Methods and systems for genetic analysis

Abstract

The present disclosure provides computational methods for genetic analysis as well as systems for implementing such analyses. The present disclosure provides methods of genetic analysis which utilize microhaplotypes that are associated with SNPs that are single base pair substitutions (SBSs) in preference to insertion or deletion SNPs. Analysis of such microhaplotypes is useful in forensic genetic applications, sample contamination analysis, and disease analysis, among other applications.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of priority under 35 U.S.C. § 119(e) of U.S. Serial No. 62 / 837,034, filed April 22, 2019, the entire contents of which is incorporated herein by reference in its entirety. BACKGROUND OF THE INVENTION FIELD OF THE INVENTION

[0002] The invention relates generally to genetic analysis and more specifically to methods and systems for analyses of microhaplotypes to determine genetic identity in complex DNA mixtures. BACKGROUND INFORMATION

[0003] Sequence variation in the human genome is a cornerstone in human identification and forensic applications. Genetic fingerprinting is a forensic technique used to identify individuals by characteristics of their genetic information (e.g., RNA, DNA). A genetic fingerprint is a small set of one or more nucleic acid variations that is likely to be different in all unrelated individuals, thereby being as unique to individuals as are fingerprints.

[0004] Sequence variation is useful in genetic analysis for a host of applications such as detection of contamination in a biological sample, forensic analysis, disease detection and population genetics to name a few. Single nucleotide polymorphisms (SNPs) have long been used in genetic analysis for such applications.

[0005] DNA contamination in biological samples is a wide spread problem. Contamination can occur at almost every stage of sample collection / processing. For example, slides can be contaminated while cutting, liquids can be inadvertently transferred between tubes, libraries can be mixed, and sample barcodes can be impure or have low quality sequences. Contamination is more likely to be noticeable with samples with low yield and / or poor quality DNA.

[0006] SNPCheck™ is a tool for performing batch checks for the presence of SNPs and can be utilized to confirm the presence of DNA contamination in a sample. With “well-behaved” DNA like normal tissue or cfDNA, SNPCheck™ can provide reasonable results because Minor Allele frequencies (MAFs) are nearly all around 0 or 0.5. However, extremely high contamination levels are missed because the MAFs are so high and can approach 0.5. Tumor DNA is not “well-behaved” because extreme copy number variation can lead to MAFs ranging from 0.02 to 0.98. This means that MAFs for contamination and real variants can significantly overlap.

[0007] A detection method that is independent or nearly independent of MAF is needed to be able to both detect DNA contamination and further quantitate the amount of contamination in an accurate way. SUMMARY OF THE INVENTION

[0008] The present disclosure provides methods of genetic analysis which utilize microhaplotypes that are associated with SNPs that are single base pair substitutions (SBSs) in preference to insertion or deletion SNPs. Analysis of such microhaplotypes is useful in forensic genetic applications, sample contamination analysis, and disease analysis, among other applications.

[0009] In one embodiment, the disclosure provides a method for genetic analysis which includes: a) identifying SNP sets having at least 3 microhaplotypes in a sample; and b) quantitating the frequency of haplotypes within the SNP sets with more than 2 microhaplotypes.

[0010] In another embodiment, the disclosure provides a method for genetic analysis which includes: a) identifying SNP sets having at least 3 microhaplotypes in a sample; and b) quantitating the frequency of the haplotypes within SNP sets with more than 2 microhaplotypes to determine the presence or absence of DNA contamination in the sample.

[0011] In yet another embodiment, the disclosure provides a method for genetic analysis which includes: a) identifying SNP sets having at least 3 microhaplotypes in a sample; and b) quantitating the frequency of the haplotypes within SNP sets with more than 2 microhaplotypes to determine the presence or absence of a genetic marker indicative of the disease or disorder.

[0012] In still another embodiment, the disclosure provides a method of identifying microhaplotypes in a genome. The method includes: a) identifying a region of interest of the genome; b) detecting SBSs within the region of interest thereby generating multiple sequence variant sets; c) analyzing each variant set for linkage disequilibrium to identify candidate microhaplotypes; and d) identifying candidate microhaplotypes.

[0013] In another embodiment, the disclosure provides a method for detecting SNP sets having at least three microhaplotypes from multiple subjects present in a sample. The method includes: a) identifying microhaplotypes in a genome in the sample; b) determining the number of SNP sets having at least 3 microhaplotypes in the sample; and c) quantitating the frequency of the haplotypes within SNP sets with greater than 2 microhaplotypes to determine the presence of DNA from multiple subjects in the sample, thereby detecting DNA from multiple subjects in the sample. In one embodiment, identifying includes: i) identifying a region of interest of the genome; ii) detecting SBSs within the region of interest thereby generating multiple sequence variant sets; and iii) analyzing each variant set for LD to identify microhaplotypes.

[0014] In an embodiment, the disclosure provides a method for detecting SNP sets having at least two microhaplotypes from multiple subjects present in a sample. The method includes: a) determining the presence or absence of SNP sets having more than two microhaplotypes in the sample, wherein the SNP sets comprise multiple single base pair substitutions and correspond to a genomic region set forth in Tables 5, 6 and 7; and b) quantitating the frequency of haplotypes within the SNP sets to determine the presence of DNA from multiple subjects in the sample, thereby detecting SNP sets having more than 2 microhaplotypes from multiple subjects in the sample.

[0015] In one embodiment the disclosure provides an oligonucleotide panel. The panel includes oligonucleotides for amplifying or hybrid capturing a region of a genome corresponding to one or more genomic regions set forth in Tables 5, 6 and 7.

[0016] In another embodiment, the disclosure provides a method of genetic analysis that includes: a) amplifying a region of a genome present in a sample, the region corresponding to a genomic region set forth in Tables 5, 6, and 7 thereby generating an amplicon; and b) sequencing the amplicon to determine the nucleic acid sequence of the amplicon.

[0017] In a further embodiment, the disclosure provides a method for detecting a disease or disorder in a subject. The method includes: a) obtaining a sample from the subject; b) identifying microhaplotypes in DNA molecules present in a sample; c) determining the presence or absence of SNP sets having more than 2 microhaplotypes in the sample; and d) quantitating the frequency of haplotypes within SNP sets to determine the presence or absence of a genetic marker indicative of the disease or disorder, thereby detecting the disease or disorder. In one embodiment, identifying includes: i) identifying a region of interest, wherein the region of interest is associated with the disease or disorder; ii) detecting SBSs within the region of interest region of interest thereby generating multiple sequence variant sets; and iii) analyzing each variant set for LD to identify microhaplotypes.

[0018] In an embodiment the disclosure provides a genetic analysis system. The system includes: a) at least one processor operatively connected to a memory; b) a receiver component configured to receive DNA analysis information including microhaplotype sequence information generated from PCR amplification of DNA in a DNA sample; and c) an analysis component, executed by the at least one processor, configured to: i)                 identify microhaplotypes in the sample based on the presence of single base pair substitutions; ii) confirm presence of the number of SNP sets for microhaplotypes in the DNA sample; and iii) 2020262082   04 Jun 2026 quantitate the frequency of genotypes within SNP sets with more than 2 microhaplotypes in the DNA sample.

[0019] In a related embodiment the disclosure provides a genetic analysis system configured to perform a method of the disclosure. The system includes: a) at least one processor operatively connected to a memory; b) a receiver component configured to receive DNA analysis information including microhaplotype sequence information generated from PCR amplification of DNA in a DNA sample; and c) an analysis component, executed by the at least one processor, configured to perform a method of the disclosure.

[0020] In still another embodiment, the invention provides a non-transitory computer readable storage medium encoded with a computer program. The program includes instructions that, when executed by one or more processors, cause the one or more processors to perform operations that implement a method of the disclosure.

[0021] In yet another embodiment, the invention provides a computing system. The system includes a memory, and one or more processors coupled to the memory, with the one or more processors being configured to perform operations that implement a method of the disclosure. [0021A] In yet another embodiment, the invention provides a method of identifying microhaplotypes in a genome comprising: a) identifying a region of interest of the genome; b) determining an additional bordering region for the region of interest, wherein the bordering region comprises regions flanking the region of interest, and wherein the regions flanking the region of interest comprise less than about 200 nucleotide base pairs; c) detecting single base pair substitutions (SBSs) within the region of interest and the additional bordering region, thereby generating multiple sequence variant sets; d) filtering the multiple sequence variant sets based on a frequency threshold for the detected SBSs; e) analyzing each of the remaining sequence variant sets for linkage disequilibrium to identify candidate microhaplotypes, wherein the linkage disequilibrium is evaluated using one or more population-scale genomic databases and the candidate microhaplotypes are identified based on a presence of at least three microhaplotypes, and wherein a third microhaplotype and any subsequent 2020262082   04 Jun 2026 microhaplotypes have a total frequency of greater than 1% when compared against data from the one or more population-scale genomic databases; and f) providing the candidate microhaplotypes. [0021B] In yet another embodiment, the invention provides a method for detecting single nucleotide polymorphism (SNP) sets having at least three microhaplotypes from multiple subjects present in a sample comprising: a) identifying microhaplotypes in a genome in the sample, wherein identifying comprises: i) identifying a region of interest of the genome; ii) determining an additional bordering region for the region of interest, wherein the bordering region comprises regions flanking the region of interest, and wherein the regions flanking the region of interest comprise less than about 200 nucleotide base pairs; iii) detecting single base pair substitutions (SBSs) within the region of interest thereby generating multiple sequence variant sets; iv) filtering the multiple sequence variant sets based on a frequency threshold for the detected SBSs; and v) analyzing each of the remaining sequence variant sets for linkage disequilibrium to identify candidate microhaplotypes, wherein the linkage disequilibrium is evaluated using one or more population-scale genomic databases and the candidate microhaplotypes are identified based on a presence of at least three microhaplotypes, and wherein a third microhaplotype and any subsequent microhaplotypes have a total frequency of greater than 1% when compared against data from the one or more population-scale genomic databases; b) determining the number of SNP sets having at least 3 microhaplotypes in the sample; and c) quantitating the frequency of the SNP sets with greater than 2 microhaplotypes to determine the presence of DNA from multiple subjects in the sample, thereby detecting DNA from multiple subjects in the sample. [0021C] In yet another embodiment, the invention provides a method for detecting single nucleotide polymorphism (SNP) sets having at least three microhaplotypes from multiple subjects present in a sample comprising: a) determining the presence or absence of SNP sets having more than two microhaplotypes in the sample, wherein the SNP sets comprise multiple single base 2020262082   04 Jun 2026 pair substitutions and correspond to a genomic region selected from regions 1) the genomic regions: chr1:120057158-120057246, chr1:156846120-156846233, chr1:226589833-226589958, chr1:23885498-23885599, chr10:104386934-104387019, chr10:43615505-43615633, chr10:70332580-70332672, chr11:534197-534242, chr11:8246326-8246343, chr12:121416622-121416650, chr12:121431272-121431300, chr12:121435427-121435475, chr12:121437114-121437221, chr12:133208886-133208979, chr12:133226159-133226196, chr12:133253995-133254083, chr12:18656174-18656225, chr12:56494991-56494998, chr13:21562832-21562948, chr14:102568296-102568367, chr14:104165753-104165927, chr14:105239146-105239192, chr14:105258892-105258893, chr14:35872792-35872926, chr15:40998305-40998342, chr15:41857216-41857303, chr15:41860411-41860490, chr15:67457335-67457485, chr16:2138269-2138398, chr16:2138398-2138422, chr16:68857289-68857441, chr16:81819768-81819820, chr16:89806343-89806347, chr16:89849583-89849629, chr16:89858505-89858525, chr17:1782952-1782957, chr17:78599562-78599655, chr17:78820329-78820374, chr17:78865546-78865630, chr17:78897547-78897561, chr17:78921117-78921211, chr19:10267011-10267077, chr19:17937758-17937786, chr19:17955001-17955021, chr19:2226676-2226772, chr19:3119184-3119239, chr19:50919797-50919828, chr19:5210622-5210782, chr19:5210762-5210782, chr19:5212380-5212482, chr19:7166376-7166388, chr2:112754828-112754880, chr2:112754943-112755001, chr2:141259283-141259376, chr2:29416366-29416481, chr2:29416481-29416615, chr2:29446184-29446202, chr2:48010488-48010558, chr20:40714307-40714479, chr20:40714539-40714540, chr20:57478807-57478939, chr20:9543622-9543681, chr21:42845374-42845383, chr22:21337266-21337325, chr22:21348914-21349037, chr22:24158895-24158899, chr3:178922222-178922274, chr3:183211906-183212026, chr4:106196829-106196951, chr4:143043340-143043404, chr4:143324036-143324094, chr4:187534362-187534375, chr4:187629497-187629538, chr5:149456772-149456811, chr5:149495287-149495395, chr5:176517326-176517461, chr5:176523562-176523597, chr5:176721198-176721272, chr5:180046209-180046344, chr5:180051003-180051118, chr5:180057231-180057293, chr5:231111-231143, chr5:35861068-35861159, chr5:35871190-35871273, chr5:57754808-57754851, chr5:67522722-67522851, chr6:117725448-117725578, chr6:117730673-117730819, chr6:152382311-152382325, 2020262082   04 Jun 2026 chr6:26056549-26056708, chr6:30865115-30865204, chr6:32188603-32188642, chr7:100410597-100410657, chr7:6026775-6026942, chr7:78119109-78119199, chr8:30999122-30999123, chr8:31024638-31024654, chr8:90958422-90958530, chr9:139403268-139403280, chr9:139405093-139405261, chr9:139410424-139410589, chr9:139411714-139411880, chr9:21968159-21968199, chr9:93639846-93639973, chr9:93641175-93641199, chr9:98238358-98238379, 2) the genomic regions: chr1:3743319-3743391, chr1:10431132-10431158, chr1:32672908-32672932, chr1:94544234-94544276, chr1:154832290-154832304, chr1:159409857-159409884, chr1:171168545-171168584, chr1:183616884-183616926, chr11:4928841-4928866, chr11:5345128-5345170, chr11:5566030-5566051, chr11:63883985-63884027, chr11:85436303-85436352, chr11:116703640-116703671, chr12:6030405-6030437, chr12:40834918-40834955, chr12:113348849-113348870, chr12:121600180-121600253, chr12:132688115-132688137, chr13:25367282-25367301, chr14:23549285-23549319, chr14:65263300-65263347, chr14:96136775-96136794, chr15:41819283-41819322, chr15:79310256-79310288, chr15:89398330-89398407, chr15:94945704-94945719, chr16:2812890-2812939, chr16:87678144-87678165, chr17:1782952-1782957, chr17:3101578-3101590, chr17:3352294-3352309, chr17:6331803-6331836, chr17:10223697-10223714, chr17:33772658-33772689, chr17:42989063-42989088, chr17:45695832-45695914, chr17:80887206-80887244, chr18:56204747-56204768, chr19:4510530-4510560, chr19:8148301-8148314, chr19:9362297-9362343, chr19:11227554-11227602, chr19:36237227-36237245, chr19:44352639-44352666, chr19:58131576-58131623, chr19:58213952-58213969, chr19:58572959-58572979, CHR2:33623720-33623734, CHR2:37579937-37579971, CHR2:71058184-71058226, CHR2:231775094-231775144, CHR2:239184569-239184581, chr20:744382-744415, chr20:5904028-5904040, chr20:52645534-52645541, chr20:62597666-62597694, chr21:43557698-43557736, chr21:46321659-46321677, chr22:17589209-17589246, chr22:19951207-19951271, chr22:21377301-21377334, chr22:33253280-33253292, chr22:35817553-35817597, chr22:44322922-44322970, chr3:122003757-122003769, chr3:129155451-129155463, chr3:136574501-136574521, chr3:142277536-142277575, chr3:178968634-178968660, chr4:156289900-156289917, chr5:147024476-147024509, chr5:148206440-148206473, chr5:150666933-150666962, chr5:150901613- 2020262082   04 Jun 2026 150901630, chr5:174870150-174870196, chr6:4069133-4069166, chr6:29913201-29913266, chr6:30080231-30080274, chr6:30993533-30993590, chr6:31170514-31170528, chr6:31930441-31930462, chr6:33141253-33141280, chr6:36291985-36292007, chr6:167754702-167754721, chr7:4213975-4214023, chr7:21640361-21640405, chr7:27196069-27196113, chr7:30795288-30795331, chr7:55220177-55220202, chr7:100677455-100677523, CHR8:142490120-142490166, CHR8:145639681-145639726, chr9:117166206-117166246, chr9:125315542-125315557, chr9:134385435-134385436, chr9:136412255-136412296, chrX:23019317-23019346, 3) the genomic regions: chr1:10431132-10431158, chr1:120057158-120057246, chr1:154832290-154832304, chr1:156846120-156846233, chr1:159409857-159409884, chr1:171168545-171168584, chr1:183616884-183616926, chr1:226573364-226573402, chr1:226589833-226589958, chr1:23885498-23885599, chr1:32672908-32672932, chr1:3743319-3743391, chr1:94544234-94544276, chr10:104386934-104387019, chr10:123194558-123194609, chr10:123199092-123199095, chr10:123275662-123275666, chr10:123335839-123335866, chr10:123346116-123346190, chr10:123396728-123396806, chr10:123406645-123406663, chr10:43611708-43611865, chr10:43615505-43615633, chr10:70332580-70332672, chr11:116703640-116703671, chr11:4928841-4928866, chr11:534197-534242, chr11:5345128-5345170, chr11:5566030-5566051, chr11:63883985-63884027, chr11:69412090-69412124, chr11:8246326-8246343, chr11:85436303-85436352, chr12:113348849-113348870, chr12:12009741-12009874, chr12:12013572-12013612, chr12:12016008-12016089, chr12:12020114-12020170, chr12:12035649-12035664, chr12:121416622-121416650, chr12:121431272-121431300, chr12:121435427-121435475, chr12:121437114-121437221, chr12:121600180-121600253, chr12:132688115-132688137, chr12:133208886-133208979, chr12:133226159-133226196, chr12:133253995-133254083, chr12:18656174-18656225, chr12:40834918-40834955, chr12:4346169-4346177, chr12:4351884-4352027, chr12:4376089-4376091, chr12:4399036-4399087, chr12:4399917-4399970, chr12:4411639-4411683, chr12:4417127-4417232, chr12:56494991-56494998, chr12:6030405-6030437, chr12:69169222-69169316, chr12:69265196-69265278, chr12:69277127-69277165, chr13:21562832-21562948, chr13:25367282-25367301, chr13:32986219-32986340, chr14:102568296-102568367, chr14:104165753- 2020262082   04 Jun 2026 104165927, chr14:105239146-105239192, chr14:105258892-105258893, chr14:23549285-23549319, chr14:35872792-35872926, chr14:65263300-65263347, chr14:96136775-96136794, chr15:40998305-40998342, chr15:41819283-41819322, chr15:41857216-41857303, chr15:41860411-41860490, chr15:67457335-67457485, chr15:79310256-79310288, chr15:88488326-88488428, chr15:88549118-88549151, chr15:88646922-88647038, chr15:88667852-88667948, chr15:89398330-89398407, chr15:94945704-94945719, chr16:2138269-2138398, chr16:2138398-2138422, chr16:2812890-2812939, chr16:68857289-68857441, chr16:81819768-81819820, chr16:87678144-87678165, chr16:89806343-89806347, chr16:89849480-89849629, chr16:89858505-89858525, chr17:1782952-1782957, chr17:3101578-3101590, chr17:33772658-33772689, chr17:37832279-37832315, chr17:37834715-37834808, chr17:41616392-41616456, chr17:42989063-42989088, chr17:45695832-45695914, chr17:6331803-6331836, chr17:78599562-78599655, chr17:78820329-78820374, chr17:78865546-78865630, chr17:78896488-78896529, chr17:78897547-78897561, chr17:78921117-78921211, chr17:80887206-80887244, chr18:56204747-56204768, chr19:10267011-10267077, chr19:11227554-11227602, chr19:17937758-17937786, chr19:17955001-17955021, chr19:2226676-2226772, chr19:30253901-30253998, chr19:30255068-30255090, chr19:30290349-30290357, chr19:30340381-30340412, chr19:30361995-30362112, chr19:3119184-3119239, chr19:36237227-36237245, chr19:41724820-41724885, chr19:41781493-41781579, chr19:44352639-44352666, chr19:4510530-4510560, chr19:50919797-50919828, chr19:5210622-5210782, chr19:5210762-5210782, chr19:5212380-5212482, chr19:58131576-58131623, chr19:58213952-58213969, chr19:58572959-58572979, chr19:7163154-7163230, chr19:7166376-7166388, chr19:8148301-8148314, chr19:9362297-9362343, chr2:112754828-112754880, chr2:112754943-112755001, chr2:113983937-113984033, chr2:113984503-113984594, chr2:113989236-113989267, chr2:141259283-141259376, chr2:16042003-16042051, chr2:16073257-16073263, chr2:16112814-16112828, chr2:16113594-16113723, chr2:202122956-202122995, CHR2:231775094-231775144, CHR2:239184569-239184581, chr2:29416366-29416481, chr2:29416481-29416615, chr2:29446184-29446202, chr2:29446701-29446721, chr2:29447108-29447253, CHR2:33623720-33623734, CHR2:37579937-37579971, chr2:47800577-47800603, chr2:47852559-47852643, chr2:48010488-48010558, CHR2:71058184-71058226, chr20:30729488-30729523, chr20:40714307-40714479, chr20:40714479-40714540, chr20:40714539-40714540, chr20:52645534- 2020262082   04 Jun 2026 52645541, chr20:57478807-57478939, chr20:5904028-5904040, chr20:62597666-62597694, chr20:744382-744415, chr20:9543622-9543681, chr21:42845374-42845383, chr21:42876400-42876447, chr21:43557698-43557736, chr21:46321659-46321677, chr22:17589209-17589246, chr22:17640022-17640045, chr22:19951207-19951271, chr22:21337266-21337325, chr22:21348914-21349037, chr22:21377301-21377334, chr22:24158895-24158899, chr22:29690246-29690345, chr22:33253280-33253292, chr22:35817553-35817597, chr22:44322922-44322970, chr3:122003757-122003769, chr3:12649857-12649937, chr3:129155451-129155463, chr3:136574501-136574521, chr3:138327951-138328016, chr3:142277536-142277575, chr3:178922222-178922274, chr3:178968634-178968660, chr3:178984575-178984679, chr3:178986121-178986203, chr3:178990402-178990462, chr3:183211906-183212026, chr3:36986932-36986992, chr3:71247257-71247304, chr4:106196829-106196951, chr4:143043340-143043404, chr4:143324036-143324094, chr4:156289900-156289917, chr4:1745492-1745500, chr4:1750487-1750584, chr4:1788994-1789044, chr4:1796629-1796636, chr4:1797741-1797852, chr4:187534362-187534375, chr4:187629497-187629538, chr4:54269096-54269173, chr4:54657737-54657790, chr4:55208737-55208788, chr4:55501109-55501195, chr4:55582037-55582068, chr4:55619846-55619859, chr4:55982752-55982784, chr4:56026865-56026914, chr5:147024476-147024509, chr5:148206440-148206473, chr5:149456772-149456811, chr5:149495287-149495395, chr5:150666933-150666962, chr5:150901613-150901630, chr5:174870150-174870196, chr5:176517326-176517461, chr5:176523562-176523597, chr5:176531772-176531857, chr5:176721198-176721272, chr5:180046209-180046344, chr5:180051003-180051118, chr5:180057231-180057293, chr5:231111-231143, chr5:35861068-35861159, chr5:35871190-35871273, chr5:56178111-56178217, chr5:57754808-57754851, chr5:67477132-67477234, chr5:67492589-67492652, chr5:67517563-67517646, chr5:67522722-67522851, chr5:67534039-67534057, chr5:67553771-67553827, chr6:117725448-117725578, chr6:117730673-117730819, chr6:152382311-152382325, chr6:167754702-167754721, chr6:26056549-26056708, chr6:29913201-29913266, chr6:30080231-30080274, chr6:30865115-30865204, chr6:30993533-30993590, chr6:31170514-31170528, chr6:31930441-31930462, chr6:32188603-32188642, chr6:32190390-32190484, chr6:33141253-33141280, chr6:36291985-36292007, chr6:4069133-4069166, chr6:41924853-41924931, chr6:42013020-42013049, chr6:42039487-42039542, chr6:42039551-42039666, 2020262082   04 Jun 2026 chr6:42052577-42052667, chr7:100410597-100410657, chr7:100416139-100416250, chr7:100677455-100677523, chr7:116336880-116336947, chr7:116471122-116471227, chr7:21640361-21640405, chr7:27196069-27196113, chr7:30795288-30795331, chr7:4213975-4214023, chr7:55220177-55220202, chr7:55251541-55251648, chr7:6026775-6026942, chr7:6026942-6026988, chr7:78119109-78119199, chr8:128700175-128700233, chr8:128713221-128713364, chr8:128889285-128889371, CHR8:142490120-142490166, CHR8:145639681-145639726, chr8:145737636-145737816, chr8:30999122-30999123, chr8:31024638-31024654, chr8:38299624-38299715, chr8:38310910-38311001, chr8:38350292-38350315, chr8:38361379-38361430, chr8:90958422-90958530, chr9:117166206-117166246, chr9:125315542-125315557, chr9:134385435-134385436, chr9:136412255-136412296, chr9:139401504-139401577, chr9:139403268-139403280, chr9:139405093-139405261, chr9:139410424-139410589, chr9:139411714-139411880, chr9:21968159-21968199, chr9:5408242-5408358, chr9:5415025-5415111, chr9:5420254-5420266, chr9:5458035-5458095, chr9:5484100-5484203, chr9:87478135-87478172, chr9:93639846-93639973, chr9:93641175-93641199, chr9:98238358-98238379, chrX:23019317-23019346.; and b) quantitating the frequency of the SNP sets to determine the presence of DNA from multiple subjects in the sample, thereby detecting SNP sets having at least 3 microhaplotypes from multiple subjects in the sample. [0021D] Throughout this specification the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. [0021E] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each of the appended claims. BRIEF DESCRIPTION OF THE FIGURES

