Using C-JAMP to Investigate Epistasis and Pleiotropy

Area of Science:

• Genomics and Bioinformatics
• Statistical Genetics
• Computational Biology

Background:

• Understanding complex biological mechanisms in human health and disease is challenging.
• Genomic data allows detailed investigation of genetic variant interactions and their effects on traits.
• Jointly modeling epistasis and pleiotropy requires advanced statistical methods.

Purpose of the Study:

• To outline the C-JAMP method for investigating epistatic effects on multiple traits.
• To demonstrate the application of C-JAMP in advancing the understanding of biological processes.
• To discuss future extensions including polygenic risk scores and ancestry-specific modeling.

Main Methods:

• Utilizes C-JAMP, a statistical method based on copula functions.
• Applies the method to analyze epistatic effects across multiple traits using genomic data.
• Discusses integration of polygenic risk scores and ancestry-specific modeling.

Main Results:

• C-JAMP provides a framework for jointly modeling epistasis and pleiotropy.
• The method facilitates a deeper understanding of genetic influences on multiple traits.
• Identifies potential areas for future software development and research.

Conclusions:

• C-JAMP is a valuable tool for dissecting complex genetic architectures.
• Joint modeling of genetic effects enhances biological insights.
• Future extensions will broaden the applicability of C-JAMP in genetic research.