Clinical detection of deletion structural variants in whole-genome sequences

Abstract

Optimal management of acutely ill infants with monogenetic diseases requires rapid identification of causative haplotypes. Whole-genome sequencing (WGS) has been shown to identify pathogenic nucleotide variants in such infants. Deletion structural variants (DSVs, >50 nt) are implicated in many genetic diseases, and tools have been designed to identify DSVs using short-read WGS. Optimisation and integration of these tools into a WGS pipeline could improve diagnostic sensitivity and specificity of WGS. In addition, it may improve turnaround time when compared with current CNV assays, enhancing utility in acute settings. Here we describe DSV detection methods for use in WGS for rapid diagnosis in acutely ill infants: SKALD (Screening Konsensus and Annotation of Large Deletions) combines calls from two tools (Breakdancer and GenomeStrip) with calibrated filters and clinical interpretation rules. In four WGS runs, the average analytic precision (positive predictive value) of SKALD was 78%, and recall (sensitivity) was 27%, when compared with validated reference DSV calls. When retrospectively applied to a cohort of 36 families with acutely ill infants SKALD identified causative DSVs in two. The first was heterozygous deletion of exons 1–3 of MMP21 in trans with a heterozygous frame-shift deletion in two siblings with transposition of the great arteries and heterotaxy. In a newborn female with dysmorphic features, ventricular septal defect and persistent pulmonary hypertension, SKALD identified the breakpoints of a heterozygous, de novo 1p36.32p36.13 deletion. In summary, consensus DSV calling, implemented in an 8-h computational pipeline with parameterised filtering, has the potential to increase the diagnostic yield of WGS in acutely ill neonates and discover novel disease genes.

Whole genome analysis identifies the association of TP53 genomic deletions with lower survival in Stage III colorectal cancer

“…Performance-wise, low-coverage WGS consistently showed high concordance with microarray and high-coverage WGS in our analysis. It As WGS studies become less expensive, we foresee that in the future low-coverage WGS may prove to be replacing clinical microarray testing for cancers 33 , developmental disabilities, congenital anomalies [34][35][36] , autism spectrum disorder 37 , and many other genetic diseases 29 . Citing the benefits of WGS, a recent study compared the performance of low-coverage WGS versus microarrays on rare and undiagnosed cases.…”

“…At 2-4x coverage, WGS provides thousands of read pairs per 100kb segment, a substantive amount enough to enable sensitive CNA detection. This provides an improved resolution compared to SNP microarrays in which there are approximately 60 probes per 100kb segment.Identifying copy number variation with high coverage WGS data have been studied extensively in basic and clinical research settings[29][30][31] . For example, in a recent systematic benchmark, Trost et al reported good performance of using >20x WGS data for identifying small-scale CNVs (1 -100kb)32 .…”

The NSIGHT1 Randomized Controlled Trial: Rapid Whole Genome Sequencing for Accelerated Etiologic Diagnosis in Critically Ill Infants

“…Clinical WGS continues to improve with respect to rate of genetic diagnosis and time to diagnosis 27 . In particular, the diagnostic rate is increasing through ongoing identification of novel disease genes, improved reference genome sequences, and better identification of disease-causing copy number, repeat expansion, regulatory, splicing and structural variations 32, [43][44][45][46][47][48][49][50] . These recent advances were not reflected in the current study.…”

Prioritisation of structural variant calls in cancer genomes

“…Clinical detection of SVs in Mendelian diseases has been considered by e.g., Noll et al (2016) but to our knowledge no prioritisation approach for oncology is publicly available. The mechanisms for oncogenic driver generation include activating fusions combining the coding frames (quite often in the intronic regions) of two genes, as well as truncating mutations in tumor suppressor genes or whole exon losses.…”