Bayesian latent variable collapsing model for detecting rare variant interaction effect in twin study

By analyzing more next-generation sequencing data, researchers have affirmed that rare genetic variants are widespread among populations and likely play an important role in complex phenotypes. Recently, a handful of statistical models have been developed to analyze rare variant (RV) association in different study designs. However, due to the scarce occurrence of minor alleles in data, appropriate statistical methods for detecting RV interaction effects are still difficult to develop. We propose a hierarchical Bayesian latent variable collapsing method (BLVCM), which circumvents the obstacles by parameterizing the signals of RVs with latent variables in a Bayesian framework and is parameterized for twin data. The BLVCM can tackle nonassociated variants, allow both protective and deleterious effects, capture SNP-SNP synergistic effect, provide estimates for the gene level and individual SNP contributions, and can be applied to both independent and various twin designs. We assessed the statistical properties of the BLVCM using simulated data, and found that it achieved better performance in terms of power for interaction effect detection compared to the Granvil and the SKAT. As proof of practical application, the BLVCM was then applied to a twin study analysis of more than 20,000 gene regions to identify significant RVs associated with low-density lipoprotein cholesterol level. The results show that some of the findings are consistent with previous studies, and we identified some novel gene regions with significant SNP-SNP synergistic effects.