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    NON-INVASIVE DETECTION OF FETAL GENETIC ABNORMALITY The present invention is directed to methods for non-invasive detection of fetal genetic abnormalities through large-scale sequencing of nucleotides from the maternal biological sample. Methods are also provided to remove the GC trend from the sequencing results due to the difference in the GC content of a chromosome. The present invention not only performs detection with more precision, but also represents a method for detecting fetal aneuplodia including disorders of sex chromosomes, such as X0, XXX, XXY and XYY, etc.
    公开号:BR112012033760B1
    申请号:R112012033760-2
    申请日:2011-06-29
    公开日:2020-11-17
    发明作者:Fuman Jiang；Huifei Chen；Xianghua Chai；Yunying Yuan；Xiuqing Zhang；Fang Chen
    申请人:Bgi Health Service Co., Ltd；
    IPC主号:
    专利说明:

    Technical field
    The invention relates to non-invasive methods for detecting fetal genetic abnormalities through the DNA sequence of samples from pregnant women. More particularly, this invention is related to the analysis of data to obtain DNA samples. This invention is also related to statistical analysis to detect fetal genetic abnormalities, such as chromosomal abnormalities, including aneuploidy. Background of the invention
    Conventional prenatal diagnostic methods with invasive procedures, such as chorionic villus sampling and amniocentesis, offer potential risks for both the mother and the fetus. Noninvasive studies of fetal aneuploidy, using maternal serum and ultrasound markers, are available, but have limited sensitivity and specificity (Kagan, et al., Human Reproduction (2008) 23: 1968-1975; Malone, et al., N Engl J Med (2005) 353: 2001-2011).
    Recent studies have shown that non-invasive detection of fetal aneuploidy through parallel sequencing of DNA molecules in the plasma of pregnant women is feasible. Fetal DNA was detected and quantified in plasma and maternal serum. (Lo, et al., Lancet (1997) 350: 485 487; Lo, et al., AM. J. hum. Genet. (1998) 62: 768-775). Multiple fetal cell types occur in the maternal circulation, including fetal granulocytes, lymphocytes, nucleated red blood cells and trophoblast cells (Pertl and Bianchi, Obstetrics and Gynecology (2001) 98: 483-490). Fetal DNA can be detected in the serum at the 17th week of pregnancy and increases with the termination of pregnancy. The fetal DNA present in maternal serum and plasma is comparable to the concentration of DNA obtained from fetal cell isolation protocols.
    Circulating fetal DNA was used to determine the sex of the fetus (Lo, et al., Am. J. hum. Genet. (1998) 62: 768-775). The fetal Rh D genotype was detected using fetal DNA. However, the diagnosis and clinical applications of circulating fetal DNA are limited to genes present in the fetus and not in the mother (Pertl and Bianchi, Obstetrics and Gynecology (2001) 98: 483-490). Therefore, there is a need for a non-invasive method that can determine the fetal DNA sequence, and offer a definitive diagnosis of chromosomal abnormalities in a fetus.
    The discovery of fetal cells and nucleic acids in maternal blood in recent decades and the application of high-performance random sequencing of DNA free from maternal plasma cells has made it possible to detect small changes in the representation of chromosomes produced by an aneuploid fetus in a plasma sample maternal. Noninvasive detection of trisomy of chromosome 13, 18 and 21 can be achieved.
    However, as some studies demonstrate, the GC trend introduced by amplification and sequencing has established a practical limit on the sensitivity of aneuploidy detection. The GC trend can be introduced during sample preparation and the sequencing process, under different conditions, such as reagent composition, cluster density and temperature, producing a differential sampling of DNA molecules with different GC composition and significant variation in sequencing data, which can be rich or poor in GC.
    To improve sensitivity, protocols were developed to remove the effect of the GC trend. Fan and Quake developed a method to isolate the GC trend statistically using weight for each GC density, based on the genomic content of the local GC, to improve the number of readings mapped in each bin, multiplying the corresponding weight ((Fan and Quake PLoS ONE ( 2010) 5: e10439) .However, the method presented difficulties regarding sex chromosome disorders, especially those relevant to the Y chromosome, due to the possibility of the process causing a slight distortion of the data, which may interfere with the accuracy of detection.
    In the present invention, a method is described to statistically isolate the GC tendency to obtain greater sensitivity in detecting fetal genetic abnormality, as well as avoiding data distortion. This method defines parameters used for statistical tests according to the GC content. In addition, the fetal fraction estimated in the diagnosis by binary hypothesis was introduced, presenting greater sensitivity and specificity. The method of the present invention also demonstrated that it is possible to increase the sensitivity of non-invasive detection of fetal genetic abnormality by precisely defining the maternal sample containing a small fraction of fetal DNA, by sequencing more polynucleotide fragments. A new sample of maternal plasma in the last weeks of pregnancy may increase the sensitivity of the diagnosis. Summary of the invention
    The present invention is directed to methods of non-invasive detection of fetal genetic abnormalities through large-scale sequencing of the nucleotides of the maternal biological sample. Methods are also provided to isolate the GC trend from sequencing results due to different GC contents of a chromosome.
    Therefore, in one aspect, a method is provided to establish a relationship between the depth of coverage and the content of a chromosome, whose method comprises: obtaining information on the sequence of multiple polynucleotide fragments that make up the aforementioned chromosome. more than one sample; assignment of said fragments to chromosomes, based on information from said sequence; calculation of the depth of the cover and the GC content of the mentioned chromosome, according to the information of the mentioned sequence for each sample; and determining the relationship between the depth of coverage and the GC content of the said chromosome.
    In one embodiment, polynucleotide fragments range from about 10 to about 100 bp in length. In another embodiment, polynucleotide fragments range from about 15 to about 500 bp in length. In yet another embodiment, polynucleotide fragments vary from about 20 to about 200 bp in length. In yet another embodiment, polynucleotide fragments vary from about 25 to about 100 bp in length. In another embodiment, the polynucleotide fragments are about 35 bp in length.
    In one embodiment, the sequence information is obtained by parallel geometric sequencing. In another modality, the determination of the fragment in relation to the chromosomes is made by comparing the sequence of the fragments with a human reference sequence. The reference human genomic sequence can be any published and / or suitable human genome configuration, such as hg18 or hg19. Fragments that correspond to more than one chromosome, or do not correspond to any chromosome can be disregarded.
    In one embodiment, the depth of coverage of a chromosome is the ratio between the number of fragments that correspond to the chromosome, and its number of reference readings. In another modality, the coverage depth is normalized. In yet another modality, normalization is calculated in relation to the coverage of all other autosomes. In another mode, normalization is calculated in relation to the coverage of all other chromosomes.
    In one mode, the relationship is in the formula:
    where f (GCij) represents the function of the relationship between the depth of the standardized cover and the corresponding GC content of sample i, chromosome j, Si, j represents the residue of sample i, chromosome j. In some modalities, the relationship between depth of coverage and GC content is calculated by local weighted polynomial regression. In some embodiments, the relationship may be a non-resistant linear relationship. In some modalities, the relationship is determined by the Loess algorithm.
    In some modalities, the method also comprises the adjusted calculation of the coverage depth, according to the formula: crij = f (.GCiJ), j = 1,2, -, 22, X, Y
    In some modalities, the method also comprises the calculation of the standard variation according to the formula:
    where ns represents the number of reference samples.
    In some modalities, the method also comprises the calculation of the student's t-statistical test, according to the formula:
    In one embodiment, the GC content of a chromosome is the average GC content of all fragments that belong to the chromosome. The GC content of a fragment can be calculated by dividing the number of G / C nucleotides in the fragment by the total number of nucleotides in the fragment. In another embodiment, the GC content of a chromosome is the aggregated GC content of the reference readings on the chromosome.
    In some modalities, at least 2, 5, 10, 20, 50, 100, 200, 500 or 1000 samples are used. In some modalities, the chromosome is 1,2, ..., 22, X or Y.
    In one embodiment, the samples are from pregnant women. In other modalities, the samples are from men. In yet another modality, the samples are from both women and men.
    In some embodiments, the samples are biological samples. In some modalities, they come from peripheral blood.
