Researchers from Princeton University (NJ, USA) and Simons Foundation (NY, USA) have developed a new technique for a genome-wide approach to identifying genes involved in autism spectrum disorder (ASD). The machine-learning approach has now raised the number of genes predicted to be involved in ASD from 65 to 2500.
At present, only 65 autism genes have been identified via sequencing studies, despite estimates predicting this number to actually be between 400 and 1000 genes. Due to the complexity of the disorder, sequencing studies are suggested to be inadequate to uncover the full genetic basis of ASD.
In the new study, published in Nature Neuroscience, the research team developed a complementary machine-learning approach. Utilizing a functional brain map, they were able to produce a genome-wide prediction of autism risk genes – including hundreds for which there is very little or no prior evidence. Following this, the team validated the approach in a large independent case-control sequencing study. Furthermore, the researchers have built an interactive web portal where clinicians and researchers can access the study’s findings.
“Our work is significant because geneticists can use our predictions to direct future sequencing studies, enabling much faster and cheaper discovery of autism genes,” commented lead author Arjun Krishnan, Princeton University. “Researchers can use our predictions to prioritize and interpret results of whole-genome sequencing studies of ASD. Biomedical researchers can use these predictions and our analysis to put any gene in specific autism-associated functional, developmental and anatomical contexts.
“We provide a systematic prioritization of potential ‘causal’ genes within eight of the most frequent autism-linked large copy number variant intervals. We find that perturbations caused by these intervals converge on specific pathways linking them to autism.”
Senior author Olga Troyanskaya, Princeton University, added: “Our paper describes the first prediction of genes associated with ASD across the whole human genome. The method we developed can, for the first time, identify ASD-associated genes even if they have not been previously linked to autism. We achieve this by using a functional map of the brain (brain-specific gene network) generated by integrating thousands of genomic datasets.”
Source: Krishnan A, Zhang R, Yao V et al. Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nat. Neurosci. doi:10.1038/nn.4353 (2016) (Epub ahead of print); www.eurekalert.org/pub_releases/2016-08/pu-pri072916.php