WO2020124391A1 - 骨密度性状遗传力分析方法及装置 - Google Patents

骨密度性状遗传力分析方法及装置 Download PDF

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WO2020124391A1
WO2020124391A1 PCT/CN2018/121861 CN2018121861W WO2020124391A1 WO 2020124391 A1 WO2020124391 A1 WO 2020124391A1 CN 2018121861 W CN2018121861 W CN 2018121861W WO 2020124391 A1 WO2020124391 A1 WO 2020124391A1
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heritability
analysis
bone density
gene
significant
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PCT/CN2018/121861
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English (en)
French (fr)
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朱木春
殷鹏
艾红
胡帆
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深圳先进技术研究院
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Publication of WO2020124391A1 publication Critical patent/WO2020124391A1/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

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  • the present application relates to the technical field of heritability analysis, in particular to a method and device for analyzing heritability of bone density traits.
  • bone density is an important indicator of bone strength. It is expressed in grams per cubic centimeter and is an absolute value. Bone mineral density is an important indicator of bone quality, reflecting the degree of osteoporosis and an important basis for predicting the risk of fracture. Many related studies have shown that bone density has a strong genetic component, and its heritability is about 50%-80%.
  • the existing methods for analyzing heritability of bone density traits mainly compare DNA sequencing data in large samples through whole-gene association research methods, and find single nucleotide polymorphism sites related to bone density. Through these genetic variation sites Assess the heritability of bone density traits.
  • the analysis of the heritability of the bone density shape by the gene association research method not only requires a large number of samples, genotyping wastes a lot of manpower and material resources, and the accuracy of evaluating the heritability of the bone density shape is low.
  • the purpose of this application is to provide a method and device for analyzing heritability of bone density traits, in order to solve the analysis of heritability of bone density shape through gene association research methods in the prior art. Not only requires a large number of samples, but also genotyping The technical problem of wasting a lot of manpower and material resources, and the low accuracy of assessing the heritability of the bone density shape.
  • an embodiment of the present application provides a heritability analysis method for bone density traits, including:
  • the heritability of bone density traits is obtained by genetic analysis of the TWAS significant genes.
  • the embodiments of the present application provide a first possible implementation manner of the first aspect, wherein the step of determining the transcriptome association analysis TWAS significant gene based on the genotype data includes:
  • the TWAS significant gene was analyzed for bone mineral density traits using the TWAS method to obtain the TWAS significant gene.
  • the examples of the present application provide a second possible implementation manner of the first aspect, wherein the step of obtaining a TWAS significant gene by performing a bone density trait analysis on the GWAS significant gene using the TWAS method includes:
  • Bone density trait analysis was performed on the standard amount using the TWAS method to obtain TWAS significant genes.
  • the embodiments of the present application provide a third possible implementation manner of the first aspect, wherein the genetic analysis of the TWAS significant gene to obtain the heritability of the bone density trait includes:
  • Correlation analysis is performed on the gene expression matrix and the two-dimensional data to obtain the heritability of the bone density trait.
  • the embodiments of the present application provide a fourth possible implementation manner of the first aspect, wherein the step of preprocessing the standard quantity to obtain a usable standard quantity includes:
  • the standard quantity whose P value is greater than the significance threshold is removed to obtain the usable standard quantity.
  • the embodiments of the present application provide a fifth possible implementation manner of the first aspect, which further includes:
  • the genetic risk score is determined according to the heritability of the bone density trait.
  • the embodiments of the present application provide a sixth possible implementation manner of the first aspect, wherein the calculation formula of the genetic risk score is as follows:
  • R 2 is the genetic risk score
  • Trait Heritability bone density M e is the number of single nucleotide polymorphisms obtained by the GWAS
  • N is the number of samples in an individual.
  • an embodiment of the present application further provides a heritability analysis device for bone density traits, including:
  • An acquisition module the acquisition module is used to acquire genotype data of the object to be detected
  • a determination module determines a TWAS significant gene based on the genotype data for association analysis of the whole transcriptome
  • An analysis module which is used to perform genetic analysis on the TWAS significant gene to obtain the heritability of the bone density trait.
  • an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, when the processor executes the computer program Steps to implement the above method.
  • an embodiment of the present application further provides a computer-readable medium having a non-volatile program code executable by a processor, where the program code causes the processor to perform the above method.
  • the embodiments of the present application obtain the bone mineral density by performing genetic analysis on the TWAS significant gene by obtaining genotype data of the object to be detected; determining the transcriptome association analysis based on the genotype data; Trait heritability, using the transcriptome management analysis method TWAS to achieve re-screening of GWAS significant genes for genome-wide association analysis, reducing the experimental analysis of GWAS significant genes, not only the accuracy of heritability is high, but also through the analysis of heritability Risk scoring, to further make genetic interpretability scientifically.