    A method is also provided to determine a fetal genetic abnormality, the method of which comprises: a) obtaining information on the sequence of multiple polynucleotide fragments from a sample; b) correlation of the said fragments to the chromes-we are based on the mentioned sequence information; c) calculation of the coverage depth and GC content of a chromosome based on the mentioned sequence information; d) calculation of the depth of the adjusted coverage of said chromosome, using the mentioned GC content of said chromosome, and a relationship established between the depth of coverage and the GC content of said chromosome; and e) comparison of the mentioned depth of coverage adjusted with the depth of coverage of the mentioned chromosome, where a difference between them indicates a fetal genetic abnormality.
    In some embodiments, the method also comprises step f) determining the fetal sex. Fetal sex can be determined according to the formula:
    where cr.aj'X and cr.ai, y are normalized relative coverage of X and Y chromosomes, respectively.
    In some modalities, the method also comprises step g) estimation of the fetal fraction. The fetal fraction can be calculated according to the formula:
    where cη. v = f (GCi Kf) is the adjusted coverage depth calculated from the Y chromosome depth ratio and corresponding GC content of samples from pregnant women with a female fetus, crixf = f (GCi Xf) refers to the depth coverage calculated from the relationship of the depth of the Y chromosome and the corresponding GC content of men. Alternatively, the fetal fraction can be calculated according to the formula: where criXf = f (GCiXf) is the adjusted depth of coverage calculated from the relationship between the depth of the X chromosome and the corresponding GC content of samples from pregnant women with a female fetus, criXf = f (GCixf) refers to the depth of coverage calculated from the relationship between the depth of the X chromosome and the corresponding GC content of men. Therefore, the fetal fraction can be calculated according to the formula:
    where cη v = f (GCiXf) is the adjusted depth of coverage calculated from the relationship between the depth of the X chromosome and the corresponding GC content of samples from pregnant women to a female fetus, criXf = f (GCiXf) refers to coverage depth calculated from the Y chromosome depth ratio and the corresponding GC content of samples from pregnant women with a female fetus, criXm = f (GCi, xm) refers to the adjusted coverage depth calculated from the ratio tion of the X chromosome depth and corresponding GC content of male samples, crj Ym = f (GCiYm) refers to the depth of coverage calculated from the relationship of the depth of the Y chromosome and corresponding GC content of men.
    In one embodiment, the genetic abnormality is a chromosomal abnormality. In another modality, the genetic abnormality is aneuploidy. In yet another modality, fetal aneuploidy is a disorder of an autosome selected from the group consisting of trisomy of chromosome 13, 18 and 21. In yet another modality, fetal aneuploidy is a disorder for a sex chromosome selected from the group consisting of XO, XXX, XXY and XYY.
    In some modalities, the comparison of the mentioned depth of coverage adjusted with the mentioned depth of the chromosome is conducted by a statistical test of hypotheses, where one hypothesis is that the fetus is euploid (HO) and the other hypothesis is that the fetus is aneuploid (H1). A statistic can be calculated for both hypotheses. In some modalities, the student's t-statistic is calculated for HO and H1 according to the formula:
    , respectively, where ^ is the fetal fraction. In some modalities the proportion of the logarithmic probability of t1 and t2 is calculated according to the formula: Ltj = log (p (tl ;. p degree ID)) / log (/ (72 (J, degree IT)), where level (degree) refers to the t distribution, D refers to diploidy, T refers to trisomy, ep (tl, rdegree l *), * = D, T represents conditional probability density provided at a t distribution level.
    In one embodiment, the gender of the fetus is female, and the student's t-test is calculated according to the formula: tlÍX = {criX -criXf) l stdXf, where criYf = f (GCiYf) is the calculated adjusted coverage depth from the relationship between the depth of coverage of the X chromosome and the corresponding GC content of samples from pregnant women with a female fetus. In some modalities, It11> 3.13 indicates that the fetus may be XXX or XO. In some modalities, It11> 5 indicates that the fetus is XXX or XO.
    In another modality, the sex of the fetus is male, and the student's t-test is calculated according to the formula: t2. = (Cr / JC- (l-fyil2) -criXf) / stdxfQv úe cηYf = f ( GCiYf) is the adjusted coverage depth calculated from the ratio of the X chromosome coverage depth and the corresponding GC content of samples from pregnant women to a female fetus. In some modalities, It2l> 3.13 indicates that the fetus may be XXY or XYY. In some modalities, It2l> 5 indicates that the fetus is XXY or XYY.
    A method is also provided to determine a fetal genetic abnormality, the method of which comprises: a) obtaining information on the sequence of multiple polynucleotide fragments from a chromosome from more than one normal sample; b) correlation of said fragments to chromosomes based on said sequence information; c) calculation of the coverage depth and GC content of the mentioned chromosome based on the mentioned sequence information of said normal samples; d) determination of the relationship between the depth of coverage and the GC content of the mentioned chromosome; e) obtaining information on the sequence of multiple polynucleotide fragments from a biological sample; f) correlation of said fragments to chromosomes, based on the aforementioned information on the sequence of the said biological sample; g) calculation of the coverage depth and the GC content of the mentioned chromosome, based on the information of the mentioned sequence of the mentioned biological samples; h) calculation of the adjusted coverage depth of said chromosome using the said GC content of said chromosome and the relationship between the depth of coverage and the GC content of said chromosome; and i) comparison of each adjusted depth of coverage with the depth of coverage of the aforementioned chromosome, where a difference between them indicates a fetal genetic abnormality.
    In another aspect, a computer-readable medium is provided in the present invention comprising a plurality of instructions for performing a prenatal diagnosis of a fetal genetic abnormality, which comprises the steps of: a) receiving the sequence information of multiple polynucleotide fragments of a sample; b) correlation of said polynucleotide fragments to chromosomes based on the mentioned sequence information; c) calculation of the coverage depth and GC content of a chromosome based on the aforementioned sequence information; d) calculation of the adjusted coverage depth of said chromosome using said GC content of said chromosome and a stabilized relationship between the depth of coverage and GC content of said chromosome; and e) comparison of the cited depth of coverage adjusted in relation to the cited depth of coverage of said chromosome, where a difference between them indicates a genetic abnormality.
    In yet another aspect, a system for determining fetal genetic abnormality is provided in the present invention, the method of which comprises: a) means for obtaining the information on the sequence of multiple polynucleotide fragments of a sample; and b) computer-readable medium comprising a plurality of instructions for making a prenatal diagnosis of a fetal genetic abnormality. In some embodiments, the system also comprises a biological sample obtained from a pregnant woman, where the biological sample includes fragments of multiple polynucleotides. Brief description of the figures
    Figure 1 shows a scheme for calculating the depth and GC content using the sequential information of polynucleotide fragments.
    Figure 2 illustrates the correlation established between standardized depth of coverage and GC content using data from 300 reference cases. The standardized depth of coverage for each case is plotted against the corresponding sequenced GC content. Crosses signify cases with euploid female fetuses, squares signify cases with euploid male fetuses. The long line is the line of adjustment between the depth of coverage and the GC content.
    Figure 3 illustrates the trend between the normalized depth of coverage and the corresponding GC content by the chromosome arrangement with its inherent ascending GC content. The inherent ascending GC content of each chromosome refers to the average GC content of chromosome sequence tags from 300 reference cases.
    Figure 4 shows different compositions of the GC class for each chromosome. The GC content of each 35 bp reading of the reference readings was calculated for each chromosome, the GC content was classified into 36 levels, and the percentage of each level was calculated as the GC composition of each chromosome. The chromosomes were mapped with heat, plotted on a graph and grouped hierarchically.
    Figure 5 demonstrates the trend of sequencing by introducing the correlation shown in Figure 2 by manual simulation of the preference process.
    Figure 6 shows the plot of the standard variation in relation to the total number of polynucleotide fragments. In 150 samples, the adjusted standard variance of each chromosome has a linear relationship with the square root of the number of single readings.
    Figure 7 shows the Q-Q plot of residues for each chromosome calculated by formula 3. A linear relationship is demonstrated with a normal distribution.
    Figure 8 shows the histogram of the depth of coverage of the Y chromosome. There are two peaks that indicate that the gender of the cases can be distinguished by the depth of coverage of the Y chromosome. The curve is the distribution of the depth of coverage of the Y chromosome calculated by the density estimate with Gaussian kernel.
