Method and system for determining para-line homologous genes

Abstract

Disclosed herein are systems and methods for diagnosing spinal muscular atrophy (SMA) from whole genome sequencing data. In one embodiment, a method includes aligning a whole genome sequencing (WGS) read of an individual sample to a modified reference sequence, such as a modified reference genomic sequence. The method may adjust the coverage and determine the number of functional SMN1 gene copies after counting the reading of the supported quasi-alleles at the selected locations of the reference sequence. The method may determine the affected or carrier status of the individual based on the number of copies of the functional SMN1 gene.

Description

Related Applications

[0002] This application is a divisional application of Chinese patent application No. 201780086093.6, and claims priority to U.S. Provisional Application No. 62 / 434,876 filed on December 15, 2016, the contents of which are expressly incorporated herein by reference in their entirety. Background Art Technical Field

[0003] The present application relates generally to the field of disease diagnostics, and more specifically to the use of genome-wide measurement data to determine affected or carrier status for diseases caused by defective genes with highly similar paralogous genes, such as spinal muscular atrophy.

[0004] Related technical description

[0005] Motor neuron disease (MND) is a group of progressive neurological disorders that destroy motor neurons, cells that control basic voluntary muscle activities such as speaking, walking, breathing and swallowing. Normally, messages from motor nerve cells in the brain (called upper motor neurons) are transmitted to motor nerve cells in the brainstem and spinal cord (called lower motor neurons), and messages from the lower motor neurons are transmitted to specific muscles. Upper motor neurons instruct lower motor neurons to produce movements such as walking or chewing. Lower motor neurons control movements of the arms, legs, chest, face, throat and tongue. Spinal motor neurons are also called anterior horn cells.

[0006] Spinal muscular atrophy (SMA) is an autosomal recessive neuromuscular disorder characterized by motor neuron loss and progressive muscle atrophy, often leading to death at an early age. The disorder is caused by a genetic defect in the SMN1 gene, which encodes the survival motor neuron (SMN) protein, a protein expressed in all eukaryotic cells and required for motor neuron survival. Low levels of this protein lead to functional loss of neuronal cells in the anterior horn of the spinal cord and subsequent system-wide muscle atrophy (dystrophy).

[0007] A person is affected by SMA if they have only a defective copy of the SMN1 gene. A person is a carrier of SMA if one of their chromosomes contains at least one normal copy of the SMN1 gene and at least one of their chromosomes does not contain a normal copy of the SMN1 gene (i.e., has no copies of SMN1 or has only a defective copy of SMN1).

[0008] Small amounts of SMN protein are produced from a gene similar to SMN1 called SMN2. Several different forms of SMN protein are produced from the SMN2 gene, but only one form (called isoform d) is full-size and fully functional. The other forms are smaller and may be easily broken down. The full-size protein produced from the SMN2 gene is the same as that produced from SMN1; however, the SMN2 gene produces much less full-size SMN protein than the SMN1 gene. The SMN1 and SMN2 genes are almost identical and encode the same protein. The sequence difference between the two is a single nucleotide in exon 7, which is thought to be an exonic splicing enhancer. It is thought that a gene conversion event may have involved the two genes, resulting in a sequence swap between SMN1 and SMN2. SUMMARY OF THE INVENTION

[0010] Disclosed herein are systems and methods for diagnosing diseases based on mutations in non-unique portions of the genome. The systems and methods can be used to determine the affected or carrier status of indications such as spinal muscular atrophy (SMA). In one embodiment, the system and method use whole genome sequencing (WGS) data to determine the affected or carrier status. In one embodiment, the method may include: aligning WGS reads with a modified reference genome sequence; counting reads of quasi-allelic genes supported at selected positions of the reference sequence, and adjusting coverage and determining the number of functional SMN1 gene copies. The modified reference genome sequence may be a reference genome sequence in the form of: it has SMN2 bases converted into a string of Ns of equal length (also referred to as a reference genome sequence evacuated from SMN2). The method may further include: determining WGS reads including known inactivating mutations in the SMN1 gene. The method may also include: counting reads of other quasi-allelic genes supported at selected positions; adjusting coverage; and determining the number of copies of the SMN2 gene. The method described herein can be extended to diagnose based on mutations in other non-unique portions of the genome.

[0011] In some embodiments, the system includes: a hardware processor configured to execute computer executable instructions to perform any method disclosed herein; and a data storage device configured to store whole genome measurement data or diagnostic results. In some embodiments, the computer readable medium includes a software program including logic or instructions for performing any method disclosed herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a flow chart showing an exemplary method for aligning whole genome sequencing read data to an SMN2-depleted reference genome for spinal muscular atrophy diagnosis.

[0013] Figure 2 Is used to generate Figure 1 Schematic representation of the relationship between input and output of WGS reads derived from SMN1 or SMN2 aligned to SMN1.

[0014] Figure 3 Is displayed using Figure 1 Flowchart of an exemplary method for aligning whole genome sequencing read data to an SMN2-depleted reference genome sequence.

[0015] Figures 4A-4C Schematically shows the Figure 3 The relationship between inputs and outputs in the diagnosis of spinal muscular atrophy.

[0016] Figure 5A and 5B Schematic illustration of methods for graph-based variant calling, e.g., distinguishing single nucleotide polymorphisms, structural variants, and paralogous genes.

[0017] Figure 6 is a flow chart showing an exemplary graph-based method of determining SMA status.

[0018] Figure 7 An overall architecture of an exemplary computing device configured to perform spinal muscular atrophy diagnosis based on whole genome measurement data is shown.

[0019] Figure 8 is an exemplary plot of the sum of read counts supporting SMN2 versus the sum of read counts supporting SMN1, which can be used to determine SMN1 and SMN2 specific copy numbers. DETAILED DESCRIPTION OF THE INVENTION

[0021] In the following detailed description, reference is made to the accompanying drawings which form a part of this document. In the accompanying drawings, similar symbols generally identify similar components unless the context indicates otherwise. The exemplary embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be adopted and other changes may be made without departing from the spirit or scope of the subject matter presented herein. It is readily understood that the various aspects of the present application as described herein and shown in the accompanying drawings may be arranged, replaced, combined, separated, and designed in a variety of different configurations, all of which are expressly contemplated herein and constitute a part of the disclosure herein.

[0022] All patents, published patent applications, other publications, and sequences from GenBank and other databases mentioned herein are incorporated by reference in their entirety with respect to the relevant art.

[0023] definition

[0024] Unless otherwise defined, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application 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 the purposes of this application, the following terms are defined as follows.

[0025] Overview

[0026] Disclosed herein are systems and methods for diagnosing disease based on mutations in non-unique portions of the genome. The systems and methods can be used to determine the affected or carrier status of an individual for SMA using whole genome sequencing (WGS) data. If the individual has only a defective copy of the SMN1 gene, the individual is affected by SMA. If the individual has at least one chromosome containing at least one normal copy of the SMN1 gene and at least one chromosome without a normal copy of SMN1 (i.e., no SMN1 copy or only a defective copy of SMN1), the individual is a carrier of SMA.

