Methods for fine-mapping with chromatin and expression data.

Area of Science:

• Genomics and Epigenetics
• Computational Biology
• Gene Regulation

Background:

• Thousands of genomic regions are linked to chromatin modifications, potentially influencing gene expression.
• Previous research used heuristic methods to study genome-epigenome-expression links.
• No prior work has explicitly modeled the causal chain: genetic variants -> chromatin modifications -> gene expression.

Purpose of the Study:

• To introduce a novel hierarchical fine-mapping framework.
• To integrate genetic, chromatin, and gene expression data.
• To identify causal variants and chromatin marks influencing gene expression.

Main Methods:

• Developed a new hierarchical fine-mapping framework.
• Integrated multi-omics data (genetics, chromatin, gene expression).
• Utilized simulation studies and analyzed empirical data from diverse ancestries.

Main Results:

• The proposed method demonstrated higher accuracy in simulations for identifying causal marks influencing expression compared to existing approaches.
• Analysis of empirical data revealed prioritized causal paths consistent with the modeled gene regulation chain.
• Identified likely functional regions associated with these prioritized paths.

Conclusions:

• The hierarchical fine-mapping framework effectively models the causal chain from genetic variants to gene expression via chromatin.
• The method improves the identification of regulatory relationships.
• Findings provide insights into the functional mechanisms underlying gene regulation in different populations.