{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Weiner DJ"],"funding":["Simons Foundation","U.S. National Library of Medicine","NIMH NIH HHS","SFARI","NHGRI NIH HHS","NLM NIH HHS","National Human Genome Research Institute"],"pagination":["405-416"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8948166"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["109(3)"],"pubmed_abstract":["Unknown SNP-to-gene regulatory architecture complicates efforts to link noncoding GWAS associations with genes implicated by sequencing or functional studies. eQTLs are often used to link SNPs to genes, but expression in bulk tissue explains a small fraction of disease heritability. A simple but successful approach has been to link SNPs with nearby genes via base pair windows, but genes may often be regulated by SNPs outside their window. We propose the abstract mediation model (AMM) to estimate (1) the fraction of heritability mediated by the closest or k<sup>th</sup>-closest gene to each SNP and (2) the mediated heritability enrichment of a gene set (e.g., genes with rare-variant associations). AMM jointly estimates these quantities by matching the decay in SNP enrichment with distance from genes in the gene set. Across 47 complex traits and diseases, we estimate that the closest gene to each SNP mediates 27% (SE: 6%) of heritability and that a substantial fraction is mediated by genes outside the ten closest. Mendelian disease genes are strongly enriched for common-variant heritability; for example, just 21 dyslipidemia genes mediate 25% of LDL heritability (211× enrichment, p = 0.01). Among brain-related traits, genes involved in neurodevelopmental disorders are only about 4× enriched, but gene expression patterns are highly informative, as they have detectable differences in per-gene heritability even among weakly brain-expressed genes."],"journal":["American journal of human genetics"],"pubmed_title":["Partitioning gene-mediated disease heritability without eQTLs."],"pmcid":["PMC8948166"],"funding_grant_id":["F30 MH129009","R00 HG010160","T15 LM007092","T32 HG002295","704413","T15LM007092"],"pubmed_authors":["Robinson EB","O'Connor LJ","Weiner DJ","Gazal S"],"additional_accession":[]},"is_claimable":false,"name":"Partitioning gene-mediated disease heritability without eQTLs.","description":"Unknown SNP-to-gene regulatory architecture complicates efforts to link noncoding GWAS associations with genes implicated by sequencing or functional studies. eQTLs are often used to link SNPs to genes, but expression in bulk tissue explains a small fraction of disease heritability. A simple but successful approach has been to link SNPs with nearby genes via base pair windows, but genes may often be regulated by SNPs outside their window. We propose the abstract mediation model (AMM) to estimate (1) the fraction of heritability mediated by the closest or k<sup>th</sup>-closest gene to each SNP and (2) the mediated heritability enrichment of a gene set (e.g., genes with rare-variant associations). AMM jointly estimates these quantities by matching the decay in SNP enrichment with distance from genes in the gene set. Across 47 complex traits and diseases, we estimate that the closest gene to each SNP mediates 27% (SE: 6%) of heritability and that a substantial fraction is mediated by genes outside the ten closest. Mendelian disease genes are strongly enriched for common-variant heritability; for example, just 21 dyslipidemia genes mediate 25% of LDL heritability (211× enrichment, p = 0.01). Among brain-related traits, genes involved in neurodevelopmental disorders are only about 4× enriched, but gene expression patterns are highly informative, as they have detectable differences in per-gene heritability even among weakly brain-expressed genes.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2025-04-19T23:15:40.377Z","creation":"2025-04-19T23:15:40.377Z"},"accession":"S-EPMC8948166","cross_references":{"pubmed":["35143757"],"doi":["10.1016/j.ajhg.2022.01.010"]}}