    Figure 9 presents a diagram of the process for diagnosing 903 test samples for chromosomal abnormality.
    Figure 10 shows the result of aneuploidy: cases of trisomy 13, 18, 21 and XO, XXY, XYY and cases of normality. Figure 10A shows the coverage depth markings normalized in relation to the GC content of chromosomes 13, 18 and 21. Figure 10B shows the markings of the X and Y chromosomes. The circles represent the coverage depth markings with the content GC of normal female fetuses, and the dotted dots represent normal male fetuses. The continuous line is the line for adjusting the relative coverage and the GC content, the dashed lines have an absolute t value equal to 1, the dotted lines have a t value equal to 2, and the dashed lines with dotted values: absolute value of t equals to 3.
    Figure 11 compares the confidence value of different diagnostic attempts.
    Figure 12 shows the relationship between the fetal DNA fraction and gestational age. The fraction of fetal DNA in maternal plasma is related to gestational age. The fraction of fetal DNA was calculated by X and Y chromosomes. There is a statistically significant correlation between the average fetal DNA fraction and gestational age (P <0.001). Note that the value of R2 that represents the square of the correlation coefficient is small. The minimum fraction is 3.49%.
    Figure 13 shows the relationship between the standard variance and the number of cases required for detection. The standardized variances computed by formula 5 for each chromosome can vary according to different numbers of samples. The standard variance becomes stable when the number of samples is greater than 100.
    Figure 14 shows the estimated number of single readings for detecting fetal aneuploidy in cellless plasma as a function of the fetal DNA fraction. The estimates are based on the confidence level of the t-value greater than 3 for aneuploidy of chromosomes 13, 18, 21 and X, even Y (of the relationship between X and Y), each presenting a different length. As the fraction of fetal DNA decreases, the total number of necessary random sequences increases. With a sequencing performance of 4 million sequence readings per channel in the flow cell, chromosome 21 trisomy can be detected if 3.5% of the cell-free DNA is fetal. X chromosome aneuploidy is not easily detected when the fraction and the number of single readings is small, such as 4% and 5 million readings. Different chromosomes require different levels of the fetal DNA fraction and unique number of readings, which can be caused by the chromosome's GC structure.
    Figure 15 presents a contour plot of sensitivity mapped by volume of data and gestational age (weeks) to detect trisomy of chromosome 13 of female fetuses, for each gestational week and each point of the data volume.
    Figure 16 shows a contour plot of sensitivity mapped by volume of data and gestational age (weeks) for detecting trisomy 18 of female fetuses, for each gestational week and each point of the data volume.
    Figure 17 shows a contour plot of sensitivity mapped by volume of data and gestational age (weeks) for detecting chromosome 21 trisomy of female fetuses, for each gestational week and each point of the data volume.
    Figure 18 presents a contour plot of sensitivity mapped by volume of data and gestational age (weeks) to detect trisomy X chromosome of female fetuses, for each gestational week and each point of the data volume.
    Figure 19 shows a contour plot of sensitivity mapped by volume of data and gestational age (weeks) for detecting chromosome 13 trisomy in male fetuses. For each gestational week and each point of the data volume, its empirical distribution of the fetal DNA fraction and standard variance was computed for the data volume initially provided, and comparing the fraction estimated by XY or Y, the sensitivity of each type of aneuploidy.
    Figure 20 shows a contour plot of sensitivity mapped by volume of data and gestational age (weeks) to detect trisomy 18 of male fetuses.
    Figure 21 shows a contour plot of sensitivity mapped by volume of data and gestational age (weeks) for detecting chromosome 21 trisomy of male fetuses. Detailed description of the invention
    The present invention is directed to methods of non-invasive detection of fetal genetic abnormalities through the large-scale sequencing of poly-nucleotide fragments from maternal biological samples. Methods are also provided to isolate the GC trend from sequencing results due to the difference in the GC content of a chromosome, based on the relationship between the depth of coverage of a chromosome and its GC content. In this way, a method is provided in the present invention to adjust the reference parameters by computer, which are used in calculating the Student's t-test with the GC contents, by local polynomial regression to adjust the depth of coverage of a chromosome of each sample in relation to the GC content of polynucleotide fragments.
    A method is also provided to determine the genetic abnormality of a fact by statistical analysis, using a hypothetical statistical test. In addition, methods are provided to calculate quality control standards (DQC) useful in determining the amount of clinical samples needed for a level of statistical significance. Definitions
    Unless otherwise stated, all technical and scientific terms used in the present invention are understood by those skilled in the art to which that invention belongs. All patents, applications, published applications and other publications mentioned in the present invention are incorporated by reference in their entirety. If a definition described in this section is contrary or otherwise inconsistent with a definition described in patents, orders, published orders and other publications that are incorporated into the present invention as a reference, the definition described in that section will prevail over the others.
    As used in the present invention, the singular forms "one", "one", and "a / o" include the plural forms, unless otherwise indicated. For example, "one" dimer includes one or more dimers.
    The term "chromosomal abnormality" refers to a deviation between the individual's chromosome structure and a normal homologous chromosome. The term “normal” refers to the predominant karyotype or pattern found in healthy individuals of a particular species. A chromosomal abnormality can be numerical or structural, and includes, but is not limited to, aneuploidy, polyploidy, inversion, trisomy, monosomy, duplication, deletion, deletion of part of a chromosome, addition, addition of part of a chromosome, insertion , a fragment of a chromosome, a region of a chromosome, a chromosomal rearrangement, and translocation. A chromosomal abnormality can be correlated with the presence of a pathological condition or with a predisposition to develop a pathological condition. As defined in the present invention, a single nucleotide polymorphism ("SNP") is not a chromosomal abnormality.
    X chromosome monosomy (XO, absence of an X chromosome) is the most common type of Turner syndrome, occurring in 1 in 2500, and 1 in 3000 girls who are born alive (Sybert and McCauley N Engl J Med (2004) 351 : 1227-1238). XXY syndrome is a condition where men have an extra X chromosome, occurring in 1 out of every 1000 men (Bock, Understanding Klinefelter Syndrome: A Guide for XXY Males and their families. NIH Pub. No. 93-3202 (1993)). XYY syndrome is an aneuploidy of the sex chromosome where the man receives an extra Y chromosome, receiving a total of 47 chromosomes in place of the usual 46, affecting 1 in 1000 births, causing male infertility (Aksglaede, etal., J Clin Endocrinol Metab (2008) 93: 169-176).
    Turner's syndrome encompasses several conditions, where monosomy of the X-chromosome (X0, absence of a sex chromosome, Barr's body) is the most common. Normal women have both X chromosomes, but in Turner's syndrome, one of these chromosomes is lost. Occurring in 1 in 2000 to 1 in 5000 women who are typical, the syndrome manifests itself in several ways. Klinefelter syndrome is a condition where men have an extra X chromosome. In humans, Klinefelter's syndrome is the most common chromosomal sexual disorder, and the second most common condition caused by the presence of extra chromosomes. The condition occurs in 1 out of every 1000 men. XYY syndrome is an aneuploidy of sex chromosomes where a man receives an extra Y chromosome, totaling 47 chromosomes instead of 46. This produces a 47, XYY karyotype. This condition is generally asymptomatic and affects 1 in 1000 men and can cause male infertility.
    Trisomy 13 (Patau's syndrome), trisomy 18 (Edward's syndrome) and trisomy 21 (Down's syndrome) are the most clinically important autosomal trisomies, and detecting them is always an important aspect. The detection of the fetal chromosomal aberrations described above is of great importance in prenatal diagnosis (Ostler, Diseases of the eve and skin: a color atlas. Lippincott Williams & Wilkins, pp. 72. ISBN 9780781749992 (2004); Driscoll and Gross N Engl J Med (2009) 360: 2556-2562; Kagan, etal., Human Reproduction (2008) 23: 1968-1975).
    The term "single reference reading" refers to fragments of a chromosome that have a unique sequence. Therefore, such fragments can be attributed to a unique location on the chromosome. Unique reference readings from a chromosome can be constructed based on a published reference genomic sequence, such as hg18 or hg19.