[0022] Figure 1 is a graph showing data generated using the method of the disclosure in one embodiment of the invention. 2020262082   04 Jun 2026

[0023] Figure 2 is a graph showing data generated using the method of the disclosure in one embodiment of the invention.

[0024] Figure 3 is an image depicting microhaplotype frequency in the presence of contamination in embodiments of the invention. DETAILED DESCRIPTION OF THE INVENTION

[0025] The present invention is based on innovative methods and systems for genetic analysis of microhaplotypes. Before the present compositions and methods are described, it is to be understood that this invention is not limited to particular methods and experimental conditions described, as such compositions, methods, and conditions may vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only in the appended claims.

[0026] As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, references to “the method” includes one or more methods, and / or steps of the type described herein which will become apparent to those persons skilled in the art upon reading this disclosure and so forth.

[0027] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the invention, the preferred methods and materials are now described.

[0028] The present disclosure provides innovative methods and systems for genetic analysis utilizing microhaplotypes. The methods utilize SBS SNPs and in embodiments SBS changes in low error genomic regions. This allows for increased accuracy in detection of DNA contamination, detection of disease as well as forensic analysis. The methods disclosed herein use SBSs in preference to STRs or insertion / deletion SNPs because the latter have an unacceptably high error rate that affects detection of low levels of contamination in a sample. All of the methods of the disclosure focus on SNP variants with a short genetic distance between them so they can ideally be on a single sequence read. Long read technologies allow longer distances as long as the SNP variants are on a single read. While longer distances can be used, using a paired read leads to a higher error rate and coverage is lower the further away the variants are. Further, certain methods of the disclosure advantageously utilize a two-phase analysis, first to detect contamination and then to quantitate it. Detection of DNA contamination via the method disclosed herein relies on the number of microhaplotypes for each SNP set and / or the frequency of 3rd / 4th haplotypes, not on the MAFs of individual SNPs.

[0029] Previous investigations have illustrated the utility of multiple closely linked SNP-based markers in anthropology for population relationship and their capacity to provide a plausible explanation for the pattern of recent human variation. In addition, multi-allelic SNPs have been promoted as suitable markers for addressing relevant forensic questions such as family / clan, lineage inference, and individual identification. Aiming to complement current DNA typing tools for forensics and population genetics, the Kidd laboratory proposed a novel type of genetic marker named microhaplotypes (e.g., “microhaps” or MHs). These are short segments of DNA (< 300 nucleotides, thus “micro”), characterized by the presence of two or more closely linked SNPs that present three or more allelic combinations (i.e., “haplotypes”) within a population. The short distance between SNPs implies an extremely low recombination rate among them. The level of heterozygosity of the microhaplotypes is dependent upon different factors, including historical accumulation of allelic variants at different positions within the targeted region, incidence of rare crossover events, occurrence of random genetic drift, and / or selection. Since microhaplotypes are multi-SNP haplotypes, they can provide, on a per locus basis, a larger assembly of information than a stand-alone SNP marker.

[0030] Further, when variants are near each other on the genome, they tend to be correlated. Each different set of SNPs on a single chromosomal allele is called a haplotype (a set of linked SNP alleles that tend to always occur together (i.e., that are associated statistically)). Because each individual has 2 copies of his / her genome, each person has 2 haplotypes in autosomal chromosomal regions. These haplotypes can be different (heterozygous) or identical (homozygous). As discussed above, a microhaplotype is a short haplotype that is about 300 nucleotides or less or longer distances for long reads. For the purposes of the methods described herein, a microhaplotype is short enough in length such that the variants are on the same sequencing read so can be unambiguously phased. Most microhaplotypes are not particularly useful in genetic analysis since 2 and only 2 microhaplotypes are ever found in a population. However, the methods of the present invention allow for identification of microhaplotypes that can provide statistically useful information such as those microhaplotypes where there can be 3, 4, 5, or even more different haplotypes found among different individuals (but never more than 2 in one individual).

[0031] As used herein, a “SNP” is a single- nucleotide substitution of one base (e.g., cytosine, thymine, uracil, adenine, or guanine) for another at a specific position, or locus, in a genome, where the substitution is present in a population to an appreciable extent (e.g., more than 1% of the population).

[0032] In certain embodiments, the methods of the disclosure relate to determining and quantitating the presence of DNA contamination in a DNA sample.

[0033] In related embodiments, the methods of the disclosure relate to determining whether a sample includes a complex mixtures of DNA from multiple individuals. Such individuals may be mother and offspring, as well as related or unrelated individuals.

[0034] Conventional forensics analysis uniquely identifies individual DNA samples through extraction of short tandem repeats (STRs) and / or determination of mitochondrial DNA (mtDNA) sequences. Capillary electrophoresis is often used to quantify STR lengths and mtDNA sequences. This methodology has been proven accurate for individual profile identification.

[0035] Of significance to the methods to the disclosure, the ability of these methods to deconvolute complex DNA mixtures into component profiles does not require any prior knowledge of the components. For example, the methods described herein are effective to deconvolute complex DNA mixtures into component profiles without any knowledge of genetic markers or DNA sequences belonging to any individual or component that contributes to any one of the complex DNA mixtures. Thus, one of the superior properties of the methods of the disclosure is that the methods do not require any prior knowledge or data regarding individual profdes, contributors, or components of a complex DNA mixture.

[0036] In some aspects, techniques described herein can be used to determine the ethnicity of an individual associated with DNA present in a biological sample.

[0037] In embodiments, the disclosure provides a method of identifying microhaplotypes in a genome. The microhaplotypes are useful for use in any of the methods disclosed herein, for example, in detection of sample contamination, disease analysis and / or complex sample deconvolution.

[0038] Accordingly, the disclosure provides a method of identifying microhaplotypes in a genome. The method includes: a) identifying a region of interest of the genome; b) detecting SBSs within the region of interest thereby generating multiple sequence variant sets; c) analyzing each variant set for LD to identify candidate microhaplotypes; and d) identifying candidate microhaplotypes.

[0039] Also, provided is a method that includes: a) identifying SNP sets having at least 3 microhaplotypes in a sample; and b) quantitating the frequency of haplotypes within the SNP sets with more than 2 microhaplotypes.

[0040] Additionally, the disclosure also provides a method that includes: a) identifying SNP sets having at least 3 microhaplotypes in a sample; and b) quantitating the frequency of haplotypes within the SNP sets with more than 2 microhaplotypes to determine the presence or absence of DNA contamination in the sample.

[0041] A method for genetic analysis is also provided that includes: a) identifying SNP sets having at least 3 microhaplotypes in a sample; and b) quantitating the frequency of the haplotypes within SNP sets with more than 2 microhaplotypes to determine the presence or absence of a genetic marker indicative of the disease or disorder.

[0042] In various embodiments, the methodology of the disclosure may further include quantitating the frequency of SNP sets having at least 3, 4, 5, 6 or more microhaplotypes in the sample. This may be performed to determine the amount of DNA contamination in the sample. In embodiments, as discussed in Example 1, the method further includes calibrating cutoff values for candidate microhaplotypes. Sample contamination can be assessed utilizing determined cutoff values for frequency of candidate microhaplotypes having SNP sets with at least 3, 4, 5, 6, 7, 8 or more microhaplotypes.

[0043] The microhaplotypes of the present invention can use different SNP sets but principles of choosing them are the same. As discussed here, the principles include: use of databases such as gnomAD™ (for exons, ~52% European, 7% East Asian, 6% African), for picking candidate SNPs, 1000 Genomes™ database (~20% European, 20% East Asian, 26% African) for evaluating LD; selecting a final set of SNPs based on 1000 Genomes frequency (or similar database) of third / fourth haplotypes to equalize variation across ancestries (use of the gnomAD database leads to slightly higher variation among Europeans); variants must be close enough to be on same sequence read; use of single base substitutions, avoiding repeat sequences / indels, to minimize error rate; avoidance of homopolymer and low confidence sequence regions; choice of SNPs in low LD so frequency of 3rd / 4th haplotype is high; maximization of distance between SNP sets so information is independent; and test of candidate SNP sets against real samples to ensure high coverage, diverse genotypes, and low rate of 3rd / 4th haplotypes in pure samples.

[0044] The methodology of the present disclosure may include identification of candidate variant sets for analysis as discussed in Example 1.

[0045] This may include identifying a region of interest of the genome and determining the nucleotide sequence of the region for use in analysis. The region of interest is examined for the presence of SBSs. In embodiments, the SBS frequency is typically between about 5-95% which may be determined using a suitable genomic database, for example the gnomAD™ database (gnomad.broadinstitute.org / ).

[0046] In embodiments, the region of interest utilized optionally includes flanking regions which are also examined for the presence of SBSs with a frequency also determined to be between about 5-95%. In various embodiments, the regions flanking the region of interest include less than about 50,100, 150, 180 or 200 nucleotide base pairs. In various embodiment, the total length of the region of interest, optionally including flanking regions is less than about 500, 450, 400, 350, 300, 250, 200, 150, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10 base pairs.

[0047] In embodiments, the candidate variant pairs that are identified are then examined for LD.      This may be performed using the 1000 Genomes™ database (ldlink.nci.nih.gov / ?tab=ldhap).

[0048] Pairs, triplets, quartets, and the like with at least three haplotypes and the third and greater haplotypes having a total frequency of >1% are then considered as candidates for use. In various embodiments, microhaplotype variant sets were chosen to avoid insertions / deletions because the intrinsic sequencing error rate in such variants is higher and more likely to generate noise. In some embodiments, variants may not be found in the 1000 Genomes™ database and therefore cannot be easily assessed for LD. However, such variants may be utilized if the MAFs observed in the gnomAD™ database suggest it is appropriate.

[0049] It will be appreciated that the region of interest may be within a gene, an intron and / or an exon or between genes. Alternatively, the region of interest may be within an exome. In embodiments, the region of interest may include a genetic marker associated with a disease. In embodiments, the region of interest may include a genetic marker associated with a particular ethnicity.

[0050] Utilizing this approach, oligonucleotide panels may be generated for amplifying or hybrid capturing the particular regions which include the microhaplotypes that are identified using the methods of the disclosure. In one embodiment, the oligonucleotide panel includes oligonucleotides for amplifying or hybrid capturing a region of a genome corresponding to one or more genomic regions set forth in Table 5. In another embodiment, the oligonucleotide panel includes oligonucleotides for amplifying or hybrid capturing a region of a genome corresponding to one or more genomic regions set forth in Table 6 or 7.

