Abstract The identification of rare variants that contribute to complex diseases is challenging due to low statistical power. Here we propose a novel and powerful rare variants association test based on the deviation of the observed mutational burden in a genomic region from a baseline mutation burden predicted by weighted recursive truncated negative-binomial regression (RUNNER) on genomic features available from public data. Simulation studies show that RUNNER is substantially more powerful than state-of-the-art rare variant association methods (including SKAT, CMC and KBAC), while maintaining correct type 1 error rates under population stratification and in small samples. Applied to real data, RUNNER “rediscovered” known genes of Hirschsprung disease missed by current methods, and detected promising new candidate genes, including NXPE4 for Hirschsprung disease and CXCL16 for Alzheimer’s disease. The proposed approach provides a powerful and robust method to identify rare risk variants for complex diseases.

Deviation from baseline mutation burden provides powerful and robust rare-variants association test for complex diseases