX3 1.68131102 9.48619E-08 USP31 1.35428511 0.007618314

SHANK2 3.08586078 9.57305E-08 PCSK5 1.29446783 0.007618314

AK7 3.11167056 1.04568E-07 SLC16A5 1.25930381 0.007670005

DIXDCl 2.20355836 1.04568E-07 NOL3 1.2781252 0.00767895

ACCN2 1.63822574 1.04568E-07 FBXL8 1.43124805 0.007687014

TBX1 1.62839701 1.05101E-07 SNRNP25 1.28739727 0.007722414

HYDIN 3.64358909 1.0567E-07 CDCA7L 1.34644696 0.007787269

C13orf30 3.57465645 1.06437E-07 MOSPD3 1.27745533 0.007817906

ANKRD37 2.08781744 1.06496E-07 CACNB3 1.33319457 0.007881717

POMT2 1.77671355 1.06496E-07 ACBD7 1.5826075 0.007886797

C21orf58 3.15402189 1.14416E-07 ADCY2 1.66275163 0.007889009

CNTRL 1.98315627 1.15119E-07 CGNL1 1.27908311 0.007934511

SIX2 1.56975674 1.16144E-07 PLEKHH3 1.24634845 0.007946023

GLB1L2 1.87516329 1.18115E-07 CN M2 1.38525605 0.007983142

ZNF440 1.62497497 1.18115E-07 FIZ1 1.28867102 0.00798317

SYTL3 1.60669405 1.18115E-07 DNHD1 1.38047028 0.008084565

ERCC1 1.55757069 1.18115E-07 PHPT1 1.26190344 0.008084565

DNAHl 2.22541262 1.18941E-07 TSPYL5 1.36008323 0.008097033

FAM154B 3.2374058 1.20444E-07 IRX5 1.25420627 0.008212841 EFCAB1 3.41783606 1.24931E-07 STK11IP 1.23490937 0.008220192

BBS1 1.62663444 1.26292E-07 CHPF 1.27265262 0.00823526

PRU E2 3.09870519 1.26484E-07 STOX2 1.3946561 0.00826187

H1FX 1.54347559 1.26484E-07 TTBK2 1.3997974 0.008275791

IFT57 2.02384988 1.27781E-07 CBX8 1.36626331 0.008275791

ARMC3 3.6866857 1.28185E-07 PPP1R3F 1.32059699 0.008334819

ClorEOl 1.97130635 1.32673E-07 JOSD2 1.48865236 0.008361772

C20orfl2 2.16851256 1.35408E-07 C17orf59 1.28230989 0.008361772

FAM183A 3.43889722 1.35507E-07 DECR2 1.23796832 0.008455759

ZBBX 3.75926958 1.37771E-07 TMEM143 1.37235803 0.008476405

Clorf88 3.33179192 1.44064E-07 OPLAH 1.25881928 0.008476405

EFHB 3.24198197 1.45387E-07 MYPOP 1.29609705 0.008483284

YSK4 3.13700382 1.50138E-07 CEL 1.93651713 0.008531505

CCDC60 2.03255306 1.50341E-07 BCL2 1.39092608 0.00871498

TUSC3 1.69381639 1.50981E-07 NGEF 1.52005004 0.008775214

CES4A 2.40159419 1.51353E-07 USP21 1.31913668 0.008780827

CAP2 2.30419698 1.5299E-07 RAD9A 1.25389182 0.008780827

STOML3 3.56916735 1.54086E-07 LGALS3BP 1.24961354 0.008801136

PCYT2 1.54216983 1.61706E-07 LGALS9C 1.43680372 0.008865252

SLFN13 2.24221791 1.6531E-07 UPF1 1.25440678 0.008873906

DNAL4 1.73946873 1.6531E-07 LEMD2 1.20960949 0.008877864

C2CD2L 1.53455465 1.65577E-07 ZFP41 1.34143098 0.009044513

IFT46 1.9344197 1.7083E-07 SEPN1 1.26474089 0.009084

DNAH6 3.67492559 1.74274E-07 PLLP 1.31604938 0.00913286

RSPH4A 3.32798921 1.74274E-07 CUL7 1.27441781 0.009164349

DTHD1 3.32521784 1.74542E-07 KRBAl 1.27792781 0.00923669

SLC12A7 1.58126148 1.7563E-07 FAM195B 1.21801424 0.009241888

DPCD 1.93856115 1.76542E-07 ATG9B 1.43120177 0.009248504

DNAH7 3.36255762 1.78119E-07 ARHGEF17 1.30638434 0.009248504

NTN1 1.52761436 1.78206E-07 NUAKl 1.2674662 0.009299617

CLDN3 1.84043179 1.8233E-07 ENDOV 1.39721558 0.009324361

RHOBTB 1 1.75019548 1.87553E-07 SCARA3 1.32119045 0.009332766

APOBEC4 3.28732642 1.8767E-07 LAMB1 1.50281672 0.009344234

FAM174A 1.51418232 1.90288E-07 CIDEB 1.28399596 0.009344234

ARMC9 1.90867648 1.91275E-07 KLHDC7A 1.30138188 0.009386153

PLTP 1.60313361 1.98108E-07 WLS 1.23889735 0.009435274 CCDC146 2.6710312 2.0177E-07 FAM161B 1.36982011 0.009478536

C14orf45 2.54462539 2.13129E-07 PACS2 1.26997864 0.009508236

OBSCN 1.86629325 2.1622E-07 SLC25A23 1.26489355 0.009521659

WDR96 4.51826736 2.1911E-07 FAM164A 1.50789785 0.009626128

SFXN3 1.59966258 2.19516E-07 Clorfl lO 1.3202239 0.00963096

GALM 1.59756388 2.19516E-07 CENPB 1.18615837 0.009652916

FAM81B 3.17612876 2.22082E-07 ZNF704 1.33301508 0.009690515

EFEMP2 1.61941953 2.24048E-07 C19orf6 1.20316007 0.009730685

RABL2A 2.30603938 2.28887E-07 KIAA0753 1.30653182 0.009784699

WDR78 3.09268044 2.33992E-07 CST3 1.21230246 0.009784699

C10orfl07 3.16756032 2.44725E-07 SLC41A3 1.25668605 0.00979418

C9orfl35 2.86769508 2.44725E-07 PEX10 1.27191387 0.009844346

NEURL1B 2.13311341 2.44782E-07 C12orf76 1.42258291 0.009870686

BCAM 2.0015908 2.44782E-07 SLC1A5 1.24890407 0.009910692

PKD1 1.53249813 2.46006E-07 RAP 1 GAP 1.3443049 0.009932188

FBRSL1 1.50952964 2.46006E-07 GRAMD1C 1.36938141 0.009956926

DNAJA4 1.55609308 2.5244E-07 NME3 1.33160165 0.010064843

Cl lorf63 2.22050183 2.53161E-07 ABHD8 1.27046682 0.010270086

MAGIX 1.61223309 2.64993E-07 ANKS1A 1.28882538 0.010380221

CLMN 2.07549994 2.87911E-07 SLC25A38 1.29944952 0.010501494

TNS1 1.77612203 3.08503E-07 SERPINF2 1.3305424 0.010548835

SPA17 2.66711922 3.17135E-07 TP53I13 1.32153864 0.010567211

CRY2 1.54310386 3.48954E-07 PANX2 1.31303008 0.010589648

IQCA1 2.54545108 3.85583E-07 ALKBH5 1.25805436 0.010606283

IFT27 2.00349955 3.85583E-07 CHST6 1.25428683 0.01060947

C6orfl65 3.3160697 3.90768E-07 WDR83 1.31345803 0.010637404

SPATA6 1.86634548 3.91415E-07 SERPINBl l 1.4704188 0.010638878

ARMC4 3.33542089 4.12418E-07 SIX5 1.33395042 0.01072225

MNS1 2.96005772 4.20421E-07 KIAA0319 1.34703243 0.010736018

AP2B1 1.82011977 4.27029E-07 ABCC10 1.26473091 0.01082689

ABHD12B 1.65078768 4.58254E-07 EPCAM 1.2567134 0.010932803

RABL2B 2.18769571 4.60153E-07 C15orf38 1.30075878 0.010969472

DNAH11 3.39839639 4.78493E-07 AXIN2 1.29402405 0.011001282

TCTEX1D2 2.32862285 4.92481E-07 NISCH 1.25096394 0.011018413

SNCAIP 2.15177999 5.25094E-07 IGF2BP2 1.30475867 0.011048991

PRR15 1.52053242 5.39026E-07 MOSC2 1.47927047 0.011053117 TRAPPC9 1.49825676 5.47471E-07 KIAA1908 1.35564703 0.01110532

Cl lorf70 3.19682649 5.52587E-07 SESN1 1.31752072 0.011207697

MTSS1L 1.51447468 5.77745E-07 Clorf86 1.28409107 0.011320516

IQCC 1.76671873 5.85222E-07 G6PC3 1.2125164 0.011409549

MIPEP 1.60770446 5.87639E-07 B3GALT6 1.22733693 0.011440605

CAPSL 3.22810829 6.13092E-07 KIF3A 1.38292341 0.011569466

FBX031 1.52038127 6.15582E-07 FM05 1.38477766 0.011656611

IGFBP7 3.46134083 6.47155E-07 FOXP2 1.37687706 0.011656611

GLTSCR2 1.39112797 6.63441E-07 EP400 1.28435344 0.011755788

CASC1 2.94972846 7.41883E-07 CYP2S1 1.27545746 0.011755788

AKAP6 2.21859968 7.65044E-07 VEGFB 1.22471026 0.011755788

CDC14A 1.71863036 7.65644E-07 TRIM32 1.29368942 0.011769481

GPR172B 1.68332351 7.75027E-07 TSNAREl 1.3634355 0.011803378

KIF3B 1.53993685 8.08875E-07 LSM4 1.23306793 0.012045042

NSUN7 1.55243313 8.71403E-07 SAMHDl 1.35015325 0.01211293

CBY1 1.69853505 9.10803E-07 GALT 1.33655074 0.012150017

MORN2 2.28391481 9.392E-07 CHST12 1.29296088 0.012150017

FAM134B 2.02733713 9.45965E-07 SUMF2 1.24339802 0.012170682

LRRIQ1 3.26113554 9.58549E-07 C14orf80 1.29511855 0.012344687

ZNF446 1.52395776 9.58549E-07 TFPI2 1.6495853 0.012357876

TTC26 2.53343738 9.80114E-07 NUDT7 1.51871011 0.012357876

CALML4 1.62740933 9.95113E-07 PNKP 1.24958927 0.012357876

LRP11 1.49024896 1.02382E-06 PFKM 1.29401217 0.012409059

TMPRSS3 1.80633832 1.04835E-06 MDCl 1.29181732 0.012467682

MDM1 1.71360038 1.07116E-06 C17orfl08 1.32080282 0.012502986

PAQR4 1.56647668 1.16048E-06 MRPL4 1.22051577 0.012531908

SEMA5A 1.65992081 1.18574E-06 CTTNBP2 1.34156692 0.012602161

IDH2 1.48906176 1.22485E-06 NEK6 1.24934177 0.01272017

SLC2A4RG 1.473539 1.28937E-06 APCDDl 1.37290114 0.012767663

WDR27 1.86298354 1.29757E-06 SNAPCl 1.31811966 0.012784092

MB 1.56393059 1.35535E-06 CUL9 1.24321273 0.012798949

PLCH1 2.31329264 1.36675E-06 DCBLD2 1.29914309 0.012917806

FOXN4 2.43309713 1.49276E-06 CHID1 1.23513008 0.012952152

CETN2 2.31001093 1.51913E-06 PELP1 1.19235772 0.012973503

ECU 1.46030427 1.63719E-06 IL2RB 1.87694069 0.012983156

ACOT1 1.71878182 1.65012E-06 EBPL 1.24533429 0.013071502 SPEF2 3.00394567 1.69058E-06 TMEM110 1.29864886 0.013215192

ENKUR 3.17038628 1.69235E-06 EGFR 1.28277513 0.013226151

ANKRD42 1.7433919 1.70496E-06 AC ATI 1.27648584 0.013237073

CSMD1 2.01483263 1.71638E-06 FADD 1.22480421 0.013237073

LRRC49 2.42707576 1.81419E-06 NCOR2 1.24365674 0.013251736

LRRC6 2.41771576 2.0278E-06 DUSP23 1.18759129 0.0134367

PDF 1.72789067 2.0278E-06 MIPOL1 1.35481022 0.013580231

AP3M2 1.6599425 2.0278E-06 IFT52 1.32547528 0.013981771

ATP6V0E2 1.51739952 2.23414E-06 FGGY 1.38422354 0.014047872

CYBASC3 1.47190218 2.47918E-06 ACTRIB 1.24578421 0.014079645

MGC2752 1.51302987 2.49691E-06 TRIOBP 1.21105055 0.014166645

CTGF 2.44083959 2.53147E-06 MTR 1.29454229 0.01416807

NME7 2.30993461 2.56434E-06 C16orf45 1.33701418 0.014182012

ICAIL 1.87405521 2.59186E-06 TECPR1 1.26017688 0.014209406

KIAA1377 2.35492722 2.63213E-06 ZNF362 1.2501977 0.014247609

WNT4 1.62388727 2.66608E-06 TMEM25 1.31255258 0.014250634

CCDC66 1.78966672 2.69319E-06 ATP13A1 1.21286134 0.0142645

DMD 1.60710731 2.70822E-06 ALDH4A1 1.29508866 0.014386525

RGMA 1.77597556 2.76587E-06 GHDC 1.2679717 0.014585547

BCL7A 1.54768303 2.79246E-06 USP13 1.6468891 0.014645502

ARL3 1.52985757 2.88426E-06 IQCB1 1.30311921 0.014724122

FKRP 1.59965333 3.01403E-06 PRMT7 1.26823696 0.014724122

RORC 1.52931081 3.01403E-06 SORB S3 1.22860767 0.014731446

ULK2 1.59698142 3.04102E-06 RASA3 1.47946487 0.014788674

ACSS1 1.55253699 3.07996E-06 WDR18 1.22894705 0.014815312

FfflAT 1.60739942 3.08587E-06 UBB 1.21302285 0.014959845

EFNB3 2.4297676 3.45813E-06 ZNF626 1.36143599 0.014974802

B3GNT9 1.55740701 3.51732E-06 CCHCR1 1.25121215 0.01509939

SLC25A4 1.49801843 3.55964E-06 C12orfl0 1.22594687 0.015249346

CCDC138 1.80406427 3.56785E-06 RGS12 1.1884216 0.015281037

PABPN1 1.44608578 3.69532E-06 GGA2 1.23527724 0.015332188

SMPD2 1.47546999 3.70938E-06 C9orf21 1.34640634 0.015553398

ZNF580 1.47324953 3.73581E-06 GAS2L1 1.27610616 0.015568411

OLFML2A 1.68087252 3.7554E-06 USP11 1.25199232 0.015568411

C7orf50 1.44237361 3.94008E-06 LAGE3 1.2733059 0.015599785

LEPREL2 1.95758996 3.94011E-06 CHST10 1.36346099 0.015732751 DZIP3 2.22081454 4.02528E-06 Clorf35 1.25664328 0.015735658

NCRNA00287 1.69130571 4.03026E-06 CPSF1 1.20966706 0.015929418

C3orf67 1.72190896 4.09892E-06 GJD3 1.22729981 0.016081967

IL17RE 1.48542123 4.16438E-06 DLG5 1.23092203 0.01610673

DUSP18 1.76643191 4.2E-06 FAM83E 1.21694985 0.016195244

HEATR2 1.53592007 4.2E-06 TRIM41 1.23404295 0.016320404

CERS4 1.46651735 4.55413E-06 TMEM213 1.41958146 0.016484036

EFHC2 2.54152611 4.67467E-06 POR 1.21138529 0.016499043

EBF4 1.50785283 4.71457E-06 LOC642852 1.46862266 0.016517072

SCAMP4 1.44146628 4.91032E-06 SDHAFl 1.24223826 0.016806901

HEY1 1.51597477 5.00328E-06 SIAH2 1.21834713 0.016864416

CSPP1 2.05160927 5.01668E-06 ZNF532 1.28788883 0.017020986

NCS1 1.53990962 5.02214E-06 PHF17 1.25357933 0.017175754

ZNF837 1.67092737 5.22131E-06 ZMYM3 1.30001737 0.0171865

CCDC104 1.59507824 5.28987E-06 OCEL1 1.28256237 0.0171865

DNAL1 1.92925734 5.86073E-06 RSG1 1.28718113 0.017273993

TTC38 1.47562236 5.88772E-06 NPTXR 1.53025827 0.01727628

KIF27 2.05357283 6.13829E-06 LONP1 1.20031058 0.017332363

THRA 1.49828801 6.16885E-06 GLT8D1 1.26957746 0.017460181

GNAL 1.51789304 6.24393E-06 ORAI2 1.41328301 0.017490601

LCA5 2.05878538 6.76347E-06 TIMM17B 1.19661829 0.017535321

IDAS 1.71281695 7.04626E-06 HEXDC 1.25292301 0.017542776

KIAA0556 1.48330058 7.50539E-06 UGT2A1 1.36534557 0.017548434

PYCR2 1.49939954 7.88147E-06 URBl 1.25831813 0.017553338

TRPV4 1.47758825 7.88147E-06 ARMC5 1.22604157 0.017553338

TMEM98 1.46244012 8.21506E-06 TFF3 2.31909088 0.017587024

DYRK1B 1.445023 8.35968E-06 ASPSCR1 1.20844515 0.017624999

MEGF8 1.4698702 8.57212E-06 MRPS26 1.23168805 0.017646918

FAM149A 1.61900561 8.90473E-06 TMEM134 1.2288306 0.017825679

FTO 1.54233263 9.20995E-06 STK11 1.17914687 0.017837909

RBKS 1.66266555 9.25498E-06 XRRAl 1.39947437 0.017892419

ORAI3 1.46516304 9.45553E-06 PYROXD2 1.34484651 0.018019021

NDUFAF3 1.44305183 9.66172E-06 GNA11 1.25697334 0.018040997

C16orf80 1.53411506 1.07805E-05 AGRN 1.21988217 0.018182474

CCDC34 1.95285314 1.08031E-05 PDE4A 1.24320237 0.018184742

FAM104B 1.64584961 1.08935E-05 MSH3 1.29294165 0.018305998 NME5 2.35890292 1.0967E-05 DEGS2 1.28509551 0.018381891

SRGAP3 1.51025268 1.10599E-05 L3MBTL2 1.25584577 0.018599944

ALMS1 1.75968611 1.10615E-05 C4orfl4 1.26050592 0.018761187

COL9A2 1.46064849 1.10777E-05 ProSAPiPl 1.22530581 0.018761187

CNTNAP3 1.64650311 1.11243E-05 CTNNALl 1.37868612 0.018768235

HDAC10 1.43909133 1.12656E-05 SGCB 1.36337998 0.018840796

WDR35 1.79775411 1.18311E-05 NT5DC2 1.22263296 0.018877812

PRR12 1.44830825 1.24302E-05 PHYHD1 1.27403407 0.018894874

SNX29 1.49309166 1.25697E-05 ZNF768 1.26202922 0.018933778