[0027] In one embodiment, the genetic state of an individual can be determined by aligning the WGS reads with a modified reference sequence. The modified reference sequence may include an SMN1 reference sequence (chr5, 70220767-70248842 on the human genome reference sequence hg19 or GRCh37). The modified genomic sequence may have a base of an SMN2 sequence (chr5, 69345350-69373422) converted into a string of Ns of equal length (also referred to as an SMN2 evacuated or masked reference genomic sequence). The mapped WGS reads can then be counted to determine the quasi-alleles at the selected positions of the modified reference sequence. "Quasi-alleles" refer to sequence differences between the mapped WGS reads and the modified reference sequence. The difference may be due to a polymorphism of the SMN gene or due to a difference between the SMN1 and SMN2 genes. The SMN gene refers to the SMN1 gene or the SMN2 gene, and the difference may be due to a polymorphism of the SMN1 gene or the SMN2 gene. The selected position of the modified reference sequence can include the position of the fixed difference between SMN1 and SMN2. The method can then adjust the coverage (average read depth or number of reads per unit length of the genome) and then determine the number of functional SMN1 gene copies based on the counted number of reads supporting the quasi-allele at the selected position of the modified reference sequence. In some embodiments, the method can adjust the coverage by normalizing the coverage depth (i.e., read count) of the whole genome or whole chromosome average of the analyzed sample. Therefore, for the same sample, coverage normalization is performed for other regions of the genome.

[0028] In other embodiments, the method can determine WGS reads containing known inactivating mutations of SMN1 by determining the sequence of WGS reads at known inactivating mutations. The method can also count the number of reads supporting other quasi-alleles at selected positions. The method can then adjust the coverage and then determine the copy number of SMN2 based on the counted number of reads supporting quasi-alleles at selected positions of the modified reference sequence. The methods described herein can be extended to diagnose based on mutations in other non-unique parts of the genome.

[0029] In some embodiments, when paralogous genes (or paralogous exons) are similar enough in the genomic reference sequence to make the read alignment ambiguous, the methods disclosed herein can be used to distinguish paralogous genes. For example, paralogous genes can be SMN1 / 2, DUX4, RPS17, CYP2D6 / 7.

[0030] Alignment of whole genome sequencing reads to a modified reference genome

[0031] Spinal muscular atrophy (SMA) affected or carrier status can be determined based on whole-genome sequencing (WGS) reads. Figure 1 1 is a flow chart showing an exemplary method 100 for aligning WGS read data with a modified reference genome sequence, the modified reference genome sequence being specifically an SMN2 evacuated reference genome sequence. The SMN2 evacuated reference genome sequence is a reference genome sequence in which the sequence of SMN2 is converted into a string of N of equal length. After starting from the start box 104, the method 100 proceeds to box 108. At box 108, the method 100 receives WGS read data of a sample. The sample can be from an individual such as a human individual. WGS is a laboratory process that can determine the complete DNA sequence of an organism's genome at one time, including the organism's chromosomal DNA, as well as the DNA contained in the mitochondria. Technologies for generating WGS include sequencing technologies, such as sequencing by synthesis using MINISEQ, MISEQ, NEXTSEQ, HISEQ, and NOVASEQ sequencing instruments from Illumina, Inc. (San Diego, CA).

[0032] From box 108, method 100 proceeds to box 112, where the method 100 aligns the WGS reads with a reference genome sequence. The reference genome sequence of the human individual can be a human reference genome sequence, such as hg16, hg17, hg18, hg19, or hg38 reference human genome sequences (these reference human genome sequences can be obtained from http: / / hgdownload.cse.ucsc.edu / downloads.html). The method of aligning the WGS reads with the reference genome sequence can use an aligner such as Burrows-Wheeler Aligner (BWA) and iSAAC. Other alignment methods include BarraCUDA, BFAST, BLASTN, BLAT, Bowtie, CASHX, Cloudburst, CUDA-EC, CUSHAW, CUSHAW2, CUSHAW2-GPU, drFAST, ELAND, ERNE, GNUMAP, GEM, GensearchNGS, GMAP and GSNAP, GeneiousAssembler, LAST, MAQ, mrFAST and mrsFAST, MOM, MOSAIK, MPscan, Novoaligh and 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.

[0033] From block 112, method 100 proceeds to block 116, where method 100 selects WGS reads that align to a portion of a reference genome sequence corresponding to an SMN1 or SMN2 gene for further evaluation. WGS reads corresponding to an SMN1 or SMN2 gene may be selected regardless of the confidence of the alignment. The alignment confidence may be represented by an alignment confidence score such as a MAPQ score.

[0034] From box 116, method 100 proceeds to box 120. At box 120, method 100 aligns the WGS reads selected at box 116 with a modified reference sequence (also referred to as re-aligning the WGS reads because after the WGS reads are aligned with the reference sequence, the WGS reads are aligned with the modified reference sequence). At box 120, re-aligning the WGS reads produces reads derived from SMN1 or SMN2 aligned with SMN1. The modified reference sequence can be a version of the reference sequence used in box 112, in which the bases of SMN2 are converted to a string of Ns of equal length. The modified reference sequence can be referred to as an SMN2 evacuated reference sequence. The sequence differences between the mapped WGS reads and the modified reference sequence can be referred to as "quasi-alleles". The differences may be due to polymorphisms of the SMN gene or due to differences between the SMN1 and SMN2 genes. The SMN gene refers to the SMN1 gene or the SMN2 gene, and the differences may be due to polymorphisms of the SMN1 gene or the SMN2 gene. The method 100 ends at block 124 .

[0035] Figure 2 is used to generate Figure 1 Schematic diagram of the relationship between input and output of WGS reads of SMN1 or SMN2 aligned to SMN1 in the reference genome sequence 208. WGS read data 204, including WGS reads, is aligned to the reference genome sequence 208 at box 212. At box 216, WGS reads aligned to SMN1 or SMN2 in the reference genome sequence 208 can be selected to be re-aligned to the SMN2-depleted reference genome sequence 218 at box 220. The re-alignment at box 220 produces reads 224 derived from SMN1 or SMN2 aligned to SMN1.

[0036] Determining Affected and Carrier Status for Spinal Muscular Atrophy

[0037] Figure 3 Is displayed using Figure 1 Flow chart of an exemplary method 300 for diagnosing spinal muscular atrophy using whole genome sequencing read data aligned to a reference genome sequence emptied of SMN2 in . The exemplary method 300 can be performed after performing the method 100 discussed above, such that block 308 occurs after block 120 described above.

[0038] The reads aligned to SMN1 in block 120 can be used to determine the copy number and possible variants in SMN1 and SMN2. For example, aligning the WGS reads to a reference emptied of SMN2 allows for high confidence identification of reads that originate from SMN1 or SMN2. Thus, reads aligned to a highly repetitive portion of SMN1 with a high confidence score are less likely to originate from other regions of the reference sequence. These realigned reads can be used to estimate the total copy number of SMN1 and SMN2 in the genome of an individual, the SMN1-specific copy number, and the SMN2-specific copy number. These realigned reads can also be used to estimate small variations between the SMN1 reference sequence and the copies of SMN1 or SMN2 in the individual whose sequence is being analyzed. Thus, several information about the affected SMA or carrier status information can be obtained.

[0039] Reads aligned to SMN1 on an SMN2-depleted reference can be further processed before diagnosing SMA status.

[0040] After method 300 begins at block 304, method 300 generates "quasi-variant" calls using reads derived from SMN1 or SMN2 aligned to SMN1 for variant calls at block 308. Quasi-variant calls indicate differences from the SMN1 reference sequence. Such quasi-variants may also indicate fixed differences between SMN1 and SMN2 in the sample, polymorphisms, or mutations in SMN1 or SMN2.

