To identify risk loci and alleles in complex diseases, researchers have used various study designs, each targeting a unique aspect of the genetic architecture of the disorder. These can range from different technologies (e.g., chromosomal arrays to detect copy number variation), cohorts (e.g., populations or families), variation type (e.g., whole-exome sequencing for rare variants or genome wide associatinos studies for common alleles), etc. Each study design has pros and cons, but generally all return genomic locations and hopefully disease gene candidates. Ideally one would wish to combine the results of these studies. Yet doing so is challenging, mainly because the different study designs are carefully calibrated to enrich for meaningful signals and thus reflect different effects which cannot be naively compared.

Typically, overlapping functional properties of candidate disease genes are exploited to sort out the positive and negative results, but simply grouping gene candidates from different studies ignores the strength of their individual contributions. We develop a general solution which integrates the disparate genetic components constrained by their observed effect sizes to determine functional convergence. Our approach looks not only for similarities in the functional conclusions drawn from each study type individually but also those which are consistent with the known effect sizes across these studies. We name this the “functional effect size trend” and it can be understood as a generalization of a classic meta-analytic method, the funnel plot test.

This repository contains the gene sets used in testing for the functional effect size trend, some results, and methods for performing similar tests.