Machine Learning Used To Investigate The Genetics Of Autism

Machine Learning Is Helping Us Find the Genetics of Autism.

Princeton Researchers Are Working Smarter, Not Harder.

Dave Gershgorn | August 3, 2016

The genetic cause of autism spectrum disorder is notoriously hard to research. Genetic markers for the disorder are tough to match from patient to patient because they’re so rare—one of the most common genetic signifiers is only found in less than one percent of those diagnosed with autism. Even when genetic anomalies are found, they must be checked against family members genomes to ensure it’s not attributable to a more commonly inherited mutation that doesn’t cause disease.
Researchers at Princeton and the Simons Foundation turned the traditional approach on its head, teaching a machine learning algorithm to look for the genetic relationships that could cause autism.

[Click to Enlarge. See link for interactive features.] "...biology of Autism Spectrum Disorders (ASD)..." Source: https://gene.sfari.org/autdb/Welcome.do (SFARI Gene is licensed by the Simons Foundation from MindSpec.)

[Click to enlarge for readability.] "Network of autism-associated genes." Source: http://newsroom.cumc.columbia.edu/blog/2014/12/22/diverse-autism-mutations/

<more at http://www.popsci.com/machine-learning-unlocks-potential-autism-causing-genes; related articles and links: https://www.researchgate.net/blog/post/nature-study-uses-machine-learning-to-predict-autism-genes (Nature study uses machine learning to predict Autism genes. The Princeton study discovered several new candidate genes linked to Autism. August 1, 2016) and http://www.nature.com/neuro/journal/vaop/ncurrent/full/nn.4353.html (Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Arjun Krishnan, Ran Zhang, Victoria Yao, Chandra L. Theesfeld, Aaron K. Wong, Alicja Tadych, Natalia Volfovsky, Alan Packer, Alex Lash and Olga G. Troyanskaya. Nature Neuroscience (2016) doi:10.1038/nn.4353. [Abstract: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes—about 65 genes out of an estimated several hundred—are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case–control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu.])>