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Genome-wide association and Mendelian randomization analysis provide insights into the shared genetic architecture between high-dimensional electrocardiographic features and ischemic heart disease

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Abstract

Observational studies have revealed that ischemic heart disease (IHD) has a unique manifestation on electrocardiographic (ECG). However, the genetic relationships between IHD and ECG remain unclear. We took 12-lead ECG as phenotypes to conduct genome-wide association studies (GWAS) for 41,960 samples from UK-Biobank (UKB). By leveraging large-scale GWAS summary of ECG and IHD (downloaded from FinnGen database), we performed LD score regression (LDSC), Mendelian randomization (MR), and polygenic risk score (PRS) regression to explore genetic relationships between IHD and ECG. Finally, we constructed an XGBoost model to predict IHD by integrating PRS and ECG. The GWAS identified 114 independent SNPs significantly (P value < 5 × 10–8/800, where 800 denotes the number of ECG features) associated with ECG. LDSC analysis indicated significant (P value < 0.05) genetic correlations between 39 ECG features and IHD. MR analysis performed by five approaches showed a putative causal effect of IHD on four S wave related ECG features at lead III. Integrating PRS for these ECG features with age and gender, the XGBoost model achieved Area Under Curve (AUC) 0.72 in predicting IHD. Here, we provide genetic evidence supporting S wave related ECG features at lead III to monitor the IHD risk, and open up a unique approach to integrate ECG with genetic factors for pre-warning IHD.

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Data availability

The datasets analyzed during the current study are available in the FinnGen, https://www.finngen.fi/en, and UK Biobank https://www.ukbiobank.ac.uk/.

Abbreviations

IHD:

Ischemic heart disease

ECG:

Electrocardiographic

UKB:

UK Biobank

SMR:

Summary databased Mendelian randomization

GWAS:

Genome-wide association studies

LDSC:

Linkage disequilibrium score regression

SNP:

Single nucleotide polymorphism

MR:

Mendelian randomization

IVW:

Inverse variance-weighted

GSMR:

Generalized summary data-based Mendelian randomization

CAUSE:

Causal analysis using summary effect estimates

LCV:

Latent causal variable

HEIDI:

Heterogeneity in dependent instruments

GCP:

Genetic causality proportion

CI:

Confidence intervals

PRS:

Polygenic risk score

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Acknowledgements

We’d like to thank the UK Biobank (application #65805) and the FinnGen project for making the data available.

Funding

The work was funded by the National Key Research and Development Program of China (2020YFB0204803), the Natural Science Foundation of China (81801132, 81971190, 61772566), Guangdong Key Field Research and Development Plan (2019B020228001,2018B010109006, and 2021A1515010256), Introducing Innovative and Entrepreneurial Teams (2016ZT06D211), Guangzhou Science and Technology Research Plan (202007030010), and Mater Foundation.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Jishou University, Jishou, China

    Xinfeng Wang

  2. Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, 500001, People’s Republic of China

    Xinfeng Wang, Mengling Qi & Huiying Zhao

  3. School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China

    Haoyang Zhang & Yuedong Yang

  4. Key Laboratory of Machine Intelligence and Advanced Computing of MOE, Guangzhou, China

    Xinfeng Wang

  5. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China

    Mengling Qi & Huiying Zhao

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  1. Xinfeng Wang
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Contributions

H. Zhao and Y.Yang designed the study. X.Wang performed the analyses with assistance from H. Zhang, M.Qi. X.Wang and H.Zhao wrote the manuscript. Y. Yang and H. Zhao supervised the study. All authors discussed the results and interpretation, and contributed to the final version of the paper.

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Correspondence to Huiying Zhao.

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Wang, X., Qi, M., Zhang, H. et al. Genome-wide association and Mendelian randomization analysis provide insights into the shared genetic architecture between high-dimensional electrocardiographic features and ischemic heart disease. Hum. Genet. 143, 49–58 (2024). https://doi.org/10.1007/s00439-023-02614-5

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