CRIPl 2.21165686 1.25722E-05 TMEM109 1.23710661 0.019040413

SOBP 1.70952245 1.29589E-05 VWA1 1.19869747 0.019040413

SLC9A3R2 1.38857255 1.31279E-05 TM9SF1 1.24665895 0.019041146

PHC1 1.60359663 1.38781E-05 CLPP 1.16917032 0.019115843

PKN1 1.44709171 1.38781E-05 ROM1 1.26671873 0.019116421

TRIP 13 2.13571915 1.40793E-05 ABHD6 1.29541914 0.019153377

SPAG16 1.5476954 1.41052E-05 WDR81 1.23318896 0.019364381

TBC1D8 1.64734934 1.44514E-05 TBCB 1.24205622 0.019442997

METTL7A 1.54943803 1.45491E-05 IL27RA 1.33040297 0.019493867

NPM2 1.64770549 1.49453E-05 LZTR1 1.26790326 0.019526164

TSGA14 1.83369437 1.53621E-05 KDELC2 1.30411719 0.01972224

ABCA3 1.56393698 1.53948E-05 CMBL 1.34033189 0.019737295

EPB41L4B 1.46546865 1.55092E-05 TMEM201 1.26474637 0.019843105

SCGB2A1 1.85264034 1.58836E-05 ANKS3 1.22989376 0.019990665

WDR69 3.13080652 1.59712E-05 DEN D1A 1.22638955 0.020155103

MCAT 1.44452413 1.59712E-05 RGL1 1.24300802 0.020233871

HSPG2 1.44631976 1.69312E-05 ARHGEF38 1.32067809 0.020237336

LRRC26 1.74351209 1.73709E-05 CD40 1.24570811 0.020269619

KIAA0195 1.42018377 1.73709E-05 ALKBH7 1.26247813 0.020284142

RFX1 1.41884581 1.80687E-05 SLC27A3 1.2354561 0.020421322

WDR19 1.89888711 1.82737E-05 TMEM93 1.31673383 0.020430106

ANKRD35 1.4184045 1.89416E-05 SIRT3 1.2475777 0.0205475

BBS9 1.59591845 1.90715E-05 SLC25A14 1.36204426 0.020560099

CCDC41 1.73056217 1.92145E-05 IQCK 1.28636095 0.020640164

FARPl 1.43058432 1.92684E-05 TCEANC2 1.28423081 0.020664899

NGRN 1.41426222 1.93043E-05 COL21A1 1.50109849 0.020759278

DCAKD 1.5245559 2.01031E-05 RAB40B 1.25324034 0.020759278 KATNAL2 1.83549945 2.03357E-05 TNS3 1.2532701 0.020795029

AUTS2 1.44446141 2.10708E-05 COL7A1 1.57647835 0.020944269

SLC7A2 2.78449202 2.13078E-05 CEP 120 1.31831944 0.021016979

ZDHHC24 1.41648471 2.14062E-05 MCM2 1.29689526 0.021126757

SLC41A1 1.52318986 2.14929E-05 ABHD11 1.18994397 0.021329494

C8orf47 1.59908668 2.15109E-05 LOC399744 1.31540057 0.021430758

SHROOM3 1.49391839 2.15542E-05 SLC22A23 1.24944619 0.021446138

SUV420H2 1.47743036 2.17189E-05 ATP6V0C 1.17416259 0.021478528

TMEM132A 1.3601549 2.17189E-05 C17orf61 1.26534127 0.021518422

CITED4 1.54649834 2.21855E-05 MACROD2 1.37686707 0.021629967

LMCD1 1.54313711 2.26856E-05 LRP5 1.24470319 0.021949014

MAGED2 1.42577997 2.28093E-05 FBXL15 1.29192497 0.021972553

RPGRIP1L 2.30088761 2.32284E-05 PTPRU 1.22543283 0.021972553

MT1X 1.75550879 2.34342E-05 MUC15 1.3122479 0.02203807

REPIN1 1.40482269 2.35893E-05 MIDI 1.27948316 0.022099398

DNER 2.54706 2.35943E-05 HOOK2 1.24529255 0.022099398

KATNB1 1.41230234 2.40285E-05 CMAHP 1.21368898 0.022099398

C14orf50 2.0041349 2.42509E-05 SPRYD3 1.20858839 0.022099398

IFT88 1.81175502 2.53479E-05 CEP78 1.33075635 0.022122696

POLQ 1.82761614 2.58084E-05 FKBP11 1.26304562 0.022134566

HSD17B13 2.1583746 2.61563E-05 DHCR7 1.25305322 0.022252456

TSPAN8 1.57248017 2.69759E-05 PLOD3 1.25880788 0.022278867

MAP9 2.17752296 2.70383E-05 SLC29A2 1.2646493 0.02232075

CD6 1.66024598 2.70383E-05 MAP3K14 1.21534306 0.022542624

CUEDC1 1.44127151 2.70383E-05 TUBGCP2 1.20510805 0.022542624

PALMD 1.84259482 2.73396E-05 C12orf74 1.26087188 0.022618056

CCDC88C 1.44651505 2.9513E-05 C9orfl03 1.35312494 0.022704588

GSTA2 3.04364309 2.99797E-05 ACSF2 1.24126062 0.022731424

LOC728392 2.45352889 3.13987E-05 DBP 1.21193124 0.022905376

SOX2 1.42277901 3.25439E-05 SCMH1 1.30660024 0.023010481

WDR73 1.45128947 3.2565E-05 DPYSL3 1.75851448 0.023022128

KRT15 1.66470618 3.25997E-05 SLC25A1 1.19992302 0.023167199

ARVCF 1.4675952 3.46454E-05 H2AFX 1.21471359 0.023460117

UNC93B1 1.3350195 3.6432E-05 AC02 1.24219638 0.023491443

FBF1 1.58227897 3.82227E-05 SETD1A 1.23864333 0.02358174

NLRC3 1.6969175 3.93238E-05 HIGD2A 1.19776928 0.02358174 MLF1 2.10274167 3.97233E-05 TNC 1.50094825 0.023589815

ACACB 1.49814786 4.01764E-05 ZNF653 1.28833815 0.023589815

ADCY9 1.51669291 4.03583E-05 SPG7 1.21091885 0.023768493

DIAPH2 1.56970385 4.08846E-05 PCP4L1 1.22918723 0.02383071

TCEAL3 1.44291146 4.16479E-05 IBA57 1.24180643 0.023836751

AGBL5 1.44132278 4.20047E-05 C17orfl01 1.25096951 0.023840587

A KZF1 1.44697405 4.20298E-05 MICALL2 1.22125277 0.024144748

TCEA2 1.52429185 4.23984E-05 SLC25A6 1.18752058 0.024216742

BAHCCl 1.49917059 4.27983E-05 HLF 1.35897608 0.024265873

SYT17 1.56742434 4.28886E-05 LDHD 1.2236788 0.024265873

HSD17B8 1.44037694 4.30152E-05 HICl 1.32339144 0.02431121

RPS6KA2 1.44445649 4.35723E-05 CDAN1 1.2574241 0.024430835

PHTF1 1.48986592 4.40703E-05 BLVRB 1.19730184 0.024565321

TTC30B 1.71522649 4.43779E-05 FANCF 1.30835319 0.024591866

TMEM67 2.20416717 4.46512E-05 C21orf33 1.23065152 0.02463506

PYCR1 1.68525202 4.5225E-05 EPB41L2 1.26976906 0.024700064

Cl lorE 1.34624129 4.7456E-05 RANBP1 1.23115634 0.024823686

PDE8B 2.32876958 4.79301E-05 NUCB2 1.23698305 0.02484779

GAL3ST2 1.52140934 4.82899E-05 NCKAP5L 1.2397669 0.024923181

MYCL1 1.49285532 4.91023E-05 ZBED1 1.21522185 0.024923181

TULP3 1.50475936 4.92334E-05 KBTBD6 1.4316415 0.025051133

FBLN5 1.48050793 4.97709E-05 THADA 1.27276897 0.025121918

AMN 1.65761529 4.99842E-05 GLIS2 1.33309074 0.02512733

EVL 1.38952418 5.22713E-05 ZNF787 1.16942772 0.025159688

KLC4 1.40405768 5.24118E-05 AES 1.16914969 0.025347775

WNK2 1.41616046 5.30142E-05 C14orfl69 1.25236913 0.025508325

C3orf39 1.45324602 5.54577E-05 CAPN10 1.20119334 0.02551561

LRP4 1.93508583 5.79675E-05 CX3CL1 2.03560065 0.02571443

FAM179B 1.49020563 5.79675E-05 TP53BP1 1.30144588 0.025752829

DYNC2H1 2.39772393 5.80606E-05 EEF2K 1.22751357 0.026121177

IFT81 1.85697674 6.05797E-05 ZNF629 1.19878625 0.026179758

SYNPO 1.43007758 6.05797E-05 PTK7 1.26249033 0.026187159

C7orf63 2.2475395 6.07346E-05 CYB5R3 1.22279029 0.026187912

LIG1 1.46051313 6.2636E-05 GSDMB 1.22615544 0.026402701

NR2F6 1.37135336 6.26657E-05 ECHDC2 1.17956917 0.026402701

PPDPF 1.33519823 6.37715E-05 GSDMD 1.22611348 0.026430687 COQ10A 1.57553325 6.42865E-05 RAB26 1.3029921 0.026534641

ADPRHL1 1.57602912 6.48279E-05 LFNG 1.27842536 0.02667787

PLXNB1 1.36748122 6.51603E-05 SREBF2 1.22653731 0.027051285

LIPT2 1.57209714 6.54735E-05 DNAJC27 1.33234962 0.027090378

GFER 1.38601943 6.57227E-05 TMEM178 1.32401023 0.027240857

PRAF2 1.48691496 6.62534E-05 IVD 1.24553409 0.027240857

MAK 2.11010178 6.6389E-05 PEMT 1.2385554 0.02725035

LPAR3 1.61372461 6.6389E-05 HIST2H2BF 1.25568147 0.027417938

CEP68 1.43585034 6.86926E-05 TNRC18 1.20092173 0.027612815

MGAT3 1.63032562 6.88196E-05 PPP5C 1.25860277 0.027781088

SELM 1.68910302 6.90845E-05 AHSA2 1.33551621 0.027828419

PRKCDBP 1.75929603 6.95654E-05 FAM171A1 1.2547829 0.027880091

GMPR 1.74175023 7.09348E-05 CYP2B6 1.89206892 0.02801745

NUDT4 1.66108324 7.1223E-05 QSOX2 1.30285256 0.0282336

TMC4 1.37606676 7.32423E-05 SCD5 1.24820591 0.0282336

C18orf32 1.4680673 7.49847E-05 CEP 164 1.25975237 0.028265449

BBS4 1.48414852 7.55039E-05 RPL13 1.19710205 0.028278399

TTC15 1.37927452 7.55039E-05 BANFl 1.22270928 0.02848803

PCM1 1.44508492 7.57285E-05 ZNF777 1.22715757 0.028513321

AHDC1 1.39404544 7.57907E-05 EPHX1 1.19634133 0.028554468

GPT2 1.37898662 7.83202E-05 TRPM4 1.19491647 0.028592325

KIAA0895 1.83866761 8.00835E-05 KIFAP3 1.32574468 0.028652927

UFC1 1.42750311 8.07E-05 SULT1A1 1.35803402 0.028720872

EPHX2 1.47972778 8.11114E-05 C1QBP 1.2250998 0.028744187

AGR3 2.49250589 8.14424E-05 SH2B1 1.23275523 0.028748064

STUB1 1.40578727 9.07013E-05 CYP2B7P1 1.3709621 0.029004147

MFSD2A 1.41538916 9.08106E-05 CMIP 1.18939283 0.029028829

TM7SF2 1.36011903 9.49179E-05 SLC2A11 1.34050851 0.029279513

BCAS3 1.39837526 9.50537E-05 SMG6 1.2413887 0.029305629

GYLTL1B 1.50326839 9.52925E-05 ARL2 1.23879567 0.029305629

CDT1 1.68706876 9.60694E-05 TTC7B 1.41937755 0.029317704

EDARADD 1.40821946 9.72324E-05 CTDP1 1.16949182 0.029509238

KIAA1841 1.63727867 9.74561E-05 LOXL1 1.29289943 0.02952562

PDLIM4 1.33499063 9.91746E-05 CDS1 1.24920822 0.030016095

FBXL2 1.70441332 0.000100287 BOD1 1.24305642 0.030061948

CCP110 1.62862095 0.000100436 PTPRS 1.25084066 0.030069163 PLA2G6 1.41041592 0.000101028 ARHGEF19 1.23306546 0.030316941

COL4A6 1.81881069 0.000101469 PPAP2C 1.19053642 0.030316941

COG7 1.41067778 0.000101469 TRAF3 1.23277663 0.030350579

LSS 1.46102295 0.00010236 ZNF707 1.23412475 0.030818439

PITPNM1 1.36286761 0.00010236 DIS3L 1.25442333 0.031179257

IFT74 1.49355699 0.000102847 GGA1 1.19942103 0.031209924

SIPA1L3 1.43775294 0.000102847 SNTB1 1.23919253 0.031230312

WDR13 1.31401675 0.000107509 KCTD13 1.22015811 0.031269564

ARMCX2 1.63758171 0.000108288 SOX21 1.25686272 0.031295938

CKB 1.57645121 0.000109216 SLC9A3R1 1.19749434 0.031709604

STK36 1.48863192 0.000112154 GLTPD1 1.19038361 0.031717891

FN3K 1.51834554 0.00011281 WTIP 1.26447786 0.031869682

LOC81691 1.62456618 0.000114135 RHOBTB2 1.26176919 0.032458791

FAM108A1 1.31380714 0.000114728 POLRMT 1.19980497 0.032991066

SQLE 1.69434086 0.000119836 SERTAD4 1.28870378 0.033069887

KCNQ1 1.33310218 0.000122927 MPST 1.16862519 0.033104411

BRF1 1.37864866 0.000124633 ZNRF3 1.34876959 0.033173043

PROS1 2.25991725 0.000125307 P4HA2 1.25705664 0.033701888

IGSF10 2.12624227 0.000125978 MPV17L 1.26662253 0.03402012

ZNF358 1.35163158 0.000126256 ARHGEF18 1.20479337 0.03402012

CHCHD6 1.46348972 0.000133584 ZNF385A 1.17649674 0.034069213

CES3 1.45903662 0.000138413 DDAHl 1.28088496 0.034092835

VWA2 1.45385588 0.000138791 MLLT6 1.20261495 0.0341598

TTC5 1.52203224 0.00014006 CPNE2 1.21968246 0.034227225

SLC27A1 1.39126087 0.000141835 MRPS31 1.27242786 0.034296798

CYB561 1.37921792 0.000141835 DHODH 1.2852554 0.034427626

RPGR 1.85326766 0.000142075 DIP2C 1.25542149 0.03464283

VMAC 1.41981554 0.000146443 SUSD3 1.28440939 0.034683637

IK 1.37718344 0.000148072 PRKARIB 1.23530537 0.034768811

CEP89 1.5127697 0.000148549 CIRBP 1.18770113 0.034785942

CEBPA 1.33935794 0.000149104 CSNK1G2 1.13123724 0.034785942

GPX8 1.72869825 0.00015137 TCEAL1 1.28209383 0.035208866

TUT1 1.35214327 0.000152136 IP013 1.24220969 0.035208866

PEX6 1.52324996 0.000155204 RCCD1 1.335678 0.035266459

MT1E 1.67168253 0.000155534 SLC23A2 1.23369819 0.035486274

LOC441869 1.43946774 0.000157594 HSF2 1.24483768 0.035535946 S1PR5 1.51757959 0.0001604 COG1 1.21528079 0.035737318

CD81 1.32468108 0.000161488 ZNF607 1.28896111 0.035814809

ENPP5 1.75733353 0.000162553 ZNF473 1.30191148 0.03587568

ZNF204P 1.75883566 0.000165462 PRPF6 1.1570728 0.035909989

ClOorKl 1.40543082 0.000165462 SLC7A8 1.24579493 0.035915271

Cl lorf74 1.86106419 0.000171801 DMWD 1.26441363 0.036031824

CRTC1 1.42765953 0.000172249 C7orf55 1.20257164 0.036467386

DDR1 1.36166857 0.000172682 LOC152217 1.19366436 0.036569637

THSD4 1.53230415 0.000178414 TMEM223 1.22267466 0.036595833

TAF6L 1.35674158 0.000179973 HDAC11 1.2172885 0.03684229

AKD1 1.62744603 0.000180844 AKT3 1.32799964 0.037008607

LZTFL1 1.71503476 0.000184545 LMTK3 1.29813131 0.037095716

PARP10 1.36830665 0.000189223 TRAPPC5 1.20831411 0.037095716

ZNF3 1.36744076 0.000189238 ITFG2 1.23730793 0.037115391

SEMA4C 1.40268633 0.000189752 KIAA1161 1.22160862 0.037232096

ZNF584 1.48555318 0.000191741 TFAP4 1.39134809 0.037263881

NFATC1 1.38421478 0.000191741 MAP1S 1.17464502 0.037440506

ZNF414 1.39531526 0.000194572 CAPN9 1.39055066 0.037748465

KIAA1797 1.48460385 0.000201377 COG8 1.2314403 0.038062365

C22orf23 1.47274344 0.000207275 UPF3A 1.24255729 0.038707203

FAM113A 1.37538478 0.000207701 XPNPEP3 1.29860558 0.038818491

GAS6 1.41786846 0.000211066 MFSD10 1.17159262 0.038901436

C14orfl35 1.50529153 0.000227989 CD8A 1.58747274 0.03893846

BAIAP2 1.32638974 0.000236186 SLC25A22 1.24064395 0.039092773

TUSCl 1.39360539 0.000247174 PAQR8 1.29464418 0.039244293

RSPH3 1.43059912 0.00024733 HIRIP3 1.22398822 0.039367991

C14orfl42 1.62415045 0.000249361 TRIM8 1.18882424 0.039367991

C13orfl5 1.35861972 0.000254195 OAF 1.23071976 0.039512526

PAQR7 1.38092355 0.000258484 SNCA 1.27821293 0.040095856

MCF2L 1.40608658 0.000258709 8-Sep 1.18728437 0.040095856

ZFPM1 1.60585901 0.000259986 C3 1.52927726 0.040833841

PARVA 1.39640833 0.00026033 C17orf89 1.218819 0.041044444

SMPD3 1.41764514 0.000263709 TRIM28 1.18909519 0.041103346

C7orf41 1.39659057 0.00026517 CARDIO 1.23773554 0.041297199

TSGA10 1.87725514 0.000266725 TMEM141 1.19110714 0.041365589

ATPIF1 1.34495974 0.000269242 Cl lorO l 1.14760658 0.041444485 TRIM3 1.42603668 0.000269692 THTPA 1.2910393 0.041760045

