{"doi":"10.1101/2020.11.23.20235846","title":"Biobank scale pharmacogenomics informs the genetic underpinnings of simvastatin use","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Background and Purpose</jats:title>\n                  <jats:p>Studying drug metabolizing enzymes, encoded by pharmacogenes (PGx), may inform biological mechanisms underlying the diseases for which a medication is prescribed. Until recently, PGx loci could not be studied at biobank scale. Here we analyze PGx haplotype variation to detect associations with medication use in the UK Biobank.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Methods</jats:title>\n                  <jats:p>\n                    In 7,649 unrelated African-ancestry (AFR) and 326,214 unrelated European-ancestry (EUR) participants from the UK Biobank, aged 37-73 at time of recruitment, we associated clinically-relevant PGx haplotypes with 265 (EUR) and 17 (AFR) medication use phenotypes using generalized linear models covaried with sex, age, age\n                    <jats:sup>2</jats:sup>\n                    , sex×age, sex×age\n                    <jats:sup>2</jats:sup>\n                    , and ten principal components of ancestry. Haplotypes across 50 genes were assigned with Stargazer. Our analyses focused on the association of PGx haplotype dose (quantitative predictor), diplotype (categorical predictor), and rare haplotype burden on medication use.\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>\n                    In EUR,\n                    <jats:italic>NAT2</jats:italic>\n                    metabolizer phenotype (OR=1.05, 95% CI: 1.03-1.08, p=7.03×10\n                    <jats:sup>−6</jats:sup>\n                    ) and activity score (OR=1.09, 95% CI: 1.05-1.14, p=2.46×10\n                    <jats:sup>−6</jats:sup>\n                    ) were associated with simvastatin use. The dose of N-acetyltransferase 2 (\n                    <jats:italic>NAT2</jats:italic>\n                    )*1 was associated with simvastatin use relative to\n                    <jats:italic>NAT2</jats:italic>\n                    *5 (\n                    <jats:italic>NAT2</jats:italic>\n                    *1 OR=1.04, 95% CI=1.03-1.07, p=1.37×10\n                    <jats:sup>−5</jats:sup>\n                    ) and was robust to effects of low-density lipoprotein cholesterol (LDL-C) concentration (\n                    <jats:italic>NAT2</jats:italic>\n                    *1 given LDL-C concentration: OR=1.07, 95% CI=1.05-1.09, p=1.14×10\n                    <jats:sup>−8</jats:sup>\n                    ) and polygenic risk for LDL-C concentration (\n                    <jats:italic>NAT2</jats:italic>\n                    *1 given LDL-C PRS: OR=1.09, 95% CI=1.04-1.14, p=2.26×10\n                    <jats:sup>−4</jats:sup>\n                    ). Interactive effects between\n                    <jats:italic>NAT2</jats:italic>\n                    *1, simvastatin use, and LDL-C concentration (OR: 0.957, 95% CI=0.916-0.998, p=0.045) were replicated in eMERGE PGx cohort (OR: 0.987, 95% CI: 0.976-0.998, p=0.029).\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions and relevance</jats:title>\n                  <jats:p>\n                    We used biobank-scale data to uncover and replicate a novel association between\n                    <jats:italic>NAT2</jats:italic>\n                    locus variation (and suggestive evidence with several other genes) and better response to simvastatin (and other statins) therapy. The presence of\n                    <jats:italic>NAT2</jats:italic>\n                    *1 versus\n                    <jats:italic>NAT2</jats:italic>\n                    *5 may therefore be useful for making clinically informative decisions regarding the potential benefit (e.g., absolute risk reduction) in LDL-C concentration prior to statin treatment.\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Subject terms</jats:title>\n                  <jats:p>genetics, genetic association studies, cardiovascular disease</jats:p>\n                </jats:sec>","journal":null,"year":null,"id":609612,"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":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":1566964,"name":"Dora Koller","orcid":null,"position":1,"is_corresponding":false},{"id":1566965,"name":"Gita A Pathak","orcid":null,"position":2,"is_corresponding":false},{"id":232951,"name":"Daniel Jacoby","orcid":"0000-0002-9182-275X","position":3,"is_corresponding":false},{"id":1566966,"name":"Edward J Miller","orcid":null,"position":4,"is_corresponding":false},{"id":229994,"name":"Renato Polimanti","orcid":"0000-0003-0745-6046","position":5,"is_corresponding":false},{"id":230694,"name":"Frank R. Wendt","orcid":"0000-0002-2108-6822","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Biobank scale pharmacogenomics informs the genetic underpinnings of simvastatin use","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Background and Purpose</jats:title>\n                  <jats:p>Studying drug metabolizing enzymes, encoded by pharmacogenes (PGx), may inform biological mechanisms underlying the diseases for which a medication is prescribed. Until recently, PGx loci could not be studied at biobank scale. Here we analyze PGx haplotype variation to detect associations with medication use in the UK Biobank.