Approximately 30 percent of autism spectrum disorder (ASD) cases can be explained by rare de novo variants disrupting gene function. Recent studies have used accurate models of the background mutation rate to create a framework for evaluating de novo mutations in ASD and other disorders. This work has shown that ASD cases have far more de novo mutations than expected in highly constrained genes.
Most studies of the genetics of ASD have focused exclusively on single nucleotide polymorphisms (SNPs) or copy number variants (CNVs), and have ignored the effects of more complex variants such as short tandem repeats (STRs). Multiple lines of evidence support a role of STRs in neurodevelopmental disorders. STRs are involved in dozens of Mendelian diseases, many of which have neurological or psychological phenotypes. Additionally, STRs play an emerging role in complex traits and have been shown to alter gene expression. Finally, STRs are estimated to contribute more de novo variants per generation than all other classes of variants combined, providing a rich source of variation whose role in developmental disorders is almost completely unexplored.
Recent work from Melissa Gymrek’s and Yaniv Erlich’s laboratories has overcome challenges in STR analysis by providing a mechanism to robustly determine STR genotype1 and a framework for using mutational constraint to evaluate the impact of individual STRs2. Gymrek and colleagues now propose to leverage bioinformatics advances in STR analysis to comprehensively evaluate the role of de novo STR variants in the Simons Simplex Collection (SSC) cohort. To that end, they will 1) measure per-locus STR constraint in the SSC data using their established method; 2) identify and characterize de novo STR mutations in ASD and control samples; 3) apply STR constraint scores to characterize de novo mutations and candidate STRs in ASD and 4) develop an interactive web app to make constraint metrics accessible to non-bioinformaticians. This work will help elucidate the importance of STR mutations in ASD, helping to further our understanding of the ASD genetic risk spectrum.