CEP290 1.50717501 0.000273516 VKORC1 1.18718687 0.041892204

SCAMP5 1.39934588 0.00027358 SELENBP1 1.1721689 0.042289115

8-Mar 1.39016591 0.000274885 DOHH 1.22434618 0.042312153

TSTD1 1.34032792 0.000279518 BSCL2 1.3183409 0.042641173

ATP6V1C2 1.38396906 0.000296582 FAIM 1.27952766 0.042673939

BTBD3 1.42834347 0.000299561 ZNF503 1.19706599 0.042673939

DOCK1 1.3556739 0.000307703 RNPEP 1.2030262 0.042712204

TPRXL 1.46505444 0.000308225 GPR153 1.21365345 0.042737806

C6orf48 1.36829759 0.000312557 LOC147727 1.27577433 0.042987541

RRAS 1.43157375 0.000312601 TMEM218 1.29964029 0.043031867

CTU1 1.70766673 0.000313118 DDX51 1.2431896 0.043259718

CDON 1.5312556 0.000314033 NBEA 1.24270767 0.043259718

LRFN3 1.40276367 0.000320189 KIAA0754 1.33628562 0.043584142

HHLA2 1.77249829 0.000325631 P4HA1 1.27680255 0.043633316

ATP6V0A4 1.40856456 0.000331973 NUMA1 1.18675348 0.044086191

MAZ 1.33830748 0.000331973 TPRA1 1.18791628 0.044350632

FAM131A 1.37617082 0.000334759 DHRS11 1.25981602 0.04459514

ADCK4 1.35866946 0.000345476 TMEM216 1.23211237 0.04472713

NBPF1 1.42147504 0.000346828 SEZ6L2 1.23005246 0.04472713

PLCH2 1.34487014 0.000351121 AGTRAP 1.21322042 0.04472713

TEL02 1.35293949 0.000352106 PTPLAD2 1.39497647 0.044903769

ZNF469 1.44727917 0.000378978 PTPRCAP 1.41832342 0.044929234

LMLN 1.55351859 0.000387955 C19orf29 1.20477082 0.044969597

NINL 1.42267221 0.000388085 FAM83H 1.17895261 0.045287191

PAIP2B 1.46931111 0.000391976 SP8 1.26481614 0.045370219

LRP3 1.34600766 0.000397182 PLEKHG4 1.24585626 0.045638621

ZBTB45 1.38679613 0.000405 TMEM9 1.21047154 0.045968953

AP4M1 1.42014443 0.00041951 AN RDl l 1.20248177 0.04613435

CYP2F1 1.38163537 0.000421654 PABPC4 1.19064568 0.046299186

ARHGAP44 1.46862173 0.00042522 ALKBH6 1.2014857 0.046508916

ASMTL 1.29539878 0.000447663 C19orf63 1.18088252 0.046519544

THNSL2 1.45304585 0.000449374 GIGYF1 1.17275338 0.046738543

PWWP2B 1.28979929 0.000449374 ZNF574 1.23128612 0.046937115

ALDHILI 1.33944749 0.000453928 SDF4 1.16627093 0.046954331

LRFN4 1.35765376 0.000458695 CAMK1 1.23284144 0.047106124 ANKRD16 1.50341162 0.000468893 TTLL4 1.20520638 0.047538908

ABCB11 1.85720038 0.000469016 SULT1E1 1.4294267 0.047970508

PSPH 1.54491063 0.000469099 RAB13 1.1740176 0.047981821

STRA6 1.61958548 0.00046936 SMCR7 1.20475982 0.048036512

GRTP1 1.3780124 0.00046936 SCARB1 1.2307995 0.048174963

COL6A1 1.90548754 0.00047228 LCK 1.30353093 0.048431845

LOC100506990 2.06901283 0.000472754 THBS3 1.1933001 0.048455354

KIAA1009 1.47960091 0.00047416 NCDN 1.23307681 0.048579383

SYTL1 1.29291891 0.000484701 CAD 1.24055107 0.049142937

HES4 1.54693182 0.000487686 EEF2 1.18180291 0.049567914

NEIL1 1.45846006 0.000487686 DPH1 1.21637967 0.049735202

AZI1 1.40092743 0.000487686 ASB1 1.21869366 0.049969351

Ensemble of

genes encoding

core extracellular

NABA_CORE_ matrix including

KIAA1737 1.39523823 0.000491958 2.71E-07

MATRISOME ECM

glycoproteins,

collagens and

proteoglycans

NABA ECM G Genes encoding

TTLL5 1.41074741 0.000504884 LYCOPROTEIN structural ECM 8.91E-07

S glycoproteins

REACTOME R Genes involved

ECRUITMENT_ in Recruitment of

OF_MITOTIC_C mitotic

SEPW1 1.29723354 0.000509229 2.86E-06

ENTROSOME P centrosome

ROTEINS AND proteins and

COMPLEXES complexes

REACTOME MI Genes involved

MXD4 1.32904467 0.000509323 TOTIC_G2_G2_ in Mitotic G2- 3.98E-05

M PHASES G2/M phases

REACTOME L

Genes involved

OSS_OF_NLP_F

in Loss of Nip

PCSK6 1.8750067 0.000512777 ROM MITOTIC 2.02E-04 from mitotic

CENTROSOM

centrosomes

ES Ensemble of

genes encoding

extracellular

NABA MATRIS

NQOl 1.40130035 0.000519124 matrix and 2.10E-04

OME

extracellular

matrix-associated

proteins

REACTOME C

Genes involved

HONDROITIN_

in Chondroitin

SULFATE DER

DAK 1.38150961 0.000524279 sulfate/dermatan 9.82E-04

MATAN_SULF

sulfate

ATE METABO

metabolism

LISM

REACTOME M

Genes involved

ETABOLISM O

in Metabolism of

SPATA7 1.57805661 0.000530373 F_LIPIDS_AND 9.82E-04 lipids and

LIPOPROTEIN

lipoproteins

S

KEGG GLYCO SAMINOGLYC Glycosaminoglyc

AN BIOSYNTH an biosynthesis -

ADARB2 1.68685402 0.000530837 9.82E-04

ESIS_CHONDR chondroitin

OITIN SULFAT sulfate

E

REACTOME G Genes involved

LYCOSAMINO in

PODXL2 1.36921797 0.000554801 4.40E-03

GLYCAN MET Glycosaminoglyc

ABOLISM an metabolism

Genes encoding

NABA BASEM structural

UGT2A2 1.66808039 0.000555928 ENT MEMBRA components of 7.36E-03

NES basement

membranes

REACTOME D Genes involved

NDN 1.45098648 0.000557146 EVELOPMENT in Developmental 7.76E-03

AL BIOLOGY Biology

UBAC1 1.32525498 0.000558971 REACTOME A Genes involved 8.07E-03 XON GUIDAN in Axon guidance

CE

REACTOME BI Genes involved

ERI3 1.36918331 0.000561446 OLOGICAL OX in Biological 1.04E-02

IDATIONS oxidations

REACTOME C Genes involved

MESDC1 1.32459189 0.000561446 1.82E-02

ELL CYCLE in Cell Cycle

KEGG STEROI

Steroid

FAM13A 1.45037916 0.000562906 D BIOSYNTHE 1.85E-02 biosynthesis

SIS

Genes related to

WNT SIGNALI Wnt-mediated

CABIN1 1.37646627 0.000581908 2.11E-02

NG signal

transduction

KEGG PEROXI

KIAA0649 1.35151381 0.000585764 Peroxisome 2.78E-02

SOME

Betal integrin

PID INTEGRIN

SBK1 1.42410101 0.000586514 cell surface 3.22E-02

1 PATHWAY

interactions

KEGG ARGINI

Arginine and

NE AND PROL

NUDT14 1.40941995 0.000597249 proline 3.56E-02

INE METABOL

metabolism

ISM

REACTOME SI Genes involved

C12orf52 1.36403577 0.000605472 GNALLING BY in Signalling by 4.13E-02

NGF NGF

REACTOME T Genes involved

RANSMEMBRA in

FAM107A 1.81948041 0.000607395 NE TRANSPOR Transmembrane 4.23E-02

T_OF_SMALL_ transport of small

MOLECULES molecules

KEGG_FOCAL_

NME2 1.35909489 0.000612032 Focal adhesion 4.23E-02

ADHESION

REACTOME C Genes involved

RAVER1 1.33417287 0.000638651 OLLAGEN FOR in Collagen 4.67E-02

MATION formation

BOC 1.41111691 0.000639409 PID ALPHA SY Alpha-synuclein 4.67E-02 NUCLEIN PAT signaling

