KR20200137875A - Non-invasive prenatal testing method and devices based on double Z-score - Google Patents

Abstract

The present invention relates to a non-invasive prenatal test method based on a two-step Z-score, and an apparatus thereof. More particularly, the present invention relates to a method and apparatus for improving the accuracy of non-invasive prenatal testing by significantly amplifying the Z-score by applying one of the statistical methods, Z-score, twice. Accordingly, the present invention uses a two-stage Z-score amplified by two-stage data processing to separate the number of reads of a specific chromosome from the number of reads of a normal sample group as much as possible to reduce the possibility of false-positive and false-negative determination of chromosomal abnormalities, and may be used to increase the accuracy of a non-invasive prenatal test.

Description

Non-invasive prenatal testing method and devices based on double Z-score}

The present invention relates to a non-invasive prenatal test method and apparatus based on a two-step Z-score. More specifically, Z-score, which is one of the statistical methods, is applied twice to greatly amplify the Z-score and thus the non-invasive prenatal test It relates to a method and apparatus for improving accuracy.

In general,'prenatal diagnosis' refers to the process of determining and diagnosing the presence of a fetus's disease before the fetus is born.

According to a recent domestic statistical data, about 3% of newborns with congenital malformations are reported, and about 20% of congenital malformations are reported to be due to chromosomal abnormalities. In particular, the deformity of Down syndrome, which is widely known, accounts for about 26% of congenital malformations. Interest in prenatal diagnosis is increasing day by day due to the increase in the birth rate of malformed babies and the development of various prenatal diagnostic equipment. In particular, pregnant women over 35 years of age, pregnant women with a history of delivery of children with chromosomal abnormalities, structural abnormalities in chromosomes from one of the parents, family history of genetic diseases, and risk of neural tube defects In case of suspected fetal malformation in maternal serum screening and ultrasound, it is necessary to undergo prenatal diagnosis.

Prenatal diagnosis methods can be largely divided into invasive diagnosis methods and non-invasive diagnosis methods. Examples of invasive diagnostic methods include chorionic villi sampling (CVS) performed between 10 and 12 weeks of pregnancy, and immunoassay between 15 and 20 weeks of pregnancy to measure the concentration of AFP in the amniotic fluid. There are amniocentesis to be analyzed, cordocentesis method, which is performed by extracting fetal blood directly from the umbilical cord under ultrasound guidance between 18 and 20 weeks of pregnancy. However, such invasive diagnostic methods have a problem in that they may cause miscarriage, disease, or deformity by impacting the fetus during the examination process.

Therefore, non-invasive diagnostic methods are being developed to overcome these problems. For example, the genetic diagnosis method before embryo implantation is a technique that selects embryos without genetic defects before implantation in the uterus using molecular genetic or cytogenetic techniques used in in vitro fertilization. In addition, QF-PCR (quantitative-fluorescent PCR) fluorescence quantification method for rapidly diagnosing chromosomal aneuploidy is performed by fluorescing short tandem repeats (STR) of DNA specifically present for each chromosome. This is a rapid screening test method in which the amount of amplified DNA with fluorescence is measured and analyzed by a DNA auto-base sequence analyzer after amplification by the multiplex PCR method. In addition, a chromosomal microarray (CMA) method in which a mapped DNA sequence on a glass slide is integrated and examined to detect a copy number change is known.

On the other hand, as it becomes possible to decode large-scale genome information with the development of sequencing technology, genome analysis methods based on this next-generation sequencing (NGS) technology are also used in the prenatal diagnosis area. In particular, it is known that the maternal blood contains about 10% of the whole genome of the fetus, and prenatal diagnostic methods are known to analyze the chromosome by separating the fetal cells from the maternal blood using this. . In this regard, Korean Patent Application No. 2010-7003969 discloses a method for diagnosing fetal chromosomal aneuploidy using massively parallel genomic sequencing. In addition, U.S. Patent No. 8195415 also discloses a method for quantitative analysis by mapping the sequence analysis results of DNA obtained from maternal blood to a specific length for each chromosome.

However, the conventionally reported methods have a problem that the determination of fetal malformations is not accurate due to statistical limitations. Since diagnostic errors (false positive; FP and false negative; FN) of fetal malformations can lead to serious consequences, it is very important to develop more sensitive and accurate analysis algorithms in non-invasive prenatal testing methods.

Accordingly, the inventors of the present invention have conducted extensive research in order to overcome the limitations of the prior art and improve the accuracy of non-invasive prenatal testing. As a result, the chromosomal abnormality is very accurately determined through the method of calculating two Z-scores for each chromosome. It was found that it could be discriminated and the invention was completed.

Accordingly, an object of the present invention is to (a) extract cell-free DNA (cfDNA) from the mother's blood to perform next-generation sequencing (Next Generataion Sequencing);

(b) mapping the sequenced read sequences to a reference genome sequence;

(c) dividing each chromosome of the reference genome into preset bins and calculating the number of mapped reads for each partition based on the mapped reads;

(d) calculating a median of the number of reads for each segment for each chromosome;

(e) calculating the first stage Z-score of each chromosome according to Equation 1 below;

[Equation 1]

(f) calculating a two-step Z-score of each chromosome according to Equation 2 below; And

[Equation 2]

(In the above formula, the representative chromosome group is two or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16.)

