{"doi":"10.1101/2021.08.17.456722","title":"Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes","abstract":"Abstract Recent works have shown that SNP-heritability—which is dominated by low-effect common variants—may not be the most relevant quantity for localizing high-effect/critical disease genes. Here, we introduce methods to estimate the proportion of phenotypic variance explained by a given assignment of SNPs to a single gene ( genelevel heritability ). We partition gene-level heritability across minor allele frequency (MAF) classes to find genes whose gene-level heritability is explained exclusively by “low-frequency/rare” variants (0.5% ≤ MAF < 1%). Applying our method to ~17K protein-coding genes and 25 quantitative traits in the UK Biobank (N=290K), we find that, on average across traits, ~2.5% of nonzero-heritability genes have a rare-variant component, and only ~0.8% (370 gene-trait pairs) have heritability exclusively from rare variants. Of these 370 gene-trait pairs, 37% were not detected by existing gene-level association testing methods, likely because existing methods combine signal from all variants in a region irrespective of MAF class. Many of the additional genes we identify are implicated in phenotypically related Mendelian disorders or congenital developmental disorders, providing further evidence of their trait-relevance. Notably, the rare-variant component of gene-level heritability exhibits trends different from those of common-variant gene-level heritability. For example, while total gene-level heritability increases with gene length, the rare-variant component is significantly larger among shorter genes; the cumulative distributions of gene-level heritability also vary across traits and reveal differences in the relative contributions of rare/common variants to overall gene-level polygenicity. We conclude that the proportion of gene-level heritability attributable to low-frequency/rare variation can yield novel insights into complex-trait genetic architecture.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":219244,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9458,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":311857,"name":"Kangcheng Hou","orcid":"0000-0001-7110-5596","position":1,"is_corresponding":false},{"id":551627,"name":"Yi Ding","orcid":"0000-0003-3595-2493","position":2,"is_corresponding":false},{"id":817778,"name":"Yifei Wang","orcid":"0000-0002-2371-4036","position":3,"is_corresponding":false},{"id":558848,"name":"Steven Gazal","orcid":"0000-0001-5714-7597","position":4,"is_corresponding":false},{"id":273430,"name":"Huwenbo Shi","orcid":"0000-0001-9886-877X","position":5,"is_corresponding":false},{"id":731,"name":"Bogdan Paşaniuc","orcid":"0000-0002-0227-2056","position":6,"is_corresponding":false},{"id":273431,"name":"Kathryn S. Burch","orcid":"0000-0001-9624-2108","position":0,"is_corresponding":true}],"reference_count":98,"raw_metadata":null,"created_at":"2026-07-18T23:53:38.409982Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}