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Deep Learning Models Accurately Identify Disease-Causal Regulatory Variants

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Air date: Thursday, October 24, 2024, 2:00:00 PM
Time displayed is Eastern Time, Washington DC Local
Views: Total views: 176 (95 Live, 81 On-demand)
Category: Special
Runtime: 01:00:48
Description: Abstract: Genetic association studies have identified thousands of independent signals associated with a wide range of human complex diseases. Despite these successes, pinpointing specific causal variants underlying a genetic association signal remains challenging. In this presentation, I will introduce a deep learning (DL) model designed to accurately predict disease-causal variants in the noncoding regions of the human genome. By applying this model to enhancers, we identify a specific set of causal variants linked to type 2 diabetes, several of which have been confirmed biochemically. When extending the model to silencers, we find that candidate silencers exhibit strong enrichment in disease-associated variants, with certain diseases showing a significantly stronger association with silencer variants than with enhancer variants. Nearly 52% of candidate silencers cluster together, forming silencer-rich loci. In the loci of Parkinson's disease hallmark genes TRIM31 and MAL, the associated SNPs densely populate these clustered candidate silencers rather than enhancers, showing an overall twofold enrichment of silencers compared to enhancers. The disruption of apoptosis in neuronal cells is associated with both schizophrenia and bipolar disorder and can largely be attributed to variants within candidate silencers. Our model allows for a mechanistic explanation of causative SNP effects by identifying altered binding of tissue-specific repressors and activators, validated with 70% directional concordance using SNP-SELEX. Focusing on individual silencer variants, experimental data confirms the roles of the rs62055708 SNP in Parkinson's disease, rs2535629 in schizophrenia, and rs6207121 in type 1 diabetes. In summary, our results suggest that advancements in deep learning models for discovering disease-causal variants can provide a foundation for explaining mechanisms of action and designing novel diagnostics and therapeutics.
Author: Dr. Ivan Ovcharenko & Dr. Lauren Porter.
 
CIT Live ID: 55299
Permanent link: https://videocast.nih.gov/watch=55299