HWAY

Ensemble of

genes encoding

core extracellular

NABA_CORE_ matrix including

MICAL3 1.44407861 0.000645699 2.71E-07

MATRISOME ECM

glycoproteins,

collagens and

proteoglycans

NABA ECM G Genes encoding

HN1L 1.36453955 0.000651034 LYCOPROTEIN structural ECM 8.91E-07

S glycoproteins

REACTOME R Genes involved

ECRUITMENT_ in Recruitment of

OF_MITOTIC_C mitotic

2.86E-06 ENTROSOME P centrosome

ROTEINS AND proteins and

COMPLEXES complexes

Table 2B. Under-expressed Genes and Pathways

POU2F3 0.51754048 1.01E-08 TCEB1 0.76866124 0.01050149

PRRG1 0.52569751 1.29E-08 PGM2L1 0.81470242 0.01050282

FAM40B 0.41827178 1.33E-08 ZNF207 0.78322085 0.01056721

RAB27B 0.63101586 1.81E-08 ZFC3H1 0.76322477 0.01058595

AGL 0.60797081 1.94E-08 MYOF 0.8174365 0.01072082

HS6ST2 0.50589265 4.17E-08 NEDD4 0.75183609 0.01072082

ERRFI1 0.59795439 5.59E-08 SYNJ1 0.74797515 0.01072082

MALL 0.60107268 6.80E-08 CHML 0.75999034 0.01073602

E2F2 0.54530533 9.00E-08 LYSMD3 0.81359844 0.01075889

ANKRD22 0.61522801 1.29E-07 XDH 0.7776994 0.01082657

MIER3 0.6186614 1.68E-07 STAG2 0.77433017 0.01089059

LOC100505839 0.54012654 1.86E-07 RGS1 0.428437 0.01099508

LHFPL2 0.6290898 1.89E-07 TIN AGL 1 0.76940891 0.01099801

PPARG 0.61457594 1.99E-07 PEX13 0.79652854 0.0110079

TMEM106B 0.62973645 2.17E-07 KRT6B 0.47469479 0.0110079

NRIP1 0.64071414 2.19E-07 C7orf60 0.72826754 0.01101626

TM4SF1 0.54686638 2.20E-07 ATP7A 0.78923096 0.01104899

PLK2 0.62474305 3.09E-07 UBTD2 0.78150066 0.01107608

C8orf4 0.5985907 3.40E-07 FGD4 0.76292428 0.01114875

MBOAT2 0.65711393 3.64E-07 HNRNPH3 0.78989996 0.01119847

TMPRSSl lA 0.50012157 3.90E-07 GNPNAT1 0.80178069 0.01120254

HPSE 0.63345701 4.27E-07 SERPINB7 0.59831614 0.01120254

SP6 0.50873861 4.58E-07 TARS 0.787516 0.01122418

MCTPl 0.54747859 4.82E-07 UBLCP1 0.7722069 0.01122648

ECT2 0.65574576 6.32E-07 GARS 0.79199425 0.01132108

CYR61 0.56382112 6.47E-07 TMEM2 0.80301179 0.01138085

CFL2 0.62040497 6.48E-07 ZNF185 0.79182935 0.01143669

SLC18A2 0.6252582 6.95E-07 GDPD3 0.67570566 0.01143669

OCLN 0.66000035 6.98E-07 C5orf43 0.79637974 0.01148042

F2RL1 0.65645045 7.34E-07 SIRT1 0.74221538 0.01148042

OXSR1 0.6328292 7.42E-07 MAB21L3 0.77571866 0.01156947

DKK1 0.43751201 8.08E-07 LYRM5 0.76896782 0.01156947

LDHA 0.6605144 8.88E-07 IER3IP1 0.79267292 0.01158028

FABP5 0.59566267 1.03E-06 VEGFA 0.75291474 0.0116188

SLC38A2 0.65822916 1.05E-06 TMSB4X 0.72244795 0.01165661

PDP1 0.66035671 1.06E-06 TMEM41A 0.77944137 0.01168994 R D3 0.65234528 1.06E-06 TNFAIP3 0.65538935 0.01172668

CDKN2B 0.60249001 1.08E-06 INTS6 0.76205092 0.01172886

SERPINB5 0.56356085 1.19E-06 ADAM10 0.80151014 0.01175579

GPNMB 0.60704771 1.36E-06 ARAP2 0.7953511 0.0118699

HSD17B3 0.60203529 1.60E-06 CNN3 0.80690311 0.01188901

SERPINE2 0.34777028 1.62E-06 SPTY2D1 0.77603059 0.01194061

BZW1 0.67135675 1.72E-06 PHF20L1 0.77584582 0.01195426

MYEOV 0.49219284 1.72E-06 SERPINB1 0.61773856 0.01198815

SGK1 0.68010617 1.95E-06 HOMER 1 0.75406296 0.01202166

DNAJB9 0.66020909 2.02E-06 PTK6 0.78404191 0.01213403

CALBl 0.31335579 2.19E-06 CAMSAPILI 0.78125047 0.01215002

MSR1 0.49696801 2.44E-06 RNF11 0.78944171 0.01221391

C12orf29 0.63475403 2.52E-06 PPFIBP1 0.79937047 0.01235788

PLA2G7 0.44181773 2.68E-06 RP2 0.65113711 0.01246432

CAPZA2 0.63650318 3.06E-06 LTN1 0.81447306 0.01248787

CD 109 0.56416931 3.06E-06 PAKlIPl 0.79300898 0.01253176

RAPH1 0.69473071 3.27E-06 ZNF189 0.76756049 0.01260727

CERS3 0.63914564 3.33E-06 BZW2 0.79754386 0.01273528

ETV4 0.59884423 3.74E-06 PKP1 0.71932402 0.01278409

FOXN2 0.62642545 3.75E-06 ATF1 0.80930096 0.01279478

RPS6KA3 0.67623565 4.20E-06 LIN7C 0.79913296 0.01285667

BCL10 0.65894446 4.20E-06 S100A16 0.77701197 0.01291573

SLC5A3 0.53006887 4.63E-06 Clorf52 0.74541456 0.01291781

STK38L 0.62733421 4.91E-06 MY05A 0.73515052 0.01297751

SNX16 0.63704107 5.31E-06 DEPTOR 0.79024652 0.01303209

STRN 0.67981453 5.81E-06 BAZ2B 0.7897409 0.0130574

HSPC159 0.6455435 6.64E-06 ME1 0.78969952 0.01306743

SLC01B3 0.49485284 6.90E-06 NR4A2 0.70149781 0.01312925

SACS 0.62971335 7.24E-06 ASNSD1 0.79830294 0.01315637

PLIN2 0.62600964 7.25E-06 CATSPERB 0.70538226 0.01315637

HSPA13 0.64757842 7.51E-06 FRMD4B 0.7805225 0.01321553

DDX3X 0.67297758 8.43E-06 ZNF552 0.79768046 0.01346424

SDR16C5 0.67434136 8.57E-06 MFN1 0.81509879 0.01359256

AMD1 0.67760181 8.91E-06 USOl 0.80330724 0.01359256

ITGB8 0.67887254 9.95E-06 BPGM 0.78515609 0.01359256

SLC4A7 0.65708728 1.04E-05 CXCL2 0.39887063 0.01359787 PTP4A1 0.68607621 1.05E-05 PPP1CC 0.80893126 0.01365976

HNMT 0.68400423 1.05E-05 PCNP 0.79622567 0.01368486

PGM2 0.6609215 1.09E-05 S100A11 0.74267291 0.0136932

FCH02 0.68699512 1.19E-05 ID2 0.75318731 0.0137174

OAS1 0.63160242 1.20E-05 IFRD1 0.42135251 0.0137174

MAPK6 0.684135 1.20E-05 SCFD1 0.80529038 0.01373021

GRAMD3 0.68353459 1.26E-05 EMP1 0.60588308 0.01373021

ABCA1 0.54787448 1.28E-05 LANCL3 0.68348747 0.01375217

SYTL5 0.70638291 1.28E-05 UBA6 0.79888098 0.01379958

GULP1 0.65824402 1.32E-05 RARS 0.79366989 0.0138429

PHLDA1 0.54172105 1.32E-05 C7orf73 0.76317263 0.01389162

NRIP3 0.60674778 1.35E-05 LCOR 0.81117554 0.01389191

UGT1A10 0.60272574 1.45E-05 PTPN12 0.60299739 0.01394062

TMED7 0.70617128 1.57E-05 IREB2 0.80814458 0.01401875

ZFAND6 0.67093358 1.57E-05 MACC1 0.80002988 0.01406745

CSTA 0.52443912 1.61E-05 B4GALT5 0.79715598 0.0141339

POF1B 0.69756087 1.69E-05 NAPEPLD 0.80214979 0.01416807

CLCA2 0.56020532 1.70E-05 HECA 0.72312723 0.01416807

CYP2E1 0.46030235 1.83E-05 SCEL 0.59978505 0.01427161

GPR115 0.51236684 1.94E-05 CDK19 0.75633313 0.01433637

STXBP5 0.68639477 1.95E-05 SOCS5 0.78388345 0.01441385

FHL2 0.69498993 2.13E-05 DGKA 0.78636133 0.01447758

EFNB2 0.68000514 2.13E-05 EIF3J 0.80032433 0.01469173

SPRY4 0.57593365 2.18E-05 MAP1LC3B 0.73616097 0.01472412

FRMD6 0.67585426 2.19E-05 IVL 0.51954316 0.01487199

SOX9 0.69148494 2.34E-05 SLC38A9 0.78548034 0.01488644

LYPLA1 0.68419869 2.40E-05 TXNDC9 0.80599778 0.01499161

SLC37A2 0.6397126 2.54E-05 ARHGAP29 0.79975551 0.01502574

SLC6A14 0.63108881 2.66E-05 CHMP1B 0.78649063 0.01506495

TCN1 0.63504893 2.67E-05 CREBl 0.75968742 0.01506947

STS 0.71630909 2.67E-05 AURKA 0.7291468 0.01525634

CLDN1 0.71508575 2.70E-05 DEN D1B 0.78917281 0.01528104

TGFB2 0.70221517 2.86E-05 SP3 0.80275018 0.01547056

PPP1CB 0.69356726 2.96E-05 ABCC9 0.75019099 0.01563394

COPS2 0.70745288 3.20E-05 LARP4 0.81575794 0.01573566

FNDC3B 0.70629744 3.27E-05 PSTPIP2 0.74759876 0.01576062 SLC9A2 0.70240663 3.45E-05 UBAP1 0.72271205 0.01576062

AHR 0.72189199 3.48E-05 GYG1 0.77805963 0.01581091

CPM 0.60903324 3.65E-05 KIAA1199 0.54860664 0.01593278

MRPS6 0.67128208 3.65E-05 SNRPB2 0.80292457 0.01593921

MAL2 0.71451061 4.09E-05 FBX034 0.80748644 0.01598506

SLC9A4 0.68487854 4.09E-05 NFAT5 0.80662528 0.01610673

PLAU 0.60117497 4.14E-05 PURB 0.80015013 0.01638623

KCTD9 0.68717984 4.21E-05 VTA1 0.795135 0.01638623

CYP2C18 0.67036117 4.25E-05 ZBTB38 0.80217977 0.01644708

ARHGAP5 0.72532517 4.26E-05 CYB5R2 0.77288599 0.01648404

TDG 0.7023444 4.31E-05 EXOC5 0.81382561 0.01655428

RALA 0.68246265 4.39E-05 CDR2L 0.81728606 0.01659833

AN DDIA 0.59706849 4.44E-05 SWAP70 0.80565394 0.0167099

CEACAM1 0.60936113 4.61E-05 GLRX3 0.78569526 0.0167132

TRPS1 0.68207878 4.80E-05 MMP7 0.51970705 0.01674324

GALNT5 0.70688281 4.90E-05 C18orfl9 0.80580272 0.0167524

AGPAT9 0.54621966 5.57E-05 IPPK 0.76399847 0.01679915

PLS1 0.73068821 5.63E-05 BLOC1S2 0.76302982 0.01685077

ABHD5 0.63310304 5.75E-05 PDLIM2 0.73531533 0.01685769

SLK 0.70996449 5.86E-05 OTUD6B 0.74806056 0.01696167

GNAI3 0.63637676 5.88E-05 POLR2K 0.78945634 0.01701766

GPCPD1 0.60712726 6.03E-05 ClOorfm 0.81187016 0.01703642

FAT1 0.71499305 6.16E-05 RELL1 0.71318764 0.01707764

CAPZAl 0.69202454 6.43E-05 GLA 0.60796251 0.01727628

TUBB3 0.46563825 6.48E-05 PLXDC2 0.53165839 0.01733236

DSG3 0.44745628 6.87E-05 L3MBTL3 0.77911939 0.01735666

C6orf211 0.70372086 6.91E-05 RUNX2 0.77801083 0.01735666

SLM02 0.70233453 7.10E-05 CA2 0.4922131 0.01735666

LOC100507127 0.44153481 7.20E-05 PPP4R2 0.79532914 0.01736433

MGAT4A 0.70002166 7.36E-05 LRRC8C 0.67202997 0.01753532

MST4 0.6716609 7.59E-05 ARID4B 0.77340187 0.01754278

UCA1 0.38849742 7.77E-05 SH3BGRL2 0.81075514 0.01755334

TPM4 0.69490548 7.82E-05 CPD 0.79596928 0.01755334

TBC1D23 0.70081911 8.08E-05 DNAJB6 0.78602264 0.01755334

C9orfl50 0.65660789 8.16E-05 RG9MTD1 0.78287275 0.01755334

MPZL2 0.72416465 8.45E-05 TXN 0.77853577 0.01761555 BCAT1 0.60155977 8.50E-05 UGCG 0.81279199 0.01783791

PRRG4 0.69994187 8.66E-05 ARNTL 0.7595337 0.01792236

ANKRD57 0.69957309 8.92E-05 PRSS16 0.78421252 0.01793552

DSEL 0.66917039 8.92E-05 RAP2A 0.78860475 0.01801902

CCNC 0.72104813 9.50E-05 VAMP7 0.78098348 0.01804468

FGFBP1 0.55896463 9.83E-05 JOSD1 0.66714848 0.01818247

HEPH 0.63099648 0.00010094 TNFRSF12A 0.7674609 0.01827299

TIAM1 0.68576937 0.00010103 EXOC1 0.80533345 0.018306

FAR1 0.71009803 0.00010236 ACOX1 0.77467238 0.01836883

MANSC1 0.67745897 0.00010243 IQGAPl 0.78700289 0.01837327

TET2 0.69755723 0.00010428 PFKFB2 0.79393361 0.01838189

PTPN13 0.72165544 0.00010468 ID1 0.7077695 0.01838189

PLS3 0.70700001 0.0001063 ELMOD2 0.8099594 0.01839339

GRHL3 0.62055831 0.00011182 SSR3 0.8027967 0.01861183

TRIB2 0.70025116 0.00011358 A2M 0.7095884 0.01863194

VGLL1 0.66984802 0.00011809 PSMA3 0.80198438 0.01868687

HOOK3 0.71748877 0.00012006 TTC39B 0.78773869 0.01868687

FAM3C 0.71723806 0.00012006 SREKlIPl 0.78848537 0.01871407

BAZ1A 0.68508081 0.00012035 DNAJC25 0.7466337 0.01872135

CCDC88A 0.65999086 0.00012598 TPRKB 0.74502201 0.01872135

SPATA5 0.6904431 0.00012757 DCP2 0.69555649 0.01872135

SOCS6 0.71829579 0.00013007 MCU 0.80603403 0.01876119

TOB1 0.72241206 0.00013331 PVR 0.7660582 0.01876119

HIST1H2BK 0.66691073 0.00013571 ADRB2 0.75075306 0.01876119

TOPI 0.71883193 0.00013658 ATP 13 A3 0.82040209 0.0188408

SRPK1 0.69969324 0.00014184 ESRPl 0.80880005 0.0189173

LRIF1 0.69079735 0.00014297 TC2N 0.81169068 0.01891942

SPTSSA 0.7084399 0.00014301 ANXA3 0.80049136 0.01893378

RALGPS2 0.7046366 0.00014634 SPCS2 0.79971407 0.01893378

CHMP2B 0.70500108 0.00014894 CKS2 0.82098525 0.01900244

CXADR 0.72706834 0.00015072 scoc 0.81832985 0.01902309

GSTA4 0.71794256 0.00015072 SGTB 0.63979487 0.01904115

NAA50 0.72321863 0.00015246 SYNM 0.73918101 0.01915338

SLC38A1 0.72718456 0.00015392 NET02 0.74186068 0.01921827

GPRC5A 0.67982467 0.00015492 RAB1A 0.79371888 0.01931145

HRH1 0.57142076 0.00015553 DUSP4 0.7679591 0.01932028 SGPP1 0.60446113 0.00015983 TICAMl 0.71976999 0.01949387

DSC2 0.42009312 0.00016546 RBMXL1 0.77176321 0.01959763

REL 0.70232402 0.00016796 NIPAL1 0.75859871 0.01975244

SERPINB8 0.71948572 0.00017411 ARL15 0.78712448 0.01978067

ESRG 0.50616862 0.00017416 SPECC1 0.79037053 0.01997725

GMFB 0.71115128 0.00017772 RAETIG 0.76619179 0.01997725

CYCS 0.73195986 0.00017997 KLF5 0.81561175 0.01999447

ATP1B3 0.72625915 0.00018351 IFNAR1 0.76951871 0.02007723

SCYL2 0.72159083 0.00018351 USP3 0.77565612 0.0201071

KRAS 0.73375761 0.00018545 FAM83C 0.70142413 0.0201071

ZNF518B 0.6968451 0.00019734 TRIM16 0.81115941 0.0201551

PNPLA8 0.63204178 0.00020809 NR3C1 0.78608488 0.02017233

ASPH 0.72334386 0.00021314 CDC42SE2 0.78654377 0.02019726

LAMA4 0.60508669 0.00021337 CNIH4 0.76529362 0.02023387

PDE5A 0.62146953 0.00021406 SLC40A1 0.75686068 0.02023734

LY6D 0.52174522 0.00021584 METTL21D 0.72136719 0.02031329

SLC44A5 0.47103937 0.00023984 B3GNT5 0.73325211 0.02032869

XPOl 0.74477235 0.00024253 FZD5 0.81737971 0.02042132

SLC35F2 0.67225241 0.0002428 NUP50 0.81619664 0.02042132

SH2D1B 0.59115181 0.00024453 APC 0.79253541 0.02042132

MED13 0.71820172 0.00025206 OSMR 0.75202139 0.02042132

STXBP3 0.71330561 0.00025406 APOBEC3A 0.41742626 0.02042132

CTSL1 0.65567678 0.00025521 SLC10A7 0.78781367 0.02043964

CPEB4 0.70060068 0.00025668 DTX3L 0.80221646 0.02047647

FLVCR2 0.5867205 0.00026148 NR1D2 0.82110804 0.02059914

RNF141 0.72848197 0.00026362 ANXA2 0.81057352 0.02064016

RAB5A 0.71866507 0.00026829 BNIP3L 0.7921443 0.02065952

STEAP4 0.73753612 0.00027352 EEA1 0.82047062 0.02105772

NPC1 0.71394763 0.00027481 GLTP 0.79057504 0.0211003

ACTR3 0.67613118 0.00027918 ACAP2 0.79259531 0.02112664

SLC12A6 0.64629107 0.00028121 MXD1 0.40192887 0.02113344

TMEM167A 0.73039401 0.0002839 CALU 0.82233944 0.02117432

HBP1 0.71134346 0.00029684 PPP2R1B 0.82287537 0.02147113

GPR37 0.64413044 0.00030167 MANF 0.79019152 0.02147113

FAM135A 0.73205965 0.00030188 UBXN8 0.75092566 0.02147113

C12orf36 0.67818686 0.00030805 KRT13 0.5557856 0.02147113 CD58 0.62882881 0.00031182 CD55 0.7675448 0.02147853

