Multivariate Quantitative Bayesian LASSO: Detecting rare haplotype association with multiple trait

Music City Center 

Often multiple traits are correlated, and they share underlying genetic factors. Rather than analyzing each phenotype separately, analyzing them jointly improves statistical power and allows biological insight into the shared genetic mechanisms. The Bivariate Quantitative Bayesian LASSO (QBL) was developed to detect rare haplotypes associated with two correlated continuous phenotypes by leveraging a latent variable to model their correlation and using Bayesian regularization to identify rare haplotypes associated with one or both phenotypes. However, its reliance on Markov Chain Monte Carlo (MCMC) for posterior estimation limits scalability as the number of phenotypes increases. To overcome this, we extend bivariate QBL to a Multivariate QBL (mQBL) framework, enabling efficient modeling of rare haplotype associations with multiple phenotypes. We employ Mean Field Variational Bayes (MFVB) for scalable posterior approximation, maintaining methodological rigor while significantly improving computational efficiency. Simulations demonstrate that mQBL performs comparably to bivariate QBL while being substantially faster.

Genetic Association

Rare Haplotype

Multiple Traits

Variational Bayes

Bayesian LASSO

Computational Scalability 

Section on Statistics in Genomics and Genetics