| Title: | Genome-wide association analysis of left ventricular imaging-derived phenotypes identifies 72 risk loci and yields genetic insights into hypertrophic cardiomyopathy |
| Journal: | Nature Communications |
| Published: | 30 Nov 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38036550/ |
| DOI: | https://doi.org/10.1038/s41467-023-43771-5 |
| URL: | https://www.nature.com/articles/s41467-023-43771-5.pdf |
Publication 8233
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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.</p>
35 Authors
- Caibo Ning
- Linyun Fan
- Meng Jin
- Wenji Wang
- Zhiqiang Hu
- Yimin Cai
- Liangkai Chen
- Zequn Lu
- Ming Zhang
- Can Chen
- Yanmin Li
- Fuwei Zhang
- Wenzhuo Wang
- Yizhuo Liu
- Shuoni Chen
- Yuan Jiang
- Chunyi He
- Zhuo Wang
- Xu Chen
- Hanting Li
- Gaoyuan Li
- Qianying Ma
- Hui Geng
- Wen Tian
- Heng Zhang
- Bo Liu
- Qing Xia
- Xiaojun Yang
- Zhongchun Liu
- Bin Li
- Ying Zhu
- Xiangpan Li
- Shaoting Zhang
- Jianbo Tian
- Xiaoping Miao
1 Application
| Application ID | Title |
|---|---|
| 63454 | Understand the relationship between diet, lifestyle and gene-environment interactions and healthy aging |
Enabling scientific discoveries that improve human health