[0041] Quasi-variant judgment is the determination of the presence of a sequence in the sample being analyzed that is identifiably similar to the SMN1 reference sequence, but different in detail from the SMN1 reference sequence. While standard variant judgment implies a sequence change at a specific position in the genome, a quasi-variant may mean one of three or more possibilities. These possibilities include: a) a sequence change at a specified position; b) a difference between the indicated position (in SMN1) and the corresponding portion of a highly similar region (SMN2); or c) a change in a highly similar region (SMN2) relative to a reference. These three possibilities correspond to variants in SMN1, differences between SMN1 and SMN2, and variants in SMN2. The phrase "quasi-variant" is ambiguous and not simply "variant."

[0042] From block 308, method 300 proceeds to block 312, where method 300 uses a reference of a fixed difference between SMN1 and SMN2 to count the number of reads that support a known standard allele of interest among reads derived from SMN1 or SMN2 aligned to SMN1.

[0043] From block 312, method 300 proceeds to block 316, where method 300 determines a gene-specific (SMN1 or SMN2) copy number based on the number of reads counted at block 312. The copy number of SMN1 and the copy number of SMN2 can be determined by comparing reads derived from SMN1 or SMN2 aligned to SMN1 to a fixed difference between SMN1 and SMN2.

[0044] Further, gene-specific copy number can be used to identify an individual's affected or carrier status, since a significant majority (approximately 95% of SMA cases and carrier haplotypes) are due to one of two types of changes resulting in the absence of exon 7 in the SMN1 form. This can be due to loss of exon 7 in the SMN1 form (complete deletion or loss of quantity for affected and carriers, respectively), or gene conversion of exon 7, such that the sequence in SMN1 exon 7 matches the SMN2 reference sequence. If the individual has only a defective copy of the SMN1 gene, the individual is affected by SMA. If the individual has at least one chromosome containing at least one normal copy of the SMN1 gene and at least one chromosome without a normal copy of SMN1 (i.e., no copies of SMN1 or only a defective copy of SMN1), the individual is a carrier of SMA (but not affected by SMA).

[0045] The genetics of SMA and current non-whole genome sequencing methods for SMA molecular diagnosis have been described in Prior, TW, et al., Technical standards and guidelines for spinal muscular atrophy testing, Genet Med. 2011 July, 13(7):686-94, the contents of which are incorporated herein in their entirety. In short, there is a critical single base difference between functional SMN1 and SMN2 that falls in exon 7 of the standard transcript of SMN1. A significant majority (approximately 95% of SMA cases and carrier haplotypes) are due to one of two types of changes that can be detected as SMN1 form exon 7 loss (complete deletion or loss of quantity for affected and carriers, respectively). One change is the deletion of all or part of SMN1 including exon 7. The second change is gene conversion that replaces the region including exon 7 of SMN1 with the homologous sequence of SMN2.

[0046] Therefore, the affected state of most affected individuals can be detected as the absence or almost absence (to allow for one or more sequencing errors) of a quasi-allele matching the SMN1 reference base at a specific position of exon 7. This can be determined by examining the variant judgment results of SMN2 evacuation at the relevant position of the SMN1 exon (homozygous judgment for the SMN2-specific quasi-allele, indicating the SMA-affected state) or by testing the read counts supporting the relevant quasi-allele. In some embodiments, testing the read counts supporting the relevant quasi-allele may include: if less than X reads matching the normal SMN1 sequence are observed, the sample is marked as "affected". If more than Y reads matching the normal SMN1 sequence are observed, the sample can be marked as "unaffected". Thresholds X and Y can be determined empirically. Thresholds X and Y can depend on the coverage depth. Alternatively or in addition, thresholds X and Y can be adjusted based on the desired or acceptable accuracy. In some embodiments, the desired or acceptable accuracy can be determined for borderline cases. In some embodiments, the read counts supporting the relevant quasi-allele can be tested based on a probability model. A probability model may be generated based on one or more sequencing errors or haplotype sampling. In some embodiments, population or family based priors may be incorporated into these processes.

[0047] Carrier status can be identified for most carriers by the reduction in the number of reads that can be attributed to SMN1 but not SMN2. It might appear that any or all positional differences in the reference sequences for SMN1 and SMN2 can be used to identify carrier status. However, empirical evaluations have shown that many of these differences reflect errors in the reference sequence or uncommon variants in the individual whose DNA provided the reference sequence, rather than fixed differences between paralogous copies. Therefore, the differential positions of the reference sequences for SMN1 and SMN2 cannot be reliably used to assess SMN1-specific copy number.

[0048] However, examination of a large number of unaffected individuals described in Example 1 below did identify several (>10) quasi-variants near exon 7 that were quasi-heterozygous in almost all samples, with the quasi-alleles matching differences in the reference sequences for SMN1 and SMN2. It is likely that not all variants in all samples are quasi-heterozygous, as there are samples with zero copies of SMN2 or perhaps individuals affected by SMA, which should be expected in the cohort. The read counts supporting the SMN1 quasi-alleles at these positions can be used to infer the number of complete SMN1 copies present in the sample. Similarly, the SMN2 copy number can be determined.

[0049] When determining gene-specific copies to determine affected or carrier status, method 300 can perform one or more methods of improving copy number judgment at box 316. In some embodiments, method 300 can adjust coverage by standardizing the whole genome or whole chromosome average of the analyzed sample for depth of coverage (i.e., reading counts). Therefore, for the same sample, standardize coverage for other regions of the genome. Other methods for improving copy number judgment include GC correction, standardization for control sample groups, or characterizing sequence uniqueness to improve results. GC correction has been described in Benjamini, Y, et al., Summarizing and correcting the GC content bias in high-throughput sequencing, Nucl. Acids Res., 2012, 40(10):e72, doi:10.1093 / nar / gks001, and Miller, CA, et al., ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads, PLoS One., 2011, 6:e16327. doi:10.1371 / journal.pone.0016327; the contents of each of which are incorporated herein by reference in their entirety.

[0050] From box 316, method 300 proceeds to box 320, where method 300 determines known variants based on the quasi-variant calls generated at box 308. Given a list of known variants and a set of quasi-variant calls, the quasi-variant calls can be marked as matching (i.e., consistent) or not matching (inconsistent) the known variants in the list. Not all affected individuals have a zero SMN1-like exon 7, as there are other mutations that disrupt the function of SMN1. Approximately 5% of affected individuals have one haplotype of exon 7 that is missing or gene converted, but other mutations are on another haplotype. Some of these can be identified by the presence of specific known mutations at box 320.

[0051] From box 320, method 300 proceeds to box 324, wherein the method 300 determines new variants based on the quasi-variant judgments generated at box 308. Given a list of known variants and a set of quasi-variant judgments, the quasi-variant judgments can be marked as not matching (i.e., not consistent) with the known variants in the list. These quasi-variant judgments marked as not matching the known variants can be new variants. Approximately 5% of affected individuals have a haplotype with a loss or gene conversion of exon 7, but other mutations are on another haplotype. Some of these may have new or previously uncharacterized mutations that can be identified in the quasi-variants described in box 308 as described above.

[0052] Method 300 proceeds from box 324 to box 328. In box 328, method 300 tests additional known variants by searching for reads containing specific kmers or other methods for genotyping one or more previous variants. Method 300 can determine the match between the target specific known variant and the quasi-variant judgment. If the SMN1 specific copy number is estimated to be 1, and a known or new destructive (quasi) variant is detected, the affected state can be determined as a result of compound heterozygosity. In some embodiments, in addition to single nucleotide variants (SNV) or insertion and deletion (indel) detection, the detection of known or new variants may include the use of structural variant detection methods. Indel refers to the insertion or deletion of a base in a genome. The detection of carriers containing known SMN1 destructive variants can be performed similarly. Method 300 ends at box 332.