    The terms "polynucleotides", "oligonucleotides", "nucleic acid" and "nucleic acid molecule" are used interchangeably in the present invention to refer to a polymeric form of nucleotides of any length, and may comprise ribonucleotides, deoxyribonucleotides, their analogues , or their mixtures. This term refers only to the primary structure of the molecule. Therefore, the term includes a single, double or triple-stranded deoxyribonucleic acid ("DNA"), as well as a single, double or triple-stranded ribonucleic acid ("RNA"). It can also include those modified, for example, by alkylation, and / or by encapsulation, and unmodified forms of the polynucleotide. More particularly, the terms "polynucleotide", "oligonucleotide", "nucleic acid" and "nucleic acid molecule" include polydeoxyribonucleotides (containing 2-deoxy-D-ribose), polyribonucleotides (containing D-ribose) , including tRNA, rRNA, hRNA and mRNA, with splicing (intron removal) or not, any other type of polynucleotide, which is an N- or C-glycoside of a purine or pyrimidine base, and other polymers containing normal nucleotide bases, for example, polyamide (for example, peptide nucleic acids (“PNAs”)) and polymorphic polymers (commercially available from Anti-Virals, Inc., Corvallis, OR., such as NeuGene®), and other acidic polymers nucleic nuclei with specific synthetic sequence, with polymers presenting nucleobases in a configuration that allows base pairing and stacking, as found in DNA and RNA. Therefore, these terms include, for example, 3 '- deoxy - 2', 5 '- DNA, N3' to P5 'oligodeoxyribonucleotide phosphoramidates, RNA 2' - O - substituted alkyl, hybrid between DNA and RNA or between PNAs and DNA or RNA, and also include types of known modifications, for example, labeling, alkylation, "capsules", replacement of one or more nucleotides with an analog, modifications between nucleotides such as, for example, those with no-charge bonds ( for example, methyl phosphonates, phosphotriesters, phosphoramidates, carbamates, etc.), with negatively charged bonds (e.g., phosphorothioates, phosphorodithioates, etc.) and positively charged bonds (e.g. pendant halves, such as, for example, proteins (including enzymes (eg, nucleases), toxins, antibodies, signal peptides, poly-L-lysine, etc.), those with intercalating agents (eg, acridine, psoralen, etc. .), containing them chelates (for example, metals, radioactive metals, boron, oxidative metals, etc.), those containing alkylating agents, those with modified bonds (for example, alpha anomeric nucleic acids, etc.), as well as the non- modified polynucleotide or oligonucleotide. “Massive parallel sequencing” means techniques for sequencing millions of nucleic acid fragments, for example, using randomly fragmented genomic DNA binding to an optically transparent, planar surface, and solid phase amplification to create a high density cell flow sequencing with millions of groups, each containing ~ 1,000 model copies per cm2. These models are sequenced using four-color DNA synthesis sequencing technology. View products offered by Illumina, Inc., San Diego, California. The sequence used is preferably that performed without a pre-amplification or cloning step, but it can be combined with methods based on amplification in a microfluidic chip, which presents reaction chambers for both PCR and sequencing based on a microscopic model. Only about 30 bp of random sequence information is needed to identify a sequence as belonging to a specific human chromosome. Longer strings can identify more particular targets. In the present invention, a large number of 35 bp readings were obtained. Another description of a massive parallel sequencing method is found in Rogers and Ventner, Nature (2005) 437: 326-327.
    As used in the present invention, "biological sample" refers to any sample obtained from a viral or living source, or from another source of macromolecules and biomolecules, and includes any cell type or tissue of an individual in which the nucleic acid, protein or another macromolecule can be obtained. The biological sample can be a sample taken directly from a biological source or a sample that is processed. For example, isolated nucleic acids that are amplified constitute a biological sample. Biological samples include, but are not limited to, body fluids, such as blood, plasma, serum, cerebrospinal fluid, synovial fluid, urine and sweat, tissue and organ samples from animals and plants, and derived processed samples.
    It is understood that aspects and modalities of the invention described include "consisting" and / or "consisting essentially of" aspects and modalities.
    Other objects, advantages and aspects of the present invention will become clear from the specifications described below together with the attached figures. II. Establishing a relationship between depth of coverage and GC content
    A method is provided to establish a relationship between the depth of co-coverage and the GC content of a chromosome, comprising: obtaining information on the sequence of multiple polynucleotide fragments of said chromosome from more than one sample; correlation of said fragments to chromosomes based on said sequence information; calculation of the coverage depth and GC content of the mentioned chromosome based on the mentioned sequence information for each sample; and determining the relationship between the depth of coverage and the GC content of the said chromosome. The operation steps can be performed in any order. In some embodiments, the method can be carried out in the following order: a) obtaining information on the sequence of multiple polynucleotide fragments that comprise said chromosome based on said sequence information; b) correlation of said fragments to chromosomes based on said sequence information; c) calculation of the coverage depth and GC content of a chromosome based on the aforementioned sequence information for each sample; and d) determination of the relationship between the depth of coverage and the GC content of the mentioned chromosome.
    To calculate the depth of coverage and the GC content of a chromosome location, information on the sequence of polynucleotide fragments is obtained by sequencing the DNA model obtained from a sample. In one embodiment, the DNA model contains both maternal and fetal DNA. In another modality, the DNA model is
    obtained from the blood of a pregnant woman. Blood can be collected using any standard technique for blood collection including, but not limited to, venipuncture. For example, blood can be drawn from a vein on the inside of the elbow or the dorsal part of the hand. Samples can be collected from a pregnant woman at any time during pregnancy. For example, blood samples can be collected from women 1-4, 4-8, 8-12, 12-16, 16-20, 20-24, 24-28, 28-32, 32-36, 36- 40 or 40-44 weeks of gestation, preferably between 8-28 weeks of gestation.
    Polynucleotide fragments are related to a chromosome location based on sequence information. A reference genomic sequence is used to obtain the unique reference readings. As used in the present invention, the term "unique reference readings" refers to all polynucleotide fragments related to a specific genomic site, based on a reference genomic sequence. In some embodiments, single reference readings are the same length as, for example, about 10, 12, 15, 20, 25, 30, 35, 40, 50, 100, 200, 300, 500 or 1000 bp. In other embodiments, the human genome hg18 or hg 19 constructs can be used as the reference genomic sequence. A chromosome location can be a continuous window on a chromosome that is about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600 in length , 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000 or more Kb. A chromosome location can be a single chromosome.
    As used in the present invention, the term "depth of coverage" refers to the ratio between the number of fragments that is related to the location of the chromosome, and the number of unique reference readings of the location of the chromosome using the following formula:
    where rijj is the number of single sequence readings mapped to chromosome j in sample i; Cjj is the depth of coverage on chromosome j in sample i; Nj is the number of unique reference readings on chromosome j.
    In some embodiments, fragments of polynucleotides that are not correlated to a single chromosome location or to multiple locations are discarded. In some embodiments, the depth of coverage is normalized, based on the depth of coverage of another chromosome location, another chromosome, the average of all other autosomes, the average of all other chromosomes, or the average of all chromosomes. In some modalities, the average coverage depth of 22 autosomes is used as a normalization constant to relate differences in the total number of sequence readings obtained for different samples:
    where cr, j represents the relative coverage depth of chromosome j in sample i. From this moment on, the term “relative coverage depth” for each chromosome, refers to the normalized value, and is used to compare different samples and subsequent analysis.
    The GC content of a chromosome location can be calculated by the average GC percentage of a chromosome location, based on the unique reference readings at the chromosome location, or the sequenced polynucleotide fragments that correspond to the location of the chromosome. chromosome. The GC content of a chromosome can be calculated using the following formula:
    nç.BASEi where i represents sample i, j represents chromosome j, NGCijj represents the number of bases G and C of DNA and BASE jj represents the number of DNA bases on chromosome j in sample i.
    The depth of coverage and the GC content can be based on the sequence information of polynucleotide fragments obtained from a single sample or from multiple samples. To establish the relationship between the depth of coverage and the GC content of the location of a chromosome, the calculation can be based on information on the sequence of polynucleotide fragments obtained from at least 1, 2, 5, 10, 20, 50, 100, 200, 500 or 1000 samples.
