Cell Type-Specific Annotation and Fine Mapping of Variants Associated With Brain Disorders.

Area of Science:

• Genomics
• Neuroscience
• Computational Biology

Background:

• Common genetic variants contribute to complex brain disorders but often reside in non-coding regions, making functional prediction challenging.
• Existing context-free computational methods predict variant function without considering specific tissue or cell types, limiting their utility for brain disorders.

Purpose of the Study:

• To develop and evaluate a multi-step pipeline for prioritizing disease-causal genetic variants by comparing context-specific and context-free approaches.
• To apply this pipeline to schizophrenia (SCZ) as a model complex brain disorder.

Main Methods:

• Compared over two dozen computational methods for variant prioritization.
• Assessed associations between cell/tissue-specific mapping scores, open chromatin accessibility, and SCZ risk loci.
• Developed a prioritized map of SCZ risk loci for functional validation.

Main Results:

• Identified significant differences between context-free and tissue-specific prediction approaches, highlighting their complementary roles.
• A consensus mapping approach prioritized candidate genes, including FURIN, demonstrating collective dysregulation of gene expression in stem cell-derived neurons relevant to SCZ.
• Genes not prioritized by both approaches lacked similar disease-associated characteristics.

Conclusions:

• A combined approach using both context-free and tissue-specific predictions effectively prioritizes common variants implicated in complex brain disorders.
• The developed pipeline aids in identifying and validating disease-causal variants for further research into neuropsychiatric conditions.