Methods and systems for determining a CYP2a6 genotype

Abstract

Disclosed herein are systems, devices, and methods for detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample. Also disclosed herein are systems, devices, and methods for detecting one or more single-nucleotide variants or indels in a CYP2A6 gene in a nucleic acid sample. Further disclosed herein are systems, devices, and methods for determining one or more alleles, such as star alleles in a CYP2A6 gene in a nucleic acid sample, including determining an allele or star allele diplotype in a CYP2A6 gene in a nucleic acid sample.

Description

ILLINC.800WO / IP-2621-PCT PATENT METHODS AND SYSTEMS FOR DETERMINING A CYP2A6 GENOTYPE INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 586,856, filed September 29, 2023, which is hereby incorporated by reference in its entirety. BACKGROUND Field

[0002] The disclosed technology relates to the field of nucleic acid sequencing. More particularly, the disclosed technology relates to determining a CYP2A6 genotype in a nucleic acid sample. Description of the Related Art

[0003] Cytochrome P450 2A6 (CYP2A6) is a gene belonging to the cytochrome P450 family of genes and is primarily expressed in the liver. It is involved in the metabolism of numerous clinically relevant drugs and is also a major component of the nicotine metabolic pathway, where it catalyzes the conversion of nicotine to cotinine, the metabolization of cotinine, and acts on other cigarette smoke-containing pro-carcinogens.

[0004] The CYP2A6 gene is encoded on the reverse strand of chromosome 19q13, is approximately 9kb long and has 9 exons. It shares high homology with other genes within the CYP2 gene family, and particularly with its pseudogene, CYP2A7, with which it shares 95% homology. 47 star alleles (observed CYP2A6 alleles) have been identified for CYP2A6, with star alleles continuously being added and adjusted. The CYP2A6 genotype has been identified as a risk factor for lung cancer and other smoking related behaviors. A large amount of inter-individual activity of CYP2A6 has been discovered suggesting that individuals with low CYP2A6 activity tend to smoke fewer cigarettes. In addition, allele frequencies for CYP2A6 differ between ethnic groups, leading to population specific differences in CYP2A6 activity.

[0005] Detection of star alleles in the CYP2A6 gene may be complicated by the high sequence similarity with the CYP2A7 pseudogene, and by the variable total copy numbers in the CYP2A6 gene.SUMMARY

[0006] In an aspect, disclosed herein are computer-implemented methods of detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample. In some embodiments, the method comprises obtaining sequence reads from the nucleic acid sample which align to a CYP2A6 gene or a CYP2A7 pseudogene in a human genome within the nucleic acid sample; estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample; estimating a copy number of a CYP2A6-specific base at one or more pre-determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene; determining one or more consensus copy numbers for one or more target regions of the CYP2A6 gene based on the estimated copy number of the CYP2A6-specific base at the one or more pre-determined differentiating sites; and detecting one or more structural variants in the CYP2A6 gene or the CYP2A7 pseudogene based on the one or more consensus copy numbers.

[0007] In some embodiments, estimating a copy number of a CYP2A6-specific base comprises, for each pre-determined differentiating site, counting sequence reads which include a CYP2A6-specific base at the pre-determined differentiating site, and counting sequence reads which include a CYP2A7-specific base at the pre-determined differentiating site.

[0008] In some embodiments, the one or more pre-determined differentiating sites comprises a site from the pre-determined differentiating sites of Table 1.

[0009] In some embodiments, the one or more target regions comprise a region corresponding to exons 5-9 of the CYP2A6 gene. In some embodiments, the one or more target regions comprise a region corresponding to sites 154-196 from the pre-determined differentiating sites of Table 1.

[0010] In some embodiments, the one or more target regions comprise a region corresponding to exons 3-4 of the CYP2A6 gene. In some embodiments, the one or more target regions comprise a region corresponding to sites 94-153 from the pre-determined differentiating sites of Table 1.

[0011] In some embodiments, the one or more target regions comprise a region corresponding to exons 1-2 of the CYP2A6 gene. In some embodiments, the one or moretarget regions comprise a region corresponding to sites 24-93 from the pre-determined differentiating sites of Table 1.

[0012] In some embodiments, the method comprises determining a first consensus copy number for a first target region based on estimated copy numbers of CYP2A6-specific bases at a first plurality of pre-determined differentiating sites; determining a second consensus copy number for a second target region based on estimated copy numbers of CYP2A6-specific bases at a second plurality of pre-determined differentiating sites; determining a third consensus copy number for a third target based on estimated copy numbers of CYP2A6-specific bases at a third plurality of pre-determined differentiating sites; and detecting one or more structural variants in the CYP2A6 gene based on the first, second, and third consensus copy numbers.

[0013] In some embodiments, estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene comprises counting sequence reads which align to a CYP2A6 gene or a CYP2A7 pseudogene in a human genome within the nucleic acid sample.

[0014] In some embodiments, estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene further comprises counting sequence reads which align to diploid regions in a human genome within the nucleic acid sample; and normalizing the count of the sequence reads which align to a CYP2A6 gene or a CYP2A7 pseudogene. In some embodiments, estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene further comprises binning the normalized count of the sequence reads which align to a CYP2A6 gene or a CYP2A7 pseudogene using a Gaussian mixture model.

[0015] In a further aspect, disclosed herein are methods of detecting one or more single-nucleotide variants or indels in a CYP2A6 gene in a nucleic acid sample. In some embodiments, the method includes determining a consensus copy number for a target region of a CYP2A6 gene in a human genome in the nucleic acid sample, wherein the target region includes a site of a single-nucleotide variant or indel in the CYP2A6 gene; obtaining sequence reads which align to the site of the single-nucleotide variant or indel in the CYP2A6 gene; counting sequence reads which contain a base corresponding to a variant allele at the site, and counting sequence reads which contain a base corresponding to a reference allele at the site; and determining a probability that the nucleic acid sample includes a variant allele at the site based on the count of sequence reads which contain a base corresponding to a variant allele,the count of sequence reads which contain a base corresponding to a reference allele, and the consensus copy number for the target region.

[0016] In some embodiments, the one or more single-nucleotide variants or indels comprise a single-nucleotide variant or indel listed in Table 2.

[0017] In some embodiments, determining the probability that the nucleic acid sample includes a variant allele at the site comprises determining a Poisson distribution.

[0018] In some embodiments, the target region includes one or more pre- determined differentiating sites of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample, and wherein determining a consensus copy number for a target region of a CYP2A6 gene comprises, for one or more pre-determined differentiating sites, estimating a copy number of a CYP2A6-specific base.

[0019] In some embodiments, estimating a copy number of a CYP2A6-specific base comprises, for each pre-determined differentiating site, counting sequence reads which include a CYP2A6-specific base at the pre-determined differentiating site, and counting sequence reads which include a CYP2A7-specific base at the pre-determined differentiating site. In some embodiments, the plurality of pre-determined differentiating sites comprises a site from the pre-determined differentiating sites of Table 1.

[0020] In some embodiments, the target region corresponds to exons 1-2, exons 3- 4, or exons 5-9 of the CYP2A6 gene. In some embodiments, the target region comprises a region corresponding to sites 154-196, sites 94-153, or sites 24-93 from the pre-determined differentiating sites of Table 1.

[0021] In a further aspect, disclosed herein are methods of determining one or more star alleles in a CYP2A6 gene in a nucleic acid sample. In some embodiments, the method includes detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample as described herein; detecting one or more single-nucleotide variants or indels in the CYP2A6 gene in the nucleic acid sample, and determining one or more star alleles in the CYP2A6 gene in the nucleic acid sample based on the one or more detected structural variants and based on the one or more detected single-nucleotide variants or indels in the CYP2A6 gene.

[0022] In some embodiments, detecting one or more single-nucleotide variants or indels in the CYP2A6 gene comprises: obtaining sequence reads which align to a site of asingle-nucleotide variant or indel in a CYP2A6 gene of a human genome in the nucleic acid sample; counting sequence reads which contain a base corresponding to a variant allele at the site, counting sequence reads which contain a base corresponding to a reference allele at the site; and determining a probability that the nucleic acid sample includes a variant allele at the site based on the count of sequence reads which contain a base corresponding to a variant allele, the count of sequence reads which contain a base corresponding to a reference allele, and the consensus copy number for a target region which includes the site of the single- nucleotide variant or indel.

[0023] In some embodiments, the method further comprises phasing the one or more detected single-nucleotide variants or indels in the CYP2A6 gene. In some embodiments, phasing the one or more detected single-nucleotide variants or indels in the CYP2A6 gene comprises counting sequence reads which include a first base corresponding to a first variant allele at a first site, and a second base corresponding to a second variant allele at a second site.

[0024] In a further aspect, disclosed herein are methods of determining a star allele diplotype of a CYP2A6 gene in a nucleic acid sample. In some embodiments, the method includes determining two star alleles of the CYP2A6 gene in a nucleic acid sample according to methods described herein.

[0025] In some embodiments of the various methods disclosed herein, a digital file is created including information related to a structural variant, a single-nucleotide variant or indel, a star allele, or a star allele diplotype.

[0026] In a further aspect, disclosed herein are electronic systems for detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample. In some embodiments, the system comprises a processor configured to perform a method comprising: obtaining sequence reads from the nucleic acid sample which align to a CYP2A6 gene or a CYP2A7 pseudogene in a human genome within the nucleic acid sample; estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample; estimating a copy number of a CYP2A6-specific base at one or more pre- determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene; determining one or more consensus copy numbers for one or more target regions of the CYP2A6 gene based on the estimated copy number of the CYP2A6-specific base at the one or more pre-determineddifferentiating sites; and detecting one or more structural variants in the CYP2A6 gene or the CYP2A7 pseudogene based on the one or more consensus copy numbers.

[0027] In a further aspect, disclosed herein are electronic systems for detecting one or more single-nucleotide variants or indels in a CYP2A6 gene in a nucleic acid sample. In some embodiments, the system comprises a processor configured to perform a method comprising: determining a consensus copy number for a target region of a CYP2A6 gene in a human genome in the nucleic acid sample, wherein the target region includes the site of one or more single-nucleotide variants or indels in the CYP2A6 gene; obtaining sequence reads which align to a site of a single-nucleotide variant or indel in the CYP2A6 gene; counting sequence reads which contain a base corresponding to a variant allele at the site, and counting sequence reads which contain a base corresponding to a reference allele at the site; and determining a probability that the nucleic acid sample includes a variant allele at the site based on the count of sequence reads which contain a base corresponding to a variant allele, the count of sequence reads which contain a base corresponding to a reference allele, and the consensus copy number for the target region.

[0028] In a further aspect, further disclosed herein are electronic systems for determining one or more star alleles in a CYP2A6 gene in a nucleic acid sample. In some embodiments, the system comprises a processor configured to perform a method comprising: detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample; detecting one or more single-nucleotide variants or indels in the CYP2A6 gene in the nucleic acid sample; and determining one or more star alleles in the CYP2A6 gene in the nucleic acid sample based on the detected structural variant and based on the one or more detected single-nucleotide variants or indels in the CYP2A6 gene. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Features of examples of the present disclosure will become apparent by reference to the following detailed description and drawings, in which like reference numerals correspond to similar, though perhaps not identical, components. For the sake of brevity, reference numerals or features having a previously described function may or may not be described in connection with other drawings in which they appear.

[0030] FIG. 1A schematically illustrates the CYP2A6 gene and the CYP2A7 pseudogene.

[0031] FIG. 1B schematically illustrates a cross-over event in the CYP2A6 gene leading to deletion of a CYP2A7 pseudogene.

[0032] FIG.1C schematically illustrates hybrid star alleles of the CYP2A6 gene.

[0033] FIG. 1D schematically illustrates three exemplary target regions of the CYP2A6 gene.

[0034] FIG. 1E schematically illustrates copy number variant alleles and exemplary copy number variant configurations in the CYP2A6 gene and the CYP2A7 pseudogene.

[0035] FIG. 2A is a flow diagram that schematically illustrates methods of detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample.

[0036] FIG.2B is a flow diagram that further schematically illustrates a process of estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample.

[0037] FIG.2C is a flow diagram that further schematically illustrates a process of estimating a copy number of a CYP2A6-specific base at one or more pre-determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene.

[0038] FIG. 2D is a flow diagram that schematically illustrates a method of detecting one or more single-nucleotide variants or insertions / deletions in a CYP2A6 gene in the nucleic acid sample.

[0039] FIG. 2E is a is a flow diagram that schematically illustrates methods of determining one or more star alleles in a CYP2A6 gene in a nucleic acid sample.

[0040] FIG.2F is a flow diagram that further schematically illustrates a process of detecting one or more single-nucleotide variants or indels in a CYP2A6 gene in the nucleic acid sample.

[0041] FIG.3A is a block diagram of an exemplary sequencing system that may be used to perform the disclosed methods.

[0042] FIG.3B is a block diagram of an exemplary computing device that may be used in connection with the exemplary sequencing system of FIG.3A.DETAILED DESCRIPTION

[0043] All patents, patent applications, and other publications, including all sequences disclosed within these references, referred to herein are expressly incorporated herein by reference, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference. All documents cited are, in relevant part, incorporated herein by reference in their entireties for the purposes indicated by the context of their citation herein. However, the citation of any document is not to be construed as an admission that it is prior art with respect to the present disclosure. Overview

[0044] As depicted in FIG.1A, the CYP2A6 gene 101 and its homolog, the CYP2A7 pseudogene 102, both include 9 exons and intervening introns. Genotyping the CYP2A6 gene may be complicated by its relatively high homology with its pseudogene, CYP2A7, and by the presence of structural variants within each gene. For example, FIG. 1B depicts a cross-over event 1000 wherein two alleles of the CYP2A7 pseudogene 102 and the CYP2A6 gene 101 cross-over to result in a structural variant having one copy of the CYP2A7 pseudogene 102 and two copies of the CYP2A6 gene 101. Similarly, FIG. 1C shows a series of “star alleles”, including *12, *34, and *47 / 4d. As illustrated, the *12 allele is a structural variant which includes the first two exons of the CYP2A7 pseudogene 102 (shown in solid boxes) followed by exons 3-9 of the CYP2A6 gene 101 (shown in dashed boxes). The *34 allele variant includes the first four exons of the CYP2A7 pseudogene 102 (shown in solid boxes) followed by exons 5-9 of the CYP2A6 gene 101 (shown in dashed boxes). The *47 / 4d star allele variant includes the first eight exons of the CYP2A7 pseudogene 102 (shown in solid boxes) followed by exon 9 of the CYP2A6 gene 101 (shown in dashed boxes). Each of these different variants can result in different phenotypes in a subject.

[0045] In addition to these variants, gene conversions have also been observed, where a 58bp segment of the CYP2A7 pseudogene is embedded within the 3’ end of the CYP2A6 gene. This gene conversion is present in numerous star alleles (for example, *1b, *5,*7, *8, *10, *19, *24, *28, *35, *36, *37). Otherwise, most of the polymorphisms in the CYP2A6 are single nucleotide polymorphisms (SNPs).

