{"doi":"10.1101/2025.09.08.25335346","title":"Cumulative Genetic Risk for Asthma Contributes to Disease Severity in Children with Asthma","abstract":"ABSTRACT Background Childhood-onset asthma is highly heritable, with nearly 200 risk loci identified in genome-wide association studies. Aggregated polygenic risk scores can be used to quantify genetic predisposition to asthma, but their power to predict asthma severity in multi-ancestral groups has not been previously evaluated. Objective Our aim was to examine the predictive power of biobank-derived asthma polygenic risk scores in four multi-ancestry asthma study cohorts of children living in U.S. urban environments. Methods We generated polygenic risk scores for asthma, derived from a large-scale genome-wide association meta-analysis, and assessed genetic predictions across different subphenotypes of asthma and tested for associations between genetic asthma risk and measures of asthma severity. Results Polygenic risk score prediction was significantly stronger for more symptomatic asthma phenotypes (P<0.001), and scores were significantly higher in difficult-to-control vs. easy-to-control asthma (P=0.02). Genetic risk was also significantly associated with more frequent exacerbations (P=0.03), higher blood eosinophil levels (P=0.01), and lower lung function (P<0.001). Conclusion Cumulative genetic risk for asthma is associated with disease severity and exacerbation risk in children with asthma. Key Messages Polygenic risk prediction is stronger in more symptomatic phenotypes Cumulative genetic risk for asthma is associated with greater asthma severity, higher exacerbation frequency, lower lung function, and increased eosinophil levels in children with asthma Capsule Summary We demonstrate that cumulative genetic risk for asthma is associated with disease severity in children with asthma living in urban environments.","journal":"medRxiv","year":2025,"id":574767,"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":0.9506,"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":996611,"name":"William Wentworth-Sheilds","orcid":null,"position":1,"is_corresponding":false},{"id":491825,"name":"Emma E. 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Bacharier","orcid":"0000-0003-0432-2704","position":11,"is_corresponding":false},{"id":249628,"name":"Stephanie Lovinsky‐Desir","orcid":"0000-0003-2272-4387","position":12,"is_corresponding":false},{"id":1482802,"name":"Michele A. Gill","orcid":null,"position":13,"is_corresponding":false},{"id":317027,"name":"William J. Sheehan","orcid":"0000-0002-4680-4923","position":14,"is_corresponding":false},{"id":336379,"name":"Shilpa J. Patel","orcid":"0000-0002-9287-1430","position":15,"is_corresponding":false},{"id":78355,"name":"Matthew C. Altman","orcid":"0000-0002-1784-8505","position":16,"is_corresponding":false},{"id":110411,"name":"James E. Gern","orcid":"0000-0002-6667-4708","position":17,"is_corresponding":false},{"id":110403,"name":"Cynthia M. Visness","orcid":"0000-0002-7010-3481","position":18,"is_corresponding":false},{"id":110408,"name":"Peter J. Gergen","orcid":null,"position":19,"is_corresponding":false},{"id":110409,"name":"Patrice M. 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