Context-dependent regulatory variants in Alzheimer's disease

Area of Science:

• Genomics and Bioinformatics
• Neuroscience
• Molecular Biology

Background:

• Noncoding genetic variants contribute to complex diseases like late-onset Alzheimer's disease (LOAD).
• Interpreting the functional impact of these variants is difficult due to their context-dependent regulatory roles.
• LOAD is linked to both common variants in immune cells and rare variants affecting neural development.

Purpose of the Study:

• To systematically characterize the functional impacts of common and rare noncoding variants in myeloid and neural contexts.
• To understand how cell type and cellular state influence variant effects on gene regulation.
• To develop a generalizable framework for interpreting disease-associated noncoding variants.

Main Methods:

• Combined in vitro and in vivo massively parallel reporter assays (MPRAs).
• Utilized interpretable deep-learning models for sequence-to-function analysis.
• Employed CRISPR interference to functionally validate a specific disease-associated locus.

Main Results:

• Individual variants showed differential and context-dependent regulatory functions across immune and neural cells.
• Common LOAD variants had stronger effects in immune contexts, while rare variants were more impactful in brain contexts.
• Deep-learning models identified motif disruptions and context tuning as mechanisms of variant effects.
• Silencing an enhancer at the SEC63-OSTM1 locus modulated inflammation and amyloidogenesis.

Conclusions:

• Noncoding variant effects in LOAD are highly context-dependent, varying by cell type and state.
• The study provides a framework for interpreting the functional consequences of genetic risk variants in complex diseases.
• Regulatory elements can act as gatekeepers for disease-relevant biological processes.