  • FIG. 1 is a flowchart of a method for analyzing heritability of bone density traits provided by an embodiment of the present application
  • FIG. 2 is a flowchart of the method in FIG. 1 based on step S102;
  • FIG. 3 is a flowchart of the method in FIG. 1 based on step S103;
  • step S302 is a flowchart of the method in FIG. 3 based on step S302;
  • FIG. 5 is a flowchart of the method in FIG. 4 based on step S401;
  • FIG. 6 is a schematic diagram of a device module for analyzing heritability of a bone density trait provided by an embodiment of the present application.
  • 01-acquisition module 02-determination module; 03-analysis module.
  • an embodiment of a method for analyzing heritability of bone density traits is provided. It should be noted that the steps shown in the flowchart in the drawings can be executed in a computer system such as a set of computer-executable instructions And, although the logic sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different from here.
  • FIG. 1 is a method for analyzing heritability of bone density traits according to an embodiment of the present application. As shown in FIG. 1, the method includes the following steps:
  • the embodiments of the present application provide a method for analyzing heritability of bone density traits, including:
  • Step S101 Obtain the genotype data of the object to be detected
  • genotype is information that can characterize a person’s genetic characteristics, for example, there are genotype AA, genotype Aa, and genotype aa, and obtaining the genotype data of the object to be tested includes the required research within the research object
  • genotype data includes genotype Aa, genotype BB, genotype cc... etc.
  • Step S102 Determine a TWAS significant gene based on genotype data for association analysis of the whole transcriptome
  • transcriptome-wide association study is to first perform genotyping and transcriptome sequencing in a small sample population to obtain genotype data and gene expression data. Use this part The data is used as a training set to fit a model of the relationship between gene expression and genotype to obtain an estimate of the effect of genotype on gene expression, and then use this model to estimate gene expression in a large sample population with existing genotyping results Finally, the association analysis of the phenotype and predicted gene expression of the large sample population.
  • This application uses the TWAS method to analyze genotype data to obtain TWAS significant genes. Significant genes refer to genes whose P value is less than a preset threshold after TWAS analysis. We consider these genes to be significant genes.
  • step S102 the step of determining the TWAS significant gene by transcriptome association analysis based on the genotype data is provided.
  • the embodiment of the present application also provides an implementation manner, as shown in FIG. 2, including:
  • Step S201 using the genome-wide association analysis GWAS method to perform bone density trait analysis on the genotype data to obtain GWAS significant genes;
  • a gene-wide association analysis method is used to screen out all-gene association-significant genes.
  • a gene-wide association study uses millions of single nucleotides in the genome. Morphology (single nucleotide polymorphism, SNP) is a molecular genetic marker, and a comparative analysis or correlation analysis at the genome-wide level is carried out. By comparison, gene mutation sites that affect complex traits are found, that is, significant genes. At present, the discovered SNP sites related to BMD traits are mainly found based on the GWAS method.
  • the statistical analysis of the GWAS study can use different analysis methods according to the different research designs, for example: association analysis based on unrelated individuals; family-based association research (Family-based association study), the selection of specific application methods can be based on The actual situation depends on this application. It is not limited to this.
  • the training samples of the GWAS model specifically used include: a GWAS summary data set generated by kemp et al. after GWAS analysis of BMD traits, and sample sources collected by kemp et al. In the UK Biobank database, the GWAS research method can be used directly after the training.
  • the specific model determination process is not limited in this application.
  • Step S202 using the TWAS method to analyze the bone density character of the GWAS significant gene to obtain the TWAS significant gene.
  • the GWAS significant genes are obtained first.
  • the DNA sequencing data in a large sample is mainly compared by the GWAS method, and the SNP sites related to BMD are found, and then these genetic Variant loci assess the heritability of BMD traits.
  • the sample size required for GWAS research design is large, and genotyping is costly.
  • the task of genetic statistical analysis is not only to find the association with the disease phenotype from hundreds of thousands of SNPs, but also to strictly control the false positives due to the confusion of the population, and the increased probability of type I errors due to multiple comparisons, etc. Problem, from a large number of positive results, those sequence variations within the genome that are truly related to the disease are selected.
  • the embodiment of the present application also provides an implementation manner, as shown in FIG. 3, including:
  • Step S301 Modeling the GWAS significant gene to obtain a standard amount
  • the GWAS summary data needs to be screened.