[0051] As such, the disclosure also provides a method of genetic analysis that includes: a) amplifying a region of a genome present in a sample, the region corresponding to a genomic region set forth in Tables 5, 6, and 7, thereby generating an amplicon; and b) sequencing the amplicon to determine the nucleic acid sequence of the amplicon.

[0052] As discussed herein, the microhaplotypes identified by the methods of the disclosure may be utilized for various applications, including but not limited to DNA contamination detection, disease analysis, and sample deconvolution (i.e., detection of DNA from multiple subjects or cell types in a single sample).

[0053] In one embodiment, the disclosure provides a method for detecting SNP sets having at least three microhaplotypes from multiple subjects present in a sample. The method includes: a) identifying microhaplotypes in a genome of the sample; b) determining the number of SNP sets having at least 3 microhaplotypes in the sample; and c) quantitating the frequency of the SNP sets with greater than 2 microhaplotypes to determine the presence of DNA from multiple subjects in the sample, thereby detecting DNA from multiple subjects in the sample. In one embodiment, identifying includes: i) identifying a region of interest of the genome; ii) detecting SBSs within the region of interest thereby generating multiple sequence variant sets; and iii) analyzing each variant set for LD to identify microhaplotypes.

[0054] In another embodiment, the disclosure provides a method for detecting SNP sets having at least three microhaplotypes from multiple subjects present in a sample. The method includes: a) determining the presence or absence of SNP sets having at least three microhaplotypes in the sample, wherein the SNP sets comprise multiple single base pair substitutions and correspond to a genomic region set forth in Tables 5 and 6 and 7; and b) quantitating the frequency of the SNP sets to determine the presence of DNA from multiple subjects in the sample, thereby detecting SNP sets having at least three microhaplotypes from multiple subjects in the sample.

[0055] Accordingly, the methods of the disclosure for deconvolution or resolution of a component from a complex DNA mixture may be performed by analyzing a single complex DNA mixture. In certain embodiments of the methods of the disclosure for deconvolution or resolution of a component from a complex DNA mixture, the method may analyze more than one complex DNA mixture. The resolution of DNA profdes using these methods increases as the number of SNP loci increase in the panel used. As used herein, the term complex DNA mixture refers to a DNA mixture comprised of DNA from two, or more contributors. Preferably, the complex DNA mixtures of the methods described herein include DNA from at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more contributors.

[0056] Methods of the disclosure are superior to existing methods of deconvoluting DNA profdes. Notably, applications for the methods described herein are not confined to the context of forensic analysis or DNA contamination detection. For example, the methods of the disclosure may be used for medical diagnosis and / or prognosis. To detect diseases, the region of interest may be chosen such that it includes a genetic marker that is associated with a disease or disease state, such as cancer or a fetal disorder. In this manner, the region of interest may be, for example, on chromosome 21 which allows for diagnosis of trisomy 21, also known as Down syndrome. If a sample is determined to be from a mother and fetus and the 3rd microhaplotype frequency is different on chromosome 21 relative to other chromosomes, this is indicative of a gene copy mutation, e.g., trisomy 21. Other trisomies including chrl3 and chrl8 trisomy can be detected similarly.

[0057] As such, the methods described herein may be used in a variety of ways to predict, diagnose and / or monitor diseases, such as cancer and fetal disorders. Further, the methods may be utilized to distinguish various cell types from one another.

[0058] In the field of cancer, biopsy samples often contain many cell types, of which a small proportion may form any part of a tumor. Consequently, DNA obtained from tumor biopsies is another form of complex DNA mixture and may contain somatic variants that arise on a particular DNA molecule. In the case of somatic variation, the limitation to SBSs can be relaxed because the somatic variation could be an indel or other modification that would otherwise be avoided. Moreover, within a tumor, the multitude of cells may be molecularly distinct with respect to the expression of factors indicating or facilitating, for example, vascularization and / or metastasis. A DNA mixture obtained from a tumor sample may also form a complex DNA mixture of the disclosure. In both of these non- limiting examples, the methods of the disclosure may be used to build individual profiles for each cell or cell type that contributes to the complex DNA mixture. Moreover, the methods of the disclosure may be used to deconvolute contributors to a complex DNA mixture. For instance, a complex DNA mixture obtained from a breast cancer tumor biopsy may be used to build an individual profile of the malignant cells. In the same patient, a brain cancer tumor biopsy, this individual profile may be used to deconvolute the contributors to the complex DNA mixture obtained from the brain cancer tumor biopsy to determine, for instance, if a malignant breast cancer cell from that subject metastasized to the brain to form a secondary tumor. This method would resolve a question as to whether the tumors arose independently, or, on the other hand, if these tumors are related.

[0059] Accordingly, the disclosure provides a method for detecting a disease or disorder in a subject. The method includes: a) obtaining a sample from the subject; b) identifying microhaplotypes in a DNA molecule present in a sample; c) determining the presence or absence of SNP sets having more than 2 microhaplotypes in the sample; and d) quantitating the frequency of haplotypes within SNP sets to determine the presence or absence of a genetic marker indicative of the disease or disorder, thereby detecting the disease or disorder. In one embodiment, identifying includes: i) identifying a region of interest, wherein the region of interest is associated with the disease or disorder; ii) detecting SBSs within the region of interest region of interest thereby generating multiple sequence variant sets; and hi) analyzing each variant set for LD to identify microhaplotypes.

[0060] In various embodiments, a genome is present in a biological sample taken from a subject. The biological sample can be virtually any type of biological sample, particularly a sample that contains DNA. The biological sample can be a germline, stem cell, reprogrammed cell, cultured cell, or tissue sample which contains 1000 to about 10,000,000 cells or a fluid with circulating DNA. In embodiments, the sample includes DNA from a tumor or a liquid biopsy, such as, but not limited to amniotic fluid, aqueous humour, vitreous humour, blood, whole blood, fractionated blood, plasma, serum, breast milk, cerebrospinal fluid (CSF), cerumen (earwax), chyle, chime, endolymph, perilymph, feces, breath, gastric acid, gastric juice, lymph, mucus (including nasal drainage and phlegm), pericardial fluid, peritoneal fluid, pleural fluid, pus, rheum, saliva, exhaled breath condensates, sebum, semen, sputum, sweat, synovial fluid, tears, vomit, prostatic fluid, nipple aspirate fluid, lachrymal fluid, perspiration, cheek swabs, cell lysate, gastrointestinal fluid, biopsy tissue and urine or other biological fluid. In one embodiment, the sample includes DNA from a circulating tumor cell. It is possible to obtain samples that contain numbers of cells, even a single cell, in embodiments that utilize an amplification protocol such as PCR. The sample need not contain any intact cells, so long as it contains sufficient biological material (e.g., DNA) to perform genetic analysis of one or more regions of the genome.

[0061] In some embodiments, a biological or tissue sample can be drawn from any tissue that includes cells with DNA or a fluid with circulating DNA. A biological or tissue sample may be obtained by surgery, biopsy, swab, stool, or other collection method. In some embodiments, the sample is derived from blood, plasma, serum, lymph, nerve-cell containing tissue, cerebrospinal fluid, biopsy material, tumor tissue, bone marrow, nervous tissue, skin, hair, tears, urine, fetal material, amniocentesis material, uterine tissue, saliva, feces, or sperm. Methods for isolating PBLs from whole blood are well known in the art.

[0062] As disclosed above, the biological sample can be a blood sample. The blood sample can be obtained using methods known in the art, such as finger prick or phlebotomy. Suitably, the blood sample is approximately 0.1 to 20 ml, or alternatively approximately 1 to 15 ml with the volume of blood being approximately 10 ml. Smaller amounts may also be used, as well as circulating free DNA in blood. Microsampling and sampling by needle biopsy, catheter, excretion or production of bodily fluids containing DNA are also potential biological sample sources.

[0063] In the present invention, the subject is typically a human but also can be any species, including, but not limited to, a dog, cat, rabbit, cow, bird, rat, horse, pig, or monkey.

[0064] The method of the disclosure utilizes nucleic acid sequence information, and can therefore include any method for performing nucleic acid sequencing including nucleic acid amplification, polymerase chain reaction (PCR), nanopore sequencing, 454 sequencing, insertion tagged sequencing. In embodiments, the methodology of the disclosure utilizes systems such as those provided by Illumina, Inc, (including but not limited to HiSeq™ X10, HiSeq™ 1000, HiSeq™ 2000, HiSeq™ 2500, Genome Analyzers™, MiSeq™’NextSeq, NovaSeq systems), Applied Biosystems Life Technologies (SOLiD™ System, Ion PGM™ Sequencer, ion Proton™ Sequencer) or Genapsys or BGIMGI and other systems. Nucleic acid analysis can also be carried out by systems provided by Oxford Nanopore Technologies (GridiON™, MiniON™) or Pacific Biosciences (Pacbio™ RS II or Sequel I or II). Importantly, in embodiments, sequencing may be performed using any of the methods described herein. When a long read technology such as PacBio™ or Oxford Nanopore™ is used, the length restrictions on the DNA are loosened and SNPs can be further apart consistent with the longer read lengths.

[0065] The present invention includes systems for performing steps of the disclosed methods and is described partly in terms of functional components and various processing steps. Such functional components and processing steps may be realized by any number of components, operations and techniques configured to perform the specified functions and achieve the various results. For example, the present invention may employ various biological samples, biomarkers, elements, materials, computers, data sources, storage systems and media, information gathering techniques and processes, data processing criteria, statistical analyses, regression analyses and the like, which may carry out a variety of functions.

[0066] Methods for genetic analysis according to various aspects of the present invention may be implemented in any suitable manner, for example using a computer program operating on the computer system. An exemplary genetic analysis system, according to various aspects of the present invention, may be implemented in conjunction with a computer system, for example a conventional computer system comprising a processor and a random access memory, such as a remotely-accessible application server, network server, personal computer or workstation. The computer system also suitably includes additional memory devices or information storage systems, such as a mass storage system and a user interface, for example a conventional monitor, keyboard and tracking device. The computer system may, however, comprise any suitable computer system and associated equipment and may be configured in any suitable manner. In one embodiment, the computer system comprises a stand-alone system. In another embodiment, the computer system is part of a network of computers including a server and a database.

[0067] The software required for receiving, processing, and analyzing genetic information may be implemented in a single device or implemented in a plurality of devices. The software may be accessible via a network such that storage and processing of information takes place remotely with respect to users. The genetic analysis system according to various aspects of the present invention and its various elements provide functions and operations to facilitate genetic analysis, such as data gathering, processing, analysis, reporting and / or diagnosis. For example, in the present embodiment, the computer system executes the computer program, which may receive, store, search, analyze, and report information relating to the human genome or region thereof. The computer program may comprise multiple modules performing various functions or operations, such as a processing module for processing raw data and generating supplemental data and an analysis module for analyzing raw data and supplemental data to generate quantitative assessments of contamination or a disease status model and / or diagnosis information.

[0068] The procedures performed by the genetic analysis system may comprise any suitable processes to facilitate genetic analysis and / or disease diagnosis. In one embodiment, the genetic analysis system is configured to establish a disease status model and / or determine disease status in a patient. Determining or identifying disease status may comprise generating any useful information regarding the condition of the patient relative to the disease, such as performing a diagnosis, providing information helpful to a diagnosis, assessing the stage or progress of a disease, identifying a condition that may indicate a susceptibility to the disease, identify whether further tests may be recommended, predicting and / or assessing the efficacy of one or more treatment programs, or otherwise assessing the disease status, likelihood of disease, or other health aspect of the patient.

[0069] The genetic analysis system suitably generates a disease status model and / or provides a diagnosis for a patient based on genetic data and / or additional subject data relating to the subjects. The genetic data may be acquired from any suitable biological samples as well as databases storing genetic information.

[0070] The following example is provided to further illustrate the advantages and features of the present invention, but it is not intended to limit the scope of the invention. While this example is typical of those that might be used, other procedures, methodologies, or techniques known to those skilled in the art may alternatively be used. EXAMPLES EXAMPLE 1 DETECTION OF SAMPLE CONT AMIN A TION

[0071] In this example, the methodology of the present disclose was utilized to detect sample contamination. The following provides an in-depth discussion of the method and process used for detection.

[0072] Identification of candidate variant sets.

[0073] For each region of interest, the regions targeted for sequencing along with an additional bordering region (up to 100 bp) was examined for SBS with a frequency of 10-90% according to the gnomAD™ database (gnomad.broadinstitute.org / ). Once a variant was found that was not in a low confidence region, the neighboring 180 bp in both directions was examined for additional SBSs with a frequency of 5-95%. These cutoffs may vary depending on the type of sample to be analyzed for various panels and the number of SNP sets required. All such variant pairs were then examined for LD using 1000 genomes data (ldlink.nci.nih.gov / ?tab=ldhap). Pairs, triplets, etc., with at least three haplotypes and the third and greater haplotypes having a total frequency of >1% were considered as candidates for use. These cutoffs could be expanded to include additional variant sets if necessary or constricted to retain only the most informative variant sets and minimize noise. For example, variant sets were chosen to avoid insertions / deletions because the intrinsic sequencing error rate in such variants is higher and more likely to generate noise. Similarly, other sequence contexts could be favored based on error rates. Furthermore, some variants were not found in the 1000 Genomes™ database so could not be assessed for LD but were advanced for candidate testing if the MAFs observed in gnomAD™ suggested they might be appropriate. While SNPs could in theory be present as far away as paired read partners, SNPs located closer to each other and covered by single reads were chosen to simplify analysis.

[0074] Characterization of candidate variant sets.

[0075] The candidate variant sets were further evaluated in real samples to ensure that there were enough reads with both / all variants on the read such that a phased haplotype could be generated. A cutoff of lOOx median coverage for each SBS was used so that all or nearly all SNP sets could be included in each comparison. High coverage is necessary to maximize sensitivity of the analysis. For other panels, the exact set of SBSs used will vary depending on the panel to be interrogated. Furthermore, some sequence contexts have higher error rates than others and use of those variants could lead to additional, artifactual microhaplotypes. Variant sets prone to too many third / fourth microhaplotypes in purportedly pure samples were eliminated from use because they could generate a high level of noise relative to signal.

[0076] A set of 106 variants was chosen for use with a 507 gene panel (Table 5) based on high coverage and low background noise level. To the extent possible, distance between SBS sets was maximized to minimize redundant information. The MAFs listed for SBSs in this table were obtained from “All Populations” of 1000 Genomes™ database and are different than the original MAFs obtained from gnomAD™.

[0077] Estimating contamination levels.

[0078] Because any sample could, in theory, be contaminated, it was necessary to characterize samples prior to use for calibration so that the process could start with pure samples. Furthermore, the variant and microhaplotype frequencies can vary significantly across ethnicities so it is useful to characterize samples with different ethnicities to ensure that a given set of SBSs will work with all samples and contaminants. For this data set, five African, five Asian, and six European (all self-identified) were selected based on coverage of at least 105 / 106 variant sets and no more than 2 variant sets with >2 microhaplotypes. These samples and their characteristics are shown in Table 1. The European samples have a non-significantly lower number of single microhaplotype SBSs. TABLE 1: Samples used for calibration. Sample 1 MH 2 MH 3 MH 4 MH Total Ethnicity AATF094T 44 62 0 0 106 Afri AATF217T 57 49 0 0 106 Afri AATF218T 56 49 1 0 106 Afri AATF219T 47 59 0 0 106 Afri PGRD00454T 66 39 0 1 106 Afri Mean 54 51.6 0.2 0.2 106 AATF355T 49 56 1 0 106 Asian AATF595T 57 47 2 0 106 Asian AATF597T 59 47 0 0 106 Asian AATF731T 45 60 0 1 106 Asian AATF735T 58 46 1 1 106 Asian Mean 53.6 51.2 0.8 0.4 106 AATF110T 42 61 1 1 105 Euro AATF375T 48 56 2 0 106 Euro AATF389T 45 60 1 0 106 Euro AATF391T 57 49 0 0 106 Euro AATF417T 47 58 1 0 106 Euro AATF088T 56 49 1 0 106 Euro Mean 49.2 55.5 1 0.17 105.8

[0079] To mimic contamination in silico, unfiltered fastQ™ reads from pure samples were computationally mixed with other samples in order to generate artificially “contaminated” samples. For a targeted contamination of X%, 100-X% of the reads from the principle sample were mixed with X% of the reads from the “contaminant”. These mixed samples were then run through the pipeline and aligned and called using our standard methods. The number of haplotypes at each SBS set and their frequency was counted and tabulated for each sample. The frequency of the third haplotype for each SBS set, if any, was then examined within each sample and the minimum, maximum, median, and mean calculated for each set of 3rd haplotype frequencies. The mixes were then examined to see how well contamination could be predicted by these parameters.

[0080] Prior to examining the results in detail, multiple technical and biological confounding factors were considered for how they may affect results. As observed with even the “pure” samples, there is technical noise that leads to a small number of 3rd / 4th haplotypes. In order to avoid these interfering with contamination detection, a minimum number of 3rd / 4th haplotypes was set. The desired level of contamination detection is at the level of 1-2% so the minimum number of 3rd / 4th haplotypes was chosen as being in the 5-10 range. This avoids the issue of having low level technical noise being misassigned as contamination. TABLE 2: Number of SBS sets with >2 Microhaplotypes (n=70 each). % Contam 0.5 1 2 5 10 Minimum 2 5 10 13 15 Median 8 13 19 23 24 Maximum 18 23 31 32 35

[0081] The percent of SNPs with >2 microhaplotypes determines whether a sample is contaminated but it is relatively insensitive to the degree of contamination. Because the % >2 microhaplotype value rapidly achieves a maximum, contamination of 2% vs 5% vs 20% appear very similar when looking only at this parameter. To circumvent this issue, we have used the MAF for the third haplotype for quantitating the level of contamination. This value can be misleading at the low contamination due to technical artifacts. It can appear anomalously high due to the possibility that the contaminating DNA could contribute two copies of the third haplotype, making contamination appear to be 2x higher than reality (Figure 3). Extreme copy number variation often present in tumor samples can also affect apparent contamination in either direction, depending on which haplotype is in excess. This is not typically a problem with normal DNA but can be severe with tumor DNA. To avoid these issues, we use the median MAF for the third haplotype to minimize the contributions of either abnormally high or low MAFs. There is additional information found in the allele frequencies for the 2nd and 4th microhaplotype though this data was not used for the calculation. More complex analyses of haplotype frequencies can be used if there are enough sets that can be examined.

