Dataset Information

Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data.

ABSTRACT: Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the 'big data' available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters.

SUBMITTER: Huang YF

PROVIDER: S-EPMC5395419 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data.

Huang Yi-Fei YF Gulko Brad B Siepel Adam A

Nature genetics 20170313 4

Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear mode ...[more]

PMID: 28288115

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Project description:BackgroundMyoclonus-Dystonia (M-D) is a pleiotropic neuropsychiatric disorder of variable penetrance. Pathogenic variants in SGCE, a maternally imprinted gene, are the most frequent known genetic cause of M-D. The population prevalence of SGCE-linked M-D is unknown, the pathogenicity of SGCE variants identified in patients with M-D may be indeterminant, and SGCE variants predicted to be deleterious by in silico analysis may appear in patients undergoing whole-exome or whole-genome sequencing for seemingly unrelated disorders. The Genome Aggregation Database (gnomAD) v2 provides variant data on 125,748 exomes and 15,708 genomes from unrelated individuals sequenced as part of various disease-specific and population genetic studies.MethodsSGCE variants included in the gnomAD v2 dataset were analyzed with Combined Annotation Dependent Depletion (CADD), and database for nonsynonymous single nucleotide polymorphisms' functional predictions (dbNSFP). We determined the frequency of annotated SGCE variants, ranked by scores of deleteriousness, within the gnomAD v2 dataset. Deleteriousness scores were compared to a subset of published disease associated SGCE pathogenic variants.ResultsWithin gnomAD v2, there were 56, 408, and 1250 alleles harboring SGCE variants with CADD scores greater than 30, 25, and 20, respectively. We estimate that approximately 1/348 individuals in the United States population harbors an SGCE variant with a CADD score ≥ 25.DiscussionSGCE M-D may be underdiagnosed due to pleiotropy, mild phenotypes, variable penetrance, and impaired access to genetic testing. Due to the high population prevalence of deleterious SGCE variants, caution should be used when asserting pathogenicity without co-segregation analyses and expert neurological examination of phenotypes within pedigrees.HighlightsIn silico analyses of a large population database of genetic variants revealed that over 0.2% of individuals in the United States harbor a highly deleterious SGCE variant. This finding suggests that M-D and minor phenotypic variants such as mild isolated myoclonus may be underdiagnosed.

Dataset Information

Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data.

Publications

Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data.

Similar Datasets

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