Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes.

ABSTRACT: In genetic association studies, joint modeling of related traits/phenotypes can utilize the correlation between them and thereby provide more power and uncover additional information about genetic etiology. Moreover, detecting rare genetic variants are of current scientific interest as a key to missing heritability. Logistic Bayesian LASSO (LBL) has been proposed recently to detect rare haplotype variants using case-control data, that is, a single binary phenotype. As there is currently no haplotype association method that can handle multiple binary phenotypes, we extend LBL to fill this gap. We develop a bivariate model by using a latent variable to induce correlation between the two outcomes. We carry out extensive simulations to investigate the bivariate LBL and compare with the univari

SUBMITTER: Yuan X 

PROVIDER: S-EPMC6836722 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature