Zhang Daiwei, Gao Boran, Feng Qidi, Manichaikul Ani, Peloso Gina M, Tracy Russell P, Durda Peter, Taylor Kent D, Liu Yongmei, Johnson W Craig, Gabriel Stacey, Gupta Namrata, Smith Joshua D, Aguet Francois, Ardlie Kristin G, Blackwell Thomas W, Gerszten Robert E, Rich Stephen S, Rotter Jerome I, Scott Laura J, Zhou Xiang, Lee Seunggeun
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.
Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
HGG Adv. 2025 Jan 9;6(1):100383. doi: 10.1016/j.xhgg.2024.100383. Epub 2024 Nov 14.
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.
血脂特征是可治疗的心脏病遗传风险因素，心脏病是全球主要死因。尽管全基因组关联研究（GWAS）已经发现了数百种与人类血脂相关的变异，但大多数血脂的因果机制仍然未知。为了更好地理解脂质代谢背后的生物学过程，我们研究了血浆蛋白水平与血液中总胆固醇（TC）、甘油三酯（TG）、高密度脂蛋白（HDL）胆固醇和低密度脂蛋白（LDL）胆固醇之间的关联。我们基于动脉粥样硬化多民族研究（MESA）中的样本训练了蛋白质预测模型，并使用全球脂质遗传学联盟（GLGC）的数据将其应用于脂质的全蛋白质组关联研究（PWAS）。在测试的749种蛋白质中，42种与至少一种血脂特征显著相关。此外，我们使用在MESA外周血单核细胞（PBMC）样本和基因型-组织表达（GTEx）项目的49种组织上训练的9714个基因表达预测模型，对脂质进行了全转录组关联研究（TWAS）。我们发现，尽管PWAS和TWAS在单个基因中可能显示不同的关联方向，但在49种组织中的40种组织中，所有基因的PWAS和TWAS符号化p值之间呈正相关，这表明蛋白质组-脂质关联和转录组-脂质关联之间具有高度一致性。