[0082] For samples having above a set number of 3rd / 4th haplotypes, a variety of factors could interfere with accurate frequency determination. In the calibration series, one technical issue is whether the nominal contamination level is actually accurate. Though the number of reads added can be precisely controlled, each sample has different properties in terms of DNA quality that may affect the functional level of contamination. Samples with divergent DNA lengths due to different DNA qualities or different fractions of on-target reads due to different capture efficiencies will have different functional levels of contamination because the frequency of SNP sets appearing on the same read is dependent on the length. This would mean that 1% added reads may be functionally equivalent to 0.5% or 2% or anywhere in between. For this reason, each sample and its contaminant were interchanged as sample and contaminant in parallel. Thus, this normalizes quality differences to some extent and provides a better estimate of the functional level of contamination. When these methods are applied to real samples, functional rather than stoichiometric contamination is more important when considering the likelihood that incorrect variant calls could be made.

[0083] There are also biological reasons for quantitation issues. A pure sample could have one or two microhaplotypes at each SBS set and the incoming contaminants one or two microhaplotypes could match one, two or neither of the primary sample’s microhaplotypes. When contamination is low and the signal just emerging, the new 3rd haplotypes would preferentially be composed of double contributions that do not match the sample’s microhaplotypes while there will be a mix of single / double contributions at higher contamination levels. Thus, one should not expect a simple, linear relation between level of contamination and the frequencies of various haplotypes. Superimposed on this difficulty is the occurrence of extensive copy number variation among tumor samples that can also have a major impact on haplotype frequency. Because of these caveats, an empirical estimation of contamination was used because low contamination levels will be overestimated and high contamination levels underestimated if one looks simply at the 3rd haplotype frequencies. With many more variant sets at very high coverage levels, it would be possible to fit the frequency data to better estimate functional contamination. As shown in Table 3, ~2% is the region where the over- and undercounting balance out to yield a relatively accurate contamination estimation with this set of SNPs and coverage conditions. Since this is around the level at which we would like to set sensitivity, median frequency of the 3rd haplotype will be used as an approximation of the level of contamination, realizing that venturing far from 2% could lead to issues with accuracy. For accurate estimation of other contamination levels, it will be necessary to examine more mixes as has been done with other SBS sets. TABLE 3: Median frequency of 3rd Haplotypes by ethnicity. % Contamination Freq of 3rd Haplotype Afri Asian Euro 0.5 1.0 1.2 1.2 1 1.2 1.4 1.7 2 1.8 2.4 2.6 5 4.1 4.4 4.9 10 7.0 7.7 8.0

[0084] Applications to real samples.

[0085] The samples used in the in silico contaminant mixes were chosen based on their high quality. Unfortunately, there is much greater variation in real samples so it is necessary to set criteria for which samples can be analyzed and how that analysis should be done. Ideally, all samples would have >100x coverage at all 106 SBS sets but this is often not the case. Missing SBS sets leads to inconsistent comparisons and low coverage at particular SBSs may lead to grossly overestimated or missing 3rd haplotype frequencies. Thus, 1000 samples were run through the standard pipeline to examine microhaplotype data. Of these 1000 samples, 151 samples had failed standard quality control metrics, leaving 849 for microhaplotype analysis. In order for an SBS to be counted, we require a minimum coverage of 20. The vast majority of samples (709) have data for all 106 SBS sets. However, there are samples with significantly fewer SBS sets meeting the minimum criteria. The point at which more samples fail than pass other quality control metrics is 100 SBS calls. Thus, for the analyses below, only the 825 passing samples with >100 SBS calls are used. Of these 825 samples, 24 failed the previously used SNPCheck™ method for monitoring sample contamination.

[0086] Table 4 shows the effects of varying the cutoffs on contamination detection for these 825 samples. Samples pass by either having fewer than the cutoff number of >2 microhaplotype SBS sets or having a 3rd microhaplotype median MAF below a set threshold. Based on the in silico experiments above, that number of SBS sets with >2 microhaplotypes should be in the 5-10 range with these microhaplotypes. In addition, even if there are more than the cutoff number of microhaplotypes, samples with a median 3rd haplotype frequency of <1.5% are also deemed to pass. Using these cutoffs, 804-811 samples pass including 18-19 samples that failed SNPCheck™. If the 3rd haplotype frequency is 2-4%, it is optional that the sample be checked to see if that level of contamination would cause a problem based on the observed somatic mutation frequency. 4-5 of these 11-18 samples failed SNPCheck™. Samples with >4% 3rd microhaplotype frequency would fail. In all cases, this would be three samples, 1 of which failed SNPCheck™. In addition to the 825 passing runs described above, SNPCheck™ had been run on samples that failed other QC metrics or had too few SBSs called in the microhaplotype method of the disclosure. Of the 4 QC and SNPCheck™-failed samples, 3 failed the microhaplotype method with contamination >10%. Of the 7 SNPCheck™-failed samples which would not typically be evaluated by the microhaplotype with fewer than 101 SBSs called, 4 also failed by the microhaplotype method regardless of cutoffs while another one would have failed with some cutoff values. TABLE 4: Comparison of Microhaplotypes to SNPCheck™. Category Suggested Status # Samples (cutoff 5) Failed SNPCheck™ # Samples (cutoff 6) Failed SNPCheck™ # Samples (cutoff 8) Failed SNPCheck™ # Samples (cutoff 10) Failed SNPCheck™ <MH Cutoff Pass 652 16 701 16 746 17 779 19 Median <2% Pass 152 2 107 2 64 1 32 0 Median 23% Check 13 2 9 2 7 2 7 2 Median 34% Check 5 3 5 3 5 3 4 2 Median 45% Fail 1 0 1 0 1 0 1 0 Median >5% Fail 2 1 2 1 2 1 2 1

[0087] A perfect match between the method of the invention and SNPCheck™ was not expected. SNPCheck™ fails some tumor samples with very high copy number variation by calling pure samples contaminated, leading to false positives. False negatives are also known to arise when the level of contamination is very high and that variation is misinterpreted as germline variation.

[0088] Contamination detection in exomes.

[0089] Many of the SBSs used in the 507 gene panel are in non-coding regions so are of no value in an exome analysis. Thus, a new set of SBSs was chosen for examination of exomes. Because exome coverage is lower on a per ROI basis, it is more important to capture variants with as much of the coverage as possible. Thus, SBS sets were chosen with a shorter intervariant spacing and localized closer to the exons than in the 507 gene panel. Because there are so many more ROIs, efforts were made to include more informative SBSs and chosen in ROIs that had higher than average coverage. These were then examined in a set of exome data and SBSs with >80 median coverage and diverse haplotypes chosen for use in the panel. These SBS sets are listed in Table 6. Using methods similar to those described above, two exomes suspected to be contaminated were examined and found to be >15% contaminated using this SBS set.

[0090] With the initial set of microhaplotypes used for the 507-gene panel, differences were observed in sensitivity among different ancestry groups. This issue was likely caused by both the biases in the databases used to select microhaplotype sets but also by the differences in the heterozygosity rate among different ancestries. To correct for this, population haplotype frequencies from the 1000 genomes project were used to balance the 3rd / 4th haplotype frequencies so they were approximately equal across all ancestries. The frequency of 3rd / 4th haplotypes among SNP sets was summed and SNP sets which contributed to excess frequency in over-represented ancestries were dropped. This allowed the generation of a set of microhaplotypes such that the expected average number of 3rd / 4th haplotypes is the same for those with East Asian, African, and European ancestry. It was not possible to simultaneously generate the same frequencies for the other two 1000 genome ancestries, Admixed American and South Asian. Both of these ancestries had higher 3rd / 4th microhaplotype frequencies than the other three so contamination should be easily detected using the same thresholds as the other ancestries.

[0091] To further improve performance characteristics, efforts were made to choose only microhaplotype sets with high coverage and low noise among pure samples. Minimum mean coverage for SNP sets was raised from 100 to 250. High coverage, however, is a double-edged sword. While it allows greater sensitivity and higher accuracy, it can also generate artifactual 3rd haplotypes caused by inherent sequencing errors that are typically around the level of 0.1%. To minimize the impact of such technical errors, low frequency haplotypes can be eliminated from consideration. The level at which this should be set can be optimized based on the coverage and sequencing quality. For these experiments, the threshold was set at 0.2% where any haplotype with a frequency below 0.2% was not considered as real. Other thresholds can be used depending on the sequence quality and other factors.

[0092] In addition, more SNP sets were used to enhance the signal and allow more precision in contamination estimates. Based on these considerations, 164 SNP sets were chosen for a second microhaplotype panel that meets all these criteria. 51 of these SNP sets were also present in the first panel and both sets are listed in Table 7 with locations, dbSNP numbers, and 1000 genome frequencies of 3rd / 4th haplotypes.

[0093] As discussed above, generation of samples with precise levels of contamination is extremely challenging. In silico combination of samples provides a mixed sample with exact levels of contamination but the functional impact is not necessarily precise. Because detection of microhaplotypes is dependent on the length of sequenced molecules, samples with the same fractional component but different DNA quality will have differential impacts on microhaplotype frequencies. To minimize the impact of this, samples were analyzed in pairs, interchanging “sample” and “contaminant” and results then averaged within each pair. 15 such pairs for each category (African, East Asian, European, and Mixed) were then analyzed for the number of 3rd / 4th microhaplotypes as a function of contamination level. As shown in Figure 1, the 3rd / 4th MH number for individuals of East Asian and European ancestry were nearly superimposable. The 3^ / 4* MH number for individuals of African-American ancestry and mixes of ancestries were higher than East Asian / European but similar to each other. The African-American discrepancy is likely due to the composition of the 1000 genomes African panel which includes 5 sub-groups from Africa and 2 from African-Americans. These two are admixed to some extent and thus generate higher numbers than the other groups. The combination of more even 3rd / 4th microhaplotype frequencies and larger number of microhaplotype sets tested will provide more robust identification of contaminated samples.

[0094] Even though the number of 3rd / 4th microhaplotypes varies slightly among different ancestries, the median 3rd microhaplotype frequency as a function of contamination level is nearly identical among those ancestries, including samples mixed from different ancestries (Figure 2). This relation is linear starting at around 1%. Contamination levels below 1% are impacted heavily by sequencing artifacts as well as the potential presence of additional contaminating DNAs beyond the intended one. Above 1%, the observed median frequency is roughly half the contamination level. This is expected based on the manner in which 3rd MHs are generated, as shown in Figure 3. At higher levels of contamination this begins to drop off due to a number of factors including the chance that the 3rd microhaplotype may actually be from the sample rather than the contaminant.

[0095] Using the relation of contamination level = 2 x Median 3rd microhaplotype level, the detection of contamination levels at different levels is shown in Table 8 for each ancestry. The patterns are similar with a decreasing fraction of samples being detected at higher contamination levels when the predicted contamination level is twice the 3rd microhaplotype level. This table provides guidance as to where thresholds need to be set to achieve near 100% detection of contamination at a given level. For example, if one wishes to detect nearly all samples contaminated at 2%, setting a cutoff of 3rd microhaplotype = 0.75% will detect 97% of samples contaminated at 2% while also including 82% of samples contaminated at 1.5% and only 15% of samples contaminated at 1% and none contaminated at 0.5%. Choice ofthresholds can be done based on relative level of false positives and false negatives. EXAMPLE 2 USING MICROHAPLOTYPES FOR NIPT DETECTION OF CHROMOSOMAL ABNORMALITIES

[0096] Non-Invasive PreNatal testing (NIPT) for chromosomal abnormality detection is carried out by taking a blood sample from the mother and assessing it for circulating fetal DNA in the presence of a large background fraction of maternal DNA. Typically, sequence reads are simply aligned and the number aligning to each chromosome counted. If there is an excess of reads aligning to chromosomes most susceptible to trisomy (usually chrl 3, chr 18 and chr21), a positive diagnosis is made. This test is typically done at week 10 or later when the amount of fetal DNA in the maternal blood is sufficient for test accuracy. Use of microhaplotypes will allow testing to be done earlier because more accurate quantitation is possible at lower DNA concentrations and provide a more accurate result due to independence from benign copy number variation pre-existing in the mother that can lead to interpretation errors.

[0097] The behavior of NIPT samples will be more straightforward than for tumor samples for two reasons. Firstly, the complication of extensive copy number variation will be less of an issue. Secondly, one of the fetal haplotypes will be already present in the mother and the incoming 3rd haplotype from the father will be single copy only so will not be overcounted at low levels. Thus, a more predictable increase in frequency would be expected.

[0098] For most trisomy 21 cases, the extra chromosome arises from the mother, deflating the contribution of the new paternal haplotype on that chromosome. Thus, the paternal haplotype frequency on unaffected chromosomes would be determined and compared to the paternal haplotype frequency on potentially affected chromosomes. Because many SBS sets would be available for use, it will be straightforward to generate a list of well-behaved SBSs. These could be enriched via target capture or PCR amplification to allow earlier detection than is currently possible. Unbiased PCR amplification of DNA for typical NIPTs is challenging because slight non-linearities can have an impact on quantitation. Because the microhaplotype method is not simply counting the number of reads but rather looking at the ratio of microhaplotypes, it is less susceptible to amplification biases. Accuracy can be further enhanced by selecting SBS sets that are less prone to sequencing errors or by choosing multiSB S sets that generate 2 or more sequence changes going from the maternal microhaplotype to the paternal microhaplotype. In addition, the fetal fraction of DNA can be readily determined via examination of the frequencies of genotypes in SNP sets with 3 microhaplotypes. The fetal fraction will be twice the 3rd microhaplotype frequency. Knowledge of the fetal fraction and its variation will provide more accurate determinations of whether a test result is valid or indeterminate.

