Patent Publication Number: US-11649500-B2

Title: Target-enriched multiplexed parallel analysis for assessment of fetal DNA samples

Description:
CROSS REFERENCE TO RELATED PATENT APPLICATIONS 
     This application is a U.S. national phase application of International Application No. PCT/EP2018/068414, which was filed on Jul. 6, 2018, and claims priority to U.S. Provisional Application No. 62/529,790, which was filed on Jul. 7, 2017. The content of these earlier filed applications is hereby incorporated by reference herein in its entirety. 
    
    
     INCORPORATION OF THE SEQUENCE LISTING 
     The present application contains a sequence listing that was submitted herewith in ASCII format via EFS-Web, containing the file name “37578_0071U1_Revised_Sequence Listing.txt,” which is 389,120 bytes in size, created on Jun. 23, 2022, and is herein incorporated by reference in its entirety pursuant to 37 C.F.R. § 1.52(e)(5). 
     FIELD OF THE INVENTION 
     The invention is in the field of biology, medicine and chemistry, more in particular in the field of molecular biology and more in particular in the field of molecular diagnostics. 
     BACKGROUND OF THE INVENTION 
     The discovery of free fetal DNA (ffDNA) in maternal circulation (Lo, Y. M. et al. (1997) Lancet 350:485-487) was a landmark towards the development of non-invasive prenatal testing for chromosomal abnormalities and has opened up new possibilities in the clinical setting. However, direct analysis of the limited amount of ffDNA in the presence of an excess of maternal DNA is a great challenge for Non-Invasive Prenatal Testing (NIPT) of chromosomal abnormalities. The implementation of next generation sequencing (NGS) technologies in the development of NIPT has revolutionized the field. In 2008, two independent groups demonstrated that NIPT of trisomy 21 could be achieved using next generation massively parallel shotgun sequencing (MPSS) (Chiu, R. W. et al. (2008)  Proc. Natl. Acad. Sci. USA  105:20458-20463; Fan, H. C. et al. (2008)  Proc. Natl. Acad. Sci. USA  105:16266-162710). The new era of NIPT for chromosomal abnormalities has opened new possibilities for the implementation of these technologies into clinical practice. Biotechnology companies that are partly or wholly dedicated to the development of NIPT tests have initiated large-scale clinical studies towards their implementation (Palomaki, G. E. et al. (2011)  Genet. Med.  13:913-920; Ehrich, M. et al. (2011)  Am. J. Obstet. Gynecol.  204:205e1-11; Chen, E. Z. et al. (2011)  PLoS One  6:e21791; Sehnert, A. J. et al. (2011)  Clin. Chem.  57:1042-1049; Palomaki, G. E. et al. (2012);  Genet. Med.  14:296-305; Bianchi, D. W. et al. (2012)  Obstet. Gynecol.  119:890-901; Zimmerman, B. et al. (2012)  Prenat. Diag.  32:1233-1241; Nicolaides, K. H. et al. (2013)  Prenat. Diagn.  33:575-579; Sparks, A. B. et al. (2012)  Prenat. Diagn.  32:3-9) 
     Initial NIPT approaches used massively parallel shotgun sequencing (MPSS) NGS methodologies (see e.g., U.S. Pat. Nos. 7,888,017; 8,008,018; 8,195,415; 8,296,076; 8,682,594; US Patent Publication 20110201507; US Patent Publication 20120270739). Thus, these approaches are whole genome-based, in which the entire maternal sample containing both maternal DNA and free fetal DNA is subjected to amplification, sequencing and analysis. 
     More recently, targeted-based NGS approaches for NIPT, in which only specific sequences of interest are sequenced, have been developed. For example, a targeted NIPT approach using TArget Capture Sequences (TACS) for identifying fetal chromosomal abnormalities using a maternal blood sample has been described (PCT Publication WO 2016/189388; US Patent Publication 2016/0340733; Koumbaris, G. et al. (2015)  Clinical chemistry,  62(6), pp. 848-855). 
     Such targeted approaches require significantly less sequencing than the MPSS approaches, since sequencing is only performed on specific loci on the target sequence of interest rather than across the whole genome. Additional methodologies for NGS-based approaches are still needed, in particular approaches that can target specific sequences of interest, thereby greatly reducing the amount of sequencing needed as compared to whole genome-based approaches, as well as increasing the read-depth of regions of interest, thus enabling detection of low signal to noise ratio regions. In particular, additional methodologies are still needed that allow for genetic aberrations present in diminutive amounts in a sample can be reliably detected. For example, additional methodologies are still needed that allow for analysis of DNA samples that contain predominantly fetal or embryonic DNA, since such samples contain only diminutive amounts of fetal or embryonic DNA. 
     SUMMARY OF THE INVENTION 
     This invention provides improved methods for enriching targeted genomic regions of interest to be analyzed by multiplexed parallel sequencing, wherein the DNA sample used in the method contains predominantly or only fetal/embryonic DNA. Accordingly, the methods allow for analysis of very small starting amounts of fetal or embryonic DNA. The methods of the disclosure can be used in the analysis of fetal or embryonic DNA samples, e.g., for the presence of genetic abnormalities, for example for purposes of IVF Pre-implantation Genetic Screening (PGS) and Diagnosis (PGD). The methods of the invention utilize a pool of TArget Capture Sequences (TACS) designed such that the sequences within the pool have features that optimize the efficiency, specificity and accuracy of genetic assessment. In one embodiment, the pool of TACS comprises member sequences whose binding encompasses all chromosomes within the human genome (chromosomes 1-22, X and Y), thereby allowing for evaluation of the entire human genome in a single fetal/embryonic DNA sample. 
     Accordingly, in one aspect, the invention pertains to a method of testing for risk of a genetic abnormality in a DNA sample comprising predominantly fetal or embryonic DNA and comprising genomic sequences of interest, the method comprising:
         (a) preparing a sequencing library from the DNA sample comprising predominantly fetal or embryonic DNA;   (b) hybridizing the sequencing library to a pool of double-stranded TArget Capture Sequences (TACS) wherein the pool of TACS comprises sequences that bind to one or more genomic sequences of interest comprising a genetic abnormality;   (c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library;   (d) amplifying and sequencing the enriched library; and   (e) performing statistical analysis on the enriched library sequences to thereby determine risk of a genetic abnormality in the DNA sample.   In one embodiment:
           (i) each member sequence within the pool of TACS is between 100-500 base pairs in length, each member sequence having a 5′ end and a 3′ end;   (ii) each member sequence binds to the same genomic sequence of interest at least 50 base pairs away, on both the 5′ end and the 3′ end, from regions harboring Copy Number Variations (CNVs), Segmental duplications or repetitive DNA elements; and   (iii) the GC content of the pool of TACS is between 19% and 80%, as determined by calculating the GC content of each member within the pool of TACS.   
               

     In various embodiments, the DNA sample is from, for example, a pre-implantation embryo, intact trophoblasts collected from a maternal Papanicolaou smear or a fetal cell found in maternal plasma. In one embodiment, the DNA sample is obtained directly from fetal or embryonic tissue. In certain embodiments, the DNA sample is obtained directly from fetal tissue, or amniotic fluid, or chorionic villi, or medium where products of conception were grown. 
     In one embodiment, the pool of TACS comprises members that bind to chromosomes 1-22, X and Y of the human genome. In one embodiment, each member sequence within the pool of TACS is at least 160 base pairs in length. In certain embodiments, the GC content of the pool of TACS is between 19% and 80% or is between 19% and 46%. Alternative % ranges for the GC content of the pool of TACS are described herein. 
     In one embodiment, the pool of TACS comprises a plurality of TACS families, wherein each member of a TACS family binds to the same target sequence of interest but with different start and/or stop positions on the sequence with respect to a reference coordinate system (i.e., binding of TACS family members to the target sequence is staggered) to thereby enrich for target sequences of interest, followed by massive parallel sequencing and statistical analysis of the enriched population. The use of families of TACS with the TACS pool that bind to each target sequence of interest, as compared to use of a single TACS within the TACS pool that binds to each target sequence of interest, significantly increases enrichment for the target sequences of interest, as evidenced by a greater than 50% average increase in read-depth for the family of TACS versus a single TACS. 
     Accordingly, in one embodiment, the pool of TACS comprises a plurality of TACS families directed to different genomic sequences of interest, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest. 
     In certain embodiments, each TACS family comprises at least 3 member sequences or at least 5 member sequences. Alternative numbers of member sequences in each TACS family are described herein. In one embodiment, the pool of TACS comprises at least 50 different TACS families. Alternative numbers of different TACS families within the pool of TACS are described herein. In certain embodiments, the start and/or stop positions for the member sequences within a TACS family, with respect to a reference coordinate system for the genomic sequence of interest, are staggered by at least 3 base pairs or by at least 10 base pairs. Alternative lengths (sizes) for the number of base pairs within the stagger are described herein. 
     In one embodiment, the genomic abnormality is a chromosomal aneuploidy. In other embodiments, the genomic abnormality is a structural abnormality, including but not limited to copy number changes including microdeletions and microduplications, insertions, deletions, translocations, inversions and small-size mutations including point mutations and mutational signatures. 
     In one embodiment, the pool of TACS is fixed to a solid support. For example, in one embodiment, the TACS are biotinylated and are bound to streptavidin-coated magnetic beads. 
     In one embodiment, amplification of the enriched library is performed in the presence of blocking sequences that inhibit amplification of wild-type sequences. 
     In one embodiment, members of the sequencing library that bind to the pool of TACS are partially complementary to the TACS. 
     In one embodiment, the statistical analysis comprises a segmentation algorithm, for example, likelihood-based segmentation, segmentation using small overlapping windows, segmentation using parallel pairwise testing, and combinations thereof. In one embodiment, the statistical analysis comprises a score-based classification system. In on embodiment, sequencing of the enriched library provides a read-depth for the genomic sequences of interest and read-depths for reference loci and the statistical analysis comprises applying an algorithm that tests sequentially the read-depth of the loci of from the genomic sequences of interest against the read-depth of the reference loci, the algorithm comprising steps for: (a) removal of inadequately sequenced loci; (b) GC-content bias alleviation; and (c) ploidy status determination. In one embodiment, GC-content bias is alleviated by grouping together loci of matching GC content. In one embodiment, sequencing of the enriched library provides the number and size of sequenced fragments for TACS-specific coordinates and the statistical analysis comprises applying an algorithm that tests sequentially the fragment-size proportion for the genomic sequence of interest against the fragment-size proportion of the reference loci, the algorithm comprising steps for: (a) removal of fragment-size outliers; (b) fragment-size proportion calculation; and (c) ploidy status determination. 
     In another aspect, the invention pertains to a method of testing for risk of a genetic abnormality in a DNA sample comprising predominantly fetal or embryonic DNA and comprising genomic sequences of interest, the method comprising:
         (a) preparing a sequencing library from the DNA sample comprising predominantly fetal or embryonic DNA;   (b) hybridizing the sequencing library to a pool of double-stranded TArget Capture Sequences (TACS), wherein the pool of TACS comprises a plurality of TACS families directed to different genomic sequences of interest, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest, and further wherein:
           (i) each member sequence within each TACS family is between 100-500 base pairs in length, each member sequence having a 5′ end and a 3′ end;   (ii) each member sequence binds to the same genomic sequence of interest at least 50 base pairs away, on both the 5′ end and the 3′ end, from regions harboring Copy Number Variations (CNVs), Segmental duplications or repetitive DNA elements; and   (iii) the GC content of the pool of TACS is between 19% and 80%, as determined by calculating the GC content of each member within each family of TACS;   
           (c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library;   (d) amplifying and sequencing the enriched library; and   (e) performing statistical analysis on the enriched library sequences to thereby determine risk of a genetic abnormality in the DNA sample.       