  • the input data must meet the following conditions: 1. No missing values, 2. INFO is greater than 0.9, 3, MAF is greater than 0.1, 4, P value cannot exceed the limit, 5 1. There is no synonymous SNP. 6. No repeated SNP. The specific added conditions and the order of execution can be determined according to the actual situation, which is not limited in this application.
  • step S302 a standard amount of bone density trait analysis is performed using the TWAS method to obtain a TWAS significant gene.
  • the standard quantity is the data in the standard input format required by the TWAS method.
  • TWAS processing process reference may be made to the above embodiments.
  • Those skilled in the art can clearly understand that for the convenience and conciseness of description, for the specific working process of the system and device described above, reference may be made to the corresponding process in the foregoing method embodiments, and details are not described herein again.
  • Step S103 Perform genetic analysis on the TWAS significant gene to obtain the heritability of the bone density trait.
  • bone density is bone mineral density, which is an important indicator of bone strength. It is expressed in grams per cubic centimeter and is an absolute value. When the bone mineral density value is used clinically, because the absolute value of different bone mineral density detectors is different, the T value is usually used to judge whether the bone mineral density is normal. Bone density is an important sign of bone quality, reflecting the degree of osteoporosis and an important basis for predicting the risk of fracture. Heritability refers to the contribution of genetic factors during the formation of polygenic diseases. The greater the inheritance, the greater the contribution of genetic factors. There are many related studies showing that bone density has a strong genetic component, and its heritability is about 50%-80%.
  • the heritability of significant genes is analyzed by genetic analysis to determine the size of the genetic component in bone density and to predict disease. Has an important meaning.
  • the specific genetic analysis method used may depend on the actual situation.
  • step S103 the step of performing genetic analysis on the TWAS significant gene to obtain the heritability of the bone density trait.
  • the embodiment of the present application also provides an implementation manner, as shown in FIG. 4, including:
  • Step S401 preprocessing the TWAS significant gene to obtain a usable TWAS significant gene
  • the pre-processing process includes a threshold screening process. Based on step S401, the step of pre-processing the standard amount to obtain a usable standard amount, the embodiment of the present application also provides an implementation manner, as shown in FIG. 5, include:
  • Step S501 Obtain the P value corresponding to the significance threshold and the standard value
  • each gene corresponds to a P value
  • the genes with a P value greater than the preset threshold are screened out by a preset P value threshold, thereby obtaining available TWAS significant genes.
  • the purpose is that the genetics cannot be analyzed. Gene knockout.
  • step S502 the standard quantity whose P value is greater than the significance threshold is removed to obtain an available standard quantity.
  • the setting of the threshold may be determined according to the actual situation.
  • the reference panel selected in the embodiment of the present application selects the calculated genes in bone tissue, blood, and peripheral blood.
  • the Thousand Talents Project Genome is selected as the reference panel.
  • the reference panel is the gene expression level of genes in a certain tissue that need to be predicted.
  • the transcriptome gene panel that can be used as a reference for genes in a pre-selected database, the linkage between SNPs
  • the imbalance reference panel indicates the linkage disequilibrium relationship existing among SNPs.
  • There are 7450 genes in the three reference tissues. Through the strict Bonferroni test, the significance threshold of TWAS analysis is 0.05/7450 6.67 ⁇ 10 -6 .
  • Step S402 Determine the gene name, characteristic value and P value according to the available TWAS significant genes
  • Step S403 a gene expression matrix is determined according to the gene name and feature value, and two-dimensional data is determined according to the gene name and P value;
  • Step S404 Perform correlation analysis on the gene expression matrix and the two-dimensional data to obtain the heritability of the bone density trait.
  • the heritability can be calculated by a preset program in the computer, and the specific method selected can be determined according to the actual situation.
  • the process of calculating the heritability in this application is as follows: Assume that the gene expression matrix is one of m*M Matrix, where m is the gene and M is the SNP, each value in the matrix is the effect of M on m; assuming that the two-dimensional array of T significant genes is n*2, first set the sliding window to 10, and then make it at n Swipe up, and then perform a dot product. After the calculation, you can get an array of n*2, then average the columns, and finally calculate the average of the rows to calculate the heritability.
  • the genetic risk score was determined according to the heritability of bone density traits.
  • the calculation method of the genetic risk factor score is the multi-gene scoring rule based on SNP for the GWAS analysis result, but for the TWAS analysis result, the gene-based scoring rule needs to be adopted.
  • TWAS analysis found the association between gene-traits. And it can make genetic interpretability from a scientific point of view.
  • the calculation formula of genetic risk score is as follows:
  • R 2 is the genetic risk score
  • Trait Heritability bone density M e is the number of single nucleotide polymorphisms obtained by the GWAS
  • N is the number of samples in an individual.
  • the embodiments of the present application also provide a heritability analysis device for bone density traits.
