Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes with two correlated continuous phenotypes - Sajal, Ibrahim Hossain; Biswas, Swati (Frontiers in Genetics, 2023)
In genetic association studies, multivariate analysis of correlated phenotypes offers statistical and biological advantages compared to analyzing one phenotype at a time. The joint analysis utilizes additional information contained in the correlation and avoids multiple testing. It also provides an opportunity to investigate and understand shared genetic mechanisms of multiple phenotypes. Bivariate logistic Bayesian LASSO (LBL) was proposed earlier to detect rare haplotypes associated with two binary phenotypes or one binary and one continuous phenotypes jointly. There is currently no haplotype association test available that can handle multiple continuous phenotypes. Employing the framework of bivariate LBL, here we propose bivariate quantitative Bayesian LASSO (QBL) to detect rare haplotypes associated with two continuous phenotypes. Bivariate QBL weeds out the unassociated haplotypes by regularizing the regression coefficients and utilizes a latent variable to model correlation between the two phenotypes. We carry out extensive simulations to investigate the performance of bivariate QBL and compare it with that of a standard (univariate) haplotype association test, Haplo.score (applied twice to two phenotypes individually). Bivariate QBL performs better than Haplo.score in all simulations with varying degree of power gain. We analyze Genetic Analysis Workshop 19 exome sequence data on systolic and diastolic blood pressures and detect several rare haplotypes associated with the two phenotypes jointly.