{"doi":"10.1101/2022.07.14.22276656","title":"Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common controls","abstract":"<jats:title>ABSTRACT</jats:title>\n                <jats:p>Genome-wide association studies (GWAS) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common controls from biobanks and extensive consortiums is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the controls are not well-characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of cases to population-based common controls regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen exposed cases and population-based common controls, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well characterized controls, and population-based common controls from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance-associations. These findings suggest that the choice of controls is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.</jats:p>","journal":null,"year":null,"id":623850,"datarank":0.13408280522314472,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.030110728139152908,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.030110728139152908,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":397475,"name":"Candelaria Vergara","orcid":"0000-0003-3297-8057","position":1,"is_corresponding":false},{"id":414209,"name":"Chloe L. Thio","orcid":"0000-0002-8851-8319","position":2,"is_corresponding":false},{"id":310876,"name":"Prosenjit Kundu","orcid":"0000-0002-4732-0314","position":3,"is_corresponding":false},{"id":608956,"name":"Nilanjan Chatterjee","orcid":"0000-0003-3213-3839","position":4,"is_corresponding":false},{"id":12944,"name":"David L. Thomas","orcid":"0000-0003-1491-1641","position":5,"is_corresponding":false},{"id":247496,"name":"Genevieve L. Wojcik","orcid":"0000-0001-7206-8088","position":6,"is_corresponding":false},{"id":115227,"name":"Priya Duggal","orcid":"0000-0001-5809-2081","position":7,"is_corresponding":false},{"id":1267511,"name":"Dylan Duchen","orcid":"0000-0001-5629-2012","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common controls","abstract":"<jats:title>ABSTRACT</jats:title>\n                <jats:p>Genome-wide association studies (GWAS) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common controls from biobanks and extensive consortiums is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the controls are not well-characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of cases to population-based common controls regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen exposed cases and population-based common controls, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well characterized controls, and population-based common controls from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance-associations. These findings suggest that the choice of controls is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.</jats:p>","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4285677114","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2023,"count":1}],"oa_status":"green","license":"cc-by-nc-nd","oa_locations":[{"url":"https://www.medrxiv.org/content/medrxiv/early/2022/07/17/2022.07.14.22276656.full.pdf","host_type":"repository"},{"url":"https://www.medrxiv.org/content/medrxiv/early/2022/07/17/2022.07.14.22276656.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2022.07.14.22276656","host_type":"publisher"},{"url":"https://doi.org/10.1101/2022.07.14.22276656","host_type":"repository"},{"url":"https://europepmc.org/article/PPR/PPR519622","host_type":"Europe_PMC"},{"url":"https://europepmc.org/api/fulltextRepo?pprId=PPR519622&type=FILE&fileName=EMS151042-pdf.pdf&mimeType=application/pdf","host_type":"Europe_PMC"}],"fields_of_study":["Genetic Associations and Epidemiology","Hepatitis C virus research","Liver Disease Diagnosis and Treatment"],"mesh_terms":[],"keywords":["Genome-wide association study","Spurious relationship","Population","Biology","Genetic association","Pathogen","Biobank","Genetics","Immunology","Single-nucleotide polymorphism","Medicine","Genotype","Environmental health","Statistics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Life in Land"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"refsnp"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-04T01:38:35.679584Z","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":[]}