Genome-wide sequencing as a first-tier screening test for short tandem repeat expansions

Abstract

Background: Screening for short tandem repeat (STR) expansions in next-generation sequencing data can enable diagnosis, optimal clinical management/treatment, and accurate genetic counseling of patients with repeat expansion disorders. We aimed to develop an efficient computational workflow for reliable detection of STR expansions in next-generation sequencing data and demonstrate its clinical utility.

Methods: We characterized the performance of eight STR analysis methods (lobSTR, HipSTR, RepeatSeq, ExpansionHunter, TREDPARSE, GangSTR, STRetch, and exSTRa) on next-generation sequencing datasets of samples with known disease-causing full-mutation STR expansions and genomes simulated to harbor repeat expansions at selected loci and optimized their sensitivity. We then used a machine learning decision tree classifier to identify an optimal combination of methods for full-mutation detection. In Burrows-Wheeler Aligner (BWA)-aligned genomes, the ensemble approach of using ExpansionHunter, STRetch, and exSTRa performed the best (precision = 82%, recall = 100%, F1-score = 90%). We applied this pipeline to screen 301 families of children with suspected genetic disorders.

Results: We identified 10 individuals with full-mutations in the AR, ATXN1, ATXN8, DMPK, FXN, or HTT disease STR locus in the analyzed families. Additional candidates identified in our analysis include two probands with borderline ATXN2 expansions between the established repeat size range for reduced-penetrance and full-penetrance full-mutation and seven individuals with FMR1 CGG repeats in the intermediate/premutation repeat size range. In 67 probands with a prior negative clinical PCR test for the FMR1, FXN, or DMPK disease STR locus, or the spinocerebellar ataxia disease STR panel, our pipeline did not falsely identify aberrant expansion. We performed clinical PCR tests on seven (out of 10) full-mutation samples identified by our pipeline and confirmed the expansion status in all, showing absolute concordance between our bioinformatics and molecular findings.

Conclusions: We have successfully demonstrated the application of a well-optimized bioinformatics pipeline that promotes the utility of genome-wide sequencing as a first-tier screening test to detect expansions of known disease STRs. Interrogating clinical next-generation sequencing data for pathogenic STR expansions using our ensemble pipeline can improve diagnostic yield and enhance clinical outcomes for patients with repeat expansion disorders.

Keywords: Clinical bioinformatics; Machine learning; Next-generation sequencing; Repeat expansion; Short tandem repeats.

Fig. 1

Decision tree model and its performance metrics on modified analysis of BWA-aligned EGA genomes. a Decision tree generated on the training dataset (n = 940). Node #0 at the top of the tree is the root node. Each node lists an STR tool (feature). The “samples” number represents the total number of genotype calls in a particular node, and “value” shows the number of expanded (or full-mutation, FM) and non-expanded (non-FM) genotypes. Gini index shows the impurity at each node. The terminal nodes or leaves with a Gini value of 0 have genotypes belonging entirely to either the expanded or non-expanded class. EHv3, ExpansionHunter version 3; wCtrls, analysis performed with controls. b Classification report summarizing the performance metrics of the model on test data (n = 236). Macro and weighted average (avg) show the unweighted and weighted mean of performance metrics calculated for Expanded and Not_Expanded class labels, respectively. c Receiver operating characteristics and precision-recall curves. d Confusion matrix showing the number of predicted and true labels on x- and y-axis, respectively. e Feature importance plot showing the STR tool on x-axis and the tool’s normalized (Gini) importance on y-axis