    In some modalities, the relationship between the depth of coverage and the GC content is an unstable linear relationship. The Loess algorithm, or local weighted polynomial regression, can be used to assess nonlinear relationships (correlations) between pairs of values, such as depth of coverage and GC content. III. Determining a fetal genetic abnormality
    A method is provided to determine fetal genetic abnormality, the method of which comprises: a) obtaining information on the sequence of multiple polynucleotide fragments from a sample; b) correlation of said fragments to chromosomes based on said sequence information; c) calculation of the coverage depth and GC content of a chromosome based on the aforementioned sequence information for each sample; d) calculation of the adjusted coverage depth of said chromosome using said GC content of said chromosome and a relationship established between the depth of coverage and the GC content of said chromosome; and e) comparison of said depth of coverage adjusted to said depth of coverage of said chromosome, where a difference between them indicates a fetal genetic abnormality.
    The methods can be used to detect femal chromosomal abnormalities, and are especially useful in detecting aneuploidy, polyploidy, monosomy, trisomy, trisomy 21, trisomy 13, trisomy 14, trisomy 15, trisomy 16, trisomy 18, trisomy 22, triploidy, tetraploidy, and sex chromosome abnormalities including XO, XXY, XYY and XXX. They can also concentrate on certain regions within the human genome according to the methods of the present invention to identify partial monosomies and trisomies. For example, the methods may involve analyzing sequential data in a “window” of a defined chromosome portion, such as continuous, overlapping 50 kb regions spread across the chromosome. Partial trisomies of 13q, 8p (8p23.1), 7q, 6p distal, 5p, 3q (3q 25.1), 2q, 1q (1q42.1 and 1q21-qter), partial monosomy Xpand 4q 35.1 have been reported, among others. For example, partial duplications of the long arm of chromosome 18 can cause Edwards syndrome, in the case of duplication of 18q21.1-qter (Mewar, etal., Am J Hum Genet. (1993) 53: 1269-78) .
    In some embodiments, the fetal fraction is estimated based on information obtained from the sequence of the polynucleotide fragments in a sample. The depth of co-coverage and the GC content of the X and Y chromosome can be used to calculate the fetal fraction. In some embodiments, the sex of the fetus is determined based on the sequence information obtained for the polynucleotide fragments in a sample. The depth of coverage and the GC content of the X and Y chromosome can be used to determine the sex of the fetus.
    In some modalities, the comparison of the mentioned depth of coverage adjusted in relation to the mentioned depth of coverage of the chromosome is carried out through a statistical hypothesis test, where one hypothesis is that the fetus is euploid (HO) and the other hypothesis is that the fetus is aneuploid (H1). In some modalities, the student's statistical test is calculated for both hypotheses such as t1 and t2, respectively. In some modalities, the proportion of the logarithmic probability of t1 and t2 is calculated. In some modalities, a proportion of the logarithmic probability> 1 indicates fetal trisomy. IV. Computer-readable reading medium and system for diagnosing a fetal genetic abnormality
    In another aspect, a computer-readable means of reading comprising a plurality of instructions for performing prenatal diagnosis of a fetal genetic abnormality is provided in the present invention, comprising the steps of: a) receiving said sequence information; b) correlation of said fragments to chromosomes based on said sequence information; c) calculation of the coverage depth and GC content of a chromosome based on the aforementioned sequence information for each sample; d) calculation of the adjusted coverage depth of said chromosome using said GC content of said chromosome and a relationship established between the depth of coverage and the GC content of said chromosome; and e) comparison of the mentioned depth of coverage adjusted to the mentioned depth of coverage of the mentioned chromosome, where a difference between them indicates a fetal genetic abnormality.
    In yet another aspect, a system is provided in the present invention to determine fetal aneuploidy, the method of which comprises: a) means for obtaining information on the sequence of said polynucleotide fragments; and b) computer-readable reading medium comprising a plurality of instructions for making a prenatal diagnosis of a fetal genetic abnormality. In some embodiments, the system also comprises a biological sample obtained from a pregnant woman, with fragments of poly-nucleotides.
    It will be understood by those skilled in the art that a number of different sequencing methods can be used. In one embodiment, sequencing is performed using massive parallel. Massive parallel sequencing, such as that obtained on platform 454 (Roche) (Margulies, et al., Nature (2005) 437: 376-380), on the Genome Illumina Analyzer (or Solexa ™ platform) or SOLiD System (Applied Biosystems) or Helicos True DNA molecule sequencing technology (Harris, etal., Science (2008) 320: 106-109), Pacific Bioscience single molecule real-time technology (SMRT ™), and by nanoporous sequencing (Soni and Meller, Clin Chem (2007) 53: 1996-2001), allows the sequencing of many nucleic acid molecules isolated from a sample with high levels of multiplexing in parallel (Dear, Brief Funct Genomic Proteomic (2003) 1: 397-416). Each of these platform sequences expands or even simple, non-amplified molecules of nucleic acid fragments. Commercially available sequencing equipment can be used to obtain sequence information for polynucleotide fragments. V. Examples
    The following examples are provided to illustrate, and not to limit the invention.
    Example 1. Analysis of factors affecting detection sensitivity: GC trend and gender
    A schematic of the procedure for calculating the coverage depth and GC content is illustrated in figure 1. Software was used to produce the unique reference readings by breaking the hg18 reference sequences into 1-mer (1-mer is a reading that is artificially decomposed from the human sequence reference with the same length “I” as the sample sequencing readings), and defining these 1-mer repeat units as unique reference readings. Second, we map our sequenced sample readings according to the unique reference readings for each chromosome. Third, we delete the outlier values by applying the cut method of the fifth part to obtain a clear data configuration. Finally, the depth of coverage count of each chromosome for each sample and the GC content of single sequence readings mapped for each chromosome of each sample were evaluated.
    To research how the GC content affects the data, 300 cases of euploidy karyotypes were chosen, and the data from the sequential readings of their coverage depths and the GC contents were distributed in a graph, demonstrating an intense correlation between them, and this phenomenon it has not been previously reported (figure 2). In figure 2, the depth of coverage is closely related to the GC content, and shows a downward direction of some chromosomes, such as 4.13, etc., while showing an upward direction of other chromosomes, such as 19, 22, etc. All chromosomes were arranged in ascending order according to their GC content, and a downward trend is present in chromosomes with lesser GC content, while another group of chromosomes with higher GC content shows an upward trend, as shown in Figure 3. It can be interpreted that if the poly-nucleotide fragments that are being sequenced for one sample have a greater GC content than the other sample, the depth of coverage that this sample represents will drop compared to that of the other sample with smaller GC content , while increasing in chromosomes with higher GC content.
    The possible explanation for this tendency to change between chromosomes with different GC contents is the differences in the composition of the GC content in the different chromosomes shown in figure 4 combined with the CG tendency introduced in the sequencing process. The GC content of each 35-mer single reference reading for each chromosome is used to classify the GC content into 36 levels. The percentage of each level as the GC composition of each chromosome was calculated, and then used to create the map by heating with the Heatmap2 software. Using chromosome 13 as an example, most of it consists of sequence segments with smaller GC content, but its smaller part consists of sequence segments with larger GC content. If the conditions during the sequencing or PCR process are favorable to segments with a higher GC content, then a larger part of chromosome 13 with low GC content will present difficulties in sequencing, causing the reduction of the depth of coverage on chromosome 13 of this sample. In comparison, in a group with a higher GC content, such as chromosome 19, the depth of coverage in the chromosome 19 sample becomes greater in this part with the highest GC content, facilitating sequencing. No matter the chromosome, segments with greater or lesser GC content have difficulty in sequencing, but the influence introduced by the GC trend was different for the different chromosomes with different GC compositions. Each reference chromosome was divided into 1KB bins, and
    The GC content of each reference reading in the bin was calculated. The GC content of each bin in the proper range [0.3, 0.6], divided by the step size of 0.001, and the relative coverage value in each range is calculated. Figure 5 shows markings of the coverage depth and GC content for each chromosome.
    The influence of fetus gender on the data was analyzed using a two-sample independent t test. No difference was found between autosomes except for sex chromosomes with approximately the same GC content, but there is an obvious difference in UR% between men and women (Chiu et al., (2008) Proc Natl Acad Sei USA 105: 20458-20463) , suggesting that there is no need to distinguish the sex of the fetus to detect autosomal aneuploidy, but there is a need to distinguish sex initially, to detect aneuploidy of the sex chromosome, such as X0, XYY, etc. Example 2 - Statistical model
    Based on the phenomenon discussed above, local polynomial regression was used to adjust the relationship between the depth of coverage and the corresponding GC content. The coverage depth consists of a GC function and a residual of the normal distribution as follows:
    where / (GCi, /) represents the function for the relationship between the depth of coverage and the corresponding GC content of sample i, chromosome j, 8, jj represents the residual of sample i, chromosome j.
