Dataset Information

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.

SUBMITTER: Noll AC

PROVIDER: S-EPMC5685307 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Clinical detection of deletion structural variants in whole-genome sequences.

Noll Aaron C AC Miller Neil A NA Smith Laurie D LD Yoo Byunggil B Fiedler Stephanie S Cooley Linda D LD Willig Laurel K LK Petrikin Josh E JE Cakici Julie J Lesko John J Newton Angela A Detherage Kali K Thiffault Isabelle I Saunders Carol J CJ Farrow Emily G EG Kingsmore Stephen F SF

NPJ genomic medicine 20160803

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 ...[more]

PMID: 29263817

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Project description:BackgroundStallion fertility is an economically important trait due to the increase of artificial insemination in horses. The availability of whole genome sequence data facilitates identification of rare high-impact variants contributing to stallion fertility. The aim of our study was to genotype rare high-impact variants retrieved from next-generation sequencing (NGS)-data of 11 horses in order to unravel harmful genetic variants in large samples of stallions.MethodsGene ontology (GO) terms and search results from public databases were used to obtain a comprehensive list of human und mice genes predicted to participate in the regulation of male reproduction. The corresponding equine orthologous genes were searched in whole genome sequence data of seven stallions and four mares and filtered for high-impact genetic variants using SnpEFF, SIFT and Polyphen 2 software. All genetic variants with the missing homozygous mutant genotype were genotyped on 337 fertile stallions of 19 breeds using KASP genotyping assays or PCR-RFLP. Mixed linear model analysis was employed for an association analysis with de-regressed estimated breeding values of the paternal component of the pregnancy rate per estrus (EBV-PAT).ResultsWe screened next generation sequenced data of whole genomes from 11 horses for equine genetic variants in 1194 human and mice genes involved in male fertility and linked through common gene ontology (GO) with male reproductive processes. Variants were filtered for high-impact on protein structure and validated through SIFT and Polyphen 2. Only those genetic variants were followed up when the homozygote mutant genotype was missing in the detection sample comprising 11 horses. After this filtering process, 17 single nucleotide polymorphism (SNPs) were left. These SNPs were genotyped in 337 fertile stallions of 19 breeds using KASP genotyping assays or PCR-RFLP. An association analysis in 216 Hanoverian stallions revealed a significant association of the splice-site disruption variant g.37455302G>A in NOTCH1 with the de-regressed estimated breeding values of the paternal component of the pregnancy rate per estrus (EBV-PAT). For 9 high-impact variants within the genes CFTR, OVGP1, FBXO43, TSSK6, PKD1, FOXP1, TCP11, SPATA31E1 and NOTCH1 (g.37453246G>C) absence of the homozygous mutant genotype in the validation sample of all 337 fertile stallions was obvious. Therefore, these variants were considered as potentially deleterious factors for stallion fertility.ConclusionsIn conclusion, this study revealed 17 genetic variants with a predicted high damaging effect on protein structure and missing homozygous mutant genotype. The g.37455302G>A NOTCH1 variant was identified as a significant stallion fertility locus in Hanoverian stallions and further 9 candidate fertility loci with missing homozygous mutant genotypes were validated in a panel including 19 horse breeds. To our knowledge this is the first study in horses using next generation sequencing data to uncover strong candidate factors for stallion fertility.

Dataset Information

Clinical detection of deletion structural variants in whole-genome sequences.

Publications

Clinical detection of deletion structural variants in whole-genome sequences.

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