[0099] In order to determine trisomy or other DNA copy-number abnormality, the 3rd microhaplotype frequencies from different regions are compared. If the third microhaplotype frequency from any large genomic region (partial or full chromosome) is different than the frequency of other genomic regions it will signify trisomy or other amplification (increased 3rd microhaplotype frequency) or deletion (no 3rd microhaplotypes). SUPPLEMENTARY TABLES TABLE 5: SBS sets for the 507 gene panel. Location Length SNP1 SNP2 SNP3 Middle Pos 1 3rd MH 4th+ MH SNP1 MAF SNP2 MAF SNP3 MAF chrl:120057158 -120057246 89 rs6203 rs45609334 0.167 0.367 0.167 chrl:156846120 -156846233 114 rsl800880 rs6334 0.213 0.232 0.213 chr 1:226589833 -226589958 126 rs 1805407 rsl805404 0.218 0.263 0.218 chrl :23885498-23885599 102 rsl1574 rs2067053 0.109 0.109 0.464 chr 10:10438693 4-104387019 86 rsl7114803 rsl2414407 0.246 0.246 0.280 chrl0:43615505 -43615633 129 rs2472737 rsl800863 0.173 0.173 0.172 chrl0:70332580 -70332672 93 rsl0823229 rsl2773594 0.172 0.259 0.172 chrl 1:534197- 534242 46 rs41258054 rsl 2628 0.077 0.077 0.297 chrl 1:8246326- 8246343 18 rs34544683 rs3816490 0.158 0.158 0.232 WO 2020 / 219444                                                 PCT / US2020 / 029113 chrl2:12141662 2-121416650 29 rsl169289 rsl169288 0.138 0.428 0.298 chrl2:12143127 2-121431300 29 rs2071190 rsl169301 0.252 0.252 0.319 chrl2:12143542 7-121435475 49 rs2464196 rs2464195 0.042 0.318 0.360 chrl2:12143711 4-121437221 108 rs55834942 rsl169304 0.063 0.714 0.223 chrl2:13320888 6-133208979 94 rs5745023 rs5745022 0.134 0.435 0.301 chrl2:13322615 9-133226196 38 rs4883613 rs4883537 0.143 0.271 0.414 chrl2:13325399 5-133254083 89 rs5744751 rs5744750 0.057 0.057 0.435 chrl2:18656174 -18656225 52 rsl1044141 rsl1044142 0.027 0.134 0.161 chrl2:56494991 -56494998 8 rs2271189 rs773123 0.066 0.252 0.067 chrl3:21562832 -21562948 117 rs2770928 rs558614 0.150 0.150 0.370 chr 14:10256829 6-102568367 72 rsl0873531 rs8005905 0.137 0.336 0.199 chrl4:10416575 3-104165927 175 rs861539 rsl799796 0.217 0.217 0.247 chrl4:10523914 6-105239192 47 rs3803304 rs2494732 0.221 0.221 0.426 WO 2020 / 219444                                                 PCT / US2020 / 029113 chrl4:10525889 2-105258893 2 rs2494748 rs2494749 0.291 0.356 0.291 chrl4:35872792 -35872926 135 rs2233415 rsl050851 0.098 0.333 0.102 chrl5:40998305 -40998342 38 rs45592734 rs45457497 0.204 0.204 0.354 chrl5:41857216 -41857303 88 rs 11639399 rs2277536 0.160 0.160 0.267 chrl5:41860411 -41860490 80 rs7171675 rsl2148316 0.154 0.333 0.155 chrl5:67457335 -67457485 151 rsl065080 rs2289261 0.166 0.166 0.485 O' chrl6:2138269-2138398 130 rsl748 rsl3332221 0.128 0.020 0.276 0.168 chrl6:2138398-2138422 25 rsl3332221 rsl3332222 0.033 0.168 0.201 chrl6:68857289 -68857441 153 rs2276330 rsl801552 0.058 0.058 0.281 chrl6:81819768 -81819820 53 rsl143685 rs4294811 0.265 0.267 0.286 chrl6:89806343 -89806347 5 rs 11647746 rs7195906 0.141 0.141 0.293 chr 16:898495 83 -89849629 47 rs2239360 rsl2448860 0.072 0.387 0.324 WO 2020 / 219444                                                 PCT / US2020 / 029113 4^ tO chrl6:89858505 -89858525 21 rs6500452 rsl800287 chrl7:1782952-1782957 6 rs5030755 rs2230930 chrl7:78599562 -78599655 94 rsl7848685 rs901065 chrl7:78820329 -78820374 46 rs3751945 rs2589156 chrl7:78865546 -78865630 85 rs2289764 rs2289765 chrl7:78897547 -78897561 15 rs7217786 rs6565491 chrl7:78921117 -78921211 95 rs4969231 rs9912373 chrl9:10267011 -10267077 67 rs4804490 rs2228611 chrl9:17937758 -17937786 29 rs3212798 rs3212797 chrl9:17955001 -17955021 21 rs3212713 rs3212712 rs32127 chrl9:2226676-2226772 97 rs3815308 rs2302061 0.172 0.468 0.297 0.029 0.029 0.271 ND Not in IK 0.321 0.077 0.437 0.077 0.161 0.281 0.230 0.148 0.249 0.148 0.119 0.198 0.119 0.204 0.204 0.466 0.028 0.206 0.188 11 17955003 0.051 0.411 0.463 0.407 0.225 0.226 0.256 WO 2020 / 219444                                                 PCT / US2020 / 029113 chrl9:3119184- 3119239 56 rs308046 rs4900 0.225 0.226 0.349 chrl9:50919797 -50919828 32 rs3218776 rs3218760 0.278 0.408 0.278 chr 19:5210622-5210782 161 rs2302224 rsl143698 0.086 0.033 0.282 0.335 chrl9:5210762-5210782 21 rs 1143699 rsl143698 0.101 0.101 0.335 chrl9:5212380-5212482 103 rs 1064300 rs2230611 0.144 0.318 0.145 chrl9:7166376-7166388 13 rs2059806 rs2229429 0.245 0.245 0.257 00 chr2:112754828 -112754880 53 rs3811632 rs3811633 0.190 0.304 0.190 chr2:l 12754943 -112755001 59 rs3811634 rs2230515 0.190 0.191 0.439 chr2:141259283 -141259376 94 rs35296183 rs35164907 0.022 0.104 0.126 chr2:29416366-29416481 116 rsl881421 rsl881420 0.176 0.019 0.427 0.415 chr2:29416481-29416615 135 rsl881420 rs56132472 0.059 0.415 0.059 chr2:29446184-29446202 19 rs2276550 rs4622670 0.177 0.421 0.176 WO 2020 / 219444                                                 PCT / US2020 / 029113 chr2:48010488-48010558 71 rs 1042821 rs 1042820 0.069 0.201 0.069 chr20:40714307 -40714479 173 rs3092662 rs2016647 0.062 0.063 0.144 chr20:40714539 -40714540 2 rsl569547 rsl569548 0.107 0.108 0.244 chr20:57478807 -57478939 133 rs7121 rs3730168 0.127 0.124 0.356 0.353 chr20:9543622-9543681 60 rs2297345 rs2297346 0.165 0.485 0.350 chr21:42845374 -42845383 10 rs2298659 rsl7854725 0.151 0.059 0.209 0.366 chr22:21337266 -21337325 60 rsl78280 rsl3054014 0.285 0.357 0.285 chr22:21348914 -21349037 124 rs4822790 rsl78292 0.168 0.169 0.248 chr22:24158895 -24158899 5 rs9608192 rs2070457 0.105 0.105 0.271 chr3:178922222 -178922274 53 rs3729676 rs2699896 0.273 0.273 0.415 chr3:183211906 -183212026 121 rsl520101 rs2256061 0.151 0.302 0.151 chr4:106196829 -106196951 123 rs34402524 rs2454206 0.092 0.092 0.230 WO 2020 / 219444                                                 PCT / US2020 / 029113 chr4:143043340 -143043404 65 rs2270658 rsl3133767 0.101 0.149 0.101 chr4:143324036 -143324094 59 rsl982965 rsl982966 0.252 0.454 0.253 chr4:187534362 -187534375 14 rs2249916 rs2249917 0.194 0.389 0.418 chr4:187629497 -187629538 42 rs458021 rs3733413 0.084 0.422 0.339 chr5:149456772 -149456811 40 rs60844779 rs3829987 0.197 0.310 0.197 chr5:149495287 -149495395 109 rs2229561 rs246388 ND Not in IK 0.285 o chr5:176517326 -176517461 136 rs422421 rs446382 0.077 0.147 0.224 chr5:176523562 -176523597 36 rs31777 rs31776 0.068 0.147 0.215 chr5:176721198 -176721272 75 rs28580074 rsl1740250 0.108 0.229 0.108 chr5:180046209 -180046344 136 rs446003 rs448012 0.070 0.021 0.368 0.417 chr5:180051003 -180051118 116 rs307826 rs728986 0.053 0.053 0.116 chr5:180057231 -180057293 63 rs3736061 rs34221241 0.039 0.059 0.039 WO 2020 / 219444                                                 PCT / US2020 / 029113 chr5:231111-231143 33 rsl126417 rs2288459 0.247 0.347 0.247 chr5:35861068-35861159 92 rsl494558 rsl1567705 rs969128 35861152 0.234 0.128 0.400 0.234 0.128 chr5:35871190-35871273 84 rsl494555 rs2228141 0.129 0.333 0.129 chr5:57754808-57754851 44 rs697133 rs702722 0.170 0.260 0.170 chr5:67522722-67522851 130 rs706713 rs706714 0.035 0.029 0.419 0.425 chr6:117725448 -117725578 131 rsl998206 rs2243378 0.168 0.168 0.325 chr6:117730673 -117730819 147 rsl 7634067 rs2273601 0.060 0.059 0.360 chr6:152382311 -152382325 15 rs2273206 rs2273207 0.115 0.277 0.162 chr6:26056549-26056708 160 rsl0425 rs2230653 rsl2204800 26056604 0.175 0.117 0.239 0.175 0.117 chr6:30865115-30865204 90 rs2239517 rs2267641 0.125 0.407 0.282 chr6:32188603-32188642 40 rs520803 rs520692 rs520688 32188605 0.012 0.268 0.268 0.280 chr7:100410597 -100410657 61 rs2230585 rs770657085 0.149 0.276 0.424 WO 2020 / 219444                                                 PCT / US2020 / 029113 chr7:6026775-6026942 168 rs2228006 rsl805323 0.112 0.117 0.112 chr7:78119109-78119199 91 rs3735442 rsl990577 ND 0.323 Not in IK chr8:30999122-30999123 2 rs3024239 rs2737335 0.130 0.375 0.495 chr8:3102463 8-31024654 17 rsl801196 rsl346044 0.193 0.274 0.193 chr8:90958422-90958530 109 rs 1061302 rs2308962 0.026 0.353 0.379 chr9:139403268 -139403280 13 rs3125000 rsl1145765 0.088 0.238 0.088 chr9:139405093 -139405261 169 rs36119806 rs3125001 0.107 0.108 0.414 chr9:139410424 -139410589 166 rs3125006 rs4880099 0.115 0.116 0.313 chr9:139411714 -139411880 167 rsl1145767 rs9411254 0.080 0.395 0.474 chr9:21968159-21968199 41 rs3088440 rsl 1515 0.098 0.170 0.098 chr9:93639846-93639973 128 rs290223 rs2290888 ND Not in IK 0.197 chr9:93641175-93641199 25 rs2306041 rs2306040 0.068 0.198 0.131 chr9:98238358-98238379 22 rs2066836 rsl805155 0.092 0.092 0.112 WO 2020 / 219444                                                 PCT / US2020 / 029113 TABLE 6: SBS sets for exome analysis. Location Length Start SNP Middle SNP End SNP Middle Pos 1 3rd MH 4th+ MH SNP1 MAF SNP2 MAF SNP3 MAF chrl:3743319- 3743391 73 rs6663840 rs58111155 rs6688969 4E+06 0.2 0.18 0.47 0.05 0.33 chrl:10431132- 10431158 27 rsl2141192 rsl7411502 0.14 0.14 0.25 chrl:32672908- 32672932 25 rs3903683 rsl2032332 0.1 0.23 0.1 chrl:94544234- 94544276 43 rs3112831 rs4147830 0.22 0.22 0.49 chrl:154832290- 154832304 15 rsl061122 rs4845397 0.07 0.22 0.28 chrl:159409857- 159409884 28 rsl2048482 rsl2118628 0.13 0.48 0.13 chrl:171168545- 171168584 40 rs2307492 rs2020862 0.12 0.12 0.47 chrl:183616884- 183616926 43 rsl0911390 rsll74657 0.09 0.09 0.37 chrll:4928841- 4928866 26 rs7108225 rs7941509 0.06 0.06 0.4 chrll:5345128- 5345170 43 rsl0837814 rs7952293 0.24 0.44 0.24 WO 2020 / 219444                                                 PCT / US2020 / 029113 0.38 0.41 0.48 0.29 0.1 0.48 0.47 0.47 0.43 0.25 0.43 0.28 0.33 0.11 0.47 0.09 0.23 0.16 0.05 0.1 0.47 0.05 0.16 0.05 0.47 0.18 0.05 0.11 0.12 0.09 0.23 0.07 0.05 0.1 0.11 0.05 0.16 0.05 0.19 0.15 rsl995157 rs614035 rs641393 rs4225 rs3741904 00 00 o UI rsll31454 rs208294 rs7486927 rsll58061 rsl885097 rs229586 rs2249778 rsl995158 rs614397 rs3851177 rs5128 rs3741903 rs4768261 rs7955146 rs208293 rsll246991 00 LT) LT) UI rs3751501 rs229587 rs2296310 22 43 50 32 33 00 CD 22 74 23 20 35 48 20 chrll:5566030-5566051 chrll:63883985-63884027 chrll:85436303-85436352 chrll:116703640-116703671 chrl2:6030405-6030437 chrl2:40834918-40834955 chrl2:113348849-113348870 chrl2:121600180-121600253 chrl2:132688115-132688137 chrl3:25367282-25367301 chrl4:23549285-23549319 chrl4:65263300-65263347 chrl4:96136775-96136794 0.31 0.16 0.08 0.38 0.41 0.19 0.27 0.15 0.17 0.49 0.46 0.07 0.14 0.33 0.06 ND 0.24 0.33 0.35 0.03 0.28 0.27 0.42 0.24 0.09 0.06 0.31 0.06 ND 0.24 0.26 0.19 0.03 0.15 0.17 0.09 0.22 0.07 0.06 rs2297380 rs2304994 rs3743398 00 (TJ U5 00 rs2240140 rs3751725 rs2230930 rs2469791 rsll556563 rsl2453262 rs2074877 rsl2943866 rs2289681 rs2297379 rsl6970441 rs3743399 CXI 00 o 00 rs2240141 rs918368 rs5030755 rs2241091 ci 00 00 00 Ui rs8075035 rs2074876 rs8072510 rsll26642 40 33 78 16 50 22 kO 13 16 34 18 32 26 chrl5:41819283-41819322 chrl5:79310256-79310288 chrl5:89398330-89398407 chrl5:94945704-94945719 chrl6:2812890-2812939 chrl6:87678144-87678165 chrl7:1782952-1782957 chrl7:3101578-3101590 chrl7:3352294-3352309 chrl7:6331803-6331836 chrl7:10223697-10223714 chrl7:33772658-33772689 chrl7:42989063-42989088 0.38 0.24 0.06 0.36 0.32 0.47 0.28 0.4 0.39 0.09 0.14 0.22 0.32 0.46 0.32 0.2 0.07 0.12 0.09 0.09 0.1 0.15 0.07 0.17 0.27 0.01 0.15 0.08 0.23 0.06 0.07 0.12 0.09 0.09 0.1 0.15 0.07 0.14 0.22 4E+07 rs3760371 rsll27986 rs3809974 rs7251858 rsl7160149 rs2240927 00 00 UI rs2293688 69Z.1901SJ rsl0413455 rsll878316 rsl469087 rs2356437 rs3760370 rs729124 rs3826593 rs7250947 rsl7202517 rsl2980833 rsl799898 rs3817622 89Z.1901SJ rsl0414451 rs2074078 rs2288274 83 39 22 31 14 47 49 19 00 CM 48 18 21 chrl7:45695832-45695914 chrl7:80887206-80887244 chrl8:56204747-56204768 chrl9:4510530-4510560 chrl9:8148301-8148314 chrl9:9362297-9362343 chrl9:11227554-11227602 chrl9:36237227-36237245 chrl9:44352639-44352666 chrl9:58131576-58131623 chrl9:58213952-58213969 chrl9:58572959-58572979 0.22 0.14 0.14 0.05 0.23 0.18 0.23 0.05 0.49 0.29 0.12 0.12 0.31 0.29 0.16 0.2 0.07 0.09 0.18 0.3 0.07 0.22 0.14 0.27 0.22 0.14 0.14 0.05 0.07 0.09 0.18 0.05 0.07 0.22 0.12 0.12 rs622716 rs2255991 rs2080390 rsl992187 rsl0462023 rs3746804 rs742711 rs2072127 rs817329 rs220178 rs5030669 rs879576 rs8970 rs2302652 rsl3421115 rs3749073 rsl3391269 rs3746803 rs742710 rs466264 rs45486695 rs3819142 rs55865320 rs879577 15 35 43 51 13 34 13 00 29 39 19 00 CD CHR2:33623720-33623734 CHR2:37579937-37579971 CHR2:71058184-71058226 CHR2:231775094-231775144 cn LO LT) rd oo 00 LD ’-i Ch 00 CO rd CM (N SO n- CM ZE chr20:744382-744415 chr20:5904028-5904040 chr20:52645534-52645541 chr20:62597666-62597694 chr21:43557698-43557736 chr21:46321659-46321677 chr22:17589209-17589246 0.37 0.17 0.14 0.45 0.12 0.21 0.07 0.09 0.4 0.07 0.17 0.37 0.3 0.37 0.35 0.16 0.07 0.09 0.11 0.29 0.31 0.32 0.37 ND 0.04 0.3 0.17 0.14 0.16 0.04 0.09 0.07 0.09 0.29 0.07 0.17 ND o 00 UI rsl548412 rsll547635 rsl33431 rs2076212 rsl042636 rs2307289 rsl052620 rs2227930 rsll70672 rs3733391 rs2116765 00 00 UI 00 LT) UI rs9862 rs2071744 rs2076213 rsl801725 rsl40693 rsl052618 rs2227929 rs7645550 rs3733390 rs2116766 65 34 13 45 49 13 13 21 40 27 18 34 chr22:19951207-19951271 chr22:21377301-21377334 chr22:33253280-33253292 chr22:35817553-35817597 chr22:44322922-44322970 chr3:122003757-122003769 chr3:129155451-129155463 chr3:136574501-136574521 chr3:142277536-142277575 chr3:178968634-178968660 chr4:156289900-156289917 chr5:147024476-147024509 0.2 0.1 0.1 0.17 0.45 0.2 0.12 0.2 0.38 0.15 0.1 0.28 0.21 0.48 0.25 0.22 0.25 ND 0.15 0.07 0.44 0.13 0.35 0.36 0.11 0.25 0.2 0.1 0.1 0.17 ND 0.15 0.07 0.13 0.13 0.15 0.1 0.11 3E+07 rsl042714 rsl2520516 rs3734049 rs5326 rs595413 rsl061156 rs2517598 rsl2179536 rs9263871 rs429608 rs2855430 rs7751928 rs4713420 rsl042713 rs375396 rs2053028 rs4532 rsl0485172 rs41557912 rs3734838 rs2523898 rs9263870 rs592229 rs9277932 rs7751919 34 30 18 47 34 99 44 58 15 22 00 CM 23 chr5:148206440-148206473 chr5:150666933-150666962 chr5:150901613-150901630 chr5:174870150-174870196 chr6:4069133-4069166 chr6:29913201-29913266 chr6:30080231-30080274 chr6:30993533-30993590 chr6:31170514-31170528 chr6:31930441-31930462 chr6:33141253-33141280 chr6:36291985-36292007 0.06 0.09 0.23 0.38 0.33 0.37 0.18 0.16 0.39 0.41 0.44 0.49 0.2 0.22 0.23 0.25 0.45 0.2 0.22 0.25 0.22 0.38 0.02 0.17 0.02 0.06 0.07 0.22 0.15 0.25 0.21 0.04 0.16 0.24 0.18 0.18 rs9457304 00 00 UI rsl0224537 rs2301721 rs2302340 rs845561 rsl0238201 rs7838192 rs2272662 rs2274159 rsl831370 rs909546 rs671694 rsl0269582 rs2301720 rs2302339 rsll506105 o 00 LT) o UI rs2748416 rsl871534 rs2274158 rsl831369 20 49 45 45 44 26 69 47 46 41 16 chr6:167754702-167754721 chr7:4213975-4214023 chr7:21640361-21640405 chr7:27196069-27196113 chr7:30795288-30795331 chr7:55220177-55220202 chr7:100677455-100677523 CHR8:142490120-142490166 CHR8:145639681-145639726 chr9:117166206-117166246 chr9:125315542-125315557 chr9:134385435- 134385436 2 rs3887873 rs2296949 0.08 0.08 0.13 chr9:136412255- 136412296 42 rs2073876 rs2073877 0.1 0.28 0.1 chrX:23019317- 23019346 30 rs5925720 rs5926203 0.16 0.16 0.34 TABLE 7: SNP sets. Location Ist Panel 2ND Panel Exo me Median Panel Cov Pure, MH>2 Length SNP1 SNP2 SNP3 African 3+4 East Asian 3+4 European 3+4 Admix Amer 3+4 South Asian 3+4 chrl:10431132- 10431158 Yes 0 0 27 rsl2141192 rsl7411502 chrl:120057158- 120057246 Yes 689 3 89 rs6203 rs45609334 0.033 0.082 0.235 chrl:154832290- 154832304 Yes 0 0 15 rsl061122 rs4845397 chrl:156846120- 156846233 Yes Yes 1526 2 114 rsl800880 rs6334 0.105 0.139 0.065 0.117 0.24 chrl:159409857- 159409884 Yes 0 0 28 rsl2048482 rsl2118628 