Integrating Multiple Genomic Data to Predict Disease-Causing Nonsynonymous Single Nucleotide Variants in Exome Sequencing Studies

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The detection of causative nonsynonymous single nucleotide variants (SNVs) is essential for the understanding of the pathogenesis of human inherited diseases. In this paper, we propose a statistical method called SPRING (Snv PRioritization via the INtegration of Genomic data) to combine six functional effect scores calculated by existing methods and five association scores derived from multiple genomic data sources to estimate the statistical significance that a nonsynonymous SNV is pathogenic for a query disease. We find that SPRING is effective in identifying disease-causing SNVs for diseases whose genetic bases are either partly known or completely unknown across a variety of inheritance styles. With real exome sequencing data, we show the qualified potential of SPRING in not only the detection of causative SNVs in simulation studies but also the identification of pathogenic de novo mutations for autism, epileptic encephalopathies and intellectual disability.

Vyšlo v časopise: Integrating Multiple Genomic Data to Predict Disease-Causing Nonsynonymous Single Nucleotide Variants in Exome Sequencing Studies. PLoS Genet 10(3): e32767. doi:10.1371/journal.pgen.1004237
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004237

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