[0046] Due to the complex genetic architecture of the CYP2A6 gene and its high homology with the CYP2A7 pseudogene, SNP array genotyping methods of the prior art have limited capacity to type all the different varieties of star alleles. Whole genome sequencing has also proven difficult through the CYP2A6 gene due to the homology with the CYP2A7 pseudogene and due to structural variants, as sequence reads can, in some cases, be misaligned to the wrong gene or can be mapped with equal confidence to both genes, leading to zero mapping quality (MAPQ).

[0047] Described herein are methods and systems for determining one or more star alleles in a CYP2A6 gene in a nucleic acid sample, thereby determining a CYP2A6 genotype. In some embodiments, the methods and systems can determine CYP2A6 star alleles with whole genome sequencing (WGS) data. In some embodiments, the systems and methods disclosed herein solve the technical problems of the prior art by providing a more accurate method for calling star alleles in the CYP2A6 gene, despite the complex genetic architecture of the CYP2A6 gene and its high homology with the CYP2A7 pseudogene. In some embodiments, the systems and methods disclosed herein allow for a greater number of star alleles to be called (45 out of 47 star alleles), compared to methods of the prior art.

[0048] In some embodiments, the methods and systems detect a structural variant in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample. In some embodiments, the methods and systems obtain sequence reads from the nucleic acid sample which align to a CYP2A6 gene or a CYP2A7 pseudogene in a human genome within the nucleic acid sample. In some embodiments, the methods and systems estimate a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample. In some embodiments, the methods and systems estimate a copy number of a CYP2A6-specific base at one or more pre-determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene. In some embodiments, the methods and systems determine one or more consensus copy numbers for one or more target regions of the CYP2A6 gene based on the estimated copy number of the CYP2A6-specific base at the one or more pre-determined differentiating sites.

[0049] For example, the one or more target regions may include three target regions, as depicted in FIG. 1D. As shown in FIG. 1D, a first target region 110 may includeexons 5 through 9 of the CYP2A6 gene 101. A second target region 120 may include exons 3 and 4 of the CYP2A6 gene 101, and a third target region 130 may include exons 1 and 2 of the CYP2A6 gene 101. In some embodiments, the methods and systems detect one or more structural variants in the CYP2A6 gene or the CYP2A7 pseudogene based on the one or more consensus copy numbers.

[0050] As illustrated in FIG. 1E, different structural variants (such as a deletion, duplication, or hybrid structural variant) across the CYP2A6 gene 101 and the CYP2A7 pseudogene 102 may result in star alleles having a different consensus copy number across the one or more target regions (such as the first target region 110, second target region 120, and third target region 130). Accordingly, in some embodiments, a structural variant such as a deletion, duplication, or hybrid structural variant may be detected based on the one or more consensus copy numbers.

[0051] In some embodiments, the methods and systems detect one or more single- nucleotide variants or insertions / deletions (indels) in a CYP2A6 gene in a nucleic acid sample. In some embodiments, the methods and systems determine a consensus copy number for a target region of a CYP2A6 gene in a human genome in the nucleic acid sample, wherein the target region includes a site of a single-nucleotide variant or indel in the CYP2A6 gene. In some embodiments, the methods and systems obtain sequence reads which align to the site of the single-nucleotide variant or indel in the CYP2A6 gene. In some embodiments, the methods and systems count sequence reads which contain a base corresponding to a variant allele at the site, and count sequence reads which contain a base corresponding to a reference allele at the site. In some embodiments, the methods and systems determine a probability that the nucleic acid sample includes a variant allele at the site based on the count of sequence reads which contain a base corresponding to a variant allele, the count of sequence reads which contain a base corresponding to a reference allele, and the consensus copy number for the target region.

[0052] In some embodiments, the methods and systems determine one or more star alleles in a CYP2A6 gene in a nucleic acid sample. In some embodiments, a star allele may be determined based on structural variants and based on single-nucleotide variants or indels (collectively, “small variants”) detected within the nucleic acid sample, as described herein. In some embodiments, the methods and systems determine a star allele diplotype of a CYP2A6gene in a nucleic acid sample by determining two star alleles of the CYP2A6 gene in the nucleic acid sample. Definitions

[0053] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs. See, for example, Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, NY 1994); Sambrook et al., Molecular Cloning, A Laboratory Manual, Cold Spring Harbor Press (Cold Spring Harbor, NY 1989). For purposes of the present disclosure, the following terms are defined below.

[0054] As used herein, a “nucleotide” includes a nitrogen containing heterocyclic base, a sugar, and one or more phosphate groups. Nucleotides are monomeric units of a nucleic acid sequence. Examples of nucleotides include, for example, ribonucleotides or deoxyribonucleotides. In ribonucleotides (RNA), the sugar is a ribose, and in deoxyribonucleotides (DNA), the sugar is a deoxyribose, i.e., a sugar lacking a hydroxyl group that is present at the 2' position in ribose. The nitrogen containing heterocyclic base can be a purine base or a pyrimidine base. Purine bases include adenine (A) and guanine (G), and modified derivatives or analogs thereof. Pyrimidine bases include cytosine (C), thymine (T), and uracil (U), and modified derivatives or analogs thereof. The C-1 atom of deoxyribose is bonded to N-1 of a pyrimidine or N-9 of a purine. The phosphate groups may be in the mono- , di-, or tri-phosphate form. These nucleotides may be natural nucleotides, but it is to be further understood that non-natural nucleotides, modified nucleotides or analogs of the aforementioned nucleotides can also be used.

[0055] As used herein, “base” or “nucleobase” is a heterocyclic base such as adenine, guanine, cytosine, thymine, uracil, inosine, xanthine, hypoxanthine, or a heterocyclic derivative, analog, or tautomer thereof. A nucleobase can be naturally occurring or synthetic. Non-limiting examples of nucleobases are adenine, guanine, thymine, cytosine, uracil, xanthine, hypoxanthine, 8-azapurine, purines substituted at the 8 position with methyl or bromine, 9-oxo-N6-methyladenine, 2-aminoadenine, 7-deazaxanthine, 7-deazaguanine, 7- deaza-adenine, N4-ethanocytosine, 2,6- diaminopurine, N6-ethano-2,6-diaminopurine, 5- methylcytosine, 5-(C3-C6)- alkynylcytosine, 5-fluorouracil, 5-bromouracil, thiouracil,pseudoisocytosine, 2-hydroxy-5-methyl-4-triazolopyridine, isocytosine, isoguanine, inosine, 7,8-dimethylalloxazine, 6-dihydrothymine, 5,6-dihydrouracil, 4-methyl-indole, ethenoadenine and the non-naturally occurring nucleobases described in U.S. Pat. Nos. 5,432,272 and 6,150,510 and PCT applications WO 92 / 002258, WO 93 / 10820, WO 94 / 22892, and WO 94 / 24144, and Fasman (“Practical Handbook of Biochemistry and Molecular Biology”, pp. 385-394, 1989, CRC Press, Boca Raton, LO), all herein incorporated by reference in their entireties.

[0056] The term “nucleic acid” or “polynucleotide” refers to a deoxyribonucleotide or ribonucleotide polymer in either single- or double-stranded form, and unless otherwise limited, encompasses known analogs of natural nucleotides that hybridize to nucleic acids in manner similar to naturally occurring nucleotides, such as peptide nucleic acids (PNAs) and phosphorothioate DNA. Unless otherwise indicated, a particular nucleic acid sequence includes the complementary sequence thereof. Nucleotides include, but are not limited to, ATP, dATP, CTP, dCTP, GTP, dGTP, UTP, TTP, dUTP, 5-methyl-CTP, 5-methyl-dCTP, ITP, dITP, 2-amino-adenosine-TP, 2-amino-deoxyadenosine-TP, 2-thiothymidine triphosphate, pyrrolo-pyrimidine triphosphate, and 2-thiocytidine, as well as the alphathiotriphosphates for all of the above, and 2ƍ-O-methyl-ribonucleotide triphosphates for all the above bases. Modified bases include, but are not limited to, 5-Br-UTP, 5-Br-dUTP, 5-F-UTP, 5-F-dUTP, 5-propynyl dCTP, and 5-propynyl-dUTP.

[0057] As used herein the term “chromosome” refers to the heredity-bearing gene carrier of a living cell, which is derived from chromatin strands comprising DNA and protein components (especially histones). The conventional internationally recognized individual human genome chromosome numbering system is employed herein.

[0058] A “genome” refers to the complete genetic information of an organism or virus, expressed in nucleic acid sequences.

[0059] As used herein, the term “reference genome” or “reference sequence” refers to any particular known genome sequence, whether partial or complete, of any organism or virus which may be used to reference identified sequences from a subject. For example, a reference genome used for human subjects as well as many other organisms is found at the National Center for Biotechnology Information at ncbi.nlm.nih.gov. In various embodiments, the reference sequence is significantly larger than the reads that are aligned to it. For example,it may be at least about 100 times larger, or at least about 1000 times larger, or at least about 10,000 times larger, or at least about 105times larger, or at least about 106times larger, or at least about 107times larger. In one example, the reference sequence is that of a full-length genome. Such sequences may be referred to as genomic reference sequences. For example, the reference sequence can be a reference human genome sequence, such as hg19 (for example, available at GenBank assembly accession GCA_000001405.1) or hg38 (for example, available at GenBank assembly accession GCA_000001405.15). In another example, the reference sequence is limited to a specific human chromosome such as chromosome 13. In some embodiments, a reference Y chromosome is the Y chromosome sequence from human genome version hg19. Such sequences may be referred to as chromosome reference sequences. Other examples of reference sequences include genomes of other species, as well as chromosomes, sub-chromosomal regions (such as strands), etc., of any species. In various embodiments, the reference sequence is a consensus sequence or other combination derived from multiple individuals. However, in certain applications, the reference sequence may be taken from a particular individual.

[0060] The term “nucleic acid sample” herein may refer to a sample, typically derived from one or more biological fluids, cells, tissues, organs, or organisms, comprising a nucleic acid or a mixture of nucleic acids comprising at least one nucleic acid sequence. In certain embodiments the nucleic acid sample comprises at least one nucleic acid sequence whose copy number is suspected of having undergone variation. Such samples may include, but are not limited to sputum / oral fluid, amniotic fluid, blood, a blood fraction, or fine needle biopsy samples (such as surgical biopsy, fine needle biopsy, etc.), urine, peritoneal fluid, pleural fluid, and the like. Although the sample is often taken from a human subject (such as a patient), the sample may be from any mammal, including, but not limited to dogs, cats, horses, goats, sheep, cattle, pigs, etc. The sample may be used directly as obtained from the biological source or following a pretreatment to modify the character of the sample. For example, such pretreatment may include preparing plasma from blood, diluting viscous fluids and so forth. Methods of pretreatment may also involve, but are not limited to, filtration, precipitation, dilution, distillation, mixing, centrifugation, freezing, lyophilization, concentration, amplification, nucleic acid fragmentation, inactivation of interfering components, the addition of reagents, lysing, etc. If such methods of pretreatment areemployed with respect to the sample, such pretreatment methods are typically such that the nucleic acid(s) of interest remain in the test sample, sometimes at a concentration proportional to that in an untreated test sample (such as namely, a sample that is not subjected to any such pretreatment method(s)). Such “treated” or “processed” samples are still considered to be biological “test” samples with respect to the methods described herein. A “nucleic acid sample” may also include nucleic acid sequence information stored in a memory, and which was originally obtained from a source such as one or more biological fluids, cells, tissues, organs, or organisms.

[0061] The term “read” or “sequence read” (or sequencing reads) refers to a sequence obtained from a portion of a nucleic acid sample. A read may be represented by a string of nucleotides sequenced from any part or all of a nucleic acid molecule. Typically, though not necessarily, a read represents a short sequence of contiguous base pairs in the sample. The read may be represented symbolically by the base pair sequence (in A, T, C, or G) of the sample portion. It may be stored in a memory device and processed as appropriate to determine whether it matches a reference sequence or meets other criteria. A read may be obtained directly from a sequencing apparatus or indirectly from stored sequence information concerning the sample. In some cases, a read is a DNA sequence of sufficient length (such as at least about 25 bp) that can be used to identify a larger sequence or region, for example, that can be aligned and specifically assigned to a chromosome or genomic region or gene. For example, a sequence read may be a short string of nucleotides (such as 20-150 bases) sequenced from a nucleic acid fragment, a short string of nucleotides at one or both ends of a nucleic acid fragment, or the sequencing of the entire nucleic acid fragment that exists in the biological sample. Sequence reads may be obtained by any method known in the art. For example, a sequence read may be obtained in a variety of ways, such as using sequencing techniques or using probes, such as in hybridization arrays or capture probes, or amplification techniques, such as the polymerase chain reaction (PCR) or linear amplification using a single primer or isothermal amplification. Sequence reads can be generated by techniques such as sequencing by synthesis, sequencing by binding, or sequencing by ligation. Sequence reads can be generated using instruments such as MINISEQ, MISEQ, NEXTSEQ, HISEQ, and NOVASEQ sequencing instruments from Illumina, Inc. (San Diego, CA).

[0062] The term “sequencing depth,” as used herein, generally refers to the number of times a locus is covered by a sequence read aligned to the locus. The locus may be as small as a nucleotide, or as large as a chromosome arm, or as large as the entire genome. Sequencing depth can be expressed as 50×, 100×, etc., where “×” refers to the number of times a locus is covered with a sequence read. Sequencing depth can also be applied to multiple loci, or the whole genome, in which case x can refer to the mean number of times the loci or the haploid genome, or the whole genome, respectively, is sequenced. When a mean depth is quoted, the actual depth for different loci included in the dataset spans over a range of values. Ultra-deep sequencing can refer to at least 100× in sequencing depth.

[0063] As used herein, the terms “aligned,” “alignment,” or “aligning” refer to the process of comparing a read or tag to a reference sequence and thereby determining the likelihood of the reference sequence contains the read sequence. If the reference sequence contains the read, the read may be mapped to the reference sequence or, in certain embodiments, to a particular location in the reference sequence. For example, the alignment of a read to the reference sequence for human chromosome 13 will tell the likelihood of the read is present in the reference sequence for chromosome 13. In some cases, an alignment additionally indicates a location where the read or tag maps to in the reference sequence. For example, if the reference sequence is the whole human genome sequence, an alignment may indicate that a read is present on chromosome 13, and may further indicate that the read is on a particular strand and / or site of chromosome 13. A “site” may be a unique position on a polynucleotide sequence or a reference genome (i.e. chromosome ID, chromosome position and orientation). In some embodiments, a site may provide a position for a residue, a sequence tag, or a segment on a sequence.

[0064] Aligned reads or tags are one or more sequences that are identified as a match in terms of the order of their nucleic acid molecules to a known sequence from a reference genome. Alignment can be done manually, although it is typically implemented by a computer algorithm, as it would be impossible to align reads in a reasonable time period for implementing the methods disclosed herein. The matching of a sequence read in aligning can be a 100% sequence match or less than 100% (non-perfect match).

