Leveraging the transcriptome to further our understanding of GWAS findings: eQTLs associated with genes related to LDL and LDL subclasses, in a cohort of African Americans

Background: GWAS discoveries often pose a significant challenge in terms of understanding their underlying mechanisms. Further research, such as an integration with expression quantitative trait locus (eQTL) analyses, are required to decipher the mechanisms connecting GWAS variants to phenotypes. An eQTL analysis was conducted on genes associated with low-density lipoprotein (LDL) cholesterol and its subclasses, with the aim of pinpointing genetic variants previously implicated in GWAS studies focused on lipid-related traits. Notably, the study cohort consisted of African Americans, a population characterized by a heightened prevalence of hypercholesterolemia. Methods: A comprehensive differential expression (DE) analysis was undertaken, with a dataset of 17,948 protein-coding mRNA transcripts extracted from the whole-blood transcriptomes of 416 samples to identify mRNA transcripts associated with LDL, with further granularity delineated between small LDL and large LDL subclasses. Subsequently, eQTL analysis was conducted with a subset of 242 samples for which whole-genome sequencing data were available to identify single-nucleotide polymorphisms (SNPs) associated with the LDL-related mRNA transcripts. Lastly, plausible functional connections were established between the identified eQTLs and genetic variants reported in the GWAS catalogue. Results: DE analysis revealed 1,048, 284, and 94 mRNA transcripts that exhibited differential expression in response to LDL, small LDL, and large LDL, respectively. The eQTL analysis identified a total of 9,950 significant SNP-mRNA associations involving 6,955 SNPs including a subset 101 SNPs previously documented in GWAS of LDL and LDL-related traits. Conclusion: Through comprehensive differential expression analysis, we identified numerous mRNA transcripts responsive to LDL, small LDL, and large LDL. Subsequent eQTL analysis revealed a rich landscape of eQTL-mRNA associations, including a subset of eQTL reported in GWAS studies of LDL and related traits. The study serves as a testament to the important role of integrative genomics in unraveling the enigmatic GWAS relationships between genetic variants and the complex fabric of human traits and diseases.

[1]  David E. James,et al.  Systems genetics approaches for understanding complex traits with relevance for human disease , 2023, eLife.

[2]  S. Gabriel,et al.  Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies , 2023, bioRxiv.

[3]  S. Piro,et al.  Evaluation of high‐density lipoprotein‐bound long non‐coding RNAs in subjects with familial hypercholesterolaemia , 2023, European Journal of Clinical Investigation.

[4]  C. Howlett,et al.  The effect of HLA-DRB1*04:01 on a mouse model of atherosclerosis , 2023, Journal of translational autoimmunity.

[5]  D. Gong,et al.  Structural insight into the human SID1 transmembrane family member 2 reveals its lipid hydrolytic activity , 2023, Nature Communications.

[6]  Megumi Narisawa,et al.  Overview of multifunctional cysteinyl cathepsins in atherosclerosis-based cardiovascular disease: from insights into molecular functions to clinical implications , 2023, Cell & Bioscience.

[7]  R. Hegele,et al.  Physiological Bases for the Superiority of Apolipoprotein B Over Low‐Density Lipoprotein Cholesterol and Non–High‐Density Lipoprotein Cholesterol as a Marker of Cardiovascular Risk , 2022, Journal of the American Heart Association.

[8]  Yingying Song,et al.  The functions of SID1 transmembrane family, member 2 (Sidt2) , 2022, The FEBS journal.

[9]  Daoxiu Zhou,et al.  eQTLs play critical roles in regulating gene expression and identifying key regulators in rice , 2022, Plant biotechnology journal.

[10]  M. Hoekstra,et al.  Stabilin 1 and 2 are important regulators for cellular uptake of apolipoprotein B-containing lipoproteins in zebrafish. , 2022, Atherosclerosis.

[11]  Rizwan Kalani,et al.  Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association , 2022, Circulation.

[12]  Sina A. Gharib,et al.  Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression , 2021, Nature Genetics.

[13]  L. Vaca,et al.  Genome-Wide Association Study Identifies a Functional SIDT2 Variant Associated With HDL-C (High-Density Lipoprotein Cholesterol) Levels and Premature Coronary Artery Disease , 2021, Arteriosclerosis, thrombosis, and vascular biology.

[14]  S. Kaptoge,et al.  Association of small, dense LDL-cholesterol concentration and lipoprotein particle characteristics with coronary heart disease: A systematic review and meta-analysis , 2020, PloS one.

[15]  M. Olesińska,et al.  Current Understanding of an Emerging Role of HLA-DRB1 Gene in Rheumatoid Arthritis–From Research to Clinical Practice , 2020, Cells.

[16]  M. Perera,et al.  Discovery of novel hepatocyte eQTLs in African Americans , 2020, PLoS genetics.

[17]  C. Murray,et al.  US Health Care Spending by Payer and Health Condition, 1996-2016. , 2020, JAMA.