     In another aspect, kits for performing the methods of the invention are also encompassed. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. 
         FIG.  1    is a schematic diagram of multiplexed parallel analysis of targeted genomic regions for non-invasive prenatal testing using TArget Capture Sequences (TACS). 
         FIG.  2    is a listing of exemplary chromosomal regions for amplifying TACS that bind to for example chromosomes 13, 18, 21 or X. A more extensive list is shown in Table 1 below. The TACS in Table 1 are those preferred herein. 
         FIG.  3    is a schematic diagram of TACS-based enrichment of a sequence of interest (bold line) using a single TACS (left) versus TACS-based enrichment using a family of TACS (right). 
         FIGS.  4 A- 4 B  are graphs showing enrichment using families of TACS versus a single TACS, as illustrated by increase in the average read-depth.  FIG.  4 A  shows loci enriched using a family of TACS (square symbol) as compared to loci enriched using a single TACS (X symbol), with different target sequences shown on the X-axis and the fold change in read-depth shown on the Y-axis.  FIG.  4 B  is a bar graph illustrating the average fold-increase in read-depth (54.7%) using a family of TACS (right) versus a single TACS (left). 
         FIG.  5    is a graph of results from fetal DNA samples that underwent ploidy status determination using likelihood-based segmentation analysis and whole-genome sequencing data. The horizontal blue line indicates the average read-depth of each chromosome. The red lines indicate threshold intervals of expected diploids. Data above the top red line is classified as more than diploid and data below the red line is classified as less than diploid. The top panel illustrates the results of a euploid female sample (i.e., a female fetus with diploid X chromosome, no Y chromosome, and without any ploidy abnormalities present). The bottom panel illustrates the results of a female aneuploid sample (i.e., a female fetus with diploid X chromosome and no Y chromosome) with monosomy 18 and monosomy 20. Values on the y-axis are log of read-depth. 
         FIG.  6    is a graph of results from fetal DNA samples that underwent ploidy status determination by whole genome sequencing, followed by segmentation analysis using small overlapping windows analysis. The horizontal blue line indicates the average read-depth of each chromosome. The red lines indicate threshold intervals of expected diploids. The top panel illustrates the results of a euploid male sample (i.e., a male fetus with a single copy of X and Y chromosomes and without any ploidy abnormalities present). The bottom panel illustrates the results of an aneuploid male sample (i.e., a male fetus with a single copy of X and Y chromosomes) and with aneuploidies on chromosomes 13 and 19 (trisomy 13 and mosaicism on chromosome 19). Values are log of read-depth. 
         FIG.  7    is a graph of results from fetal DNA samples that underwent ploidy status determination by whole genome sequencing, followed by segmentation analysis using parallel pairwise testing. The top panel illustrates the results of a normal (euploid) sample and the bottom panel illustrates the results of an aneuploidy sample with aneuploidies on chromosomes 1, 2, 13, 15, 16, 19, and 20. 
         FIG.  8    is a graph depicting results from fetal DNA samples that underwent ploidy status determination using TACS-based enrichment, followed by a score-based classification. As per the key, samples plotted with N indicate normal ploidy status, the sample plotted with P illustrates partial trisomy, the samples plotted with T indicate trisomy and the samples plotted with M indicate monosomy. 
         FIG.  9    is a dot plot graph showing results of a fragments-based test for detecting increased numbers of smaller-size fragments in a mixed sample. An abnormal, aneuploid sample, with an estimated fetal fraction of 2.8%, was correctly detected using this method. The black dots are individual samples. The x-axis shows the sample index. The y-axis shows the score result of the fragments-size based method. A score result greater than the threshold shown by the grey line indicates a deviation from the expected size of fragments illustrating the presence of aneuploidy. 
         FIG.  10    is a graph of results from fetal DNA samples that underwent ploidy status determination using likelihood-based segmentation analysis and TACS-based enrichment whole genome sequencing data. The horizontal blue line indicates the average read-depth of each chromosome. The red lines indicate threshold intervals of expected diploids. Data above the top red line is classified as more than diploid and data below the red line is classified as less than diploid. The top panel illustrates the results of a euploid male sample (i.e., a male fetus with one copy of chromosome X chromosome and one copy of chromosome Y, and without any ploidy abnormalities present). The bottom panel illustrates the results of a male aneuploid sample with trisomy 13 and monosomy 21. Values on the y-axis are log-based transformations of read-depth. 
         FIG.  11    is a graph of results from fetal DNA samples that underwent ploidy status determination using likelihood-based segmentation analysis and TACS-based enrichment data. The horizontal blue line indicates the average read-depth of each chromosome. The red lines indicate threshold intervals of expected diploids. Data above the top red line is classified as more than diploid and data below the red line is classified as less than diploid. The top panel illustrates the results of a euploid male sample (i.e., a male fetus with one copy of chromosome X chromosome and one copy of chromosome Y, and without any ploidy abnormalities present). The bottom panel illustrates the results of a male aneuploid sample with trisomy 13 and monosomy 21. Values on the y-axis are log-based transformations of read-depth. 
     
    
    
     Table 1 shows exemplary and preferred TACS positions. The corresponding sequences are depicted in the sequence protocol. 
     
       
         
           
               
               
               
               
               
             
               
                   
                   
               
               
                   
                 Ch. 
                 Start 
                 Stop 
                 GC content 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 chr1 
                 1321966 
                 1322216 
                 0.348 
               
               
                   
                 chr1 
                 2223227 
                 2223477 
                 0.348 
               
               
                   
                 chr1 
                 3047692 
                 3047942 
                 0.348 
               
               
                   
                 chr1 
                 4134402 
                 4134652 
                 0.348 
               
               
                   
                 chr1 
                 5007713 
                 5007963 
                 0.348 
               
               
                   
                 chr1 
                 5865510 
                 5865760 
                 0.348 
               
               
                   
                 chr1 
                 6714342 
                 6714592 
                 0.348 
               
               
                   
                 chr1 
                 7651255 
                 7651505 
                 0.348 
               
               
                   
                 chr1 
                 8470924 
                 8471174 
                 0.348 
               
               
                   
                 chr1 
                 9407377 
                 9407627 
                 0.324 
               
               
                   
                 chr1 
                 10209181 
                 10209431 
                 0.296 
               
               
                   
                 chr1 
                 11076652 
                 11076902 
                 0.348 
               
               
                   
                 chr1 
                 12295996 
                 12296246 
                 0.348 
               
               
                   
                 chr1 
                 13923467 
                 13923717 
                 0.348 
               
               
                   
                 chr1 
                 14770392 
                 14770642 
                 0.348 
               
               
                   
                 chr1 
                 15578046 
                 15578296 
                 0.348 
               
               
                   
                 chr1 
                 16593363 
                 16593613 
                 0.348 
               
               
                   
                 chr1 
                 17424880 
                 17425130 
                 0.348 
               
               
                   
                 chr1 
                 18298306 
                 18298556 
                 0.348 
               
               
                   
                 chr1 
                 19423315 
                 19423565 
                 0.348 
               
               
                   
                 chr1 
                 20230997 
                 20231247 
                 0.348 
               
               
                   
                 chr1 
                 21064982 
                 21065232 
                 0.348 
               
               
                   
                 chr1 
                 22007055 
                 22007305 
                 0.348 
               
               
                   
                 chr1 
                 22807861 
                 22808111 
                 0.344 
               
               
                   
                 chr1 
                 23611830 
                 23612080 
                 0.348 
               
               
                   
                 chr1 
                 24692851 
                 24693101 
                 0.348 
               
               
                   
                 chr1 
                 25500621 
                 25500871 
                 0.348 
               
               
                   
                 chr1 
                 26321343 
                 26321593 
                 0.348 
               
               
                   
                 chr1 
                 27450716 
                 27450966 
                 0.348 
               
               
                   
                 chr1 
                 28296472 
                 28296722 
                 0.296 
               
               
                   
                 chr1 
                 29098007 
                 29098257 
                 0.348 
               
               
                   
                 chr1 
                 30034947 
                 30035197 
                 0.348 
               
               
                   
                 chr1 
                 30884476 
                 30884726 
                 0.348 
               
               
                   
                 chr1 
                 31759697 
                 31759947 
                 0.348 
               
               
                   
                 chr1 
                 32646478 
                 32646728 
                 0.348 
               
               
                   
                 chr1 
                 33479257 
                 33479507 
                 0.348 
               
               
                   
                 chr1 
                 34305150 
                 34305400 
                 0.348 
               
               
                   
                 chr1 
                 35132601 
                 35132851 
                 0.348 
               
               
                   
                 chr1 
                 35939215 
                 35939465 
                 0.348 
               
               
                   
                 chr1 
                 36744730 
                 36744980 
                 0.336 
               
               
                   
                 chr1 
                 37623596 
                 37623846 
                 0.348 
               
               
                   
                 chr1 
                 38444825 
                 38445075 
                 0.348 
               
               
                   
                 chr1 
                 39248090 
                 39248340 
                 0.348 
               
               
                   
                 chr1 
                 40135959 
                 40136209 
                 0.348 
               
               
                   
                 chr1 
                 41158448 
                 41158698 
                 0.348 
               
               
                   
                 chr1 
                 42642199 
                 42642449 
                 0.348 
               
               
                   
                 chr1 
                 43546530 
                 43546780 
                 0.308 
               
               
                   
                 chr1 
                 44429847 
                 44430097 
                 0.348 
               
               
                   
                 chr1 
                 45307055 
                 45307305 
                 0.348 
               
               
                   
                 chr1 
                 46108116 
                 46108366 
                 0.348 
               
               
                   
                 chr1 
                 47100462 
                 47100712 
                 0.324 
               
               
                   
                 chr1 
                 48012499 
                 48012749 
                 0.348 
               
               
                   
                 chr1 
                 48821604 
                 48821854 
                 0.268 
               
               
                   
                 chr1 
                 49632073 
                 49632323 
                 0.336 
               
               
                   
                 chr1 
                 50440111 
                 50440361 
                 0.332 
               
               
                   
                 chr1 
                 51241903 
                 51242153 
                 0.312 
               
               
                   
                 chr1 
                 52076744 
                 52076994 
                 0.348 
               
               
                   
                 chr1 
                 53710264 
                 53710514 
                 0.348 
               
               
                   
                 chr1 
                 54512550 
                 54512800 
                 0.344 
               
               
                   
                 chr1 
                 55394792 
                 55395042 
                 0.348 
               
               
                   
                 chr1 
                 56384481 
                 56384731 
                 0.348 
               
               
                   
                 chr1 
                 57349269 
                 57349519 
                 0.348 
               
               
                   
                 chr1 
                 58229509 
                 58229759 
                 0.348 
               
               
                   
                 chr1 
                 59040876 
                 59041126 
                 0.348 
               
               
                   
                 chr1 
                 59858357 
                 59858607 
                 0.304 
               
               
                   
                 chr1 
                 60930291 
                 60930541 
                 0.348 
               
               
                   
                 chr1 
                 62103549 
                 62103799 
                 0.304 
               
               
                   
                 chr1 
                 62916429 
                 62916679 
                 0.348 
               
               
                   
                 chr1 
                 64067557 
                 64067807 
                 0.348 
               
               
                   
                 chr1 
                 64969248 
                 64969498 
                 0.348 
               
               
                   
                 chr1 
                 65878461 
                 65878711 
                 0.348 
               
               
                   
                 chr1 
                 67063532 
                 67063782 
                 0.336 
               
               
                   
                 chr1 
                 67873342 
                 67873592 
                 0.348 
               
               
                   
                 chr1 
                 70446150 
                 70446400 
                 0.272 
               
               
                   
                 chr1 
                 71372533 
                 71372783 
                 0.348 
               
               
                   
                 chr1 
                 72327150 
                 72327400 
                 0.348 
               
               
                   
                 chr1 
                 73213150 
                 73213400 
                 0.332 
               
               
                   
                 chr1 
                 74040085 
                 74040335 
                 0.344 
               
               
                   
                 chr1 
                 74845564 
                 74845814 
                 0.348 
               
               
                   
                 chr1 
                 75862550 
                 75862800 
                 0.316 
               
               
                   
                 chr1 
                 76678210 
                 76678460 
                 0.348 
               
               
                   
                 chr1 
                 77512868 
                 77513118 
                 0.348 
               
               
                   
                 chr1 
                 78324741 
                 78324991 
                 0.284 
               
               
                   
                 chr1 
                 79622150 
                 79622400 
                 0.324 
               
               
                   
                 chr1 
                 79622150 
                 79622400 
                 0.324 
               
               
                   
                 chr1 
                 81028150 
                 81028400 
                 0.28 
               
               
                   
                 chr1 
                 81829490 
                 81829740 
                 0.3 
               
               
                   
                 chr1 
                 82631657 
                 82631907 
                 0.344 
               
               
                   
                 chr1 
                 83432297 
                 83432547 
                 0.348 
               
               
                   
                 chr1 
                 84232408 
                 84232658 
                 0.348 
               
               
                   
                 chr1 
                 85186300 
                 85186550 
                 0.348 
               
               
                   
                 chr1 
                 85987798 
                 85988048 
                 0.312 
               
               
                   
                 chr1 
                 86792219 
                 86792469 
                 0.3 
               
               
                   
                 chr1 
                 88716354 
                 88716604 
                 0.348 
               
               
                   
                 chr1 
                 89574150 
                 89574400 
                 0.344 
               
               
                   
                 chr1 
                 90818292 
                 90818542 
                 0.348 
               
               
                   
                 chr1 
                 91937586 
                 91937836 
                 0.332 
               
               
                   
                 chr1 
                 92757305 
                 92757555 
                 0.32 
               
               
                   
                 chr1 
                 93564210 
                 93564460 
                 0.328 
               
               
                   
                 chr1 
                 94366822 
                 94367072 
                 0.348 
               
               
                   
                 chr1 
                 94473766 
                 94474015 
                 0.53012 
               
               
                   
                 chr1 
                 94476259 
                 94476508 
                 0.566265 
               
               
                   
                 chr1 
                 94496446 
                 94496695 
                 0.546185 
               
               
                   
                 chr1 
                 94508204 
                 94508453 
                 0.554217 
               
               
                   
                 chr1 
                 94508724 
                 94508973 
                 0.570281 
               
               
                   
                 chr1 
                 94517145 
                 94517394 
                 0.369478 
               
               
                   
                 chr1 
                 94525991 
                 94526240 
                 0.477912 
               
               
                   
                 chr1 
                 95460410 
                 95460660 
                 0.336 
               
               
                   
                 chr1 
                 96550309 
                 96550559 
                 0.348 
               
               
                   
                 chr1 
                 97375580 
                 97375830 
                 0.348 
               
               
                   
                 chr1 
                 98175941 
                 98176191 
                 0.256 
               
               
                   
                 chr1 
                 99069150 
                 99069400 
                 0.3 
               
               
                   
                 chr1 
                 99919444 
                 99919694 
                 0.34 
               
               
                   
                 chr1 
                 100316484 
                 100316734 
                 0.34 
               
               
                   
                 chr1 
                 100316495 
                 100316745 
                 0.348 
               
               
                   
                 chr1 
                 100340820 
                 100341070 
                 0.344 
               
               
                   
                 chr1 
                 100346760 
                 100347010 
                 0.38 
               
               
                   
                 chr1 
                 100381830 
                 100382080 
                 0.28 
               