[0065] Alignment may be performed by modifications and / or combinations of methods such as Burrows-Wheeler Aligner (BWA), iSAAC, BarraCUDA, BFAST, BLASTN,BLAT, Bowtie, CASHX, Cloudburst, CUDA-EC, CUSHAW, CUSHAW2, CUSHAW2-GPU, drFAST, ELAND, ERNE, GNUMAP, GEM, GensearchNGS, GMAP and GSNAP, Geneious Assembler, LAST, MAQ, mrFAST and mrsFAST, MOM, MOSAIK, MPscan, Novoaligh & NovoalignCS, NextGENe, Omixon, PALMapper, Partek, PASS, PerM, PRIMEX, QPalma, RazerS, REAL, cREAL, RMAP, rNA, RT Investigator, Segemehl, SeqMap, Shrec, SHRiMP, SLIDER, SOAP, SOAP2, SOAP3 and SOAP3-dp, SOCS, SSAHA and SSAHA2, Stampy, SToRM, Subread and Subjunc, Taipan, UGENE, VelociMapper, XpressAlign, and ZOOM.

[0066] The term “mapping” used herein refers to specifically assigning a sequence read to a larger sequence, such as a reference genome, by alignment.

[0067] A “genetic variation” or “genetic alteration” refers to a particular genotype present in certain individuals, and often a genetic variation is present in a statistically significant sub-population of individuals. The presence or absence of a genetic variance can be determined using a method or apparatus described herein. In certain embodiments, the presence or absence of one or more genetic variations is determined according to an outcome provided by methods and apparatuses described herein. In some embodiments, a genetic variation is a chromosome abnormality (such as aneuploidy), partial chromosome abnormality or mosaicism, each of which is described in greater detail herein. Non-limiting examples of genetic variations include one or more deletions (such as micro-deletions), duplications (such as micro-duplications), insertions, mutations, polymorphisms (such as single-nucleotide polymorphisms), fusions, repeats (such as short tandem repeats), distinct methylation sites, distinct methylation patterns, the like and combinations thereof. An insertion, repeat, deletion, duplication, mutation or polymorphism can be of any length, and in some embodiments, is about 1 base or base pair (bp) to about 250 megabases (Mb) in length. In some embodiments, an insertion, repeat, deletion, duplication, mutation or polymorphism is about 1 base or base pair (bp) to about 1,000 kilobases (kb) in length (for example about 10 bp, 50 bp, 100 bp, 500 bp, 1 kb, 5 kb, 10 kb, 50 kb, 100 kb, 500 kb, or 1000 kb in length).

[0068] A genetic variation is sometimes a deletion. In certain embodiments a deletion is a mutation (such as a genetic aberration) in which a part of a chromosome or a sequence of DNA is missing. A deletion is often the loss of genetic material. Any number of nucleotides can be deleted. A deletion can comprise the deletion of one or more entire chromosomes, a segment of a chromosome, an allele, a gene, an intron, an exon, any non-coding region, any coding region, a segment thereof or combination thereof. A deletion can comprise a microdeletion. A deletion can comprise the deletion of a single base.

[0069] A genetic variation is sometimes a genetic duplication. In certain embodiments a duplication is a mutation (such as a genetic aberration) in which a part of a chromosome or a sequence of DNA is copied and inserted back into the genome. In certain embodiments a genetic duplication (i.e. duplication) is any duplication of a region of DNA. In some embodiments a duplication is a nucleic acid sequence that is repeated, often in tandem, within a genome or chromosome. In some embodiments a duplication can comprise a copy of one or more entire chromosomes, a segment of a chromosome, an allele, a gene, an intron, an exon, any non-coding region, any coding region, segment thereof or combination thereof. A duplication can comprise a microduplication. A duplication sometimes comprises one or more copies of a duplicated nucleic acid. A duplication sometimes is characterized as a genetic region repeated one or more times (such as repeated 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 times). Duplications can range from small regions (thousands of base pairs) to whole chromosomes in some instances. Duplications frequently occur as the result of an error in homologous recombination or due to a retrotransposon event. Duplications have been associated with certain types of proliferative diseases. Duplications can be characterized using genomic microarrays or comparative genetic hybridization (CGH).

[0070] A genetic variation is sometimes an insertion. An insertion is sometimes the addition of one or more nucleotide base pairs into a nucleic acid sequence. An insertion is sometimes a microinsertion. In certain embodiments an insertion comprises the addition of a segment of a chromosome into a genome, chromosome, or segment thereof. In certain embodiments an insertion comprises the addition of an allele, a gene, an intron, an exon, any non-coding region, any coding region, segment thereof or combination thereof into a genome or segment thereof. In certain embodiments an insertion comprises the addition (i.e., insertion) of nucleic acid of unknown origin into a genome, chromosome, or segment thereof. In certain embodiments an insertion comprises the addition (i.e., insertion) of a single base.

[0071] A genetic variation sometimes includes copy number variations, i.e., variations in the number of copies of a nucleic acid sequence present in a test sample in comparison with the copy number of the nucleic acid sequence present in a reference sample. In certain embodiments, the nucleic acid sequence is 1 kb or larger. In some cases, the nucleicacid sequence is a whole chromosome or significant portion thereof. A copy number variant may refer to the sequence of nucleic acid in which copy-number differences are found by comparison of a nucleic acid sequence of interest in test sample with an expected level of the nucleic acid sequence of interest. For example, the level of the nucleic acid sequence of interest in the test sample is compared to that present in a qualified sample. Copy number variants / variations may include deletions, including microdeletions, insertions, including microinsertions, duplications, multiplications, and translocations. CNVs encompass chromosomal aneuploidies and partial aneuploidies.

[0072] “Phasing” refers to analyzing linkage information between sequence reads of a nucleic acid to determine whether two subsequences of a nucleic acid (such as alleles or variants) are located on a single chromosome or two separate chromosomes (for example, a maternally or paternally inherited chromosome).

[0073] As used herein, a “file” includes digital files. In some embodiments, a file is on a computer storage medium (such as a computer hard drive, for example a spinning magnetic disk drive or a solid state drive). In some embodiments, the digital file is stored in the format of a BAM, FASTQ, SAM, CRAM, JSON, CIGAR, or VCF file.

[0074] As used herein, “star allele” refers to a specific variant of the cytochrome P450 (CYP2A6) gene that is identified by a star (*) symbol followed by a number. Many of these star alleles are haplotype patterns at the gene level and may be associated with protein activity levels. Examples of star alleles include but are not limited to *1b, *5, *7, *8, *10, *19, *24, *28, *35, *36, *37. Embodiments of Methods and Systems of Detecting One or More Structural Variants in a CYP2A6 Gene or a CYP2A7 Pseudogene

[0075] FIG. 2A is a block diagram that schematically illustrates an exemplary method 200 of detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample. In some embodiments, the method 200 is implemented on a computer. The method 200 may be embodied in a set of executable program instructions stored on a computer-readable medium, such as one or more disk drives, of a computing system. For example, the server device 3102 shown in FIGS. 3A and 3B and described in greater detail below can execute a set of executable program instructions to implement themethod 200. When the method 200 is initiated, the executable program instructions can be loaded into a memory, such as RAM, and executed by one or more processors of a server device 3102. Although the method 200 is described with respect to the server device 3102 shown in FIG. 3B, the description is illustrative only and is not intended to be limiting. In some embodiments, the method 200 or portions thereof may be performed serially or in parallel by multiple computing systems. Obtaining Sequence Reads

[0076] As shown in FIG. 2A, the method 200 for detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample may start from start block 210. The method 200 may proceed to block 220, wherein sequence reads from the nucleic acid sample which align to a CYP2A6 gene or a CYP2A7 pseudogene in a human genome within the nucleic acid sample are obtained. In some embodiments, the sequence reads are generated from a nucleic acid sample obtained from a subject.

[0077] Sequence reads can be generated by techniques such as sequencing by synthesis, sequencing by binding, or sequencing by ligation. Sequence reads can be generated using instruments such as MINISEQ, MISEQ, NEXTSEQ, HISEQ, and NOVASEQ sequencing instruments from Illumina, Inc. (San Diego, CA). Sequence reads can be, for example, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1250, 1500, 1750, 2000, or more base pairs (bps) in length each. For example, sequence reads are about 100 base pairs to about 1000 base pairs in length each. The sequence reads can comprise paired-end sequence reads. The sequence reads can comprise single-end sequence reads. The sequence reads can be generated by whole genome sequencing (WGS). The WGS can be clinical WGS (cWGS). The sample can comprise cells, cell-free DNA, cell-free fetal DNA, amniotic fluid, a blood sample, a biopsy sample, or a combination thereof.

[0078] In some embodiments, the sequence reads are aligned to a reference sequence (not shown). For example, sequence reads obtained from a sample may be aligned to a CYP2A6 gene or a CYP2A7 pseudogene in the reference sequence. Sequence reads may also be aligned to diploid regions of a reference sequence as further described herein. In some embodiments, a computing system stores the first plurality of sequence reads in memory. The computing system may load the first plurality of sequence reads into memory. In someembodiments, the sequence reads are obtained from a digital file containing sequencing information. In some embodiments, the digital file is on a computer storage medium (such as a computer hard drive, for example a spinning magnetic disk drive or a solid state drive). Estimating a Combined Copy Number

[0079] The method 200 may proceed to process block 230, wherein a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample is estimated. FIG. 2B is a block diagram that illustrates the details on the methods taking place within the process block 230 described above, wherein a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample is estimated. Estimate Copy Number of a CYP2A6-Specific Base at Pre-Determined Differentiating Sites

[0080] The method 200 may proceed to process block 240, wherein a copy number of a CYP2A6-specific base at one or more pre-determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene is estimated. FIG. 2C is a block diagram that further illustrates the methods taking place within the process block 240 described above, wherein a copy number of a CYP2A6-specific base at one or more pre-determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene is estimated. Determining One or More Consensus Copy Numbers

[0081] After estimating a copy number of a CYP2A6-specific base at one or more pre-determined differentiating sites at process block 240, the method 200 may proceed to block 250, wherein one or more consensus copy numbers for one or more target regions of the CYP2A6 gene are determined.

[0082] In some embodiments, at block 250, the systems and methods may determine the one or more consensus copy numbers based on the estimated copy number of the CYP2A6-specific base at the one or more pre-determined differentiating sites. For example, a target region may include one or more pre-determined differentiating sites. At block 250, the systems and methods may determine a consensus copy number for each target region of the one or more target regions based on the estimated copy number(s) of the CYP2A6-specific base at each of the one or more pre-determined differentiating sites within the target region.

[0083] A consensus copy number may be determined from one or more estimated copy numbers of a CYP2A6-specific base using methods known to those of skill in the art. For example, the consensus copy number may be the most common copy number among a plurality of estimated copy numbers of a CYP2A6-specific base over a plurality of pre-determined differentiating sites within a target region.

[0084] In some embodiments, a target region is a subsection of the CYP2A6 gene. For example, in some embodiments, the one or more target regions comprise a target region corresponding to exons 5-9 of the CYP2A6 gene. In some embodiments, the one or more target regions comprise a target region corresponding to exons 3-4 of the CYP2A6 gene. In some embodiments, the one or more target regions comprise a target region corresponding to exons 1-2 of the CYP2A6 gene. In some embodiments, the one or more target regions include three target regions, with a first target region corresponding to exons 5-9, a second target region corresponding to exons 3-4, and a third target region corresponding to exons 1-2.

[0085] In some embodiments, the one or more target regions comprise a region corresponding to sites 154-196 from the pre-determined differentiating sites of Table 1, or any subset of these sites. In some embodiments, the one or more target regions comprise a region corresponding to sites 94-153 from the pre-determined differentiating sites of Table 1, or any subset of these sites. In some embodiments, the one or more target regions comprise a region corresponding to positions sites 24-93 from the pre-determined differentiating sites of Table 1, or any subset of these sites. In some embodiments, the one or more target regions include three target regions, with a first target region corresponding to sites 154-196 (or a subset of these sites), a second target region corresponding to sites 94-153 (or a subset of these sites), and a third target region corresponding to positions sites 24-93 (or a subset of these sites) from the pre-determined differentiating sites of Table 1.

[0086] The method 200 may next proceed to decision state 260, wherein the methods and systems determine whether there are additional target regions to be analyzed. If there are additional target regions to be analyzed, the method 200 may return to block 250 and the workflow may proceed as previously described. For example, in some embodiments, the method 200 includes determining a first consensus copy number for a first target region based on estimated copy numbers of CYP2A6-specific bases at a first plurality of pre-determined differentiating sites; determining a second consensus copy number for a second target regionbased on estimated copy numbers of CYP2A6-specific bases at a second plurality of pre- determined differentiating sites; and determining a third consensus copy number for a third target based on estimated copy numbers of CYP2A6-specific bases at a third plurality of pre- determined differentiating sites. Detect a Structural Variant Based on Consensus Copy Numbers

[0087] If there are no additional target regions to be analyzed, the method 200 may proceed to block 270, wherein one or more structural variants in the CYP2A6 gene or the CYP2A7 pseudogene are detected based on the one or more consensus copy numbers. In some embodiments, a deletion, duplication, or a hybrid structural variant may be detected based on the one or more consensus copy numbers. For example, as two copies of the CYP2A6 gene are expected, a structural variant may be detected if one or more consensus copy numbers deviate from 2 (for example, if there is a consensus copy number of 0, 1, 3, or 4).

[0088] In some embodiments, a copy number variant (cnv) tag may be determined which includes one or more consensus copy numbers. For example, in an embodiment which includes three target regions as described above, a cnv tag may be determined across the three target regions representing the consensus copy number for that region, for example 2_2_2 may specify a sample containing a consensus copy number of 2 for each of the three target regions (i.e., with no structural variant). For example, a cnv tag of 2_2_1 or 2_1_1 may correspond to a hybrid structural variant, cnv tag of 0_0_0 or 1_1_1 may correspond to a deletion, and a cnv tag of 3_3_3 may correspond to a duplication. In some embodiments, one or more structural variants are detected in the CYP2A6 gene based on the first, second, and third consensus copy numbers.

[0089] The method 200 may end at block 280. Processes of Estimating a Combined Copy Number

[0090] FIG.2B is a block diagram that illustrates the details on the methods taking place within the process block 230 described above, wherein a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample is estimated. As shown in FIG.2B, the method of process block 230 may start from process block 2010.

[0091] The method of process block 230 may proceed to block 2020, wherein sequence reads which align to the CYP2A6 gene or to the CYP2A7 pseudogene are counted. In some embodiments, at block 2020, the methods and systems counting sequence reads which align to a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample. For example, sequence reads may have been previously aligned to a reference sequence as described. In some embodiments, counting the total number of reads aligning to either the CYP2A6 gene or to the CYP2A7 pseudogene in a reference genome sequence includes counting sequence reads which can be mapped with equal confidence either to the CYP2A6 gene or to the CYP2A7 pseudogene (leading to zero mapping quality). In some embodiments, sequence reads align to regions in both the CYP2A6 gene or to the CYP2A7 pseudogene with a mapping quality of zero because the sequence is identical between the two regions, due to the high homology between regions of the CYP2A6 gene and the CYP2A7 pseudogene. Thus, in some embodiments, the method jointly counts sequence reads that map to either the CYP2A6 gene or the CYP2A7 pseudogene, including sequence reads with zero mapping quality (for example, sequence reads which have the same alignment score in either the CYP2A6 gene or the CYP2A7 pseudogene). In some embodiments, disambiguation of the sequencing depth for the CYP2A6 gene and the CYP2A7 pseudogene is done in a downstream step based on a predetermined set of differentiating sites.

