Specific structural variants discovered with non-mendelian inheritance
OwnerMichael R. Garvin, Daniel A. Jacobson +2inventors
InventorsMichael R. Garvin, Daniel A. Jacobson, David Kainer, Erica Teixeira Prates
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Abstract
The present disclosure is directed to methods of identifying structural variants (SVs) from single nucleotide polymorphisms (SNPs) that demonstrate non-Mendelian inheritance pattern (NMI) and finding the biological relevance of the SVs through machine learning. Also disclosed are processors programmed to identify biologically-relevant SVs and computer-readable storage devices comprising instructions to identify biologically-relevant SVs.
Claims36
A method of identifying at least one structural variation in a genome, the method comprising:
- assembling single nucleotide polymorphism (SNP) data from parents and their offspring;
- analyzing the SNP data for a plurality of non-Mendelian inheritance (NMI) patterns, wherein each NMI in the plurality of NMI patterns is a potential structural variation;
- scoring the NMIs to identify large structural variations, wherein a run of at least three SNPs with NMI indicates a large structural variation;
- removing SNPs that demonstrate NMI in the offspring but that overlap with at least one known existing variation;
- identifying conserved regions of the genome to filter regions that should be conserved but include a structural variation;
- identifying biologically important structural variations;
- and
- classifying the identified biologically important structural variations using a machine learning algorithm.
A computer-implemented method of training a machine learning algorithm for identifying at least one structural variation in a genome, the method comprising
- training the machine learning algorithm using a training set, wherein the training set is created by:
- assembling single nucleotide polymorphism (SNP) data from parents and their offspring;
- analyzing the SNP data for a plurality of non-Mendelian inheritance patterns (NMI), wherein each NMI is a potential structural variation;
- scoring the NMIs to identify large structural variations, wherein presence of at least three neighboring SNPs that demonstrate NMI in the offspring indicates a large structural variation;
- removing SNPs that demonstrate NMI in the offspring but that overlap with at least one known existing variation;
- identifying conserved regions of the genome to filter regions that should be conserved but include a structural variation;
- and
- identifying potentially biologically important structural variations.
A processor programmed to perform:
- assembling single nucleotide polymorphism (SNP) data from parents and their offspring;
- analyzing the SNP data for a plurality of non-Mendelian inheritance (NMI) patterns, wherein each NMI in the plurality of NMI patterns is a potential structural variation;
- scoring the NMIs to identify large structural variations, wherein the presence of at least three neighboring SNPs that demonstrate NMI in the offspring indicates a large structural variation;
- removing SNPs that demonstrate NMI in the offspring but that overlap with at least one known existing variation;
- identifying conserved regions of the genome to filter regions that should be conserved but include a structural variation;
- identifying biologically important structural variations;
- and
- classifying the identified biologically important structural variations using a machine learning algorithm.
A computer-readable storage device, comprising instructions to perform:
- assembling single nucleotide polymorphism (SNP) data from parents and their offspring;
- analyzing the SNP data for a plurality of non-Mendelian inheritance (NMI) patterns, wherein each NMI in the plurality of NMI patterns is a potential structural variation;
- scoring the NMIs to identify large structural variations, wherein the presence of at least three neighboring SNPs that demonstrate NMI in the offspring indicates a large structural variation;
- removing SNPs that demonstrate NMI in the offspring but that overlap with at least one known existing variation;
- identifying conserved regions of the genome to filter regions that should be conserved but include a structural variation;
- identifying biologically important structural variations;
- and
- classifying the identified biologically important structural variations using a machine learning algorithm.
A method comprising:
- obtaining a biological sample from a subject,
- detecting in the biological sample whether at least one gene or genomic region selected from Table 1 or Table 2 has a structural variation;
- and
- determining that the subject is at risk of Autism Spectrum Disorder if the at least one gene or genomic region has a structural variation.
Dependencies are inferred from the claim text.