(g) To provide a non-invasive prenatal test method comprising the step of determining whether or not a chromosome abnormality is detected by checking the Z-score value in the second step calculated for each chromosome.

Another object of the present invention is (a) a sequencing unit for extracting cell-free DNA (cfDNA) from the mother's blood and performing Next Generataion Sequencing;

(b) a mapping unit for mapping the sequenced read sequences to a reference genome sequence;

(c) a read number calculation unit that divides each chromosome of the reference genome into preset bins and calculates the number of mapped reads for each partition based on the mapped reads;

(d) a median value calculator for calculating a median of the number of reads for each chromosome;

(e) a first-stage Z-score calculator for calculating a first-stage Z-score of each chromosome according to Equation 1 below;

[Equation 1]

(f) a two-step Z-score calculating unit for calculating a two-step Z-score of each chromosome according to Equation 2 below; And

[Equation 2]

(In the above formula, the representative chromosome group is two or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16.)

(g) It is to provide a non-invasive prenatal test apparatus including a determination unit for determining whether or not a chromosome abnormality is determined by checking the two-step Z-score value calculated for each chromosome.

In order to achieve the above object, the present invention comprises the steps of: (a) extracting cell-free DNA (cfDNA) from the mother's blood and performing Next Generataion Sequencing;

(b) mapping the sequenced read sequences to a reference genome sequence;

(c) dividing each chromosome of the reference genome into preset bins and calculating the number of mapped reads for each partition based on the mapped reads;

(d) calculating a median of the number of reads for each segment for each chromosome;

(e) calculating the first stage Z-score of each chromosome according to Equation 1 below;

[Equation 1]

(f) calculating a two-step Z-score of each chromosome according to Equation 2 below; And

[Equation 2]

(In the above formula, the representative chromosome group is two or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16.)

(g) It provides a non-invasive prenatal test method including the step of determining whether or not a chromosome abnormality is detected by checking the Z-score value calculated for each chromosome.

In order to achieve another object of the present invention, the present invention includes: (a) a sequencing unit for extracting cell-free DNA (cfDNA) from the mother's blood and performing Next Generataion Sequencing;

(b) a mapping unit for mapping the sequenced read sequences to a reference genome sequence;

(c) a read number calculation unit that divides each chromosome of the reference genome into preset bins and calculates the number of mapped reads for each partition based on the mapped reads;

(d) a median value calculator for calculating a median of the number of reads for each chromosome;

(e) a first-stage Z-score calculator for calculating a first-stage Z-score of each chromosome according to Equation 1 below;

[Equation 1]

(f) a two-step Z-score calculating unit for calculating a two-step Z-score of each chromosome according to Equation 2 below; And

[Equation 2]

(In the above formula, the representative chromosome group is two or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16.)

(g) It provides a non-invasive prenatal test apparatus including a determination unit that determines whether or not a chromosome is abnormal by checking the Z-score value in the second step calculated for each chromosome.

Hereinafter, the present invention will be described in detail.

The present invention comprises the steps of: (a) extracting cell-free DNA (cfDNA) from the mother's blood and performing Next Generataion Sequencing;

(b) mapping the sequenced read sequences to a reference genome sequence;

(c) dividing each chromosome of the reference genome into preset bins and calculating the number of mapped reads for each partition based on the mapped reads;

(d) calculating a median of the number of reads for each segment for each chromosome;

(e) calculating the first stage Z-score of each chromosome according to Equation 1 below;

[Equation 1]

(f) calculating a two-step Z-score of each chromosome according to Equation 2 below; And

[Equation 2]

(In the above formula, the representative chromosome group is two or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16.)

(g) It provides a non-invasive prenatal test method including the step of determining whether or not a chromosome abnormality is detected by checking the Z-score value calculated for each chromosome.

(a) extracting cell-free DNA (cfDNA) from the mother's blood and performing Next Generataion Sequencing;

In the present invention, the mother is about 1 week to about 45 weeks of fetal pregnancy (e.g., 1 week to 4 weeks, 4 weeks to 8 weeks, 8 weeks to 12 weeks, 12 weeks to 16 weeks, 16 weeks of fetal pregnancy) To 20 weeks, 20 to 24 weeks, 24 to 28 weeks, 28 to 32 weeks, 32 to 36 weeks, 36 to 40 weeks, or 40 to 44 weeks), often from about 5 weeks of fetal pregnancy About 28 weeks (e.g. 6 weeks, 7 weeks, 8 weeks, 9 weeks, 10 weeks, 11 weeks, 12 weeks, 13 weeks, 14 weeks, 15 weeks, 16 weeks, 17 weeks, 18 weeks of fetal pregnancy, 19 weeks, 20 weeks, 21 weeks, 22 weeks, 23 weeks, 24 weeks, 25 weeks, 26 weeks, or 27 weeks) may mean a female.

Analysis of fetal DNA found in maternal blood can be carried out, for example, by using whole blood, serum or plasma. Methods for preparing serum or plasma from maternal blood are known. For example, the blood of a pregnant woman is contained in tubes or special commercial products containing EDTA to prevent blood clotting, such as Vacutainer SST (Becton Dickinson, Franklin Lakes, NJ). Can be placed, and then plasma can be obtained from whole blood via centrifugation. Serum can be obtained with or without centrifugation after blood coagulation. When centrifugation is used, the centrifugation is typically (but not exclusively) carried out at an appropriate speed, for example 1,500xg to 3,000xg. Plasma or serum can be subjected to an additional centrifugation step before being transferred to a new tube for DNA extraction.

