Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.

ABSTRACT: Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the Di

SUBMITTER: Mukherjee S 

PROVIDER: S-EPMC8546038 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature