{"doi":"10.1093/ije/dyaa288","title":"The use of negative control outcomes in Mendelian randomization to detect potential population stratification","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>A key assumption of Mendelian randomization (MR) analysis is that there is no association between the genetic variants used as instruments and the outcome other than through the exposure of interest. One way in which this assumption can be violated is through population stratification, which can introduce confounding of the relationship between the genetic variants and the outcome and so induce an association between them. Negative control outcomes are increasingly used to detect unobserved confounding in observational epidemiological studies. Here we consider the use of negative control outcomes in MR studies to detect confounding of the genetic variants and the exposure or outcome. As a negative control outcome in an MR study, we propose the use of phenotypes which are determined before the exposure and outcome but which are likely to be subject to the same confounding as the exposure or outcome of interest. We illustrate our method with a two-sample MR analysis of a preselected set of exposures on self-reported tanning ability and hair colour. Our results show that, of the 33 exposures considered, genome-wide association studies (GWAS) of adiposity and education-related traits are likely to be subject to population stratification that is not controlled for through adjustment, and so any MR study including these traits may be subject to bias that cannot be identified through standard pleiotropy robust methods. Negative control outcomes should therefore be used regularly in MR studies to detect potential population stratification in the data used.</jats:p>","journal":"International Journal of Epidemiology","year":2021,"id":594878,"datarank":0.7193685818395114,"base_score":4.795790545596741,"endowment":4.795790545596741,"self_citation_contribution":0.7193685818395114,"citation_network_contribution":0.0,"self_endowment_contribution":0.7193685818395114,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":120,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"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":240623,"name":"Tom G. Richardson","orcid":"0000-0002-7918-2040","position":1,"is_corresponding":false},{"id":21773,"name":"Gibran Hemani","orcid":"0000-0003-0920-1055","position":2,"is_corresponding":false},{"id":1528,"name":"George Davey Smith","orcid":"0000-0002-1407-8314","position":3,"is_corresponding":false},{"id":50525,"name":"Eleanor Sanderson","orcid":"0000-0001-5188-5775","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"The use of negative control outcomes in Mendelian randomization to detect potential population stratification","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>A key assumption of Mendelian randomization (MR) analysis is that there is no association between the genetic variants used as instruments and the outcome other than through the exposure of interest. One way in which this assumption can be violated is through population stratification, which can introduce confounding of the relationship between the genetic variants and the outcome and so induce an association between them. Negative control outcomes are increasingly used to detect unobserved confounding in observational epidemiological studies. Here we consider the use of negative control outcomes in MR studies to detect confounding of the genetic variants and the exposure or outcome. As a negative control outcome in an MR study, we propose the use of phenotypes which are determined before the exposure and outcome but which are likely to be subject to the same confounding as the exposure or outcome of interest. We illustrate our method with a two-sample MR analysis of a preselected set of exposures on self-reported tanning ability and hair colour. Our results show that, of the 33 exposures considered, genome-wide association studies (GWAS) of adiposity and education-related traits are likely to be subject to population stratification that is not controlled for through adjustment, and so any MR study including these traits may be subject to bias that cannot be identified through standard pleiotropy robust methods. Negative control outcomes should therefore be used regularly in MR studies to detect potential population stratification in the data used.</jats:p>","is_dataset_classified":null,"base_score":4.795790545596741,"endowment":4.795790545596741,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"33570130","pmcid":"PMC8407870","openalex_id":"https://openalex.org/W3123863341","authors":[],"funders":[{"funder_name":"Integrative Epidemiology Unit which is funded by the University of Bristol and the Medical Research Council","grant_id":"MC_UU_00011/1","title":"Mendelian randomization to hypothesis-free causal inference"},{"funder_name":"UKRI Innovation Research Fellow","grant_id":"MR/S003886/1","title":null},{"funder_name":"Wellcome Trust","grant_id":"208806/Z/17/Z","title":null},{"funder_name":"Medical Research Council","grant_id":"MC_UU_00011/2","title":null}],"total_grants":4,"fwci":13.7816,"citation_percentile":0.99314088,"influential_citations":0,"citation_trend":[{"year":2020,"count":1},{"year":2021,"count":15},{"year":2022,"count":13},{"year":2023,"count":26},{"year":2024,"count":33},{"year":2025,"count":22},{"year":2026,"count":10}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.1093/ije/dyaa288","host_type":"journal"},{"url":"https://doi.org/10.1093/ije/dyaa288","host_type":"publisher"},{"url":"http://academic.oup.com/ije/article-pdf/50/4/1350/40146561/dyaa288.pdf","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/33570130","host_type":"repository"},{"url":"https://research-information.bris.ac.uk/en/publications/6b31f3ae-6d36-4291-9058-d7a165b4de19","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8407870","host_type":"repository"},{"url":"https://hdl.handle.net/1983/6b31f3ae-6d36-4291-9058-d7a165b4de19","host_type":""},{"url":"https://europepmc.org/articles/PMC8407870","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC8407870?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1093/ije/dyaa288","host_type":""},{"url":"https://dx.doi.org/10.1093/ije/dyaa288","host_type":""},{"url":"https://research-information.bris.ac.uk/ws/files/268775222/dyaa288.pdf","host_type":""},{"url":"https://doi.org/https://doi.org/10.1093/ije/dyaa288","host_type":""}],"fields_of_study":["Genetic Associations and Epidemiology","Genetic and phenotypic traits in livestock","Genetic Mapping and Diversity in Plants and Animals","0301 basic medicine","0303 health sciences","03 medical and health sciences"],"mesh_terms":["Humans","Phenotype","Bias","Polymorphism, Single Nucleotide","Adiposity","Genome-Wide Association Study","Mendelian Randomization Analysis"],"keywords":["Mendelian randomization","Population stratification","Confounding","Observational study","Population","Genetic association","Genome-wide association study","Selection bias","Pleiotropy","Medicine","Causal inference","Biology","Genetics","Internal medicine","Phenotype","Single-nucleotide polymorphism","Genetic variants","Environmental health","Pathology","Genotype","Negative Control Outcomes","610","Mendelian Randomization Analysis","Polymorphism, Single Nucleotide","Bias","Methods","Humans","Adiposity"],"sdg_mappings":[{"sdg_number":2,"sdg_label":"2. Zero hunger"},{"sdg_number":10,"sdg_label":"10. No inequality"},{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-27T15:47:34.192532Z","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":[]}