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

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.

Shared genetic links between bladder cancer and obesity-related traits: A conjunctional false discovery rate study

“…To address these issues, a method called conditional false discovery rate (cFDR) has been developed, which leveraged pleiotropic effects and made full use of relevant auxiliary covariates to increase the power of the analysis. [ 12 , 13 ] While cFDR can identify risk loci associated with 1 trait, complex disorders and multifactorial phenotypes require a method called conditional co-FDR (ccFDR), which can investigate the risk loci of 2 traits simultaneously. [ 10 ] Our study applied both cFDR and ccFDR to investigate the risk loci associated with bladder cancer (BCa) and obesity-related traits.…”