Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis

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• 作者：Longfei Wang ; Sungyoung Lee ; Jungsoo Gim ; Dandi Qiao ; Michael Cho ; Robert C Elston ; Edwin K Silverman and Sungho Won
• 刊名：Genetic Epidemiology
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：40
• 期：6
• 页码：502-511
• 全文大小：202K
• ISSN：1098-2272

文摘

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at kr/software/mfarvat/" title="Link to external resource: http://healthstat.snu.ac.kr/software/mfarvat/" target="_blank">http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.