  • the heritability analysis device for bone density traits is mainly used to perform the heritability analysis method for bone density traits provided in the above contents of the embodiments of the present application.
  • the heredity analysis device for bone density traits provided by the example is introduced in detail.
  • the heritability analysis device for a bone density trait mainly includes:
  • Acquisition module 01 which is used to acquire genotype data of the object to be detected
  • the determination module 02 determines the TWAS significant gene based on the genotype data to analyze the whole transcriptome association;
  • Analysis module 03 analysis module 03 is used for genetic analysis of TWAS significant genes to obtain heritability of bone density traits.
  • An embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor.
  • the processor implements the computer program to implement the above method step.
  • Embodiments of the present application also provide a computer-readable medium having a non-volatile program code executable by a processor, where the program code causes the processor to perform the above method.
  • connection should be understood in a broad sense, for example, it may be a fixed connection or a detachable connection , Or integrally connected; it can be a mechanical connection or an electrical connection; it can be directly connected, or it can be indirectly connected through an intermediary, or it can be the connection between two components.
  • connection should be understood in a broad sense, for example, it may be a fixed connection or a detachable connection , Or integrally connected; it can be a mechanical connection or an electrical connection; it can be directly connected, or it can be indirectly connected through an intermediary, or it can be the connection between two components.
  • connection should be understood in a broad sense, for example, it may be a fixed connection or a detachable connection , Or integrally connected; it can be a mechanical connection or an electrical connection; it can be directly connected, or it can be indirectly connected through an intermediary, or it can be the connection between two components.
  • the disclosed system, device, and method may be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the unit is only a division of logical functions.
  • multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some communication interfaces, devices or units, and may be in electrical, mechanical, or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the functions are implemented in the form of software functional units and sold or used as independent products, they may be stored in a non-volatile computer-readable storage medium executable by a processor.
  • the technical solution of the present application essentially or part of the contribution to the existing technology or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to enable a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code .

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Abstract

一种骨密度性状遗传力分析方法及装置，涉及遗传力分析技术领域，该方法和装置通过获取待检测对象的基因型数据（S101）；根据所述基因型数据确定全转录组关联分析TWAS显著基因（S102）；对所述TWAS显著基因进行遗传性分析得到骨密度性状遗传力（S103），利用全转录组管理分析方法TWAS实现对全基因组关联分析GWAS显著基因进行再筛选，减少了对于GWAS显著基因的实验分析，不仅遗传力的精确度高，并且还通过对遗传力的风险评分，在科学角度上进一步做出遗传可解释性。

Description

骨密度性状遗传力分析方法及装置 技术领域
本申请涉及遗传力分析技术领域，尤其是涉及一种骨密度性状遗传力分析方法及装置。
背景技术
骨密度全称是骨骼矿物质密度，是骨骼强度的一个重要指标，以克/每立方厘米表示，是一个绝对值。骨密度是骨质量的一个重要标志，反映骨质疏松程度，预测骨折危险性的重要依据。有许多相关的研究表明骨密度有很强的遗传成分，其遗传性大约在50％-80％。现有的骨密度性状遗传力分析方法中，主要是通过全基因关联研究方法比较大样本中DNA测序数据，发现与骨密度相关的单核苷酸多态性位点，通过这些遗传变异位点评估骨密度性状的遗传力。
但是通过基因关联研究方法对骨密度形状遗传力进行分析不仅需要大量样本，基因分型浪费大量人力物力，且评估出骨密度形状遗传力的精确度低。
发明内容
有鉴于此，本申请的目的在于提供了一种骨密度性状遗传力分析方法及装置，以解决现有技术中通过基因关联研究方法对骨密度形状遗传力进行分析不仅需要大量样本，基因分型浪费大量人力物力，且评估出骨密度形状遗传力的精确度低的技术问题。
第一方面，本申请实施例提供了一种骨密度性状遗传力分析方法，包括：
获取待检测对象的基因型数据；
根据所述基因型数据确定全转录组关联分析TWAS显著基因；
对所述TWAS显著基因进行遗传性分析得到骨密度性状遗传力。
结合第一方面，本申请实施例提供了第一方面的第一种可能的实施方式，其中，根据所述基因型数据确定全转录组关联分析TWAS显著基因的步骤，包括：
利用全基因组关联分析GWAS方法对所述基因型数据进行骨密度性状分析得到GWAS显著基因；
利用TWAS方法对所述GWAS显著基因进行骨密度性状分析得到TWAS显著基因。
结合第一方面，本申请实施例提供了第一方面的第二种可能的实施方式，其中，利用TWAS方法对所述GWAS显著基因进行骨密度性状分析得到TWAS显著基因的步骤，包括：
对所述GWAS显著基因进行模式化处理得到标准量；
利用TWAS方法对所述标准量进行骨密度性状分析得到TWAS显著基因。
结合第一方面，本申请实施例提供了第一方面的第三种可能的实施方式，其中，对所述TWAS显著基因进行遗传性分析得到骨密度性状遗传力的步骤，包括：
对TWAS显著基因进行预处理得到可用TWAS显著基因；
根据所述可用TWAS显著基因确定基因名、特征值和P值；
根据所述基因名与所述特征值确定基因表达量矩阵，根据所述基因名与所述P值确定二维数据；
对所述基因表达量矩阵和所述二维数据进行关联性分析得到所述骨密度性状遗传力。
结合第一方面，本申请实施例提供了第一方面的第四种可能的实施方式，其中，对标准量进行预处理得到可用标准量的步骤，包括：
获取显著性阈值和标准量对应的P值；
剔除所述P值大于所述显著性阈值的标准量，得到所述可用标准量。
结合第一方面，本申请实施例提供了第一方面的第五种可能的实施方式，其中，还包括：
根据所述骨密度性状遗传力确定基因风险评分。
结合第一方面，本申请实施例提供了第一方面的第六种可能的实施方式，其中，所述基因风险评分的计算公式如下所示：
Figure PCTCN2018121861-appb-000001
其中，R 2是所述基因风险评分，
Figure PCTCN2018121861-appb-000002
是骨密度性状遗传力，M e是通过GWAS获得的单核苷酸多态性位点数量，N是个体内采样数量。
第二方面，本申请实施例还提供一种骨密度性状遗传力分析装置，包括：
获取模块，所述获取模块用于获取待检测对象的基因型数据；
确定模块，所述确定模块根据所述基因型数据确定全转录组关联分析TWAS显著基因；
分析模块，所述分析模块用于对所述TWAS显著基因进行遗传性分析得到骨密度性状遗传力。
第三方面，本申请实施例还提供一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上 述方法的步骤。
第四方面，本申请实施例还提供一种具有处理器可执行的非易失的程序代码的计算机可读介质，所述程序代码使所述处理器执行上述方法。
