Journal article
Forecasting risk gene discovery in autism with machine learning and genome-scale data
Scientific reports, Vol.10(1), pp.4569-4569
03/12/2020
DOI: 10.1038/s41598-020-61288-5
PMCID: PMC7067874
PMID: 32165711
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 method, forecASD, integrates brain gene expression, heterogeneous network data, and previous gene-level predictors of autism association into an ensemble classifier that yields a single score indexing evidence of each gene's involvement in the etiology of autism. We demonstrate that forecASD has substantially better performance than previous predictors of autism association in three independent trio-based sequencing studies. Studying forecASD prioritized genes, we show that forecASD is a robust indicator of a gene's involvement in ASD etiology, with diverse applications to gene discovery, differential expression analysis, eQTL prioritization, and pathway enrichment analysis.
Details
- Title: Subtitle
- Forecasting risk gene discovery in autism with machine learning and genome-scale data
- Creators
- Leo Brueggeman - University of Iowa, Medical Scientist Training Program, Iowa City, IA, USATanner Koomar - University of Iowa, Interdisciplinary Genetics Program, Iowa City, IA, USAJacob J Michaelson - University of Iowa, Interdisciplinary Genetics Program, Iowa City, IA, USA. jacob-michaelson@uiowa.edu
- Resource Type
- Journal article
- Publication Details
- Scientific reports, Vol.10(1), pp.4569-4569
- DOI
- 10.1038/s41598-020-61288-5
- PMID
- 32165711
- PMCID
- PMC7067874
- NLM abbreviation
- Sci Rep
- ISSN
- 2045-2322
- eISSN
- 2045-2322
- Publisher
- England
- Grant note
- R01 DC014489 / NIDCD NIH HHS T32 GM007337 / NIGMS NIH HHS R01 MH105527 / NIMH NIH HHS
- Language
- English
- Date published
- 03/12/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Communication Sciences and Disorders; Psychiatry; Iowa Neuroscience Institute
- Record Identifier
- 9984070253802771
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