    There is no strong linear relationship between the depth of coverage and the corresponding GC content, so a Loess algorithm is applied to adjust the depth of coverage with the corresponding GC content, from which an important value is calculated for the model of the invention, which is the adjusted coverage depth.

    With the coverage depth adjusted, the standard variance and the Student's t-test were calculated according to formulas 6 and 7:
    Example 3. Calculation of fetal fraction
    Because the fetal fraction is very important for the detection of the present invention, it was calculated before the test procedure. As mentioned earlier, 19 adult men were sequenced, and when their depths of coverage were compared with cases of female fetuses, it was found that the depth of coverage of the X chromosome of men is almost half that of women, and the depth of coverage of the Y chromosome for men it is almost 0.5 higher than for women. Then, we can calculate the fetal fraction depending on the X and Y chromosome coverage depth according to formulas 8, 9 and 10, considering the GC correlation:
    where criXf = f (GCiXf) is the depth of coverage adjusted by the regression correlation of the depth of coverage of the X chromosome and the corresponding GC content of cases with female fetuses, criYf = f (GCiYf) refers to the depth of coverage adjusted by the correlation of the Y chromosome coverage depth regression and the corresponding GC content of cases with female fetuses, criXm = f (GCiXm) refers to the depth of coverage adjusted by the correlation of the X chromosome coverage depth regression and corresponding CG content of men , cri Ym = f (GCiYm) refers to the coverage depth adjusted by the regression correlation of the coverage depth of the Y chromosome and corresponding GC content of adult men. For calculation only, σxf and σx m are equal and σY f and ôY m are equal. Example 4. Calculation of the residual of each chromosome
    Figure 6 shows that the standard variation (see formula 3) for each chromosome with a certain total number of unique readings is influenced by the reference numbers of participating cases. The standard variation only increases when the number of selected cases was greater than 150, with the proviso that 1.7 million unique readings were sequenced for each case. However, the standard variation was different for the different chromosomes. After considering the GC trend, the method of the present invention showed a moderate standard variation for chromosome 13 (0.0063), chromosome 18 (0.0066) and chromosome 21 (0.0072). The standard variation of the X chromosome is greater than that of the chromosomes mentioned above, and may require more strategies for accurate detection of abnormalities.
    Figure 7 shows the Q-Q mark, where the residual is compiled for normal distribution, considering the calculation of the Student's t-test reasonable.
    Example 5. Distinguishing the sex of the fetus
    In order to discover abnormalities of the sex chromosome, it is better to distinguish the femal sex. There are two obvious peaks when the frequency distribution of the depth of coverage of the Y chromosome is investigated in the 300 cases of the present invention, suggesting the distinction of sex by the depth of coverage of the Y chromosome. Cases with a depth of coverage less than 0.04 can be related to female fetuses, while greater than 0.051 can indicate male fetuses, however, it is not possible to distinguish sex with values between 0.04 and 0.051, according to figure 8. For these cases of aneuploidy and doubtful sex determination, logistic regression was used to predict sex according to formula 11 (Fan, etal., Proc NatlAcad Sei USA (2008) 42: 16266-16271):
    where cr.αi x and cr.ai y are normalized in relation to the coverage of X and Y, respectively.
    In comparison with the karyotype result, the method of the present invention to distinguish fetal sex was successful in the 300 reference cases with 100% accuracy, however, it presented an error in the realization of the 901st case, and the depth value of Y chromosome of this case remained between 0.04 and 0.051. Example 6. Diagnostic performance of the GC correlation test t attempt Sample recall
    Nine hundred and three (903) participants were recruited from Shenzhen People Hospital and Shenzhen Maternal and child care center with their karyotype results. Approvals were obtained from institutional review groups at each convocation site, and all participants gave written consent. The mother's ages and gestational ages in the blood samples were recorded. The 903 cases included 2 cases of trisomy 13, 15 cases of trisomy 18, 16 cases of trisomy 21, 3 cases of X0, 2 cases of XXY and 1 case of XYY. The distribution of karyotype results is shown in figure 9.
    Sequencing of maternal plasma DNA
    Peripheral venous blood (5 mL) was collected from each pregnant participant in EDTA tubes, and centrifuged at 1,600 g / 10 minutes for 4 hours. The plasma was transferred to microcentrifuge tubes and centrifuged again at 16,000 g / 10 minutes, to remove residual cells. Plasma without cells was stored at 80 ° C until DNA extraction. Each plasma sample was frozen and thawed only once.
    For massive parallel genomic sequencing, all the DNA extracted from the amount of 600 pL of maternal plasma was used to build the DNA bank, according to a modified protocol from Illumina. Briefly, a repair on the ends of fragments of maternal plasma DNA was performed using T4 DNA polymerase, Kleno-w ™ polymerase and T4 polynucleotide kinase. Commercially available adapters (Illumina) were attached to the DNA fragments after adding terminal A residues. The DNA attached to the adapter was then further expanded using a 17-cycle PCR with multiple standard primers. The 60 mL Agencourt AMPure ™ kit (Beckman) was used for the purification of PCR products. The size distribution of the sequencing banks was analyzed with a DNA 1000 kit in Bioanalyzer ™ 2100 (Agilent) and quantified with real-time PCR. The sequencing banks with different indexes were then pooled in similar amounts prior to the clustering station in Illumina GA II ™ (single-ended sequencing).
    Euploid samples from 19 men were sequenced for subsequent analysis of the fetal DNA fraction. A new correlated GC t test was developed in the invention for the diagnosis of trisomy 13, trisomy 21, and sex chromosome abnormalities. This method has been compared with the other two methods mentioned below on diagnosing performance. Example 7. Detection of fetal aneuploidy such as trisomy 13,18 and 21
    To determine whether the number of copies of a chromosome within a patient's case deviated from normal, the depth of coverage of a chromosome was compared with that of all other reference cases. All previous studies presented only one null hypothesis. A null hypothesis (H0: the fetus is euploid) stipulated that the average depth of coverage of the distribution of patient cases and the average depth of coverage of all normal reference distribution were equal, which meant that the patient was euploid if this null hypothesis were accepted. Using Student's t test, t1 can be calculated by formula 12:

    The other null hypotheses (H1: the fetus is aneuploid) stipulated that the average coverage depth of the distribution of cases of patients with an imprecise fetal fraction was equal to the average coverage depth of the distribution of aneuploid cases with the same fetal fraction, which it meant that this patient was aneuploid if that null hypothesis was accepted. Using Student's t test, t2 was calculated using formula 13:
    IT11> 3 and It2l <3 may indicate a case of aneuploidy especially when the distributions between cases of euploidy and aneuploidy are completely broken down, although in another condition, such as in insufficient precision or insufficient fetal fraction, and so on, It11 can be less than 3, but the fetus is abnormal. T1 and t2 combined can help in obtaining a more accurate decision, so the logarithmic probability ratio of t1 and t2 according to formula 14 was used:
    where Ljj is the proportion of logarithmic probability. If the proportion is greater than 1, we can conclude that the fetus may have trisomy.
    However, in cases of female fetuses, it is difficult to assess their fetal fraction, so it is impossible to calculate. However, a reference value (RV) of 7% can be provided, according to the empirical distribution of the fetal fraction.
    Nine hundred and three (903) cases were researched, among them 866 contained euploid fetuses, of which 300 cases were randomly selected to perform the t test correlated with GC. In addition, 2 cases of trisomy 13, 12 cases of trisomy 18, 16 cases of trisomy 21.4X (consisting of 3 cases of X0, 1 case of chimera 45, xo / 46, xx (27:23), and 2 cases of XXY and 1 of XYY participated in the study.After alignment, an average of 1.7 million data (SD = 306185) of single aligned readings per case was obtained, with no errors. Using the correlated Student t test with GC, of the present invention, all cases of T13 (2 out of 2) were successfully identified, while 901 out of 901 cases of non-occurrence of trisomy 13 were correctly classified (figure 10A) .The sensitivity and specificity of this test were 100% and 100% (table 1).
