Abstract: Genome-wide association studies may allow for detection of variants that are statistically significantly associated with disease risk. However, inferring which are the genes underlying these variant associations may be difficult. The presently disclosed approaches utilize machine learning techniques to predict genes from genome-wide association study summary statistics that substantially improves causal gene identification in terms of both precision and recall compared to other techniques.