[0053] One challenge of accurate carrier status testing is the presence of haplotypes containing two (complete) SMN1 copies. An individual with one such haplotype and another haplotype without a complete SMN1 copy will be a carrier, because a zero-copy haplotype can be transmitted. Since carrier status is largely detected as a copy number change, these individuals can often receive false negative results in carrier screening using standard methods. The method described here may be more or less limited by this. Method 300 can implement one or more techniques to reduce the impact of this problem by detecting known haplotypes carrying two SMN1 copies. An example of this technology is described in Luo, M, et al., An Ashkenazi Jewish SMN1haplotype-specific to duplication alleles improves pan-ethnic carrier screening for spinal muscular atrophy, Genet Med 2014, 16: 149-56, the contents of which are incorporated herein in their entirety.

[0054] The above methods may give inaccurate answers. Copy number methods may be confounded by random deviations from the expected number of reads or by gene conversions that affect only a subset of the SMN1 / SMN2 differentiated quasi-variants. Potentially damaging quasi-variants may be attributed to SMN1 when in fact they belong to SMN2, or vice versa. These potential errors limit the sensitivity and specificity of the test, but are expected to be uncommon and similarly affect the accepted (non-NGS) methods of SMA testing.

[0055] Figures 4A-4C Schematically shows Figure 3 The relationship between inputs and outputs for spinal muscular atrophy diagnosis in block 408 is described. The reads 224 derived from SMN1 or SMN2 aligned to SMN1 can be compared to the fixed difference list 404 between SMN1 and SMN1 to determine the number of reads that support the known target standard allele in the reads derived from SMN1 or SMN2 aligned to SMN1 at block 408. After normalizing the number of reads that support the known target standard allele at block 410, the gene-specific (SMN1 or SMN2) copy number is determined.

[0056] At block 416, reads 224 derived from SMN1 or SMN2 aligned with SMN1 can be compared to a list 414 of known disruptive SMN1 variants using kmer-based variant genotyping to test for additional known SMN1 variants. After detecting single nucleotide variants (SNVs), indels, or structural variants (SVs) using reads 224 derived from SMN1 or SMN2 aligned with SMN1 at block 418, additional known SMN1 variants can be tested at block 424 by determining intersections of known disruptive SMN1 variants 414 and the detected SNVs or indels at block 419. SNVs and indels can be detected using tools or methods such as GATK, FreeBayes, Platypus, or Strelka. CNVs can be detected using tools or methods such as CANVAS, GenomeSTRIP, or CNVnator. SVs can be detected using tools or methods such as MANTA, BreakDancer, or Pindel.

[0057] At block 428, reads derived from SMN2 can be subtracted from the SNVs or indels detected at block 418 based on the SMN1 / SMN2 differences and the SMN2 variant list 426. The resulting reads can be annotated to identify candidate novel SMN1 disruptive variants 420 at block 430.

[0058] SMA state determination based on graph

[0059] Figure 5A and5B A graph-based method for distinguishing paralogous genes (such as SMN1 and SMN2) is schematically shown. The graph-based method can encode the differences between paralogous genes and the differences between variants of each paralogous gene as different paths in the graph. The graph can represent the reference sequence of the first paralogous gene, the reference sequence of the second paralogous gene, and the variants of each paralogous gene. When paralogous genes (or paralogous exons) are similar enough in the genome reference sequence to make the read alignment ambiguous, this method can be used to distinguish, such as DUX4, RPS17, CYP2D6 / 7.

[0060] See also Figure 5A , the graph 500a may include two non-branching nodes 504a, 504b and two branching nodes 508a, 508b connected by edges. The non-branching nodes 504a, 504b represent the sequences of paralogous genes that are unchanged within each paralogous gene and between paralogous genes. For example, the non-branching nodes 504a, 504b may represent portions of the SMN1 and SMN2 sequences that are unchanged within SMN1, within SMN2, and between SMN1 and SMN2. The nodes 504a, 504b, 508a, 508b form two paths 504a-508a-504b, 504a-508b-504b, which encode variants of paralogous genes such as SMN1. The variant of the paralogous gene may be a cytosine base or a thymine base at position 873 in exon 7 of the SMN1 reference sequence, which corresponds to the chromosomal position 70247773 on chromosome 5. Position 70247773 on chromosome 5 in the reference sequence is a cytosine base. If this chromosomal position has a thymine base, the resulting splice variant is translated into an inactive SMN1 protein. Sequence reads 512a-512g from individuals with paralogous genes can be aligned with graph 500a to determine which variant an individual has. Figure 5A As shown, three of the seven sequence reads 512a, 512b, 512e can be aligned with non-branch nodes 504a, 504b representing the invariant sequence of paralogous genes. Two of the seven sequence reads 512c, 512d can be aligned along a path comprising nodes 504a, 508b, 504b, and nodes 504a, 508b, 504b represent one of the two variants. The remaining two of the seven sequence reads 512f, 512g can be aligned with a path comprising nodes 504a, 508a, 504b representing another variant. Therefore, it can be determined that the individual has two variants represented by branch nodes 508a, 508b.

[0061] See also Figure 5B, graph 500b may include 5 non-branching nodes 516a-516c connected by edges. The edge connecting non-branching node 516a and non-branching node 516c represents the absence of at least one nucleotide in the invariant sequence represented by non-branching nodes 516a and 516c. The missing sequence is represented by node 516b. Non-branching nodes 516a, 516-b, 516c form two paths: 516a-516b-516c representing variants without deletions, and 516a-516c representing variants with deletions. Node 516d represents an insertion sequence of at least one nucleotide between the invariant sequences represented by nodes 516c and 516e, and the edge connecting node 516c and node 516e represents the absence of an alternative to the insertion. Nodes 516c, 516d, 516e form two paths: 516c-516e representing variants without insertions, and 516c-516d-516e representing variants with insertions. In one embodiment, the insertions and deletions represented by the paths in Figure 500b represent differences between two paralogous genes. Thus, Figure 500b encodes all four combinations of variants with or without deletions and with or without insertions. For example, there is a common long deletion that removes most of SMN1 (chr5: 70244113-70250418) or SMN2 (chr5: 69351655-69374999) including exon 7. Such deletions can be integrated into the figure using edges between non-branching nodes.

[0062] like Figure 5B As shown, one of the three sequence reads 520a can be aligned with non-branch nodes 516a, 516c along the edge 516a-516c representing a variant with a deletion. One of the sequence reads 520b can be aligned with a path containing non-branch node 516c and non-branch node 516d representing a variant with an insertion. The remaining sequence read 520c can be aligned with non-branch node 516d representing a variant with an insertion. Therefore, it can be determined that the individual has the variant represented by the paths 516a-516c, 516c-516d-516e.

[0063] Graph-based methods that distinguish paralogous genes such as SMN1 and SMN2 can be used to determine the SMA status of an individual, including copy number estimation. Figure 6 6 is a flow chart showing an exemplary graph-based method 600 for determining SMA status. After the method 600 begins at block 604, the method 600 proceeds to block 608, where the computing system (e.g., reference Figure 7 The computing device 700 described above receives multiple sequence reads of SMN1 or SMN2 of an individual.

