Patterns of de novo tandem repeat mutations and their role in autism.

Autism spectrum disorder (ASD) is an early-onset developmental disorder characterized by deficits in communication and social interaction and restrictive or repetitive behaviours1,2. Family studies demonstrate that ASD has a substantial genetic basis with contributions both from inherited and de novo variants3,4. It has been estimated that de novo mutations may contribute to 30% of all simplex cases, in which only a single child is affected per family5. Tandem repeats (TRs), defined here as sequences of 1 to 20 base pairs in size repeated consecutively, comprise one of the major sources of de novo mutations in humans6. TR expansions are implicated in dozens of neurological and psychiatric disorders7. Yet, de novo TR mutations have not been characterized on a genome-wide scale, and their contribution to ASD remains unexplored. Here we develop new bioinformatics methods for identifying and prioritizing de novo TR mutations from sequencing data and perform a genome-wide characterization of de novo TR mutations in ASD-affected probands and unaffected siblings. We infer specific mutation events and their precise changes in repeat number, and primarily focus on more prevalent stepwise copy number changes rather than large expansions. Our results demonstrate a significant genome-wide excess of TR mutations in ASD probands. Mutations in probands tend to be larger, enriched in fetal brain regulatory regions, and are predicted to be more evolutionarily deleterious. Overall, our results highlight the importance of considering repeat variants in future studies of de novo mutations.

TRTools: a toolkit for genome-wide analysis of tandem repeats.

SUMMARY: A rich set of tools have recently been developed for performing genome-wide genotyping of tandem repeats (TRs). However, standardized tools for downstream analysis of these results are lacking. To facilitate TR analysis applications, we present TRTools, a Python library and suite of command line tools for filtering, merging and quality control of TR genotype files. TRTools utilizes an internal harmonization module, making it compatible with outputs from a wide range of TR genotypers. AVAILABILITY AND IMPLEMENTATION: TRTools is freely available at https://github.com/gymreklab/TRTools. Detailed documentation is available at https://trtools.readthedocs.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The impact of short tandem repeat variation on gene expression.

Short tandem repeats (STRs) have been implicated in a variety of complex traits in humans. However, genome-wide studies of the effects of STRs on gene expression thus far have had limited power to detect associations and provide insights into putative mechanisms. Here, we leverage whole-genome sequencing and expression data for 17 tissues from the Genotype-Tissue Expression Project to identify more than 28,000 STRs for which repeat number is associated with expression of nearby genes (eSTRs). We use fine-mapping to quantify the probability that each eSTR is causal and characterize the top 1,400 fine-mapped eSTRs. We identify hundreds of eSTRs linked with published genome-wide association study signals and implicate specific eSTRs in complex traits, including height, schizophrenia, inflammatory bowel disease and intelligence. Overall, our results support the hypothesis that eSTRs contribute to a range of human phenotypes, and our data should serve as a valuable resource for future studies of complex traits.

Profiling the genome-wide landscape of tandem repeat expansions.

Tandem repeat (TR) expansions have been implicated in dozens of genetic diseases, including Huntington's Disease, Fragile X Syndrome, and hereditary ataxias. Furthermore, TRs have recently been implicated in a range of complex traits, including gene expression and cancer risk. While the human genome harbors hundreds of thousands of TRs, analysis of TR expansions has been mainly limited to known pathogenic loci. A major challenge is that expanded repeats are beyond the read length of most next-generation sequencing (NGS) datasets and are not profiled by existing genome-wide tools. We present GangSTR, a novel algorithm for genome-wide genotyping of both short and expanded TRs. GangSTR extracts information from paired-end reads into a unified model to estimate maximum likelihood TR lengths. We validate GangSTR on real and simulated data and show that GangSTR outperforms alternative methods in both accuracy and speed. We apply GangSTR to a deeply sequenced trio to profile the landscape of TR expansions in a healthy family and validate novel expansions using orthogonal technologies. Our analysis reveals that healthy individuals harbor dozens of long TR alleles not captured by current genome-wide methods. GangSTR will likely enable discovery of novel disease-associated variants not currently accessible from NGS.