There are a number of known methods for extracting DNA from blood. The general DNA preparation method (described, for example, Sambrook and Russell, Molecular Cloning: A Laboratory Manual 3d ed., 2001) can be followed; Various commercially available reagents or kits, such as Qiagen's QIAamp Cyclic Nucleic Acid Kit, QiaAmp DNA Mini Kit or QiaAmp DNA Blood Mini Kit (Qiagen, Hilden, Germany), GenomicPrep? ? Blood DNA Isolation Kit (Promega, Madison, Wis.), and GFX?? A genomic blood DNA purification kit (Amersham, Piscataway, NJ) can also be used to obtain DNA from blood samples from pregnant women. More than one of these methods may be used in combination.

In particular, the serum cell free DNA (cfDNA) is very small, and even a small amount of maternal cells mixed in the serum can affect the sensitivity, so it is important to completely remove the maternal cells in the serum. .

As used herein, the term “cell-free DNA” may refer to a nucleic acid isolated from a source that is substantially free of cells, and may refer to “cell free” nucleic acids, “cell free circulating nucleic acids” (eg, CCF fragments) and/ Or as a “cell free circulating” nucleic acid. Extracellular nucleic acids can be present in the blood and can be obtained from this blood.

The cell-free DNA extracted from the maternal blood may be sequenced through Next Generation Sequencing. The term "next-generation sequencing (NGS)" may be used interchangeably with "massive parallel sequencing" or second-generation sequencing. Next-generation sequencing refers to a technique for simultaneously sequencing millions of fragments of nucleic acid. Next-generation sequencing is, for example, 454 platform (Roche), GS FLX titanium, Illumina MiSeq, Illumina HiSeq, Illumina Genome Analyzer, Solexa platform, SOLiD System (Applied Biosystems), Ion Proton (Life Technologies), Complete Genomics, Helicos Biosciences Heliscope. , Pacific Biosciences' single molecule real-time (SMRT??) technology, or a combination thereof.

In some embodiments, nucleic acids can be fragmented or cleaved before, during or after the methods described herein. Fragmented or truncated nucleic acids may be about 5 to about 10,000 base pairs, about 100 to about 1,000 base pairs, about 100 to about 500 base pairs, or about 10, 15, 20, 25, 30 , 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 200, 300, 400 Nominal, average of, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000 or 9000 base pairs ) Or mean length. Fragments can be produced by suitable methods known in the art, and the average, average or nominal length of nucleic acid fragments can be adjusted by selecting an appropriate fragment generation procedure. Nucleic acid fragments may contain overlapping nucleotide sequences, and such overlapping sequences may facilitate construction of the nucleotide sequence of the non-fragmented counterpart nucleic acid or segment thereof.

In some embodiments, nucleic acids are fragmented or cleaved by suitable methods, and non-limiting examples of such methods include physical methods (e.g., shearing, e.g., sonication, French press, heating, UV irradiation, etc.), Enzymatic methods (e.g., enzymatic cleavage agents (e.g., suitable nucleases, suitable restriction enzymes, suitable methylation sensitive restriction enzymes)), chemical methods (e.g., alkylation, DMS, pipery Din, acid hydrolysis, base hydrolysis, heating, etc., or a combination thereof) and the like, or combinations thereof.

The step (a) of the present invention may further include preparing a nucleic acid library to perform next-generation sequencing. The nucleic acid library can be prepared according to the method of next-generation sequencing. That is, a nucleic acid library can be prepared according to the manufacturer's instructions for providing next-generation sequencing.

The sequence information of the obtained nucleic acid fragment may be referred to as reads.

(b) mapping the sequenced read sequences to a reference genome sequence;

In the step (b) of the present invention, the sequenced sequence reads may be mapped, and the number of reads mapped to a specified nucleic acid region (eg, a chromosome, or a portion or segment thereof) may be referred to as a count. Any suitable mapping method (eg, process, algorithm, program, software, module, etc., or a combination thereof) may be used in step (b) of the present invention. Some aspects of the mapping process are described below.

The mapping of sequence reads (i.e., sequence information from fragments for which the physical genomic location is unknown) can be performed in a number of ways, often with the step of aligning the obtained sequence reads with matching sequences in the reference genome. Include. In such an alignment, the sequence reads are generally aligned with a reference genomic sequence, and the sequence reads to be aligned may be designated as “mapped”, “mapped sequence reads” or “mapped reads”. In some embodiments, mapped sequence reads may be referred to as “hits” or “counts”.

As used herein, the terms “aligned”, “aligned” or “aligned” refer to two or more nucleic acid sequences that can be identified as a match (eg, 100% identity) or partial match. Alignment may be performed manually or may be performed by a computer (e.g., software, program, module or algorithm), and non-limiting examples of such computer programs include nucleotide data distributed as part of the Illumina genomic analysis pipeline. Includes the efficient local alignment (ELAND) computer program. Alignment of sequence reads can be 100% sequence identity. In some cases, the alignment is less than 100% sequence match (ie, non-perfect match, partial match, partial alignment). In some embodiments, the alignment is about 99%, 98%, 97%, 96%, 95%, 94%, 93%, 92%, 91%, 90%, 89%, 88%, 87%, 86%, It may be 85%, 84%, 83%, 82%, 81%, 80%, 79%, 78%, 77%, 76% or 75% consistent. In some embodiments, the alignment can include a mismatch

Various computer-aided methods can be used to map each sequence read to portions. Non-limiting examples of computer algorithms that can be used to align sequences include BWA, BLAST, BLITZ, FASTA, BOWTIE 1, BOWTIE 2, ELAND, MAQ, PROBEMATCH, SOAP or SEQMAP, or a modification or combination thereof. Including, but not limited to these, may be most preferably BWA.

In some embodiments, sequence reads may be aligned with sequences within a reference genome. In some embodiments, sequence reads are, for example, UCSC database (hg19, hg38), GenBank, dbEST, dbSTS, EMBL (European Molecular Biology Laboratory) and DDBJ (Japanese DNA Databank). (DNA Databank of Japan)), can be found in nucleic acid databases known in the art and/or can be aligned with sequences in such databases, most preferably aligned with sequences in UCSC databases (hg19, h38). Can be.

(c) dividing each chromosome of the reference genome into preset bins and calculating the number of mapped reads for each partition based on the mapped reads;

In some embodiments, the mapped reads are grouped together according to various parameters and assigned to specific portions (eg, portions of a reference genome). Often, individual mapped reads can be used to identify the compartments (eg, presence, absence or amount of parts) present in the sample. In some embodiments, the amount of a partition indicates the amount of a larger sequence (eg, chromosome) in the sample.

The term “compartment” may also be referred to herein as “genomic compartment”, “bin”, “region”, “portion”, “part of a reference genome”, “part of a chromosome” or “genomic part” . In some embodiments, the compartment may be an entire chromosome, a segment of a chromosome, a segment of a reference genome, a segment spanning multiple chromosomes, multiple chromosomal segments, and/or combinations thereof. In some embodiments, the partitions may be predefined based on certain parameters (eg, indicators). In some embodiments, the partitions may be randomly or non-randomly defined based on the division of the dielectric (eg, divided by size, GC content, contiguous region, randomly defined contiguous region, etc.). In some embodiments, the partitions are discrete genomic bins, genomic bins with contiguous sequences of a predetermined length, variable-size bins, point-based views of smoothed coverage maps, and/or It can be selected from combinations of these.

In some embodiments, the partitions may be described based on one or more parameters including, for example, the length of the sequence or specific features or features. Such compartments may be selected from consideration and/or may be filtered and/or removed by using any suitable criteria known in the art or described herein. In some embodiments, the partitions are based on a specific length of the genomic sequence. In some embodiments, the compartments may have approximately the same length, or may have different lengths. In some embodiments, portions are of equal length. In some embodiments, portions of different lengths can be adjusted or weighted.

Preferably, in the present invention, the compartment may be about 10 kilobases (kb) to about 500 kb, about 10 kb to about 300 kb, about 20 kb to about 200 kb, about 20 kb to about 100 kb, and preferably May be from about 30 kb to about 80 kb, and most preferably from 40 kb to 60 kb.

In the present invention, the partition is not limited to a sequence of sequences. Thus, the partitions may consist of contiguous and/or non-contiguous sequences. In addition, the compartment is not limited to a single chromosome. In some embodiments, the compartment may comprise all or part of one chromosome, or all or part of two or more chromosomes. In some embodiments, the compartments may span one or two or more entire chromosomes. Additionally, the compartment may span linked or unlinked regions of multiple chromosomes.

In addition, in the present invention, the compartments can often be processed according to one or more features, parameters, criteria and/or methods described herein or known in the art (e.g., standardization, filtration, selection, etc. or Can be processed by a combination of). The compartment can be processed according to any suitable parameters by any suitable method. Non-limiting examples of features and/or parameters that can be used to filter and/or select the partitions include count, coverage, mappability, variability, level of uncertainty, guanine-cytosine (GC) content, CCF fragment length, and/or Or read length (eg, fragment length ratio (FLR), fetal ratio statistics (FRS)), DNaseI sensitivity, methylation status, acetylation, histone distribution, chromatin structure, and the like, or a combination thereof.

Each chromosome of the reference genome is divided into the above-described partitions, and the number of mapped reads per partition is calculated based on the mapped reads.

In some embodiments, mapped or segmented sequence reads can be quantified based on a selected feature or variable to determine the number of reads mapped to one or more partitions (eg, a partition of a reference genome). In some embodiments, the number of reads mapped to partitions may be referred to as a count (eg, a count value). In some embodiments, counts for two or more partitions (eg, a set of partitions) can be manipulated mathematically (eg, calculating averaging, adding, normalizing, etc., or a combination thereof). In some embodiments, counts can be measured from some or all of the reads mapped to (ie, associated with) the partitions. In some embodiments, the number of mapped reads can be measured from a predetermined subset of mapped reads. A predetermined subset of mapped sequence reads can be defined or selected by using any suitable feature or variable. In some embodiments, the predetermined subset of mapped reads may include 1 to n reads, where n represents a number equal to the sum of all reads generated from a test subject or reference subject sample.

