Localizing Rare-Variant Association Regions via Multiple Testing Embedded in an Aggregation Tree

Abstract

Rare variants (RVs) genetic association studies enable researchers to uncover the variation in phenotypic traits left unexplained by common variation. Traditional single-variant analysis lacks power; thus, researchers have developed various methods to aggregate the effects of RVs across genomic regions to study their collective impact. Some existing methods utilize a static delineation of genomic regions, often resulting in suboptimal effect aggregation, as neutral subregions within the test region will result in an attenuation of signal. Other methods use varying windows to search for signals but often result in long regions containing many neutral RVs. To pinpoint short genomic regions enriched for disease-associated RVs, we developed a novel method, DYNATE (DYNamic Aggregation TEsting). DYNATE dynamically and hierarchically aggregates smaller genomic regions into larger ones and performs multiple testing for disease associations with controlled weighted false discovery rate. Extensive numerical simulations demonstrate the superior performance of DYNATE under various scenarios compared to existing methods. Importantly, DYNATE-identified regions have higher enrichment levels of disease-associated RVs. We applied DYNATE to an amyotrophic lateral sclerosis (ALS) study and identified a new gene, EPG5, harboringpossibly pathogenic mutations.