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Published on: September 20, 2024
Genome-wide association study of cardiometabolic multimorbidity in the UK Biobank
Chenxuan Zhao1,2,3, Tianqi Ma2,3, Xunjie Cheng2,3
1Department of Cardiovascular Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Insights
Identifying causal factors for cardiometabolic multimorbidity (CMM) is vital. This study used genetic analysis to find shared genetic factors and potential causal links for CMM, aiding future prevention strategies.
Area of Science:
- Genetics
- Cardiovascular Medicine
- Metabolic Disorders
Background:
- Cardiometabolic multimorbidity (CMM) has high prevalence and poor prognosis.
- Identifying causal factors for CMM is crucial for prevention.
- Mendelian randomization (MR) is a key method, but requires knowledge of SNP effects on CMM.
Purpose of the Study:
- To analyze genetic overlap among cardiometabolic diseases (CMDs).
- To identify genetic loci and SNPs associated with CMM.
- To explore potential causal factors of CMM using MR.
Main Methods:
- Genome-wide association study (GWAS) and post-GWAS analyses in UK Biobank participants (N=407,949).
- Analysis of genetic correlations and shared loci among CMDs.
- Polygenic risk score modeling and two-sample MR analysis.
Main Results:
- Strong positive genetic correlations and shared loci were observed among CMDs.
- Eleven loci and 12 lead SNPs associated with CMM were identified.
- MR analysis suggested causal effects of total cholesterol, serum urate, BMI, and smoking on CMM.
Conclusions:
- Identified shared genetic architecture underlying CMDs and CMM.
- Provided genetic loci and SNPs for future MR studies on CMM.
- Highlighted potential causal pathways for CMM, informing prevention strategies.
Abstract:
Considering the high prevalence and poor prognosis of cardiometabolic multimorbidity (CMM), identifying causal factors and actively implementing preventive measures is crucial. However, Mendelian randomization (MR), a key method for identifying the causal factors of CMM, requires knowledge of the effects of SNPs on CMM, which remain unknown. We first analyzed the genetic overlap of single cardiometabolic diseases (CMDs) using the latest genome-wide association study (GWAS) for evidential support and comparison. We observed strong positive genetic correlations and shared loci among all CMDs. Further, GWAS and post-GWAS analyses of CMM were performed in 407 949 European ancestry individuals from the UK Biobank. Eleven loci and 12 lead SNPs were identified. By comparison, we found these SNPs were a subset of SNPs associated with CMDs, including both shared and non-shared SNPs. Then, the polygenic risk score model predicted the risk of CMM (C-index = 0.62) and we identified candidate genes related to lipid metabolism and immune function. Finally, as an example, two-sample MR analysis based on the GWAS revealed potential causal effects of total cholesterol, serum urate, body mass index, and smoking on CMM. These results provide a basis for future MR research and inspire future studies on the mechanism and prevention of CMM.
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