在本申请实施例中，本申请实施例通过获取待检测对象的基因型数据；根据所述基因型数据确定全转录组关联分析TWAS显著基因；对所述TWAS显著基因进行遗传性分析得到骨密度性状遗传力，利用全转录组管理分析方法TWAS实现对全基因组关联分析GWAS显著基因进行再筛选，减少了对于GWAS显著基因的实验分析，不仅遗传力的精确度高，并且还通过对遗传力的风险评分，在科学角度上进一步做出遗传可解释性。
本申请的其他特征和优点将在随后的说明书中阐述，并且，部分地从说明书中变得显而易见，或者通过实施本申请而了解。本申请的目的和其他优点在说明书、权利要求书以及附图中所特别指出的结构来实现和获得。
为使本申请的上述目的、特征和优点能更明显易懂，下文特举较佳实施例，并配合所附附图，作详细说明如下。
附图说明
为了更清楚地说明本申请具体实施方式或现有技术中的技术方案，下面将对具体实施方式或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本申请的一些实施方式，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例提供的骨密度性状遗传力分析方法流程图；
图2为图1中基于步骤S102的方法流程图；
图3为图1中基于步骤S103的方法流程图；
图4为图3中基于步骤S302的方法流程图；
图5为图4中基于步骤S401的方法流程图；
图6为本申请实施例提供种骨密度性状遗传力分析装置模块示意图。
图标：
01-获取模块；02-确定模块；03-分析模块。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合附图对本申请的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
根据本申请实施例，提供了一种骨密度性状遗传力分析的方法实施例，需要说明的是，在附图的流程图示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行，并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
图1是根据本申请实施例的骨密度性状遗传力分析方法，如图1所示，该方法包括如下步骤：
本申请实施例提供了一种骨密度性状遗传力分析方法，包括：
步骤S101，获取待检测对象的基因型数据；
在本申请实施例中，基因型是可以表征一个人遗传特性的信息，例如：存在基因型AA、基因型Aa和基因型aa，获取待检测对象的基因型数据中包括研究对象内所需研究的各部分基因型，例如：基因型数据中包括基因型Aa、基因型BB、基因型cc….等。
步骤S102，根据基因型数据确定全转录组关联分析TWAS显著基因；
在本申请实施例中，全转录组关联分析(transcriptome－wide association study，TWAS)，是先在小样本人群中进行基因分型和转录组测序以获得基因型数据和基因表达数据，用这部分数据作为训练集，拟合基因表达量和基因型之间关系的模型，得到基因型对基因表达量的效应估计值，然后利用该模型估计已有基因分型结果的大样本人群的基因表达量，最后对大样本的人群的表型和预测的基因表达量进行关联分析。本申请利用TWAS方法对基因型数据进行分析得到TWAS显著基因，显著基因是指经过TWAS分析后，基因的P值小于预设阈值的基因，这些基因我们认为是显著基因。
基于步骤S102，根据基因型数据确定全转录组关联分析TWAS显著基因的步骤，本申请实施例还提供了一种实施方式，如图2所示，包括：
步骤S201，利用全基因组关联分析GWAS方法对基因型数据进行骨密度性状分析得到GWAS显著基因；
在本申请实施例中，首先采用全基因关联分析方法筛选出全基因关联显著基因，全基因关联研究(gene-wide association study，GWAS)是应用基因组中数以百万计的单核苷酸多态性(single nucleotide polymorphism，SNP)为分子遗传标记，进行全基因组水平上的对照分析或相关性分析，通过比较发现影响复杂性状的基因变异位点，即显著基因。目前，已发现的与BMD性状相关的SNP位点，主要是基于GWAS方法发现的。GWAS研究的统计分析依据研究设计的不同可以采用不同的分析方法，例如：基于无关个体(Unrelated individual)的关联分析；基于家系的关联研究(Family-based association study)，具体应用的方法选取可以依据实际情况而定，本申请对此不做限定，具体采用的GWAS模型的训练样 本包括：kemp等人对BMD性状做GWAS分析后产生的一个GWAS总结性的数据集，kemp等人收集的样本来源于英国生物银行数据库，训练完成后可以直接使用GWAS研究方法，具体的确定模型过程本申请对此也不做出限定。
步骤S202，利用TWAS方法对GWAS显著基因进行骨密度性状分析得到TWAS显著基因。
在本申请实施例中，首先获取GWAS显著基因，现有的BDM性状遗传力分析方法中，主要是通过GWAS方法比较大样本中DNA测序数据，发现与BMD相关的SNP位点，然后通过这些遗传变异位点评估BMD性状的遗传力。GWAS研究设计所需样本量大,基因分型耗资巨大。遗传统计分析的任务不仅要从几十万个SNPs中发现与疾病表型的关联,同时需要严格控制由于人群混杂可能带来的假阳性,以及因多重比较而带来的I类错误概率扩大等问题,从大量的阳性结果中筛选出那些与疾病真正相关的基因组内序列变异。这也是GWAS方法遇到的困难，也是GWAS方法的缺点。GWAS研究所需样本量大，各种研究设计方法以及遗传统计方法无法从根本上消除人群混杂、多重比较造成的假阳性，即使采用通过重复研究方法来保证遗传标记与疾病间的真关联。但其发现的SNP位点，及相应的基因也存在很多假信号。而且也会忽略大部分的非编码区上的调控基因、表达数据基因座的SNP信号，以及一些罕见的SNP位点。因此，应用GWAS方法发掘的SNP，并没有解释太多BMD性状的遗传力，所以需要使用TWAS方法进行第二次筛选，获得更加精确的显著基因，即TWAS显著基因。基于步骤S202，利用TWAS方法对GWAS显著基因进行骨密度性状分析得到TWAS显著基因的步骤，本申请实施例还提供了一种实施方式，如图3所示，包括：
