DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders

In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance of benign heterozygous variants that act as massive background noise (typically, in a  400:1 excess ratio). To reduce this overflow of false positives in next-generation sequencing (NGS) screens, we developed DOMINO, a tool assessing the likelihood for a gene to harbor dominant changes. Unlike commonly-used predictors of pathogenicity, DOMINO takes into consideration features that a re the properties of genes, rather than of variants.

Source: The American Journal of Human Genetics - October 5, 2017 Category: Genetics & Stem Cells Authors: Mathieu Quinodoz, Beryl Royer-Bertrand, Katarina Cisarova, Silvio Alessandro Di Gioia, Andrea Superti-Furga, Carlo Rivolta Tags: Report Source Type: research