Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.
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.
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
Longfei
L
Wang
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
Sungyoung
S
Lee
Institute of Health and Environment, Seoul National University, Seoul, Korea.
Jungsoo
J
Gim
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
Dandi
D
Qiao
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.|Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.
Michael
M
Cho
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America.
Robert C
RC
Elston
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.|Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.
Edwin K
EK
Silverman
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.|Graduate School of Public Health, Seoul National University, Seoul, Korea.
Sungho
S
Won
D003198:Computer Simulation; D056726:Genetic Association Studies; D014644:Genetic Variation; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D010641:Phenotype
association analysis; family-based design; multivariate phenotypes; rare variants