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Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.

ABSTRACT: Exome sequencing has been widely used in detecting pathogenic nonsynonymous single nucleotide variants (SNVs) for human inherited diseases. However, traditional statistical genetics methods are ineffective in analyzing exome sequencing data, due to such facts as the large number of sequenced variants, the presence of non-negligible fraction of pathogenic rare variants or de novo mutations, and the limited size of affected and normal populations. Indeed, prevalent applications of exome sequencing have been appealing for an effective computational method for identifying causative nonsynonymous SNVs from a large number of sequenced variants. Here, we propose a bioinformatics approach called SPRING (Snv PRioritization via the INtegration of Genomic data) for identifying pathogenic nonsynonymous SNVs for a given query disease. Based on six functional effect scores calculated by existing methods (SIFT, PolyPhen2, LRT, MutationTaster, GERP and PhyloP) and five association scores derived from a variety of genomic data sources (gene ontology, protein-protein interactions, protein sequences, protein domain annotations and gene pathway annotations), SPRING calculates the statistical significance that an SNV is causative for a query disease and hence provides a means of prioritizing candidate SNVs. With a series of comprehensive validation experiments, we demonstrate that SPRING is valid for diseases whose genetic bases are either partly known or completely unknown and effective for diseases with a variety of inheritance styles. In applications of our method to real exome sequencing data sets, we show the capability of SPRING in detecting causative de novo mutations for autism, epileptic encephalopathies and intellectual disability. We further provide an online service, the standalone software and genome-wide predictions of causative SNVs for 5,080 diseases at http://bioinfo.au.tsinghua.edu.cn/spring.

SUBMITTER: Wu J

PROVIDER: S-EPMC3961190 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.

Wu Jiaxin J Li Yanda Y Jiang Rui R

PLoS genetics 20140320 3

Exome sequencing has been widely used in detecting pathogenic nonsynonymous single nucleotide variants (SNVs) for human inherited diseases. However, traditional statistical genetics methods are ineffective in analyzing exome sequencing data, due to such facts as the large number of sequenced variants, the presence of non-negligible fraction of pathogenic rare variants or de novo mutations, and the limited size of affected and normal populations. Indeed, prevalent applications of exome sequencing ...[more]

PMID: 24651380

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Project description:BACKGROUND:Although mosaic variation has been known to cause disease for decades, high-throughput sequencing technologies with the analytical sensitivity to consistently detect variants at reduced allelic fractions have only recently emerged as routine clinical diagnostic tests. To date, few systematic analyses of mosaic variants detected by diagnostic exome sequencing for diverse clinical indications have been performed. METHODS:To investigate the frequency, type, allelic fraction, and phenotypic consequences of clinically relevant somatic mosaic single nucleotide variants (SNVs) and characteristics of the corresponding genes, we retrospectively queried reported mosaic variants from a cohort of ~?12,000 samples submitted for clinical exome sequencing (ES) at Baylor Genetics. RESULTS:We found 120 mosaic variants involving 107 genes, including 80 mosaic SNVs in proband samples and 40 in parental/grandparental samples. Average mosaic alternate allele fraction (AAF) detected in autosomes and in X-linked disease genes in females was 18.2% compared with 34.8% in X-linked disease genes in males. Of these mosaic variants, 74 variants (61.7%) were classified as pathogenic or likely pathogenic and 46 (38.3%) as variants of uncertain significance. Mosaic variants occurred in disease genes associated with autosomal dominant (AD) or AD/autosomal recessive (AR) (67/120, 55.8%), X-linked (33/120, 27.5%), AD/somatic (10/120, 8.3%), and AR (8/120, 6.7%) inheritance. Of note, 1.7% (2/120) of variants were found in genes in which only somatic events have been described. Nine genes had recurrent mosaic events in unrelated individuals which accounted for 18.3% (22/120) of all detected mosaic variants in this study. The proband group was enriched for mosaicism affecting Ras signaling pathway genes. CONCLUSIONS:In sum, an estimated 1.5% of all molecular diagnoses made in this cohort could be attributed to a mosaic variant detected in the proband, while parental mosaicism was identified in 0.3% of families analyzed. As ES design favors breadth over depth of coverage, this estimate of the prevalence of mosaic variants likely represents an underestimate of the total number of clinically relevant mosaic variants in our cohort.

Dataset Information

Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.

Publications

Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.

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