Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies

• Published in: American journal of human genetics Vol. 105; no. 4; pp. 763 - 772
• Main Authors: Fang, Huaying, Hui, Qin, Lynch, Julie, Honerlaw, Jacqueline, Assimes, Themistocles L., Huang, Jie, Vujkovic, Marijana, Damrauer, Scott M., Pyarajan, Saiju, Gaziano, J. Michael, DuVall, Scott L., O’Donnell, Christopher J., Cho, Kelly, Chang, Kyong-Mi, Wilson, Peter W.F., Tsao, Philip S., Ramoni, Rachel, Breeling, Jim, Huang, Grant, Muralidhar, Sumitra, Moser, Jennifer, Whitbourne, Stacey B., Brewer, Jessica V., Warren, Stuart, Argyres, Dean P., Stephens, Brady, Brophy, Mary T., Humphries, Donald E., Do, Nhan, Shayan, Shahpoor, Nguyen, Xuan-Mai T., Hauser, Elizabeth, Sun, Yan, Zhao, Hongyu, Wilson, Peter, McArdle, Rachel, Dellitalia, Louis, Harley, John, Whittle, Jeffrey, Beckham, Jean, Wells, John, Gutierrez, Salvador, Gibson, Gretchen, Kaminsky, Laurence, Villareal, Gerardo, Kinlay, Scott, Xu, Junzhe, Hamner, Mark, Haddock, Kathlyn Sue, Bhushan, Sujata, Iruvanti, Pran, Godschalk, Michael, Ballas, Zuhair, Buford, Malcolm, Mastorides, Stephen, Klein, Jon, Ratcliffe, Nora, Florez, Hermes, Swann, Alan, Murdoch, Maureen, Sriram, Peruvemba, Yeh, Shing Shing, Washburn, Ronald, Jhala, Darshana, Aguayo, Samuel, Cohen, David, Sharma, Satish, Callaghan, John, Oursler, Kris Ann, Whooley, Mary, Ahuja, Sunil, Gutierrez, Amparo, Schifman, Ronald, Greco, Jennifer, Rauchman, Michael, Servatius, Richard, Oehlert, Mary, Wallbom, Agnes, Fernando, Ronald, Stapley, Todd, Sherman, Scott, Anderson, Gwenevere, Sonel, Elif, Boyko, Edward, Meyer, Laurence, Gupta, Samir, Fayad, Joseph, Hung, Adriana, Lichy, Jack, Hurley, Robin, Robey, Brooks, Striker, Robert, Sun, Yan V., Tang, Hua
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 03.10.2019; Elsevier
• Subjects: Algorithms; biobank; Continental Population Groups - genetics; Ethnic Groups - genetics; ethnicity-specific trait loci; genetic ancestry; Genome-Wide Association Study; Humans; Machine Learning; multi-ethnic cohort; self-reported race/ethnicity; stratified analysis; Support Vector Machine; trans-ethnic GWAS; multi-ethnic cohort; ethnicity-specific trait loci; self-reported race/ethnicity; trans-ethnic GWAS; stratified analysis; genetic ancestry; biobank
• ISSN: 0002-9297; 1537-6605
• DOI: 10.1016/j.ajhg.2019.08.012

Large-scale multi-ethnic cohorts offer unprecedented opportunities to elucidate the genetic factors influencing complex traits related to health and disease among minority populations. At the same time, the genetic diversity in these cohorts presents new challenges for analysis and interpretation. We consider the utility of race and/or ethnicity categories in genome-wide association studies (GWASs) of multi-ethnic cohorts. We demonstrate that race/ethnicity information enhances the ability to understand population-specific genetic architecture. To address the practical issue that self-identified racial/ethnic information may be incomplete, we propose a machine learning algorithm that produces a surrogate variable, termed HARE. We use height as a model trait to demonstrate the utility of HARE and ethnicity-specific GWASs.