Chunk #66 — ONLINE METHODS — Enrichment of genes in associated loci in known and novel pathways — Data-Driven Expression-Prioritized Integration for Complex Traits (DEPICT) analysis
The DEPICT method (T.H.P. et al., unpublished data; see Geller et al.29 for an earlier application of DEPICT) relies on pre-computed predictions of gene function based on a heterogeneous panel of 77,840 expression arrays (Fehrmann et al., manuscript in review; ref. 30), 5,984 molecular pathways (based on 169,810 high-confidence experimentally derived protein-protein interactions31), 2,473 phenotypic gene sets (based on 211,882 gene-phenotype pairs from the Mouse Genetics Initiative (see URLs)), 737 Reactome pathways32, 5,083 Gene Ontology terms14, and 184 KEGG pathways33. The method leverages these predictions to extend the functional annotations of genes, including genes that previously had only a few or no functional annotations. DEPICT facilitates the analysis of GWAS data by (1) assessing whether genes in associated loci are enriched in tissue-specific expression, (2) identifying reconstituted gene sets that are enriched in genes from associated loci, and (3) systematically identifying the most likely causal gene(s) at a given locus (see Supplementary Note for a more detailed description of DEPICT). In order to run DEPICT, we first clumped the summary statistics from the meta-analysis using 500kb flanking regions, r2>0.1, and
