Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits

• Traditional univariate genome-wide association studies generate false positives and nega-tives due to difficulties distinguishing associated variants from variants with spurious non-zero effects that do not directly influence the trait. Recent efforts have been directed atidentifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict modelassumptions. Here, we present gene-ε, a new approach for identifying statistical associa-tions between sets of variants and quantitative traits. Our key insight is that enrichment stud-ies on the gene-level are improved when we reformulate the genome-wide SNP-level nullhypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-ε efficiently identifies enriched genes under a variety of simulated geneticarchitectures, achieving greater than a 90% true positive rate at 1% false positive rate forpolygenic traits. Lastly, we apply gene-ε to summary statistics derived from six quantitativetraits using European-ancestry individuals in the UK Biobank, and identify enriched genesthat are in biologically relevant pathways.

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