[0092] In some embodiments, by counting sequence reads with a low mapping quality (including a mapping quality of zero), a combined copy number of the CYP2A6 gene and the CYP2A7 pseudogene may be estimated despite the high sequence homology. For example, in some prior art methods, sequence reads with low or zero mapping quality (MAPQ) are filtered out and are not used in determining the sequence of a region or detecting variants in the CYP2A6 gene or the CYP2A7 pseudogene. The systems and methods described herein may, in some embodiments, use sequence reads with low or zero mapping quality to determine a combined copy number of the CYP2A6 gene and the CYP2A7 pseudogene. In some embodiments, this may allow for estimation of combined copy in a region which, due to high sequence similarity between the CYP2A6 gene and the CYP2A7 pseudogene, it may be difficult to otherwise detect variants determine a combined copy number. Without being bound by theory, because ambiguity in mapping of zero mapping quality sequencing reads in CYP2A6 / CYP2A7 regions due to sequencing reads aligning to those two regions (rather thanelsewhere in the genome), in some embodiments, the ambiguously-mapped reads are used in the estimation of the combined copy number of CYP2A6+CYP2A7. Such embodiments may advantageously allow for copy number estimation even in regions where there are only reads with zero mapping quality, and may avoid deviations in the depth signal in regions where a significant proportion of reads have zero mapping quality (MAPQ =0).

[0093] The method of process block 230 may proceed to block 2030, wherein sequence reads which align to diploid regions in the human genome within the nucleic acid sample are counted. For example, sequence read obtained from the human genome within the nucleic acid sample can be obtained and aligned as previously described herein to regions of a reference sequence previously designated as diploid regions. Sequence reads which align to one or more diploid regions may be counted. In some embodiments, the diploid sequence reads are obtained from regions of a human genome that are expected or observed to consistently be diploid across populations. For example, a diploid region may be a region which is diploid in over 90%, over 95%, or over 99% of nucleic acid samples from a population. For example, in some embodiments, the diploid sequence reads are obtained from about 3,000 genomic regions of approximately 2000 bp in length, which are expected to be consistently diploid across populations.

[0094] The method of process block 230 may proceed to block 2040, wherein the count of the sequence reads which align to a CYP2A6 gene or a CYP2A7 pseudogene is normalized. In some embodiments, block 2430 includes normalizing the count of the sequence reads which align to the RCCX region of the human genome. In some embodiments, the sequence read count is normalized by the length of the CYP2A6 gene or the CYP2A7 pseudogene. In some embodiments, the sequence read count may be normalized by the length of the region and against a set of diploid sequence reads. In some embodiments, at block 2430, the methods and systems normalize the count of the sequence reads which align to a CYP2A6 gene or a CYP2A7 pseudogene using (1a) a depth of the sequence reads aligned to the CYP2A6 gene or the CYP2A7 pseudogene, (1b) a length of each of the CYP2A6 gene and / or the CYP2A7 pseudogene, (2a) a depth of sequence reads aligned to diploid regions, and (2b) a length of each of the diploid regions. For example, in some embodiments, the length of the diploid regions used for normalization is about 2kbp, and there are about 3,000 such regions. In some embodiments, the length of the CYP2A6 regions is 13,966 bps and the length of the CYP2A7(pseudogene) region is 13,870 bps. In some embodiments, the size of the regions is used to normalize the read counts, by dividing the read counts in those regions by the length of the region. In some embodiments, both steps (1a) / (1b) and (2a) described above are done simultaneously. In some embodiments, the normalization step allows for the depths in regions of different lengths to be comparable, as longer regions will have proportionally higher number of reads mapping to them (higher depth).

[0095] In some embodiments, at block 2040, the methods and systems GC-correct the sequence read count. For example, in some embodiments, the combined normalized sequence read count (for example, a sequence read count normalized by length of the region) for the CYP2A6 gene and the CYP2A7 pseudogene is pooled together with sequence read counts (for example, a sequence read count normalized by length of the region) for diploid regions. Normalizing the count of sequence reads which align to the CYP2A6 gene or the CYP2A7 pseudogene by the count of sequence reads which align to diploid regions may, in some embodiments, correct for bias in sequencing coverage due to variable GC content among different regions. For example, the count of sequence reads aligned to the CYP2A6 gene or the CYP2A7 pseudogene may be corrected for GC content using (1) a GC content of each of the CYP2A6 gene and the CYP2A7 pseudogene and (2) a GC content of each of diploid regions. In some embodiments, in block 2430, the methods and systems determine a normalized and / or GC-corrected combined copy number. In some embodiments, the normalized and / or GC- corrected combined copy number is a float copy number, including a non-integer number such as 1.2, 2.4, etc.

[0096] The method of process block 230 may proceed to block 2050, wherein the normalized count of the sequence reads which align to a CYP2A6 gene or a CYP2A7 pseudogene is binned using a Gaussian mixture model. In some embodiments, at block 2050, the methods and systems bin the normalized and / or GC-corrected count of the sequence reads which align to a CYP2A6 gene or a CYP2A7 pseudogene using a Gaussian mixture model (GMM). For example, a normalized and / or GC-corrected count of sequence reads may be a non-integer number such as 3.4, 3.2, etc., and a Gaussian mixture model may be used to determine an integer copy number such as “3.” For example, a Gaussian mixture model may be used to infer the most likely combined copy number of the CYP2A6 gene and the CYP2A7 pseudogene based on the observed normalized depth signal.

[0097] The combined copy number can be, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more copies. The Gaussian mixture model can comprise a one-dimensional Gaussian mixture model. The plurality of Gaussians of the Gaussian mixture model can represent integer copy numbers, for example, 0 to 5, 0 to 6, 0 to 7, 0 to 8, 0 to 9, 0 to 10, 0 to 11, 0 to 12, 0 to 13, 0 to 14, or 0 to 15. For example, the plurality of Gaussians of the Gaussian mixture model can represent integer copy numbers from 0 to 10. A mean of each of the plurality of Gaussians can be the integer copy number represented by the Gaussian. A mean of each of the plurality of Gaussians can be the integer copy number represented by the Gaussian (such as copy numbers of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or more). The standard deviation of a Gaussian can be or be about, for example, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, or more. The plurality of Gaussians of the Gaussian mixture model can comprise, for example, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or more, Gaussians. For example, the plurality of Gaussians of the Gaussian mixture model can comprise 5 Gaussians. In some embodiments, the GMM includes pre-defined parameters such as shift, prior, mean, and standard deviation (sd). In some embodiments, a normalized and GC-corrected depth is first scaled by a shift value that corrects for alignment bias between target region and diploid regions.

[0098] At block 2050, the methods and systems may determine an estimated combined copy number. In some embodiments, the posterior probability of CN = i given scaled depth is computed for i = 0-X (X being a maximum copy number) based on the pre-trained mean, sd, and prior values from the Gaussian mixture model. In some embodiments, the integer copy number with highest posterior probability is then selected as candidate for the estimated combined copy number. The computing system can determine the estimated combined copy number using the Gaussian mixture model and a predetermined posterior probability threshold, given the normalized number of the sequence reads aligned to the RCCX region. The predetermined posterior probability threshold can be, for example, 0.7, 0.75, 0.8, 0.85, 0.95, or more. In some embodiments, the predetermined posterior probability threshold is 0.95. In some embodiments, the estimated combined copy number is an integer copy number.

[0099] The method of process block 230 may end at block 2060.Processes of Estimating a Copy Number of a CYP2A6-Specific Base

[0100] FIG.2C is a block diagram that further illustrates the methods taking place within the process block 240 described above, wherein a copy number of a CYP2A6-specific base at one or more pre-determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene is estimated. As shown in FIG. 2C, the method of process block 240 may start from process block 2110.

[0101] The method of process block 240 may proceed to block 2120, wherein sequence reads which include a CYP2A6-specific base at a pre-determined differentiating site are counted to determine a CYP2A6 sequence read count. Next, the method of process block 240 may proceed to block 2130, wherein sequence reads which include a CYP2A7-specific base at a pre-determined differentiating site are counted to determine a CYP2A7 sequence read count.

[0102] In some embodiments, at block 2120 and block 2130, the methods and systems count sequence reads at a pre-determined differentiating site. In some embodiments, at block 2120 and at block 2130, sequence information (such as a nucleotide base) is assessed at a pre-determined differentiating site. For example, sequence reads which include a CYP2A6- specific base at a first pre-determined differentiating site may be counted at block 2120. For example, sequence reads which include a CYP2A7-specific base at a first pre-determined differentiating site may be counted at block 2130.

[0103] In some embodiments, a pre-determined differentiating site is a site where, if the sequence of the CYP2A6 gene and the sequence of the CYP2A7 pseudogene are aligned to one another, the sequence of the CYP2A6 gene and the CYP2A7 pseudogene differ. In some embodiments, the sequence of the CYP2A6 gene and the CYP2A7 pseudogene differ at the pre- determined differentiating site in at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% of a population of nucleic acid samples.

[0104] In some embodiments, a pre-determined differentiating sites comprises a site corresponding to a position selected from the positions listed in the table below, which lists genomic coordinates in reference genome HG38. The genomic coordinates may vary based on the reference genome being used to define them. Accordingly, the pre-determined differentiating sites may include equivalent genomic coordinates in other reference genomes.Table 1 Site Chromosome CYP2A6 CYP2A6- CYP2A7 CYP2A7-Specific No. Position Specific Base Position Base 1 chr19 40841665 g 40873546 a 2 chr19 40841677 g 40873558 a 3 chr19 40841700 g 40873581 a 4 chr19 40841711 t 40873592 c 5 chr19 40841716 t 40873597 c 6 chr19 40841755 g 40873636 a 7 chr19 40841770 c 40873651 t 8 chr19 40841787 g 40873668 a 9 chr19 40841791 a 40873672 t 10 chr19 40841846 g 40873726 a 11 chr19 40841849 t 40873729 a 12 chr19 40841873 a 40873753 g 13 chr19 40841885 c 40873765 g 14 chr19 40841894 a 40873774 g 15 chr19 40841901 a 40873781 c 16 chr19 40841902 c 40873782 a 17 chr19 40841903 a 40873783 g 18 chr19 40841906 a 40873786 g 19 chr19 40841917 a 40873797 t 20 chr19 40841926 g 40873806 a 21 chr19 40841948 t 40873828 c 22 chr19 40842171 t 40874051 c 23 chr19 40843333 c 40875216 g 24 chr19 40843418 g 40875301 t 25 chr19 40844444 a 40876340 c 26 chr19 40844456 c 40876352 t 27 chr19 40844570 a 40876466 tchr19 40844600 c 40876496 a chr19 40844725 a 40876621 g chr19 40844902 c 40876798 g chr19 40844906 t 40876802 a chr19 40844922 g 40876818 a chr19 40844977 c 40876873 t chr19 40844979 t 40876875 g chr19 40844983 g 40876879 c chr19 40845005 g 40876902 a chr19 40845010 c 40876907 g chr19 40845019 t 40876916 a chr19 40845031 g 40876928 t chr19 40845071 g 40876968 t chr19 40845077 a 40876974 t chr19 40845080 t 40876977 a chr19 40845110 a 40877007 g chr19 40845151 c 40877048 t chr19 40845174 t 40877071 c chr19 40845203 g 40877100 t chr19 40845205 t 40877102 a chr19 40845206 c 40877103 g chr19 40845223 t 40877120 g chr19 40845227 t 40877124 c chr19 40845228 g 40877125 a chr19 40845233 g 40877130 t chr19 40845249 g 40877146 a chr19 40845272 g 40877169 a chr19 40845306 g 40877202 a chr19 40845551 c 40877447 t chr19 40845556 t 40877452 cchr19 40845570 t 40877466 c chr19 40845591 a 40877487 g chr19 40845711 t 40877607 g chr19 40845723 t 40877619 c chr19 40845850 t 40877746 g chr19 40845866 c 40877762 t chr19 40845883 g 40877779 a chr19 40846014 g 40877910 c chr19 40846026 c 40877922 t chr19 40846027 c 40877923 g chr19 40846055 c 40877951 t chr19 40846062 t 40877958 c chr19 40846321 c 40878217 t chr19 40846368 t 40878253 c chr19 40846399 c 40878284 t chr19 40846406 c 40878291 g chr19 40846415 a 40878300 g chr19 40846433 g 40878318 c chr19 40846437 c 40878322 t chr19 40846465 t 40878350 c chr19 40846521 c 40878406 g chr19 40846528 c 40878413 t chr19 40846546 a 40878431 g chr19 40846557 a 40878442 g chr19 40846606 t 40878491 c chr19 40846608 c 40878493 t chr19 40846609 c 40878494 g chr19 40846612 a 40878497 g chr19 40846639 g 40878524 a chr19 40846669 c 40878554 tchr19 40846777 t 40878662 c chr19 40846787 t 40878672 c chr19 40846791 c 40878676 t chr19 40846799 a 40878684 g chr19 40846806 t 40878691 c chr19 40846820 a 40878705 t chr19 40846902 a 40878787 g chr19 40847166 g 40879051 a chr19 40847189 c 40879074 t chr19 40847271 a 40879156 g chr19 40847294 t 40879179 g chr19 40847295 t 40879180 c chr19 40847299 c 40879184 t chr19 40847582 g 40879467 c chr19 40847590 t 40879475 a chr19 40847674 a 40879559 g chr19 40847849 c 40879734 g chr19 40847864 c 40879749 t chr19 40848100 a 40879965 g chr19 40848128 g 40879993 c chr19 40848130 c 40879995 g chr19 40848266 g 40880131 t chr19 40848269 a 40880134 g chr19 40848293 t 40880158 c chr19 40848388 a 40880253 g chr19 40848392 c 40880257 a chr19 40848602 t 40880467 a chr19 40848606 c 40880471 t chr19 40848608 g 40880473 a chr19 40848616 c 40880481 tchr19 40848617 c 40880482 g chr19 40848618 a 40880483 c chr19 40848623 c 40880488 t chr19 40848629 g 40880494 t chr19 40848633 g 40880498 c chr19 40848701 g 40880566 t chr19 40848783 a 40880648 g chr19 40848784 a 40880649 g chr19 40849173 c 40880944 t chr19 40849191 a 40880962 t chr19 40849325 t 40881096 c chr19 40849331 t 40881102 c chr19 40849386 a 40881157 g chr19 40849400 g 40881171 a chr19 40849406 c 40881177 g chr19 40849408 c 40881179 a chr19 40849421 a 40881192 g chr19 40849422 c 40881193 g chr19 40849430 g 40881201 a chr19 40849439 a 40881210 g chr19 40849464 c 40881235 t chr19 40849465 a 40881236 g chr19 40849473 c 40881244 a chr19 40849482 a 40881253 g chr19 40849487 g 40881258 c chr19 40849488 g 40881259 a chr19 40849513 a 40881284 g chr19 40849548 t 40881319 c chr19 40849696 g 40881467 a chr19 40849708 t 40881479 cchr19 40849713 a 40881484 g chr19 40849714 c 40881485 t chr19 40849733 t 40881504 g chr19 40849760 g 40881531 c chr19 40849773 t 40881544 a chr19 40849782 a 40881553 g chr19 40849799 c 40881570 a chr19 40849991 t 40881762 c chr19 40850019 t 40881790 c chr19 40850053 t 40881824 a chr19 40850056 g 40881827 a chr19 40850064 g 40881835 c chr19 40850069 g 40881840 a chr19 40850071 a 40881842 c chr19 40850175 c 40881959 t chr19 40850217 c 40882001 t chr19 40850231 c 40882015 t chr19 40850255 g 40882039 t chr19 40850261 t 40882045 c chr19 40850262 g 40882046 a chr19 40850263 t 40882047 c chr19 40850265 c 40882049 t chr19 40850268 c 40882052 g chr19 40850286 c 40882070 g chr19 40850309 a 40882093 g chr19 40850338 t 40882122 c chr19 40850358 a 40882142 g chr19 40850389 a 40882173 g chr19 40850411 t 40882195 g chr19 40850437 g 40882221 achr19 40850443 t 40882227 c chr19 40850453 c 40882237 t chr19 40850456 c 40882240 g chr19 40850543 t 40882327 c chr19 40850558 c 40882342 g chr19 40850560 t 40882344 c chr19 40850561 g 40882345 a chr19 40850695 a 40882479 c chr19 40850701 g 40882485 a chr19 40850714 g 40882498 a chr19 40850797 c 40882581 t chr19 40850802 g 40882586 c chr19 40850803 g 40882587 a chr19 40850809 g 40882593 a chr19 40850868 t 40882653 c chr19 40850921 t 40882706 c chr19 40851170 a 40882954 g chr19 40851182 a 40882967 t chr19 40851193 a 40882978 g chr19 40851588 c 40883373 t chr19 40851614 a 40883399 g chr19 40851934 t 40883719 c chr19 40852795 g 40884580 a chr19 40852811 c 40884596 g chr19 40852826 g 40884611 a chr19 40854256 g 40886041 a chr19 40854270 t 40886055 c chr19 40854305 t 40886090 c chr19 40854411 c 40886196 g chr19 40854422 a 40886207 g208 chr19 40854962 a 40886748 g 209 chr19 40854975 c 40886761 g 210 chr19 40855195 c 40886981 t 211 chr19 40855201 a 40886987 g 212 chr19 40855219 g 40887005 a 213 chr19 40855237 a 40887023 g 214 chr19 40855294 a 40887079 g 215 chr19 40855308 c 40887093 t 216 chr19 40855316 c 40887101 g 217 chr19 40855322 g 40887107 c 218 chr19 40855389 t 40887174 c 219 chr19 40855394 g 40887179 t 220 chr19 40855402 t 40887187 c 221 chr19 40855585 g 40887370 t