MALATl 0.35629204 0.00031256 PKP2 0.84172061 0.02150051

YWHAZ 0.7300418 0.0003126 PLAT 0.56494138 0.0215063

HBEGF 0.36825648 0.0003126 NEAT1 0.72062622 0.02173452

CLEC2B 0.41375232 0.00031403 NCOA3 0.81904203 0.02181149

CYB5R4 0.62282326 0.00031499 ZC3H12C 0.79419138 0.02181149

ATP10B 0.73014866 0.00032141 FAM49B 0.51183042 0.02209803

KCTD6 0.6982837 0.00032602 CUL4B 0.81000302 0.0220994

ITGA2 0.73729371 0.00032753 SCD 0.81856731 0.02225105

MGST1 0.74936959 0.00033476 FXYD5 0.61611839 0.02227887

CDRT1 0.6679511 0.00034261 C3orf58 0.7929907 0.02231832

SPRR1A 0.45298366 0.00034579 SOS2 0.78441202 0.02242783

UGT8 0.6364024 0.00036052 EPPK1 0.71847068 0.02247716

BIRC3 0.63931884 0.00036805 UBE4A 0.81949437 0.02247809

PAM 0.73943259 0.00036851 RLF 0.76493297 0.02249613

SMC4 0.72845839 0.00036886 MAGT1 0.81754733 0.02251014

ACTR2 0.7257177 0.00037179 DCTN6 0.79087132 0.02255614

RAB21 0.71063184 0.00038679 ITCH 0.81832417 0.02261806

SEC24A 0.74242518 0.00038918 TXNL1 0.80210696 0.02270459

ELL2 0.73642285 0.00039252 EPHA2 0.80043392 0.02270459

ARPC5 0.66218112 0.00039424 SLC10A5 0.75403621 0.02270459

PRDM1 0.56977817 0.00039519 CLEC7A 0.40086257 0.02273095

GK 0.56146426 0.00039726 ALG6 0.79281819 0.02273251

C14orfl29 0.73022452 0.00040878 TMX3 0.82502213 0.02283395

CCDC99 0.72023731 0.00041286 RAB8B 0.51178041 0.02283395

PRSS3 0.42409665 0.00042522 ENPP4 0.82969342 0.02290538

USP25 0.71934778 0.00042769 SAMD4A 0.80115193 0.02290538

PKN2 0.71899998 0.00043042 GNG12 0.81800792 0.02290834

GPR87 0.73061781 0.00043214 MITF 0.79669058 0.02302213

RORA 0.70094713 0.00043625 UBE2J1 0.80232214 0.02305656

GGCT 0.7344833 0.00044515 KIAA1324L 0.84134374 0.02309417

ZNHIT6 0.76417154 0.00045036 TGFBR1 0.77759794 0.02324532

TMBIMl 0.72290834 0.00046454 CHM 0.82558253 0.02329511

TFPI 0.61640577 0.00048755 TMEM41B 0.80778275 0.02342002

BCAP29 0.72684992 0.00049294 JARID2 0.7674422 0.02350843

RCOR1 0.70144121 0.00049756 DYNC1LI1 0.79569175 0.02350861 LEOl 0.72295774 0.00051807 DNAJA1 0.80469715 0.0235662

OTUB2 0.6388429 0.00052599 CXCL3 0.57876868 0.0235662

TMPRSS11D 0.60003871 0.0005336 AFTPH 0.80550055 0.02358174

CP 0.73425817 0.000553 SCGB1A1 0.68088861 0.02358174

IKZF2 0.7513508 0.00055695 BMP3 0.81011626 0.02365337

ROD1 0.73886335 0.0005605 CCRL2 0.6009859 0.02365337

HPGD 0.74086493 0.00056145 SEL1L 0.82277025 0.0238405

NAPG 0.73799305 0.00056145 CASP7 0.81804453 0.0238405

RIT1 0.7194234 0.00056717 MED4 0.7939477 0.0238405

CLCA4 0.63982609 0.00059724 SLURP 1 0.58553775 0.0238405

PPP3R1 0.70906132 0.00060194 C12orf4 0.82963799 0.02394378

GABPA 0.72611695 0.00060812 DENR 0.81434832 0.02394378

SPCS3 0.75238433 0.00061101 MKI67 0.65325272 0.02394378

ITGAV 0.74691451 0.00061101 CD84 0.70733746 0.02421674

LOC100289255 0.69618504 0.00061787 PGM3 0.82981262 0.02433953

ADAM9 0.75133718 0.00061987 VPS4B 0.81124865 0.02443084

HIF1A 0.62106857 0.00061987 SLC7A11 0.7055667 0.02443084

GAN 0.67925484 0.00062053 CD44 0.77927941 0.02445288

EIF1AX 0.76260769 0.00062186 SLC1A1 0.75927386 0.02456729

WASL 0.74896466 0.00062186 CLPX 0.80928724 0.024572

UBE2W 0.64239921 0.00063811 MOSPD1 0.80026606 0.02459523

RCAN1 0.71096698 0.00064856 ZC3H15 0.80450651 0.02467764

SSR1 0.7514502 0.00065077 RAB11A 0.80437379 0.02482369

PHACTR2 0.75203507 0.00065103 DNAJB1 0.80659609 0.02483132

NCK1 0.73821734 0.00065616 SC5DL 0.81585449 0.02492318

SDS 0.43860257 0.00065851 PON2 0.79911935 0.02492318

ZNF460 0.6508334 0.00066048 WAC 0.80996863 0.02494557

SPAG9 0.7041979 0.00066393 IRAK2 0.78621119 0.02498706

ETFA 0.7376278 0.0006674 MAN2A1 0.80945847 0.02501316

TBL1XR1 0.77064376 0.00066959 NRP1 0.75842343 0.02501316

MET 0.75295132 0.00066959 NFKBIA 0.64409994 0.02509502

LOC100499177 0.6435527 0.00066959 ZNF143 0.78375832 0.02519086

RC3H1 0.71187912 0.00067619 OSTC 0.81380824 0.02520621

PPP1R15B 0.72604754 0.000685 DHX15 0.80218767 0.0252546

RBMS1 0.72833819 0.00069497 USP32 0.69625972 0.02547673

PAPSS2 0.73311321 0.00070388 CMAS 0.80689954 0.02563124 FGFR10P2 0.72583355 0.00070539 ATP6V1G1 0.79750807 0.02563124

PHF6 0.74176092 0.00071648 ARPC3 0.74025507 0.02567149

RAB27A 0.69715587 0.00072005 PTARl 0.82246466 0.02577645

MAP4K4 0.69994514 0.00072785 ABCEl 0.8206001 0.02577645

PRKAR2B 0.7353908 0.00074015 ZNF260 0.81726679 0.02577645

ANXA1 0.73823795 0.00074408 VNN1 0.47957675 0.02591115

LOC100134229 0.73183087 0.00074435 TPM3 0.77578302 0.02596422

OSTM1 0.71670885 0.00075171 CN M1 0.75796579 0.02596422

SMOX 0.59247896 0.00075968 MED21 0.78624253 0.02601824

RTKN2 0.67259731 0.00076669 GM2A 0.80553342 0.02604295

TMEM64 0.751443 0.00076931 PSMC2 0.81330981 0.02617976

BRWD3 0.70874449 0.00077331 RAP IB 0.79847594 0.02618716

YTHDF3 0.73166588 0.00077638 CYP4X1 0.71483031 0.02618716

CLDN4 0.71007023 0.00077802 PHTF2 0.81641271 0.0262022

MMP1 0.55376446 0.00077869 UBE2V2 0.81033911 0.02626899

KCNN4 0.68465172 0.00079015 ARHGAP20 0.78890875 0.02632695

CLDN12 0.76454862 0.0007909 RHBDL2 0.79592484 0.0264027

COQ10B 0.71874588 0.00079995 SMAPl 0.81113172 0.02649101

LRP12 0.71964731 0.00080097 KRT10 0.68898712 0.02653464

FOSL1 0.51166802 0.00082386 RFK 0.80461614 0.02655103

PARD6B 0.74223837 0.00082622 RAP1GDS1 0.8420239 0.02657993

LOC439990 0.69267458 0.00083354 MAPKIIPIL 0.82200085 0.02658191

PDLIM5 0.76185114 0.00084129 SLC35A5 0.81757126 0.02659754

LTBP1 0.73928714 0.00084166 GDAP2 0.776095 0.02667787

HI GDI A 0.74108416 0.00084269 MIB1 0.82312043 0.02681784

RANBP6 0.72113191 0.00085429 ITPR2 0.72381288 0.02688482

AFF4 0.75419694 0.00086212 PGRMC2 0.82715791 0.02695215

RCBTB2 0.72276464 0.00088071 RAB14 0.8177047 0.02700102

DEFB1 0.56084482 0.00088306 ARL4A 0.82412052 0.02702553

SORB SI 0.69135874 0.00090133 RYBP 0.69095215 0.02702816

LACTB2 0.75713601 0.00092553 TDP2 0.68722637 0.02707132

DAB2 0.69448887 0.00092633 CBX3 0.80911237 0.02714575

ZNF431 0.70801523 0.00092668 TBC1D15 0.79826732 0.02725035

MAN1A1 0.74578309 0.00093774 ZNF292 0.79336479 0.02727831

RNF19A 0.7499563 0.00094857 DEK 0.79668216 0.02738693

SRD5A3 0.68412211 0.00094857 GTF2F2 0.79408033 0.0273958 SDCBP2 0.69112547 0.00096472 CCNG2 0.66348611 0.02746122

GLS 0.55743607 0.00096829 FBXW7 0.77030162 0.02750752

ARRDC3 0.73257404 0.00098514 NCOA7 0.67006969 0.02759494

PDZD8 0.74504511 0.00101932 SLC39A10 0.81569938 0.02762611

NT5C2 0.74411832 0.00102102 CXCL1 0.5037887 0.02773044

DDX52 0.74116607 0.00102436 LMBRD2 0.79862543 0.02773263

ZNF326 0.73410121 0.00104743 RNF139 0.77894417 0.0277779

SDCBP 0.51524162 0.00106089 ATXN3 0.81712764 0.02778695

TAB2 0.73583939 0.00106325 HMGCS1 0.83634026 0.02780334

MDFIC 0.75928971 0.00107939 GAB l 0.75314903 0.02799812

FAM126B 0.65824303 0.00109786 DR1 0.79711312 0.02810783

MAT2A 0.76256991 0.00110997 TJP1 0.815017 0.02814271

SAMD9 0.60678126 0.00110997 SSFA2 0.81751861 0.02821836

OSBPL8 0.69459764 0.00111029 SH3GLB1 0.80551167 0.02824311

LIG4 0.73079298 0.0011228 EDIL3 0.73606278 0.02837228

THRB 0.76151823 0.00114313 CMTM6 0.73956197 0.02838961

TNFRSFIOD 0.62060304 0.00114435 PIK3C2A 0.83154276 0.02851279

RIOK3 0.73962901 0.00115102 PHACTR4 0.82152956 0.02867344

6-Mar 0.69528665 0.00117913 CD86 0.44546002 0.02875144

VPS26A 0.74010152 0.0012058 RSL24D1 0.80075639 0.02876288

GRHL1 0.74125467 0.00121284 MAP4K3 0.82252973 0.02880875

SEC23A 0.74746817 0.00122351 C4orf32 0.73140848 0.02889681

CLOCK 0.75080448 0.00124549 TGIF1 0.80327776 0.02900415

SAT1 0.70085873 0.00128002 NFYA 0.79091615 0.02900415

POLB 0.7265576 0.00129411 XRCC4 0.79014548 0.02906143

TAF13 0.74566967 0.00129461 BACH1 0.60345946 0.02933929

DSC3 0.67776861 0.00129939 PRPF18 0.79195926 0.02934951

SAMD8 0.73394378 0.00131822 HSPA5 0.82254051 0.02939332

NPEPPS 0.7437029 0.00132561 COBLL1 0.80869858 0.02939332

TPD52 0.75898328 0.00135933 STRN3 0.81460651 0.02940888

NCEH1 0.7474324 0.00136541 C16orf52 0.80347457 0.02940888

AP1S3 0.80504206 0.00136961 ACAD SB 0.81872232 0.02951968

USP53 0.75319991 0.00137958 CLCF1 0.79372787 0.02959393

EDEM1 0.75561796 0.00139667 SBDS 0.82630688 0.02972834

MBNL1 0.74932328 0.00141178 Clorf96 0.73892616 0.02980835

TMEM33 0.74560237 0.00141178 SVIL 0.77354524 0.02993904 NMU 0.50565668 0.00141984 FRS2 0.82504155 0.02998364

CCPG1 0.74604118 0.0014299 DNAJB14 0.79384122 0.02998364

TBK1 0.73752066 0.00144402 IL8 0.12605808 0.02998364

PCMTD1 0.75791312 0.00146293 GJB4 0.79743165 0.03001609

SMNDC1 0.72111534 0.00147433 UBE2E1 0.8132693 0.03004003

ARNTL2 0.73486575 0.00151723 PRC1 0.76311242 0.03009422

CHPT1 0.72326837 0.00151723 KPNA4 0.79641384 0.03021352

SEC61G 0.7105942 0.00151723 ALDH3B2 0.80496463 0.03021519

SHISA2 0.59853622 0.00152782 ARFIPl 0.81639333 0.03031551

XIST 0.44631578 0.00155743 BMPR2 0.83541357 0.03031694

TMOD3 0.77533314 0.00157527 PUS10 0.73256187 0.03037422

HERC4 0.73058905 0.00159354 CENPN 0.76828791 0.03047261

FEM1C 0.76590656 0.00160833 YES1 0.82057502 0.03053073

TFRC 0.7570632 0.0016402 ZNF468 0.84177205 0.03072911

F8A1 0.7386134 0.00164374 PIK3CG 0.53271288 0.03078134

ATP IB 1 0.76704609 0.0016534 LPCAT2 0.61892931 0.03081115

ZDHHC13 0.75504945 0.00166529 MAGOHB 0.77202271 0.03087813

ERV3.1 0.68654538 0.00167391 PGGT1B 0.81716901 0.03087848

TMEM30A 0.75615819 0.00169183 SIKE1 0.81047669 0.03087848

CCNYL1 0.74297343 0.00169817 C15orf52 0.7677753 0.03095296

IBTK 0.76516915 0.0017406 CHST4 0.75379626 0.03109953

KLF6 0.64386779 0.0017406 SLC28A3 0.80134905 0.03115551

MAP2K4 0.73093628 0.00175469 GTDC1 0.77009529 0.03131057

PICALM 0.60342183 0.00178068 ITPRIP 0.62964124 0.03136065

DCUN1D1 0.78777005 0.00178761 PERP 0.81957926 0.03145735

SRP19 0.73007773 0.00179995 PSMD5 0.81822219 0.03147226

GNE 0.76363264 0.00180792 CNIH 0.8396771 0.03158417

TMEM56 0.72176614 0.00184076 PDE4B 0.15925174 0.03166939

NUS1 0.76925969 0.00185255 FAM105A 0.76759455 0.03184924

TMED5 0.75920484 0.00185255 GABRE 0.72174883 0.03184924

PMAIP1 0.61359208 0.00185497 UHMK1 0.83795019 0.03186968

TM9SF3 0.76920471 0.00186378 CDK6 0.84259905 0.03206511

ARL8B 0.75277703 0.001865 GSPT1 0.81333116 0.03211789

CSTB 0.7246213 0.0018664 CLINT 1 0.84129485 0.03258105

TAOK1 0.76340931 0.00187476 SPTLC1 0.82243139 0.03262099

FRK 0.74737271 0.00187862 OXR1 0.82634351 0.03273304 KRT6A 0.50297318 0.00188266 SYNCRIP 0.82737388 0.03294625

ZRANB2 0.73683865 0.00188671 TWSG1 0.82516604 0.03294625

MAOA 0.75804286 0.00190091 TUFT1 0.78129892 0.03294625

UBE2K 0.75499291 0.00193919 FAM98A 0.82227343 0.03311064

ZCCHC6 0.64117131 0.00197834 ANGPTL4 0.62447345 0.03316298

TACC1 0.73591479 0.00201604 SPIN1 0.82919111 0.03336936

TRAMl 0.76688878 0.00202235 FTSJD1 0.82751547 0.03348945

PNRC2 0.76237127 0.00202235 THBS1 0.3372848 0.03405027

CDC25B 0.73376831 0.00205757 YPEL2 0.83006226 0.03422723

MTHFD2 0.71278467 0.0020715 CIGALTICI 0.82711113 0.03422723

ARL5B 0.65205708 0.00208123 SFT2D2 0.79342076 0.03422723

VBP1 0.7564177 0.00208303 NBPF14 0.62423931 0.03436711

IRS1 0.74430144 0.00209694 APPBP2 0.81820437 0.03439503

GALNT1 0.75884893 0.0021133 SUB1 0.79595423 0.03442763

CD68 0.69932459 0.0021133 CSTF2 0.81280844 0.03457978

ALDHlAl 0.78129241 0.00211381 SERPINB 13 0.74386568 0.03462984

GALNT3 0.7706992 0.00216886 TAF12 0.75776079 0.03465156

ANKRD50 0.77616647 0.00217264 EAF2 0.73385631 0.03465156

PMP22 0.44713619 0.00220309 ACER2 0.81769965 0.03468364

ARF4 0.76387404 0.00223255 KIAA1370 0.8310723 0.03478594

ER01L 0.75005002 0.00224373 C6orfl l5 0.7920281 0.03480856

KIAA1033 0.74890236 0.00224373 TMEM161B 0.82837568 0.03482004

UBASH3B 0.73513497 0.00225969 SERPINB4 0.58217203 0.03526646

CARD6 0.74899398 0.00228664 TMEM206 0.76722577 0.03530246

RABGEF1 0.71844668 0.00230748 TMEM87A 0.81927656 0.03544177

MZT1 0.71720898 0.00230944 TAOK3 0.79902307 0.03567122

ASPHD2 0.74295902 0.00238373 KIF5B 0.83603725 0.03581481

2-Mar 0.72623707 0.00241931 ATP6AP2 0.81457493 0.03586138

PPP1R12A 0.72959311 0.00243185 SPRR3 0.55146539 0.03606441

TRA2A 0.7429305 0.00243585 BTBD10 0.80108306 0.03618119

TRAPPC6B 0.73528091 0.00244989 CBR4 0.81257455 0.03620449

RAP2C 0.68175561 0.0024659 LAD1 0.80458232 0.03629508

C6orf62 0.75844544 0.00251409 SMC2 0.82005575 0.03648829

PPIP5K2 0.78387164 0.00252188 MOSPD2 0.61436673 0.03648829

TGFBI 0.52785345 0.00252749 NPAS2 0.83232392 0.03656964

RBI 0.77191438 0.00252877 FBX032 0.80298304 0.03658334 IMPA1 0.78178293 0.00254095 PLEKHA2 0.80322887 0.03677678

TNPOl 0.78650015 0.00256633 KLHL2 0.79563549 0.03677678

FBX028 0.77608259 0.00259197 RPH3AL 0.79452691 0.03677678

GALNT7 0.78732986 0.0026183 AGFG1 0.79019227 0.03677678 cm 0.71982264 0.00262033 MY06 0.83241148 0.03684746

ACVR2A 0.74257908 0.00262047 AEBP2 0.80355723 0.03686652

FAM18B1 0.76176472 0.00262281 CREB3L2 0.84749284 0.03709572

CXCL6 0.33096087 0.00262687 RANBP9 0.81802251 0.03709572

ERBB2IP 0.7639335 0.00266838 KLHL15 0.65857368 0.03709572

APOBEC3B 0.59242482 0.00270511 CUL3 0.8096363 0.03710186

DHRS9 0.75871115 0.002728 RAB22A 0.80433101 0.03711539

PIGA 0.73677237 0.00273775 OSBPL11 0.78407533 0.0371207

DUSP5 0.6422383 0.00276958 KIAA1539 0.69819167 0.03714167

CLIC4 0.73379796 0.00278346 DLG1 0.83009251 0.03726826

TMEM139 0.75516298 0.00278911 UBXN2B 0.7072684 0.03738914

SMAGP 0.75555643 0.00280753 IRAK4 0.79536496 0.03758668

PDCD4 0.75886671 0.00281775 PI3 0.58243222 0.03758668

PSMC6 0.75273204 0.00282496 C2orf69 0.80329365 0.03766295

MMP13 0.57119817 0.00284506 ZFAND2A 0.77084332 0.03768355

LLPH 0.73355098 0.00288026 APAFl 0.66297493 0.0378646

WBP5 0.71785926 0.0028814 GCOM1 0.68735303 0.03797817

ANKRD36 0.67810421 0.0028814 CA13 0.80329168 0.03802656

ERGIC2 0.76423191 0.00290561 CASP3 0.82104836 0.03806237

KLF3 0.78570378 0.00290614 CPEB2 0.77921871 0.03806237

ZNF770 0.78511401 0.00290848 IPCEF1 0.7139869 0.03808773

ATP 1 IB 0.75855302 0.00291572 CHIC1 0.82883135 0.0381983

SLC16A7 0.7565461 0.00298357 TMTC1 0.78485797 0.03831128

ST3GAL4 0.72572041 0.00300271 USMG5 0.79549212 0.03832104

PPP3CA 0.7448162 0.00304887 FRYL 0.84203988 0.03853779

ZNF117 0.50142805 0.00306525 RASAL1 0.75179941 0.0387072

KDM6A 0.77213154 0.00308418 NBN 0.83154425 0.03872393

PLXND1 0.72142004 0.00308418 HIVEP2 0.78765473 0.03881849

MIER1 0.73557856 0.00313244 TXLNG 0.83712784 0.03882687

OVOL1 0.62502792 0.00317568 DOCK5 0.64601096 0.03890144

SERINC1 0.75179781 0.00321045 LPHN2 0.79892749 0.03891655

RNF13 0.72052005 0.00322686 CRNKL1 0.798853 0.03894719 ZNF323 0.77734232 0.00324034 LYPLAL1 0.79886604 0.03899625

NCOA4 0.74867373 0.00324034 SPPL2A 0.80742034 0.03902383

MTAP 0.75495838 0.00324226 COROIC 0.7980739 0.03903911

NUFIP2 0.77357636 0.00325406 PANK3 0.83224164 0.03915089

EREG 0.33784392 0.00333776 RMND5A 0.79488445 0.03951253

RAB9A 0.75777512 0.00340898 SKIL 0.76881016 0.03955317

CTSL2 0.55240955 0.00342468 EXOC6 0.81125111 0.03955891

TMEM87B 0.78519368 0.00346666 LOC100294145 0.80974179 0.03965787

NCKAP1 0.78570783 0.00352262 CYLD 0.79867583 0.03971547

