Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning

Dimitrios Vitsios (), Ryan S. Dhindsa, Lawrence Middleton, Ayal B. Gussow and Slavé Petrovski ()
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Dimitrios Vitsios: BioPharmaceuticals R&D, AstraZeneca
Ryan S. Dhindsa: BioPharmaceuticals R&D, AstraZeneca
Lawrence Middleton: BioPharmaceuticals R&D, AstraZeneca
Ayal B. Gussow: National Library of Medicine
Slavé Petrovski: BioPharmaceuticals R&D, AstraZeneca

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate intolerance to variation, functional genomic annotations and primary genomic sequence to build JARVIS: a comprehensive deep learning model to prioritize non-coding regions, outperforming other human lineage-specific scores. Despite being agnostic to evolutionary conservation, JARVIS performs comparably or outperforms conservation-based scores in classifying pathogenic single-nucleotide and structural variants. In constructing JARVIS, we introduce the genome-wide residual variation intolerance score (gwRVIS), applying a sliding-window approach to whole genome sequencing data from 62,784 individuals. gwRVIS distinguishes Mendelian disease genes from more tolerant CCDS regions and highlights ultra-conserved non-coding elements as the most intolerant regions in the human genome. Both JARVIS and gwRVIS capture previously inaccessible human-lineage constraint information and will enhance our understanding of the non-coding genome.

Date: 2021
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DOI: 10.1038/s41467-021-21790-4

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