{"doi":"10.1101/2022.05.02.22274580","title":"Genome-wide investigation of maximum habitual alcohol intake (MaxAlc) in 247,755 European and African Ancestry U.S. Veterans informs the relationship between habitual alcohol consumption and alcohol use disorder","abstract":"Abstract Importance Alcohol genome-wide association studies (GWAS) have generally focused on alcohol consumption and alcohol use disorder (AUD); few have examined habitual drinking behaviors like maximum habitual alcohol intake (MaxAlc). Objective Identify MaxAlc loci and elucidate the genetic architecture across alcohol traits. Design The MaxAlc GWAS was performed in Million Veteran Program (MVP) participants enrolled from January 10, 2011 to September 30, 2020. Ancestry-specific GWAS were conducted in European (EUR) (n=218,623) and African (AFR) (n=29,132) ancestry subjects, then meta-analyzed (N=247,755). Linkage-disequilibrium score regression (LDSC) was used to estimate SNP-heritability and genetic correlations (r g ) with other alcohol and psychiatric traits. Genomic structural equation modeling (gSEM) was used to evaluate genetic relationships between MaxAlc and other alcohol traits. Mendelian randomization (MR) was used to examine causal relationships. MTAG (multi-trait analysis of GWAS) was used to analyze MaxAlc and problematic alcohol use (PAU) jointly. Setting The study was performed in a sample of U.S military Veterans. Participants Participants were 92.68% male and had mean age=65.92 ( SD =11.70). 36.92% reported MaxAlc ≥ the binge-drinking threshold. Main Outcomes(s) and Measure(s) MaxAlc was defined from survey item: “in a typical month, what is/was the largest number of drinks of alcohol you may have had in one day?” with ordinal responses from 0 ≥ 15 drinks. Results The MaxAlc GWAS resulted in 15 genome-wide significant (GWS) loci. Top associations in EUR and AFR were with known functional variants ADH1B *rs1229984 ( p =3.12×10 −104 ) and rs2066702 ( p =6.30×10 −17 ), respectively. Multiple novel associations were found. The SNP-heritability was 6.65% (s.e.=0.41%) in EUR and 3.42% (s.e.=1.46%) in AFR. MaxAlc was positively correlated with PAU (r g =0.79; p =3.95×10 −149 ) and AUD (r g =0.76; p =1.26×10 −127 ), and had negative r g with “alcohol usually taken with meals” (r g =-0.53; p =1.40×10 −50 ). For psychiatric traits, MaxAlc had the strongest r g with suicide attempt (r g =0.40; p =3.02×10 −21 ). gSEM supported a two-factor model with MaxAlc loading on a factor with PAU and AUD, and other alcohol consumption measures loading a separate factor. MR supported a small causal effect of MaxAlc on the liver enzyme gamma-glutamyltransferase (β=0.012; p =2.66×10 −10 ). MaxAlc MTAG resulted in 31 GWS loci. Conclusions and Relevance MaxAlc closely aligns genetically with the etiology of problematic alcohol use traits. Key Points Question What is the genetic etiology of maximum habitual alcohol intake (MaxAlc) and how does it compare to other alcohol consumption measures. Findings This MaxAlc study in 247,455 European and African ancestry individuals identified 15 genome-wide significant loci, including multiple novel associations. MaxAlc was strongly genetically correlated (r g ) with measures of alcohol-related problems, demonstrated significantly different r g with psychiatric traits compared to other alcohol consumption traits, and loaded on a factor with alcohol problem traits while alcohol consumption state measures loaded on a separate factor. Meaning MaxAlc is genetically different from trait consumption measures in relation to problematic alcohol use.","journal":"medRxiv","year":2022,"id":298642,"datarank":0.26876392038420827,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9287,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":229992,"name":"Daniel F. Levey","orcid":"0000-0001-8431-9569","position":1,"is_corresponding":false},{"id":230694,"name":"Frank R. Wendt","orcid":"0000-0002-2108-6822","position":2,"is_corresponding":false},{"id":987654,"name":"Hang Zhou","orcid":"0000-0002-9159-8655","position":3,"is_corresponding":false},{"id":563704,"name":"Marco Galimberti","orcid":"0000-0001-6052-156X","position":4,"is_corresponding":false},{"id":11401,"name":"Henry R. Kranzler","orcid":"0000-0002-1018-0450","position":5,"is_corresponding":false},{"id":11341,"name":"J. Michael Gaziano","orcid":"0000-0002-5384-9767","position":6,"is_corresponding":false},{"id":22832,"name":"Murray B Stein","orcid":"0000-0001-9564-2871","position":7,"is_corresponding":false},{"id":229994,"name":"Renato Polimanti","orcid":"0000-0003-0745-6046","position":8,"is_corresponding":false},{"id":988005,"name":"The Million Veteran Program","orcid":null,"position":9,"is_corresponding":false},{"id":230005,"name":"Joel Gelernter","orcid":"0000-0002-4067-1859","position":10,"is_corresponding":false},{"id":813452,"name":"Joseph D. Deak","orcid":"0000-0002-0540-6080","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:31:36.269611Z","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":[]}