Abstract
Univariate genome-wide association analysis of quantitative and qualitative traits has been investigated extensively in the literature. In the presence of correlated phenotypes, it is more intuitive to analyze all phenotypes simultaneously. We describe an efficient likelihood-based approach for the joint association analysis of quantitative and qualitative traits in unrelated individuals. We assume a probit model for the qualitative trait, under which an unobserved latent variable and a prespecified threshold determine the value of the qualitative trait. To jointly model the quantitative and qualitative traits, we assume that the quantitative trait and the latent variable follow a bivariate normal distribution. The latent variable is allowed to be correlated with the quantitative phenotype. Simultaneous modeling of the quantitative and qualitative traits allows us to make more precise inference on the pleiotropic genetic effects. We derive likelihood ratio tests for the testing of genetic effects. An application to the Genetic Analysis Workshop 17 data is provided. The new method yields reasonable power and meaningful results for the joint association analysis of the quantitative trait Q1 and the qualitative trait disease status at SNPs with not too small MAF.
Highlights
Statistical methods for the univariate association analysis of quantitative and qualitative traits have been well developed in the literature
Let Xi denote a vector of covariates, including the intercept and environmental variables, and let Zi denote a vector of genotype score(s) at the major single-nucleotide polymorphism (SNP) locus
In this paper, we described a likelihood-based approach for the joint association analysis of quantitative and qualitative traits in unrelated individuals
Summary
Statistical methods for the univariate association analysis of quantitative and qualitative traits have been well developed in the literature. Complex human diseases are often characterized by multiple traits. These traits tend to be correlated with each other because of common environmental and genetic factors. In the genetic analysis of complex diseases, it is natural to account for the correlations among multiple traits and to model them simultaneously. Joint genetic linkage analysis of multiple correlated phenotypes has been studied by Jiang and Zeng [1], Mangin et al [2], Amos and Laing [3], Almasy et al [4], Blangero et al [5], Wijsman and Amos [6], and Williams et al [7,8], among others. Joint linkage analysis of multiple correlated traits can potentially improve the power to detect linkage signals at genes that jointly influence a complex disease
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