[0064] From block 608, method 600 proceeds to block 612, where the computing system maps each sequence read to a path comprising at least one node in a graph representing an SMN1 reference sequence and a difference between the SMN1 reference sequence and an SMN2 reference sequence. The graph includes a plurality of paths. Each path can be represented as an ordered list of one or more nodes in a plurality of branch nodes and non-branch nodes, wherein an edge exists between every two subsequent nodes. By connecting the sequences of these nodes in the order listed, the path can represent a survival motor neuron gene 1 (SMN1) reference sequence, a sequence difference between the SMN1 reference sequence and a survival motor neuron gene 2 (SMN2) reference sequence, a variant of SMN1, and a variant of SMN2. For example, known variants in SMN2 can be used to exclude these variants from being considered possible SMN1 disruptions, and also avoid overestimating the number of intact SMN2 copies.

[0065] The plurality of connected branch nodes and non-branch nodes may represent a graph of paths formed by connected nodes encoding or representing an SMN1 reference sequence, a difference between an SMN1 reference sequence and an SMN2 reference sequence, a variant of SMN1, and a variant of SMN2. The computing system may store the graph as a data structure for determining an SMA status of an individual. The computing system may generate a data structure representing the plurality of branch nodes and the plurality of non-branch nodes connected by the plurality of edges. The computing system may graphically display or cause the graph including the plurality of branch nodes and the plurality of non-branch nodes connected by the plurality of edges to be displayed as a graph.

[0066] The plurality of non-branch nodes and a subset of the plurality of branch nodes connected by two or more edges may represent an SMN1 reference sequence. Figure 5A , non-branch nodes 504a, 504b and branch node 508a may represent an SMN1 reference sequence. In one embodiment, two non-branch nodes connected to the same two non-branch nodes may represent a difference between an SMN1 reference sequence and an SMN2 reference sequence, a difference between an SMN1 reference sequence and a variant of SMN1, a difference between an SMN2 reference sequence and a variant of SMN2, or any combination thereof. For example, Figure 5A The branch nodes 508a, 508b connected to the same two non-branch nodes 504a, 504b in the example may represent differences between the SMN1 reference sequence and the SMN2 reference sequence. In another embodiment, a non-branch node connected to two non-branch nodes may represent at least one nucleotide inserted into the SMN1 reference sequence, or at least one nucleotide deleted from the SMN1 reference sequence. Figure 5B, a non-branch node 516c connected to two non-branch nodes 516a and 516b indicates that the sequence represented by the non-branch node 516b is missing from the SMN1 reference sequence. A non-branch node 516e connected to two non-branch nodes 516c and 516d may indicate that the sequence represented by the non-branch node 516d is inserted into the SMN1 reference sequence.

[0067] See also Figure 6 From block 612, method 600 proceeds to block 616, where the computing system determines the number of sequence reads mapped to a path that includes each branch node, non-branch node, and / or edge connecting two nodes. Figure 5A , each sequence read 512a-512g can be mapped to one or more nodes 504a, 504b, 508a, 508b, 508B based on the sequence read and the sequence represented by the node 504a, 504b, 508a, 508b. Figure 5B , each sequence read can be mapped to one or more nodes 516a-516e. In one embodiment, the alignment method determines the best local alignment to the graph, and does not calculate the read sequence with multiple different best alignments, to exclude useless readings for eliminating ambiguity between paralogous gene variants. The reading excluded can be compared with two or more paths with identical or similar alignment scores.

[0068] See also Figure 6 , method 600 proceeds from box 616 to box 620, wherein the computing system determines the spinal muscular atrophy (SMA) status of the individual based on the number of sequence reads mapped to each of the multiple branch nodes and edges. In one embodiment, determining the SMA status of the individual may include determining the number of sequence reads mapped to a node (such as branch node 508a) representing a sequence difference between an SMN1 reference sequence and an SMN2 reference sequence. For example, branch node 508a may represent a cytosine base at position 873 in exon 7 of the SMN1 reference sequence. If the number of sequence reads mapped to the branch node representing the SMN1 reference sequence is below a threshold, the SMA status of the individual may be determined as an affected state. If the number of sequence reads mapped to the branch node representing the SMN1 reference sequence is not below a threshold, the SMA status of the individual may be determined as a carrier state or an unaffected state. The threshold may be an absolute number of reads, a percentage of the total number of reads, or a percentage of the total number of SMN1 and SMN2 reads. The threshold may be a number of reads mapped to branch node 508a and any associated branch nodes (such as Figure 5AAs another example, determining the SMA status of an individual can include determining the number of sequence reads that map to two or more branch nodes (such as branch nodes 508a, 508b) that represent sequence differences between an SMN1 reference sequence and an SMN2 reference sequence. Branch nodes 508a, 508b can represent single base differences between SMN1 and SMN2 that affect splicing, which can be used to determine the affected and unaffected states of SMA for an individual.

[0069] In one embodiment, a branch node may represent a functionally significant variant of SMN1. Determining the SMA status of an individual may include determining the number of sequence reads mapped to a branch node representing a functionally significant variant of SMN1. If the number of sequence reads mapped to a branch node representing a functionally significant variant is above a threshold, the SMA status of the individual may be determined as an affected state or a carrier state. The threshold may be the absolute number of reads, the percentage of the total number of reads, the percentage of the total number of SMN1 and SMN2 reads, or the percentage of the number of SMN1 and SMN2 reads mapped to the branch node and / or any associated branch nodes. Thus, method 600 may be used to detect known but rare functionally significant variants in SMN1 to aid in identifying other individuals affected.

[0070] In another embodiment, determining the SMA status of an individual includes determining the SMN1 copy number. The computing system can determine the SMN1 copy number by first determining the number of sequence reads mapped to a first branch node representing a first subsequence of an SMN1 reference sequence (such as a cytosine base at position 873 in exon 7 of an SMN1 reference sequence). The first branch node is also referred to herein as a functional site. The computing system can determine the number of sequence reads mapped to a second branch node representing a second subsequence of the SMN1 reference sequence. The second branch node may be referred to herein as a junction site. The first subsequence and the second subsequence may have a high probability of co-occurrence. Table 1 shows exemplary functional site and junction site sequences for SMN1.

[0071] Table 1. Tight junction variants

[0072] chromosome Chromosome location Site classification Reference sequence Optional sequence chr5 70247773 Function C T chr5 70246793 connect G A chr5 70247290 connect T C chr5 70247724 connect G A chr5 70247921 connect A G chr5 70248036 connect A G

[0073] Therefore, the SMN1 copy number can be determined based on the number of sequence reads mapped to the second non-branch node representing the connection site and / or the number of sequence reads mapped to the first branch node representing the functional site. For example, if the number of sequence reads mapped to the first branch node representing the functional site is equal to a threshold value (such as 0) or is lower than the threshold value, the SMN1 copy number can be determined to be zero. If the number of sequence reads mapped to the first branch node representing the functional site is lower than the first threshold value, the SMN1 copy number can be determined to be one or more. If the number of sequence reads mapped to the second branch node representing the connection site is lower than the second threshold value, the SMN1 copy number can be determined to be 1. If the number of sequence reads mapped to the second branch node representing the connection site is higher than the second threshold value, the SMN1 copy number can be determined to be two (or more). The threshold value can be the absolute number of reads, the percentage of the total number of reads, the percentage of the total number of SMN1 and SMN2 reads, the percentage of the number of SMN1 and SMN2 reads mapped to the branch node representing the functional site, or the percentage of the number of SMN1 and SMN2 reads mapped to the non-branch node representing the connection site.