In particular, in the present invention, the number of reads mapped per division can be manipulated or converted (e.g., can be standardized, can be combined, can be added, can be filtered, can be selected, or It can be calculated as an average, derived as an average, or processed by a combination thereof). Preferably, in the present invention, the number of mapped reads for each partition may be standardized.

The standardization can be carried out by any suitable method described in the present invention or known in the art. In some embodiments, normalizing comprises adjusting values measured on different scales to a conceptually common scale. In some embodiments, normalization involves sophisticated mathematical adjustments that align the probability distribution of the adjusted values. In some embodiments, normalizing comprises aligning the distribution to a standard distribution. In some embodiments, normalization includes mathematical adjustments that allow comparison of corresponding normalized values for different data sets in a manner that eliminates the influence of some significant outcomes (eg, errors and exceptions). In some embodiments, normalizing includes scaling. Standardization often involves dividing one or more sets of data into predetermined variables or expressions.

In the present invention, bin-wise normalization of the standardization method, standardization by GC content, linear least squares regression, nonlinear least squares regression, LOESS, GC LOESS, LOWESS, PERUN, repeat masking (RM) ), GC-normalizatin and repeat masking (GCRM), conditional quantile normalization (cQn), and a combination thereof, preferably by GC content It can be standardization, LOESS method, and combinations thereof.

Most preferably, the relationship between the GC content for each section of the reference genome and the number of mapped reads for each section is calculated by calculating the bias by the GC content through the LOESS regression method, and correcting the bias by the GC content. Can be standardized. Explaining this step in detail, the most influencing on the diagnosis of chromosome number abnormalities using next-generation sequencing is the bias of sequence reads due to the GC content generated in the next-generation sequencing process (Benjamini, Y., & Speed, TP (2012). ).Summarizing and correcting the GC content bias in high-throughput sequencing.Nucleic Acids Research, 40(10)).

In other words, this bias is known to occur in the PCR amplification process that exists in the large-scale parallel genome sequencing process, and it is a result of good sequencing in the part of the genome with a high GC content or, conversely, the part with a low GC content in the genome.

The result of chromosomal abnormalities may be detected differently from the actual due to such a bias by the GC content.For example, if a part with a particularly high GC content is to be diagnosed using the result of well sequencing, the ratio of the chromosome with a high GC content It will appear to be higher than this reality. This leads to observation results different from the actual one. Therefore, when diagnosing chromosomal aneuploidy using next-generation sequencing, it is desirable to strongly suppress such variations due to GC content.

In one embodiment of the present invention, the GC content deviation correction method is used to determine the degree of deviation due to GC content through the LOESS regression method to determine the relationship between the number of reads for each chromosome segment and the GC content of the segment obtained as a result of next-generation sequencing. After calculation, the deviation was reversely removed using this method.

Meanwhile, the LOESS method is a regression modeling method known in the art that uses multiple regression models in combination with a k-nearest neighbor base meta-model. LOESS is often referred to as a locally weighted polynomial regression. In some embodiments, GC LOESS applies the LOESS model to the relationship between fragment counts (eg, sequence reads, counts) and GC composition for a portion of a reference genome. Using LOESS to construct a smooth curve through a set of data points is often referred to as a LOESS curve, especially when each smoothed value is provided by a weighted quadratic least squares regression over a range of values of the y-axis scatterplot reference variable. do. For each point in the data set, the LOESS method fits a low-degree polynomial to a subset of the data with the value of the explanatory variable near the point at which the response is evaluated. The polynomial is fitted by using a weighted least squares which gives more weight to points near the point on which the response is being evaluated and less weight to points further away. Then, the value of the regression function for a point is obtained by evaluating the local polynomial using the explanatory variable value for that data point. The LOESS fit is often considered complete after the regression function values have been calculated for each data point. Most of the details of this method, such as the order and weight of the polynomial model, are variable.

(d) calculating a median of the number of reads for each segment for each chromosome;

In the step (d) of the present invention, the number of mapped reads for each partition, preferably the median value of the number of standardized reads calculated in step (c), is calculated for each chromosome.

In this extinction, the median is according to the conventional definition.

(e) calculating the first stage Z-score of each chromosome according to Equation 1 below;

[Equation 1]

(f) calculating a two-step Z-score of each chromosome according to Equation 2 below; And

[Equation 2]

(In the above formula, the representative chromosome group is two or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16.)

The present invention may be characterized in that the number of reads calculated for each chromosome in steps (e) and (f) is processed over two steps to calculate the first step Z-score and the second step Z-score.

In the present invention, chromosome i means a chromosome for which chromosome abnormality is to be detected, and chromosomes 1 to 22 mean human autosomal.

In the step (f) of the present invention, the representative chromosome group used to calculate the step 2 Z-score refers to a group of two or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16, preferably Specifically, it may mean a group of 5 or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16, more preferably 9 or more selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16. It may mean a chromosome group, and most preferably, it may be a group consisting of chromosomes 1 to 12 and chromosomes 14 to 16.

As described above, in the second Z-score calculation step, chromosomes 13, 18 and 21, which may affect the result due to the risk of trisomy, are excluded, and the GC content is particularly high or low 19 and 20. And because chromosome 22 is excluded from data processing, a bias that may occur due to them is reduced, so that more accurate results can be obtained.

