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Research Paper|Volume 13, Issue 3|pp 3618—3644

Identification of novel SNPs associated with coronary artery disease and birth weight using a pleiotropic cFDR method

Xinrui Wu1, Xu Lin2, Qi Li3, Zun Wang4, Na Zhang1, Mengyuan Tian1, Xiaolei Wang1, Hongwen Deng5,6, Hongzhuan Tan1
  • 1Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, China
  • 2Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
  • 3Xiangxi Center for Disease Prevention and Control, Jishou 416000, China
  • 4Xiangya Nursing School, Central South University, Changsha 410013, China
  • 5School of Basic Medical Science, Central South University, Changsha 410013, China
  • 6Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70112, USA
Received: September 19, 2020Accepted: November 11, 2020Published: December 19, 2020
https://doi.org/10.18632/aging.202322

Copyright: © 2020 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Objectives: Clinical and epidemiological findings indicate an association between coronary artery disease (CAD) and low birth weight (BW). However, the mechanisms underlying this relationship are largely unknown. Here, we aimed to identify novel single-nucleotide polymorphisms (SNPs) associated with CAD, BW, and their shared pleiotropic loci, and to detect the potential causal relationship between CAD and BW.

Methods: We first applied a genetic pleiotropic conditional false discovery rate (cFDR) method to two independent genome-wide association studies (GWAS) summary statistics of CAD and BW to estimate the pleiotropic enrichment between them. Then, bi-directional Mendelian randomization (MR) analyses were performed to clarify the causal association between these two traits.

Results: By incorporating related traits into a conditional analysis framework, we observed the significant pleiotropic enrichment between CAD and BW. By applying the cFDR level of 0.05, 109 variants were detected for CAD, 203 for BW, and 26 pleiotropic variants for both traits. We identified 11 CAD- and/or BW-associated SNPs that showed more than three of the metabolic quantitative trait loci (metaQTL), protein QTL (pQTL), methylation QTL (meQTL), or expression QTL (eQTL) effects. The pleiotropic SNP rs10774625, located at ATXN2, showed metaQTL, pQTL, meQTL, and eQTL effects simultaneously. Using the bi-directional MR approach, we found a negative association from BW to CAD (odds ratio [OR] = 0.68, 95% confidence interval [CI]: 0.59 to 0.80, p = 1.57× 10-6).

Conclusion: We identified several pleiotropic loci between CAD and BW by leveraging GWAS results of related phenotypes and identified a potential causal relationship from BW to CAD. Our findings provide novel insights into the shared biological mechanisms and overlapping genetic heritability between CAD and BW.