title: A rare variant association test in family-based designs and non-normal quantitative traits.
abstract: Rare variant studies are now being used to characterize the genetic diversity between individuals and may help to identify substantial amounts of the genetic variation of complex diseases and quantitative phenotypes. Family data have been shown to be powerful to interrogate rare variants. Consequently, several rare variants association tests have been recently developed for family-based designs, but typically, these assume the normality of the quantitative phenotypes. In this paper, we present a family-based test for rare-variants association in the presence of non-normal quantitative phenotypes. The proposed model relaxes the normality assumption and does not specify any parametric distribution for the marginal distribution of the phenotype. The dependence between relatives is modeled via a Gaussian copula. A score-type test is derived, and several strategies to approximate its distribution under the null hypothesis are derived and investigated. The performance of the proposed test is assessed and compared with existing methods by simulations. The methodology is illustrated with an association study involving the adiponectin trait from the UK10K project.
authors.affiliation: Département de mathématiques et statistique, Université Laval, Québec, G1V 0A6, Québec, Canada.
authors.forename: Lajmi
authors.initials: L
authors.lastname: Lakhal-Chaieb
authors.affiliation: Département de mathématiques, Université de Québec À Montréal, Montreal, Québec, Canada.
authors.forename: Karim
authors.initials: K
authors.lastname: Oualkacha
authors.affiliation: Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.|Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.|Department of Twin Research, King's College London, London, U.K.
authors.forename: Brent J
authors.initials: BJ
authors.lastname: Richards
authors.affiliation: Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.|Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.|Departments of Oncology and Human Genetics, McGill University, Montreal, Québec, Canada.
authors.forename: Celia M T
authors.initials: CM
authors.lastname: Greenwood
mesh_terms: D052242:Adiponectin / Q000032:analysis / Q000235:genetics*; D003198:Computer Simulation; D005190:Family; D056726:Genetic Association Studies; D014644:Genetic Variation; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D016011:Normal Distribution; D010641:Phenotype; D040641:Quantitative Trait Loci; D014427:Twins / Q000235:genetics*; D006113:United Kingdom
chemical_list: D052242:Adiponectin
keywords: Gaussian copulas; Kernel machine regression; association tests; rare variants; region-based tests; score test; variance components