Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation...

Area of Science:

• Genomics
• Computational Biology
• Neuroscience

Background:

• Predicting the functional impact of noncoding genetic variants is crucial for understanding complex traits.
• Current computational scores offer insights but require multi-step analyses for custom variant sets and trait association.
• Integrating variant functional predictions with complex trait data, particularly brain traits, remains a challenge.

Purpose of the Study:

• To present a standardized protocol for prioritizing genetic variants based on deep-learning-predicted functional scores.
• To demonstrate the association of these functional scores with complex brain traits.
• To provide a generalizable framework for variant prioritization and phenotype correlation.

Main Methods:

• Generation of deep-learning-based functional scores for custom variant sets.
• Statistical comparison of variant scores.
• Correlation analysis between functional scores and brain phenotypes.
• Functional enrichment analysis to interpret variant significance.

Main Results:

• The protocol enables the generation and application of deep-learning functional scores for variant prioritization.
• Demonstrated the successful association of predicted functional scores with specific brain traits.
• The methodology proved effective in identifying potentially impactful variants.

Conclusions:

• The developed protocol offers a robust and generalizable method for functional variant prioritization.
• This approach facilitates the integration of computational predictions with complex trait analysis, particularly in neuroscience.
• The protocol can be adapted for various genetic models and phenotypic data.