According to an embodiment of the present invention, it was confirmed that the trisomy risk of chromosome 21, which was not possible to be discriminated through the first step Z-score, can be discriminated through the second step Z-score, and this result is soon This means that the second step Z-score can significantly reduce false negative results in the fetal prenatal test method and increase the accuracy of the test.

The method of the present invention may further include the step of determining the risk of a chromosomal abnormality of the fetus using a preset cut-off value for each chromosome after step (g). That is, as a result of analysis according to the method of the present invention, a cell-free DNA obtained from a specific mother, when the second stage Z-score value of a specific chromosome is greater than or less than the cut-off, the mother's fetus indicates an abnormality of the chromosome. It can be determined that the risk is high.

In particular, the cut-off value of the specific chromosome is greater than the maximum value of the second stage Z-score indicated in the mother group with the normal fetus, and the second stage indicated in the mother group with the fetus showing abnormalities in the chromosome. It may be less than the minimum value of the Z-score, and preferably the cut-off value of the specific chromosome is the maximum value of the second-stage Z-score expressed in the mother group with a normal fetus, and the abnormality in the chromosome. It can be set as the average of the minimum value of the two-stage Z-score represented by the maternal group with visible fetuses, but it is obvious that such a cut-off value can be easily set for each chromosome within a given experimental environment. Can be understood.

Meanwhile, an abnormality of a chromosome that can be determined through the method of the present invention may be an aneuploidy of a fetal chromosome.

In the present invention, the term "aneuploidy" does not refer to a specific number of chromosomes, but refers to a situation in which a given cell of an organism or chromosomal content in cells is abnormal. In some embodiments, the term “aneuploidy” herein refers to an imbalance in genetic material caused by the loss or gain of an entire chromosome or a portion of a chromosome. “Aneuploidy” may refer to the deletion and/or insertion of one or more segments of a chromosome. In some embodiments, the term "euploid" refers to the normal complement of a chromosome.

As an example of the aneuploidy in the present invention, "monochromosomal" refers to the lack of one chromosome of the normal complement. Partial monosomy may arise from an imbalanced translocation or deletion, with only a segment of the chromosome present as a single copy.

As another example of the aneuploidy in the present invention, "trisomy" refers to the presence of 3 copies instead of 2 copies of a specific chromosome. The presence of an additional chromosome 21 found in human Down syndrome is a prime example. Trisomy 18 and trisomy 13 are two other human autosomal trisomy.

The chromosomal abnormality can be caused by a variety of mechanisms. Mechanisms are (i) non-separation resulting from weakened mitotic checkpoints, (ii) inactive mitotic checkpoints resulting in non-segregation in multiple chromosomes, and (iii) one centroid is bilateral mitotic spindle. Merotelic attachment that occurs when attached to, (iv) a multipole spindle formed when more than two spindle poles are formed, and (v) a single pole spindle formed when only one spindle pole is formed. , And (vi) tetraploid intermediates arising as the end result of a monopole spindle mechanism.

As another example of chromosomal aneuploidy, “partial monosomy” and “partial trisomy” refer to an imbalance of genetic material caused by the loss or acquisition of a portion of a chromosome. Partial monosomy or partial trisomy can arise from an imbalanced translocation, wherein the individual has a derivative chromosome formed from the cleavage and fusion of two different chromosomes. In this situation, an individual will have 3 copies of a portion of one chromosome (2 normal copies, and a segment present on the derivative chromosome), and only 1 copy of a portion of the other chromosome included in the derivative chromosome.

The type of chromosomal aneuploidy that can be detected by the method of the present invention is not particularly limited, and non-limiting examples and related diseases thereof are shown below.

Most preferably, in the present invention, the aneuploidy of the chromosome may be selected from the group consisting of a trisomy of chromosome 13, a trisomy of chromosome 18, a trisomy of chromosome 21, and combinations thereof.

The present invention also includes (a) a sequencing unit for extracting cell-free DNA (cfDNA) from the mother's blood and performing Next Generataion Sequencing;

(b) a mapping unit for mapping the sequenced read sequences to a reference genome sequence;

(c) a read number calculation unit that divides each chromosome of the reference genome into preset bins and calculates the number of mapped reads for each partition based on the mapped reads;

(d) a median value calculator for calculating a median of the number of reads for each chromosome;

(e) a first-stage Z-score calculator for calculating a first-stage Z-score of each chromosome according to Equation 1 below;

[Equation 1]

(f) a two-step Z-score calculating unit for calculating a two-step Z-score of each chromosome according to Equation 2 below; And

[Equation 2]

(In the above formula, the representative chromosome group is two or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16.)

(g) It provides a non-invasive prenatal test apparatus including a determination unit that determines whether or not a chromosome is abnormal by checking the Z-score value in the second step calculated for each chromosome.

Accordingly, the present invention reduces the possibility of false-positive and false-negative determination of chromosome abnormalities by separating the number of reads of a specific chromosome from the number of reads of the normal sample group as much as possible by using a two-stage Z-score amplified by data processing in two stages It can be used to increase the accuracy of non-invasive prenatal testing.

