Identifying causal variants and genes using functional genomics in specialized cell types and contexts.

Area of Science:

• Human genetics
• Genomics
• Molecular biology

Background:

• Genome-wide association studies (GWAS) have identified numerous genetic loci linked to polygenic diseases.
• Most disease-associated loci are in non-coding regions, complicating the identification of specific causal variants.
• Linkage disequilibrium in these regions poses challenges for pinpointing causal variants and genes.

Purpose of the Study:

• To review current methods for identifying causal variants of polygenic diseases.
• To highlight the importance of using disease-relevant cell types in functional genomics analyses.
• To discuss challenges and best practices in leveraging functional genomics data for variant discovery.

Main Methods:

• Utilizing functional genomics assays (e.g., ATAC-seq, ChIP-seq, RNA-seq) to annotate variants.
• Applying statistical and experimental approaches to infer regulatory relationships between variants and genes.
• Integrating data from specialized cell types to contextualize variant function.

Main Results:

• Functional genomics data significantly aids in annotating variants and understanding regulatory networks.
• Selection of appropriate, disease-relevant cell types is crucial for accurate mechanism elucidation.
• Advances in genomics provide powerful tools for dissecting polygenic disease architecture.

Conclusions:

• Functional genomics, particularly when applied to specialized cell types, is essential for identifying causal variants in polygenic diseases.
• Best practices are needed to navigate the complexities of variant interpretation in non-coding regions.
• Future research should focus on integrating diverse functional genomics data to refine our understanding of disease risk.