A Hybrid Likelihood Model for Sequence-Based Disease Association Studies

In the past few years,case-control studies of common diseases have shifted their focus from single genes to whole exomes. new sequencing technologies now routinely detect hundreds of thousands of sequence variants in a single study,many of which are rare or even novel. the limitation of classical single-marker association analysis for rare variants has been a challenge in such studies. a new generation of statistical methods for case-control association studies has been developed to meet this challenge. a common approach to association analysis of rare variants is the burden-style collapsing methods to combine rare variant data within individuals across or within genes. here,we propose a new hybrid likelihood model that combines a burden test with a test of the position distribution of variants. in extensive simulations and on empirical data from the dallas heart study,the new model demonstrates consistently good power,in particular when applied to a gene set (e.g.,multiple candidate genes with shared biological function or pathway),when rare variants cluster in key functional regions of a gene,and when protective variants are present. when applied to data from an ongoing sequencing study of bipolar disorder (191 cases,107 controls),the model identifies seven gene sets with nominal p-values<0.05,of which one mapk signaling pathway (kegg) reaches trend-level significance after correcting for multiple testing. © 2013 chen et al.