[0105] A pre-determined differentiating site may be determined as further described below. In some embodiments, the method comprises identifying single-base differences between the sequence of a first gene (such as CYP2A6) and a paralog gene (such as CYP2A7) in a reference sequence. For example, a reference sequence of the first gene may be compared with a reference sequence of a paralog gene by aligning the sequences to each other and noting all sites with single base differences between the two gene sequences. The genomic coordinates of those differentiating sites in both the first and the paralog genes may then be stored to an electronic storage. For example, a digital file may be created including a list of the single base differences. In some embodiments, the method includes selecting, as differentiating sites, single-base differences which are fixed across a population. For example, the method may include, for a plurality of nucleic acid samples (such as a plurality of nucleic acid samples from a population of individuals), receiving a plurality of sequence reads which align to the first gene and the paralog gene. In some embodiments, the plurality of nucleic acid samples are derived from individuals of a population, such as more than 100, more than 500, more than 1,000, more than 5,000, or more than 10,000 individuals. In some embodiments, the plurality of samples are taken from the 1000 Genomes Project. In some embodiments, thepopulation is a diverse population, such as a genetically diverse population including individuals from a plurality of ethnic groups, such as to account for differences in population types and increase the likelihood that single-base differences do not comprise differences due to population type. The method may further include, for each of the plurality of nucleic acid samples, estimating a gene-specific copy number for the first gene and a copy number for the paralog gene. The method may further include selecting a subset of nucleic acid samples of the plurality of nucleic acid samples, wherein the subset of nucleic acid samples comprises nucleic acid samples which are estimated to be diploid for the first gene and diploid for the paralog gene (such as using only the data from samples which are estimated to not contain a recombination event between the first gene and the paralog gene). The method may further include selecting single-base differences which have copy numbers consistent with diploidy for the first gene and the paralog gene in at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% of the nucleic acid samples of the subset of nucleic acid samples.

[0106] After obtaining a CYP2A6 sequence read count at block 2120 and a CYP2A7 sequence read count at block 2130, the method of process block 240 may proceed to block 2140, wherein a CYP2A6 copy number is determined for the pre-determined differentiating site based on combined copy number, the CYP2A6 sequence read count, and the CYP2A6 sequence read count. For example, in some embodiments, at block 2140, the methods and systems assess the proportion of sequence reads including a CYP2A6-specific base at the pre- determined differentiating site as compared to a CYP2A7-specific base. For example, if the estimated combined copy number is 3, and roughly two thirds of sequence reads support a CYP2A6-specific base while one third support a CYP2A7-specific base, then a probability may be determined that the copy number of the CYP2A6-specific base is 1.

[0107] In some embodiments, a probability of a CYP2A6 copy number of 0 up to a number corresponding to the estimated combined copy number is determined based on the CYP2A6 sequence read count,the CYP2A7 sequence read count, and the estimated combined copy number. For example, a probability of a CYP2A6 copy number of each integer from 0 up to the integer corresponding to the estimated combined copy number may be determined. For example, at block 2140, if the estimated combined copy number is 4, the system may determine a probability of a CYP2A6 copy number of 0 based on the CYP2A6 sequence read count, the CYP2A7 sequence read count, and the estimated combined copy number. The system may thendetermine a probability of a CYP2A6 copy number of 1 based on the CYP2A6 sequence read count, the CYP2A7 sequence read count, and the estimated combined copy number. The system may then iteratively determine the probability of further CYP2A6 copy numbers 2, then 3, and finally 4 (the estimated combined copy number), based on the CYP2A6 sequence read count, the CYP2A7 sequence read count, and the estimated combined copy number.

[0108] In some embodiments, at block 2140, a probability of a CYP2A6 copy number is determined based on a prior probability of a CYP2A6 copy number of 0 up to a number corresponding to the estimated combined copy number. In some embodiments, the prior probability is a uniform prior. In some embodiments, at block 2140, a Poisson posterior probability is determined to estimate the copy number of the CYP2A6-specific base at the pre- determined differentiating site. In some embodiments, a probability is determined comparing the expected depth under each possible copy number (from 0 up to the combined copy number) to the actual read depth. In some embodiments, expected depth is determined as the (sum of read counts to the CYP2A6 gene (or CYP2A7 gene) multiplied by the possible copy number (from 0 up to the combined copy number), and divided by the total copy number.

[0109] The methods and systems may determine the CYP2A6 copy number with the highest determined probability. The methods and systems may report and / or display the CYP2A6 copy number with the highest determined probability. In some embodiments, the methods and systems disclosed herein further include a step of creating a digital file including an estimated CYP2A6 copy number for the pre-determined differentiating site. In some embodiments, the digital file further includes an estimated combined copy number, and / or an estimated CYP2A7 copy number.

[0110] In some embodiments (not illustrated), the systems and methods may also determine a CYP2A7 copy number for the pre-determined differentiating site using steps similar to those described with reference to block 2120, block 2130, and block 2140. For example, a CYP2A7 copy number may be determined based on the combined copy number, the CYP2A6 sequence read count and the CYP2A7 sequence read count. For example, in some embodiments, the methods and systems assess the proportion of sequence reads including the CYP2A7-specific base at the pre-determined differentiating site as compared to the CYP2A6- specific base.

[0111] The method of process block 240 may proceed to decision state 2150, wherein the system may decide whether there are any additional pre-determined differentiating sites to be analyzed. If there is an additional pre-determined differentiating site, the method of process block 240 may return to block 2120 and the method of process block 240 may proceed for the additional pre-determined differentiating site as previously described. If there are no additional pre-determined differentiating sites to be analyzed, the method of process block 240 may end at block 2180. Embodiments of Methods and Systems of Detecting One or More Single-Nucleotide Variants or Indels in a CYP2A6 Gene in a Nucleic Acid Sample

[0112] In another aspect, disclosed herein are methods and systems of detecting one or more single-nucleotide variants or indels in a CYP2A6 gene in a nucleic acid sample. FIG. 2D is a block diagram that schematically illustrates an exemplary method 2200 of detecting one or more single-nucleotide variants or indels in a CYP2A6 gene in a nucleic acid sample. In some embodiments, the method 2200 is implemented on a computer. The method 2200 may be embodied in a set of executable program instructions stored on a computer- readable medium, such as one or more disk drives, of a computing system. For example, the server device 3102 shown in FIGS. 3A and 3B and described in greater detail below can execute a set of executable program instructions to implement the method 2200. When the method 2200 is initiated, the executable program instructions can be loaded into a memory, such as RAM, and executed by one or more processors of a server device 3102. Although the method 2200 is described with respect to the server device 3102 shown in FIG. 3B, the description is illustrative only and is not intended to be limiting. In some embodiments, the method 2200 or portions thereof may be performed serially or in parallel by multiple computing systems. Determining a Consensus Copy Number

[0113] As shown in FIG. 2D, the method 2200 for detecting one or more single- nucleotide variants or indels in a CYP2A6 gene in a nucleic acid sample may start from start block 2210. The method 2200 may proceed to block 2220, wherein a consensus copy number for a target region of a CYP2A6 gene in a human genome in the nucleic acid sample is determined.

[0114] For example, the target region may be one of the target regions previously described with respect to block 250 of the method 200. In some embodiments, the target region includes one or more sites of one or more single-nucleotide variants or indels in the CYP2A6 gene.

[0115] In some embodiments, a consensus copy number for the target region is determined as previously described with respect to block 250 of the method 200. For example, in some embodiments, the target region includes one or more pre-determined differentiating sites of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample. The one or more pre-determined differentiating sites may be the pre-determined differentiating sites previously described with respect to block 240 of the method 200. For example, in some embodiments, determining a consensus copy number for a target region of a CYP2A6 gene comprises, for one or more pre-determined differentiating sites, estimating a copy number of a CYP2A6-specific base as previously described with respect to process block 240 of the method 200. For example, in some embodiments, estimating a copy number of a CYP2A6-specific base comprises, for each pre-determined differentiating site, counting sequence reads which include a CYP2A6-specific base at the pre-determined differentiating site. Obtaining Sequence Reads which Align to a Site of a Single-Nucleotide Variant or Indel

[0116] In some embodiments, the method 2200 may proceed to block 2230, wherein sequence reads which align to a site of a single-nucleotide variant or indel in the CYP2A6 gene are obtained. This may be performed similar to as described with respect to block 220 of the method 200. For example, sequence reads may be aligned to a reference sequence over the site of the single-nucleotide variant or indel.

[0117] In some embodiments, the one or more single-nucleotide variants or indels are selected from the table below, which lists genomic coordinates in reference genome HG38. The genomic coordinates may vary based on the reference genome being used to define them. Accordingly, the sites of the one or more single-nucleotide variants or indels may include equivalent genomic coordinates in other reference genomes. Table 2 CYP2A6 CYP2A6 CYP2A7 CYP2A7 Amino Acid Variant Position Reference Position Reference Change Base Basechr19 40843827 A 40875724 C,G R485L 6600G>T chr19 40843845 A 40875742 C G479V 6582G>T chr19 40843854 C 40875751 T K476R 6573A>G chr19 40843869 G 40875766 A I471T 6558T>C chr19 40843896 G 40875793 A L462P 6531T>C chr19 40843969 A 40875866 T N438Y 6458A>T chr19 40844677 G 40876573 C E419D 5750G>C chr19 40844682 C 40876578 T N418D 5745A>G chr19 40844759 A 40876655 T Y392F 5668A>T chr19 40844766 T 40876662 C,G E390K 5661G>A chr19 40845362 T 40877258 C V365M 5065G>A chr19 40845404 G 40877300 A Y351H 5023A>G chr19 40846020 A 40877916 G T303I 4406C>T chr19 40846903 G 40878788 A I268T 3524T>C chr19 40846912 T 40878797 C R265Q 3515G>A chr19 40847036 G 40878921 A S224P 3391T>C chr19 40848263 TTG 40880128 TGCG R203 2161- Frameshift 2163GC>A chr19 40848266 A 40880131 G,C,T R203C 2161C>T chr19 40848266 T 40880131 G,A,C R203S 2161C>A chr19 40848284 CTG 40880149 CTTTG K196 2141- Frameshift 2142delAA chr19 40848293 C 40880158 T K194E 2134A>G chr19 40848628 T 40880493 A L160H 1799T>A chr19 40848623 CCCGGAT 40880488 CCCGGAG L160I 1798C>A chr19 40848660 C 40880525 G I149M 1767C>G chr19 40848716 C 40880581 A S131A 1711T>G chr19 40848724 A 40880589 C,T R128L 1703G>T chr19 40848724 T 40880589 C,A R128Q 1703G>A chr19 40848755 G 40880620 A F118L 1672T>Cchr19 40849833 G 40881604 C V110L 594G>C chr19 40849959 T 40881730 C V68M 468G>A chr19 40850341 T 40882125 C S29N 86G>A chr19 40850411 G 40882195 T M6L 16A>C chr19 40850414 T 40882198 C G5R 13G>A chr19 40850474 C 40882258 A TATABox -48T>G chr19 40849860 A 40881631 G R101X 567C>T chr19 40846030 G 40877926 A I300T 4397T>C chr19 40844725 C 40876621 A S403R 5702T>G chr19 40843915 T 40875812 C V456I 6512G>A Counting Sequence Reads at the Site

[0118] In some embodiments, the method 2200 may proceed to block 2240, wherein sequence reads which contain a base corresponding to a variant allele at the site are counted. Next, the method 2200 may proceed to block 2250, wherein sequence reads which contain a base corresponding to a reference allele at the site are counted.

