Organ-specific prioritization and annotation of non-coding regulatory variants in the human genome

Abstract

AbstractIdentifying non-coding regulatory variants in the human genome remains a challenging task in genomics. Recently we advanced our leading regulatory variant database, RegulomeDB, to its second version. Building upon this comprehensive database, we developed a novel machine-learning architecture with stacked generalization, TLand, which utilizes RegulomeDB-derived features to predict regulatory variants at cell or organ-specific levels. In our holdout benchmarking, TLand consistently outperformed state-of-the-art models, demonstrating its ability to generalize to new cell lines or organs. We trained three types of organ-specific TLand models to overcome the common model bias toward high data availability cell lines or organs. These models accurately prioritize relevant organs for 2 million GWAS SNPs associated with GWAS traits. Moreover, our analysis of top-scoring variants in specific organ models showed a high enrichment of relevant GWAS traits. We expect that TLand and RegulomeDB will further advance our ability to understand human regulatory variants genome-wide.