scVMAP: a comprehensive platform for integrating single-cell chromatin accessibility regions with causal variants

Area of Science:

• Genomics
• Computational Biology
• Systems Biology

Background:

• Genome-wide association studies (GWAS) identify genetic variants associated with traits, but their functional impact at the single-cell level remains challenging to elucidate.
• Single-cell assay for transposase-accessible chromatin with high-throughput sequencing (scATAC-seq) provides insights into cell-type-specific chromatin accessibility and regulatory elements.
• Integrating GWAS and scATAC-seq data is crucial for understanding the genetic architecture of complex traits and diseases.

Purpose of the Study:

• To develop scVMAP, a user-friendly database for exploring trait-relevant cell populations at single-cell resolution.
• To facilitate the comprehensive analysis and efficient exploration of integrated genetic variation and single-cell genomic data.
• To provide a resource for understanding the functional localization of genetic variations and their impact on cellular phenotypes.

Main Methods:

• Integration of 183 scATAC-seq datasets with 15,884 fine-mapping results.
• Development of a database (scVMAP) to store and query over 32.1 billion trait-cell pairs.
• Implementation of analytical pipelines to compute trait relevance scores (TRSs), cell-type-specific differential gene and transcription factor (TF) activities, and regulatory networks.

Main Results:

• scVMAP provides trait relevance scores for individual cells, enabling the identification of cell populations associated with specific traits.
• The database offers insights into cell-type-specific gene and TF activities and their regulatory interactions linked to phenotypic traits.
• Comprehensive visualization and analysis tools are available for exploring trait-cell population relationships at single-cell resolution.

Conclusions:

• scVMAP serves as a valuable resource for researchers investigating the functional consequences of genetic variations in a single-cell context.
• The database enhances the understanding of how phenotypic associations are mapped to specific cell types and regulatory mechanisms.
• scVMAP facilitates the discovery of novel biological insights by integrating large-scale genetic and single-cell genomic data.