Gazal, StevenWeissbrod, OmerHormozdiari, FarhadDey, KushalNasser, JosephJagadeesh, KarthikWeiner, DanielShi, HuwenboFulco, CharlesO’Connor, LukePasaniuc, BogdanEngreitz, Jesse M.Price, Alkes L.2023-08-212022-06Gazal, Steven, Omer Weissbrod, Farhad Hormozdiari, Kushal Dey, Joseph Nasser, Karthik Jagadeesh, Daniel Weiner et al. "Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity." Nat Genet 54, no. 6 (2022): 827-836. DOI: 10.1038/s41588-022-01087-y1061-40361546-1718https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37376879Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis. Here, we developed a heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk. Our optimal combined S2G strategy (cS2G) included seven constituent S2G strategies and achieved a precision of 0.75 and a recall of 0.33, more than doubling the recall of any individual strategy. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 5,095 causal SNP–gene-disease triplets (with S2G-derived functional interpretation) with high confidence. We further applied cS2G to provide an empirical assessment of disease omnigenicity; we determined that the top 1% of genes explained roughly half of the SNP heritability linked to all genes and that gene-level architectures vary with variant allele frequency.en-USGeneticsJournal Article2023-08-2110.1038/s41588-022-01087-y