    For cases of trisomy 18, 12 out of 12 cases of trisomy 18, and 888 out of 891 cases of non-occurrence of trisomy 18 could be correctly identified (figure 10A). The sensitivity and specificity of this test were 100% and 99.66%, respectively. For trisomy 21, 16 of 16 cases of trisomy 21, and 16 of 16 cases of non-occurrence of trisomy 21 could also be correctly detected (figure 10A). The sensitivity and specificity of this test were 100% and 100% respectively. Example 8. Detection of X0, XXX, XXY. XYY
    We believe that the detection of trisomy for autosomes, and for disorders of sex chromosomes, such as X0, XXX, XXY and XYY, can also be performed by the method of the present invention.
    Initially, sex was confirmed by sex distinction. If a case has been confirmed for a female fetus, the t1 value of the student test tlÍX = (c ^ x ~ cri, xf}! St <^ xf needed to calculate the detection of XXX or X0, where criXf and stdXf are the same as in formula 10; if t1 is greater than 3.13, this case may be XXX or X0, but considering that the accuracy was limited by the wide variation in the depth of coverage for the X chromosome, a new plasma sample was collected and the experiment was repeated to obtain a more accurate decision when It11 <5, or It11> 3.13 The value of It11> 5 was confirmed as being aneuploid in this case. All detection processes start from the premise that the data satisfy the control standard quality.
    If the test sample was confirmed to have male fetuses, the fraction of fetal DNA was estimated initially by Y and X. However, we can extrapolate the coverage depth adjusted for the X chromosome with the fraction of fetal DNA calculated only by the coverage depth of the Y chromosome, and t2 can be calculated
    If t2 has a value that is too high (greater than 5) or too small (less than -5), the fetus can be either XXY or XYY. In addition, the interval between fetal fractions estimated by X and Y independently can provide information for detecting sex chromosome disorders.
    In XO detection, three out of 4 XO cases were detected, and the case was not detected as a chimera case (figure 10B). The sensitivity and specificity of this test were 75% (100% if the chimera is not considered) and 99.5% respectively. For XXY cases, all two cases were successfully identified, while 901 of 901 cases of non-XXY were correctly classified (figure 10B) with 100% sensitivity 100% specificity. For the XYY case, it was correctly identified (Figure 10B), and the sensitivity and specificity were 100% and 100% respectively.
    To assess whether the new test of the invention has advantages when compared to two other tests, z-score and z-score with correction for GC, the three tests were carried out to analyze the 900 cases, with the same 300 reference cases. The accuracy of a measurement was expressed in the confidence value (CV). In a survey of the invention, the confidence value (CV) of the standard z-score test is higher than that of other tests of clinical interest on chromosome 18 and 21 (figure 11), generating a lower sensitivity rate for trisomy of the 18 and 21 (table 1). Table 1. Comparison of sensitivity and specificity of different methods Diagnosis (# of cases)


    For the z-score test with GC correlation, the CV value of chromosome 13 is 0.0066 with a 100% sensitivity rate and a 100% specificity rate. For the new Student t test with GC correlation discussed in the present invention, the CV value of chromosome 13 is 0.0063, with a 100% sensitivity rate and a 100% specificity rate. On chromosome 18, the CV of these two tests were 0.0062 and 0.0066, respectively, both with rates of 100% sensitivity and 100% and specificity rates of 99.89% and 99.96% respectively. The performance was similar when comparing the CV of these two tests for chromosome 21: 0.0088 and 0.0072, respectively. Both had the same sensitivity rate of 100% in the study of small cases and obtained the same specificity rate of 100%. And these two methods performed better than the standard z-score test. The new test developed in the invention with GC correction was not only comparable to the GC correction test with good performance, but also presented another advantage in detecting abnormalities related to sex chromosomes, such as X0, XXY and XYY. The data demonstrate that when the GC correction test is used, it can be difficult to distinguish the sex of the fetuses by the deviation of data that represent the sex chromosomes, introducing the number of expressed sequence tags multiplied by the weight factor, making it difficult to detect the disorder of the fetus. sex chromosome. Example 9. Theoretical performance of the t-test correlated with GC in relation to the size of the data, weeks of gestation and fraction of fetal DNA
    Aneuploid measurement remains a challenge due to the high concentration of maternal DNA (Fan etal., Proc NatlAcad Sei USA (2008) 42: 16266 - 16271), and a small fraction of fetal DNA is the most significant restriction factor for detecting aneuploidy by sequencing the massive parallel genome (MPGS). However, there is no major advance in the clinical determination of the minimal fraction of fetal DNA prior to detection by MPGS, especially in female fetuses, although gestational weeks are the only clinical sign related to the fetal DNA fraction. It has been previously reported that there is a statistically significant correlation between fetal DNA fraction and gestational age (Lo, et al., Am. J. Human Genet. (1998) 62: 768-775). In the study of the invention, to research the relationship between the estimated fetal DNA fraction and the gestational age, the fetal DNA fraction of all participating cases with male fetuses (total of 427 cases) was calculated in figure 12, calculated using the formula 10. The fraction of fetal DNA estimated for each sample is correlated with gestational age (P less than 0.0001). It has also been shown that even at gestational age 20, there were 4 out of 65 cases with a fetal DNA fraction less than 5%, which could affect the accuracy of detection.
    To evaluate the method of calculating the fetal fraction, some cases were selected distributed in the estimated fetal fraction, and then Q-PCR helped to calculate another relative fetal fraction. Then, a standard curve was obtained demonstrating a strong correlation between them, and that the calculation of the fetal fraction by the method of the present invention is reliable.
    However, the depth of sequencing (the number of unique total readings) was another significant factor that affected the accuracy of the detection of aneuploidy employed in the value of the standard variation. The standard variation for each chromosome used in the GC-related test can be fixed according to a certain level of sequencing depth, when the number of reference cases reaches the value of 150 (figure 13). To investigate how the depth of sequencing influences the standard variation for each chromosome, one hundred and fifty (150) cases were sequenced not only at the 1.7 million level, but at another level with the total number of unique readings reaching 5 million (SD = 1.7 million). Depending on these two configurations, it was found that the standard variance is linear with the inverse of the square root of the total number of unique readings shown in figure 6.
    For a given fraction of fetal DNA, we can estimate the total number of single readings, according to the method of the present invention, to detect the deviation in the number of copies of the chromosome from normal, with t1 equal to 3 (figure 14). This demonstrates that the smaller the fraction of DNA, the greater the depth of sequencing required. With a configuration of 1.7 million unique readings, the test is able to detect fetuses with aneuplodia on chromosome 13 and X with a fraction of fetal DNA greater than 4.5%, and fetuses with aneuploidy on chromosome 21 and 18 with more 4%; while in the 5 million reference configuration, the test was able to detect trisomy on chromosome 18 and 21, still with a fetal DNA fraction of about 3%. For the identification of fetuses with abnormalities on the X chromosome, such as XXX or X0, with a fetal fraction of about 4%, the total number of single readings required in these cases and in the corresponding cases should reach 5 million. If the fetal DNA is less than 3.5%, the sequencing depth requirement will exceed 20M. And if the fetal DNA fraction is smaller, detection will be difficult and unreliable, so another strategy is proposed, such as collecting a new sample of maternal plasma, redoing the experiment and reanalyzing the data when pregnancy is more advanced. , that is, when the fraction of DNA becomes larger with increasing gestational age. And this strategy can also be applied to samples that have a small fraction of fetal DNA.
    Even if the tests perform well, they are not convincing without a large number of cases of abnormalities. To calculate the sensitivity of the method of the present invention of the correlation of the student's t-test with GC, the theoretical sensitivity was published considering different gestational ages and different sequencing depths.
