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.

Abstract: A computer-implemented method of performing an optimized burden test for a particular gene, in which an optimal combination of a maximum allele count and a minimum pathogenicity score threshold that maximize significance of burden testing for rare deleterious variants are determined using a grid search protocol. Each combination of maximum allele count and minimum pathogenicity score threshold is tested with a t-test to obtain effect size and p-value. The combination of allele count and pathogenicity score threshold with the most significant p-value is selected as the optimal parameters for the rare deleterious variant burden test for a particular gene.

Abstract: A computer-implemented method of quantifying a strength of association of genes associated with a phenotype and a contribution of rare variants to a phenotype response by calculating a weighted burden score for a plurality of associated genes with a specified phenotype, wherein the burden score identifies identifying consequential, non-random association in a cohort between carrier status of each of the associated genes and a phenotype response to presence in the associated genes of one or more rare pathogenic variants. Respective effective strength scores are determined for the consequential, non-random association for genes selected from the associated genes based on respective burden scores at per-gene resolution.

Abstract: A computer-implemented method of predicting phenotypic shift in response to usage of a plurality of drugs on a plurality of phenotypes of a cohort of individuals with a plurality of confounders. The cohort of individuals has associated phenotype measurements, covariate measurements, and drug usage patterns for two separate time points. The phenotype measurements for the first and second time points are covariate-corrected and drug-usage corrected through the use of biostatistics.