[0074] In another embodiment, known variants in SMN1 can be used to identify specific haplotypes that can be used to detect silent carrier haplotypes with two copies of SMN1 on a single chromosome, thereby improving carrier status testing. For example, a computing system can determine the SMA status of an individual by determining the number of sequence reads mapped to branch nodes representing SMN1 variants; if the number of sequence reads mapped to branch nodes representing SMN1 variants is above a threshold, the spinal muscular atrophy (SMA) status of the individual is determined to be a silent carrier haplotype. In one embodiment, a branch node can represent a carrier tag variant of SMN1, the presence of which indicates a high probability of a carrier state. Determining the SMA status of an individual can include determining the number of sequence reads mapped to branch nodes representing carrier marker variants. Table 2 shows exemplary carrier tag variants.

[0075] Table 2. Carrier tag variants

[0076] chromosome Chromosome location Reference sequence Optional sequence chr5 70243571 G A chr5 70246957 A G chr5 70247901 T G chr5 70248471 CTA C

[0077] Computing Devices

[0078] Figure 7 The general architecture of an exemplary computing device 700 is shown, which is configured to learn a demographic model and generate predictions using the model. Figure 7 The general architecture of computing device 700 shown in FIG. 7 includes an arrangement of computer hardware and software components. Computing device 700 may include Figure 7The elements shown in more (or less) elements. This is not necessary, but all of these generally provide feasible disclosures for conventional elements. As shown, computing device 700 includes: processing unit 740, network interface 745, computer-readable medium drive 750, input / output device interface 755, display 760 and input device 765, all of which can communicate with each other by means of communication bus. Network interface 745 can provide connection to one or more networks or computing systems. Therefore, processing unit 740 can receive information and instructions from other computing systems or services via the network. Processing unit 740 can also communicate with memory 770, and also provide output information for optional display 760 via input / output device interface 755. Input / output device interface 755 can also accept input from optional input device 765, such as keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game controller, accelerometer, gyroscope or other input device.

[0079] The memory 770 may include computer program instructions (grouped into modules or components in one embodiment) that the processing unit 740 executes to implement one or more embodiments. The memory 770 typically includes RAM, ROM and / or other persistent, auxiliary or non-temporary computer-readable media. The memory 770 may store an operating system 772, which provides computer program instructions for use in the general management and operation of the processing unit 740 in the computing device 700. The memory 770 may also include computer program instructions and other information for implementing various aspects of the present application. For example, in one embodiment, the memory 770 includes a spinal muscular atrophy state determination module 774, which determines the affected or carrier state of spinal muscular atrophy. In addition, the memory 770 may include a data store 780 and / or one or more other data stores (which store data for analysis or analysis results) or communicate with it.

[0080] This application also relates to the following implementation schemes:

[0081] Embodiment 1. A system for determining the status of a paralogous gene in an individual, comprising:

[0082] a non-transitory memory configured to store executable instructions; and

[0083] A hardware processor programmed by executable instructions to perform a method comprising the steps of:

[0084] collecting nucleotide sequence data from the individual, including first paralogous gene sequence data and second paralogous gene sequence data;

[0085] aligning the nucleotide sequence data with a first reference sequence of a first paralogous gene to determine a plurality of alignments;

[0086] Determining sequence differences between the first paralogous gene sequence data and the reference sequence based on the alignment;

[0087] determining a copy number of the first paralog gene based on (i) the sequence differences and (ii) a plurality of sequence differences between a reference sequence of the first paralog gene sequence data and a reference sequence of the second paralog gene; and

[0088] Based on the first paralog copy number, the paralog status of an individual was determined.

[0089] Embodiment 2. A system as described in embodiment 1, wherein collecting nucleotide sequence data includes receiving whole genome sequence data of the individual.

[0090] Embodiment 3. The system of embodiment 1, wherein the first paralogous gene sequence data comprises survival motor neuron 1 (SMN1), DUX4, RPS17 or CYP2D6 / 7 gene data.

[0091] Embodiment 4. The system of embodiment 1, wherein aligning the nucleotide sequence data comprises aligning the first paralogous gene sequence data with the first reference sequence and aligning the second paralogous gene sequence data with the first reference sequence.

[0092] Embodiment 5. A system as described in embodiment 1, wherein determining the sequence difference includes: determining at least one sequence difference between (1) a first sequence read of the sequence data aligned with a reference sequence of the first paralogous gene and (2) a corresponding subsequence of the reference sequence of the first paralogous gene.

[0093] Embodiment 6. The system of embodiment 1, wherein the paralogous gene status of the individual comprises the copy number or disease status of the first paralogous gene based on the plurality of sequence differences.

[0094] Embodiment 7. A system for diagnosing spinal muscular atrophy (SMA) in an individual, comprising:

[0095] a non-transitory memory configured to store executable instructions; and

[0096] A hardware processor programmed by executable instructions to perform a method comprising the steps of:

[0097] aligning the individual's survival motor neuron gene 1 (SMN1) sequence data and the survival motor neuron gene 2 (SMN2) sequence data to an SMN1 reference sequence to generate an alignment;

[0098] Determining sequence differences between the SMN1 sequence data and the SMN2 sequence data and the SMN1 reference sequence based on the alignment terms;

[0099] determining the SMN1 copy number based on (i) the plurality of sequence differences and (ii) the plurality of differences between the SMN1 reference sequence and the SMN2 reference sequence; and

[0100] The SMA status of an individual is determined based on the SMN1 copy number.

[0101] Embodiment 8. A system as described in Embodiment 7, wherein aligning the SMN1 sequence data and the SMN2 sequence data with the SMN1 reference sequence includes: aligning the sequence data including the SMN1 sequence data and the SMN2 sequence data with the SMN1 reference sequence and the SMN2 reference sequence.

[0102] Embodiment 9. The system of embodiment 8, wherein aligning the SMN1 sequence data and the SMN2 sequence data to the SMN1 reference sequence further comprises:

[0103] selecting sequence data aligned to said SMN1 reference sequence or SMN2 reference sequence; and

[0104] The selected sequence data were aligned with the SMN1 reference sequence.

[0105] Embodiment 10. The system of embodiment 7, wherein determining the sequence difference comprises determining at least one sequence difference between a first sequence read of the sequence data of SMN1 and a corresponding sequence of the SMN1 reference sequence.

[0106] Embodiment 11. The system of embodiment 7, wherein the hardware processor is further programmed by the executable instructions to:

[0107] generating quasi-variant base calls based on differences between alignments of the SMN1 sequence data and the SMN2 sequence data with the SMN1 reference sequence; and

[0108] Based on the quasi-variant calls, the presence of known variants in the SMN1 sequence data and SMN2 sequence data is determined.

[0109] Embodiment 12. A system as described in embodiment 11, wherein the hardware processor is further programmed by the executable instructions to: determine new variants in the SMN1 sequence data and SMN2 sequence data based on the quasi-variant judgment.

[0110] Embodiment 13. A system for distinguishing paralogous genes, comprising:

[0111] a non-transitory memory configured to store executable instructions and a data structure representing a plurality of paths including a plurality of branch nodes and a plurality of non-branch nodes, wherein the plurality of paths represent: a reference sequence of a first paralog gene, a sequence difference between the reference sequence of the first paralog gene and the reference sequence of a second paralog gene, a variant of the first paralog gene, and a variant of the second paralog gene; and

[0112] A hardware processor programmed by executable instructions to perform a method comprising the steps of:

[0113] Receiving sequence data of a first paralogous gene and a second paralogous gene of an individual;

[0114] mapping the sequence data to at least one branch node or non-branch node associated with a path in the plurality of paths;

[0115] determining a number of sequence reads of the sequence data that map to each branch node or non-branch node; and

[0116] The paralogous gene status of an individual was determined based on the number of sequence reads mapping to each branch node or non-branch node.

