Identification of genetic loci simultaneously associated with multiple cardiometabolic traits
Author
Wood, Alexis C.Arora, Amit
Newell, Michelle
Bland, Victoria L.
Zhou, Jin
Pirastu, Nicola
Ordovas, Jose M.
Klimentidis, Yann C.
Affiliation
Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of ArizonaBIO5 Institute, University of Arizona
Issue Date
2022-01
Metadata
Show full item recordPublisher
Elsevier BVCitation
Wood, A. C., Arora, A., Newell, M., Bland, V. L., Zhou, J., Pirastu, N., Ordovas, J. M., & Klimentidis, Y. C. (2022). Identification of genetic loci simultaneously associated with multiple cardiometabolic traits. Nutrition, Metabolism and Cardiovascular Diseases.Rights
© 2022 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Background and aims: Cardiometabolic disorders (CMD) arise from a constellation of features such as increased adiposity, hyperlipidemia, hypertension and compromised glucose control. Many genetic loci have shown associations with individual CMD-related traits, but no investigations have focused on simultaneously identifying loci showing associations across all domains. We therefore sought to identify loci associated with risk across seven continuous CMD-related traits. Methods and results: We conducted separate genome-wide association studies (GWAS) for systolic and diastolic blood pressure (SBP/DBP), hemoglobin A1c (HbA1c), low- and high- density lipoprotein cholesterol (LDL-C/HDL-C), waist-to-hip-ratio (WHR), and triglycerides (TGs) in the UK Biobank (N = 356,574–456,823). Multiple loci reached genome-wide levels of significance (N = 145–333) for each trait, but only four loci (in/near VEGFA, GRB14-COBLL1, KLF14, and RGS19-OPRL1) were associated with risk across all seven traits (P < 5 × 10−8). We sought replication of these four loci in an independent set of seven trait-specific GWAS meta-analyses. GRB14-COBLL1 showed the most consistent replication, revealing nominally significant associations (P < 0.05) with all traits except DBP. Conclusions: Our analyses suggest that very few loci are associated in the same direction of risk with traits representing the full spectrum of CMD features. We identified four such loci, and an understanding of the pathways between these loci and CMD risk may eventually identify factors that can be used to identify pathologic disturbances that represent broadly beneficial therapeutic targets.Note
12 month embargo; published: 07 January 2022ISSN
0939-4753Version
Final accepted manuscriptSponsors
National Institutes of Healthae974a485f413a2113503eed53cd6c53
10.1016/j.numecd.2022.01.002