chrl:171168545- 171168584 Yes 0 0 40 rs2307492 rs2020862 chrl:183616884- 183616926 Yes 0 0 43 rsl0911390 rsll74657 WO 2020 / 219444                                                 PCT / US2020 / 029113 0.183 0.100 0.240 0.243 0.160 0.000 0.114 0.013 0.073 0.125 0.147 0.147 0.238 0.082 0.156 0.050 0.055 0.022 0.131 0.192 0.159 0.154 0.242 0.093 0.242 0.075 0.000 0.071 0.000 0.100 0.151 0.205 0.251 0.028 0.250 0.211 0.023 0.000 0.113 00 o o 0.176 0.227 0.143 0.115 0.011 0.224 0.051 0.283 0.211 0.017 0.195 0.029 00 o o 6968899SJ rsl805408 1701790815.1 rs2067053 rsl2032332 LT) LT) T—1 T—1 T—1 T—1 00 LT) tn S— rs4147830 rsl2414407 rs6585731 rs2114689 rs2981453 91769880ISJ 00 kO O') T—1 (N T—1 tn L— rsl614303 rs7923788 rsl805414 rsl805407 rsll574 rs3903683 0178E999SJ T—1 cn 00 CM T—1 T—1 cn tn S— rsl7114803 rs7911440 rs4752560 T—1 kO CM T—1 O') CM tn S— T—1 T—1 kO T—1 cn kO LT) tn S— LT) LT) T—1 00 O') (N tn L— 0Z.96061SJ rsl0788194 39 126 102 25 73 43 98 52 LT) 28 75 79 O') T—1 T—1 CM 25 o o o o o CM T—1 T—1 o (N 2011 361 692 o o o 250 384 1151 320 1055 420 331 669 Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chrl:226573364-226573402 chrl:226589833-226589958 chrl:23885498-23885599 chrl:32672908-32672932 chrl:3743319-3743391 chrl:94544234-94544276 chrl0:104386934-104387019 chrl0:123194558-123194609 chrl0:123199092-123199095 chrl0:123275662-123275666 chrl0:123335839-123335866 chrl0:123346116-123346190 chrl0:123396728-123396806 chrl0:123406645-123406663 0.284 0.160 0.271 0.076 0.031 0.079 0.146 0.212 0.187 0.151 0.137 0.127 0.249 0.347 0.161 0.193 0.185 0.056 0.000 0.125 0.190 0.211 0.218 0.121 0.173 0.153 0.232 0.098 0.240 0.196 0.060 0.105 0.023 0.000 0.254 0.022 0.181 0.232 o LT) LT) kO LT) CM CM tn S— rsl800863 rsl2773594 rs4225 rs7941509 00 CM kO CM T—1 tn S— rs7952293 rsl995157 rs614035 on 00 on CM T—1 T—1 tn S— rs3816490 rs641393 rsll31454 rs743614 E91788179SJ 896H7Z.SJ on CM CM tn S— on CM CM m CM 00 o T—1 tn S— 00 CM T—1 LT) tn S— LT) CM CM 00 O T—1 tn S— rs41258054 rsl0837814 00 LT) T—1 LT) on on T—1 tn S— rs614397 rs79274134 rs34544683 rs3851177 rs7955146 kO (N T—1 00 cn (N (N tn L— rs2855708 158 129 93 32 26 46 43 22 43 35 00 T—1 50 22 134 41 CM LT) T—1 o o T—1 o o o T—1 <0 o o (N m 629 463 549 o o 2026 o o o 2968 287 o o 379 647 Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chrl0:43611708-43611865 chrl0:43615505-43615633 chrl0:70332580-70332672 chrll:116703640-116703671 chrll:4928841-4928866 chrll:534197- 534242 chrll:5345128-5345170 chrll:5566030-5566051 chrll:63883985-63884027 chrll:69412090-69412124 chrll:8246326-8246343 chrll:85436303-85436352 chrl2:113348849-113348870 chrl2:12009741-12009874 chrl2:12013572-12013612 0.104 0.054 0.159 0.151 0.182 0.049 0.050 0.042 0.000 0.216 990’0 0.251 0.112 0.272 0.219 0.222 0.045 0.000 0.144 0.075 0.194 0.132 0.236 0.062 0.166 0.135 0.135 0.100 0.000 0.248 0.111 0.271 0.049 0.255 0.000 0.000 0.105 0.107 0.105 0.000 0.125 0.241 0.126 0.082 0.118 0.014 0.012 0.173 0.105 0.000 0.099 T—1 m T—1 00 cn CM CM tn S— rs2416944 rs7973930 rs2739085 00 00 CM kO T—1 T—1 tn S— T—1 O cn O') kO T—1 T—1 tn S— rs2464195 rsll69304 rs208294 rs7486927 rs5745022 rs4883537 rs5744750 rsll044142 rsl0784618 o cn T—1 00 cn CM CM tn S— rs2723805 rs2710310 oi 00 CM kO T—1 T—1 tn S— rs2071190 rs2464196 rs55834942 cn O') CM 00 o CM tn S— T—1 O') O') kO CM T—1 T—1 tn S— rs5745023 rs4883613 rs5744751 rsll044141 rs4768261 82 57 kO T—1 29 29 49 108 74 23 94 38 68 52 38 cn T—1 T—1 CM o T—1 o o o (N (N T—1 T—1 o 1488 637 2052 3076 1774 3503 1919 o o 739 00 LT) 448 381 o Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chrl2:12016008-12016089 chrl2:12020114-12020170 chrl2:12035649-12035664 chrl2:121416622-121416650 chrl2:121431272-121431300 chrl2:121435427-121435475 chrl2:121437114-121437221 chrl2:121600180-121600253 chrl2:132688115-132688137 chrl2:133208886-133208979 chrl2:133226159-133226196 chrl2:133253995-133254083 chrl2:18656174-18656225 chrl2:40834918-40834955 o 00 o 6 CN CT O 6 T—1 6 CT 6 00 00 T—1 6 r5 CN 6 CN T—1 6 O O 6 o o o 6 LD O T—1 6 Ln T—1 rsi 6 CO Ln T—1 6 rsi LD rsi 6 CN o 6 LD O 6 T—1 LD T—1 6 T—1 CT 6 co co T—1 6 CN T—1 6 CN 00 CN 6 LD LD O 6 00 o 6 T—1 T—1 T—1 6 co co T—1 6 00 o 6 rsi 6 00 m o 6 co CO O 6 T—1 00 T—1 6 T—1 6 o CN CN 6 co Ln CN 6 co CN 6 o T—1 T—1 6 CT Ln o 6 rsi rsi O 6 rsi T—1 6 o 00 o 6 m T—1 6 CT o 6 co T—1 T—1 6 co co o 6 T—1 CT CO 6 T—1 o 6 00 LD o 6 rsi co 6 o o o 6 LD T—1 o 6 rsi CT T—1 6 rsi LD T—1 6 o o o 6 o rsi 6 00 T—1 m 6 T—1 m o 6 CT T—1 T—1 6 Ln CN CO 6 co T—1 6 r5 T—1 6 CT 6 co o 6 LD Ln rsi 6 o T—1 CO 6 LD rsi T—1 6 Ln T—1 6 O T—1 6 T—1 LD 00 T—1 CN CO co 4_ CT LD 00 T—1 CN CO co 4_ rsi LD m T—1 LD < / ) L_ 00 CN m CN m 00 T—1 T—1 < / ) 4_ co CN CN LD LD co 4_ LD LD 00 T—1 00 CN T—1 CO 4_ O LD 00 T—1 CN CO co 4_ 00 LD 00 T—1 CN CO co 4_ LD CN CT T—1 CN CO co 4_ o Ln 00 LD LD CT co 4_ co rsl T—1 CO L_ o CT T—1 CO < / ) L_ Ln LD co co co < / ) L_ co LD CO CT rsi rsi ch L_ 00 00 Ln co LD LD T—1 < / ) L_ T—1 LD 00 Ln Ln ch L_ T—1 LD O 00 m T—1 T—1 < / ) L_ o rsi co LD O rsi ch L_ CN Ln o co LD O T—1 T—1 < / ) 4_ Ln Ln Ln Ln CT co 4_ CO T—1 o 00 co CN co 4_ CT Ln 00 T—1 CN CO co 4_ LD 00 T—1 CN CO co 4_ Ln CN CT T—1 CN CO co 4_ CO rsl co co co T—1 CO 4_ CT 00 T—1 T—1 rsl rsl ch L_ CO o CT T—1 CO < / ) L_ co co 00 T—1 00 Ln LD < / ) L_ Ln o LD T—1 00 CO < / ) L_ Ln 00 00 00 o T—1 < / ) L_ 00 rsi CT o rsi ch L_ 00 LD m T—1 m T—1 < / ) L_ CT T—1 CO LD O rsi ch L_ CT T—1 co CN Ln Ln Ln LD O T—1 00 co co Ln CT CO 00 CT CO T—1 T—1 o rsi O Ln CN CN CN T—1 T—1 LD o co o T—1 CO O o LD LD 00 LD LD O CO CT T—1 LD T—1 CN CT 00 LD CO T—1 rsl rsl T—1 00 CO CO o o 00 LD CO Ln T—1 T—1 O co T—1 co < / ) > ch co co co CO 0J co CO 0J CO 0J < / ) > < / ) < / ) < / ) < / ) < / ) chrl2:4346169-4346177 4 00 oo rd rs| Ln q 22 rsi Ln rsi 22 T—1 "'J _c CT 00 O bP O 22 r*' rsi 22 T—1 _c LD CO o 22 oo CT Q 22 cn rsi 22 T—1 _c T—1 CT Q CT « 22 22 cr> rsi 22 T—1 _c CT co iq co oo 3 s rsi 5 T—1 _c rsl rH rsl ■> co 3 K Tt <-• rsl T—1 _c T—1 CT 22 oo 5 22 s ? s__ _C in o r O rn co o o m 3 s _c CN rsi rsi 0^1 tH iq m 2 S S 2 s__ _C LD CT rd 00 Ln rs, LD rsl CT LD rsl S 2 s__ _C r5 rH Ln ld £ £ cr LD rsl 2 2 s__ _C CN CO 00 00 rsl ^2 cn m rsi tH ld rsi |_T) s s s__ _C CN 00 rsl t-i 2 g m Ln ld C'l m s s s__ _C CT T—1 rsi q 22 ™ CT LO 00 02 Ch s a s__ _C 0.123 0.302 0.245 0.220 0.261 0.068 o oi CM O T—1 cn CM o 00 o o O1 o o 0.017 LT) O1 CM O T—1 T—1 O CM oi o o cn T—1 CM O cn LT) T—1 O 00 o cn o cn T—1 O O1 cn T—1 O cn LT) T—1 O 0.049 m o o O1 O o cm CM T—1 O 01 T—1 O o r\j T—1 T—1 O r\j T—1 O o T—1 O o 00 cn (N o LT) cn T—1 O 0.278 T—1 T—1 o oi kO T—1 o o CM T—1 O o CM O O O o o kO O1 o o LT) O1 O O cn cn T—1 O 00 00 o o rs8005905 rsl799796 rs2494732 rs2494749 rsl885097 T—1 LT) 00 O LT) O T—1 tn S— kO 00 LT) O1 CM CM tn S— rs2249778 rs45457497 rs2297380 rs2277536 kO T—1 cn 00 T—1 (N T—1 tn L— T—1 kO (N O1 00 (N (N tn L— rs2304994 rsl369426 rsl0873531 oi m LT) T—1 kO 00 tn S— rs3803304 rs2494748 rs3751501 rs2233415 00 LT) O1 CM CM tn S— o T—1 cn kO O1 (N (N tn L— rs45592734 rs2297379 O1 O1 cn O1 cn kO T—1 T—1 tn L— rs7171675 0809901SJ rsl6970441 rs8042993 72 LT) T—1 CM LT) cn LT) cn T—1 00 o (N 00 m o 00 00 o 00 T—1 LT) T—1 cn m o LT) T—1 o T—1 o O o o (N (N o T—1 696 LT) kO T—1 CM LT) m o cn kO o O LT) T—1 (N o 00 (N LT) T—1 O kO 00 LT) o O o 00 T—1 tn tn tn tn tn tn tn tn tn Yes tn tn tn tn tn tn tn tn chrl4:102568296-102568367 chrl4:104165753-104165927 chrl4:105239146-105239192 chrl4:105258892-105258893 chrl4:23549285-23549319 chrl4:35872792-35872926 chrl4:65263300-65263347 chrl4:96136775-96136794 chrl5:40998305-40998342 chrl5:41819283-41819322 chrl5:41857216-41857303 chrl5:41860411-41860490 chrl5:67457335-67457485 chrl5:79310256-79310288 chrl5:88488326-88488428 nnnnncn / in 1 0.079 0.000 0.151 ND 0.123 0.000 0.051 0.126 0.134 0.133 0.024 0.133 0.010 0.225 ND 0.017 0.013 0.056 0.271 0.035 0.043 0.020 0.124 0.000 0.217 ND 0.116 0.000 0.120 0.282 0.074 0.064 0.073 0.102 0.015 0.132 0.100 ND 0.000 0.000 0.068 0.141 0.013 0.013 0.012 0.000 0.266 0.110 0.192 ND 0.249 0.118 0.000 0.140 0.161 0.032 0.177 0.000 rsl2324332 rs76506232 rs3784410 rs3743398 rs7178698 T—1 CM CM CM cn cn cn T—1 IS) S— rsl3332222 rs2240140 CM LT) LT) T—1 O 00 T—1 IS) S— rs4294811 rs3751725 906961Z.SJ rsl2448860 rsl800287 rs2230930 rsll073758 LT) LT) CM T—1 kO T—1 tn S— rs3784411 rs3743399 CM 00 kO O 00 T—1 IS) S— rsl748 T—1 CM CM CM cn cn cn T—1 IS) S— rs2240141 rs2276330 rsll43685 00 kO cn 00 T—1 O') <S) L— rsll647746 O') LT) cn O') cn (N (N <S) L— rs6500452 rs5030755 78 kO T—1 130 25 50 153 53 22 LT) 150 T—1 (N <0 o T—1 o o o o o T—1 T—1 o (N (N m T—1 1763 975 1099 o o 941 2026 o 215 2558 o 601 275 869 1284 Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chrl5:88549118-88549151 chrl5:88646922-88647038 chrl5:88667852-88667948 chrl5:89398330-89398407 chrl5:94945704-94945719 chrl6:2138269-2138398 chrl6:2138398-2138422 chrl6:2812890-2812939 chrl6:68857289-68857441 chrl6:81819768-81819820 chrl6:87678144-87678165 chrl6:89806343-89806347 chrl6:89849480-89849629 chrl6:89858505-89858525 chrl7:1782952-1782957 0.053 0.235 0.181 ND 0.115 0.115 0.307 CT rsl 6 o LD O 6 CN LD O 6 96T0 OTTO ND 00 o 6 OTTO 0.291 0.111 0.114 0.016 00 o m 6 LD O T—1 6 ND 0.107 0.111 0.321 0.122 0.124 o o o 6 E9T0 0.150 ND o o o 6 o 6 96T0 0.199 CT O 6 0.194 O 6 o o o 6 ND CN 00 o 6 CT 00 CN 6 0.154 0.031 0.022 rs2469791 rsl2943866 rs2934953 T—1 CT CN CO 00 CN co 4_ rs7222604 T—1 00 LD CT 00 CN CN co 4_ rs3760371 CN LD CN CO Ln rsi T—1 CO 4_ 990T06SJ LD Ln T—1 CT 00 Ln CN co 4_ rs2289765 rs2271603 T61z9999SJ rs9912373 rsll27986 T—1 CT o T—1 CN CN co 4_ rs8072510 00T961zTSJ CO o LD O m T—1 CN T—1 CO 4_ rs76280498 rsll26642 rs3760370 rs8075035 rsl7848685 rs3751945 rs2289764 CN O LD T—1 CN CN co rs7217786 rs4969231 rs729124 m T—1 m CO 94 LD CN CO 00 co 94 46 Ln 00 42 Ln T—1 Ln CT CT CO o o T—1 Ln T—1 O o o o o CO o rsl O o o 00 o T—1 1558 1646 O o o o CN T—1 CN 3252 631 LD CN CN 1725 1576 o Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chrl7:31O1578-3101590 chrl7:33772658-33772689 chrl7:37832279-37832315 chrl7:37834715-37834808 chrl7:41616392-41616456 chrl7:42989063-42989088 chrl7:45695832-45695914 chrl7:6331803-6331836 chrl7:78599562-78599655 chrl7:78820329-78820374 chrl7:78865546-78865630 chrl7:78896488-78896529 T9SZ.6882 -Z.1z9Z.688Z.:Z.TJM3 chrl7:78921117-78921211 chrl7:80887206-80887244 0.224 o o o 6 0.203 0.073 0.252 0.213 0.263 0.012 0.275 0.275 LD O 6 00 T—1 6 0.022 0.172 0.104 0.232 o 00 T—1 6 0.293 00 LD O 6 T0T0 00 T—1 6 LD LD O 6 00 LD o 6 0.052 o o o 6 0.143 o o o 6 0.250 6 0.121 0.037 9ET0 0.224 O O O 6 0.281 o o o 6 o o o 6 0.182 0.221 0.310 LD O T—1 6 00 o 6 0.104 0.233 0.179 0.073 0.171 0.074 0.197 0.034 o o o 6 0.043 0.085 0.216 00 T—1 6 LD LD T—1 6 CT) O 6 0.067 T—1 T—1 CN T—1 CN m < / ) 4_ rs3809974 T—1 T—1 LD 00 CN CN CN < / ) 4_ 889SJ CT) CN T—1 CN m co 4_ CN T—1 CN T—1 CN m < / ) 4_ rs2302061 rs4805475 rs8099838 CN 00 O LD T—1 T—1 T—1 < / 1 4_ rs929814 T—1 CN Ln m < / ) L_ OO617SJ rs2293688 rs28364580 rs9304592 rs3826593 o CT) o 00 < / ) 4_ rsl799898 00 CT) CN T—1 CN m < / ) 4_ m T—1 CN T—1 CN m < / ) 4_ rs3815308 rsll7342492 996E0T8SJ rsl473201 rs929813 rs255270 rs308046 rs3817622 rs2301236 rs8103839 CN CN to 49 CT) CN T—1 CN CT) m CN CT) m LD Ln CT) T—1 LD LD O o o O T—1 T—1 T—1 m T—1 O O O 265 o 1721 1946 2349 768 495 CN m 593 290 1438 O CT) O CN O O T—1 Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chrl8:56204747-56204768 chrl9:10267011-10267077 chrl9:11227554-11227602 chrl9:17937758-17937786 chrl9:17955001-17955021 chrl9:2226676-2226772 chrl9:30253901-30253998 chrl9:30255068-30255090 chrl9:30290349-30290357 chrl9:30340381-30340412 chrl9:30361995-30362112 chrl9:3119184-3119239 chrl9:36237227-36237245 chrl9:41724820-41724885 6Z.9T8Z.T1z -SeizTSZT^eTJq:’ 0.275 0.090 0.056 0.141 0.262 00 T—1 6 0.134 T800 00 LD O 6 0.144 0.075 0.126 0.099 0.124 0.065 0.191 00 CN 6 00 CN 6 0.139 LD LD O 6 o o o 6 o o o 6 0.025 0.065 LD O T—1 6 LD O T—1 6 0.125 LD LD T—1 6 0.222 0.115 LD 00 T—1 6 0.179 E0T0 0.104 rslO61769 rs2356437 00 Ln 00 T—1 m CN ch 4_ rs3218760 rsll43698 rsll43698 T—1 T—1 LD O CO CN CN ch 4_ rsl0413455 rsll878316 rsl469087 rs2245648 CT CN CT CN CN CN ch 4_ rsl7160149 rs2240927 rs3811633 Ln T—1 Ln o CO CN CN < / ) rslO61768 rs7250947 rs3218776 CN CN CN O CO CN ch 4_ rsll43699 OOE179OTSJ T—1 Ln T—1 o T—1 CH 4_ rs2074078 CN 00 00 CN CN ch 4_ rs2963 rs2059806 T—1 Ln CN o CN T—1 CH 4_ CO co 00 o 00 CT CN T—1 < / ) CN CO LD T—1 T—1 00 co < / ) rs3811634 00 CN T—1 CO CN CO 161 T—1 CN 103 48 00 T—1 T—1 CN r- CO T—1 T—1 47 co Ln CT Ln O o Ln O T—1 o o O CN o o T—1 CO O o LD 00 00 CN 740 4185 1945 o o O 810 1028 o o 366 747 Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chrl9:44352639-44352666 chrl9:4510530-4510560 chrl9:50919797-50919828 chrl9:5210622-5210782 chrl9:5210762-5210782 chrl9:5212380-5212482 chrl9:58131576-58131623 chrl9:58213952-58213969 chrl9:58572959-58572979 chrl9:7163154-7163230 chrl9:7166376-7166388 chrl9:8148301-8148314 chrl9:9362297-9362343 chr2:112754828-112754880 chr2:112754943-112755001 0.229 00 m o 6 0.183 0.264 0.115 0.131 0.245 0.043 0.027 O 6 0.203 0.015 SETO 00 o 6 0.138 6ST0 0.058 6 0.183 0.114 0.127 0.065 0.222 CN LD O 6 0.163 OTTO 0.163 00 o 6 0.177 T0T0 00 00 o 6 0.217 o 6 0.150 0.123 0.236 0.026 LD 00 O 6 0.013 0.074 o o o 6 0.177 o o o 6 0.276 00 CN 6 o o o 6 o o o 6 o o o 6 0.054 T800 0.203 0.142 0.017 0.021 0.113 0.052 0.022 0.052 o o o 6 o 6 00 o 6 0.259 0.412 rs3748916 rs67776659 rs2863243 rs35164907 rs67056216 rsl2986949 rs6716344 rs6741005 rs3769823 00 T—1 CN CT) CT) T—1 < / ) 4_ rsl0462023 o T—1 00 00 T—1 < / ) 4_ rs56132472 rs4622670 rs4665447 rs3748915 rs2241975 rs2863242 rs35296183 rs2693006 rsl2986946 rsl6863159 rs34339850 rs3769824 rs3749073 rsl3391269 T—1 CN T—1 00 00 T—1 < / ) 4_ o T—1 00 00 T—1 < / ) 4_ rs2276550 rsl2619049 CT) CN CT) m 94 49 r- Ln T—1 130 40 T—1 m CO T—1 116 135 CT) T—1 T—1 CN T—1 O CN T—1 T—1 T—1 o o o Ln T—1 o T—1 776 O O T—1 CT) O O T—1 446 392 1546 835 368 1337 o o 677 750 2130 686 Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chr2:113983937-113984033 chr2:113984503-113984594 chr2:113989236-113989267 chr2:141259283-141259376 chr2:16042003-16042051 chr2:16073257-16073263 chr2:16112814-16112828 chr2:16113594-16113723 chr2:202122956-202122995 CHR2:231775094-231775144 CHR2:239184569-239184581 chr2:29416366-29416481 chr2:29416481-29416615 chr2:29446184-29446202 chr2:29446701-29446721 0.173 LD 00 O 6 0.061 0.053 0.054 0.138 69T0 0.232 0.065 0.104 0.137 0.092 0.167 0.120 0.254 O 6 0.093 0.175 0.026 CT O 6 0.242 0.231 LD 00 CN 6 0.141 0.154 LD O 6 o o o 6 0.085 0.073 0.074 0.073 T600 0.390 o 6 0TT0 o o 6 LD O CN 6 o o o 6 0.114 o o o 6 LD 00 T—1 6 rs6723311 rs622716 T—1 CT CT m Ln CN CN ch 4_ rs3814360 CN O 00 Ln LD T—1 o T—1 CH 4_ o CN 00 CN o T—1 CH 4_ rs2080390 1Z6T6809SJ rs2016647 rsl569548 rsl569548 rs2072127 rs3730168 rs742711 rs817329 rs4387740 rs8970 CN Ln LD CN o co CN co 4_ rs56239373 rs6722699 T—1 CN 00 CN O T—1 CH 4_ Ln T—1 T—1 T—1 CN CO T—1 CH 4_ rs6089193 rs3092662 rs2016647 rsl569547 rs466264 rs7121 rs742710 rs45486695 146 Ln T—1 Ln CO CN Ln 00 T—1 CO LD CO 173 CN LD CN 00 133 CO T—1 CT CN T—1 o o O Ln CN o CN CO T—1 CN T—1 o 00 o O 448 o o 1072 293 1461 o 3150 307 1095 1134 o 711 o O Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chr2:29447108-29447253 CHR2:33623720-33623734 CHR2:37579937-37579971 E09008Z.17 -Z.Z.9008Z.1z:3JM3 chr2:47852559-47852643 chr2:48010488-48010558 