Ming-Huei Chen1, Qiong Yang1. 1. Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA, Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart Lung and Blood Institute, National Institutes of Health, Framingham, MA 01702, USA.
Abstract
UNLABELLED: Family-based designs offer unique advantage for identifying rare risk variants in genetic association studies. There are existing tools for analyzing rare variants in families but lacking components to handle binary traits properly and survival traits. In this report, we introduce an R software package RVFam (Rare Variant association analysis with Family data) designed to analyze continuous, binary and survival traits against rare and common sequencing variants in genome-wide association studies (GWAS) involving family data. Single and multiple variant association tests were implemented while accounting for arbitrary family structures. Extensive simulation studies were performed to evaluate all the approaches implemented in RVFam. AVAILABILITY AND IMPLEMENTATION: http://cran.r-project.org/web/packages/RVFam/.
UNLABELLED: Family-based designs offer unique advantage for identifying rare risk variants in genetic association studies. There are existing tools for analyzing rare variants in families but lacking components to handle binary traits properly and survival traits. In this report, we introduce an R software package RVFam (Rare Variant association analysis with Family data) designed to analyze continuous, binary and survival traits against rare and common sequencing variants in genome-wide association studies (GWAS) involving family data. Single and multiple variant association tests were implemented while accounting for arbitrary family structures. Extensive simulation studies were performed to evaluate all the approaches implemented in RVFam. AVAILABILITY AND IMPLEMENTATION: http://cran.r-project.org/web/packages/RVFam/.
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