FIG. 1 shows the results of using HMMcopy, a tool that divides all mapped reads into 50 kb bins and standardizes them according to GC content.
2 is a graph showing the result of comparing the number of reads before standardization of data (Readcount) and the number of reads standardized according to the GC content (upper drawing: before standardization, lower drawing: after standardization)
3 shows the median of each chromosome used to calculate the first stage Z-score.
4 shows values in the process of calculating the second step Z-socre after calculating the first step Z-score from the median of each chromosome. In the case of trisomy 21, it can be seen that the value of chromosome 21 increases gradually as data processing.
FIG. 5 shows values in the process of calculating the first step Z-score from the median of each chromosome and then calculating the second step Z-socre as in FIG. 4. In the case of a normal sample, it can be seen that the difference between the first step Z-score and the second step Z-score is not large in the data processing process.
6 shows the results of detecting chromosomal aneuploidy in both the first stage Z-score and the second stage Z-score in a sample with Down syndrome disease (chromosome 21 trisomy). It can be seen that the Z-score value of the second step is significantly increased compared to the Z-score of the first step.
7 shows the result of detecting a chromosomal abnormality in the first stage Z-score in the sample with Down syndrome disease (chromosome 21 trisomy), but the second stage Z-score value. It can be seen that the possibility of false negative is reduced by using the second Z-score value as a sample with the possibility of false negative that can be missed when using only the first Z-score.
8 shows the result of detecting a chromosomal abnormality in the Z-score of the first stage in the sample with Down syndrome disease (chromosome trisomy 21), but the chromosomal abnormality was detected in the Z-score of the second stage.
9 is a result of confirming the difference in detection of chromosome abnormality according to selection of a representative chromosome group in Equation 2 for calculating the second step Z-score in the present invention (a: representative chromosome groups 1 to 12 and 14 Select chromosome ~16, b: Select chromosome 1~22 as the representative chromosome group).

Hereinafter, a preferred embodiment is presented to aid the understanding of the present invention. However, the following examples are provided for easier understanding of the present invention, and the content of the present invention is not limited thereby.

<Example 1> Step of preprocessing next-generation sequencing (NGS) data

Blood was collected from the mother, plasma was separated using a centrifuge, and then 30 ng or more of cfDNA (cell-free DNA) was extracted from the separated plasma to prepare a library. After performing bead size selection and pooling using the Illumina Adapter, the nucleotide sequence was decoded using next-generation sequencing (NGS).

Pre-treatment was performed by mapping the sequences (reads) produced using next-generation sequencing (NGS) to the human reference genome sequence and removing PCR duplication.

<Example 2> Step of standardizing according to GC content using pre-processed data

Sequences (reads) subjected to the pretreatment process were split into 50 kb bins and normalized according to the GC content. HMMcopy, a tool that normalizes and corrects existing data according to the GC content bias, was used using the LOESS model. Whole genome samples were divided into 50 kb bins, and the number of reads was normalized according to the GC content bias of each 50 kb bin.

In FIG. 1, when one sample is divided into 50 kb bins, the reads column, which is the number of reads, the gc column, which is the GC content in each bin, and the cor.gc column, which is the standardized reads value according to the GC content bias.

In FIG. 2, before and after standardization according to the GC content bias was compared, the number of preprocessed reads before standardization has a large variation in each bin, and is between log values 1 and 1.5. However, after standardization according to the GC content bias, the number of reads fluctuates less for each section, and the log value is located between 0.7 and 1.3.

<Example 3> Calculation of the median of sequences partitioned with 50 kb

In FIG. 1, the median value (median) for each chromosome was calculated using the number of reads (cor.gc column) standardized according to the GC content in each 50 kb bin.

In FIG. 3, the black dot means the number of reads (cor.gc column) standardized according to the GC content in each 50kb bin, and the median value (median) of the cor.gc column in each chromosome is calculated as a solid yellow line. .

<Example 4> Calculation of the first step Z-score and the second step Z-score

In general, when calculating the Z-score, an average value and a standard deviation value are used.In the present invention, the average value and standard deviation value of the median values of the number of standardized reads of each chromosome are used when calculating the first stage Z-score. Then, it was calculated by substituting it into Equation 1.

[Equation 1]

Thereafter, the average value and the standard deviation value were calculated once again using the first stage Z-score of each chromosome, and finally substituted into Equation 2 to calculate the second stage Z-score.

[Equation 2]

(In the above formula, the representative chromosome group consists of chromosomes 1 to 12 and 14 to 16)

<Example 5> Determination of chromosomal abnormalities through the second step Z-score

Each chromosomal abnormality determination cut-off value was used to determine whether it was a normal group or an abnormal group according to the second step Z-score.

The cut-off value is for each chromosome to separate the two groups by collecting the two-stage Z-score values of the normal group (pregnant women pregnant with a steady-state baby) and abnormal group (pregnant women pregnant with a chromosomal abnormality). A possible value was selected as the cut-off value. The cut-off value was set to be smaller than the minimum value of the second stage Z-score value in the abnormal group and greater than the maximum value of the second stage Z-score value in the normal group.

As shown in FIG. 6, in the sample obtained from the mother, aneuploidy (trisomy) of chromosome 21 was determined not only in the second stage Z-score but also in the first stage Z-score.

However, as shown in FIG. 7, in the sample obtained from another mother, a chromosomal abnormality was determined with the second stage Z-score, but the chromosomal abnormality was not determined with the first stage Z-score. It means that it can prevent false negative determination of chromosomal abnormalities in fetal prenatal examination.

The usefulness of the second-stage Z-score in the present invention can also be confirmed through the results of FIG. 8.

