Poster only 3rd Metabolic Diseases; Breakthrough Discoveries in Diabetes & Obesity 2022

Genetic determinants of lipidomic variation and their role in cardiometabolic disease risk (#125)

Corey Giles 1 2 3 , Gemma Cadby 4 , Kevin Huynh 1 2 3 , Tingting Wang 2 3 , Phillip E Melton 4 5 , Sonia Shah 6 , Naomi R Wray 6 7 , Natalie Mellett 3 , Gavriel Olshansky 3 , Michael Inouye 3 , Eric Moses 5 , Peter J Meikle 1 2 3 8
  1. Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia
  2. Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
  3. Baker Heart and Diabetes Institute, Melbourne, VICTORIA, Australia
  4. The Curtin UWA Centre for Genetic Origins of Health and Disease, The University of Western Australia, Perth, Western Australia, Australia
  5. Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
  6. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
  7. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
  8. Monash University, Melbourne, Victoria, Australia

Dysregulation of lipid metabolism is as an important – and modifiable – risk factor for the initiation and progression of cardiometabolic diseases. Although lipid metabolism is established as crucial to numerous biological processes, the genetic factors that influence inter-individual variation are still not well understood. To address this issue, we apply an integrative approach to link genetic variants with altered lipid metabolism and cardiometabolic disease risk.

Lipidomic profiling was performed using liquid chromatography coupled electrospray-ionization tandem-mass spectrometry on 4,492 genotyped individuals from the Busselton Family Health Study. We performed genome-wide association analysis of 596 lipid species and 33 lipid classes using linear-mixed models, correcting for age, sex, their interactions, 10 genomic principal components and an empirical genetic relatedness matrix. To account for lipoprotein mediated associations, the analysis was repeated with HDL-C, triglycerides, and total cholesterol as covariates. Validation of genome-wide significant associations is supported by replication in the Australian Imaging, Biomarker & Lifestyle Study of Ageing (n=1,112) and Alzheimer's Disease Neuroimaging Initiative (n=757) cohorts.

Over 3,300 independent genome-wide significant (p<5x10-8) lipid-loci were identified. Over 70% of the genomic regions are novel associations for lipid species and metabolites. Approximately 95% of the observed associations are independent of lipoprotein measures. Candidate causal genes were identified using genetic variant functional annotation and integration of results from expression-quantitative trait locus (QTL), methylation-QTL and protein-QTL studies. We assess the health implications of these lipid-associated loci in 450,000 participants of the UK Biobank.

By linking lipid metabolism to cardiometabolic diseases – through genetic associations – we highlight potential therapeutic targets for monitoring, prevention, and treatment of a wide range of metabolic conditions.