[0117] Embodiment 14. The system of embodiment 13, wherein the first paralogous gene comprises a survival motor neuron 1 (SMN1), DUX4, RPS17, or CYP2D6 / 7 gene sequence.

[0118] Embodiment 15. The system of embodiment 13, wherein the sequence data of the first paralogous gene and the second paralogous gene comprises a plurality of sequence reads of the survival motor neuron gene 1 (SMN2) and the survival motor neuron gene 2 (SMN2) of the individual.

[0119] Embodiment 16. A system as described in embodiment 13, wherein receiving sequence data includes receiving whole genome sequence data of an individual.

[0120] Embodiment 17. A system as described in embodiment 13, wherein mapping the sequence data to at least one branch node or non-branch node of the path includes: determining a comparison item between the sequence read of the sequence data and at least one branch node or non-branch node of the path based on the sequence read and the sequence represented by the branch node or non-branch node.

[0121] Embodiment 18. A system as described in embodiment 13, wherein determining the number of sequence reads of sequence data mapped to each branch node or non-branch node includes: when the sequence read is mapped to the branch node or non-branch node, increasing the count associated with the branch node or non-branch node.

[0122] Embodiment 19. The system of embodiment 13, wherein the paralogous gene status of the individual comprises: the number of copies of the first paralogous gene or a disease state associated with the number of copies of the first paralogous gene.

[0123] Embodiment 20. A system as described in Embodiment 13, wherein the copy number is determined based on two or more nodes having a high probability of occurring simultaneously.

[0124] Embodiment 21. A system for diagnosing spinal muscular atrophy, comprising:

[0125] a non-transitory memory configured to store executable instructions and a data structure representing a plurality of paths including a plurality of branch nodes and a plurality of non-branch nodes, wherein the plurality of paths represent: a survival motor neuron gene 1 (SMN1) reference sequence, sequence differences between the SMN1 reference sequence and a survival motor neuron gene 2 (SMN2) reference sequence, variants of SMN1, and variants of SMN2; and

[0126] A hardware processor programmed by executable instructions to perform a method comprising the steps of:

[0127] receiving a plurality of sequence reads of SMN1 or SMN2 of an individual;

[0128] mapping each of the plurality of sequence reads to at least one branch node or non-branch node of a path in the plurality of pathways;

[0129] determining a number of sequence reads mapped to each of the plurality of branch nodes; and

[0130] Based on the number of sequence reads mapped to each of the plurality of branch nodes, a spinal muscular atrophy (SMA) status of the individual is determined.

[0131] Embodiment 22. The system of embodiment 21, wherein determining the SMA status of the individual comprises:

[0132] determining a number of sequence reads mapped to branch nodes representing sequence differences between the SMN1 reference sequence and the SMN2 reference sequence; and

[0133] The SMA status of an individual is determined as:

[0134] Affected if the number of sequence reads mapped to a branch node representing the SMN1 reference sequence is below a threshold, and

[0135] Otherwise, it is a carrier state or an unaffected state.

[0136] Embodiment 23. The system of Embodiment 22, wherein the branch node represents a cytosine base at position 873 in exon 7 of the SMN1 reference sequence.

[0137] Embodiment 24. The system of embodiment 21, wherein determining the SMA status of the individual comprises:

[0138] determining the number of sequence reads mapping to branch nodes representing functionally significant variants of SMN1; and

[0139] The SMA status of an individual is determined as:

[0140] If the number of sequence reads mapped to the branch node representing the functionally significant variant is above a threshold, then the affected state or carrier state is obtained.

[0141] Embodiment 25. The system of embodiment 21, wherein determining the SMA status of the individual comprises determining the SMN1 copy number.

[0142] Embodiment 26. The system of embodiment 25, wherein determining the SMN1 copy number comprises: determining the SMN1 copy number based on a number of sequence reads mapped to a branch node.

[0143] Embodiment 27. The system of embodiment 25, wherein determining the SMN1 copy number comprises: determining a number of sequence reads mapped to a first branch node representing a first subsequence of the SMN1 reference sequence.

[0144] Embodiment 28. The system of embodiment 25, wherein determining the SMA status of the individual comprises: determining a number of sequence reads that map to branch nodes representing variants of SMN1.

[0145] Embodiment 29. The system of embodiment 21, wherein the hardware processor is further programmed by the executable instructions to generate a data structure representing the multiple paths.

[0146] Embodiment 30. The system of embodiment 21, wherein the hardware processor is further programmed by the executable instructions to graphically display the plurality of branch nodes and the plurality of non-branch nodes as a graph.

[0147] Embodiment 31. The system of embodiment 21, wherein a path of the plurality of paths comprising one or more non-branching nodes and one or more branching nodes represents the SMN1 reference sequence.

[0148] Embodiment 32. A system as described in Embodiment 21, wherein the two branch nodes represent: the difference between the SMN1 reference sequence and the SMN2 reference sequence, the difference between the SMN1 reference sequence and a variant of SMN1, the difference between the SMN2 reference sequence and a variant of SMN2, or any combination thereof.

[0149] Embodiment 33. The system of embodiment 21, wherein a non-branch node indicates that at least one nucleotide is inserted into the SMN1 reference sequence, or at least one nucleotide is deleted from the SMN1 reference sequence. Example

[0150] Some aspects of the embodiments discussed herein are disclosed in further detail in one or more of the following examples, which are not intended to limit the scope of the present application in any way.

[0151] Example 1

[0152] Determination of SMN1 and SMN2 specific copy number

[0153] This example describes the use of read counts supporting quasi-alleles at multiple positions to determine SMN1 and SMN2 specific copy number.

[0154] Figure 8 is an exemplary plot of the sum of read counts supporting SMN2 relative to the sum of read counts supporting SMN1, which can be used to determine SMN1 and SMN2 specific copy numbers. More than 1300 samples were analyzed using whole genome sequencing using an Illumina sequencer. As described in reference Figure 1 The sequencing data from each sample were processed and analyzed by aligning them to the SMN2-depleted reference genome as described and as described in reference Figure 3 Determining affected and carrier status for spinal muscular atrophy. Figure 8 Each point in corresponds to a sample. The x-value is the sum of the number of reads supporting the SMN1 reference "allele" at each position (across the "almost always het" sites). The y-value is the sum of the number of reads supporting the SMN2 reference "allele" at each position (at the same site). Ellipses were added to highlight the identified clusters of samples. The slope of each ellipse matches the slope of the line through the origin and the center of the cluster identified by the ellipse. The clusters appear to correspond to the number of copies of SMN1 and SMN2. The dashed line is the determination of the boundary between carriers and non-carriers.

[0155] The following is a list of the locations in the SMN1 gene (on chromosome 5, using the hg19 human reference genome sequence) that were used to generate Figure 8 : 70244142, 70245876, 70246019, 70246156, 70246320, 70246793, 70246864, 70246919, 70247219, 70247290, 70247724, 70247773, 70247921, and 70248036. The bases at these positions in SMN1 are different from the analogous positions in SMN2, thus generating quasi-heterozygous calls in almost all samples analyzed.

[0156] Together, these data suggest that there are at least 14 positions in the SMN1 gene that are quasi-heterozygous in nearly all samples. Read counts supporting SMN1 quasi-alleles at these positions can be used to infer the full SMN1 copy number present in a sample. Similarly, SMN2 copy number can be determined.