               
                   
                 chr1 
                 100381939 
                 100382189 
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     DETAILED DESCRIPTION 
     The invention pertains to a method for analyzing genetic abnormalities that involves hybridization-based enrichment of selected target regions across the human genome in a multiplexed panel assay, followed by quantification, coupled with a novel bioinformatics and mathematical analysis pipeline. An overview of the method is shown schematically in  FIG.  1   . 
     In-solution hybridization enrichment has been used in the past to enrich specific regions of interest prior to sequencing (see e.g., Meyer, M and Kirchner, M. (2010)  Cold Spring Harb. Protoc.  2010(6):pdbprot5448; Liao, G. J. et al. (2012)  PLoS One  7:e38154; Maricic, T. et al. (2010)  PLoS One  5:e14004; Tewhey, R. et al. (2009)  Genome Biol.  10:R116; Tsangaras, K. et al. (2014)  PLoS One  9:e109101; PCT Publication WO 2016/189388; US Patent Publication 2016/0340733; Koumbaris, G. et al. (2015)  Clinical chemistry,  62(6), pp. 848-855). However, for the methods of the invention, the target sequences (referred to as TArget Capture Sequences, or TACS) used to enrich for specific regions of interest have been optimized for maximum efficiency, specificity and accuracy and, furthermore, allow for analysis of very small starting amounts of fetal or embryonic DNA in samples containing only or predominantly fetal or embryonic DNA. 
     Furthermore, in certain embodiments, the TACS used in the methods are families of TACS, comprising a plurality of members that bind to the same genomic sequence but with differing start and/or stop positions, such that enrichment of the genomic sequences of interest is significantly improved compared to use of a single TACS binding to the genomic sequence. The configuration of such families of TACS is illustrated schematically in  FIG.  3   , showing that the different start and/or stop positions of the members of the TACS family when bound to the genomic sequence of interest results in a staggered binding pattern for the family members. 
     The use of families of TACS with the TACS pool that bind to each target sequence of interest, as compared to use of a single TACS within the TACS pool that binds to each target sequence of interest, significantly increases enrichment for the target sequences of interest, as evidenced by a greater than 50% average increase in read-depth for the family of TACS versus a single TACS. Comparison of use of a family of TACS versus a single TACS, and the significantly improved read-depth that was observed, is described in detail in Example 5. 
     Analysis of Fetal/Embryonic DNA Samples 
     The methods and kits of the disclosure are used in the analysis of fetal or embryonic DNA samples, e.g., for the presence of genetic abnormalities, for example for purposes of IVF Pre-implantation Genetic Screening (PGS) and Diagnosis (PGD). Accordingly, in the methods of the invention, the DNA sample comprises predominantly or only fetal or embryonic DNA. The methods can be used with samples from a single or only a few fetal or embryonic cells. As used herein “a few” fetal or embryonic cells refers to 10 fetal or embryonic cells or less. Accordingly, the methods allow for analysis of very small amounts of fetal or embryonic DNA. The fetal or embryonic DNA sample contains predominantly or only fetal/embryonic DNA, described further below in the subsection on sample preparation. An exemplification of use of the method with samples from 3-day and 5-day biopsy embryos is described in Example 6. 
     Accordingly, in one aspect, the invention pertains to a method of testing for risk of a genetic abnormality in a DNA sample comprising predominantly fetal or embryonic DNA and comprising genomic sequences of interest, the method comprising:
         (a) preparing a sequencing library from the DNA sample comprising predominantly fetal or embryonic DNA;   (b) hybridizing the sequencing library to a pool of double-stranded TArget Capture Sequences (TACS), wherein the pool of TACS comprises sequences that bind to one or more genomic sequences of interest comprising a genetic abnormality;   (c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library;   (d) amplifying and sequencing the enriched library; and   (e) performing statistical analysis on the enriched library sequences to thereby determine risk of a genetic abnormality in the DNA sample.   In one embodiment:
           (i) each member sequence within the pool of TACS is between 100-500 base pairs in length, each member sequence having a 5′ end and a 3′ end;   (ii) each member sequence binds to the same genomic sequence of interest at least 50 base pairs away, on both the 5′ end and the 3′ end, from regions harboring Copy Number Variations (CNVs), Segmental duplications or repetitive DNA elements; and   (iii) the GC content of the pool of TACS is between 19% and 80%, as determined by calculating the GC content of each member within the pool of TACS.   
               

     In one embodiment, the pool of TACS comprises a plurality of TACS families, wherein each member of a TACS family binds to the same target sequence of interest but with different start and/or stop positions on the sequence with respect to a reference coordinate system (i.e., binding of TACS family members to the target sequence is staggered) to thereby enrich for target sequences of interest, followed by massive parallel sequencing and statistical analysis of the enriched population. The use of families of TACS with the TACS pool that bind to each target sequence of interest, as compared to use of a single TACS within the TACS pool that binds to each target sequence of interest, significantly increases enrichment for the target sequences of interest, as evidenced by a greater than 50% average increase in read-depth for the family of TACS versus a single TACS. 
     Accordingly, in one embodiment, the pool of TACS comprises a plurality of TACS families directed to different genomic sequences of interest, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest. 
     Thus, in another aspect, the invention pertains to a method of testing for risk of a genetic abnormality in a DNA sample comprising predominantly fetal or embryonic DNA and comprising genomic sequences of interest, the method comprising:
         (a) preparing a sequencing library from the DNA sample comprising predominantly fetal or embryonic DNA;   (b) hybridizing the sequencing library to a pool of double-stranded TArget Capture Sequences (TACS), wherein the pool of TACS comprises a plurality of TACS families directed to different genomic sequences of interest, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest;   (c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library;   (d) amplifying and sequencing the enriched library; and   (e) performing statistical analysis on the enriched library sequences to thereby determine risk of a genetic abnormality in the DNA sample.   In one embodiment,
           (i) each member sequence within each TACS family is between 100-500 base pairs in length, each member sequence having a 5′ end and a 3′ end;   (ii) each member sequence binds to the same genomic sequence of interest at least 50 base pairs away, on both the 5′ end and the 3′ end, from regions harboring Copy Number Variations (CNVs), Segmental duplications or repetitive DNA elements; and   (iii) the GC content of the pool of TACS is between 19% and 80%, as determined by calculating the GC content of each member within each family of TACS.   
               

