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Detection of rare disease variants in extended pedigrees using RVS.
Sherman, Thomas; Fu, Jack; Scharpf, Robert B; Bureau, Alexandre; Ruczinski, Ingo.
Affiliation
  • Sherman T; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.
  • Fu J; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.
  • Scharpf RB; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
  • Bureau A; Département de Médecine Sociale et Préventive, Université Laval, Québec, QC, Canada.
  • Ruczinski I; Centre de Recherche CERVO, Québec, QC, Canada.
Bioinformatics ; 35(14): 2509-2511, 2019 07 15.
Article in En | MEDLINE | ID: mdl-30500888
ABSTRACT

SUMMARY:

Family-based sequencing studies enable researchers to identify highly penetrant genetic variants too rare to be tested in conventional case-control studies, by studying co-segregation of variant and disease phenotypes. When multiple affected subjects in a family are sequenced, the probability that a variant or a set of variants is shared identical-by-descent by some or all affected relatives provides evidence against the null hypothesis of complete absence of linkage and association. The Rare Variant Sharing software package RVS implements a suite of tools to assess association and linkage between rare genetic variants and a dichotomous disease indicator in family pedigrees. AVAILABILITY AND IMPLEMENTATION RVS is available as open source software from the Bioconductor webpage at https//bioconductor.org/packages/release/bioc/html/RVS.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Rare Diseases Type of study: Diagnostic_studies / Observational_studies / Risk_factors_studies Limits: Humans Language: En Journal: Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2019 Type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Rare Diseases Type of study: Diagnostic_studies / Observational_studies / Risk_factors_studies Limits: Humans Language: En Journal: Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2019 Type: Article