[0157] In at least some of the aforementioned embodiments, one or more elements used in one embodiment may be interchangeably used in another embodiment unless such replacement is technically infeasible. It will be appreciated by those skilled in the art that various other omissions, additions and modifications may be made to the methods and structures described herein without departing from the scope of the subject matter claimed. All of these modifications and variations are intended to fall within the subject matter scope defined by the appended claims.

[0158] With respect to the use of substantially any plural and / or singular terms herein, those skilled in the art may appropriately convert from the plural to the singular and / or from the singular to the plural, depending on the context and / or application. For clarity, various singular / plural arrangements may be expressly set forth herein. As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly indicates otherwise. Unless otherwise indicated, any reference to "or" herein is intended to encompass "and / or."

[0159] Those skilled in the art will understand that, in general, the terms used herein, especially in the appended claims (e.g., the bodies of the appended claims), are generally intended to be “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “including but not limited to,” etc.). Those skilled in the art will further understand that if a specific number of claim recitations is intended to be introduced, such an intent will be explicitly stated in the claim, and in the absence of such a statement, no such intent is present. For example, to aid understanding , the following appended claims may contain the use of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of these phrases should not be construed as implying that the introduction of a claim recitation by the indefinite article "a" or "an" limits any particular claim containing such introduced claim recitation to that embodiment containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one", indefinite articles such as "a" or "an" (e.g., "a" and / or "an" should be construed as "at least one" or "one or more"); the same is true for the use of definite articles used to introduce claim recitations.

[0160] Furthermore, even if a specific number of an introduced claim recitation is explicitly recited, one skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., a simple recitation of "two items," without other modifiers, means at least two items, or two or more items). Furthermore, in those cases where a convention similar to "at least one of A, B, and C, etc." is used, such construction is generally intended in the sense that one skilled in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but is not limited to systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). In those cases where a convention similar to "at least one of A, B, or C, etc." is used, such construction is generally intended in the sense that one skilled in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include but is not limited to systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). Those skilled in the art will further understand that virtually any transitional word and / or phrase presenting two or more alternative terms, whether in the specification, claims or drawings, should be understood to contemplate the possibility of including one of these terms, any one term or both terms. For example, the phrase "A or B" will be understood to include the possibility of "A", or "B", or "A and B".

[0161] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0162] As will be understood by those skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible sub-ranges and combinations of sub-ranges thereof. Any listed range can be easily recognized as fully describing and making the same range decomposed into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed here can be easily decomposed into a lower third, a middle third, and an upper third, etc. As will be understood by those skilled in the art, all languages ​​such as "up to", "at least", "greater than", "less than", etc., include the described numbers, and refer to ranges that can be subsequently decomposed into sub-ranges as discussed herein. Finally, as will be understood by those skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 objects refers to a group having 1, 2, or 3 objects. Similarly, a group having 1-5 objects refers to a group having 1, 2, 3, 4, or 5 objects, etc.

[0163] Although various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and not limitation, with the true scope and spirit being indicated by the appended claims.

Claims

1. A system for determining the status of a paralogous gene in an individual, comprising: a non-transitory memory configured to store executable instructions; and A hardware processor programmed by executable instructions to perform a method comprising the steps of: collecting nucleotide sequence data from the individual, including first paralogous gene sequence data and second paralogous gene sequence data; aligning the nucleotide sequence data with a first reference sequence of a first paralogous gene to determine a plurality of alignments; Determining sequence differences between the first paralogous gene sequence data and the reference sequence based on the alignment; determining a copy number of the first paralog gene based on (i) the sequence differences and (ii) a plurality of sequence differences between a reference sequence of the first paralog gene sequence data and a reference sequence of the second paralog gene; and Based on the first paralog copy number, the paralog status of an individual was determined.

2. The system of claim 1, wherein collecting nucleotide sequence data comprises receiving whole genome sequence data of the individual.

3. The system of claim 1, wherein the first paralogous gene sequence data comprises survival motor neuron 1 (SMN1), DUX4, RPS17 or CYP2D6 / 7 gene data.

4. The system of claim 1, wherein aligning the nucleotide sequence data comprises aligning the first paralogous gene sequence data to the first reference sequence and aligning the second paralogous gene sequence data to the first reference sequence.

5. The system of claim 1, wherein determining sequence differences comprises: At least one sequence difference is determined between (1) a first sequence read of the sequence data aligned to a reference sequence of the first paralogous gene and (2) a corresponding subsequence of the reference sequence of the first paralogous gene.

6. The system of claim 1, wherein the paralogous gene status of the individual comprises a copy number or a disease status of a first paralogous gene based on the plurality of sequence differences.

7. A system for diagnosing spinal muscular atrophy (SMA) in an individual, comprising: a non-transitory memory configured to store executable instructions; and A hardware processor programmed by executable instructions to perform a method comprising the steps of: aligning the individual's survival motor neuron gene 1 (SMN1) sequence data and the survival motor neuron gene 2 (SMN2) sequence data to an SMN1 reference sequence to generate an alignment; Determining sequence differences between the SMN1 sequence data and the SMN2 sequence data and the SMN1 reference sequence based on the alignment terms; determining the SMN1 copy number based on (i) the plurality of sequence differences and (ii) the plurality of differences between the SMN1 reference sequence and the SMN2 reference sequence; and The SMA status of an individual is determined based on the SMN1 copy number.

8. The system of claim 7, wherein aligning the SMN1 sequence data and the SMN2 sequence data to the SMN1 reference sequence comprises: The sequence data including the SMN1 sequence data and the SMN2 sequence data are aligned with the SMN1 reference sequence and the SMN2 reference sequence.

9. Systems for distinguishing paralogous genes, including: a non-transitory memory configured to store executable instructions and a data structure representing a plurality of paths including a plurality of branch nodes and a plurality of non-branch nodes, wherein the plurality of paths represent: a reference sequence of a first paralog gene, a sequence difference between the reference sequence of the first paralog gene and the reference sequence of a second paralog gene, a variant of the first paralog gene, and a variant of the second paralog gene; and A hardware processor programmed by executable instructions to perform a method comprising the steps of: Receiving sequence data of a first paralogous gene and a second paralogous gene of an individual; mapping the sequence data to at least one branch node or non-branch node associated with a path in the plurality of paths; determining a number of sequence reads of the sequence data mapped to each branch node or non-branch node; and The paralogous gene status of an individual was determined based on the number of sequence reads mapping to each branch node or non-branch node.

10. A system for diagnosing spinal muscular atrophy, comprising: a non-transitory memory configured to store executable instructions and a data structure representing a plurality of paths including a plurality of branch nodes and a plurality of non-branch nodes, wherein the plurality of paths represent: a survival motor neuron gene 1 (SMN1) reference sequence, a sequence difference between the SMN1 reference sequence and a survival motor neuron gene 2 (SMN2) reference sequence, a variant of SMN1, and a variant of SMN2; and A hardware processor programmed by executable instructions to perform a method comprising the steps of: receiving a plurality of sequence reads of SMN1 or SMN2 of an individual; mapping each of the plurality of sequence reads to at least one branch node or non-branch node of a path in the plurality of pathways; determining a number of sequence reads mapped to each of the plurality of branch nodes; and Based on the number of sequence reads mapped to each of the plurality of branch nodes, a spinal muscular atrophy (SMA) status of the individual is determined.

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