Reducing the exome search space for mendelian diseases using genetic linkage analysis of exome genotypes.
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ABSTRACT:
SUBMITTER: Smith KR
PROVIDER: S-EPMC3308048 | biostudies-literature | 2011 Sep
REPOSITORIES: biostudies-literature
Publications
Reducing the exome search space for mendelian diseases using genetic linkage analysis of exome genotypes.
Smith Katherine R KR Bromhead Catherine J CJ Hildebrand Michael S MS Shearer A Eliot AE Lockhart Paul J PJ Najmabadi Hossein H Leventer Richard J RJ McGillivray George G Amor David J DJ Smith Richard J RJ Bahlo Melanie M
Genome biology 20110914 9
Many exome sequencing studies of Mendelian disorders fail to optimally exploit family information. Classical genetic linkage analysis is an effective method for eliminating a large fraction of the candidate causal variants discovered, even in small families that lack a unique linkage peak. We demonstrate that accurate genetic linkage mapping can be performed using SNP genotypes extracted from exome data, removing the need for separate array-based genotyping. We provide software to facilitate suc ...[more]
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Project description:MotivationIncreasingly, cost-effective high-throughput DNA sequencing technologies are being utilized to sequence human pedigrees to elucidate the genetic cause of a wide variety of human diseases. While numerous tools exist for variant prioritization within a single genome, the ability to concurrently analyze variants within pedigrees remains a challenge, especially should there be no prior indication of the underlying genetic cause of the disease. Here, we present a tool, variant analysis of sequenced pedigrees (VASP), a flexible data integration environment capable of producing a summary of pedigree variation, providing relevant information such as compound heterozygosity, genome phasing and disease inheritance patterns. Designed to aggregate data across a sequenced pedigree, VASP allows both powerful filtering and custom prioritization of both single nucleotide variants (SNVs) and small indels. Hence, clinical and research users with prior knowledge of a disease are able to dramatically reduce the variant search space based on a wide variety of custom prioritization criteria.Availability and implementationSource code available for academic non-commercial research purposes at https://github.com/mattmattmattmatt/VASP.
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