Genome-wide association analysis of left ventricular imaging-derived phenotypes identifies 72 risk loci and yields genetic insights into hypertrophic cardiomyopathy

Caibo Ning^1,2,3,11, Linyun Fan^1,2,3,11, Meng Jin^4,11, Wenji Wang^5,11, Zhiqiang Hu^5,11, Yimin Cai^1,11, Liangkai Chen^6,11, Zequn Lu¹, Ming Zhang¹, Can Chen¹, Yanmin Li¹, Fuwei Zhang¹, Wenzhuo Wang¹, Yizhuo Liu¹, Shuoni Chen¹, Yuan Jiang¹, Chunyi He¹, Zhuo Wang¹, Xu Chen¹, Hanting Li¹, Gaoyuan Li¹, Qianying Ma¹, Hui Geng¹, Wen Tian¹, Heng Zhang¹, Bo Liu ⁴, Qing Xia⁵, Xiaojun Yang⁷, Zhongchun Liu⁸ , Bin Li¹ , Ying Zhu^1,2,3,9, Xiangpan Li², Shaoting Zhang^5,10 , Jianbo Tian^1,2,3,9 & Xiaoping Miao^1,2,3,9

Abstract

Left ventricular regional wall thickness (LVRWT) is an independent predictor of morbidity and mortality in cardiovascular diseases (CVDs). To identify specific genetic influences on individual LVRWT, we established a novel deep learning algorithm to calculate 12 LVRWTs accurately in 42,194 individuals from the UK Biobank with cardiac magnetic resonance (CMR) imaging. Genome-wide association studies of CMR-derived 12 LVRWTs identified 72 significant genetic loci associated with at least one LVRWT phenotype (P < 5 × 10−8), which were revealed to actively participate in heart development and contraction pathways. Significant causal relationships were observed between the LVRWT traits and hypertrophic cardiomyopathy (HCM) using genetic correlation and Mendelian randomization analyses (P < 0.01). The polygenic risk score of inferoseptal LVRWT at end systole exhibited a notable association with incident HCM, facilitating the identification of high-risk individuals. The findings yield insights into the genetic determinants of LVRWT phenotypes and shed light on the biological basis for HCM etiology.