Single Nucleotide Polymorphisms' Causal Structure Robustness within Coronary Artery Disease Patients.

Area of Science:

• Genetics and Bioinformatics
• Cardiovascular Disease Research
• Computational Biology

Background:

• Advancements in technology generate vast datasets across scientific fields.
• Causal models offer a powerful approach to understanding complex variable relationships.
• Interpreting large datasets is crucial for scientific discovery and knowledge advancement.

Purpose of the Study:

• To assess the robustness of the causal structure of single nucleotide polymorphisms (SNPs) in coronary artery disease (CAD).
• To investigate how interventions, simulated by data exclusion, affect causal structure under varying Syntax Score categories.
• To explore the local causal structure around the Syntax Score in patients with positive Syntax Scores.

Main Methods:

• Analysis of causal structures using data from 963 coronary artery disease patients.
• Simulated interventions by randomly excluding patients from datasets categorized by Syntax Score (zero and positive).
• Global and local causal structure assessment under different intervention strengths.

Main Results:

• The causal structure of single nucleotide polymorphisms demonstrated greater robustness under milder data interventions.
• Stronger interventions led to an increased impact on the overall causal structure.
• The local causal structure around the Syntax Score was found to be resilient, even with strong interventions in positive Syntax Score cases.

Conclusions:

• Causal models can enhance the understanding of the biological underpinnings of coronary artery disease.
• SNP causal structures are sensitive to data intervention levels, with local structures showing resilience.
• This approach provides valuable insights into genetic factors influencing CAD complexity.