[18]  R. Brook,et al.  Effects of Race on Statin Prescribing for Primary Prevention With High Atherosclerotic Cardiovascular Disease Risk in a Large Healthcare System , 2019, Journal of the American Heart Association.

[19]  V. Ranzani,et al.  PCSK7 gene variation bridges atherogenic dyslipidemia with hepatic inflammation in NAFLD patients[S] , 2019, Journal of Lipid Research.

[20]  Shayne D. Wierbowski,et al.  Extracting Complementary Insights from Molecular Phenotypes for Prioritization of Disease-Associated Mutations. , 2018, Current opinion in systems biology.

[21]  D. Torrents,et al.  Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort , 2018, Journal of Medical Genetics.

[22]  J. Wilkins,et al.  Discordance between lipoprotein particle number and cholesterol content: an update , 2018, Current opinion in endocrinology, diabetes, and obesity.

[23]  Xuefan Gu,et al.  Sidt2 regulates hepatocellular lipid metabolism through autophagy , 2018, Journal of Lipid Research.

[24]  N. Risch,et al.  A large electronic health record-based genome-wide study of serum lipids , 2018, Nature Genetics.

[25]  C. Fernández-Hernando,et al.  MicroRNAs and lipid metabolism , 2017, Current opinion in lipidology.

[26]  A. Orekhov,et al.  Small Dense Low-Density Lipoprotein as Biomarker for Atherosclerotic Diseases , 2017, Oxidative medicine and cellular longevity.

[27]  Lale Tokgözoğlu,et al.  Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel , 2017, European heart journal.

[28]  Maayan Levy,et al.  The Role of the Immune System in Metabolic Health and Disease. , 2017, Cell metabolism.

[29]  Jennifer G. Robinson,et al.  Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci. , 2016, Human molecular genetics.

[30]  I. Klein,et al.  Thyroid Disease and the Heart. , 2016, Current problems in cardiology.

[31]  G. Maurer,et al.  Association of Small Dense LDL Serum Levels and Circulating Monocyte Subsets in Stable Coronary Artery Disease , 2015, PloS one.

[32]  Kathleen F. Kerr,et al.  Association of low-density lipoprotein cholesterol-related genetic variants with aortic valve calcium and incident aortic stenosis. , 2014, JAMA.

[33]  P. Toth.  Insulin resistance, small LDL particles, and risk for atherosclerotic disease. , 2014, Current vascular pharmacology.

[34]  A. Lusis,et al.  Systems genetics approaches to understand complex traits , 2013, Nature Reviews Genetics.

[35]  Andrew D. Johnson,et al.  Gene Expression Signatures of Coronary Heart Disease , 2013, Arteriosclerosis, thrombosis, and vascular biology.

[36]  Cristen J Willer,et al.  Finding genes and variants for lipid levels after genome-wide association analysis , 2012, Current opinion in lipidology.

[37]  Andrey A. Shabalin,et al.  Matrix eQTL: ultra fast eQTL analysis via large matrix operations , 2011, Bioinform..

[38]  Tanya M. Teslovich,et al.  Biological, Clinical, and Population Relevance of 95 Loci for Blood Lipids , 2010, Nature.

[39]  M. Robinson,et al.  A scaling normalization method for differential expression analysis of RNA-seq data , 2010, Genome Biology.

[40]  Davis J. McCarthy,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[41]  S. Baranzini,et al.  Uncoupling the Roles of HLA-DRB1 and HLA-DRB5 Genes in Multiple Sclerosis1 , 2008, The Journal of Immunology.

[42]  Bjarni V. Halldórsson,et al.  Many sequence variants affecting diversity of adult human height , 2008, Nature Genetics.

[43]  Robert M. Plenge,et al.  Defining the Role of the MHC in Autoimmunity: A Review and Pooled Analysis , 2008, PLoS genetics.

[44]  E. Jacobson,et al.  The HLA gene complex in thyroid autoimmunity: from epidemiology to etiology. , 2008, Journal of autoimmunity.

[45]  Dolores Corella,et al.  Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans , 2008, Nature Genetics.

[46]  Michael J Pencina,et al.  LDL Particle Number and Risk of Future Cardiovascular Disease in the Framingham Offspring Study - Implications for LDL Management. , 2007, Journal of clinical lipidology.

[47]  W. Cromwell,et al.  Lipoprotein particle analysis by nuclear magnetic resonance spectroscopy. , 2006, Clinics in laboratory medicine.

[48]  S. Heymsfield,et al.  Waist Circumference Correlates with Metabolic Syndrome Indicators Better Than Percentage Fat , 2006, Obesity.

[49]  A. M. Leone,et al.  Inflammation, genetics, and ischemic heart disease: focus on the major histocompatibility complex (MHC) genes. , 2005, Cytokine.

[50]  W. Cromwell,et al.  Measurement issues related to lipoprotein heterogeneity. , 2002, The American journal of cardiology.

[51]  Abbas Dehghan,et al.  Genome-Wide Association Studies. , 2018, Methods in molecular biology.

[52]  Edinburgh Research Explorer Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits , 2022 .