[0119] In some embodiments, at block 2240 and block 2250, the methods and systems count sequence reads at the site of a single-nucleotide variant or indel. In some embodiments, at block 2240 and at block 2250, sequence information (such as a nucleotide base) are assessed at the site of a single-nucleotide variant or indel. For example, sequence reads which include a basecall corresponding to a variant allele may be counted at block 2240. For example, sequence reads which include a basecall corresponding to a reference allele may be counted at block 2250. Determining a Probability that the Nucleic Acid Sample Includes a Variant Allele at the Site

[0120] The method 2200 may proceed to block 2260, wherein a probability that the nucleic acid sample includes a variant allele at the site is determined based on the count of sequence reads which contain a base corresponding to a variant allele, the count of sequence reads which contain a base corresponding to a reference allele, and the consensus copy number for the target region. For example, in some embodiments, at block 2260, the methods and systems assess the proportion of sequence reads which include sequence information corresponding to a variant allele at the site as compared to a reference allele.

[0121] In some embodiments, at block 2260, the consensus copy number of the region containing the variant position is used as the expected total copy number in a Poisson distribution that determines the probability that the variant allele is present based on the count of sequence reads which contain a base corresponding to the variant allele, and based on the count of sequence reads which contain a base corresponding to the reference allele.

[0122] For example, for each small variant, the methods and systems may obtain the consensus copy number for the target region including the site of the small variant. Then the methods and systems may determine a probability of a variant at the site with a Poisson model using the consensus copy number, the count of reads including a base corresponding to a variant allele at the site, and the count of reads including a base corresponding to a reference allele at the site.

[0123] For example, at block 2260, the methods and systems may estimate a copy number of a variant allele at the site. This may be performed similarly to the method of estimating a copy number at pre-differentiating sites at block 2140 of FIG. 2C. For example, the methods and systems may determine an expected depth for every possible copy number of a variant allele being considered. For example, the most probable copy number of the variant allele may be determined based on the posterior probabilities. In some embodiments, when the estimated variant allele copy number is not 0, the variant is predicted to be in the sample.

[0124] In some embodiments, the copy number calculation uses a minimum read cut off. In other words, in some embodiments, the methods and systems will estimate a copy number of 0 for the variant allele if there are fewer than a pre-determined minimum number of sequence reads which include a base corresponding to a variant allele at the site, and if there are greater than the pre-determined minimum number of sequence reads which include a base corresponding to a reference allele at the site. In some embodiments, the minimum read cut off number is 2. A minimum read cut off number may be higher or lower depending on the specific small variant. For example, for the small variant "6582G>T", the cut off number may be 7.

[0125] The method 2200 may proceed to decision state 2270, wherein the system may decide whether there are any sites of a single-nucleotide variant or indel in a CYP2A6 gene to be analyzed. If there is an additional site, the method 2200 may return to block 2230 and the method 2200 may proceed for the additional site as previously described. If there are no additional sites to be analyzed, the method 2200 may end at block 2280.Embodiments of Methods and Systems for Determining One or More Star Alleles

[0126] In another aspect, disclosed herein are methods and systems for determining one or more star alleles in a CYP2A6 gene in a nucleic acid sample. FIG.2E is a block diagram that schematically illustrates an exemplary method 2300 of determining one or more star alleles n a CYP2A6 gene in a nucleic acid sample. In some embodiments, the method 2300 is implemented on a computer. The method 2300 may be embodied in a set of executable program instructions stored on a computer-readable medium, such as one or more disk drives, of a computing system. For example, the server device 3102 shown in FIGS. 3A and 3B and described in greater detail below can execute a set of executable program instructions to implement the method 2300. When the method 2300 is initiated, the executable program instructions can be loaded into a memory, such as RAM, and executed by one or more processors of a server device 3102. Although the method 2300 is described with respect to the server device 3102 shown in FIG.3B, the description is illustrative only and is not intended to be limiting. In some embodiments, the method 2300 or portions thereof may be performed serially or in parallel by multiple computing systems. Detecting One or More Structural Variants

[0127] As shown in FIG. 2E, the method 2300 for determining one or more star alleles in a CYP2A6 gene in a nucleic acid sample may start from start block 2310. The method 2300 may proceed to block 2320, wherein one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample is detected.

[0128] In some embodiments, at block 2320, one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample is detected as described with respect to the method 200. For example, sequence reads may be obtained from the nucleic acid sample which align to a CYP2A6 gene or a CYP2A7 pseudogene in a human genome within the nucleic acid sample; a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample may be estimated; a copy number of a CYP2A6-specific base at one or more pre-determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene may be estimated; one or more consensus copy numbers for one or more target regions of the CYP2A6 gene may be determined based on the estimated copy number of the CYP2A6-specific base at the one or more pre-determined differentiating sites; and one or morestructural variants in the CYP2A6 gene or the CYP2A7 pseudogene may be detected based on the one or more consensus copy numbers, as further described herein. Detecting One or More Single Nucleotide Variants or Indels in the CYP2A6 Gene

[0129] The method 2300 may then proceed to process block 2330, wherein one or more single-nucleotide variants or indels in the CYP2A6 gene in the nucleic acid sample are detected. FIG. 2F is a block diagram that further illustrates the methods taking place within the process block 2230 described above, wherein one or more single-nucleotide variants or indels in the CYP2A6 gene in the nucleic acid sample are detected. The method of process block 2330 may start from process block 2410.

[0130] The method of process block 2330 may proceed to block 2420, wherein sequence reads which align to a site of a single-nucleotide variant or indel in a CYP2A6 gene of a human genome in the nucleic acid sample are obtained. This may be performed similar to as described with respect to block 2230 of the method 2200.

[0131] The method of process block 2330 may proceed to block 2430, wherein sequence reads which contain a base corresponding to a variant allele at a site of a single- nucleotide variant or indel are counted. This may be performed similarly to as described with respect to block 2240

[0132] The method of process block 2330 may proceed to block 2440, wherein sequence reads which contain a base corresponding to a reference allele at the site are counted. This may be performed similar to as described with respect to block 2250.

[0133] The method of process block 2330 may proceed to block 2450, wherein a probability that the nucleic acid sample includes a variant allele at the site may be determined. This may be performed similar to as described with respect to block 2260. For example, the probability may be determined based on the count of sequence reads which contain a base corresponding to a variant allele, the count of sequence reads which contain a base corresponding to a reference allele, and the consensus copy number for a target region which includes the site of the single-nucleotide variant or indel.

[0134] The target region may be a target region of the one or more target regions described with respect to block 2320 of the method 2300. Accordingly, the consensus copy number for a target region which includes the site of the single-nucleotide variant or indel maybe a consensus copy number determined for a target region of the one or more target regions described with respect to block 2320 of the method 2300.

[0135] The method of process block 2330 may proceed to decision state 2460, wherein the system may decide whether there are any sites of a single-nucleotide variant or indel in a CYP2A6 gene to be analyzed. If there is an additional site, the method of process block 2330 may return to block 2420 and the method of process block 2330 may proceed for the additional site as previously described. If there are no additional sites to be analyzed, the method of process block 2330 may end at block 2470. Determining One or More Star Alleles in the CYP2A6 Gene

[0136] The method 2300 may proceed to process block 2340, wherein one or more one or more star alleles in the CYP2A6 gene in the nucleic acid sample are determined. In some embodiments, the one or more star alleles are determined based on the one or more detected structural variants and / or based on the one or more detected single-nucleotide variants or indels in the CYP2A6 gene.

[0137] For example, the combination of detected structural variant(s) and detected single-nucleotide variant(s) or indel(s) may be matched against a predetermined definition of a particular star allele, for example publicly available definitions of star alleles, such as those available from databases such as PharmVar (available at https: / / www.pharmvar.org / gene / CYP2A6).

[0138] In some embodiments, a star allele may be detected based on one or more detected single-nucleotide variants or indels without having any detected structural variants, such as based on one or more detected single-nucleotide variants or indels alone. In some embodiments, a star allele may be detected based on one or more detected structural variants without having any detected single-nucleotide variants or indels, such as based on one or more detected structural variants alone. In some embodiments, a star allele may be detected based on an absence of any detected single-nucleotide variants or indels and / or an absence of any detected structural variants.

[0139] In some embodiments, determining one or more star alleles includes phasing the one or more detected single-nucleotide variants or indels and / or the one or more detected structural variants in the CYP2A6 gene. For example, some star allele diplotypes (for example, diplotypes *1 / *36 and *7 / *35, and diplotypes *11 / *10 and *7 / *8) may share a variantprofile, such that these pairs of diplotypes may share the same combination of detected structural and small variants. In some embodiments, these pairs of diplotypes may be differentiated from each other by the phase of the detected variants.

[0140] For example, the methods and systems may detect that a diploid sample includes variant alleles A and B. Without phasing, it may be ambiguous whether the sample has a diplotype corresponding to (No variant) / (A + B), or to A / B. For example, star allele *1 has no detected variants, while star allele *36 includes the variants 6558T>C and 6458A>T. Diplotype *7 / *35 has the same combination of detected variants, but star allele *7 has 6558T>C while star allele *35 has (6458A>T). As another example, diplotype *1 (no variant) / *10 (6558T>C,6600G>T) may be confused with *7(6558T>C) / *8(6600G>T), unless phase of the variants is determined.

[0141] Accordingly, in some embodiments, the methods and systems determine the phase of the one or more detected single-nucleotide variants or indels and / or the one or more detected structural variants in the CYP2A6 gene. To determine the star allele diplotype, in some embodiments, the methods and systems phase the one or more structural variants by counting sequence reads which include two variant alleles in the same sequence read.

[0142] In some embodiments, phasing comprises counting sequence reads which include a first base corresponding to a first variant allele at a first site, and a second base corresponding to a second variant allele at a second site. For example, in order to differentiate whether the variants occur on the same allele or not, the bases at two or more sites of each sequence read covering two or more sites of a variant (such as a single-nucleotide variant or indel) may be counted. For example, for the haplotype 6600G>T_6558T>C; the methods and systems may count the frequency of each base present in each sequence read for site 1 (chr19:40843827) and site 2 (chr19:40843869) for gene 1, and site 1 (chr19:40875724) and site 2 (chr19:40875766) in gene 2. The methods and systems may the asses the frequency that two variants in the haplotype (6600G>T_6558T>C) occur on the same sequence read. If it occurs on the same read then the systems and methods may determine that the one or more star alleles are *1 / *36 or *1 / *10, instead of a *7 / *35 or a *7 / *8 respectively.

[0143] In some embodiments, the systems and methods may determine a star allele diplotype of a CYP2A6 gene in a nucleic acid sample. For example, the systems and methodsmay determine two star alleles of the CYP2A6 gene in a nucleic acid sample according to the methods and systems described.

[0144] In some embodiments, the methods and systems may create a digital file including information related to a structural variant, a single-nucleotide variant or indel, a star allele, a star allele diplotype, or any combination of these.

[0145] The method 2300 may end at block 2350. Embodiments of Sequencing Systems

[0146] FIG. 3A illustrates a diagram of an environment in which a copy number detection system can operate in accordance with one or more implementations. The following paragraphs describe the copy number detection system with respect to illustrative figures that portray example implementations and embodiments. For example, FIG. 3A illustrates a schematic diagram of a computing system 3000 in which a structural variant detection application 3106 operates in accordance with one or more implementations. As illustrated, the computing system 3000 includes one or more server device(s) 3102 connected to a user client device 3108, a local device 3118, and a sequencing device 3114 via a network 3112. The network 3112 can comprise any suitable network over which computing devices can communicate.

[0147] As shown in FIG. 3A, the computing system 3000 includes the server device(s) 3102. In various implementations, the server device(s) 3102 may generate, receive, analyze, store, and transmit digital data, such as data for nucleobase calls or sequenced nucleic- acid polymers. In some implementations, the server device(s) 3102 receive various data from the sequencing device 3114, such as data from a sample genome and / or sequence reads. The server device(s) 3102 may also communicate with the user client device 3108. In particular, the server device(s) 3102 can send data for sequence reads, direct nucleobase calls, nucleobase calls, and / or sequencing metrics to the user client device 3108.

[0148] As shown, the server device(s) 3102 includes a sequencing application 3110. In general, the sequencing application 3110 analyzes the data (such as call data) received from the sequencing device 3114 or elsewhere to determine nucleobase sequences for nucleic- acid polymers. For example, the sequencing application 3110 can receive raw data from the sequencing device 3114 and determine a nucleobase sequence for a sample genome or anucleic-acid segment. In some implementations, the sequencing application 3110 determines the sequences of nucleobases in DNA and / or RNA segments or oligonucleotides.

[0149] As also shown, the sequencing application 3110 includes the structural variant detection application 3106. As described below, in some embodiments, the structural variant detection application 3106 can detect one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample. For example, in some embodiments, the structural variant detection application 3106 obtains sequence reads from the nucleic acid sample which align to a CYP2A6 gene or a CYP2A7 pseudogene in a human genome within the nucleic acid sample. In some embodiments, the structural variant detection application 3106 estimates a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample. In some embodiments, the structural variant detection application 3106 estimates a copy number of a CYP2A6-specific base at one or more pre-determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene. In some embodiments, the structural variant detection application 3106 determines one or more consensus copy numbers for one or more target regions of the CYP2A6 gene based on the estimated copy number of the CYP2A6-specific base at the one or more pre-determined differentiating sites. In some embodiments, the structural variant detection system 3106 detects one or more structural variants in the CYP2A6 gene or the CYP2A7 pseudogene based on the one or more consensus copy numbers.

[0150] While the sequencing application 3110 has been described as including the structural variant detection application 3106, other systems or methods may be included within the sequencing application 3110, such as a small variant detection system and / or a star allele caller system (not illustrated).

[0151] For example, in some embodiments, the sequencing application 3110 includes a small variant detection application (not illustrated). As described below, in some embodiments, the small variant detection application can detect one or more single-nucleotide variants or indels in a CYP2A6 gene in a nucleic acid sample.

[0152] For example, in some embodiments, the sequencing application 3110 includes a star allele caller application (not illustrated). As described below, in some embodiments, the star allele caller application can determine one or more star alleles in a CYP2A6 gene in a nucleic acid sample.

[0153] Moreover, while the structural variant detection application 3106 is described being implemented on the server device(s) 3102, as part of the sequencing application 3110, in some implementations, the structural variant detection application 3106 is implemented by (such as located entirely or in part) on the user client device 3108, the sequencing device 3114, and / or the local device 3118. As mentioned, in some implementations, the structural variant detection application 3106 is implemented by one or more other components of the computing system 3000, such as the sequencing device 3114. In particular, the structural variant detection application 3106 can be implemented in a variety of different ways across the server device(s) 3102, the network 3112, the user client device 3108, the local device 3118, and the sequencing device 3114.

[0154] As further shown in FIG.3A, the computing system 3000 includes the user client device 3108. In various implementations, the user client device 3108 can generate, store, receive, and send digital data. In particular, the user client device 3108 can receive the data from the sequencing device 3114. As further illustrated, the user client device 3108 includes a sequencing application 3110. The sequencing application 3110 may be a web application or a native application stored and executed on the user client device 3108 (e.g., a mobile application, desktop application, or web application). The sequencing application 3110 can receive data from the sequencing application 3110 and / or structural variant detection application 3106. For example, the user client device 3108 can receive variant call files and / or alignment files from the sequencing application 3110.

