Improved functional mapping with GSA-MiXeR implicates biologically specific gene-sets and estimates enrichment magnitude

While genome-wide association studies (GWAS) are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. We developed the GSA-MiXeR analytical tool for gene-set analysis (GSA), which fits a model for gene-set heritability enrichments for complex traits, accounting for linkage disequilibrium across variants, and allowing the quantification of partitioned heritability and fold enrichment for small gene-sets. We validate the method using extensive simulations and sensitivity analyses. When applied to height and schizophrenia, GSA-MiXeR implicates gene-sets with greater biological specificity compared to standard GSA approaches, including insulin-like growth factor for height, as well as calcium channel function, GABAergic and dopaminergic signaling for schizophrenia. Such biologically relevant gene-sets, often with less than ten genes, are more likely to provide new insights into the pathobiology of complex diseases and highlight potential drug targets.