Fine-mapping candidate neuropsychiatric regulatory variants using cell type-aware comparative genomics.

Area of Science:

• Genomics
• Computational Biology
• Evolutionary Biology

Background:

• Identifying functional genomic loci often relies on sequence conservation, but this fails when regulatory function is maintained despite sequence divergence.
• Regulatory elements can evolve to maintain function across cell types and species, even with significant sequence changes.

Purpose of the Study:

• To introduce the Cell Type-Aware Conservation Inference Toolkit (CTACIT) for identifying trait-associated regulatory variants.
• To improve the imputation of regulatory function by integrating sequence conservation with cell type-specific chromatin data.
• To enhance the prioritization of variants for functional characterization in disease research.

Main Methods:

• CTACIT integrates sequence conservation scores with multi-species, cell type-specific open chromatin data.
• The toolkit imputes regulatory function for genomic loci across numerous species.
• Computational analysis was applied to neuropsychiatric trait loci, comparing CTACIT results with traditional methods.

Main Results:

• CTACIT identified higher heritability enrichment and more fine-mapped variants in neuropsychiatric loci compared to sequence conservation or human chromatin data alone.
• In vivo reporter assays validated CTACIT predictions for enhancers near the DRD2 schizophrenia risk locus.
• The toolkit successfully prioritized variants in regions of conserved regulatory function.

Conclusions:

• CTACIT effectively integrates genome conservation and multi-species chromatin data to identify functional regulatory variants.
• The toolkit addresses a key challenge in linking genetic associations to mechanistic understanding of diseases.
• CTACIT enhances the discovery of trait-associated regulatory variants, particularly for complex diseases.