Identification of disease-causing single nucleotide variants in

第十届中国R会议【直播】

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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. Here, we propose bioinformatics approaches, SPRING, snvForest and GLINTS, for identifying pathogenic nonsynonymous SNVs for a given query disease. SPRING integrates six functional effect scores calculated by existing methods and five association scores derived from a variety of genomic data sources to calculate the statistical significance that an SNV is causative for a query disease. snvForest adopts an ensemble learning method to assign prediction scores to candidate SNVs. These methods are designed to use with a set of seed genes known as associated with the disease of interest, and thus is suitable for studies on diseases with some prior knowledge. GLINTS further incorporates three disease phenotype similarity data to facilitate the detection of causative SNVs without any knowledge of seed genes for a query disease. This method is therefore suitable for research on diseases whose genetic bases are completely unknown. With a series of comprehensive validation experiments, we demonstrate the effectiveness of these methods, not only in simulation studies, but also in detecting causative de novo mutations for autism, epileptic encephalopathies and intellectual disability.

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