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Family‐Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis
Author(s) -
Wang Longfei,
Lee Sungyoung,
Gim Jungsoo,
Qiao Dandi,
Cho Michael,
Elston Robert C,
Silverman Edwin K,
Won Sungho
Publication year - 2016
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21985
Subject(s) - multivariate statistics , multivariate analysis , phenotype , computational biology , homogeneous , association test , association (psychology) , biology , computer science , genetics , gene , mathematics , genotype , machine learning , psychology , single nucleotide polymorphism , combinatorics , psychotherapist
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 http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.