Detecting Expansions of Tandem Repeats in Cohorts Sequenced with Short-Read Sequencing Data

Abstract

Repeat expansions cause more than 30 inherited disorders, predominantly neurogenetic. These can present with overlapping clinical phenotypes, making molecular diagnosis challenging. Single-gene or small-panel PCR-based methods can help to identify the precise genetic cause, but they can be slow and costly and often yield no result. Researchers are increasingly performing genomic analysis via whole-exome and whole-genome sequencing (WES and WGS) to diagnose genetic disorders. However, until recently, analysis protocols could not identify repeat expansions in these datasets. We developed exSTRa (expanded short tandem repeat algorithm), a method that uses either WES or WGS to identify repeat expansions. Performance of exSTRa was assessed in a simulation study. In addition, four retrospective cohorts of individuals with eleven different known repeat-expansion disorders were analyzed with exSTRa. We assessed results by comparing the findings to known disease status. Performance was also compared to three other analysis methods (ExpansionHunter, STRetch, and TREDPARSE), which were developed specifically for WGS data. Expansions in the assessed STR loci were successfully identified in WES and WGS datasets by all four methods with high specificity and sensitivity. Overall, exSTRa demonstrated more robust and superior performance for WES data than did the other three methods. We demonstrate that exSTRa can be effectively utilized as a screening tool for detecting repeat expansions in WES and WGS data, although the best performance would be produced by consensus calling, wherein at least two out of the four currently available screening methods call an expansion.