Novel common variants associated with body mass index and coronary artery disease detected using a pleiotropic cFDR method

• Published in: Journal of molecular and cellular cardiology 2017-11, Vol.112, p.1-7
• Main Authors: Lv, Wan-Qiang, Zhang, Xue, Zhang, Qiang, He, Jing-Yang, Liu, Hui-Min, Xia, Xin, Fan, Kun, Zhao, Qi, Shi, Xue-Zhong, Zhang, Wei-Dong, Sun, Chang-Qing, Deng, Hong-Wen
• Format: Article
• Language: English
• Subjects: Body Mass Index; Conditional FDR; Coronary artery disease; Coronary Artery Disease - genetics; Genetic Pleiotropy; Genetic Variation; Genome-Wide Association Study; Humans; Obesity; Pleiotropy; Reproducibility of Results
• ISSN: 0022-2828; 1095-8584; 1095-8584
• DOI: 10.1016/j.yjmcc.2017.08.011

Genome-wide association studies (GWAS) have been successfully applied in identifying single nucleotide polymorphisms (SNPs) associated with body mass index (BMI) and coronary heart disease (CAD). However, the SNPs to date can only explain a small percentage of the genetic variances of traits. Here, we applied a genetic pleiotropic conditional false discovery rate (cFDR) method that combines summary statistic p values from different multi-center GWAS datasets, to detect common genetic variants associated with these two traits. The enrichment of SNPs associated with BMI and CAD was assessed by conditional Q-Q plots and the common variants were identified by the cFDR method. By applying the cFDR level of 0.05, 7 variants were identified to be associated with CAD (2 variants being novel), 34 variants associated with BMI (11 variants being novel), and 3 variants associated with both BMI and CAD (2 variants being novel). The SNP rs653178 (ATXN2) is noteworthy as this variant was replicated in an independent analysis. SNP rs12411886 (CNNM2) and rs794356 (HIP1) were of note as the annotated genes may be associated with processes that are functionally important in lipid metabolism. In conclusion, the cFDR method identified novel variants associated with BMI and/or CAD by effectively incorporating different GWAS datasets. •Applied a pleiotropic cFDR method to GWAS data to identify potential novel variants for complex trait.•Identified 2 potential pleiotropic variants for body mass index and coronary artery disease.•Identified 2 and 11potential novel variants for body mass index and coronary artery disease, respectively.