Forecasting risk gene discovery in autism with machine learning and genome-scale data.

ABSTRACT: Genetics has been one of the most powerful windows into the biology of autism spectrum disorder (ASD). It is estimated that a thousand or more genes may confer risk for ASD when functionally perturbed, however, only around 100 genes currently have sufficient evidence to be considered true "autism risk genes". Massive genetic studies are currently underway producing data to implicate additional genes. This approach - although necessary - is costly and slow-moving, making identification of putative ASD risk genes with existing data vital. Here, we approach autism risk gene discovery as a machine learning problem, rather than a genetic association problem, by using genome-scale data as predictors to identify new genes with similar properties to established autism risk genes. This ensemble met

SUBMITTER: Brueggeman L 

PROVIDER: S-EPMC7067874 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature