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Exome-based mapping and variant prioritization for inherited Mendelian disorders.


ABSTRACT: Exome sequencing in families affected by rare genetic disorders has the potential to rapidly identify new disease genes (genes in which mutations cause disease), but the identification of a single causal mutation among thousands of variants remains a significant challenge. We developed a scoring algorithm to prioritize potential causal variants within a family according to segregation with the phenotype, population frequency, predicted effect, and gene expression in the tissue(s) of interest. To narrow the search space in families with multiple affected individuals, we also developed two complementary approaches to exome-based mapping of autosomal-dominant disorders. One approach identifies segments of maximum identity by descent among affected individuals; the other nominates regions on the basis of shared rare variants and the absence of homozygous differences between affected individuals. We showcase our methods by using exome sequence data from families affected by autosomal-dominant retinitis pigmentosa (adRP), a rare disorder characterized by night blindness and progressive vision loss. We performed exome capture and sequencing on 91 samples representing 24 families affected by probable adRP but lacking common disease-causing mutations. Eight of 24 families (33%) were revealed to harbor high-scoring, most likely pathogenic (by clinical assessment) mutations affecting known RP genes. Analysis of the remaining 17 families identified candidate variants in a number of interesting genes, some of which have withstood further segregation testing in extended pedigrees. To empower the search for Mendelian-disease genes in family-based sequencing studies, we implemented them in a cross-platform-compatible software package, MendelScan, which is freely available to the research community.

SUBMITTER: Koboldt DC 

PROVIDER: S-EPMC3951946 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Exome sequencing in families affected by rare genetic disorders has the potential to rapidly identify new disease genes (genes in which mutations cause disease), but the identification of a single causal mutation among thousands of variants remains a significant challenge. We developed a scoring algorithm to prioritize potential causal variants within a family according to segregation with the phenotype, population frequency, predicted effect, and gene expression in the tissue(s) of interest. To  ...[more]

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