ACTG1 0.76392092 0.00353277 C6orf204 0.77428898 0.03971547

STEAP1 0.70400557 0.0035547 MAP3K5 0.80607409 0.03976224

C20orf54 0.6725607 0.00357863 PRKAA2 0.82840521 0.03988755

GTF2A2 0.75863446 0.00358684 CHUK 0.81785294 0.04058768

LAMP2 0.72705142 0.0035881 SNX6 0.81732751 0.04097796

B4GALT4 0.76856871 0.00359353 PSMB2 0.82520067 0.04109294

ETFDH 0.75965073 0.00359783 F3 0.84871606 0.04152053

BLNK 0.75809879 0.00362427 CHST2 0.77943848 0.04178592

FREM2 0.72246394 0.00366469 STX3 0.67806804 0.04184764

PSMD12 0.76433814 0.00368788 MBD2 0.8052338 0.04189529

SRP72 0.7794528 0.00375595 MKLNl 0.82564266 0.04192489

PLEKHF2 0.77591424 0.0038141 LNPEP 0.81160431 0.04207684

TMX1 0.77242467 0.00382017 USP15 0.57814041 0.042141

CD2AP 0.78829185 0.00383168 QKI 0.66036133 0.04236353

SPIRE1 0.74145864 0.0038936 DERL2 0.80411723 0.0425095

MYD88 0.71278412 0.00392321 ZMAT3 0.81595879 0.04264891

SLMAP 0.80047015 0.00393122 ARFGEF1 0.8346722 0.04298754

TUBB6 0.64642059 0.00397194 ERP44 0.80464897 0.04298754

ADAMDECl 0.56927435 0.00403827 HR 0.7668347 0.04298754

BCL2L15 0.7904988 0.00404876 PITPNC1 0.77723239 0.04308056

DDX21 0.77375237 0.0040688 CCDC59 0.76646023 0.04319013

TOPORS 0.72470814 0.00408953 PHF14 0.83670922 0.0432236

ARMC1 0.78022166 0.0041395 ACP5 0.70586156 0.04325972

DTWD2 0.7787722 0.0041562 ARPC2 0.79251427 0.04329313

FMR1 0.77028713 0.00419389 WDFY3 0.81539874 0.04355816

LIN54 0.74726623 0.00423614 ST 17B 0.59142405 0.04356623

KRT23 0.7309985 0.00423614 ATL3 0.81419607 0.04369002 CAV2 0.77823069 0.00428967 FAM84B 0.81682318 0.04373954

KLHL24 0.78910432 0.00432043 SRSF1 0.84262736 0.04402008

EPB41L5 0.74889943 0.00437807 LRRC4 0.76990857 0.04408044

CAV1 0.63489736 0.00443521 EPT1 0.82795078 0.04408619

PNP 0.67837892 0.00444139 CDC42 0.82028228 0.04412194

SRSF3 0.76672922 0.00446884 NBEALl 0.84458841 0.04417812

PLOD2 0.77561134 0.00450756 CLTC 0.83625892 0.04423619

ATP6V1A 0.76889678 0.00450756 KAT2B 0.80534479 0.04435063

A2ML1 0.612115 0.00451131 NDFIP2 0.83214986 0.0444398

ETF1 0.75295148 0.00452275 PEX11A 0.81101355 0.04453493

PPP2CA 0.76256592 0.00459161 NSF 0.83222465 0.04459514

SLC16A4 0.69724257 0.00459161 MRPS36 0.78965942 0.04459514

TPD52L1 0.75565633 0.00462225 IFNGR2 0.72554575 0.04459514

ABI1 0.78984533 0.00462963 PPM1D 0.75457637 0.0446064

HSPB8 0.54030013 0.00463892 CCDC90B 0.83348758 0.04465495

RAP1A 0.6286857 0.00466577 KRR1 0.8321851 0.04472713

UBE2D3 0.71948245 0.00469068 S100A2 0.55244156 0.04472713

ANKRD36BP1 0.75516672 0.00472447 SPAST 0.82037816 0.04490377

ZMPSTE24 0.78103406 0.0047778 NFYB 0.80065627 0.0449696

EIF4E 0.7660037 0.00485502 RBM27 0.83065796 0.04524741

EIF2S1 0.77037082 0.0048821 FBXO30 0.81207512 0.04524741

TIMP3 0.595252 0.00491633 C16orf87 0.8049152 0.04524741

RPS6KB1 0.77598677 0.0049242 FUT1 0.79442719 0.04556648

NMD3 0.77550502 0.0049698 SNX27 0.81137971 0.04590608

ZNF148 0.76729032 0.00501501 TGFA 0.80946531 0.04594414

GLRX 0.72655698 0.0050292 SNAP23 0.76908603 0.04621429

TOR1AIP2 0.75049332 0.00505042 SS18L2 0.75904606 0.04629091

PDCD10 0.77565396 0.00508211 MED13L 0.80323764 0.04639414

MALT1 0.75049905 0.00508211 KHDRBS3 0.79154107 0.04641655

CHD1 0.66214755 0.00508211 ZNF165 0.76560285 0.04651954

XKRX 0.73215187 0.00508311 RASA2 0.77538631 0.04658899

SPOPL 0.67456908 0.00509812 RGS10 0.78835868 0.04662598

D4S234E 0.74950027 0.0051853 RPP30 0.8120508 0.04690347

ZNF217 0.7862703 0.0052441 LIPA 0.83791908 0.04694484

C3orfl4 0.73804789 0.00525477 ZNF438 0.62962389 0.04694484

ZFX 0.78085119 0.00529941 LIMCH1 0.83370853 0.04700596 FAM59A 0.7610016 0.0053185 LM07 0.82293913 0.04710612

LAMTOR3 0.75345856 0.00532764 PUS7L 0.80031465 0.04718282

HK2 0.78199641 0.00534013 CBFB 0.82243007 0.04719184

GOLT1B 0.78276656 0.0053411 LMBRD1 0.81532931 0.04726984

TF 0.53399053 0.00534914 RIPK2 0.69796908 0.04754754

SLC12A2 0.76713817 0.00541558 SLC36A4 0.77616278 0.04774991

BLZF1 0.76183931 0.00543208 NR4A3 0.31905163 0.04778283

MORC3 0.77320595 0.0054433 TTC13 0.79548927 0.04780477

ABHD13 0.75751055 0.0054433 PRRC1 0.84094443 0.0480836

ARHGAP10 0.76095515 0.0055016 TOMM70A 0.83565352 0.0480836

PPP6C 0.78390582 0.00565944 EIF4A3 0.79211732 0.04817496

AKTIP 0.76242019 0.00566109 FRG1 0.7766039 0.04833913

IL18 0.74117905 0.00571372 DIP2B 0.81299057 0.048344

AMMECRl 0.7666803 0.00572446 MRPL50 0.83249841 0.04843281

SMEK1 0.78090529 0.0057997 SHISA9 0.76315554 0.04871027

NXT2 0.76719049 0.00584548 ITGAX 0.21887106 0.0489067

C12orf5 0.74487036 0.00585798 FAM120AOS 0.80855619 0.04915381

NFE2L3 0.77997497 0.00588459 MAP3K1 0.81117229 0.04919247

SHOC2 0.76830128 0.00591428 BRMS1L 0.78256727 0.04924817

ERI1 0.72854148 0.00591448 ST3GAL5 0.81440085 0.04925387

ZDHHC20 0.78918118 0.00595532 RALBP1 0.82325491 0.04929206

MS4A7 0.50459021 0.00595907 GTPBP10 0.83111393 0.04933293

CTR9 0.77182568 0.00597991 DOCK4 0.8068281 0.04934341

FAM46A 0.78379873 0.005986 WDR26 0.8064914 0.04935751

CPA4 0.73474526 0.005986 CTH 0.74246418 0.04943839

TROVE2 0.71896413 0.00601438 PARP9 0.8069565 0.04958092

ARL6IP1 0.78399879 0.00601695 ANKHDl 0.68180395 0.04988035

GADD45A 0.7103299 0.00619164 TRNT1 0.82420431 0.04988205

YOD1 0.60396183 0.00619164 C15orf48 0.66963309 0.04988205

CTTNBP2NL 0.76796852 0.00625618 FERMT2 0.80386104 0.04991843

PLSCR4 0.79632728 0.00626049 REACTOME IM Genes involved 1.07E-22

MUNE_SYSTE in Immune

M System

TMEM188 0.72279412 0.00632262 REACTOME M Genes involved 1.47E-18

ETABOLISM O in Metabolism of

F_LIPIDS_AND lipids and LIPOPROTEIN lipoproteins

S

MMADHC 0.78690813 0.00643294 REACTOME A Genes involved 1.46E-15

DAPTIVE IMM in Adaptive

UNE_SYSTEM Immune System

ARG2 0.74715273 0.00650999 REACTOME H Genes involved 1.57E-14

EMOSTASIS in Hemo stasis

SLC30A6 0.7797098 0.00651052 PID_ERBBl_DO ErbBl 2.05E-13

WNSTREAM P downstream

ATHWAY signaling

SPRR2A 0.37077622 0.0065136 REACTOME PP Genes involved 1.47E-12

ARA ACTIVAT inPPARA

ES GENE EXP Activates Gene

RESSION Expression

SPINK5 0.54459219 0.00663235 PID PDGFRB P PDGFR-beta 2.22E-12

ATHWAY signaling

pathway

YWHAG 0.78943324 0.00664564 PID P53 DOW Direct p53 8.30E-12

NSTREAM_PAT effectors

HWAY

IFI16 0.78293982 0.00669397 KEGG PATHW Pathways in 1.14E-11

AYS_IN_CANC cancer

ER

CYP4F3 0.66425151 0.00672128 REACTOME F Genes involved 1.65E-11

ATTY ACID T in Fatty acid,

RIACYLGLYCE triacylglycerol,

ROL AND KET and ketone body

ONE BODY M metabolism

ETABOLISM

DSG2 0.79997277 0.00672627 NABA MATRIS Ensemble of 2.28E-10

OME ASSOCIA genes encoding

ECM-associated TED proteins including

ECM-affilaited

proteins, ECM

regulators and

secreted factors

ITGB1 0.78721307 0.00683767 REACTOME T Genes involved 2.48E-09

RANSMEMBRA in

NE TRANSPOR Transmembrane

T_OF_SMALL_ transport of small

MOLECULES molecules

SGMS2 0.80465915 0.00686207 REACTOME IN Genes involved 4.47E-09

NATE_IMMUN in Innate Immune

E SYSTEM System

DMXL2 0.75565891 0.00687227 KEGG REGUL Regulation of 5.03E-09

ATION_OF_AC actin cytoskeleton

TIN CYTOSKE LETON

UGP2 0.77377034 0.00689688 KEGG MAPK S MAPK signaling 6.01E-09

IGNALING PA pathway

THWAY

TMEM165 0.76973779 0.00694615 REACTOME DI Genes involved 7.31E-09

ABETES PATH in Diabetes

WAYS pathways

CDC73 0.76294135 0.00696238 KEGG_SMALL_ Small cell lung 7.31E-09

CELL LUNG C cancer

ANCER

MPP5 0.80257658 0.00703803 NABA ECM R Genes encoding 7.31E-09

EGULATORS enzymes and

their regulators

involved in the

remodeling of the

extracellular matrix

SP1 0.76405586 0.00705511 REACTOME A Genes involved 7.61E-09

POPTOSIS in Apoptosis

VDAC2 0.76968598 0.00707017 NABA MATRIS Ensemble of 1.09E-08

OME genes encoding

extracellular

matrix and

extracellular

matrix-associated

proteins

LRRFIP1 0.77118612 0.0070728 PID NFKAPPA Canonical NF- 1.11E-08

B CANONICAL kappaB pathway

PATHWAY

C14orfl28 0.71927857 0.00711871 KEGG APOPTO Apoptosis 1.29E-08

SIS

LYPD3 0.68004615 0.00715007 REACTOME C Genes involved 1.98E-08

LASS_I_MHC_ in Class I MHC

MEDIATED AN mediated antigen

TIGEN PROCE processing &

SSING_PRESEN presentation

TATION

PTPRZ1 0.78817053 0.00719019 REACTOME T Genes involved 2.71E-08

OLL RECEPTO in Toll Receptor

R_CASCADES Cascades

RAB18 0.76366275 0.00722127 REACTOME A Genes involved 2.71E-08

CTIVATED TL in Activated

R4 SIGNALLIN TLR4 signalling

G

AP3S1 0.75774232 0.00729569 PID CDC42 PA CDC42 signaling 2.71E-08

THWAY events

C17orf 1 0.74332375 0.00730188 KEGG NOD LI NOD-like 4.69E-08

KE RECEPTOR receptor signaling _SIGNALING_P pathway

ATHWAY

XIAP 0.79828911 0.0073532 KEGG_FOCAL_ Focal adhesion 7.43E-08

ADHESION

LOC374443 0.71361722 0.00737354 REACTOME T Genes involved 9.93E-08

RAF6 MEDIAT in TRAF6

ED INDUCTIO mediated

N OF NFKB A induction of

ND MAP KINA NFkB and MAP

SESJJPON TL kinases upon

R7_8_OR_9_AC TLR7/8 or 9

TIVATION activation

TWF1 0.79895735 0.00742683 PID TNF PATH TNF receptor 1.12E-07

WAY signaling

pathway

ELF1 0.77273855 0.00744917 KEGG EPITHE Epithelial cell 1.49E-07

LIAL CELL SI signaling in

GNALING_IN_ Helicobacter

pylori infection

HELICOBACTE R PYLORI INF ECTION

S100A14 0.76635669 0.00744917 BIOCARTA HI HIV-I Nef : 1.71E-07

VNEF PATHW negative effector

AY of Fas and TNF

SLC16A6 0.70750259 0.00745345 KEGG_P53_SIG p53 signaling 1.71E-07

NALING PATH pathway

WAY

DCUN1D3 0.56968422 0.00747439 REACTOME A Genes involved 1.79E-07

NTIGEN PROC in Antigen

ESSING_ processing:

Ubiquitination &

UBIQUITINATI

Proteasome

ON PROTEASO ME DEGRADA degradation

TION

SLC44A2 0.76320925 0.00753544 PID AP1 PATH AP-1 1.93E-07

WAY transcription

factor network

SESTD1 0.7924907 0.00756289 KEGG PATHO Pathogenic 1.93E-07

GENIC ESCHE Escherichia coli

RICHIA_COLI_ infection

INFECTION

S100P 0.64809558 0.00767001 REACTOME M Genes involved 2.31E-07

YD88_MAL_CA inMyD88:Mal

SCADE INITIA cascade initiated

TED ON PLAS on plasma

MA MEMBRA membrane

NE

ARPP19 0.78635202 0.00768701 REACTOME SI Genes involved 2.51E-07

GNALLING BY in Signalling by

NGF NGF

KLF10 0.76312973 0.00775452 KEGG UBIQUI Ubiquitin 2. 1E-07

TIN MEDIATE mediated

D PROTEOLYS proteolysis

IS

TGM1 0.55760183 0.00777418 REACTOME C Genes involved 2.56E-07

YTOKINE SIG in Cytokine

NALING_IN_IM Signaling in

MUNE_SYSTE Immune system

M

BHLHE40 0.78959699 0.00777685 KEGG NEURO Neurotrophin 3.27E-07

TROPHIN SIGN signaling

ALING PATHW pathway

AY PLBD1 0.70356721 0.00777685 REACTOME T Genes involved 3.49E-07

RIF MEDIATE in TRIF mediated

D TLR3 SIGNA TLR3 signaling

LING

MYC 0.76472327 0.00781167 BIOCARTA MA MAPKinase 3.88E-07

PK PATHWAY Signaling

Pathway

FAM91A1 0.77751938 0.00785683 REACTOME M Genes involved 4.44E-07

EMBRANE TR in Membrane

AFFICKING Trafficking

MREG 0.76267651 0.00794736 BIOCARTA SA How does 4.71E-07

LMONELLA P salmonella hijack

ATHWAY a cell

GDPD1 0.81908069 0.0079732 PID HIF 1 TFPA HIF-1 -alpha 6.39E-07

THWAY transcription

factor network

GPD2 0.80071021 0.00805078 PID TGFBR PA TGF-beta 6.45E-07

THWAY receptor signaling

PVRL4 0.77402462 0.00805078 PID_MYC_ACTI Validated targets 7.35E-07

V P ATHWAY of C-MYC

transcriptional

activation

SUCLA2 0.76523468 0.00805078 BIOCARTA AC Y branching of 7.40E-07

TINY PATHWA actin filaments

Y

ACER3 0.77959865 0.00808456 REACTOME P Genes involved 7.42E-07

HOSPHOLIPID_ in Phospholipid

METABOLISM metabolism

RABL3 0.7748714 0.00809777 PID MET PAT Signaling events 8.18E-07

HWAY mediated by

Hepatocyte

Growth Factor Receptor (c-Met)

RAB10 0.79901305 0.0082063 KEGG ENDOC Endocytosis 8.35E-07

YTOSIS

PJA2 0.7769656 0.00823489 REACTOME IN Genes involved 1.08E-06

SULIN_SYNTH in Insulin

ESIS_AND_PRO Synthesis and

CESSING Processing

CAP1 0.72655632 0.00826187 KEGG PANCRE Pancreatic cancer 1.12E-06

ATIC CANCER

RDX 0.80715808 0.00827579 KEGG_RENAL_ Renal cell 1.12E-06

CELL CARCIN carcinoma

OMA

TES 0.79507705 0.00829307 PID ATF2 PAT ATF-2 1.25E-06

HWAY transcription

factor network

MUDENG 0.79933934 0.0083017 REACTOME SL Genes involved 1.30E-06

C_MEDIATED_ in SLC-mediated

TRANSMEMBR transmembrane

ANE TRANSPO transport

RT

PPIL3 0.76235604 0.00834263 REACTOME SI Genes involved 1.40E-06

GNALING_BY_ in Signaling by

THE_B_CELL_ the B Cell

Receptor (BCR)

RECEPTOR B C R

BIRC2 0.78625068 0.00837842 PID_FOXO_PAT FoxO family 1.45E-06

HWAY signaling

CCNB1 0.7807843 0.00847331 REACTOME N Genes involved 1.46E-06

FKB AND MA in NFkB and

P_KINASES_AC MAP kinases

TIVATION ME activation DIATED BY T mediated by

LR4 SIGNALIN TLR4 signaling

G REPERTOIR repertoire

E

ATL2 0.77916813 0.0084764 REACTOME PL Genes involved 1.48E-06

ATELET ACTI in Platelet

VATION activatioa

SIGNALING A signaling and

ND AGGREGA aggregation

TION

SORD 0.75801895 0.0084879 KEGG TGF BE TGF-beta 1.74E-06

TA SIGNALIN signaling

G PATHWAY pathway

ATP11C 0.79291526 0.00853151 PID_EPHB_FW EPHB forward 1.77E-06

D PATHWAY signaling

RRAGC 0.75615041 0.00853151 REACTOME A Genes involved 1.77E-06

POPTOTIC_CLE in Apoptotic

AVAGE OF CE cleavage of

LLULAR PROT cellular proteins

EINS

IFNGR1 0.69711126 0.00853151 BIOCARTA CD Role of PI3K 2.02E-06

C42RAC_PATH subunit p85 in

WAY regulation of

Actin

Organization and

Cell Migration

STEAP2 0.78974481 0.00856925 REACTOME C Genes involved 2.04E-06

ELL CYCLE M in Cell Cycle.

ITOTIC Mitotic

WDR72 0.64839931 0.0086094 PID_CASPASE_ Caspase cascade 2.45E-06

PATHWAY in apoptosis

KRT4 0.67492283 0.00863552 REACTOME CI Genes involved 2.97E-06 RCADIAN CLO in Circadian

CK Clock

HS2ST1 0.7871526 0.00868303 ST_FAS_SIGNA Fas Signaling 3.14E-06

LING PATHWA Pathway

Y

ZCCHC10 0.75926787 0.00868842 BIOCARTA DE Induction of 3.18E-06

ATH PATHWA apoptosis through

Y DR3 and DR4/5

Death Receptors

PPP2R2A 0.79190305 0.00877521 PID_RAC1_PAT RACl signaling 3.49E-06

HWAY pathway

SQRDL 0.75607401 0.00879068 SIG_PIP3_SIGN Genes related to 4.27E-06

ALING IN CAR PIP3 signaling in

DIAC MYOCTE cardiac myocytes

S

STK38 0.78754071 0.00886943 PID BETA CAT Regulation of 4.37E-06

ENIN_NUC_PA nuclear beta

THWAY catenin signaling

and target gene

transcription

LYRM1 0.7382844 0.00898135 REACTOME A Genes involved 5.72E-06

POPTOTIC_CLE in Apoptotic

AVAGE OF CE cleavage of cell

LL ADHESION adhesion

PROTEINS proteins

SYK 0.64957988 0.00898135 PID PLK1 PAT PLK1 signaling 6.25E-06

HWAY events

S100A10 0.76365242 0.00900115 REACTOME M Genes involved 6.47E-06

ETABOLISM O in Metabolism of

F PROTEINS proteins

NTS 0.73291849 0.00900309 REACTOME B Genes involved 6.56E-06

MALl_CLOCK_ in NPAS2 ACTIV BMALl:CLOCK

ATES_CIRCADI /NPAS2

AN EXPRESSI Activates

ON Circadian

Expression

LOC440434 0.68882777 0.00901276 ST_P38_MAPK_ p38 MAPK 8.35E-06

PATHWAY Pathway

GNA13 0.63583346 0.00908917 REACTOME D Genes involved 9.75E-06

EVELOPMENT in Developmental

AL BIOLOGY Biology

STK17A 0.73661542 0.00912019 PID ARF6 TRA Arf6 trafficking 1.10E-05

FFICKING PAT events

HWAY

ITSN2 0.76584981 0.00913286 ST TUMOR NE Tumor Necrosis 1.23E-05

CROSIS_FACT Factor Pathway.

