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

doi:10.1016/j.xhgg.2024.100383

Proteome-wide association studies for blood lipids and comparison with transcriptome-wide association studies

HGG Adv. 2024 Nov 14;6(1):100383. doi: 10.1016/j.xhgg.2024.100383. Online ahead of print.

Authors

Daiwei Zhang¹, Boran Gao², Qidi Feng³, Ani Manichaikul⁴, Gina M Peloso⁵, Russell P Tracy⁶, Peter Durda⁷, Kent D Taylor⁸, Yongmei Liu⁹, W Craig Johnson¹⁰, Stacey Gabriel¹¹, Namrata Gupta¹¹, Joshua D Smith¹², Francois Aguet¹³, Kristin G Ardlie¹³, Thomas W Blackwell¹⁴, Robert E Gerszten¹⁵, Stephen S Rich⁴, Jerome I Rotter⁸, Laura J Scott¹⁶, Xiang Zhou², Seunggeun Lee¹⁷

Affiliations

¹ 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.
² Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
³ 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.
⁴ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
⁵ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
⁶ Departments of Pathology and Laboratory Medicine, and Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
⁷ Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
⁸ 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.
⁹ Department of Medicine, Divisions of Cardiology and Neurology, Duke University Medical Center, Durham, NC, USA.
¹⁰ Department of Biostatistics, University of Washington, Seattle, WA, USA.
¹¹ Genomics Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
¹² Department of Genome Sciences, Human Genetics, and Translational Genomics, University of Washington, Seattle, WA, USA.
¹³ Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
¹⁴ Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA.
¹⁵ Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
¹⁶ Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA. Electronic address: [email protected].
¹⁷ 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. Electronic address: [email protected].

Abstract

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.

Keywords: Multi-omics; blood lipids; proteome-wide association studies; transcriptome-wide association studies.