     The TACS-enrichment based method of the disclosure can be used in the detection of a wide variety of genetic abnormalities. In one embodiment, the genetic abnormality is a chromosomal aneuploidy (such as a trisomy, a partial trisomy or a monosomy). In other embodiments, the genomic abnormality is a structural abnormality, including but not limited to copy number changes including microdeletions and microduplications, insertions, translocations, inversions and small-size mutations including point mutations and mutational signatures. In another embodiment, the genetic abnormality is a chromosomal mosaicism. 
     TArget Capture Sequence Design 
     As used herein, the term “TArget Capture Sequences” or “TACS” refers to short DNA sequences that are complementary to the region(s) of interest on a genomic sequence(s) of interest (e.g., chromosome(s) of interest) and which are used as “bait” to capture and enrich the region of interest from a large library of sequences, such as a whole genomic sequencing library prepared from a biological sample. A pool of TACS is used for enrichment wherein the sequences within the pool have been optimized with regard to: (i) the length of the sequences; (ii) the distribution of the TACS across the region(s) of interest; and (iii) the GC content of the TACS. The number of sequences within the TACS pool (pool size) has also been optimized. 
     It has been discovered that TACS having a length of 100-500 base pairs are optimal to maximize enrichment efficiency. In various other embodiments, each sequence within the pool of TACS is between 150-260 base pairs, 100-200 base pairs, 200-260 base pairs, 100-350 bp in length, or 100-500 bp in length. In preferred embodiments, the length of the TACS within the pool is at least 250 base pairs, or is 250 base pairs or is 260 base pairs or is 280 base pairs. It will be appreciated by the ordinarily skilled artisan that a slight variation in TACS size typically can be used without altering the results (e.g., the addition or deletion of a few base pairs on either end of the TACS); accordingly, the base pair lengths given herein are to be considered “about” or “approximate”, allowing for some slight variation (e.g., 1-5%) in length. Thus, for example, a length of “250 base pairs” is intended to refer to “about 250 base pairs” or “approximately 250 base pairs”, such that, for example, 248 or 252 base pairs is also encompassed. 
     The distribution of the TACS across each region or chromosome of interest has been optimized to avoid high copy repeats, low copy repeats and copy number variants, while at the same time also being able to target informative single nucleotide polymorphisms (SNPs) in order to enable both aneuploidy, or structural copy number change detection, and fetal fraction (ff) estimation. Accordingly, each sequence within the TACS pool is designed such that the 5′ end and the 3′ end are each at least 50 base pairs away from regions in the genome that are known to harbour one or more of the following genomic elements: Copy Number Variations (CNVs), Segmental duplications and/or repetitive DNA elements (such as transposable elements or tandem repeat areas). In various other embodiments, each sequence within the TACS pool is designed such that the 5′ end and the 3′ end are each at least 50, 100, 150, 200, 250, 300, 400 or 500 base pairs away from regions in the genome that are known to harbour one or more of the aforementioned elements. 
     The term “Copy Number Variations” is a term of art that refers to a form of structural variation in the human genome in which there can be alterations in the DNA of the genome in different individuals that can result in a fewer or greater than normal number of a section(s) of the genome in certain individuals. CNVs correspond to relatively large regions of the genome that may be deleted (e.g., a section that normally is A-B-C-D can be A-B-D) or may be duplicated (e.g., a section that normally is A-B-C-D can be A-B-C-C-D). CNVs account for roughly 13% of the human genome, with each variation ranging in size from about 1 kilobase to several megabases in size. 
     The term “Segmental duplications” (also known as “low-copy repeats”) is also a term of art that refers to blocks of DNA that range from about 1 to 400 kilobases in length that occur at more than one site within the genome and typically share a high level (greater than 90%) of sequence identity. Segmental duplications are reviewed in, for example, Eichler. E. E. (2001)  Trends Genet.  17:661-669. 
     The term “repetitive DNA elements” (also known as “repeat DNA” or “repeated DNA”) is also a term of art that refers to patterns of DNA that occur in multiple copies throughout the genome. The term “repetitive DNA element” encompasses terminal repeats, tandem repeats and interspersed repeats, including transposable elements. Repetitive DNA elements in NGS is discussed further in, for example, Todd, J. et al. (2012)  Nature Reviews Genet.  13:36-46. 
     The TACS are designed with specific GC content characteristics in order to minimize data GC bias and to allow a custom and innovative data analysis pipeline. It has been determined that TACS with a GC content of 19-80% achieve optimal enrichment and perform best with cell free fetal DNA. Within the pool of TACS, different sequences can have different % GC content, although to be selected for inclusion with the pool, the % GC content of each sequence is chosen as between 19-80%, as determined by calculating the GC content of each member within the pool of TACS or within each family of TACS. That is, every member within the pool or within each family of TACS in the pool has a % GC content within the given percentage range (e.g., between 19-80% GC content). 
     In some instances, the pool of TACS (e.g., each member within each family of TACS) may be chosen so as to define a different % GC content range, deemed to be more suitable for the assessment of specific genetic abnormalities. Non-limiting examples of various % GC content ranges, can be between 19% and 80%, or between 19% and 79%, or between 19% and 78%, or between 19% and 77%, or between 19% and 76%, or between 19% and 75%, or between 19% and 74%, or between 19% and 73%, or between 19% and 72%, or between 19% and 71%, or between 19% and 70%, or between 19% and 69%, or between 19% and 68%, or between 19% and 67%, or between 19% and 66%, or between 19% and 65%, or between 19% and 64%, or between 19% and 63%, or between 19% and 62%, or between 19% and 61%, or between 19% and 60%, or between 19% and 59%, or between 19% and 58%, or between 19% and 57%, or between 19% and 56%, or between 19% and 55%, or between 19% and 54%, or between 19% and 53%, or between 19% and 52%, or between 19% and 51%, or between 19% and 50%, or between 19% and 49%, or between 19% and 48%, or between 19% and 47%, or between 19% and 46%, or between 19% and 45%, or between 19% and 44%, or between 19% and 43%, or between 19% and 42%, or between 19% and 41%, or between 19% and 40%. 
     As described in further detail below with respect to one embodiment of the data analysis, following amplification and sequencing of the enriched sequences, the test loci and reference loci can then be “matched” or grouped together according to their % GC content (e.g., test loci with a % GC content of 40% is matched with reference loci with a % GC content of 40%). It is appreciated that the % GC content matching procedure may allow slight variation in the allowed matched % GC range. A non-limiting instance, and with reference to the previously described example in text, a test locus with % GC content of 40% could be matched with reference loci of % GC ranging from 39-41%, thereby encompassing the test locus % GC within a suitable range. 
     To prepare a pool of TACS having the optimized criteria set forth above with respect to size, placement within the human genome and % GC content, both manual and computerized analysis methods known in the art can be applied to the analysis of the human reference genome. In one embodiment, a semi-automatic method is implemented where regions are firstly manually designed based on the human reference genome build 19 (hg19) ensuring that the aforementioned repetitive regions are avoided and subsequently are curated for GC-content using software that computes the % GC-content of each region based on its coordinates on the human reference genome build 19 (hg19). In another embodiment, custom-built software is used to analyze the human reference genome in order to identify suitable TACS regions that fulfill certain criteria, such as but not limited to, % GC content, proximity to repetitive regions and/or proximity to other TACS. 
     The number of TACS in the pool has been carefully examined and adjusted to achieve the best balance between result robustness and assay cost/throughput. The pool typically contains at least 800 or more TACS, but can include more, such as 1500 or more TACS, 2000 or more TACS or 2500 or more TACS or 3500 or more TACS or 5000 or more TACS. It has been found that an optimal number of TACS in the pool is 5000. It will be appreciated by the ordinarily skilled artisan that a slight variation in pool size typically can be used without altering the results (e.g., the addition or removal of a small number of TACS); accordingly, the number sizes of the pool given herein are to be considered “about” or “approximate”, allowing for some slight variation (e.g., 1-5%) in size. Thus, for example, a pool size of “1600 sequences” is intended to refer to “about 1600 sequences” or “approximately 1600 sequences”, such that, for example, 1590 or 1610 sequences is also encompassed. 
     In view of the foregoing, in another aspect, the invention provides a method for preparing a pool of TACS for use in the method of the invention for detecting risk of a chromosomal and/or other genetic abnormality, wherein the method for preparing the pool of TACS comprises: selecting regions in one or more chromosomes of interest having the criteria set forth above (e.g., at least 50 base pairs away on either end from the aforementioned repetitive sequences and a GC content of between 19% and 80%, as determined by calculating the GC content of each member within each family of TACS), preparing primers that amplify sequences that hybridize to the selected regions, and amplifying the sequences, wherein each sequence is 100-500 base pairs in length. 
     For use in the methods of the disclosure, the pool of TACS typically is fixed to a solid support, such as beads (such as magnetic beads) or a column. In one embodiment, the pool of TACS are labeled with biotin and are bound to magnetic beads coated with a biotin-binding substance, such as streptavidin or avidin, to thereby fix the pool of TACS to a solid support. Other suitable binding systems for fixing the pool of TACS to a solid support (such as beads or column) are known to the skilled artisan and readily available in the art. When magnetic beads are used as the solid support, sequences that bind to the TACS affixed to the beads can be separated magnetically from those sequences that do not bind to the TACS. 
     Families of TACS 
     In one embodiment, the pool of TACS comprises a plurality of TACS families directed to different genomic sequences of interest. Each TACS family comprises a plurality of members that bind to the same genomic sequence of interest but having different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest. Typically, the reference coordinate system that is used for analyzing human genomic DNA is the human reference genome built hg19, which is publically available in the art, but other coordinate systems may also be used. Alternatively, the reference coordinate system can be an artificially created genome based on built hg19 that contains only the genomic sequences of interest. Exemplary non-limiting examples of start/stop positions for TACS that bind to chromosome 13, 18, 21, X or Y are shown in  FIG.  2   . 
     Each TACS family comprises at least 2 members that bind to the same genomic sequence of interest. In various embodiments, each TACS family comprises at least 2 member sequences, or at least 3 member sequences, or at least 4 member sequences, or at least 5 member sequences, or at least 6 member sequences, or at least 7 member sequences, or at least 8 member sequence, or at least 9 member sequences, or at least 10 member sequences. In various embodiments, each TACS family comprises 2 member sequences, or 3 member sequences, or 4 member sequences, or 5 member sequences, or 6 member sequences, or 7 member sequences, or 8 member sequences, or 9 member sequences, or 10 member sequences. In various embodiments, the plurality of TACS families comprises different families having different numbers of member sequences. For example, a pool of TACS can comprise one TACS family that comprises 3 member sequences, another TACS family that comprises 4 member sequences, and yet another TACS family that comprises 5 member sequences, and the like. In one embodiment, a TACS family comprises 3-5 member sequences. In another embodiment, the TACS family comprises 4 member sequences. 
     The pool of TACS comprises a plurality of TACS families. Thus, a pool of TACS comprises at least 2 TACS families. In various embodiments, a pool of TACS comprises at least 3 different TACS families, or at least 5 different TACS families, or at least 10 different TACS families, or at least 50 different TACS families, or at least 100 different TACS families, or at least 500 different TACS families, or at least 1000 different TACS families, or at least 2000 TACS families, or at least 4000 TACS families, or at least 5000 TACS families. 
     Each member within a family of TACS binds to the same genomic region of interest but with different start and/or stop positions, with respect to a reference coordinate system for the genomic sequence of interest, such that the binding pattern of the members of the TACS family is staggered (see  FIG.  3   ). In various embodiments, the start and/or stop positions are staggered by at least 3 base pairs, or at least 4 base pairs, or at least 5 base pairs, or at least 6 base pairs, or at least 7 base pairs, or at least 8 base pairs, or at least 9 base pairs, or at least 10 base pairs, or at least 15 base pairs, or at least 20 base pairs, or at least 25 base pairs. Typically, the start and/or stop positions are staggered by 5-10 base pairs. In one embodiment, the start and/or stop positions are staggered by 5 base pairs. In another embodiment, the start and/or stop positions are staggered by 10 base pairs. 
     Sample Collection and Preparation 
     The methods of the invention can be used with a variety of biological samples that contain only or predominantly fetal or embryonic DNA. As used herein, a sample containing “predominantly fetal or embryonic DNA” is one that contains more than 50% fetal or embryonic DNA, and typically contains more than 90%, or 95% or 99% fetal or embryonic DNA. In one embodiment, the source of the sample that contains predominantly fetal or embryonic DNA is fetal or embryonic cells obtained from embryo biopsy of in vitro fertilized (IVF) pre-implantation embryos. It has been demonstrated that intact cells can be obtained from IVF pre-implantation embryos for Pre-implantation Genetic Screening (PGS) and Pre-implantation Genetic Diagnosis (PGD) processes. An ovum is fertilized through IVF and resulting cells are collected during in vitro growth of the embryo. For example, cells can be collected from a day 3 embryo or a day 5 embryo. Typically, if cell harvesting is performed at day 3 a single fetal cell is obtained, also known as a blastomere, and if harvesting is performed at day 5 a few cells are obtained, also known as trophectoderm cells. Typically, the genetic integrity of the grown fetal cells is interrogated using array Comparative Genomic Hybridization (aCGH), a technology that can detect genetic abnormalities of a certain genomic size and above. The method of the disclosure provides an alternative means for detecting genomic abnormalities in fetal cells obtained from an embryo, which enables higher resolution of the interrogated genome. 
     In another embodiment, the source of the sample that contains predominantly fetal or embryonic DNA is fetal or embryonic cells obtained non-invasively from collecting intact cells (trophoblasts) from a maternal Papanicolaou smear (pap test). Recently it has been shown that this is a simple and safe approach for obtaining fetal or embryonic genetic material non-invasively and that the cells obtained from the pap test had an abundance (near 100%) of fetal or embryonic genetic material (Jain, C. V. et al. (2016)  Science Translational Medicine  8(363):363re4-363re4). 
     In another embodiment, the source of the sample that contains predominantly fetal or embryonic DNA is one or a few fetal or embryonic cells found in maternal plasma. Thus, one or a few fetal or embryonic cells present in maternal plasma can be isolated and DNA from the one or a few cells can be used as the DNA sample in the methods of the invention. 
     In yet other embodiments, the sample containing predominantly fetal or embryonic DNA is a DNA sample that is obtained directly from fetal tissue, or from amniotic fluid, or from chorionic villi or from medium where products of conception were grown. 
     In another embodiment, the DNA sample that contains predominantly fetal or embryonic DNA is obtained directly from fetal or embryonic tissue. 
     For the biological sample preparation, typically cells are lysed and DNA is extracted using standard techniques known in the art, a non-limiting example of which is the QiAsymphony (Qiagen) protocol. 
     Following isolation, the cell free DNA of the sample is used for sequencing library construction to make the sample compatible with a downstream sequencing technology, such as Next Generation Sequencing. Typically this involves ligation of adapters onto the ends of the cell free DNA fragments, followed by amplification. Sequencing library preparation kits are commercially available. A non-limiting exemplary protocol for sequencing library preparation is described in detail in Example 1. 
     Enrichment by TACS Hybridization 
     The region(s) of interest on the chromosome(s) of interest is enriched by hybridizing the pool of TACS to the sequencing library, followed by isolation of those sequences within the sequencing library that bind to the TACS. To facilitate isolation of the desired, enriched sequences, typically the TACS sequences are modified in such a way that sequences that hybridize to the TACS can be separated from sequences that do not hybridize to the TACS. Typically, this is achieved by fixing the TACS to a solid support. This allows for physical separation of those sequences that bind the TACS from those sequences that do not bind the TACS. For example, each sequence within the pool of TACS can be labeled with biotin and the pool can then be bound to beads coated with a biotin-binding substance, such as streptavidin or avidin. In a preferred embodiment, the TACS are labeled with biotin and bound to streptavidin-coated magnetic beads. The ordinarily skilled artisan will appreciate, however, that other affinity binding systems are known in the art and can be used instead of biotin-streptavidin/avidin. For example, an antibody-based system can be used in which the TACS are labeled with an antigen and then bound to antibody-coated beads. Moreover, the TACS can incorporate on one end a sequence tag and can be bound to a solid support via a complementary sequence on the solid support that hybridizes to the sequence tag. Furthermore in addition to magnetic beads, other types of solid supports can be used, such as polymer beads and the like. 
     In certain embodiments, the members of the sequencing library that bind to the pool of TACS are fully complementary to the TACS. In other embodiments, the members of the sequencing library that bind to the pool of TACS are partially complementary to the TACS. For example, in certain circumstances it may be desirable to utilize and analyze data that are from DNA fragments that are products of the enrichment process but that do not necessarily belong to the genomic regions of interest (i.e., such DNA fragments could bind to the TACS because of part homologies (partial complementarity) with the TACS and when sequenced would produce very low coverage throughout the genome in non-TACS coordinates). 
     Following enrichment of the sequence(s) of interest using the TACS, thereby forming an enriched library, the members of the enriched library are eluted from the solid support and are amplified and sequenced using standard methods known in the art. Next Generation Sequencing is typically used, although other sequencing technologies can also be employed, which provides very accurate counting in addition to sequence information. To detect genetic abnormalities, such as but not limited to, aneuploidies or structural copy number changes requires very accurate counting and NGS is a type of technology that enables very accurate counting. Accordingly, for the detection of genetic abnormalities, such as but not limited to, aneuploidies or structural copy number changes, other accurate counting methods, such as digital PCR and microarrays can also be used instead of NGS. Non-limiting exemplary protocols for amplification and sequencing of the enriched library are described in detail in Example 3. 
     Data Analysis 
     The information obtained from the sequencing of the enriched library can be analyzed using an innovative biomathematical/biostatistical data analysis pipeline. Details of an exemplary analysis using this pipeline are described in depth in Example 4, and in further detail below. Alternative data analysis approaches for different purposes are also provided herein. For example, data analysis approaches for analyzing fetal and/or embryonic DNA samples for genetic abnormalities are described in detail in Example 6. 
     The analysis pipeline described in Example 4 exploits the characteristics of the TACS, and the high-efficiency of the target capture enables efficient detection of aneuploidies or structural copy number changes, as well as other types of genetic abnormalities. In the analysis, first the sample&#39;s sequenced DNA fragments are aligned to the human reference genome. QC metrics are used to inspect the aligned sample&#39;s properties and decide whether the sample is suitable to undergo classification. These QC metrics can include, but are not limited to, analysis of the enrichment patterns of the loci of interest, such as for example the overall sequencing depth of the sample, the on-target sequencing output of the sample, TACS performance, GC bias expectation, fraction of interest quantification. For determining the risk of a chromosomal abnormality in the fetal DNA of the sample, an innovative algorithm is applied. The steps of the algorithm include, but are not limited to, removal of inadequately sequenced loci, read-depth and fragment-size information extraction at TACS-specific coordinates, genetic (GC-content) bias alleviation and ploidy status classification. 
     Ploidy status determination is achieved using one or more statistical methods, non-limiting examples of which include a t-test method, a bootstrap method, a permutation test and/or a binomial test of proportions and/or segmentation-based methods and/or combinations thereof. It will be appreciated by the ordinarily skilled artisan that the selection and application of tests to be included in ploidy status determination is based on the number of data points available. As such, the suitability of each test is determined by various factors such as, but not limited to, the number of TACS utilized and the respective application for GC bias alleviation, if applicable. Thus, the aforementioned methods are to be taken as examples of the types of statistical analysis that may be employed and are not the only methods suitable for the determination of ploidy status. Typically, the statistical method results in a score value for the mixed sample and risk of the chromosomal abnormality in the fetal DNA is detected when the score value for the mixed sample is above a reference threshold value. 
     In particular, one aspect of the statistical analysis involves quantifying and alleviating GC-content bias. In addition to the challenge of detecting small signal changes in fetal DNA in the mixed sample, and/or other components of DNA of interest part of a mixed sample (for example, but not limited to, additional or less genetic material from certain chromosomal regions), the sequencing process itself introduces certain biases that can obscure signal detection. One such bias is the preferential sequencing/amplification of genetic regions based on their GC-content. As such, certain detection methods, such as but not limited to, read-depth based methods, need to account for such bias when examining sequencing data. Thus, the bias in the data needs to be quantified and, subsequently, suitable methods are applied to account for it such that genetic context dependencies cannot affect any statistical methods that may be used to quantify fetal genetic abnormality risk. 
     For example, one method of quantifying the GC-content bias is to use a locally weighted scatterplot smoothing (LOESS) technique on the sequencing data. Each targeted locus may be defined by its sequencing read-depth output and its&#39; GC-content. A line of best fit through these two variables, for a large set of loci, provides an estimate of the expected sequencing read-depth given the GC-content. Once this GC-bias quantification step is completed, the next step is to use this information to account for possible biases in the data. One method is to normalize the read-depth of all loci by their expected read-depth (based on each locus&#39; GC-content). In principle, this unlinks the read-depth data from their genetic context and makes all data comparable. As such, data that are retrieved from different GC-content regions, such as for example, but not limited, to different chromosomes, can now be used in subsequent statistical tests for detection of any abnormalities. Thus, using the LOESS procedure, the GC bias is unlinked from the data prior to statistical testing. In one embodiment, the statistical analysis of the enriched library sequences comprises alleviating GC bias using a LOESS procedure. 
     In an alternative embodiment, the GC-content bias is quantified and alleviated by grouping together loci of similar (matching) GC-content. Thus, conceptually this method for alleviating GC-content bias comprises of three steps, as follows:
         1) identification and calculation of GC-content in the TACS;   2) alleviation/accounting of GC-content bias using various matching/grouping procedures of the TACS; and   3) calculation of risk of any genetic abnormalities that may be present in the fetus utilizing statistical and mathematical methods on datasets produced from step 2.       