OR PATHWAY

GOLT1A 0.71280825 0.00924664 PID ECADHERI E-cadherin 1.29E-05

N_NASCENT_A signaling in the

J PATHWAY nascent adherens

junction

DIAPH1 0.77552848 0.00932056 REACTOME M Genes involved 1.29E-05

AP KINASE A in MAP kinase

CTIVATION IN activation in TLR

TLR CASCAD cascade

E

ZNF654 0.74649612 0.00934308 KEGG B CELL B cell receptor 1.31E-05

_RECEPTOR_SI signaling

GNALING PAT pathway

HWAY

FPR3 0.48825296 0.00934423 BIOCARTA MI Role of 1.40E-05

TOCHONDRIA_ Mitochondria in

Apoptotic PATHWAY Signaling

RCHY1 0.79749711 0.00935 REACTOME SI Genes involved 1.48E-05

GNALING_BY_ in Signaling by

TGF BETA RE TGF-beta

CEPTOR_COMP Receptor

LEX Complex

4-Mar 0.77086317 0.00935 SIG_INSULIN_ Genes related to 1.49E-05

RECEPTOR PA the insulin

THWAY IN CA receptor pathway

RDIAC_MYOC

YTES

REEP3 0.8126155 0.0094555 REACTOME N Genes involved 1.49E-05

OD1 2 SIGNAL in NOD 1/2

ING PATHWA Signaling

Y Pathway

TFG 0.79338065 0.00956122 ST JNK MAPK JNK MAPK 1.49E-05

PATHWAY Pathway

SNX18 0.76111449 0.00960834 REACTOME MI Genes involved 1.59E-05

TOTIC_Gl_Gl_ in Mitotic Gl- S_PHASES Gl/S phases

TMEM79 0.77640651 0.00962273 REACTOME N Genes involved 1.59E-05

GF SIGNALLIN in NGF signalling

G_VIA_TRKA_ via TRKA from

FROM THE PL the plasma

ASMA MEMBR membrane

ANE

C12orf35 0.56826344 0.00962273 REACTOME A Genes involved 1.63E-05

CTIVATION OF in Activation of

NF KAPPAB I NF-kappaB in B

N B CELLS Cells

GOLGA4 0.8023233 0.00962569 PID AVB3 0PN Osteopontin- 1.85E-05

PATHWAY mediated events PLA2R1 0.78448235 0.00972618 PID CD40 PAT CD40/CD40L 1.85E-05

HWAY signaling

SYPL1 0.80241463 0.00979309 PID RB IPATH Regulation of 1.86E-05

WAY retinoblastoma

protein

C15orf34 0.76100423 0.0098085 PID TAP63 PA Validated 2.31E-05

THWAY transcriptional

targets of TAp63

isoforms

AGA 0.77317636 0.00987069 REACTOME A Genes involved 2.31E-05

POPTOTIC_EXE in Apoptotic

CUTION PHAS execution phase

E

10-Sep 0.80194663 0.00988696 ST_ERK1_ERK2 ERK1/ERK2 2.31E-05

MAPK PATH MAPK Pathway

WAY

MFAP3 0.78771375 0.00994587 BIOCARTA CA Caspase Cascade 2.41E-05

SPASE_ in Apoptosis

PATHWAY

PID INTEGRIN Beta3 integrin 2.55E-05 3 PATHWAY cell surface

interactions

List of known asthma-associated genes that overlap with genes in the RNAseq data

IL15; IL18; ILIB; IL1R1; ILIRN; IL2RB; IL33; IL5RA; IL6R; IL8; IRAK2; IRFl;

NDFIPl; NODI; OPN3; ORMDL3; PBX2; PCDH20; PDE4D; PHFl l; RAD50; RORA; SERPINA3; SLC22A5; SMAD3; SPATS2L; SPINK5; STAT6; TAPl; TGFB 1; TIMP1; TLE4; TLR2; TLR4; VDR

Table 4. List of the genes identified in the eight classification models and unique genes comprising the asthma gene panel.

CDHR3, NWD1, TMEM190, GNAL, ZNF117,

EPDR1, DEFB1, PTAFR, SPRR2D, CHCHD10,

LOC90784, AKR1B 15, CROCCP2, S100A8,

TFPI, C3, S100A7, DUSP1, LY6D, SORD,

SERPINFl, TPSB2, NMU, GSTT1, LPAR6,

CYFIP2, CPAMD8, SLC5A8, SLC5A3, SC4MOL,

NR1D1, ARL4D, ALDH1A3, LPHN1,

LOC286002, CRABP2, CEBPD, C6orfl05,

TM4SF1, ANKRD9, PCP4L1, SLC35E2,

LOC388564, DNAI1, SLC44A5, LTBP1, CROCC,

NCRNA00152, CDH26, TPSAB1, RHCG,

CLEC7A, IER3, MMP9, ALOX15B

SVM-RFE & 119 PYCRl, TXNDC5, B3GNT6, CD177, FAM46C, Approx 0.64 SVM-Linear PPP2R2C, VWAl, PTER, KALI, GNG4, ERAP2,

SYNM, CCL5, TRIM31, DOCK1, NFKBIZ,

MGST1, SPRR1A, PLIN4, TNFRSF18, ISYNA1,

SLC9A4, SLC9A2, SLC9A3, CP A3, SERPINB11,

OSM, MSMB, LGALS9C, SDK1, G0S2,

DPYSL3, RPH3AL, KIF7, Cl lorf9, COL1A1,

HLA.C, HCAR2, SLC26A4, SHF, SERPINFl,

SPRR2D, SCGB 1A1, ZDHHC2, SEMA5A, ESR1,

VAV2, NWD1, CYP2E1, KRT13, KRTIO, GNAL,

ZNF117, EPDR1, PAX3, KLHL29, NBPFl,

GPNMB, FABP5, CLCA2, C7orfl3, SPRR2F,

LOC90784, CYP2B6, CROCCP2, TFPI, S100A7,

DUSP1, LY6D, PHYHD1, SORD, TMEM64,

C15orf48, MXRA8, IL4I1, TPSB2, NMU,

BPIFA2, ZNF528, HTR3A, STEAPl, STEAP2,

LPAR6, OBSCN, MT2A, CPAMD8, D4S234E,

ECM1, SLC16A4, LRRC26, CRCT1, SLC5A5,

ZC3H12A, NR1D1, ALDH1A3, SLC37A2,

LPHN1, CRABP2, TM4SF1, ANKRD9, CXCR7,

TF, TMEM220, LOC388564, XIST, SLC44A5,

LTBP1, RAB3B, MEX3D, TPSAB1, RHCG,

SRRM3, SCGB3A1, RNDl, REC8, SCD,

ALOX15B, ATP6V0E2, COL6A6 SVM-RFE & 119 PYCR1, TXNDC5, B3GNT6, CD177, FAM46C, Approx 0.69 Logistic PPP2R2C, VWA1, PTER, KALI, GNG4, ERAP2,

SYNM, CCL5, TRIM31, DOCK1, NFKBIZ,

MGST1, SPRR1A, PLIN4, TNFRSF18, ISYNA1,

SLC9A4, SLC9A2, SLC9A3, CP A3, SERPINB11,

OSM, MSMB, LGALS9C, SDK1, G0S2,

DPYSL3, RPH3AL, KIF7, Cl lorf9, COL1A1,

HLA.C, HCAR2, SLC26A4, SHF, SERPINFl,

SPRR2D, SCGB 1A1, ZDHHC2, SEMA5A, ESR1,

VAV2, NWD1, CYP2E1, KRT13, KRTIO, GNAL,

ZNF117, EPDR1, PAX3, KLHL29, NBPF1,

GPNMB, FABP5, CLCA2, C7orfl3, SPRR2F,

LOC90784, CYP2B6, CROCCP2, TFPI, S100A7,

DUSP1, LY6D, PHYHD1, SORD, TMEM64,

C15orf48, MXRA8, IL4I1, TPSB2, NMU,

BPIFA2, ZNF528, HTR3A, STEAPl, STEAP2,

LPAR6, OBSCN, MT2A, CPAMD8, D4S234E,

ECM1, SLC16A4, LRRC26, CRCT1, SLC5A5,

ZC3H12A, NR1D1, ALDH1A3, SLC37A2,

LPHN1, CRABP2, TM4SF1, ANKRD9, CXCR7,

TF, TMEM220, LOC388564, XIST, SLC44A5,

LTBP1, RAB3B, MEX3D, TPSAB1, RHCG,

SRRM3, SCGB3A1, RNDl, REC8, SCD,

ALOX15B, ATP6V0E2, COL6A6

LR-RFE & 90 PCSK6, HIPK2, TXNDC5, B3GNT6, CD177, Approx 0.49 AdaBoost KRT24, FCGBP, DLECl, SERPINB3, CLEC2B,

PTER, ERAP2, SYNM, CDKN1A, SPRR1A,

C12orf36, SERPINE2, XIST, SLC9A3, SCD,

TEKT2, EPPK1, RPH3AL, MS4A8B, SDK1,

IGF1, FOS, SERPINBl l, CP A3, HLA.C,

SLC26A4, CYP1B1, SCGB1A1, SEMA5A, ESR1,

CDHR3, NWD1, TMEM190, GNAL, ZNF117,

EPDR1, DEFB1, PTAFR, SPRR2D, CHCHD10,

LOC90784, AKR1B 15, CROCCP2, S100A8,

TFPI, C3, S100A7, DUSP1, LY6D, SORD,

SERPINFl, TPSB2, NMU, GSTT1, LPAR6,

CYFIP2, CPAMD8, SLC5A8, SLC5A3, SC4MOL, NR1D1, ARL4D, ALDH1A3, LPHN1,

LOC286002, CRABP2, CEBPD, C6orfl05,

TM4SF1, A KRD9, PCP4L1, SLC35E2,

LOC388564, DNAIl, SLC44A5, LTBPl, CROCC,

NCRNA00152, CDH26, TPSAB1, RHCG,

CLEC7A, IER3, MMP9, ALOX15B

LR-RFE & 90 PCSK6, HIPK2, TXNDC5, B3GNT6, CD177, Approx 0.60 RandomForest KRT24, FCGBP, DLEC1, SERPINB3, CLEC2B,

PTER, ERAP2, SYNM, CDKN1A, SPRR1A,

C12orf36, SERPINE2, XIST, SLC9A3, SCD,

TEKT2, EPPK1, RPH3AL, MS4A8B, SDK1,

IGF1, FOS, SERPINBl l, CP A3, HLA.C,

SLC26A4, CYP1B1, SCGB1A1, SEMA5A, ESR1,

CDHR3, NWD1, TMEM190, GNAL, ZNF117,

EPDR1, DEFB1, PTAFR, SPRR2D, CHCHDIO,

LOC90784, AKR1B 15, CROCCP2, S100A8,

TFPI, C3, S100A7, DUSP1, LY6D, SORD,

SERPINFl, TPSB2, NMU, GSTT1, LPAR6,

CYFIP2, CPAMD8, SLC5A8, SLC5A3, SC4MOL,

NR1D1, ARL4D, ALDH1A3, LPHN1,

LOC286002, CRABP2, CEBPD, C6orfl05,

TM4SF1, ANKRD9, PCP4L1, SLC35E2,

LOC388564, DNAIl, SLC44A5, LTBPl, CROCC,

NCRNA00152, CDH26, TPSAB1, RHCG,

CLEC7A, IER3, MMP9, ALOX15B

SVM-RFE & 123 HSPA6, GSTAl, PLIN4, TXNDC5, B3GNT6, Approx 0.50 RandomForest BHLHE40, CYP4F11, CD177, IRX5, TMX4,

DDIT4, SCCPDH, FCGBP, ARRDC4, MUC16,

TSPAN8, ACOT2, SPINK5, C19orf51, PTER,

F2R, GNG4, SERPINGl, C14orfl67, ERAP2,

MMP10, DOCKl, NFKBIZ, CHCHDIO, MGSTl,

C12orI36, CLCA2, XIST, SLC9A2, SLC9A3,

CP A3, TEKT2, EPPK1, SERPINBl l, OVCA2,

MSMB, CDC25B, TNS3, SDK1, FOS, RPH3AL,

KIF7, COL1A1, HLA.C, HCAR2, SLC26A4,

PAX3, SERPINFl, SPRR2F, DNER, GSTT1, ESR1, VAV2, CYP2E1, TMEM190, KRT13,

GNAL, RPSAP58, FABP5, MALATl, C7orfl3,

SCGB1A1, AKR1B15, CYP2B6, HBEGF, TFPI,

C3, S100A7, DUSP1, HERC2P2, SORD,

C15orf48, MXRA8, IL4I1, TPSB2, NMU,

SEMA5A, BPIFA2, PRSS3, AK4, BASP1,

HTR3A, COL21A1, LPAR6, MKI67, CYFIP2,

CPAMD8, D4S234E, CRCT1, MFSD6L, CIT,

SLC5A8, NR1D1, ALDH1A3, SLC37A2, LPHN1,

LOC286002, CRABP2, CEBPD, ANKRD9,

CXCR7, SLC35E2, LOC388564, SLC9A4,

SLC44A5, LTBP1, CRYM, RAB3B, KALI,

MEX3D, TPSAB1, NCRNA00086, HLA.DQA1,

RHCG, REC8, ALOX15B, ATP6V0E2, COL6A6

SVM-RFE & 212 IDAS, NR1D1, HIPK2, RCBTB2, PYCR1, Approx 0.55 AdaBoost TSPAN8, CPPED1, B3GNT6, HLA.DPB1,

PARD6G, IP6K3, EIF1AX, CD177, FAM46C,

IRX5, C3orfl4, IFITM1, NGEF, SCCPDH,

PPP2R2C, XYLT1, DLEC1, MUC16, SERPINB3,

ACOT2, SLC35E2, SMPDL3B, C19orf51,

LOC388796, MPV17L, SYK, SLC9A4, PTER,

F2R, GNG4, BSTl, C14orfl67, CCNO, ERAP2,

SYNM, EVL, CCL5, TRIM31, DOCK1, RRAS,

MALATl, MGSTl, SLC29A1, C12orf36, PLIN4,

SERPINE2, JUB, PTN, SLC9A2, CLEC7A,

CP A3, TEKT2, EPPK1, SERPINB11, OVCA2,

OSM, VWA1, CDC25B, LGALS9C, MS4A8B,

SDK1, S100A13, DPYSL3, PDLIM2, RPH3AL,

KIF7, Cl lorf9, TEKT4P2, PMEPA1, HLA.C,

HCAR2, SLC26A4, PAX3, NLRPl, GIMAP6,

SPRR2F, SPRR2C, DNER, ABCG1, ZDHHC2,

ZNF532, SEMA5A, ESR1, VAV2, NWD1,

CYP2E1, TMEM190, MAOB, CXCR7, GNAL,

ZNF117, GAS7, EPDR1, NCF2, DEFB1,

H2AFY2, GRTP1, NBPFl, CROCCP2,

SERPINGl, KRT5, CHCHD10, TP63, C7orfl3,

SCGB1A1, LOC90784, HICl, AKR1B15, GAS2L2, H1FX, CYP2B6, GPNMB, HBEGF,

ACAT2, TFPI, C3, S100A7, DUSP1, SLC9A3, LYSMD2, HERC2P2, PHYHD1, TOP1MT, PLCL2, SORD, TMEM64, C15orf48, PLXND1, CD8A, MXRA8, IL4I1, IL2RB, NMU, GSTT1, BPIFA2, ZNF528, IL32, WDR96, NPNT, DMRTA2, BASP1, CEBPD, HTR3A, COL21A1, OBSCN, CYFIP2, CPAMD8, XIST, D4S234E, IGF1R, ECM1, PTPRZl, CRCT1, RRM2, MLKL, CIT, SC4MOL, DDIT4, ELF5, ARL4D, ALDH1A3, SLC37A2, LPHN1, LOC286002, CRABP2, CCNJL, MEGF6, TM4SF1, AN RD9, C8orf4, SLC16A14, ALOX15B, PCP4L1, TOR1B, TF, ACOT11, HOMER3, LOC388564, CYP1B1, DNAI1, LRP12, LTBP1, ANXA6, CARD11, CROCC, CES1, ALDH3B2, NCRNA00152, RAB3B, TNC, KALI, FOXN4, MEX3D, FCGBP, TPSAB1, NCRNA00086, HLA.DOA, KRT78, RHCG, NCALD, REC8, RDH10, SERPINFl, ATP6V0E2, POLR2J3, POU2F3, TCTEX1D4

Asthma gene 275 IDAS, HSPA6, PCSK6, HIPK2, C15orf48, n/a panel (275 TXNDC5, CPPED1, HLA.DPB1, PARD6G, unique genes) CYP4F11, FAM46C, IRX5, C3orfl4, IGF1R,

NGEF, SCCPDH, PPP2R2C, MUC16, ACOT2, SMPDL3B, C19orf51, MPV17L, SYK, CLEC2B, PTER, F2R, BST1, SYNM, EVL, CDKN1A, DOCK1, G0S2, MGST1, C12orf36, PLIN4, SERPINE2, JUB, SLC9A2, CLEC7A, TEKT2, EPPKl, OVCA2, MSMB, LGALS9C, MS4A8B, SDK1, PDLIM2, FOS, RPH3AL, KIF7, COL1A1, TEKT4P2, HLA.C, PAX3, SPRR2D, GIMAP6, SPRR2F, SPRR2C, DNER, ZDHHC2, GSTT1, ESRl, CDHR3, CYP2E1, TMEM190, BHLHE40, KRT13, KRTIO, GNAL, RPSAP58, EPDR1, H2AFY2, GRTP1, NBPF1, SERPING1, PTAFR, KRT5, CHCHDIO, HICl, ZNF532, CROCCP2, HBEGF, ACAT2, S100A8, TFPI, C3, S100A7, HERC2P2, PLCL2, SORD, CD8A, MXRA8, IL2RB, NMU, LRRC26, BPIFA2, PRSS3, AK4, NPNT, SLC5A3, FCGBP, HTR3A, COL21A1, SLC5A5, MT2A, CYFIP2, XIST, ECM1, PTPRZl, SLC5A8, MFSD6L, MLKL, ZC3H12A, ALDH1A3, SLC37A2, LOC286002, CCNJL, MEGF6, TM4SF1, SLC16A14, CXCR7, HOMER3, CYP1B1, ALDH3B2, SLC44A5, LTBPl, ANXA6, IL32, CDH26, MEX3D, VWAl, TPSABl, HLA.DOA, ARRDC4, DMRTA2, SRRM3, IER3, RNDl, REC8, RDH10, ATP6V0E2, POLR2J3, COL6A6, PCP4L1, GSTA1, RCBTB2, PYCR1, TSPAN8, B3GNT6, EIF1AX, CD177, PLXND1, IFITM1, DDIT4, KLHL29, KRT24, XYLTl, DLECl, SERPINB3, IP6K3, TMEM220, LOC388796, KALI, GNG4, C14orfl67, CCNO, ERAP2, CCL5, TRIM31, RRAS, CLCA2, SLC29A1, SPRRIA, ARL4D, PTN, CP A3, OSM, TNS3, S100A13, IGF1, DPYSL3, SERPINB11, CDC25B, Cl lorf9, PMEPA1, HCAR2, SLC26A4, SHF, LOC90784, SCGB1A1, DNAI1, ABCGl, TMEM64, SEMA5A, CRYM, VAV2, NWD1, MAOB, ZNF117, GAS7, SPINK5, NCF2, DEFBl, KRT78, GPNMB, FABP5, MALAT1, MMP10, TP63, C7orfl3, NLRPl, AKR1B 15, GAS2L2, H1FX, CYP2B6, IL4I1, DUSP1, LYSMD2, PHYHD1, TOP1MT, SERPINFl, NFKBIZ, TPSB2, ZNF528, WDR96, BASP1, STEAP1, STEAP2, LPAR6, NCALD, OBSCN, MKI67, CPAMD8, D4S234E, SLC16A4, CRCT1, LY6D, RRM2, CIT, SC4MOL, NR1D1, ELF5, LPHN1, CRABP2, CEBPD, C6orfl05, ANKRD9, C8orf4, TNFRSF18, TOR1B, TF, ACOT11, SLC35E2, LOC388564, SLC9A4, LRP12, ISYNA1, CARD11, MMP9, NCRNA00152, CROCC, CES1, TMX4, RAB3B, TNC, FOXN4, NCRNA00086, HLA.DQA1, RHCG, SLC9A3, SCGB3A1, SCD,

ALOX15B, POU2F3, TCTEX1D4

Table 5. Characteristics of the external asthma cohorts used in the validation of the asthma gene panel.

