Proteome-wide association studies for blood lipids and comparison with transcriptome-wide association studies
Daiwei Zhang,
Boran Gao,
Qidi Feng,
Ani Manichaikul,
Gina M. Peloso,
Russell P. Tracy,
Peter Durda,
Kent D. Taylor,
Yongmei Liu,
W. Craig Johnson,
Stacey Gabriel,
Namrata Gupta,
Joshua D. Smith,
Francois Aguet,
Kristin G. Ardlie,
Thomas W. Blackwell,
Robert E. Gerszten,
Stephen S. Rich,
Jerome I. Rotter,
Laura J. Scott,
Xiang Zhou,
Seunggeun Lee
Affiliations
Daiwei Zhang
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA; Departments of Biostatistics and Genetics, University of North Carolina, Chapel Hill, NC, USA
Boran Gao
Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
Qidi Feng
Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
Ani Manichaikul
Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
Gina M. Peloso
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
Russell P. Tracy
Departments of Pathology and Laboratory Medicine, and Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT, USA
Peter Durda
Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
Kent D. Taylor
The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
Yongmei Liu
Department of Medicine, Divisions of Cardiology and Neurology, Duke University Medical Center, Durham, NC, USA
W. Craig Johnson
Department of Biostatistics, University of Washington, Seattle, WA, USA
Stacey Gabriel
Genomics Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
Namrata Gupta
Genomics Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
Joshua D. Smith
Department of Genome Sciences, Human Genetics, and Translational Genomics, University of Washington, Seattle, WA, USA
Francois Aguet
Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
Kristin G. Ardlie
Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
Thomas W. Blackwell
Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
Robert E. Gerszten
Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
Stephen S. Rich
Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 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, USA
Laura J. Scott
Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA; Corresponding author
Xiang Zhou
Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA; Corresponding author
Seunggeun Lee
Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea; Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA; Corresponding author
Summary: Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWASs) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWASs) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWASs) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWASs and TWASs can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p values across all the genes, which suggests high-level consistency between proteome-lipid associations and transcriptome-lipid associations.