步骤S301，对GWAS显著基因进行模式化处理得到标准量；
在本申请实施例中，需要将GWAS显著基因按照TWAS的输入格式进行模式化处理得到模式量，即标准量。首先对GWAS总结性数据做数据的筛查，数据格式的转换。TWAS输入数据的必须要有以下四列：SNP ID、A1(等位基因1，效应等位基因)、A2(等位基因2，其他等位基因)、Z(Z-scores，关于等位基因1的效应量)。在格式转换之前也需要对GWAS总结性数据做筛查，输入的数据需要满足一下条件：1、无缺失值，2、INFO大于0.9，3、MAF大于0.1，4、P值不能超出界限，5、没有同义SNP，6、没有重复SNP。具体增加的条件还有执行的顺序可以依据实际情况而定，本申请对此不做限定。
步骤S302，利用TWAS方法对标准量进行骨密度性状分析得到TWAS显著基因。
在本申请实施例中，标准量是采用TWAS方法所需的标准输入格式的数据，具体TWAS处理过程可以参照上述实施例，所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统和装置的具体工作过程，可以参考前述方法实施例中的对应 过程，在此不再赘述。
步骤S103，对TWAS显著基因进行遗传性分析得到骨密度性状遗传力。
在本申请实施例中，骨密度(bone mineral density，BMD)全称是骨骼矿物质密度，是骨骼强度的一个重要指标，以克/每立方厘米表示，是一个绝对值。在临床使用骨密度值时由于不同的骨密度检测仪的绝对值不同，通常使用T值判断骨密度是否正常。骨密度，是骨质量的一个重要标志，反映骨质疏松程度，预测骨折危险性的重要依据。遗传力是指在多基因疾病的形成过程中，遗传因素的贡献大小，遗传都越大，表明遗传因素的贡献越大。有许多相关的研究表明骨密度有很强的遗传成分，其遗传性大约在50％-80％，对显著基因进行遗传性分析得到遗传力，从而判定骨密度中的遗传成分大小，对疾病预测有着重要意义。具体使用的遗传性分析方法可以依据实际情况而定。基于步骤S103，对TWAS显著基因进行遗传性分析得到骨密度性状遗传力的步骤，本申请实施例还提供了一种实施方式，如图4所示，包括：
步骤S401，对TWAS显著基因进行预处理得到可用TWAS显著基因；
在本申请实施例中，预处理过程包括阈值筛选过程，基于步骤S401，对标准量进行预处理得到可用标准量的步骤，本申请实施例还提供了一种实施方式，如图5所示，包括：
步骤S501，获取显著性阈值和标准量对应的P值；
在本申请实施例中，每个基因对应有一个P值，通过预设的P值阈值将P值大于预设阈值的基因筛选掉，从而得到可用的TWAS显著基因，目的在于将不能分析出遗传力的基因剔除。
步骤S502，剔除P值大于显著性阈值的标准量，得到可用标准量。
在本申请实施例中，阈值的设定可以依据实际情况而定，例如：本申请实施例选用的参考面板(reference panel)选用的是已经计算好的基因分别在骨组织、血液、外周血的基因表达量，选用千人计划基因组作为参考面板，参考面板是需要预测的在某一组织上的基因的基因表达量，可以在预选的数据库上基因作为参考的转录组基因面板，SNP间的连锁不平衡参考面板表示SNP间存在的连锁不平衡关系。三个参考组织的基因共有7450个基因，通过严格的Bonferroni检验，TWAS分析的显著性阈值为0.05/7450＝6.67×10 -6。
步骤S402，根据可用TWAS显著基因确定基因名、特征值和P值；
步骤S403，根据基因名与特征值确定基因表达量矩阵，根据基因名与P值确定二维数据；
步骤S404，对基因表达量矩阵和二维数据进行关联性分析得到骨密度性状遗传力。
在本申请实施例中，可以通过计算机中的预设程序计算遗传力，具体选用的方法可以 依据实际情况而定，本申请中计算遗传力过程如下：假设基因表达量矩阵为m*M的一个矩阵，其中m为基因，M为SNP，矩阵中的每个值为M在m上的效应；假设T显著基因二维数组为n*2，首先设置滑动窗口为10，然后在使其在n上滑动，然后进行点乘，计算结束后，可以获得n*2的数组，然后求列平均，最后再求行平均计算得到遗传力。
在本申请提供的又一实施例中，还包括：
根据骨密度性状遗传力确定基因风险评分。
在本申请实施例中，遗传风险因素评分，对于GWAS分析结果采用的计算方法是基于SNP的多基因评分规则，但对于TWAS分析结果，则需要采用基于基因的评分规则。TWAS分析找到的基因-性状之间的关联关系。并可以从科学的角度做出遗传性的可解释性，基因风险评分的计算公式如下所示：
Figure PCTCN2018121861-appb-000003
其中R 2是基因风险评分，
Figure PCTCN2018121861-appb-000004
是骨密度性状遗传力，M e是通过GWAS获得的单核苷酸多态性位点数量，N是个体内采样数量。首先需要转化TWAS预测的基因表达量的格式，转化为列名为基因名、行为每一个特征值的基因表达量矩阵。接着将TWAS显著基因转换为一列是基因名，一列是TWAS检验P值的二维数据。然后对前两者做关联性分析，获得TWAS检验显著基因的遗传力
Figure PCTCN2018121861-appb-000005
通过前文可知，M e和N都是已知量。最后将各个数值代入遗传风险评分函数中，获得BMD性状的遗传风险评分。
本申请实施例还提供了一种骨密度性状遗传力分析装置，该骨密度性状遗传力分析装置主要用于执行本申请实施例上述内容所提供的骨密度性状遗传力分析方法，以下对本申请实施例提供的骨密度性状遗传力分析装置做具体介绍。
图6是根据本申请实施例的一种骨密度性状遗传力分析装置的示意图，该骨密度性状遗传力分析装置主要包括：
获取模块01，获取模块01用于获取待检测对象的基因型数据；
确定模块02，确定模块02根据基因型数据确定全转录组关联分析TWAS显著基因；
分析模块03，分析模块03用于对TWAS显著基因进行遗传性分析得到骨密度性状遗传力。
