Detecting rare haplotypes associated with complex diseases using both population and family data: Combined logistic Bayesian Lasso

Abstract

Haplotype-based association methods have been developed to understand the genetic architecture of complex diseases. Compared to single-variant-based methods, haplotype methods are thought to be more biologically relevant, since there are typically multiple non-independent genetic variants involved in complex diseases, and the use of haplotypes implicitly accounts for non-independence caused by linkage disequilibrium. In recent years, with the focus moving from common to rare variants, haplotype-based methods have also evolved accordingly to uncover the roles of rare haplotypes. One particular approach is regularization-based, with the use of Bayesian least absolute shrinkage and selection operator (Lasso) as an example. This type of methods has been developed for either case-control population data (the logistic Bayesian Lasso (LBL)) or family data (family-triad-based logistic Bayesian Lasso (famLBL)). In some situations, both family data and case-control data are available; therefore, it would be a waste of resources if only one of them could be analyzed. To make full usage of available data to increase power, we propose a unified approach that can combine both case-control and family data (combined logistic Bayesian Lasso (cLBL)). Through simulations, we characterized the performance of cLBL and showed the advantage of cLBL over existing methods. We further applied cLBL to the Framingham Heart Study data to demonstrate its utility in real data applications.

Detecting rare haplotype association with two correlated phenotypes of binary and continuous types

“…The basic method for a single binary (case‐control) phenotype was proposed by Biswas and Lin 28 and since then has been extended in many different directions. There are extensions to model gene‐environment interactions, 29‐31 data resulting from complex sampling designs, 32 family data, 33,34 continuous phenotype, 35 longitudinal phenotype, 36 and combination of population‐based and family data 37,38 . It has been also shown to be one of the most powerful methods for detecting rare haplotype association and its interaction with an environmental covariate 39‐43 .…”

“…There are extensions to model gene-environment interactions, [29][30][31] data resulting from complex sampling designs, 32 family data, 33,34 continuous phenotype, 35 longitudinal phenotype, 36 and combination of population-based and family data. 37,38 It has been also shown to be one of the most powerful methods for detecting rare haplotype association and its interaction with an environmental covariate. [39][40][41][42][43] Most recently, we extended LBL to jointly model two correlated binary disease phenotypes, referred to as bivariate LBL earlier.…”