CHR2:71058184-71058226 chr20:30729488-30729523 chr20:40714307-40714479 OlzSIzTrOiz -6LPPlL0P'0ZJ^ chr20:40714539-40714540 chr20:52645534-52645541 chr20:57478807-57478939 chr20:5904028-5904040 chr20:62597666-62597694 0.059 CT 00 T—1 6 0.212 0.058 0.234 0.142 S9T0 0.034 0.174 0.218 0.235 0.130 0.223 0.112 0.071 0.053 00 00 o 6 0.230 6T00 LD 00 O 6 0.259 9ET0 0.115 LD 00 O 6 0.214 0.115 0.017 0.035 o o CN 6 0.224 0.059 0.281 0.122 0.173 00 CN 6 0.125 9TT0 0.105 0.098 0.032 rs3746804 rs2297346 rsl7854725 rs395584 00 T—1 o CN CN co 4_ 6990E09SJ rs879576 rs7287672 0891zSJ rsl3054014 CN CT CN 00 T—1 CO 4_ CN T—1 00 Ln T—1 CO 4_ rs2070457 CT 00 T—1 T—1 CO T—1 < / ) rsll547635 rs3746803 rs2297345 rs2298659 rs7277080 CN T—1 CT T—1 00 co co 4_ rs55865320 rs879577 o co m o Ln Ln T—1 T—1 CO 4_ rs4818 rsl78280 rs4822790 T—1 T—1 00 Ln T—1 CO 4_ rs9608192 rs73156524 rs9862 m 09 o T—1 48 CT CO CT T—1 00 co 24 Ln LD 09 124 CO Ln o o T—1 CO T—1 o Ln o o o o o o o Ln o o o o 813 6909 00 CN T—1 CN o o o 00 Ln CN T—1 o 565 1246 o 713 259 o Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chr20:744382-744415 chr20:9543622-9543681 chr21:42845374-42845383 chr21:42876400-42876447 chr21:43557698-43557736 chr21:46321659-46321677 chr22:17589209-17589246 chr22:17640022-17640045 chr22:19951207-19951271 chr22:21337266-21337325 chr22:21348914-21349037 chr22:21377301-21377334 chr22:24158895-24158899 chr22:29690246-29690345 chr22:33253280-33253292 0.125 OTTO 89T0 0.132 06T0 0.045 0.216 o T—1 CO 6 T—1 o 6 Ln T—1 6 T—1 CO T—1 6 LD O 6 o Ln o 6 o LD T—1 6 LD T—1 6 00 CN o 6 o o CN 6 LD CT T—1 6 T—1 6 Ln o 6 o o o 6 CN 00 T—1 6 Ln T—1 T—1 6 00 CN o 6 o o 6 00 T—1 T—1 6 CT o T—1 6 T—1 T—1 o 6 CT T—1 T—1 6 T—1 6 o o o 6 o 6 m CN CN 6 LD T—1 6 6 00 CT o 6 o CO 6 00 Ln T—1 6 T—1 o 6 00 CN T—1 6 co o 6 CN co o co 00 CT < / ) L_ rsl33431 CN T—1 CN LD O CN co 4_ LD m LD CN o T—1 co 4_ CT Ln CT CO LD CT co 4_ CT 00 CN O CO CN co 4_ o CN LD CN Ln o T—1 co 4_ rslll398337 rs2227930 LD CT 00 CT CT LD CN co 4_ CN LD O T—1 T—1 CO 4_ rs7646600 O co 00 CT < / ) L_ co co LD CO LD < / ) L_ T—1 LD O LD Ln CN CN < / ) L_ 00 o 00 LD CN CN < / ) L_ rs2071744 m T—1 CN LD O CN co 4_ Ln CN T—1 o 00 T—1 co 4_ T—1 T—1 CO Ln Ln o CN co 4_ CO CT LD O T—1 CO 4_ 00 T—1 LD CN Ln o T—1 co 4_ rs61699523 CT CN CT CN CN CN co 4_ rs3729676 rs7645550 rs7612684 T—1 CN CT 00 00 T—1 CO < / ) L_ T—1 T—1 LD 00 CN < / ) L_ T—1 o T—1 o CN Ln T—1 < / ) L_ CT O 00 LD CN CN < / ) L_ 45 CT CO T—1 T—1 00 CO T—1 T—1 CN LD LD o CO Ln CN Ln o T—1 CO 00 T—1 LD T—1 CN T—1 T—1 LD o o o CN o o T—1 o T—1 O CN Ln T—1 CN o o o LD Ln o o CO LD LD T—1 CO CN CN T—1 O CN CO CN CO CN LD CT T—1 T—1 LD CO Ln O LD CN Yes co co co co co co co co co co < / ) < / ) < / ) CO 0J < / ) chr22:35817553-35817597 chr22:44322922-44322970 chr3:122003757-122003769 chr3:12649857-12649937 chr3:129155451-129155463 chr3:136574501-136574521 chr3:138327951-138328016 chr3:142277536-142277575 chr3:178922222-178922274 chr3:178968634-178968660 chr3:178984575-178984679 chr3:178986121-178986203 chr3:178990402-178990462 chr3:183211906-183212026 chr3:36986932-36986992 o 6 0.090 00 T—1 6 0.229 0.121 CO 6 0.061 rsl LD o 6 00 o 6 0.031 LD O 6 00 T—1 6 0.202 CT 00 O 6 0.236 0.306 o 00 T—1 6 0.346 0.141 0.144 CT 00 T—1 6 T600 0.105 0.226 LD O 6 0.140 rs| 00 O 6 00 rs| 6 0.217 0.235 CO 00 co 6 CT O 6 0.092 OTTO o o 6 0.140 CT O rsl 6 0.104 o 6 0.075 0.291 0.144 T9T0 0.233 0.051 0.056 0.281 0.085 0.133 0.111 0.163 LD LD O 6 0.016 0.032 0.126 rs| O 6 CT rsl 6 0.254 0.227 96T0 0.128 0.050 0.233 rs2037474 rs2454206 rsl3133767 rsl982966 rs3733391 rs4865467 rs73202803 00 O 00 rsl T—1 T—1 < / ) 4_ rs3135842 rs743682 rs2249917 rs3733413 rs62325166 rs4864823 rs939845 rs34402524 rs2270658 rsl982965 rs3733390 rs4865466 rs7680647 rsll248077 rs3135841 rs3135848 rs2249916 rs458021 T—1 o rsl T—1 o o o T—1 < / ) 4_ rs28489910 48 123 Ln LD CT m 00 T—1 CT 00 CT T—1 Ln 00 112 T—1 42 00 o o o rs| o rs| CO T—1 o o T—1 Ln 1098 534 351 CT o rs| o 4202 1702 678 319 995 2353 1727 557 00 00 rsi Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes chr3:71247257-71247304 chr4:106196829-106196951 chr4:143043340-143043404 chr4:143324036-143324094 chr4:156289900-156289917 OOSSizZT -Z6VSV£T:izJM3 1z8S0SZT -Z8izOSZT:iz-)Lp 1z1zO68ZT -1z6688ZT:1zJip chr4:1796629-1796636 chr4:1797741-1797852 chr4:187534362-187534375 chr4:187629497-187629538 chr4:54269096-54269173 chr4:54657737-54657790 0.317 0.223 T800 0.212 06T0 0.453 ND 0.051 ND CO co o 6 CO co o 6 o 6 CO LD T—1 6 T—1 Ln o 6 CT LD O 6 0.144 o 00 T—1 6 Q Z Ln T—1 CN 6 Q Z o o 6 00 co o 6 o o CN 6 o o CN 6 LD co o 6 CT O T—1 6 0.194 00 CN 6 Q Z T—1 CO o 6 Q Z 00 o 6 LD o 6 6 OTTO CN T—1 6 CT o T—1 6 o 6 o o CN 6 Q Z 00 LD O 6 Q Z o o o 6 o o o 6 0.202 o T—1 T—1 6 o o 6 Ln CN T—1 6 o 6 LD T—1 CN 6 Q Z co CN CN 6 Q Z CT LD T—1 6 co T—1 6 rs73234206 rs34743464 Ln T—1 T—1 00 T—1 o o T—1 < / ) 4_ rs6554197 rs3134889 rs4353958 rs34945396 rs75371420 rs2116765 rsl042714 rs3829987 rs246388 LD T—1 Ln o CN Ln CN T—1 < / ) L_ rs3734049 rs5326 rs446382 rs31776 rs2412560 96T1z999SJ rs76272262 rsll732442 rslll33360 rs4864958 rs2116766 rsl042713 CT 00 o LD ch 4_ T—1 LD Ln CT CN CN CN ch 4_ rs375396 00 CN O CO Ln o < / ) L_ rs4532 T—1 CN rsl ch L_ rs31777 m 00 T—1 CO co o Ln co co o CT O T—1 o co 00 T—1 LD CO T—1 LD CO CO Ln CO CO T—1 T—1 o o co CO o o o CO O 00 CN Ln CO T—1 CN CT 00 T—1 Ln LD Ln LD Ln o o CT O T—1 T—1 o T—1 o o o rsl Ln LD O CT CT T—1 CH CH < / ) < / ) < / ) Yes ch oj CH ch CH ch oj CH 0J < / ) CH 0J < / ) ch < / ) chr4:55208737-55208788 chr4:55501109-55501195 chr4:55582037-55582068 chr4:55619846-55619859 chr4:55982752-55982784 chr4:56026865-56026914 chr5:147024476-147024509 chr5:148206440-148206473 chr5:149456772-149456811 chr5:149495287-149495395 chr5:150666933-150666962 chr5:150901613-150901630 chr5:174870150-174870196 chr5:176517326-176517461 chr5:176523562-176523597 m 00 T—1 6 SETO CT O 6 0.239 rsi LD O 6 0.055 0.098 rsi O 6 0.118 o o T—1 6 o LD o 6 o 6 00 fs| 6 Ln o 6 T—1 cn 6 CT m 6 LD LD T—1 6 00 o 6 CT LD O 6 m o T—1 6 o 00 T—1 6 T—1 LD T—1 6 CT rsi O 6 rsi rsi 6 0.242 CT T—1 T—1 6 co 00 o 6 CO o 6 T—1 00 o 6 T—1 T—1 T—1 6 m T—1 6 T—1 6 Ln o 6 o T—1 6 m m o 6 T—1 rsi T—1 6 00 T—1 6 rsi T—1 o 6 rsi T—1 rsi 6 LD O 6 o o o 6 Ln o 6 o o o 6 o o o 6 00 Ln o 6 T—1 CT T—1 6 m Ln T—1 6 00 o T—1 6 Ln o T—1 6 K-rsi 6 m CT rsi 6 00 T—1 o 6 T—1 m o 6 m T—1 6 00 LD T—1 6 T—1 T—1 o 6 o o T—1 6 Ln T—1 o 6 o o o 6 LD T—1 6 00 rsi m 6 CT LD O 6 CT T—1 T—1 6 o m rsi 6 T—1 o 6 m O T—1 6 m rsi 6 o m T—1 6 LD T—1 rsi 6 00 rsi T—1 CT LD CT ch 4_ rsi m LD rsi T—1 m cn C- T—1 LD LD rsi m LD rsi T—1 cn C- rsl65943 o rs| o T—1 T—1 < / ) C- CN T—1 o 00 CH C- LD 00 CT 00 CN ch C- T—1 CN T—1 CN CN CO CH C- CT Ln 00 00 rsl rsl ch C- Ln o LD Ln T—1 T—1 CH 4_ T—1 T—1 00 rsi rsi rsi cn 4_ m 00 Ln rsi m 00 cn C- rsi rsi rsi O cn C- rsi rsi LD LD T—1 m cn C- m LD rsi CT o 00 m cn C- rsl rsi T—1 m 00 cn C- T—1 LD O cn C- 00 m T—1 o CT O T—1 cn C- rs7708357 o o 00 m 00 rs| ch C- co o o LD ch C- LD CN 00 O CO ch C- T—1 LD O LD CO CO CH C- T—1 LD rsl T—1 T—1 CH C- 00 Ln Ln CT T—1 CH 4_ Ln Ln Ln CT T—1 cn C- Ln rsi LD rsi rsi 00 m cn C- m m T—1 CT LD cn C- LD CT T—1 rsi m cn C- m rsi LD 00 00 T—1 m T—1 cn C- CT m CT CT LD cn C- m T—1 LD O cn C- m rsi CT o C- LD 00 Ln LD CO T—1 LD T—1 T—1 CO LD CO co rsi CT 00 LD 00 O m T—1 CT T—1 m T—1 CN T—1 CN O T—1 m T—1 O rsi O rsi T—1 T—1 o 00 rs| LD O 00 T—1 Ln LD CO 00 CN 00 T—1 Ln T—1 LD LD CO rsl T—1 Ln m Ln Ln rsi cn CT Ln m T—1 m to m rsi rsi LD rsi 00 00 cn CH ch oj ch ch ch < / ) ch ch < / ) CH 0J ch oj ch CH 0J CH ch oj ch ch ch chr5:176531772-176531857 00 3 p CN pl ld (N tH Ln Ki c_ _C CT O . CN H LD m o O S “ o 3 c_ _C rn o 00 T—1      • Ln O £ “ o 3 c_ _C a s h’s Ln CN O £ “ o 3 c_ _C T—1 T—1 t-h rn t-H m CN rd • • m £ rsi _c ob LD ~s Q CT rd Ln LD ’"j “ s 99 S cn _c 6 3 cn £ oo r< $ s cn _c T—1 a a tH 00 LD 99 g Ln _c ob oo rj l^ m oo r*s "'T r*. m {5 un m _c rsi a r*. rn cm K LD iri ld _c CT rsl Ln CT LO £ hs CT LD iri ld _c rn LD in ld r-. tH LO lh [p iri ld _c CN rs! Ki tn rs! oo a a iri ld _c CT ° 2 cn O m r* rn iri ld _c 290225940.1 290225940.1 chr5:67553771-67553827 Yes 584 1 57 rs6893676 rs34303 0.090 0.168 0.173 0.143 0.106 chr6:l 17725448-117725578 Yes 277 4 131 rs 1998206 rs2243378 0.076 0.181 0.150 0.143 0.197 chr6:l 17730673-117730819 Yes 158 0 147 rs 17634067 rs2273601 0.040 0.000 0.111 0.052 0.096 chr6:152382311-152382325 Yes 279 2 15 rs2273206 rs2273207 0.137 0.039 0.026 0.039 0.055 chr6:167754702-167754721 Yes 0 0 20 rs909546 rs9457304 chr6:26056549-26056708 Yes Yes 524 2 160 rs 10425 rs2230653 rs 12204800 0.048 0.309 0.227 0.344 0.256 chr6:29913201-29913266 Yes 0 0 66 rs41557912 rsl061156 chr6:30080231-30080274 Yes 0 0 44 rs3734838 rs2517598 chr6:30865115-30865204 Yes Yes 461 5 90 rs2239517 rs2267641 0.120 0.244 0.038 0.063 0.094 chr6:30993533-30993590 Yes 0 0 58 rs2523898 rs4713420 rsl2179536 chr6:31170514-31170528 Yes 0 0 15 rs9263870 rs9263871 chr6:31930441 -31930462 Yes 0 0 22 rs592229 rs429608 chr6:32188603-32188642 Yes Yes 1185 1 40 rs520803 rs520692 rs520688 0.000 0.047 0.000 0.000 0.011 chr6:32190390-32190484 Yes 2363 5 95 rs915894 rs8192569 0.330 0.232 0.102 0.141 0.205 chr6:33141253-33141280 Yes 0 0 28 rs9277932 rs2855430 WO 2020 / 219444                                                 PCT / US2020 / 029113 chr6:36291985-36292007 Yes 0 0 23 rs7751919 rs7751928 chr6:4069133-4069166 Yes 0 0 34 rs 10485172 rs595413 ND ND ND ND ND chr6:41924853-41924931 Yes 922 2 79 rs4623235 rsl6895130 0.095 0.110 0.210 0.156 0.138 chr6:42013020-42013049 Yes 530 0 rs9381126 rs6919122 rs6942118 0.351 0.421 0.381 0.504 0.390 chr6:42039487-42039542 Yes 651 3 56 rs9349215 rs66472208 0.023 0.245 0.020 0.048 0.127 chr6:42039551-42039666 Yes 292 1 116 rs66489927 rs7763360 rs2492927 0.192 0.148 0.300 0.248 0.322 chr6:42052577-42052667 Yes 305 0 91 rs9357387 rs2493841 rs9381136 0.050 0.163 0.176 0.161 0.139 chr7:100410597-100410657 Yes 1469 8 61 rs2230585 rs770657085 0.164 0.056 0.000 0.043 0.156 chr7:100416139-100416250 Yes 1438 3 rs3857809 rsl44173 0.185 0.059 0.000 0.301 0.173 chr7:100677455-100677523 Yes 0 0 69 rs61075804 rsl0238201 chr7:l 16336880-116336947 Yes 666 1 68 rs2237708 rs39749 0.036 0.209 0.257 0.228 0.242 chr7:116471122-116471227 Yes 297 4 106 rs41773 rs62470772 0.129 0.093 0.206 0.115 0.148 chr7:21640361-21640405 Yes 0 0 45 rs 10269582 rsl0224537 chr7:27196069-27196113 Yes 0 0 45 rs2301720 rs2301721 chr7:30795288-30795331 Yes 0 0 44 rs2302339 rs2302340 chr7:4213975-4214023 Yes 0 0 49 rs671694 rs886731 WO 2020 / 219444                                                 PCT / US2020 / 029113 chr7:55220177-55220202 Yes Yes 1118 0 26 rsll506105 rs845561 0.115 0.265 0.254 0.304 0.413 chr7:55251541-55251648 Yes 672 4 108 rs2877261 rsl3222385 rsl1771471 0.200 0.076 0.233 0.183 0.090 chr7:6026775-6026942 Yes 720 19 168 rs2228006 rsl 805323 0.000 0.122 0.046 0.017 0.106 chr7:6026942-6026988 Yes 3560 3 47 rsl805323 rsl805321 0.000 0.303 0.046 0.017 0.153 chr7:78119109-78119199 Yes 330 2 91 rs3735442 rsl 9905 77 ND ND ND ND ND chr8:128700175-128700233 Yes 496 2 59 rsl3282849 rs7005394 0.208 0.179 0.063 0.084 0.201 chr8:128713221-128713364 Yes 796 5 144 rs28548827 rs7820045 0.254 0.057 0.028 0.101 0.111 chr8:128889285-128889371 Yes 1835 1 rs6470587 rs6470588 0.081 0.165 0.210 0.202 0.230 CHR8:142490120-142490166 Yes 0 0 47 rs2748416 rs7838192 CHR8:145639681-145639726 Yes 0 0 46 rsl871534 rs2272662 chr8:145737636-145737816 Yes 485 0 rs4925828 rs4251691 0.000 0.203 0.000 0.072 0.000 chr8:30999122-30999123 Yes Yes 554 3 2 rs3024239 rs2737335 0.149 0.024 0.060 0.032 0.085 chr8:31024638-31024654 Yes Yes 432 0 17 rsl801196 rsl 346044 0.147 0.104 0.266 0.173 0.283 chr8:3 8299624-38299715 Yes 1668 5 92 rs60527016 rs6987534 0.028 0.286 0.236 0.219 0.076 chr8:38310910-38311001 Yes 1289 0 92 rsl0958700 rs4733930 0.029 0.323 0.260 0.249 0.074 WO 2020 / 219444                                                 PCT / US2020 / 029113 chr8:38350292-38350315 Yes 580 2 24 rs35305468 rs7830964 0.039 0.249 0.180 0.118 0.138 chr8:38361379-38361430 Yes 1456 2 52 rs328294 rs328293 0.309 0.172 0.126 0.115 0.283 chr8:90958422-90958530 Yes 182 1 109 rsl061302 rs2308962 0.097 0.000 0.000 0.000 0.000 chr9:l 17166206-117166246 Yes 0 0 41 rs2274158 rs2274159 chr9:125315542-125315557 Yes 0 0 16 rsl831369 rsl831370 chr9:134385435-134385436 Yes 0 0 2 rs3887873 rs2296949 chr9:136412255-136412296 Yes 0 0 42 rs2073876 rs2073877 chr9:139401504-139401577 Yes 1346 1 74 rs3124596 rs7870145 rs3829116 0.310 0.000 0.163 0.117 0.264 chr9:139403268-139403280 Yes 500 1 13 rs3125000 rsl1145765 0.046 0.000 0.095 chr9:139405093-139405261 Yes Yes 626 3 169 rs36119806 rs3125001 0.150 0.012 0.102 0.065 0.184 chr9:139410424-139410589 Yes Yes 327 2 166 rs3125006 rs4880099 0.088 0.052 0.115 0.068 0.215 chr9:139411714-139411880 Yes 428 5 167 rsl1145767 rs9411254 0.209 0.000 0.000 0.025 0.000 chr9:21968159-21968199 Yes 213 0 41 rs3088440 rsl 1515 0.164 0.019 0.079 0.078 0.052 chr9:5408242-5408358 Yes 344 3 117 rsl0758685 rsl0975098 rsl 0975 099 0.084 0.349 0.257 0.320 0.409 chr9:5415025-5415111 Yes 372 3 rs78298180 rsl0758687 0.104 0.161 0.054 0.052 0.199 WO 2020 / 219444                                                 PCT / US2020 / 029113 chr9:5420254-5420266 Yes 1180 1 13 rsl0121219 rsl1790878 0.064 0.227 0.222 0.248 0.218 chr9:5458035-5458095 Yes 323 3 61 rs7042084 rsl0481593 0.268 0.132 0.220 0.249 0.131 chr9:5484100-5484203 Yes 395 4 104 rsl 1793113 rsl1790610 rsl0122509 0.139 0.151 0.094 0.084 0.167 chr9:87478135-87478172 Yes 1016 4 38 rs7048015 rsl0780690 0.023 0.251 0.184 0.258 0.216 chr9:93639846-93639973 Yes 487 6 128 rs290223 rs2290888 ND ND ND ND ND chr9:93641175-93641199 Yes 693 2 25 rs23 06041 rs23 06040 0.062 0.000 0.064 chr9:98238358-98238379 Yes Yes 3840 0 22 rs2066836 rsl805155 0.011 0.083 0.109 0.076 0.060 chrX:23019317-23019346 Yes 0 0 30 rs5925720 rs5926203 WO 2020 / 219444                                                 PCT / US2020 / 029113 TABLE 8: Observed 3rd MH Frequency (x2). Observed 3rd IV H Free [uency (x2) Asian 1 1.5 2 2.5 3 4 5 7 9 In silico Mixing Levels 0.5 8 0 0 0 0 0 0 0 0 1 15 2 0 0 0 0 0 0 0 1.5 15 12 0 0 0 0 0 0 0 2 15 14 10 0 0 0 0 0 0 2.5 15 15 15 8 0 0 0 0 0 3 15 15 15 15 6 0 0 0 0 4 15 15 15 15 15 3 0 0 0 5 15 15 15 15 15 15 1 0 0 10 15 15 15 15 15 15 15 15 9 African 1 1.5 2 2.5 3 4 5 7 9 In silico Mixing Levels 0.5 3 0 0 0 0 0 0 0 0 1 15 0 0 0 0 0 0 0 0 1.5 15 10 0 0 0 0 0 0 0 2 15 14 5 0 0 0 0 0 0 2.5 15 15 15 4 0 0 0 0 0 3 15 15 15 14 5 0 0 0 0 4 15 15 15 15 13 1 0 0 0 5 15 15 15 15 15 12 2 0 0 10 15 15 15 15 15 15 15 14 7 European 1 1.5 2 2.5 3 4 5 7 9 In silico Mixing Levels 0.5 8 0 0 0 0 0 0 0 0 1 15 4 0 0 0 0 0 0 0 1.5 15 13 4 0 0 0 0 0 0 2 15 15 12 0 0 0 0 0 0 2.5 15 15 15 8 0 0 0 0 0 3 15 15 15 13 4 0 0 0 0 4 15 15 15 14 14 3 0 0 0 5 15 15 15 15 15 12 1 0 0 10 15 15 15 15 15 15 15 13 7 Mixed 1 1.5 2 2.5 3 4 5 7 9 In silico Mixing Levels 0.5 5 0 0 0 0 0 0 0 0 1 15 3 0 0 0 0 0 0 0 1.5 15 14 0 0 0 0 0 0 0 2 15 15 11 0 0 0 0 0 0 2.5 15 15 15 7 1 0 0 0 0 3 15 15 15 15 6 0 0 0 0 4 15 15 15 15 15 2 0 0 0 5 15 15 15 15 15 14 0 0 0 10 15 15 15 15 15 15 15 14 9 All (%) 1 1.5 2 2.5 3 4 5 7 9 In silico Mixing Levels 0.5 40 0 0 0 0 0 0 0 0 1 100 15 0 0 0 0 0 0 0 1.5 100 82 7 0 0 0 0 0 0 2 100 97 63 0 0 0 0 0 0 2.5 100 100 100 45 2 0 0 0 0 3 100 100 100 95 35 0 0 0 0 4 100 100 100 98 95 15 0 0 0 5 100 100 100 100 100 88 7 0 0 10 100 100 100 100 100 100 100 93 53