     For the t-test method, the dataset is split into two groups; the test loci and the reference loci. For each group, subsets of groups are created where loci are categorized according to their GC-content as illustrated in a non-limiting example in the sample Table 1 below: 
                             TABLE 1               GC   Reference loci read-depth   Test loci read-depth                  40%   x 1   40 , x 2   40 , . . . , x nx40   40     y 1   40 , y 2   40 , . . . , y ny40   40         41%   x 1   41 , x 2   41 , . . . , x nx41   41     y 1   41 , y 2   41 , . . . , y ny41   41         42%   x 1   42 , x 2   42 , . . . , x nx42   42     y 1   42 , y 2   42 , . . . , y ny42   42         . . .   . . .   . . .                    
It is appreciated by the ordinarily skilled artisan that subgroup creation may involve encompassing a range of appropriate GC-content and/or a subset of loci that are defined by a given GC-content and/or GC-content range. Accordingly, the % GC content given in the non-limiting example of Table 1 are to be considered “about” or “approximate”, allowing for some slight variation (e.g., 1-2%). Thus, for example, a % GC content of “40%” is intended to refer to “about 40%” or “approximately 40%”, such that, for example, “39%-41%” GC-content loci may also be encompassed if deemed appropriate.
 
Hence, when referring to a particular GC-content it is understood that the reference and test loci subgroups may comprise of any number of loci related to a particular % GC content and/or range.
 
     Subsequently, for each GC-content subgroup, a representative read-depth is calculated. A number of methods may be utilized to choose this such as, but not limited to, the mean, median or mode of each set. Thus, two vectors of representative read-depth are created where one corresponds to the reference loci and the other to the test loci (e.g., Xm, Ym). In one embodiment, the two vectors may be tested against each other to identify significant differences in read-depth. In another embodiment, the difference of the two vectors may be used to assess if there are significant discrepancies between the test and reference loci. The sample is attributed the score of the test. 
     For statistical analysis using a bootstrap approach, the dataset is split into two groups, the test loci and the reference loci. The GC-content of each locus is then calculated. Then the following procedure is performed: 
     A random locus is selected from the reference loci; its read-depth and GC-content are recorded. Subsequently, a random locus from the test loci is selected, with the only condition being that its&#39; GC-content is similar to that of the reference locus. Its read-depth is recorded. It is appreciated by the ordinarily skilled artisan that GC-content similarity may encompass a range of suitable GC-content. As such, referral to a specific % GC content may be considered as “approximate” or “proximal” or “within a suitable range” (e.g., 1%-2%) encompassing the specific % GC content under investigation. Thus, a reference-test locus pair of similar GC-content is created. The difference of the reference-test pair is recorded, say E1. The loci are then replaced to their respective groups. This process is repeated until a bootstrap sample of the same size as the number of test TACS present is created. A representative read-depth of the bootstrap sample is estimated, say E_mu, and recorded. A number of methods may be utilized to do so, such as but not limited to, the mean, mode or median value of the vector, and/or multiples thereof. 
     The process described above is repeated as many times as necessary and a distribution of E_mu is created. The sample is then attributed a score that corresponds to a percentile of this distribution. 
     For statistical analysis using a permutation test, the dataset is sorted firstly into two groups, the test-loci and the reference loci. For each group, subsets of groups are created, where loci are categorized according to their GC-content similarity (see columns 2 and 3 of the non-limiting sample Table 2 below). The number of loci present in each test subgroup is also recorded. The loci of the test group are utilized to calculate an estimate of the test-group&#39;s read-depth, say Yobs. A representative number from each GC-content subgroup may be selected to do so. Any number of methods may be used to provide a read-depth estimate, such as but not limited to, the mean, median or mode of the chosen loci. 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 GC 
                 Reference loci read- 
                 Test loci read-depth 
                 test loci 
                 Merging of loci 
               
               
                   
               
             
            
               
                 40% 
                 x 1   40 , x 2   40 , . . . , x nx40   40   
                 y 1   40 , y 2   40 , . . . , y ny40   40   
                 ny40 
                 x 1   40 , . . . , x nx40   40 , y 1   40 , . . . , y ny40   40   
               
               
                 41% 
                 x 1   41 , x 2   41 , . . . , x nx41   41   
                 y 1   41 , y 2   41 , . . . , y ny41   41   
                 ny41 
                 x 1   41 , . . . , x nx41   41 , y 1   41 , . . . , y ny41   41   
               
               
                 42% 
                 x 1   42 , x 2   42 , . . . , x nx42   42   
                 y 1   42 , y 2   42 , . . . , y ny42   42   
                 ny42 
                 x 1   42 , . . . , x nx42   42 , y 1   42 , . . . , y ny42   42   
               
               
                 . . .  
                 . . .  
                 . . .  
                 . . .  
                 . . . 
               
               
                   
               
            
           
         
       
     
     A distribution to test Yobs is then built utilizing loci irrespective of their test or reference status as follows. The test and reference loci of each GC-content subgroup (see last column of sample Table 2) are combined to allow for calculation of a new read-depth estimate. From each merged subgroup a number of loci are chosen at random, where this number is upper-bounded by the number of test-loci utilized in the original calculation of Yobs (e.g., for GC content 40%, and in the context of the non-limiting sample Table 2, this number of loci may be in the range [1,ny40]). The new read-depth estimate is calculated from all the chosen loci. The procedure is iterated as many times as necessary in order to build a distribution of observed means. A sample is then attributed a score that corresponds to the position of Yobs in this distribution using a suitable transformation that accounts for the moments of the built distribution. As with the already described methods, it is appreciated that slight variation in % GC content is allowed (e.g., 1%-2%), if deemed appropriate. Hence, reference to a specific GC-content could be taken as “about” or “approximate”, so that for example when referring to a 40% GC-content, loci that are “approximately” or “about” 40% (e.g., 39%-41%) may be utilized in the method. 
     For statistical analysis using a binomial test of proportions, fragment-sizes aligned to TACS-specific genomic coordinates are used. It has been shown that fragments of cell free genetic material originating from the placenta tend to be smaller in length when compared to other cell free genetic material (Chan, K. C. (2004)  Clin. Chem.  50:88-92). Hence, the statistic of interest is whether the proportion of small-size fragments aligned to a TACS-specific test-region deviates significantly from what is expected when comparing it to the respective proportion of other TACS-specific reference-regions, as this would indicate fetal genetic abnormalities. 
     Thus, fragment-sizes are assigned into two groups. Sizes related to the test loci are assigned to one group and fragment-sizes related to the reference loci are assigned to the other group. Subsequently, in each group, fragment sizes are distributed into two subgroups, whereby small-size fragments are assigned into one subgroup and all remaining fragments are designated to the remaining subgroup. The last step computes the proportion of small-sized fragments in each group and uses these quantities in a binomial test of proportions. The score of the test is attributed to the sample under investigation. 
     The final result of a sample may be given by combining one or more scores derived from the different statistical methods, non-limiting examples of which are given in Example 4. 
     For statistical analysis using segmentation methods, the read-depth and sequence composition of non-overlapping genomic regions of interest of fixed-size is obtained. On the obtained dataset, GC-content read-depth bias alleviation may be performed, but is not limited to, using a local polynomial fitting method in order to estimate the expected read-depth of regions based on their GC content. The expected value, dependent on GC-content, is then used to normalize regions using suitable methods known to those skilled in the art. The normalized dataset is subsequently processed using one or more segmentation-based classification routines. To do so the algorithms process consecutive data points to detect the presence of read-depth deviations which manifest in the form of a “jump/drop” from their surrounding data points. Depending on the segmentation routine used, data points are given a score which is used towards assigning membership into segments of similar performing read-depths. For example, consecutive data points with score values within a suitable range may be classified as one segment, whereas consecutive data points with score values which exceed the set thresholds may be assigned to a different segment. Details of segmentation-based routines are given in Example 6. 
     Kits of the Invention 
     In another aspect, the invention provides kits for carrying out the methods of the disclosure. In one embodiment, the kit comprises a container consisting of the pool of TACS and instructions for performing the method. In one embodiment, the TACS are provided in a form that allows them to be bound to a solid support, such as biotinylated TACS. In another embodiment, the TACS are provided together with a solid support, such as biotinylated TACS provided together with streptavidin-coated magnetic beads. 
     In one embodiment, the kit comprises a container comprising the pool of TACS and instructions for performing the method, wherein the pool of TACS comprises a plurality of member sequences, wherein:
         (i) each member sequence within the TACS pool is between 100-500 base pairs in length, each member sequence having a 5′ end and a 3′ end;   (ii) each member sequence binds to the same genomic sequence of interest at least 50 base pairs away, on both the 5′ end and the 3′ end, from regions harboring Copy Number Variations (CNVs), Segmental duplications or repetitive DNA elements; and   (iii) the GC content of the pool of TACS is between 19% and 80%, as determined by calculating the GC content of each member within the pool of TACS.       

     In one embodiment, the pool of TACS comprises a plurality of TACS families, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest, 
     Furthermore, any of the various features described herein with respect to the design and structure of the TACS can be incorporated into the TACS that are included in the kit. 
     In various other embodiments, the kit can comprise additional components for carrying out other aspects of the method. For example, in addition to the pool of TACS, the kit can comprise one or more of the following (i) one or more components for isolating cell free DNA from a biological sample (e.g., as described in Example 1); (ii) one or more components for preparing the sequencing library (e.g., primers, adapters, buffers, linkers, restriction enzymes, ligation enzymes, polymerase enzymes and the like as described in detail in Example 1); (iii) one or more components for amplifying and/or sequencing the enriched library (e.g., as described in Example 3); and/or (iv) software for performing statistical analysis (e.g., as described in Example 4). 
     Fragment-Based Analysis 
     In another aspect, the invention pertains to fragment based analysis of samples, described further in Example 7. There is evidence from the literature that fetal cell free DNA can be found in the medium of IVF products of conception and it can be used for the assessment of chromosomal abnormalities (Liu, WeiQiang, et al. (2017)). Furthermore, specific types of genetic abnormalities can be characterized by and/or associated with fragments of a smaller size than the expected size of fragments originating from healthy tissues (Jiang et al, (2015),  Proceedings of the National Academy of Sciences,  112(11), ppE1317-E1325). 
     Thus, fragments-based detection may be used to detect abnormalities. For example, a binomial test of proportions, as described Example 4, can be used for the detection of increased presence of nucleic acid material originating from abnormal cells based on fragment size. In particular, under the null hypothesis that the distribution of fragment sizes originating from both euploid and aneuploid cells is the same, a binomial test for proportions (as described in Example 4) using continuity correction can be utilized to quantify any evidence against it. 
     EXAMPLES 
     The present invention is further illustrated by the following examples, which should not be construed as further limiting. The contents of all references, appendices, Genbank entries, patents and published patent applications cited throughout this application are expressly incorporated herein by reference in their entirety. 
     Example 1: Sample Collection and Library Preparation 
     The general methodology for the TACS-based multiplexed parallel analysis approach for genetic assessment is shown schematically in  FIG.  1   . In this example, methods for collecting and processing a fetal or embryonic DNA sample, followed by sequencing library preparation for use in the methodology of  FIG.  1    are described. 
     Sample Collection 
     Fetal cell samples were obtained from 3-day and 5-day biopsy embryos respectively were subjected to the TACS methodology shown in  FIG.  1    to determine the status of genetic abnormalities. Protocols used for collecting samples for our study were approved by the Cyprus National Bioethics Committee, and informed consent was obtained from all participants. 
     Sequencing Library Preparation 
     Collected fetal cells were initially lysed and DNA extracted using the Rubicon Genomics PicoPLEX© WGA Kit (Liang, L. et al. (2013) PLoS One 8(4), p. e61838). Following a pre-amplification step, the lysed material was amplified using amplification enzyme and buffer supplied by the manufacturer. Subsequently, DNA was purified followed by fragmentation using sonication. Following fragmentation, standard library preparation methods were used with the following modifications. A negative control extraction library was prepared separately to monitor any contamination introduced during the experiment. During this step, 5′ and 3′ overhangs were filled-in, by adding 12 units of T4 polymerase (NEB) while 5′ phosphates were attached using 40 units of T4 polynucleotide kinase (NEB) in a 100 μl reaction and subsequent incubation at 25° C. for 15 minutes and then 12° C. for 15 minutes. Reaction products were purified using the MinElute® kit (Qiagen). Subsequently, adaptors P5 and P7 (see adaptor preparation) were ligated at 1:10 dilution to both ends of the DNA using 5 units of T4 DNA ligase (NEB) in a 40 μl reaction for 20 minutes at room temperature, followed by purification using the MinElute® kit (Qiagen). Nicks were removed in a fill-in reaction with 16 units of Bst polymerase (NEB) in a 40 μl reaction with subsequent incubation at 65° C. for 25 minutes and then 12° C. for 20 minutes. Products were purified using the MinElute® kit (Qiagen). Library amplification was performed using a Fusion polymerase (Herculase® II Fusion DNA polymerase (Agilent Technologies) or Pfusion® High Fidelity Polymerase (NEB)) in 50 μl reactions and with the following cycling conditions, 95° C. for 3 minutes; followed by 10 cycles at 95° C. for 30 seconds, 60° C. for 30 seconds, 72° C. for 30 seconds and finally 72° C. for 3 minutes (Koumbaris, G. et al. (2015)  Clinical chemistry,  62(6), pp. 848-855). The final library products were purified using the MinElute® Purification Kit (Qiagen) and measured by spectrophotometry. 
     Adaptor Preparation 
     Hybridization mixtures for adapter P5 and P7 were prepared separately and incubated for 10 seconds at 95° C. followed by a ramp from 95° C. to 12° C. at a rate of 0.1° C./second. P5 and P7 reactions were combined to obtain a ready-to-use adapter mix (100 μM of each adapter). Hybridization mixtures were prepared as follows: P5 reaction mixture contained adaptor P5_F (500 μM) at a final concentration of 200 μM, adaptor P5+P7_R (500 μM) at a final concentration of 200 μM with 1× oligo hybridization buffer. In addition, P7 reaction mixture contained adaptor P7_F (500 μM) at a final concentration of 200 IM, adapter P5+P7_R(500 μM) at a final concentration of 200 μM with 1× oligo hybridization buffer (Koumbaris, G. et al. (2015)  Clinical chemistry,  62(6), pp. 848-855). Sequences were as follows, wherein *=a phosphorothioate bond (PTO) (Integrated DNA Technologies): 
     