FEV1 97.6 (13.2) 78.2 (7.7) n/a 97.8 (16.5) 91.2 98.3 %predicted (10.8) (11.0)

FEV1/FVC 89.3 (5.6) 76.5 (3.2) n/a n/a n/a n/a

PC20 (mg/ml) n/a n/a n/a 4.5 (5.1) 4.4 (5.2) 28 (27.1)

Results are number (%) or mean (SD) unless otherwise indicated. ^AFor Asthmal, criteria for control per NAEPP/EPR3 criteria. For Asthma2, criteria for control not specified. *For Asthma2, data that the authors deposited in GEO GSE46171 are a subset of their published results.²⁹ GSE46171 has data for 16 of the 23 subjects with controlled asthma, 7 of the 11 subjects with uncontrolled asthma, and 5 of the 9 controls reported in the authors' publication.²⁹ The number of subjects with publically available data (GSE46171) that were used in these analyses are indicated. The summary statistics shown are drawn from the authors' publication on their reported sample.†Median (range).

Table 6. Characteristics of the external cohorts with non-asthma respiratory conditions and controls used in the validation of the asthma gene panel.

Other 100% 100% 2% 2% 2% 2% 0% 0% 0 (0%) 0 (0%)

*Data that the authors deposited in GEO GSE43523 are a subset of their published results.

GSE43523 has data for 7 of the 15 subjects with allergic rhinitis, and 5 of the 13 controls reported in the authors' publication.³⁵ The number of subjects with publically available data

(GSE43523) that were used in these analyses are indicated. The summary statistics shown are drawn from the authors' publication on their reported cohort. ^AEach subject provided a URI and control sample. The data that the authors deposited in GEO GSE46171 are a subset of their published results.²⁹ GSE46171 has data for 6 of the 9 healthy subjects reported in the authors' publication who provided samples during URI, and 5 of the 9 healthy subjects who provided samples after resolution of their URI.²⁹ The number of subjects with publically available data

(GSE46171) that were used in these analyses are indicated. The summary statistics shown are drawn from the authors' publication on their reported cohort.† Median (range).

"Definitions: Allergic Rhinitis = Rhinitis symptoms and >1 elevated slgE to aeroallergen;

Allergic rhinitis control = No symptoms, no slgE to aeroallergen, total serum IgE < population mean. URI Day 2 = Day 2 following onset of "common cold" symptoms and no underlying airway disease; URI Day 2 control = No URI symptoms and no known airway disease. URI Day

6 = Day 6 following onset of "common cold" symptoms and no underlying airway disease; URI

Day 6 control = No URI symptoms and no known airway disease. Cystic Fibrosis =

Homozygous F508del mutation; Cystic Fibrosis control = Overweight but healthy. Smoking =

>10 cigarettes/day in past month and smoking > 10 pack years; Smoking control = Never smoker, no environmental cigarette exposure and no respiratory symptoms.

Table 7. Positive and negative predictive values (PPV and NPV respectively) for the LR-RFE &

Logistic asthma gene panel.

Positive and negative predictive values (PPV and NPV respectively) obtained when the LR-RFE & Logistic asthma gene panel was applied to classifying samples in various microarray-derived data sets of subjects with non-asthma respiratory conditions and controls. Also shown in parentheses are the corresponding PPVs and NPVs obtained when random counterpart models are applied to these datasets for the same classification tasks.

References

1. Current Asthma Prevalence Percents by Age, Sex, and Race/Ethnicity, United States, 2012. Asthma Surveillance Data. National Health Interview Survey, National Center for Health Statistics, Centers for Disease Control and Prevention cdcgov/asthma/asthmadatahtm, downloaded 1/30/2017.

2. Yeatts K, Shy C, Sotir M, Music S, Herget C. Health consequences for children with undiagnosed asthma-like symptoms. Archives of pediatrics & adolescent medicine 157, 540-544 (2003).

3. Stempel DA, Spahn JD, Stanford RH, Rosenzweig JR, McLaughlin TP. The economic impact of children dispensed asthma medications without an asthma diagnosis. J Pediatr 148, 819-823

(2006).

4. Fanta CH. Asthma. N Engl J Med 360, 1002-1014 (2009).

5. Szefler SJ, et al. Asthma outcomes: Biomarkers. Journal of Allergy and Clinical Immunology 129, S9-S23 (2012).

6. Reddel HK, et al. A summary of the new GIN A strategy: a roadmap to asthma control. Eur Respir J 46, 622-639 (2015).

7. Expert Panel Report 3 : Guidelines for the Diagnosis and Management of Asthma. (ed^A(eds). National Heart Lung and Blood Institute and National Asthma Education and Prevention Program (2007).

8. Gershon AS, Victor JC, Guan J, Aaron SD, To T. Pulmonary function testing in the diagnosis of asthma: a population study. Chest 141, 1190-1196 (2012).

9. Sokol KC, Sharma G, Lin YL, Goldblum RM. Choosing wisely: adherence by physicians to recommended use of spirometry in the diagnosis and management of adult asthma. Am J Med 128, 502-508 (2015).

10. Petsky HL, et al. A systematic review and meta-analysis: tailoring asthma treatment on eosinophilic markers (exhaled nitric oxide or sputum eosinophils). Thorax 67, 199-208 (2012). 11. van Schayck CP, van Der Heijden FM, van Den Boom G, Tirimanna PR, van Herwaarden CL. Underdiagnosis of asthma: is the doctor or the patient to blame? The DIMCA project. Thorax 55, 562-565 (2000).

12. Sridhar S, et al. Smoking-induced gene expression changes in the bronchial airway are reflected in nasal and buccal epithelium. BMC Genomics 9, 259 (2008).

13. Wagener AH, et al. The impact of allergic rhinitis and asthma on human nasal and bronchial epithelial gene expression. PLoS One 8, e80257 (2013).

14. Guajardo JR, et al. Altered gene expression profiles in nasal respiratory epithelium reflect stable versus acute childhood asthma. J Allergy Clin Immunol 115, 243-251 (2005).

15. Poole A, et al. Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease. J Allergy Clin Immunol 133, 670-678 e612 (2014).

16. Byron SA, Van Keuren- Jensen KR, Engelthaler DM, Carpten JD, Craig DW. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet 17, 257- 271 (2016).

17. Mendelsohn J. Personalizing oncology: perspectives and prospects. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 31, 1904-1911 (2013).

18. Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507-2517 (2007).

19. Witten Hi, Frank E, Hall MA. Data mining : practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann (2011).

20. Demsar J. Statistical Comparisons of Classifiers over Multiple Data Sets. J Mach Learn Res 7, 1-30 (2006).

21. The Childhood Asthma Management Program (CAMP): design, rationale, and methods. Childhood Asthma Management Program Research Group. Control Clin Trials 20, 91-120 (1999).

22. Covar RA, Fuhlbrigge AL, Williams P, Kelly HW, the Childhood Asthma Management Program Research G. The Childhood Asthma Management Program (CAMP): Contributions to the Understanding of Therapy and the Natural History of Childhood Asthma. Current respiratory care reports 1, 243-250 (2012).

23. Egan M, Bunyavanich S. Allergic rhinitis: the "Ghost Diagnosis" in patients with asthma. Asthma Research and Practice 1, DOI: 10.1186/s40733-40015-40008-40730 (2015). 24. Hoffman GE, Schadt EE. variancePartition: Quantifying and interpreting drivers of variation in complex gene expression studies. bioRxiv, doi: dx.doi. org/10.1101/040170 (2016).

25. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA- seq data with DESeq2. Genome Biol 15, 550 (2014).

26. Subramanian A, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550 (2005).

27. Whalen S, Pandey OP, Pandey G. Predicting protein function and other biomedical characteristics with heterogeneous ensembles. Methods 93, 92-102 (2016).

28. Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. (2011).

29. Mathias RA. Introduction to genetics and genomics in asthma: genetics of asthma. Advances in experimental medicine and biology 795, 125-155 (2014).

30. Giovannini-Chami L, et al. Distinct epithelial gene expression phenotypes in childhood respiratory allergy. Eur Respir J 39, 1197-1205 (2012).

31. McErlean P, et al. Asthmatics with exacerbation during acute respiratory illness exhibit unique transcriptional signatures within the nasal mucosa. Genome medicine 6, 1 (2014).

32. Zhang W, et al. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biol 16, 133 (2015).

33. Su Z, et al. An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era. Genome Biol 15, 523 (2014).

34. Venet D, Dumont JE, Detours V. Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome. PLoS computational biology 7, el 002240 (2011).

35. Chibon F. Cancer gene expression signatures - the rise and fall? European journal of cancer 49, 2000-2009 (2013).

36. Imoto Y, et al. Cystatin SN upregulation in patients with seasonal allergic rhinitis. PLoS One 8, e67057 (2013).

37. Clarke LA, Sousa L, Barreto C, Amaral MD. Changes in transcriptome of native nasal epithelium expressing F508del-CFTR and intersecting data from comparable studies. Respir Res

14, 38 (2013). 38. Oliver BG, Robinson P, Peters M, Black J. Viral infections and asthma: an inflammatory interface? Eur Respir J 44, 1666-1681 (2014).

39. Scott S, Currie J, Albert P, Calverley P, Wilding JP. Risk of misdiagnosis, health-related quality of life, and BMI in patients who are overweight with doctor-diagnosed asthma. Chest 141, 616-624 (2012).

40. Kulkarni MM. Digital multiplexed gene expression analysis using the NanoString nCounter system. Current protocols in molecular biology / edited by Frederick M Ausubel [et al] Chapter 25, Unit25B 10 (2011).

41. Veldman- Jones MH, et al. Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical

Samples. Cancer research 75, 2587-2593 (2015).

42. Leong HS, et al. Efficient molecular subtype classification of high-grade serous ovarian cancer. The Journal of pathology 236, 272-277 (2015).

43. Cardoso F, et al. 70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer. N Engl J Med 375, 717-729 (2016).

44. Paik S, et al. A multigene assay to predict recurrence of tamoxifen-treated, nodenegative breast cancer. N Engl J Med 351, 2817-2826 (2004).

45. Wechsler ME. Managing asthma in primary care: putting new guideline recommendations into context. Mayo Clin Proc 84, 707-717 (2009).

46. Physician Fee Schedule Search. Centers for Medicare & Medicaid Services, available athttps://wwwcmsgov/apps/physician-fee-schedule/search/search-criteriaaspx and accessed on 1/30/2017, (2016).

47. Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of nextgeneration sequencing technologies. Nat Rev Genet 17, 333-351 (2016).

48. Asthma in the US. Centers for Disease Control and Prevention Vitalsigns http://wwwcdcgov/vitalsigns/asthma/, downloaded 1/30/2017, (2011).

49. Cowling BJ, et al. Comparative epidemiology of pandemic and seasonal influenza A in households. N Engl J Med 362, 2175-2184 (2010).

50. Bunyavanich S, Schadt EE. Systems biology of asthma and allergic diseases: A multiscale approach. J Allergy Clin Immunol, (2014). 51. Sordillo J, Raby BA. Gene expression profiling in asthma. Advances in experimental medicine and biology 795, 157-181 (2014).

52. Jain VV, Allison DR, Andrews S, Mejia J, Mills PK, Peterson MW. Misdiagnosis Among Frequent Exacerbators of Clinically Diagnosed Asthma and COPD in Absence of Confirmation of Airflow Obstruction. Lung 193, 505-512 (2015).

53. Brower V. Biomarkers: Portents of malignancy. Nature 471, S19-21 (2011).

54. Muraro A, et al. Precision medicine in patients with allergic diseases: Airway diseases and atopic dermatitis-PRACTALL document of the European Academy of Allergy and Clinical Immunology and the American Academy of Allergy, Asthma & Immunology. J Allergy Clin Immunol 137, 1347-1358 (2016).

55. Himes BE, et al. Genome-wide association analysis identifies PDE4D as an asthma susceptibility gene. Am J Hum Genet 84, 581-593 (2009).

56. Fromer M, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci, (2016).

57. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10, R25 (2009).

58. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105-1111 (2009).

59. Trapnell C, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28, 511-515 (2010).

60. DeLuca DS, et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530-1532 (2012).

61. Pedregosa F, Varoquaux Ge, Gramfort A, Michel V, Thirion B, others. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825-2830 (2011).

62. Guyon I, Weston, J, Barnhill, S, Vapnik, V. Gene selection for cancer classification using support vector machines. Machine Learning 46, 389-422 (2002).

63. Schadt EE, Friend SH, Shaywitz DA. A network view of disease and compound screening. Nature reviews Drug discovery 8, 286-295 (2009).

64. Bewick V, Cheek L, Ball J. Statistics review 14: Logistic regression. Crit Care 9, 112-118 (2005). 65. Burges CJ. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery 2, 121-167 (1998).

66. Freund Y, Schapire RE. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J Comput Syst Sci 55, 119-139 (1997).

67. Breiman L. Random Forests. Machine Learning 45, 5-32 (2001).

68. Hollander M, Wolfe DA, Chicken E. Nonparametric statistical methods. John Wiley & Sons (2013).

69. Vidaurre D, Bielza C, Larranaga P. A Survey of LI Regression. International Statistical Review 81, 361-387 (2013).

70. Barrett T, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41, D991-995 (2013).

While several possible embodiments are disclosed above, embodiments of the present invention are not so limited. These exemplary embodiments are not intended to be exhaustive or to unnecessarily limit the scope of the invention, but instead were chosen and described in order to explain the principles of the present invention so that others skilled in the art may practice the invention. Indeed, various modifications of the invention in addition to those described herein will become apparent to those skilled in the art from the foregoing description. Such modifications are intended to fall within the scope of the appended claims.

Disclosed are methods and compositions that can be used for, can be used in conjunction with, can be used in preparation for, or are products of the disclosed methods and compositions. These and other materials are disclosed herein, and it is understood that combinations, subsets, interactions, groups, etc. of these methods and compositions are disclosed.

All patents, applications, publications, test methods, literature, and other materials cited herein are hereby incorporated by reference in their entirety as if physically present in this specification.

Ill

Claims

CLAIMS What is claimed is:

1. A method for diagnosing asthma in a subject, comprising the steps of:

2. A method for detection of asthma in a subject, comprising the steps of:

3. A method for differentially diagnosing asthma from other respiratory disorders in a subject, comprising the steps of:

4. A method for classifying a subject as having asthma or not having asthma, comprising the steps of:

5. A method for monitoring asthma in a subject, comprising the steps of:

6. A method for selecting a subject for a clinical trial for asthma therapeutic compositions and/or methods, comprising the steps of:

7. A method for treating asthma in a subject, comprising the steps of:

8. The method as described in any of claims 1-7, wherein step (a) further comprises the steps of (i) brushing/swabbing/scraping/washing/sponging the patient's nose, (ii) obtaining and appropriately preserving the nasal brushing/swab/scraping/wash/sponge sample, and (iii) assaying the gene expression profile of the cells and tissue contained in the sample, whether by isolating RNA as described herein or by use of a RNA profiling system that does not require a separate isolation step.

9. The method as described in any of claims 1-8, wherein the classification analysis comprises Logistic Regression-Recursive Feature Elimination (LR-RFE) algorithms in combination with Logistic algorithm, the asthma gene panel consists of the LR-RFE & Logistic asthma gene panel, and the optimal classification threshold is about 0.76.

10. The method as described in any of claims 1-8, wherein the classification analysis comprises LR-RFE algorithm in combination with SVM-Linear algorithms, the asthma gene panel consists of the LR-RFE & SVM-Linear asthma gene panel, and the optimal classification threshold is about 0.52.

11. The method as described in any of claims 1-8, wherein the classification analysis comprises the SVM-RFE algorithm in combination with the SVM-Linear algorithms, the asthma gene panel consists of the SVM-RFE & SVM-Linear asthma gene panel, and the optimal classification threshold is about 0.64.

12. The method as described in any of claims 1-8, wherein the classification analysis comprises the SVM-RFE algorithm in combination with the Logistic algorithms, the asthma gene panel consists of the SVM-RFE & Logistic asthma gene panel, and the optimal classification threshold is about 0.69.

13. The method as described in any of claims 1-8, wherein the classification analysis comprises the LR-RFE algorithm in combination with the AdaBoost algorithms, the asthma gene panel consists of the LR-RFE & AdaBoost asthma gene panel, and the optimal classification threshold is about 0.49.

14. The method as described in any of claims 1-8, wherein the classification analysis comprises the LR-RFE algorithm in combination with the RandomForest algorithms, the asthma gene panel consists of the LR-RFE & RandomForest asthma gene panel, and the optimal classification threshold is about 0.60.

15. The method as described in any of claims 1-8, wherein the classification analysis comprises the SVM-RFE algorithm in combination with the RandomForest algorithms, the asthma gene panel consists of the SVM-RFE & RandomForest asthma gene panel, and the optimal classification threshold is about 0.50.

16. The method as described in any of claims 1-8, wherein the classification analysis comprises the SVM-RFE algorithm in combination with the AdaBoost algorithm, the asthma gene panel consists of the SVM-RFE & AdaBoost asthma gene panel, and the optimal classification threshold is about 0.55.

17. The method as described in any of the foregoing claims, wherein steps (b) and/or (c) and/or (d) are performed by a computer.

18. A kit for diagnosing and/or detecting asthma in a subject, said kit comprising probes directed towards one or more of the genes in the asthma gene panel, as described in more detail herein, wherein the probes can be used to determine the expression levels of one or more of the genes in the asthma gene panel.

19. The kit of claim 12, further comprising: a detection means; an amplification means; and control probes.