Figure 8a shows the distribution of the normal group and the abnormal group (pregnant women who are pregnant with a trisomy fetus of chromosome 21) according to the first stage Z-score, and the first stage Z-score can accurately distinguish the normal group and the abnormal group. It can be seen that setting of the existing cut-off value is impossible. That is, since it is impossible to distinguish between a pregnant woman having a first-stage Z-score of the maximum value in the normal group and a pregnant woman having a first-stage Z-score of the minimum value in the abnormal group, it may be determined that accurate chromosomal abnormality diagnosis is difficult.

Figure 8b is a distribution of the same Z-score of the normal group and the abnormal group according to the second-stage Z-score in the normal group. Since it is possible to clearly distinguish a pregnant woman representing a score, it can be determined that accurate chromosomal abnormality diagnosis is possible by setting a cut-off value.

<Example 6> Verification of the usefulness of Equation 2 for calculating the second-stage Z-score

In Example 5, the result of the chromosome analysis of a pregnant woman with a trisomy abnormality fetus of chromosome 21 was used in the same manner, but representative chromosome groups 1 to 12 in Equation 2 for calculating the second stage Z-score And, the difference between the calculation by selecting chromosomes 14 to 16 and those calculated by selecting all chromosomes 1 to 22 was compared.

As a result, as shown in FIG. 9, when the representative chromosome group is selected as chromosomes 1 to 12 and 14 to 16 in Equation 2 for calculating the second step Z-score, abnormality of chromosome 21 is clear. Although it was possible to confirm (FIG. 9A), it was found that no abnormality of chromosome 21 was confirmed when all of chromosomes 1 to 22 were selected (FIG. 9B).

The present invention uses a two-stage Z-score amplified by two-stage data processing, thereby dividing the number of reads of a specific chromosome from the number of reads of the normal sample group as much as possible to reduce the possibility of false positives and false negatives of chromosome abnormality determination It can be used to increase the accuracy of prenatal testing, so it has excellent industrial applicability.

Claims (11)

(a) extracting cell-free DNA (cfDNA) from the mother's blood and performing Next Generataion Sequencing;
(b) mapping the sequenced read sequences to a reference genome sequence;
(c) dividing each chromosome of the reference genome into preset bins and calculating the number of mapped reads for each partition based on the mapped reads;
(d) calculating a median of the number of reads for each segment for each chromosome;
(e) calculating the first stage Z-score of each chromosome according to Equation 1 below;
[Equation 1]

(f) calculating a two-step Z-score of each chromosome according to Equation 2 below; And
[Equation 2]

(In the above formula, the representative chromosome group is two or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16.)
(g) a non-invasive prenatal test method comprising the step of determining whether or not a chromosome is abnormal by checking the Z-score value calculated for each chromosome.
The method of claim 1, wherein the bin in step (c) is 10 to 500 kb.
The method of claim 1, wherein the number of reads calculated in step (c) is a standardized number of reads.
4. The method of claim 3, wherein the number of normalized reads has a reduced guanine-cytosine (GC) bias compared to the number of raw reads.
The method of claim 3, wherein the standardization is bin-wise normalization, standardization by GC content, linear least squares regression, nonlinear least squares regression, LOESS, GC LOESS, LOWESS, PERUN, repeat masking ; RM), GC-normalizatin and repeat masking (GCRM), conditional quantile normalization (cQn), and a combination thereof, characterized in that it is performed by a method selected from the group consisting of Way.
The method of claim 1, wherein the representative chromosome group of Equation 2 is composed of chromosomes 1 to 12 and 14 to 16.
The method of claim 1, further comprising the step of determining a risk of a chromosomal abnormality of the fetus using a preset cut-off value for each chromosome after the step (g).
The method of claim 7, wherein the cut-off value of a specific chromosome is greater than the maximum value of the second-stage Z-score represented in a mother group with a normal fetus, and in a mother group with a fetus exhibiting abnormalities in the chromosome. A method, characterized in that less than the minimum value of the indicated two-stage Z-score.
9. The method of any one of claims 1 to 8, wherein the chromosomal abnormality is an aneuploidy of fetal chromosomes.
The method according to claim 9, wherein the aneuploidy of the chromosome is selected from the group consisting of a trisomy of chromosome 13, a trisomy of chromosome 18, a trisomy of chromosome 21, and combinations thereof.
(a) a sequencing unit that extracts cell-free DNA (cfDNA) from the mother's blood and performs Next Generataion Sequencing;
(b) a mapping unit for mapping the sequenced read sequences to a reference genome sequence;
(c) a read number calculation unit that divides each chromosome of the reference genome into preset bins and calculates the number of mapped reads for each partition based on the mapped reads;
(d) a median value calculator for calculating a median of the number of reads for each chromosome;
(e) a first-stage Z-score calculator for calculating a first-stage Z-score of each chromosome according to Equation 1 below;
[Equation 1]

(f) a two-step Z-score calculating unit for calculating a two-step Z-score of each chromosome according to Equation 2 below; And
[Equation 2]

(In the above formula, the representative chromosome group is two or more chromosomes selected from the group consisting of chromosomes 1 to 12 and chromosomes 14 to 16.)
(g) Non-invasive prenatal testing apparatus comprising a determination unit for determining whether or not a chromosome abnormality is determined by checking the two-step Z-score value calculated for each chromosome.

Images