       
         
           
               
               
            
               
                   
                 adaptor P5_F: 
               
               
                   
                 (SEQ ID NO: XX) 
               
               
                   
                 A*C*A*C*TCTTTCCCTACACGACGCTCTTCCG*A*T*C*T 
               
               
                   
                   
               
               
                   
                 adaptor P7_F: 
               
               
                   
                 (SEQ ID NO: YY) 
               
               
                   
                 G*T*G*A*CTGGAGTTCAGACGTGTGCTCTTCCG*A*T*C*T, 
               
               
                   
                   
               
               
                   
                 adaptor_P5 + P7_R: 
               
               
                   
                 (SEQ ID NO: ZZ) 
               
               
                   
                 A*G*A*T*CGGAA*G*A*G*C 
               
            
           
         
       
     
     Example 2: TArget Capture Sequences (TACS) Design and Preparation 
     This example describes preparation of custom TACS for the detection of whole or partial chromosomal abnormalities for chromosomes 1-22, X and Y or any other chromosome, as well as other genetic abnormalities, such as but not limited to, chromosomal mosaicism, microdeletion/microduplication syndromes, translocations, inversions, insertions, and other point or small size mutations. The genomic target-loci used for TACS design were selected based on their GC content and their distance from repetitive elements (minimum 50 bp away). TACS size can be variable. In one embodiment of the method the TACS range from 100-500 bp in size and are generated through a PCR-based approach as described below. The TACS were prepared by simplex polymerase chain reaction using standard Taq polymerase, primers designed to amplify the target-loci, and normal DNA used as template. 
     All custom TACS were generated using the following cycling conditions: 95° C. for 3 minutes; 40 cycles at 95° C. for 15 seconds, 60° C. for 15 seconds, 72° C. for 12 seconds; and 72° C. for 12 seconds, followed by verification via agarose gel electrophoresis and purification using standard PCR clean up kits such as the QiAquick® PCR Purification Kit (Qiagen) or the NucleoSpin® 96 PCR clean-up (Mackerey Nagel) or the Agencourt® AMPure® XP Kit for PCR Purification (Beckman Coulter). Concentration was measured by Nanodrop (Thermo Scientific). 
     Example 3: TACS Hybridization and Amplification 
     This example describes the steps schematically illustrated in  FIG.  1    of target capture by hybridization using TACS, followed by quantitation of captured sequences by Next Generation Sequencing (NGS). 
     TACS Biotinylation 
     TACS were prepared for hybridization, as previously described (Koumbaris, G. et al. (2015)  Clinical chemistry,  62(6), pp. 848-855), starting with blunt ending with the Quick Blunting™ Kit (NEB) and incubation at room temperature for 30 minutes. Reaction products were subsequently purified using the MinElute® kit (Qiagen) and were ligated with a biotin adaptor using the Quick Ligation™ Kit (NEB) in a 40 μl reaction at RT for 15 minutes. The reaction products were purified with the MinElute® kit (Qiagen) and were denatured into single stranded DNA prior to immobilization on streptavidin coated magnetic beads (Invitrogen). 
     TACS Hybridization 
     Amplified libraries were mixed with blocking oligos (Koumbaris, G. et al. (2105)  Clinical chemistry,  62(6), pp. 848-855) (200 μM), 5 μg of Cot-1 DNA (Invitrogen), 50 μg of Salmon Sperm DNA (Invitrogen), Agilent hybridization buffer 2×, Agilent blocking agent 10×, and were heated at 95° C. for 3 minutes to denature the DNA strands. Denaturation was followed by 30 minute incubation at 37° C. to block repetitive elements and adaptor sequences. The resulting mixture was then added to the biotinylated TACS. All samples were incubated in a rotating incubator for 12-48 hours at 66° C. After incubation, the beads were washed as described previously and DNA was eluted by heating (Koumbaris, G. et al. (2105)  Clinical chemistry,  62(6), pp. 848-855). Eluted products were amplified using outer-bound adaptor primers. Enriched amplified products were pooled equimolarly and sequenced on a suitable platform. 
     Example 4: Bioinformatics Sample Analysis 
     This example describes representative statistical analysis approaches for use in the methodology illustrated in  FIG.  1    (“analysis pipeline” in  FIG.  1   ). 
     Human Genome Alignment 
     For each sample, the bioinformatic pipeline routine described below was applied in order to align the sample&#39;s sequenced DNA fragments to the human reference genome. Targeted paired-end read fragments obtained from NGS results were processed to remove adaptor sequences and poor quality reads (Q-score&lt;25) using the cutadapt software (Martin, M. et al. (2011)  EMB.netJournal  17.1). The quality of the raw and/or processed reads as well as any descriptive statistics which aid in the assessment of quality check of the sample&#39;s sequencing output were obtained using the FastQC software (Babraham Institute (2015)  FastQC ) and/or other custom-built software. Processed reads which were at least 25 bases long were aligned to the human reference genome built hg19 (UCSC Genome Bioinformatics) using the Burrows-Wheel Alignment algorithm (Li, H. and Durbin, R. (2009)  Bioinformatics  25:1754-1760) but other algorithms known to those skilled in the art may be used as well. If relevant, duplicate reads were removed post-alignment. Where applicable, sequencing output pertaining to the same sample but processed on separate sequencing lanes, was merged to a single sequencing output file. The removal of duplicates and merging procedures were performed using the Picard tools software suite (Broad Institute (2015)  Picard ) and/or the Sambamba tools software suite (Tarasov, Artem, et al. “Sambamba: fast processing of NGS alignment formats.”  Bioinformatics  31.12 (2015): 2032-2034). 
     The above software analysis resulted in a final aligned version of a sequenced sample against the human reference genome and all subsequent steps were based on this aligned version. Information in terms of Short Nucleotide Polymorphisms (SNPs) at loci of interest was obtained using bcftools from the SAMtools software suite (Li, H. et al. (2009)  Bioinformatics  25:2078-2079) and/or other software known to those skilled in the art. The read-depth per base, at loci of interest, was obtained using the mpileup option of the SAMtools software suite, from here on referred to as the mpileup file. Information pertaining to the size of the aligned fragments was obtained using the view option of the SAMtools software suite, from here on referred to as the fragment-sizes file and/or other software known to those skilled in the art. 
     The mpileup file and the fragment-sizes file were processed using custom-build application programming interfaces (APIs) written in the Python and R programming languages (Python Software Foundation (2015)  Python ; The R Foundation (2015)  The R Project for Statistical Computing ). The APIs were used to determine the ploidy state of chromosomes of interest, and/or other genetic abnormalities in regions of interest across the human genome, using a series of steps (collectively henceforth referred to as the “algorithm”) and to also collect further descriptive statistics to be used as quality check metrics, such as but not limited to fetal fraction quantification (collectively henceforth referred to as the “QC metrics”). The APIs can also be used for the assessment of genetic abnormalities from data generated when applying the described method in cases of multiple gestation pregnancies, as well as other genetic abnormalities such as, but not limited to, microdeletions, microduplications, copy number variations, translocations, inversions, insertions, point mutations and mutational signatures. 
     QC Metrics 
     QC metrics were used to inspect an aligned sample&#39;s properties and decide whether the sample was suitable to undergo classification. These metrics were, but are not limited to, the enrichment of a sample. The patterns of enrichment are indicative of whether a sample has had adequate enrichment across loci of interest in a particular sequencing experiment (herein referred to as a “run”). To assess this, various metrics are assessed, non-limiting examples of which are:
         (i) overall sample on-target read depth,   (ii) sample on-target sequencing output with respect to total mapped reads,   (iii) individual TACS performance in terms of achieved read-depth,   (iv) kurtosis and skewness of individual TACS enrichment,   (v) kurtosis and skewness moments that arise from all TACS,   (vi) fragment size distribution,   (vii) percentage of duplication   (viii) percentage of paired reads and,   (ix) percentage of aligned reads,
 
if applicable.
       

     The above checks are also taken into consideration with regards to GC-bias enrichment. Samples that fail to meet one or more of the criteria given above are flagged for further inspection, prior to classification. 
     The Algorithm 
     The algorithm is a collection of data processing, mathematical and statistical model routines arranged as a series of steps. The algorithm&#39;s steps aim in deciding the relative ploidy state of a chromosome of interest with respect to all other chromosomes of the sequenced sample and is used for the detection of whole or partial chromosomal abnormalities for chromosomes 1-22, X and Y or any other chromosome, as well as other genetic abnormalities such as, but not limited to, chromosomal mosaicism, microdeletion/microduplication syndromes and other point or small size mutations. As such the algorithm can be used, but is not limited to, the detection of whole or partial chromosomal abnormalities for chromosomes 13, 18, 21, X, Y or any other chromosome, as well as other genetic abnormalities such as, but not limited to, microdeletions, microduplications, copy number variations, translocations, inversions, insertions, point mutations and other mutational signatures. 
     For read-depth associated tests, the algorithm compares sequentially the read-depth of loci from each chromosome of interest (herein referred to as the test chromosome) against the read-depth of all other loci (herein referred to as the reference loci) to classify its ploidy state. For each sample, these steps were, but are not limited to: 
     (a) Removal of inadequately sequenced loci. The read-depth of each locus was retrieved. Loci that have not achieved a minimum number of reads, were considered as inadequately enriched and were removed prior to subsequent steps. 
     (b) Genetic (GC-content) bias alleviation. The sequencing procedure may introduce discrepancies in read-depth across the loci of interest depending on their GC content. To account for such bias, a novel sequence-matching approach that increases both sensitivity and specificity to detect chromosomal aneuploidies was employed. The GC content of each locus on the test chromosome was identified and similar genetic loci were grouped together to form genetically matched groups. The procedure was repeated for the reference loci. Then, genetically matched groups from the test chromosome were conditionally paired with their genetically matched group counterparts on the reference chromosome(s). The groups may have any number of members. The conditionally matched groups were then used to assess the ploidy status of test chromosomes. 
     (c) Genetic abnormality determination. Ploidy status determination, or other genetic abnormalities of interest such as but not limited to microdeletions, microduplications, copy number variations, translocations, inversions, insertions, point mutations and other mutational signatures was achieved using a single statistical method and/or a weighted score approach on the result from the following, but not limited to, statistical methods: 
     Statistical Method 1: The differences in read-depth of the conditionally paired groups were tested for statistical significance using the t-test formula: 
             t   =         x   ^     -   μ       s   /     n               
where t is the result of the t-test, tis the average of the differences of the conditionally paired groups, μ is the expected read-depth and is set to a value that represents insignificant read-depth differences between the two groups, s the standard deviation of the differences of the conditionally paired groups and n the length of the vector of the conditionally paired differences. The magnitude of the t-score was then used to identify evidence, if any, against the null hypothesis of same ploidy between reference and test chromosomes. Specifically, t&gt;=c1 (where c1 is a predefined threshold belonging to the set of all positive numbers) shows evidence against the null hypothesis of no difference.
 
     Statistical Method 2: Bivariate nonparametric bootstrap. The bootstrap method depends on the relationship between the random variables X (read-depth of reference loci) and Y (read-depth of test loci). Here, the read depth of baits on the reference group (random variable denoted by X) were treated as the independent covariate. The first step of the iterative procedure involved random sampling with replacement (bootstrapping) of the read-depths of loci on the reference chromosomes, i.e., (x1,g1), . . . , (xn,gn), where the parameter g is known and denotes the GC-content of the chosen bait. Then, for each randomly selected reference bait (xi,gi), a corresponding read depth was generated for a genetically matched locus i.e., (y1,g1), . . . , (yn,gn). Thus, the bivariate data (x1,y1), (x2,y2), . . . , (xn,yn) was arrived at, which was conditionally matched on their GC-content (parameter gi). The differences between the read depths of the genetically matched bootstrapped values xi and yi were used to compute the statistic of interest in each iteration. In one embodiment this statistical measure can be, but is not limited to, the mode, mean or median of the recorded differences, and/or multiples thereof. The procedure was repeated as necessary to build up the distribution of the statistic of interest from these differences. The sample was assigned a score that corresponds to a specific percentile of the built distribution (e.g. 5 th  percentile). Under the null hypothesis the ploidy between chromosomes in the reference and test groups is not different. As such, samples whose score for a particular chromosome, was greater than a predefined threshold, say c2, were classified as statistically unlikely to have the same ploidy. Other statistical measures may be employed. 
     Statistical Method 3: Stratified permutation test. The statistic of interest is the read-depth estimate of the test chromosome, denoted by, Ŷ obs  which is calculated using all loci of the test chromosome&#39;s genetically matched groups as follows: 
                 Y   ^       o   ⁢   b   ⁢   s       =         ∑     j   =   1       j   =   T       ⁢       ∑     i   =   1       i   =     N   ⁢   j         ⁢     y   ij             ∑     j   =   1       j   =   T       ⁢   Nj             
where y ij  is the read-depth of locus i part of the genetically matched group j (i.e., loci belonging to a specific group based on their GC-content), Nj is the number of test loci part of the genetically matched group j and T is the number of genetically matched groups.
 
