Adaptive clustering and adaptive weighting methods to detect disease associated rare variants.

Current statistical methods to test association between rare variants and phenotypes are essentially the group-wise methods that collapse or aggregate all variants in a predefined group into a single variant. Comparing with the variant-by-variant methods, the group-wise methods have their advantages. However, two factors may affect the power of these methods. One is that some of the causal variants may be protective. When both risk and protective variants are presented, it will lose power by collapsing or aggregating all variants because the effects of risk and protective variants will counteract each other. The other is that not all variants in the group are causal; rather, a large proportion is believed to be neutral. When a large proportion of variants are neutral, collapsing or aggregating all variants may not be an optimal solution. We propose two alternative methods, adaptive clustering (AC) method and adaptive weighting (AW) method, aiming to test rare variant association in the presence of neutral and/or protective variants. Both of AC and AW are applicable to quantitative traits as well as qualitative traits. Results of extensive simulation studies show that AC and AW have similar power and both of them have clear advantages from power to computational efficiency comparing with existing group-wise methods and existing data-driven methods that allow neutral and protective variants. We recommend AW method because AW method is computationally more efficient than AC method.