    The theoretical sensitivity of aneuploidy was calculated using the following steps. First, a regression analysis was applied to adjust the fetal DNA to the gestational age fr. = f (gsat}> where fr {is the average adjustment of the fetal DNA fraction at your gestational age, gsat and calculation of the fetal DNA fraction distribution using the Gaussian kernel density calculation (Birke, (2008) Journal of Statistical Planning and Inference 19: 2851-2862), mainly in relation to the estimated fetal DNA fraction, distributed in 19 and 20 gestational weeks before extrapolating the distribution of the fetal DNA fraction in the other weeks, according to the relationship between the fraction of fetal DNA and gestational age
    , where pdt is the density of the adjusted probability h 2 of the fetal DNA fraction at your gestational age, where X is the date of 19 and 20 gestational weeks (figure 12). Second, the standard variance was calculated according to the numbers of unique gestational readings, as mentioned earlier (σ = fftuqri), where tuqn is the total number of unique readings. Finally, to calculate the sensitivity at each gestational age at a certain level of sequencing depth, the density of the probability of the false negative in each fraction of the fetal DNA was computed (in the present invention, with the normally distributed fetal DNA fraction fluctuation), and then with its integration to obtain a false negative rate (FNR) at a gestational age with all levels of fetal DNA fraction
    , where j is chromosome j. The theoretical sensitivity at a certain depth of sequencing at this gestational age is easily calculated as 1-FNR. Figures 15-21 show the markings of the calculation results. Student's t-test greater than 3 was configured to identify female aneuploid fetuses, while for male fetuses, when computing the density of the false negative probability in each fraction, a logarithm greater than 1 was used as the critical value, as mentioned in the binary hypothesis, helping to achieve high sensitivity compared to female fetuses.
    However, our conclusion is relatively conservative due to the fact that it is difficult to obtain an approximate distribution of the actual distribution of the fetal DNA fraction along with the gestational age, especially at the beginning of pregnancy with a smaller sample scale.
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    Claims (12)
    [0001]
    1. Method implemented by computer to determine a fetal genetic abnormality which is a chromosomal aneuploidy, CHARACTERIZED by the fact that it comprises: (a) obtaining sequence information of multiple polynucleotide fragments from a sample, said sample being a sample of peripheral blood derived from a pregnant female and containing maternal and fetal DNA; (b) assign said fragments to chromosomes based on said sequence information, by comparing said fragments with Unique Reference Readings of the same size for each of said chromosomes, where Unique Reference Readings are fragments of a chromosome that it has a unique sequence that can be unequivocally attributed to a single chromosomal location based on a reference genomic sequence; (c) determine the depth of coverage and the GC content of a chromosome based on the sequence information for the fragments that were assigned to Unique Reference Readings for said chromosome in step (b), where the depth of coverage is the ratio between the number of fragments assigned solely to said chromosome and the number of Unique Reference Readings for said chromosome of the same fragment size based on said reference human genomic sequence; (d) determining the adjusted coverage depth of said chromosome using said GC content of said chromosome and establishing a relationship between the depth of coverage and the GC content of said chromosome in the absence of aneuploidy, where said established relationship was determined by a method comprising the steps of: (i) obtaining sequence information for multiple polynucleotide fragments covering said chromosome from a plurality of peripheral euploid blood samples containing genomic DNA, where the size of the fragment is the same that the fragment size of the multiple polynucleotide fragments of step (a) above; (ii) assigning said fragments to chromosomes based on said sequence information as in step (b) above; (iii) determining the depth of coverage and the GC content of said chromosome based on said sequence information for each euploid sample as in step (c) above; and (iv) use the depth of coverage and the GC content determined for each sample in step (iii) to determine the relationship between the depth of coverage and the GC content of said chromosome in the absence of aneuploidy; and (e) comparing said adjusted depth of coverage with the depth of coverage of said chromosome determined in step (c), in which a difference between them indicates fetal chromosomal aneuploidy.
    [0002]
    2. Method, according to claim 1, CHARACTERIZED by the fact that step (a) further comprises obtaining sequence information for multiple polynucleotide fragments from multiple different samples and the depth of coverage is normalized to take into account the differences in the total number of read sequences obtained for different samples, for example against the average depth of coverage of another chromosome, preferably against the average depth of coverage of all other autosomes or against the average depth of coverage of all other chromosomes.
    [0003]
    3. Method, according to claim 1 or 2, CHARACTERIZED by the fact that the GC content of the chromosome is determined as the average GC content of all fragments that are attributed to said chromosome for the purpose of step (c).
    [0004]
    4. Method, according to claim 2, CHARACTERIZED by the fact that it also comprises determining the fetal sex, for example according to the formula:
    [0005]
    5. Method, according to claim 2, CHARACTERIZED by the fact that it also comprises estimating the fetal fraction, in which the fetal fraction is calculated based on the depth of coverage of the X and / or Y chromosome, determined as defined in step ( c) of claim 1, according to a formula selected from:
    [0006]
    6. Method, according to claim 2, CHARACTERIZED by the fact that the comparison of said adjusted depth of coverage with the depth of coverage of the chromosome determined in step (c) is conducted by a statistical hypothesis test, in which a hypothesis is that the fetus is euploid (HO) and the other hypothesis is that the fetus exhibits aneuploidy for said chromosome (H1).
    [0007]
    7. Method, according to claim 6, CHARACTERIZED by the fact that Student's t-test is calculated for both hypotheses.
    [0008]
    8. Method, according to claim 7, CHARACTERIZED by the fact that Student's t-test is calculated for HO and H1 according to the formulas:
    [0009]
    9. Method, according to claim 8, CHARACTERIZED by the fact that the proportion of the logarithmic probability of t1 and t2 is calculated according to the formula: Ltj = log (p (tl ,. j, degree | D)) / log (^ (í2íy, degree | T)), where Lij is the proportion of logarithmic probability, where degree refers to the level of the distribution t, D refers to i} (11 degree I * 1 * = DT Diploidy, T refers to Trisomy, and 1 'represents the conditional probability density provided at a t distribution level, if the proportion is greater than 1, the fetus is inferred to exhibit trisomy of said chromosome.
    [0010]
    10. Method according to any one of claims 1 to 9, CHARACTERIZED by the fact that it is for use in determining an autosomal fetal aneuploidy.
    [0011]
    11. Method, according to claim 10, CHARACTERIZED by the fact that fetal aneuploidy is selected from the group consisting of trisomies 13, 18 and 21.
    [0012]
    12. Method according to claim 4, CHARACTERIZED by the fact that it is for use in determining a sex chromosome aneuploidy, such as a sex chromosome aneuploidy selected from the group consisting of XO, XXX, XXY and XYY
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    同族专利:
    公开号 | 公开日
    US20140099642A1|2014-04-10|
    WO2013000100A1|2013-01-03|
    CN103403183A|2013-11-20|
    CA2791118C|2019-05-07|
    RU2589681C2|2016-07-10|
    EP2561103A1|2013-02-27|
    ES2512448T3|2014-10-24|
    DK2561103T3|2014-10-20|
    ZA201209583B|2014-01-29|
    PL2561103T3|2015-02-27|
    CN103403183B|2014-10-15|
    SG191757A1|2013-08-30|
    CA2948939C|2021-02-02|
    SI2561103T1|2014-11-28|
    JP2014520509A|2014-08-25|
    CA2791118A1|2012-12-29|
    KR20140023847A|2014-02-27|
    EP2561103A4|2013-08-07|
    BR112012033760A2|2018-02-27|
    EP2561103B1|2014-08-27|
    KR101489568B1|2015-02-03|
    JP5659319B2|2015-01-28|
    CA2948939A1|2012-12-29|
    HK1190758A1|2014-07-11|
    AU2012261664A1|2013-01-17|
    RU2012158107A|2015-08-10|
    US9547748B2|2017-01-17|
    AU2012261664B2|2014-07-03|
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    法律状态:
    2018-04-10| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2018-10-09| B25B| Requested transfer of rights rejected|Owner name: BGI HEALTH SERVICE CO., LTD (CN) Free format text: INDEFERIDO O PEDIDO DE TRANSFERENCIA CONTIDO NA PETICAO 860140063276 DE 02/05/2014, EM VIRTUDE DO PEDIDO JA ESTAR EM NOME DO INTERESSADO. |
    2019-05-21| B06T| Formal requirements before examination [chapter 6.20 patent gazette]|
    2019-08-27| B06A| Notification to applicant to reply to the report for non-patentability or inadequacy of the application [chapter 6.1 patent gazette]|
    2020-04-07| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2020-11-17| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 29/06/2011, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    PCT/CN2011/001070|WO2013000100A1|2011-06-29|2011-06-29|Noninvasive detection of fetal genetic abnormality|
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