     Subsequently, a null distribution to test Ŷ obs  was built. To do so, for each group j, the test and reference loci were combined (exchangeability under the null hypothesis), and each group j was sampled randomly up to Nj times without replacement (stratified permutation). This created a vector of values, say yi, and from this the vector&#39;s average value, say, was calculated. The procedure was repeated as necessary to build the null distribution. Finally, Ŷ obs  was studentised against the null distribution using the formula: 
               Z     Y   ⁢   o   ⁢   b   ⁢   s       =         Y     o   ⁢   b   ⁢   s     ⋀     -     Y   ^         σ   Y             
where Ŷ and σ Y  are the first and square root of the second moment of all permuted ý i  statistic values. Samples whose Z Yobs  was greater than a predefined threshold, say c3, were statistically less likely to have the same ploidy in the reference and test groups.
 
     In the case of fragment-size associated tests, the algorithm computes the proportion of small-size fragments found in test-loci and compares it with the respective proportion in reference-loci as described in Statistical Method 4 below. 
     Statistical Method 4: Fragment Size Proportions. For each sample the number and size of fragments aligned onto the human reference genome at the corresponding TACS coordinates, is extracted. The data is subsequently filtered so as to remove fragment-sizes considered statistical outliers using the median outlier detection method. Specifically, outliers are defined as those fragments whose size is above or below the thresholds, F thr , set by equation:
 
 F   thr   =F   median ±( X ×IQR)
 
where F median  is the median fragment-size of all fragments of a sample, X is a variable that can take values from the set of R+, and IQR is the interquartile range of fragment sizes. Thereafter, a binomial test of proportions is carried out to test for supporting evidence against the null hypothesis, H0, where this is defined as:
 
H0: The proportion of small fragments of the test-region is not different from the proportion of small-fragments of the reference region.
 
     In various embodiments of the invention, small fragments are defined as those fragments whose size is less than or equal to a subset of Z+ that is upper-bounded by 160 bp. If the set of all TACS are defined as T, then the test region can be any proper subset S which defines the region under investigation, and the reference region is the relative complement of S in T. For example, in one embodiment of the invention, the set S is defined by all TACS-captured sequences of chromosome 21 and thus the reference set is defined by all TACS-captured fragments on the reference chromosomes, and/or other reference loci 
     The alternative hypothesis, H1, is defined as: 
     H1: The proportion of small fragments of the test-region is not equal to the proportion of test fragments of the reference region. 
     As such, and taking into account continuity correction, the following score is computed (Brown et. al, Harrel): 
               W     t   ⁢   e   ⁢   s   ⁢   t       =       (       p   ′     -     p     r   ⁢   e   ⁢   f         )     /           p   ′     ⁡     (     1   -     p   ′       )         N     t   ⁢   e   ⁢   s   ⁢   t                       where               p   ′     =       (       F   ′     +   0.5     )       (       N     t   ⁢   e   ⁢   s   ⁢   t       +   1     )                     p     r   ⁢   e   ⁢   f       =       (       F     r   ⁢   e   ⁢   f       +     0   .   5       )       (       N     r   ⁢   e   ⁢   f       +   1     )             
{acute over (F)} is the number of small-size fragments on the test-region, F ref  the number of small size fragments on the reference region, N test  the number of all fragments on the test region and N ref  the number of all fragments on the reference region.
 
     For each sample, the algorithm tests sequentially the proportion of fragment sizes of regions under investigation (for example, but not limited to, chromosome 21, chromosome 18, chromosome 13 or other (sub)chromosomal regions of interest) against reference regions; those not under investigation at the time of testing. For each sample a score is assigned for each test. Scores above a set-threshold, say c4, provide evidence against the null hypothesis. 
     Weighted Score method 1: In one embodiment of the method, a weighted score was attributed to each sample s, computed as a weighted sum of all statistical methods using the formula:
 
 V   S ( R,F )= z   1  max{ R   S   ,F   S }+(1− z   1 )min{ R   S   ,F   S }
 
where R S  is the run-specific corrected score arising from a weighted contribution of each read-depth related statistical method for sample s and is defined as:
 
               R   s     =       (         ∑   i     ⁢       w   i     ⁢     S     i   ⁢   s           -       R   ′     r       )       σ   r             
and Ŕ r  is the run-specific median value calculated from the vector of all unadjusted read-depth related weighted scores that arise from a single sequencing run, and σ r  is a multiple of the standard deviation of R scores calculated from a reference set of 100 euploid samples. The terms max{R S ,F S } and min{R S ,F S } denote the maximum and minimum values of the bracketed set, respectively.
 
F S  is the run-specific corrected score arising from the fragment-size related statistical method and is defined as:
 
               F   s     =       (       W     t   ⁢   e   ⁢   s   ⁢   t       -       R   ′     f       )       σ   f             
where W test  is as defined earlier, Ŕ f  is the run specific median calculated from the vector of all unadjusted fragment-related statistical scores that arise from a single sequencing run, and σ f  is a multiple of the standard deviation of F scores calculated from a reference set of 100 euploid samples.
 
     A unique classification score of less than a predefined value indicates that there is no evidence from the observed data that a sample has a significant risk of aneuploidy. 
     Weighted Score method 2: In another embodiment of the method, the weighted score arising from the statistical methods described above was used to assign each sample a unique genetic abnormality risk score using the formula: 
               R   ⁡     (     t   ,   c     )       =       ∑     j   =   0       j   =   N       ⁢       w   j     ⁢       t   j       c   j                 
where R is the weighted score result, w j  the weight assigned to method j, t j  the observed score resulting from method j, and c j  the threshold of method j.
 
     A unique classification score of less than a predefined value indicates that there is no evidence from the observed data that a sample has a significant risk of aneuploidy. 
     Since all read depths from baits in the reference group were assumed to be generated from the same population, and in order to have a universal threshold, run-specific adjustments were also employed to alleviate run-specific biases. 
     The aforementioned method(s), are also suitable for the detection of other genetic abnormalities, such as but not limited to, subchromosomal abnormalities. A non-limiting example is the contiguous partial loss of chromosomal material leading to a state of microdeletion, or the contiguous partial gain of chromosomal material leading to a state of microduplication. A known genetic locus subject to both such abnormalities is 7q11.23. In one embodiment of statistical method 1, synthetic plasma samples of 5%, 10% and 20% fetal material were tested for increased risk of microdeletion and/or microduplication states for the genetic locus 7q11.23. 
     For point mutations various binomial tests are carried out that take into consideration the fetal fraction estimate of the sample, f, the read-depth of the minor allele, r, and the total read-depth of the sequenced base, n. Two frequent, yet non-limiting examples involve assessment of the risk when the genetic abnormality is a recessive point mutation or a dominant point mutation. 
     In addition to the above, fetal sex determination methods were also developed, with non-limiting examples given below. In one embodiment of the invention, fetal sex was assigned to a sample using a Poisson test using the formula: 
               Pr   ⁡     (       r   y     ≤   k     )       =       e     -   λ       ⁢       ∑     i   =   0       i   =   k       ⁢       λ   i       i   !                     where             λ   =       f   ⁢   B   ⁢   μ     2           
and f is the fetal fraction estimate of the sample, B is the number of target sequences on chromosome Y, μ is the read-depth of the sample and k is the sum of reads obtained from all targets B. The null hypothesis of the Poisson test was that the sample is male. A value of Pr(r y ) less than a threshold c y  was considered as enough evidence to reject the null hypothesis, i.e. the sample is not male. If any of the terms for computing Pr(r y ) were unavailable, then the sample&#39;s sex was classified as NA (not available).
 
     In another embodiment of the invention, fetal sex was assigned using the average read-depth of target sequences on chromosome Y. If the average read-depth of the target-sequences was over a predefined threshold, where such threshold may be defined using other sample-specific characteristics such as read-depth and fetal-fraction estimate, the fetal sex was classified as male. If the average read-depth was below such threshold then the sample was classified as female. 
     Example 5: Target Enrichment Using Families of TACS 
     In this example, a family of TACS, containing a plurality of members that all bind to the same target sequence of interest, was used for enrichment, compared to use of a single TACS binding to a target sequence of interest. Each member of the family of TACS bound to the same target sequence of interest but had a different start and/or stop coordinates with respect to a reference coordinate system for that target sequence (e.g., the human reference genome built hg19). Thus, when aligned to the target sequence, the family of TACS exhibit a staggered binding pattern, as illustrated in  FIG.  3   . Typically, the members of a TACS family were staggered approximately 5-10 base pairs. 
     A family of TACS containing four members (i.e., four sequences that bound to the same target sequence but having different start/stop positions such that the binding of the members to the target sequence was staggered) was prepared. Single TACS hybridization was also prepared as a control. The TACS were fixed to a solid support by labelling with biotin and binding to magnetic beads coated with a biotin-binding substance (e.g., streptavidin or avidin) as described in Example 3. The family of TACS and single TACS were then hybridized to a sequence library, bound sequences were eluted and amplified, and these enriched amplified products were then pooled equimolarly and sequenced on a suitable sequencing platform, as described in Example 3. 
     The enriched sequences from the family of TACS sample and the single TACS sample were analyzed for read-depth. The results are shown in  FIGS.  4 A and  4 B . As shown in  FIG.  4 A , target sequences of interest enriched using the family of four TACS (square symbol) exhibited a fold-change in read-depth when compared to control sequences that were subjected to enrichment using only a single TACS (X symbol). Fold-change was assessed by normalizing the read-depth of each locus by the average read-depth of a sample, wherein the average read-depth was calculated from all loci enriched with a single TACS. As shown in  FIG.  4 B , an overall 54.7% average increase in read-depth was observed using the family of four TACS. 
     This example demonstrates that use of a family of TACS, as compared to a single TACS, results in significantly improved enrichment of a target sequence of interest resulting in significantly improved read-depth of that sequence. 
     Example 6: Analysis of Fetal DNA Samples from Embryo Biopsy 
     In this example, fetal DNA samples obtained from fetal cells from embryo biopsy were analyzed using the TACS-based methodology shown in  FIG.  1    to detect chromosomal abnormalities in the fetal samples. 
     Fetal Sample Collection, Library Preparation and TACS Enrichment Fetal cell samples were obtained from 3-day and 5-day biopsy embryos respectively were subjected to the TACS methodology shown in  FIG.  1    to determine the status of genetic abnormalities. All samples were previously referred for Pre-implantation Genetic Screening (PGS) and subjected to array Comparative Genomic Hybridization (aCGH) as part of the routine screening test. Results of aCGH were used as a reference standard for the results obtained. 
     Collected fetal cells were initially lysed and DNA extracted using the Rubicon Genomics PicoPLEX© WGA Kit (Liang, L. et al. (2013) PLoS One 8(4), p. e61838). 
     For certain samples in which whole-genome sequencing was to be performed, the lysed material was subjected to whole genome amplification using commercial whole genome amplification kits. Briefly, following a pre-amplification step, the lysed material was then amplified using amplification enzyme and buffer supplied by the manufacturer. Subsequently, DNA was purified followed by fragmentation using sonication. Fragmented DNA was then processed using standard sequencing library preparation methods such as described in Example 1, typically involving ligation of adapters onto the ends of the cell free DNA fragments, followed by amplification. In addition to the description provided in Example 1, sequencing library preparation kits are commercially available for this purpose. 
     For samples in which TACS-based enrichment was to be performed, then the sequencing library obtained from the above methods underwent TACS hybridization essentially as described in Example 3. The region(s) of interest on the chromosome(s) of interest were enriched by hybridizing the pool of TACS to the sequencing library, followed by isolation of those sequences within the sequencing library that bind to the TACS. To facilitate isolation of the desired, enriched sequences, typically the TACS sequences were modified such that sequences that hybridized to the TACS were separable from sequences that did not hybridize to the TACS. Typically this was achieved by fixing the TACS to a solid support such as described in Example 3, thereby allowing for physical separation of those sequences that bind the TACS from those sequences that do not bind the TACS. The pools of TACS used either can contain a plurality of single TACS that bind to different target sequences of interest or, alternatively, can contain a plurality of families of TACS containing a plurality of members that each bind to the same target sequence of interest but with different start and/or stop positions on the target sequence, as described in Example 5. 
     For analysis of fetal DNA samples by TACS-based enrichment, the pool of TACS can contain TACS that target a subset of chromosomes of interest (e.g., chromosomes 13, 18, 21, X and Y). More preferably, however, the pool of TACS contains various TACS that target every chromosome within the human genome (chromosomes 1-22, X and Y) such that the entire genome is encompassed, allowing for determination of chromosomal abnormalities in any chromosome within the human genome. 
     Next Generation Sequencing (NGS) typically was used to sequence the TACS-enriched sequences (or the whole genome for samples analyzed by whole genome sequencing), thereby providing very accurate counting as well as sequence information. Library products were pooled equimolarly and then subjected to sequencing. 
     Data Analysis 
     Sequencing data obtained from NGS were processed to remove adaptor sequences and poor quality reads. Reads whose length was at least 25 bases long post adaptor-removal were aligned to the human reference genome built hg19. If relevant, duplicate reads were removed post-alignment. Where applicable, sequencing output pertaining to the same sample but processed on separate sequencing lanes, was merged to a single sequencing output file. Software analysis provides a final aligned version of a sequenced sample against the human reference genome from which information was extracted in terms of Short Nucleotide Polymorphisms (SNPs) at loci of interest, read-depth per base and the size of aligned fragments. 
     For whole-genome sequencing and TACS-based whole-genome sequencing, the read-depth of non-overlapping genomic regions of fixed size (e.g. 50 kb or 1 Mb) was obtained by using the samtools bedcov tool, which provides the sum of all reads across a specified genomic region. The obtained value was divided by the length of the windows. For TACS targeted-based sequencing, the read-depth was obtained by using the samtools mpileup tool, which provides information on the read-depth per base, across specified contiguous sequences or the bedcov tool. The median value of the obtained information was assigned as the read-depth of a given locus. Removal of read-depth outliers was performed using either a median-based or mean-based outlier detection approach. Finally, GC-content read-depth bias alleviation was achieved using a local polynomial fitting method to estimate the expected read-depth of regions based on their GC content and then normalize regions using this expected value accordingly. 
     The normalized read-depth from all regions was used as input into
         (a) various segmentation-based classification algorithms (described further below), and/or   (b) score-based classification algorithms (described further below),
 
which were then used to determine the ploidy status of the interrogated regions, as well as the size of any genetic aneuploidies. Score-based classification algorithms were used only with targeted enrichment sequencing data.
 
