Genes (Jul 2021)
Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations
- Jia Wen,
- Munan Xie,
- Bryce Rowland,
- Jonathan D. Rosen,
- Quan Sun,
- Jiawen Chen,
- Amanda L. Tapia,
- Huijun Qian,
- Madeline H. Kowalski,
- Yue Shan,
- Kristin L. Young,
- Marielisa Graff,
- Maria Argos,
- Christy L. Avery,
- Stephanie A. Bien,
- Steve Buyske,
- Jie Yin,
- Hélène Choquet,
- Myriam Fornage,
- Chani J. Hodonsky,
- Eric Jorgenson,
- Charles Kooperberg,
- Ruth J. F. Loos,
- Yongmei Liu,
- Jee-Young Moon,
- Kari E. North,
- Stephen S. Rich,
- Jerome I. Rotter,
- Jennifer A. Smith,
- Wei Zhao,
- Lulu Shang,
- Tao Wang,
- Xiang Zhou,
- Alexander P. Reiner,
- Laura M. Raffield,
- Yun Li
Affiliations
- Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Munan Xie
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Bryce Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Jonathan D. Rosen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Amanda L. Tapia
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Huijun Qian
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Madeline H. Kowalski
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Kristin L. Young
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
- Marielisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
- Maria Argos
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL 60612, USA
- Christy L. Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
- Stephanie A. Bien
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ 08854, USA
- Jie Yin
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
- Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
- Myriam Fornage
- Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center, Houston, TX 77030, USA
- Chani J. Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
- Eric Jorgenson
- Regeneron Genetics Center, Tarrytown, NY 10591, USA
- Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Yongmei Liu
- Molecular Physiology Institute, Duke University, Durham, NC 27701, USA
- Jee-Young Moon
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Kari E. North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
- Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
- Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
- Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Lulu Shang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Tao Wang
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
- Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- DOI
- https://doi.org/10.3390/genes12071049
- Journal volume & issue
-
Vol. 12,
no. 7
p. 1049
Abstract
Background: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. Methods: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants. Results: Our results revealed 24 suggestive signals (p −4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. Conclusions: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.
Keywords