[0155] The sequencing application 3110 can also include instructions that (when executed) cause the user client device 3108 to receive data from the structural variant detection application 3106 and present data from the sequencing device 3114 and / or the server device(s) 3102. Furthermore, the sequencing application 3110 can instruct the user client device 3108 to display data for variant calls, such as nucleobase calls or an indication of a copy number variant. Indeed, the user client device 3108 can display nucleobase call results for a genome sample and / or an indication of a predicted copy number variant.

[0156] As further shown in FIG. 3A, the computing system 3000 includes the sequencing device 3114. In various implementations, the sequencing device 3114 can sequence a genomic sample or other nucleic-acid polymer. For example, the sequencing device 3114 analyzes nucleic-acid segments or oligonucleotides extracted from genomic samples togenerate data either directly or indirectly on the sequencing device 3114. More particularly, the sequencing device 3114 receives and analyzes, within nucleotide-sample slides (such as flow cells), nucleic-acid sequences extracted from genomic samples. In one or more implementations, the sequencing device 3114 utilizes SBS to sequence a genomic sample or other nucleic-acid polymers. In addition to, or in the alternative to communicating across the network 3112, in some implementations, the sequencing device 3114 bypasses the network 3112 and communicates directly with the user client device 3108.

[0157] As further depicted in FIG. 3A, in some implementations, the server device(s) 3102 includes a distributed collection of servers, where the server device(s) 3102 include several server devices distributed across the network 3112 and located in the same or different physical locations. For instance, the server device(s) 3102 can be implemented, in whole or in part, on the local device 3118. To illustrate, the local device 3118 may implement the sequencing application 3110 and / or the structural variant detection application 3106. Further, the server device(s) 3102 and / or the local device 3118 can include a content server, an application server, a communication server, a web-hosting server, or another type of server.

[0158] The user client device 3108 illustrated in FIG.3A can include various types of client devices. For example, in some implementations, the user client device 3108 includes non-mobile devices, such as desktop computers or servers, or other types of client devices. In various implementations, the user client device 3108 includes mobile devices, such as laptops, tablets, mobile telephones, or smartphones.

[0159] Though FIG. 3A illustrates the components of the computing system 3000 communicating via the network 3112, in certain implementations, the components of computing system 3000 can also communicate directly with each other, bypassing the network 3112. For instance, in some implementations, the user client device 3108 communicates directly with the sequencing device 3114. Additionally, in some implementations, the user client device 3108 communicates directly with the structural variant detection application 3106 and / or the server device(s) 3102. In some implementations, the user client device 3108 communicates directly with the local device 3118. Moreover, the structural variant detection application 3106 can access one or more databases housed on or accessed by the server device(s) 3102 or elsewhere in the computing system 3000.

[0160] FIG.3B is a block diagram of an exemplary server device 3102 that may be used in connection with the computing system 3000 of FIG. 3A. The server device 3102 may be configured to determine a copy number variant genotype in a nucleic acid sample. The general architecture of the server device 3102 depicted in FIG.3B includes an arrangement of computer hardware and software components. The server device 3102 may include many more (or fewer) elements than those shown in FIG.3B. It is not necessary, however, that all of these generally conventional elements be shown in order to provide an enabling disclosure. As illustrated, the server device 3102 includes a processing unit 310, a network interface 320, a computer readable medium drive 330, an input / output device interface 340, a display 350, and an input device 360, all of which may communicate with one another by way of a communication bus. The network interface 320 may provide connectivity to one or more networks or computing systems. The processing unit 310 may thus receive information and instructions from other computing systems or services via a network. The processing unit 310 may also communicate to and from memory 370 and further provide output information for an optional display 350 via the input / output device interface 340. The input / output device interface 340 may also accept input from the optional input device 360, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device.

[0161] The memory 370 may contain computer program instructions (grouped as modules or components in some embodiments) that the processing unit 310 executes in order to implement one or more embodiments. The memory 370 generally includes RAM, ROM and / or other persistent, auxiliary or non-transitory computer readable media. The memory 370 may store an operating system 372 that provides computer program instructions for use by the processing unit 310 in the general administration and operation of the server device 3102. The memory 370 may store a reference genome 373, such as for use by the sequencing application 3110. The memory 370 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0162] For example, in one embodiment, the memory 370 includes a sequencing application 3110, which may include a structural variant detection application 3106. The structural variant detection application 3106 can perform the methods disclosed herein. In addition, memory 370 may include or communicate with the data store 390 and / or one or moreother data stores that store one or more inputs, one or more outputs, and / or one or more results (including intermediate results) of determining a copy number variant genotype in a nucleic acid sample of the present disclosure, such the sequencing reads, the estimated copy number(s), and the variant call (for example, the detection of a copy number variant) determined.

[0163] In some embodiments, the disclosed systems and methods may involve approaches for shifting or distributing certain sequence data analysis features and sequence data storage to a cloud computing environment or cloud-based network. User interaction with sequencing data, genome data, or other types of biological data may be mediated via a central hub that stores and controls access to various interactions with the data. In some embodiments, the cloud computing environment may also provide sharing of protocols, analysis methods, libraries, sequence data as well as distributed processing for sequencing, analysis, and reporting. In some embodiments, the cloud computing environment facilitates modification or annotation of sequence data by users. In some embodiments, the systems and methods may be implemented in a computer browser, on-demand or on-line.

[0164] In some embodiments, software written to perform the methods as described herein is stored in some form of computer readable medium, such as memory, CD- ROM, DVD-ROM, memory stick, flash drive, hard drive, SSD hard drive, server, mainframe storage system and the like.

[0165] In some embodiments, the methods may be written in any of various suitable programming languages, for example compiled languages such as C, C#, C++, Fortran, and Java. Other programming languages could be script languages, such as Perl, MatLab, SAS, SPSS, Python, Ruby, Pascal, Delphi, R and PHP. In some embodiments, the methods are written in C, C#, C++, Fortran, Java, Perl, R, Java or Python. In some embodiments, the method may be an independent application with data input and data display modules. Alternatively, the method may be a computer software product and may include classes wherein distributed objects comprise applications including computational methods as described herein.

[0166] In some embodiments, the methods may be incorporated into pre-existing data analysis software, such as that found on sequencing instruments. Software comprising computer implemented methods as described herein are installed either onto a computer system directly, or are indirectly held on a computer readable medium and loaded as needed onto acomputer system. Further, the methods may be located on computers that are remote to where the data is being produced, such as software found on servers and the like that are maintained in another location relative to where the data is being produced, such as that provided by a third party service provider.

[0167] An assay instrument, desktop computer, laptop computer, or server which may contain a processor in operational communication with accessible memory comprising instructions for implementation of systems and methods. In some embodiments, a desktop computer or a laptop computer is in operational communication with one or more computer readable storage media or devices and / or outputting devices. An assay instrument, desktop computer and a laptop computer may operate under a number of different computer based operational languages, such as those utilized by Apple based computer systems or PC based computer systems. An assay instrument, desktop and / or laptop computers and / or server system may further provide a computer interface for creating or modifying experimental definitions and / or conditions, viewing data results and monitoring experimental progress. In some embodiments, an outputting device may be a graphic user interface such as a computer monitor or a computer screen, a printer, a hand-held device such as a personal digital assistant (i.e., PDA, Blackberry, iPhone), a tablet computer (such as iPAD), a hard drive, a server, a memory stick, a flash drive and the like.

[0168] A computer readable storage device or medium may be any device such as a server, a mainframe, a supercomputer, a magnetic tape system and the like. In some embodiments, a storage device may be located onsite in a location proximate to the assay instrument, for example adjacent to or in close proximity to, an assay instrument. For example, a storage device may be located in the same room, in the same building, in an adjacent building, on the same floor in a building, on different floors in a building, etc. in relation to the assay instrument. In some embodiments, a storage device may be located off-site, or distal, to the assay instrument. For example, a storage device may be located in a different part of a city, in a different city, in a different state, in a different country, etc. relative to the assay instrument. In embodiments where a storage device is located distal to the assay instrument, communication between the assay instrument and one or more of a desktop, laptop, or server is typically via Internet connection, either wireless or by a network cable through an access point. In some embodiments, a storage device may be maintained and managed by theindividual or entity directly associated with an assay instrument, whereas in other embodiments a storage device may be maintained and managed by a third party, typically at a distal location to the individual or entity associated with an assay instrument. In embodiments as described herein, an outputting device may be any device for visualizing data.

[0169] An assay instrument, desktop, laptop and / or server system may be used itself to store and / or retrieve computer implemented software programs incorporating computer code for performing and implementing computational methods as described herein, data for use in the implementation of the computational methods, and the like. One or more of an assay instrument, desktop, laptop and / or server may comprise one or more computer readable storage media for storing and / or retrieving software programs incorporating computer code for performing and implementing computational methods as described herein, data for use in the implementation of the computational methods, and the like. Computer readable storage media may include, but is not limited to, one or more of a hard drive, a SSD hard drive, a CD-ROM drive, a DVD-ROM drive, a floppy disk, a tape, a flash memory stick or card, and the like. Further, a network including the Internet may be the computer readable storage media. In some embodiments, computer readable storage media refers to computational resource storage accessible by a computer network via the Internet or a company network offered by a service provider rather than, for example, from a local desktop or laptop computer at a distal location to the assay instrument.

[0170] In some embodiments, computer readable storage media for storing and / or retrieving computer implemented software programs incorporating computer code for performing and implementing computational methods as described herein, data for use in the implementation of the computational methods, and the like, is operated and maintained by a service provider in operational communication with an assay instrument, desktop, laptop and / or server system via an Internet connection or network connection.

[0171] In some embodiments, a hardware platform for providing a computational environment comprises a processor (i.e., CPU) wherein processor time and memory layout such as random access memory (i.e., RAM) are systems considerations. For example, smaller computer systems offer inexpensive, fast processors and large memory and storage capabilities. In some embodiments, graphics processing units (GPUs) can be used. In some embodiments, hardware platforms for performing computational methods as described hereincomprise one or more computer systems with one or more processors. In some embodiments, smaller computer are clustered together to yield a supercomputer network.

[0172] In some embodiments, computational methods as described herein are carried out on a collection of inter- or intra-connected computer systems (i.e., grid technology) which may run a variety of operating systems in a coordinated manner. For example, the CONDOR framework (University of Wisconsin-Madison) and systems available through United Devices are exemplary of the coordination of multiple stand-alone computer systems for the purpose dealing with large amounts of data. These systems may offer Perl interfaces to submit, monitor and manage large sequence analysis jobs on a cluster in serial or parallel configurations. EXAMPLES

[0173] Some aspects of the embodiments discussed above are disclosed in further detail in the following examples, which are not in any way intended to limit the scope of the present disclosure. Those in the art will appreciate that many other embodiments also fall within the scope of the disclosure, as it is described herein above and in the claims. Example 1

[0174] In the following example, concordance of the methods and systems with a Get-RM dataset was tested. Methods and Systems of Determining Star Alleles

[0175] Samples were tested for structural variants in the CYP2A6 gene or the CYP2A7 pseudogene. Sequence reads were obtained from each sample which align to either the CYP2A6 gene or the CYP2A7 pseudogene. A combined copy number of the CYP2A6 gene and the CYP2A7 pseudogene was estimated by counting sequence reads the CYP2A6 gene or the CYP2A7 pseudogene, including reads with zero mapping quality. The read count was normalized by the length of the region and against a set of 3000 genomic regions of 2000bp expected to be consistently diploid across populations. After GC correction, a Gaussian Mixture Model was then used to infer the most likely combined copy number of CYP2A6 / CYP2A7 genes based on the observed normalized depth signal.

[0176] A copy number of a CYP2A6-specific base and a copy number of a CYP2A7-specific base was estimated at 172 pre-determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene (pre-determined differentiating sites 24-196 of Table 1). Copy number at a site was determined by multiplying the combined copy number with the proportion of reads which include a base corresponding to the CYP2A6 and CYP2A7 allele at each site. A Poisson distribution was determined to estimate gene specific copy numbers at each site.

[0177] A consensus copy number was determined for each of three target regions of the CYP2A6 gene based on the estimated copy number of the CYP2A6-specific base at the pre-determined differentiating sites. The three target regions corresponded to region 1: sites 154-196, region 2: sites 94-153, and region 3: sites 24-93 (of the pre-determined differentiating sites disclosed in Table 1). These target regions coincide with that of the breaks of hybrid star alleles (*12, *34). Region 3 corresponds to exons 1-2, region 2 corresponds to exons 3-4 and region 1 corresponds to exons 5-9.

[0178] Structural variants in the CYP2A6 gene or the CYP2A7 pseudogene were detected based on a copy number variant (cnv) tag including the 3 consensus copy numbers. A cnv tag was determined across the 3 target regions representing the consensus copy number for that region, for example, 2_2_2 specified a sample containing a consensus copy number of 2 for each of the three regions (no structural variant). The cnv tag was used directly to call structural variants (hybrids (2_2_ 1, 2_1_1) deletions (0_0_0, 1_1_1) and duplications (3_3_3)).

[0179] Samples were also tested for single-nucleotide variants or indels (collectively, “small variants”) in the CYP2A6 gene. Variant positions associating with their corresponding star allele were retrieved from PharmVar. Sequence reads which align to a site of each single-nucleotide variant or indel were obtained. For each small variant, sequence reads which contained a base corresponding to a variant allele at the site were counted, and sequence reads which contained a base corresponding to a reference allele at the site were counted. A probability that the sample includes a variant allele at the site was determined based on the count of sequence reads which contained a base corresponding to a variant allele, the count of sequence reads which contained a base corresponding to a reference allele, and the consensus copy number for the target region which includes the site of the small variant. To check thepresence of a variant, the consensus copy number of the region containing the variant position is used as the expected total copy number in a Poisson distribution that determines the probability the variant allele is present based on the counts of reads aligning to the variant as compared to the reference allele using a Poisson distribution.

[0180] A star allele diplotype was determined for each sample. The combination of variants identified in the sample were used to ultimately output the final diplotypes by matching against the PharmVar star allele definitions. Diplotypes *1 / *36 compared to *7 / *35 and diplotypes *11 / *10 compared to *7 / *8 share the same variant profile. In order to differentiate which diplotype is correct in a sample containing these variants, haplotype phasing was conducted across the variants, as the variants were close enough to be present in the same read. To identifying the correct diplotype, the systems and methods determined whether both variants required for *35 and *10 were found on the same read. Assessing Performance

[0181] Performance of the methods and systems was assessed using samples from the CDC Genetic Testing Reference Material program (GeT-RM). Consensus genotypes from two different assays were used: the Affymetrix DMET assay, which covers star alleles *2, *4, *6, *7, *8, *9, *11, *13, *17, *20, *28, and 387FS, and the Agena Bioscience iPLEX ADME PGx Pro assay, which covers star alleles *2, *5, *6, *7, *8, *9, *11, *12, *17, *20, *26, *1X2b, and CNV(*4). Together, these assays could call only 17 of the 47 star alleles. In comparison, the methods and systems described herein were able to call 45 star alleles (not calling newly defined *47 or *46).

