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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Human Genetics01:28

Human Genetics

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Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

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Related Experiment Video

Updated: May 18, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Shared Genetic Liability Between Heart Failure and Myocardial Infarction Revealed by Genome-Wide Cross-Trait

Ruikang Liu1,2, Chiyun Sun1,3, Nan Jiang1

  • 1Graduate School, Beijing University of Chinese Medicine, Beijing, China.

Journal of Cardiovascular Pharmacology and Therapeutics
|May 16, 2026
PubMed
Summary

Myocardial infarction (MI) and heart failure (HF) share significant genetic links, with about 90% of genetic variants overlapping. This suggests a shared genetic liability contributing to both cardiovascular diseases.

Keywords:
Myocardial infarctionTWAScondFDRheart failuremiXeR

Related Experiment Videos

Last Updated: May 18, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Area of Science:

  • Cardiovascular Genetics
  • Genomics
  • Complex Disease Etiology

Background:

  • Shared genetic architecture between myocardial infarction (MI) and heart failure (HF) is not fully understood.
  • Investigating the biological relevance of shared genetic factors is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To comprehensively analyze the shared genetic architecture between MI and HF.
  • To identify pleiotropic loci and functional variants contributing to both conditions.

Main Methods:

  • Genome-wide association studies (GWAS) summary statistics for MI and HF in European ancestry.
  • Linkage disequilibrium score regression for genetic correlation and MiXeR for polygenic overlap.
  • Conditional and conjunctional false discovery rate (condFDR/conjFDR) for locus identification.
  • Functional prioritization using fine-mapping, TWAS, and proteomic data integration (SMR).

Main Results:

  • Strong positive genetic correlation between MI and HF (rg = 0.494).
  • Substantial polygenic overlap, with ~90% of MI variants shared with HF, and concordant effect directions.
  • Identification of multiple pleiotropic loci and novel HF-associated regions; prioritized rs544366796 for MI.
  • TWAS and FOCUS highlighted known MI genes and identified MYOZ1 as HF-specific; SMR suggested apolipoprotein E as a shared protein.

Conclusions:

  • MI and HF share significant genetic liability through polygenic overlap and pleiotropic loci.
  • Integrative analyses propose a potential two-stage genetic framework from ischemic susceptibility to HF progression.
  • Findings provide a hypothesis-generating model for the genetic links between MI and HF.