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Methods</jats:title>\n                  <jats:p>\n                    In 7,649 unrelated African-ancestry (AFR) and 326,214 unrelated European-ancestry (EUR) participants from the UK Biobank, aged 37-73 at time of recruitment, we associated clinically-relevant PGx haplotypes with 265 (EUR) and 17 (AFR) medication use phenotypes using generalized linear models covaried with sex, age, age\n                    <jats:sup>2</jats:sup>\n                    , sex×age, sex×age\n                    <jats:sup>2</jats:sup>\n                    , and ten principal components of ancestry. Haplotypes across 50 genes were assigned with Stargazer. Our analyses focused on the association of PGx haplotype dose (quantitative predictor), diplotype (categorical predictor), and rare haplotype burden on medication use.\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>\n                    In EUR,\n                    <jats:italic>NAT2</jats:italic>\n                    metabolizer phenotype (OR=1.05, 95% CI: 1.03-1.08, p=7.03×10\n                    <jats:sup>−6</jats:sup>\n                    ) and activity score (OR=1.09, 95% CI: 1.05-1.14, p=2.46×10\n                    <jats:sup>−6</jats:sup>\n                    ) were associated with simvastatin use. The dose of N-acetyltransferase 2 (\n                    <jats:italic>NAT2</jats:italic>\n                    )*1 was associated with simvastatin use relative to\n                    <jats:italic>NAT2</jats:italic>\n                    *5 (\n                    <jats:italic>NAT2</jats:italic>\n                    *1 OR=1.04, 95% CI=1.03-1.07, p=1.37×10\n                    <jats:sup>−5</jats:sup>\n                    ) and was robust to effects of low-density lipoprotein cholesterol (LDL-C) concentration (\n                    <jats:italic>NAT2</jats:italic>\n                    *1 given LDL-C concentration: OR=1.07, 95% CI=1.05-1.09, p=1.14×10\n                    <jats:sup>−8</jats:sup>\n                    ) and polygenic risk for LDL-C concentration (\n                    <jats:italic>NAT2</jats:italic>\n                    *1 given LDL-C PRS: OR=1.09, 95% CI=1.04-1.14, p=2.26×10\n                    <jats:sup>−4</jats:sup>\n                    ). Interactive effects between\n                    <jats:italic>NAT2</jats:italic>\n                    *1, simvastatin use, and LDL-C concentration (OR: 0.957, 95% CI=0.916-0.998, p=0.045) were replicated in eMERGE PGx cohort (OR: 0.987, 95% CI: 0.976-0.998, p=0.029).\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions and relevance</jats:title>\n                  <jats:p>\n                    We used biobank-scale data to uncover and replicate a novel association between\n                    <jats:italic>NAT2</jats:italic>\n                    locus variation (and suggestive evidence with several other genes) and better response to simvastatin (and other statins) therapy. The presence of\n                    <jats:italic>NAT2</jats:italic>\n                    *1 versus\n                    <jats:italic>NAT2</jats:italic>\n                    *5 may therefore be useful for making clinically informative decisions regarding the potential benefit (e.g., absolute risk reduction) in LDL-C concentration prior to statin treatment.\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Subject terms</jats:title>\n                  <jats:p>genetics, genetic association studies, cardiovascular disease</jats:p>\n                </jats:sec>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"33837531","pmcid":null,"openalex_id":null,"authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"5R21DC018098-03","title":"Genome-wide Investigation of the Interplay Between Age-Related Hearing Loss and Smoking Behaviors"},{"funder_name":"National Institutes of Health","grant_id":"1R21DA047527-01","title":"Investigating the Systems Genetics of the Patterns of Polysubstance Abuse and Addiction"}],"total_grants":2,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"bronze","license":"Wiley Online Library User Agreement","oa_locations":[{"url":"https://syndication.highwire.org/content/doi/10.1101/2020.11.23.20235846","host_type":"publisher"},{"url":"https://doi.org/10.1002/cpt.2260","host_type":""},{"url":"https://doi.org/10.1101/2020.11.23.20235846","host_type":""},{"url":"https://www.medrxiv.org/content/medrxiv/early/2020/11/24/2020.11.23.20235846.full.pdf","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/33837531","host_type":""},{"url":"https://dx.doi.org/10.1101/2020.11.23.20235846","host_type":""},{"url":"https://dx.doi.org/10.1002/cpt.2260","host_type":""}],"fields_of_study":["0301 basic medicine","03 medical and health sciences"],"mesh_terms":[],"keywords":["Adult","Male","Simvastatin","Genotype","Arylamine N-Acetyltransferase","Pilot Projects","Cholesterol, LDL","Middle Aged","Cohort Studies","Phenotype","Haplotypes","Pharmacogenetics","Inactivation, Metabolic","Odds Ratio","Humans","Female","Aged","Biological Specimen Banks"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. Good health"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-31T10:57:53.635638Z","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":[]}