Ploidy Status Determination Using Segmentation Algorithms
       

     Three different types of segmentation algorithms were developed and applied to fetal DNA sample analysis: (i) Likelihood-based segmentation; (ii) Segmentation using small overlapping windows; and (iii) Segmentation using parallel pairwise testing, each of which is described further below, along with the results for application of the algorithm. 
     Each algorithm is a collection of data processing and statistical modeling routines arranged as a series of steps with aim to decide if the observed sequencing data does not support the null hypothesis, H0 defined as: 
     H0=There are no ploidy deviations from the expected ploidy state. 
     For human genomes the expected ploidy state is the diploid state. The segmentation approach aims to discover breakpoints in consecutive data where there is a clear distinction between read-depths, which in turn indicates that there is a change in ploidy state. The algorithms are described below. 
     A. Likelihood-Based Segmentation 
     Given a set of ordered data points {x_{1}, x_{2}, x_{3}, x_{4}, . . . , x_{N}}, that describe read-depth, the aim was to infer at which point x_{i} the data changes distribution (i.e. there is a significant and consecutive change in read-depth). This was labeled as the break point ϑ_{1}. For example, if the data changes distribution after x_{3} then ϑ{1}=x_{3}. If more than one break point exists, then the algorithm will label the next discovered break point as ϑ_{2}. The algorithm steps were as follows:
         (a) Given a sequence of data (i,x_{i}), where i=1 . . . N, the algorithm estimates the number of modes in the data. To this end, a process known as bivariate kernel density estimation was utilized. For example, if there was a single breakpoint, then the algorithm returned that there were 2 modes in our data distribution.   (b) Decide the position of the break point(s) in the data, if such point(s) exist(s). This was achieved with the following algorithm:   (1) Based on the number of breakpoints found in (a) define the probability density function (p.d.f) of the data, which depends on the unknown values of the breakpoints. This may be, but not limited to, a mixture of Normal distributions.   (2) Calculate the maximum likelihood estimate of the p.d.f in (1) for a fixed set of value(s) for the breakpoints.   (3) Repeat (2) for different sets of break point value(s).   (4) Select as estimated break point(s) the values that maximizes step (2).       

     It was noted that the algorithm does this by assigning membership in all combinations for all breakpoints estimated in part (a). As an example, if the probability is maximized when data points x_{1} to x_{3} come from the first distribution then ϑ_{1}=x_{3} and membership of x_{1} to x_{3} is assigned to the first distribution and x_{4} to x_{N} to the next identified distribution(s). If the likelihood is maximized with all data points x_{i} assigned to the same mode then no break-point is defined and all data points are assigned to the same distribution. Various distributions and computational methods known to those skilled in the art can be used to implement this. 
     Representative results of fetal DNA analysis using the likelihood-based segmentation algorithm are shown in  FIG.  5   . These results demonstrate that likelihood-based segmentation analysis can classify whole-chromosome aberrations in fetal DNA samples (e.g., from PGD/PGS products of conception). At the top panel of  FIG.  5   , a sample without any ploidy abnormalities subjected to whole-genome sequencing is presented. The expected read-depth of each chromosome (blue horizontal bars) lies within the red lines that indicate the range of values of normal ploidy, as decided from the data. Even if on occasion individual data points (grey dots) deviate from the confidence intervals this is not sufficient evidence of ploidy aberrations according to the probabilistic metric used. Conversely, if enough data points deviate from the confidence intervals then the probabilistic measure used can assign a different ploidy state. Such a case is presented at the bottom of  FIG.  5   , where the sample has been determined to have monosomy 18 and monosomy 20. 
     In similar fashion,  FIG.  10    presents results from the algorithm utilizing data derived from TACS specific coordinates combined with data from products of partial complementarity to the TACS that align to non-TACS coordinates thus producing low coverage throughout the genome. In the top panel of  FIG.  10    a normal male sample is presented, whereas in the bottom panel the male sample is classified as having trisomy for chromosome 13 and monosomy for chromosome 21. 
       FIG.  11    presents results from the algorithm utilizing data from TACS specific coordinates only. As with  FIG.  10   , in the top panel of  FIG.  11    a normal male sample is presented, whereas in the bottom panel the male sample is classified as having trisomy for chromosome 13 and monosomy for chromosome 21. 
     Thus, it can be seen that the algorithm successfully classifies TACS-based enrichment and TACS-based whole genome sequencing data, allowing for correct classification of chromosomal abnormalities and at the same time requiring significantly less sequencing than massively parallel shotgun sequencing approaches. 
     B. Segmentation Using Small Overlapping Windows Given a set of data points the aim was to decide membership of each data point into a set of clusters, based on a thresholding scheme. The algorithm does so as follows: 
     (a) Given a set of consecutive read-depth data x_{i} (i=1 to N) the data are divided into overlapping windows of fixed size. For example let w_{1}={x_{1}, . . . , x{10}} denote the first window, then w_{2}={x{2}, . . . , x_{11}}, w_{3}={x{3}, . . . , x_{12}} etc. 
     (b) For each window w_{k}, a score S(k)=(X_{k}−m)/m is computed, where X_{k} is the median of w_{k} and m is the median from all x_{i} from all chromosomes. 
     (c) Assign cluster membership based on a thresholding value s, whereby:
         if S(k)&lt;s, assign to cluster1   if s&lt;=S(k)&lt;C_{1}s are assigned to cluster 2,   if 2s&lt;=S(k)&lt;C_{2}s are assigned to cluster 3 etc.
 
where C_{j}. are positive real numbers greater than one. For example, if s is a particular threshold value then all consecutive w_{k} where S(k)&lt;s are assigned to cluster 1. All consecutive w_{k} where s&lt;=S(k)&lt;C_{1}s are assigned to cluster 2. All consecutive w_{k} where 2s&lt;=S(k)&lt;C_{2}s are assigned to cluster 3 etc. The threshold s can be either decided from the data or treated as a tuning parameter.
       

     Representative results of ploidy determination for fetal DNA samples (e.g., PGS/PGD products of conception) using whole genome sequencing and small overlapping windows segmentation are shown in  FIG.  6   . The top panel illustrates a normal sample. As with  FIG.  5   , the expected read-depth of each chromosome (blue horizontal bars) lies within the red lines, which indicate the range of values of normal ploidy. The expected read-depth is calculated from the individual data points (grey dots). The average read-depth and data points of chromosomes X and Y lie below the bottom red-line, indicating that there is only a single copy of each chromosome, as expected for a male sample. An aneuploid sample is presented at the bottom of  FIG.  6    where the sample is classified with trisomy 13 and mosaicism on chromosome 19. 
     C. Segmentation Using Parallel Pairwise Testing 
     This segmentation approach firstly performs full chromosome ploidy determination and then a sub-chromosomal ploidy determination as follows: 
     (a) Read-depth data from one candidate chromosome are compared with read-depth data from other chromosomes using non-parametric statistical tests. The process is repeated until all candidate chromosomes are tested. 
     (b) Perform a multiple comparisons adjustment on the results of the statistical tests to avoid false positive results. 
     (c) Depending on the statistical test result from the adjusted data, assign the relevant ploidy to candidate chromosomes that illustrate significant evidence against the null hypothesis 
     (d) Once full-chromosomal ploidy is determined then sub-chromosomal ploidy is tested by randomly splitting regions of each chromosome into smaller sizes. Each sub-chromosomal region is then tested for significant deviations from its expected full-chromosomal read-depth using similar statistical tests as in steps (a)-(c). 
     Representative results of ploidy determination for fetal DNA samples (e.g., PGS/PGD products of conception) using whole genome sequencing and small overlapping windows segmentation are presented in  FIG.  7   . The top panel illustrates a normal sample. As with  FIGS.  5 ,  6 ,  10  and  11   , the expected read-depth of each chromosome is illustrated using blue horizontal bars. In this instance, confidence interval bars have been omitted. A normal sample is presented at the top  FIG.  7    whilst a sample presenting many abnormalities is presented at the bottom panel. 
     Ploidy Status Determination Using Score-Based Classification 
     Additionally or alternatively to the segmentation-based algorithms described above, fetal DNA samples can be analyzed using score-based classification. The read-depth data were firstly transformed using square root or logarithmic transformation in order to minimize variance biases. Then methods such as those described in Example 4 were performed to decide on the ploidy status of each tested region (chromosomal and sub-chromosomal regions may be tested). 
     Representative results using a score-based classification system on the fetal DNA samples (e.g., PGS/PGD products of conception) are shown in  FIG.  8   . Green dots illustrate normal ploidy samples whilst all others that lie above or below the normal ploidy thresholds illustrate some type of abnormality. Specifically, blue dots illustrate trisomy samples, cyan dots illustrate partial trisomy samples and red dots illustrate monosomy samples. 
     In summary, this example demonstrates the successful analysis of fetal DNA samples (e.g., PGS/PGD products of conception) for chromosomal abnormalities using either whole genome sequencing data, TACS-based whole genome sequencing data and TACS-based enrichment data, using a variety of statistical analysis approaches. Furthermore, the example illustrates that the methods used with whole genome sequencing data can be successfully applied to TACS-based whole genome sequencing data and TACS-based enrichment data. 
     Example 7: Fragment Size Based Tests 
     There is evidence from the literature that unhealthy tissue can be characterized by and/or associated with fragments in the plasma having a smaller size than the expected size of fragments originating from healthy tissues (Jiang et al, (2015),  Proceedings of the National Academy of Sciences,  112(11), ppE1317-E1325). Furthermore, it has been shown that fetal cell free DNA can be found in the spent medium of embryo culture of PGS/PGD products of conception and that it can be used for the assessment of chromosomal abnormalities (Liu, WeiQiang, et al. (2017). Thus, a fragments-size based test can be utilized to detect the presence of somatic copy number variations. To this effect, a binomial test of proportions, as described Example 4, can be used for the detection of increased presence of nucleic acid material originating from non-healthy tissue based on fragment size. In particular, under the null hypothesis that the distribution of fragment sizes originating from both healthy and non-healthy cells is the same, a binomial test for proportions (as described in Example 4) using continuity correction can be utilized to quantify any evidence against it. 
     The same hypothesis holds true for fragments originating from the placenta/fetus (Chan, K. C. (2004)  Clin. Chem.  50:88-92). Specifically, placenta derived fragments are generally of smaller size when compared to fragments originating from maternal tissues/cells. Accordingly, assessment of the fragment size-based test was performed using maternal plasma samples (i.e., mixed samples where cell free DNA is of maternal and fetal origin). The size of fragments that have aligned to TACS-enriched regions can be obtained from the aligned data. Subsequently, the proportion of fragments under a specific threshold from a test region is compared respective proportion of fragments from a reference region for evidence against the null hypothesis H0, 
     H0: The proportion of small fragments of the test-region is not different from the proportion of small-fragments of the reference region. 
       FIG.  9    shows results when applying the fragment sizes method to the mixed sample containing maternal and fetal DNA. The black dots are individual samples. The x-axis shows the sample index. The y-axis shows the score result of the fragments-based method. A score result greater than the one indicated by the threshold, illustrated as a grey line, indicates a deviation from the expected size of fragments illustrating the presence of aneuploidy. The results demonstrate that an aneuploid sample, having an estimated fetal fraction equal to 2.8%, was correctly identified, illustrating that fragments-based detection may be used to detect abnormalities in samples with low signal-to-noise ratio (e.g., as is the case in detection of cancer). 
     Accordingly, this example demonstrates the successful ability of the fragments-based detection method in detecting genetic abnormalities present in diminutive amounts. In addition to this, since small-sized fragments are associated with fragments from non-healthy tissues (Jiang et al, (2015),  Proceedings of the National Academy of Sciences,  112(11), ppE1317-E1325) they can also be leveraged for the detection of small-sized mutations, such as point mutations and mutational signatures.