Work

Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example

Year: 2021

Type: article

Source: Drug and Alcohol Dependence

DOI: 10.1016/j.drugalcdep.2021.109115

Authors: Alexander S. Hatoum, Frank R. Wendt, Marco Galimberti, Renato Polimanti, Benjamin Neale, Henry R. Kranzler, Joel Gelernter, Howard J. Edenberg, Arpana Agrawal

Institutions: Washington University in St. Louis, Yale University, VA Connecticut Healthcare System, Broad Institute, Harvard University, Massachusetts General Hospital, Stanley Center for Psychiatric Research, Massachusetts Institute of Technology, Philadelphia VA Medical Center, University of Pennsylvania, Indiana University Indianapolis, Indiana University School of Medicine, Indiana University – Purdue University Indianapolis

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: U.S. Department of Veterans Affairs, Yale University, University of Pennsylvania, American Society of Clinical Psychopharmacology, National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism, National Institute of Mental Health, National Institute on Drug Abuse

Awards: G1957592153, G2195549334, G2273635344, G2988863959, Genetics of Alcohol Dependence in African-Americans, Genetics of Opioid Dependence, Genetics of Alcohol Dependence in American Populations, Genetics of Cocaine Dependence, 7/7 Psychiatric Genomics Consortium: Finding actionable variation, Genetics of Cocaine Dependence, G9000002075, G9000044128, G9000097481, G9000240960, G9000298100

OpenAlex ID: W3205507750

FWCI: 2.01

Cites: 38

Cited by: 24

Related to: 10