{"doi":"10.1101/2025.10.07.681018","title":"Comprehensive gene heritability estimation reveals the genetic architecture of rare coding variants underlying complex traits","abstract":"Whole-exome sequencing (WES) enables high-resolution interrogation of the contribution of rare coding variants to complex trait variation. However, existing methods for heritability estimation attributed to rare-coding variants are often limited by the effects of linkage disequilibrium (LD) and by the sparse nature of rare variant data. We introduce FLEX (Fast, LD-aware Estimation of eXome-wide and gene-level heritability), a scalable and flexible framework for estimating and partitioning heritability across genes or sets of genes using WES data. FLEX integrates all coding variants—from common to ultra-rare—within a unified model and corrects for LD-induced effects to improve the accuracy of heritability estimates. In addition, FLEX supports both individual-level and summary statistic data and is computationally efficient for biobank-scale datasets. Through extensive simulations, we show that FLEX is well-calibrated while providing accurate heritability estimates. We applied FLEX to WES data across N = 153, 351 unrelated European ancestry individuals and 20 quantitative traits in the UK Biobank. We identified 64 gene-trait pairs with significant gene-level heritability (p < 0.05/18, 624 accounting for the number of protein-coding genes tested), among which rare coding variants explained 38% of gene-level heritability, on average. Compared to heritability estimates from genome-wide imputed SNPs, incorporation of rare and ultra-rare coding variants led to a 24.8% increase in heritability on average, while effect sizes at rare and ultra-rare variants are substantially larger (≈18x on average). Partitioning across variant effect annotations, we find that predicted loss-of-function variants had stronger individual effects than missense variants (24% on average) while missense variants accounted for a greater share of rare coding heritability. Together, FLEX provides an adaptable and accurate approach for quantifying gene-level heritability, advancing our understanding of the genetic architecture of complex traits, and facilitating the discovery of trait-relevant genes.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":577216,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9345,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1077042,"name":"Boyang Fu","orcid":"0000-0002-0082-8735","position":1,"is_corresponding":false},{"id":1331189,"name":"Moonseong Jeong","orcid":"0009-0009-3836-2676","position":2,"is_corresponding":false},{"id":1162628,"name":"Prateek Anand","orcid":"0009-0004-8455-1308","position":3,"is_corresponding":false},{"id":1163058,"name":"Aakarsh Anand","orcid":null,"position":4,"is_corresponding":false},{"id":14658,"name":"Seon-Kyeong Jang","orcid":"0000-0002-8533-6894","position":5,"is_corresponding":false},{"id":1305622,"name":"Aditya Gorla","orcid":"0000-0003-0849-7894","position":6,"is_corresponding":false},{"id":1444037,"name":"Jiazheng Zhu","orcid":null,"position":7,"is_corresponding":false},{"id":49873,"name":"Päivi Pajukanta","orcid":"0000-0002-6423-8056","position":8,"is_corresponding":false},{"id":24618,"name":"Pier Francesco Palamara","orcid":"0000-0002-7999-1972","position":9,"is_corresponding":false},{"id":24103,"name":"Noah Zaitlen","orcid":"0000-0002-3553-3670","position":10,"is_corresponding":false},{"id":844281,"name":"Richard Border","orcid":"0000-0002-6293-2968","position":11,"is_corresponding":false},{"id":36127,"name":"Sriram Sankararaman","orcid":"0000-0003-1586-9641","position":12,"is_corresponding":false},{"id":1077457,"name":"Zhengtong Liu","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:58:04.622308Z","pmid":"41278985","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":[]}