Premium
Multivariate association test for rare variants controlling for cryptic and family relatedness
Author(s) -
Sun Jianping,
Oualkacha Karim,
Greenwood Celia M.T.,
LakhalChaieb Lajmi
Publication year - 2019
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11475
Subject(s) - univariate , multivariate statistics , copula (linguistics) , test statistic , statistic , association test , statistics , multivariate analysis , statistical hypothesis testing , summary statistics , pleiotropy , phenotype , econometrics , biology , mathematics , genetics , single nucleotide polymorphism , genotype , gene
Abstract In genetic studies of complex diseases, multiple measures of related phenotypes are often collected. Jointly analyzing these phenotypes may improve statistical power to detect sets of rare variants affecting multiple traits. In this work, we consider association testing between a set of rare variants and multiple phenotypes in family‐based designs. We use a mixed linear model to express the correlations among the phenotypes and between related individuals. Given the many sources of correlations in this situation, deriving an appropriate test statistic is not straightforward. We derive a vector of score statistics, whose joint distribution is approximated using a copula. This allows us to have closed‐form expressions for the p ‐values of several test statistics. A comprehensive simulation study and an application to Genetic Analysis Workshop 18 data highlight the gains associated with joint testing over univariate approaches, especially in the presence of pleiotropy or highly correlated phenotypes. The Canadian Journal of Statistics 47: 90–107; 2019 © 2018 Statistical Society of Canada