[00100] Although the invention has been described with reference to the above examples, it will be understood that modifications and variations are encompassed within the spirit and scope of the invention. Accordingly, the invention is limited only by the following claims.

Claims

1. A method of identifying microhaplotypes in a genome comprising:a) identifying a region of interest of the genome;b) determining an additional bordering region for the region of interest, wherein the bordering region comprises regions flanking the region of interest, and wherein the regions flanking the region of interest comprise less than about 200 nucleotide base pairs;c) detecting single base pair substitutions (SBSs) within the region of interest and the additional bordering region, thereby generating multiple sequence variant sets;d) filtering the multiple sequence variant sets based on a frequency threshold for the detected SBSs;e) analyzing each of the remaining sequence variant sets for linkage disequilibrium to identify candidate microhaplotypes, wherein the linkage disequilibrium is evaluated using one or more population-scale genomic databases and the candidate microhaplotypes are identified based on a presence of at least three microhaplotypes, and wherein a third microhaplotype and any subsequent microhaplotypes have a total frequency of greater than 1% when compared against data from the one or more population-scale genomic databases; andf) providing the candidate microhaplotypes.

2. The method of claim 1, wherein the regions flanking the region of interest comprise less than about 50, 100, 150, or 180 nucleotide base pairs capable of being sequenced by a short read sequencer.

3. The method of claim 1 or 2, wherein SBSs with a frequency of less than 10% or greater than 90% are filtered at step d).

4. The method of claim 1 or 2, wherein SBSs from the regions flanking the region of interest with a frequency of less than 5% or greater than 95% are filtered at step d).

5. The method of any one of claims 1 to 4, further comprising calibrating thresholds for candidate microhaplotypes for assessing contamination of a sample.

6. The method of claim 5, wherein only DNA sequence reads overlapping the candidate2020262082   06 Aug 2026microhaplotypes are used for calculating the thresholds for contamination detection and degree of contamination.

7. The method of claim 6, wherein the DNA sequences being used to calibrate the thresholds for contamination detection and degree of contamination are mixed pairwise in silico, alternately using each DNA sequence as primary sample and contaminant.

8. The method of claims 6 or 7, wherein the number and genotype of SNP sets with 1 and / or 2 microhaplotypes are compared between different individuals to assess identity or contamination.

9. The method of any one of claims 5 to 8, further comprising assessing sample contamination utilizing determined cutoff values for frequency of candidate microhaplotypes having single nucleotide polymorphism (SNP) sets with (i) at least 3 microhaplotypes and / or (ii) at least 4 or more microhaplotypes.

10. The method of any one of claims 5 to 9, wherein the sample comprises (i) DNA from atumor or a liquid biopsy, or (ii) DNA extracted from a formalin fixed paraffin embedded block, slide, or curl.

11. The method of claim 10, wherein the liquid biopsy is from amniotic fluid, aqueoushumour, vitreous humour, blood, whole blood, fractionated blood, plasma, serum, breast milk, cerebrospinal fluid (CSF), cerumen (earwax), chyle, chime, endolymph, perilymph, feces, breath, gastric acid, gastric juice, lymph, mucus (including nasal drainage and phlegm), pericardial fluid, peritoneal fluid, pleural fluid, pus, rheum, saliva, exhaled breath condensates, sebum, semen, sputum, sweat, synovial fluid, tears, vomit, prostatic fluid, nipple aspirate fluid, lachrymal fluid, perspiration, cheek swabs, cell lysate, gastrointestinal fluid, biopsy tissue and urine or other biological fluid.

12. The method of any one of claims 5 to 11, wherein the sample is from a circulating tumor cell.

13. The method of any one of claims 5 to 12, wherein calibrating comprises analysis of the candidate microhaplotype in multiple samples obtained from humans of different ethnicities.

14. The method of any one of claims 1 to 13, wherein the candidate microhaplotypes comprise SNP sets having at least 3, 4 or more sets of SNP sequence variants.2020262082   06 Aug 202615. The method of any one of claims 1 to 14, wherein the region of interest is within a gene, an exome, an intron and / or an exon or between genes.

16. The method of any one of claims 1 to 15, wherein the genome is from a human.

17. The method of any one of claims 1 to 16, further comprising assessing samplecontamination by analyzing median, average or other measure of microhaplotype frequency of haplotypes within SNP sets with at least 3 or 4 microhaplotypes.

18. Use of the method of any one of claims 1 to 17 to assess quality of samples from aparticular source or vendor or technician preparing or sequencing samples.

19. A method for detecting single nucleotide polymorphism (SNP) sets having at least three microhaplotypes from multiple subjects present in a sample comprising:a) identifying microhaplotypes in a genome in the sample, wherein identifying comprises:i) identifying a region of interest of the genome;ii) determining an additional bordering region for the region of interest, wherein the bordering region comprises regions flanking the region of interest, and wherein the regions flanking the region of interest comprise less than about 200 nucleotide base pairs;iii) detecting single base pair substitutions (SBSs) within the region of interest thereby generating multiple sequence variant sets;iv) filtering the multiple sequence variant sets based on a frequency threshold for the detected SBSs; andv) analyzing each of the remaining sequence variant sets for linkage disequilibrium to identify candidate microhaplotypes, wherein the linkage disequilibrium is evaluated using one or more population-scale genomic databases and the candidate microhaplotypes are identified based on a presence of at least three microhaplotypes, and wherein a third microhaplotype and any subsequent microhaplotypes have a total frequency of greater than 1% when compared against data from the one or more population-scale genomic databases;b) determining the number of SNP sets having at least 3 microhaplotypes in the sample; and2020262082   06 Aug 2026c) quantitating the frequency of the SNP sets with greater than 2 microhaplotypes to determine the presence of DNA from multiple subjects in the sample, thereby detecting DNA from multiple subjects in the sample.

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