NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans.
State-of-the-art methods assessing pathogenic non-coding variants have mostly been characterized on common disease-associated polymorphisms, yet with modest accuracy and strong positional biases. In this study, we curated 737 high-confidence pathogenic non-coding variants associated with monogenic Mendelian diseases. In addition to interspecies conservation, a comprehensive set of recent and ongoing purifying selection signals in humans is explored, accounting for lineage-specific regulatory elements. Supervised learning using gradient tree boosting on such features achieves a high predictive performance and overcomes positional bias. NCBoost performs consistently across diverse learning and independent testing data sets and outperforms other existing reference methods.
['Caron|Barthélémy|B|', 'Luo|Yufei|Y|', 'Rausell|Antonio|A|0000-0002-4832-6101']
D021901:DNA, Intergenic D030342:Genetic Diseases, Inborn D006801:Humans D020641:Polymorphism, Single Nucleotide D012641:Selection, Genetic D000069553:Supervised Machine Learning D000073336:Whole Genome Sequencing
Mendelian diseases Non-coding genetic variants Pathogenicity score Rare variant analysis Whole genome sequencing