本申请实施例所提供的装置，其实现原理及产生的技术效果和前述方法实施例相同，为简要描述，装置实施例部分未提及之处，可参考前述方法实施例中相应内容。
本申请实施例还提供一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述方法的步 骤。
本申请实施例还提供一种具有处理器可执行的非易失的程序代码的计算机可读介质，所述程序代码使所述处理器执行上述方法。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统和装置的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
另外，在本申请实施例的描述中，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通。对于本领域的普通技术人员而言，可以具体情况理解上述术语在本申请中的具体含义。
在本申请的描述中，需要说明的是，术语“中心”、“上”、“下”、“左”、“右”、“竖直”、“水平”、“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本申请和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本申请的限制。此外，术语“第一”、“第二”、“第三”仅用于描述目的，而不能理解为指示或暗示相对重要性。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，又例如，多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些通信接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个处理器可执行的非易失的计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM， Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是：以上所述实施例，仅为本申请的具体实施方式，用以说明本申请的技术方案，而非对其限制，本申请的保护范围并不局限于此，尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，其依然可以对前述实施例所记载的技术方案进行修改或可轻易想到变化，或者对其中部分技术特征进行等同替换；而这些修改、变化或者替换，并不使相应技术方案的本质脱离本申请实施例技术方案的精神和范围，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应所述以权利要求的保护范围为准。

Claims (10)

  1. 一种骨密度性状遗传力分析方法，其特征在于，包括：
    获取待检测对象的基因型数据；
    根据所述基因型数据确定全转录组关联分析TWAS显著基因；
    对所述TWAS显著基因进行遗传性分析得到骨密度性状遗传力。
  2. 根据权利要求1所述的骨密度性状遗传力分析方法，其特征在于，根据所述基因型数据确定全转录组关联分析TWAS显著基因的步骤，包括：
    利用全基因组关联分析GWAS方法对所述基因型数据进行骨密度性状分析得到GWAS显著基因；
    利用TWAS方法对所述GWAS显著基因进行骨密度性状分析得到TWAS显著基因。
  3. 根据权利要求2所述的骨密度性状遗传力分析方法，其特征在于，利用TWAS方法对所述GWAS显著基因进行骨密度性状分析得到TWAS显著基因的步骤，包括：
    对所述GWAS显著基因进行模式化处理得到标准量；
    利用TWAS方法对所述标准量进行骨密度性状分析得到TWAS显著基因。
  4. 根据权利要求1所述的骨密度性状遗传力分析方法，其特征在于，对所述TWAS显著基因进行遗传性分析得到骨密度性状遗传力的步骤，包括：
    对TWAS显著基因进行预处理得到可用TWAS显著基因；
    根据所述可用TWAS显著基因确定基因名、特征值和P值；
    根据所述基因名与所述特征值确定基因表达量矩阵，根据所述基因名与所述P值确定二维数据；
    对所述基因表达量矩阵和所述二维数据进行关联性分析得到所述骨密度性状遗传力。
  5. 根据权利要求4所述的骨密度性状遗传力分析方法，其特征在于，对标准量进行预处理得到可用标准量的步骤，包括：
    获取显著性阈值和标准量对应的P值；
    剔除所述P值大于所述显著性阈值的标准量，得到所述可用标准量。
  6. 根据权利要求4所述的方法，其特征在于，还包括：
    根据所述骨密度性状遗传力确定基因风险评分。
  7. 根据权利要求6所述的方法，其特征在于，所述基因风险评分的计算公式如下所示：
    Figure PCTCN2018121861-appb-100001
    其中R 2是所述基因风险评分，
    Figure PCTCN2018121861-appb-100002
    是骨密度性状遗传力，M e是通过GWAS获得的单核苷酸多态性位点数量，N是个体内采样数量。
  8. 一种骨密度性状遗传力分析装置，其特征在于，包括：
    获取模块，所述获取模块用于获取待检测对象的基因型数据；
    确定模块，所述确定模块根据所述基因型数据确定全转录组关联分析TWAS显著基因；
    分析模块，所述分析模块用于对所述TWAS显著基因进行遗传性分析得到骨密度性状遗传力。
  9. 一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现上述权利要求1至7任一项所述的方法的步骤。
  10. 一种具有处理器可执行的非易失的程序代码的计算机可读介质，其特征在于，所述程序代码使所述处理器执行所述权利要求1-7任一所述方法。
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