title: A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects.
abstract: For region-based sequencing data, power to detect genetic associations can be improved through analysis of multiple related phenotypes. With this motivation, we propose a novel test to detect association simultaneously between a set of rare variants, such as those obtained by sequencing in a small genomic region, and multiple continuous phenotypes. We allow arbitrary correlations among the phenotypes and build on a linear mixed model by assuming the effects of the variants follow a multivariate normal distribution with a zero mean and a specific covariance matrix structure. In order to account for the unknown correlation parameter in the covariance matrix of the variant effects, a data-adaptive variance component test based on score-type statistics is derived. As our approach can calculate the P-value analytically, the proposed test procedure is computationally efficient. Broad simulations and an application to the UK10K project show that our proposed multivariate test is generally more powerful than univariate tests, especially when there are pleiotropic effects or highly correlated phenotypes.
authors: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.|Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
authors: Jianping
authors: J
authors: Sun
authors: Department of Mathematics, Université du Québec À Montréal, Montreal, QC, Canada.
authors: Karim
authors: K
authors: Oualkacha
authors: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.|Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.|Department of Human Genetics, McGill University, Montreal, QC, Canada.|Department of Medicine, Jewish General Hospital, Montreal, QC, Canada.
authors: Vincenzo
authors: V
authors: Forgetta
authors: Institute of Aging Research, School of Medicine, Hangzhou Normal University, Hangzhou, Zhejiang, China.|Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.
authors: Hou-Feng
authors: HF
authors: Zheng
authors: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.|Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.|Department of Human Genetics, McGill University, Montreal, QC, Canada.|Department of Medicine, Jewish General Hospital, Montreal, QC, Canada.|Department of Twin Research, King's College London, London, UK.
authors: J
authors: J
authors: Brent Richards
authors: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
authors: Antonio
authors: A
authors: Ciampi
authors: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.|Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.|Department of Human Genetics, McGill University, Montreal, QC, Canada.|Department of Oncology, McGill University, Montreal, QC, Canada.
authors: Celia Mt
authors: 0000-0002-2427-5696
authors: CM
authors: Greenwood
mesh_terms: D000465:Algorithms; D058685:Genetic Pleiotropy; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015810:Linkage Disequilibrium; D011110:Polymorphism, Genetic; D012680:Sensitivity and Specificity