[0182] The methods and systems described herein were also benchmarked against calls from Stargazer. Although there were 137 GeT-RM samples with results from at least one assay, Stargazer only reported results from 70 GeT-RM samples. The methods and systems described herein disagreed with Stargazer for just 4 of these samples (6%). For two of these samples, the GeT-RM consensus call corroborated with our caller’s call (a *9 in HG00436 and a reference call in NA07055). The other two samples contained star alleles not covered by GeT-RM. Overall, Stargazer did not identify variants pertaining to a *9 star allele (HG00436), *24 (NA19174) and *16 (NA19147). Stargazer incorrectly predicted a *22 allele in one sample (NA07055).

[0183] In total, 19 of the 137 GeT-RM samples were predicted by the methods and systems described herein to contain star alleles not callable via either GeT-RM assay. Amongst the 82 samples where there was a callable consensus call from both GeT-RM assays the methods and systems described herein had a concordance of 96%. GeT-RM miscalled 3 samples: it missed a heterozygous *4 deletion in 2 samples and a *7 allele in two samples. GeT-RM noted that one of the assays (Affymetrix) are not able to detect heterozygous *4 alleles. Amongst the 36 non-consensus samples where there was a callable allele from the Agena Bioscience assay, the methods and systems described herein had a concordance of 88%. This was due to four samples with discrepant calls, two missing a *7 call, a missed heterozygous *4 and a missed hybrid allele (*12).

[0184] In conclusion, the methods and systems described herein had high accuracy when compared to a benchmark data set (Get-RM). Example 2

[0185] In the following example, concordance with orthogonal sequencing technology was tested.

[0186] To further test the accuracy of the methods and systems described herein, called star allele calls in a cohort of 259 samples were compared with results from an orthogonal long read technology (PacBio).

[0187] Star alleles were determined using the “Methods and Systems of Determining Star Alleles” described in Example 1. Star allele calls from long read data were made by assessing SNPs, indels, and structural variants determined by running dipcall (a reference-based variant calling pipeline for a pair of phased haplotype assemblies) on local phased assemblies. The methods and systems described herein achieved concordance level of 99% with the orthogonal technology. Example 3

[0188] In the following example, consistency with Mendelian inheritance was assessed.

[0189] To further validate the methods and systems described herein, a Mendelian consistency assessment was conducted using trios from the 1000 Genome Project (“1kGP”)dataset. Star alleles were determined using the “Methods and Systems of Determining Star Alleles” described in Example 1. A family was said to be Mendelian consistent if the child’s diplotype could be formed from a combination of the parents. This check was a more stringent assessment of caller performance, especially for alleles not able to be assessed with GeT-RM.

[0190] Of the 577 families, the methods and systems described herein were able to call star allele diplotypes for all three family members for 554 trios. The methods and systems described herein achieved a Mendelian consistency level of 96.5% within these families. This check further supported the overall accuracy of the methods and systems described herein. Other Considerations

[0191] The embodiments described herein are exemplary. Modifications, rearrangements, substitute processes, etc. may be made to these embodiments and still be encompassed within the teachings set forth herein. One or more of the steps, processes, or methods described herein may be carried out by one or more processing and / or digital devices, suitably programmed.

[0192] The various illustrative imaging or data processing techniques described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. The described functionality can be implemented in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosure.

[0193] The various illustrative detection systems described in connection with the embodiments disclosed herein can be implemented or performed by a machine, such as a processor configured with specific instructions, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor can be a microprocessor, but in the alternative, the processor can be a controller, microcontroller,or state machine, combinations of the same, or the like. A processor can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. For example, systems described herein may be implemented using a discrete memory chip, a portion of memory in a microprocessor, flash, EPROM, or other types of memory.

[0194] The elements of a method, process, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of computer-readable storage medium known in the art. An exemplary storage medium can be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. A software module can comprise computer-executable instructions which cause a hardware processor to execute the computer- executable instructions.

[0195] Conditional language used herein, such as, among others, “can,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and / or states. Thus, such conditional language is not generally intended to imply that features, elements and / or states are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and / or states are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” “involving,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list.

[0196] Disjunctive language such as the phrase “at least one of X, Y or Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y or Z, or any combination thereof (such as X, Y and / or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y or at least one of Z to each be present.

[0197] The terms “about” or “approximate” and the like are synonymous and are used to indicate that the value modified by the term has an understood range associated with it, where the range can be ±20%, ±15%, ±10%, ±5%, or ±1%. The term “substantially” is used to indicate that a result (such as a measurement value) is close to a targeted value, where close can mean, for example, the result is within 80% of the value, within 90% of the value, within 95% of the value, or within 99% of the value.

[0198] Unless otherwise explicitly stated, articles such as “a” or “an” should generally be interpreted to include one or more described items. Accordingly, phrases such as “a device configured to” or “a device to” are intended to include one or more recited devices. Such one or more recited devices can also be collectively configured to carry out the stated recitations. For example, “a processor to carry out recitations A, B and C” can include a first processor configured to carry out recitation A working in conjunction with a second processor configured to carry out recitations B and C.

[0199] While the above detailed description has shown, described, and pointed out novel features as applied to illustrative embodiments, it will be understood that various omissions, substitutions, and changes in the form and details of the devices or algorithms illustrated can be made without departing from the spirit of the disclosure. As will be recognized, certain embodiments described herein can be embodied within a form that does not provide all of the features and benefits set forth herein, as some features can be used or practiced separately from others. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

[0200] It should be appreciated that all combinations of the foregoing concepts (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subjectmatter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein.

[0201] The scope of the present disclosure is not intended to be limited by the specific disclosures of examples in this section or elsewhere in this specification, and may be defined by claims as presented in this section or elsewhere in this specification or as presented in the future. The language of the claims is to be interpreted broadly based on the language employed in the claims and not limited to the examples described in the present specification or during the prosecution of the application, which examples are to be construed as non- exclusive.

Claims

WHAT IS CLAIMED IS:

1. A computer-implemented method of detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample, the method comprising: obtaining sequence reads from the nucleic acid sample which align to a CYP2A6 gene or a CYP2A7 pseudogene in a human genome within the nucleic acid sample; estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample; estimating a copy number of a CYP2A6-specific base at one or more pre- determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene; determining one or more consensus copy numbers for one or more target regions of the CYP2A6 gene based on the estimated copy number of the CYP2A6- specific base at the one or more pre-determined differentiating sites; and detecting one or more structural variants in the CYP2A6 gene or the CYP2A7 pseudogene based on the one or more consensus copy numbers.

2. The method of claim 1, wherein estimating a copy number of a CYP2A6- specific base comprises, for each pre-determined differentiating site, counting sequence reads which include a CYP2A6-specific base at the pre-determined differentiating site, and counting sequence reads which include a CYP2A7-specific base at the pre-determined differentiating site.

3. The method of claim 1 or claim 2, wherein the one or more pre-determined differentiating sites comprises a site from the pre-determined differentiating sites of Table 1.

4. The method of any of claims 1-3, wherein the one or more target regions comprise a region corresponding to exons 5-9 of the CYP2A6 gene.

5. The method of any of claims 1-4, wherein the one or more target regions comprise a region corresponding to sites 154-196 from the pre-determined differentiating sites of Table 1.

6. The method of any of claims 1-5, wherein the one or more target regions comprise a region corresponding to exons 3-4 of the CYP2A6 gene.

7. The method of any of claims 1-6, wherein the one or more target regions comprise a region corresponding to sites 94-153 from the pre-determined differentiating sites of Table 1.

8. The method of any of claims 1-7, wherein the one or more target regions comprise a region corresponding to exons 1-2 of the CYP2A6 gene.

9. The method of any of claims 1-8, wherein the one or more target regions comprise a region corresponding to sites 24-93 from the pre-determined differentiating sites of Table 1.

10. The method of any of claims 1-9, wherein the method comprises: determining a first consensus copy number for a first target region based on estimated copy numbers of CYP2A6-specific bases at a first plurality of pre- determined differentiating sites; determining a second consensus copy number for a second target region based on estimated copy numbers of CYP2A6-specific bases at a second plurality of pre- determined differentiating sites; determining a third consensus copy number for a third target based on estimated copy numbers of CYP2A6-specific bases at a third plurality of pre- determined differentiating sites; and detecting one or more structural variants in the CYP2A6 gene based on the first, second, and third consensus copy numbers.

11. The method of any of claims 1-10, wherein estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene comprises counting sequence reads which align to a CYP2A6 gene or a CYP2A7 pseudogene in a human genome within the nucleic acid sample.

12. The method of claim 11, wherein estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene further comprises: counting sequence reads which align to diploid regions in a human genome within the nucleic acid sample; and normalizing the count of the sequence reads which align to a CYP2A6 gene or a CYP2A7 pseudogene.

13. The method of claim 12, wherein estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene further comprises binning the normalized count of the sequence reads which align to a CYP2A6 gene or a CYP2A7 pseudogene using a Gaussian mixture model.

14. A method of detecting one or more single-nucleotide variants or insertions / deletions (indels) in a CYP2A6 gene in a nucleic acid sample, the method comprising: determining a consensus copy number for a target region of a CYP2A6 gene in a human genome in the nucleic acid sample, wherein the target region includes a site of a single-nucleotide variant or indel in the CYP2A6 gene; and obtaining sequence reads which align to the site of the single-nucleotide variant or indel in the CYP2A6 gene; counting sequence reads which contain a base corresponding to a variant allele at the site, and counting sequence reads which contain a base corresponding to a reference allele at the site; and determining a probability that the nucleic acid sample includes a variant allele at the site based on the count of sequence reads which contain a base corresponding to a variant allele, the count of sequence reads which contain a base corresponding to a reference allele, and the consensus copy number for the target region.

15. The method of claim 14, wherein the one or more single-nucleotide variants or indels comprise a single-nucleotide variant or indel listed in Table 2.

16. The method of claim 14 or claim 15, wherein determining the probability that the nucleic acid sample includes a variant allele at the site comprises determining a Poisson distribution.

17. The method of any of claims 14-16, wherein the target region includes one or more pre-determined differentiating sites of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample, and wherein determining a consensus copy number for a target region of a CYP2A6 gene comprises, for one or more pre-determined differentiating sites, estimating a copy number of a CYP2A6-specific base.

18. The method of claims 17, wherein estimating a copy number of a CYP2A6- specific base comprises, for each pre-determined differentiating site, counting sequence readswhich include a CYP2A6-specific base at the pre-determined differentiating site, and counting sequence reads which include a CYP2A7-specific base at the pre-determined differentiating site.

19. The method of claim 17 or claim 1814, wherein the plurality of pre-determined differentiating sites comprises a site from the pre-determined differentiating sites of Table 1.

20. The method of any of claims 14-19, wherein the target region corresponds to exons 1-2, exons 3-4, or exons 5-9 of the CYP2A6 gene.

21. The method of claim 20, wherein the target region comprises a region corresponding to sites 154-196, sites 94-153, or sites 24-93 from the pre-determined differentiating sites of Table 1.

22. A method of determining one or more alleles in a CYP2A6 gene in a nucleic acid sample, the method comprising: detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample according to the method of any of claims 1-10; detecting one or more single-nucleotide variants or indels in the CYP2A6 gene in the nucleic acid sample, and determining one or more alleles in the CYP2A6 gene in the nucleic acid sample based on the one or more detected structural variants and based on the one or more detected single-nucleotide variants or indels in the CYP2A6 gene.

23. The method of claim 22, wherein detecting one or more single-nucleotide variants or indels in the CYP2A6 gene comprises: obtaining sequence reads which align to a site of a single-nucleotide variant or indel in a CYP2A6 gene of a human genome in the nucleic acid sample; counting sequence reads which contain a base corresponding to a variant allele at the site, counting sequence reads which contain a base corresponding to a reference allele at the site; and determining a probability that the nucleic acid sample includes a variant allele at the site based on the count of sequence reads which contain a base corresponding to a variant allele, the count of sequence reads which contain a base corresponding toa reference allele, and the consensus copy number for a target region which includes the site of the single-nucleotide variant or indel.

24. The method of claim 22 or claim 23, wherein the method further comprises phasing the one or more detected single-nucleotide variants or indels in the CYP2A6 gene.

25. The method of claim 24, wherein phasing the one or more detected single- nucleotide variants or indels in the CYP2A6 gene comprises counting sequence reads which include a first base corresponding to a first variant allele at a first site, and a second base corresponding to a second variant allele at a second site.

26. A method of determining an allele diplotype of a CYP2A6 gene in a nucleic acid sample, the method comprising: determining two alleles of the CYP2A6 gene in a nucleic acid sample according to any of claims 22-26.

27. The method of any of claims 1-26, further comprising creating a digital file including information related to a structural variant, a single-nucleotide variant or indel, an allele, a star allele, an allele diplotype or a star allele diplotype.

28. An electronic system for detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample, comprising a processor configured to perform a method comprising: obtaining sequence reads from the nucleic acid sample which align to a CYP2A6 gene or a CYP2A7 pseudogene in a human genome within the nucleic acid sample; estimating a combined copy number of a CYP2A6 gene and a CYP2A7 pseudogene in the nucleic acid sample; estimating a copy number of a CYP2A6-specific base at one or more pre- determined differentiating sites of the CYP2A6 gene and the CYP2A7 pseudogene; determining one or more consensus copy numbers for one or more target regions of the CYP2A6 gene based on the estimated copy number of the CYP2A6- specific base at the one or more pre-determined differentiating sites; and detecting one or more structural variants in the CYP2A6 gene or the CYP2A7 pseudogene based on the one or more consensus copy numbers.

29. An electronic system for detecting one or more single-nucleotide variants or indels in a CYP2A6 gene in a nucleic acid sample, comprising a processor configured to perform a method comprising: determining a consensus copy number for a target region of a CYP2A6 gene in a human genome in the nucleic acid sample, wherein the target region includes the site of one or more single-nucleotide variants or indels in the CYP2A6 gene; obtaining sequence reads which align to a site of a single-nucleotide variant or indel in the CYP2A6 gene; counting sequence reads which contain a base corresponding to a variant allele at the site, and counting sequence reads which contain a base corresponding to a reference allele at the site; and determining a probability that the nucleic acid sample includes a variant allele at the site based on the count of sequence reads which contain a base corresponding to a variant allele, the count of sequence reads which contain a base corresponding to a reference allele, and the consensus copy number for the target region.

30. An electronic system for determining one or more alleles in a CYP2A6 gene in a nucleic acid sample, comprising a processor configured to perform a method comprising: detecting one or more structural variants in a CYP2A6 gene or a CYP2A7 pseudogene in a nucleic acid sample; detecting one or more single-nucleotide variants or indels in the CYP2A6 gene in the nucleic acid sample; and determining one or more alleles in the CYP2A6 gene in the nucleic acid sample based on the detected structural variant and based on the one or more detected single-nucleotide variants or indels in the CYP2A6 gene.

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