Development and Validation of Clinical Whole-Exome and Whole-Genome Sequencing for Detection of Germline Variants in Inherited Disease

Context.-With the decrease in the cost of sequencing, the clinical testing paradigm has shifted from single gene to gene panel and now whole-exome and whole-genome sequencing. Clinical laboratories are rapidly implementing next-generation sequencing-based whole-exome and whole-genome sequencing. Because a large number of targets are covered by whole-exome and whole-genome sequencing, it is critical that a laboratory perform appropriate validation studies, develop a quality assurance and quality control program, and participate in proficiency testing. Objective.-To provide recommendations for wholeexome and whole-genome sequencing assay design, validation, and implementation for the detection of germline variants associated in inherited disorders. Data Sources.-An example of trio sequencing, filtration and annotation of variants, and phenotypic consideration to arrive at clinical diagnosis is discussed. Conclusions.-It is critical that clinical laboratories planning to implement whole-exome and whole-genome sequencing design and validate the assay to specifications and ensure adequate performance prior to implementation. Test design specifications, including variant filtering and annotation, phenotypic consideration, guidance on consenting options, and reporting of incidental findings, are provided. These are important steps a laboratory must take to validate and implement whole-exome and whole-genome sequencing in a clinical setting for germline variants in inherited disorders

A Simple Data-Adaptive Probabilistic Variant Calling Model

Background: Several sources of noise obfuscate the identification of single nucleotide variation (SNV) in next generation sequencing data. For instance, errors may be introduced during library construction and sequencing steps. In addition, the reference genome and the algorithms used for the alignment of the reads are further critical factors determining the efficacy of variant calling methods. It is crucial to account for these factors in individual sequencing experiments. Results: We introduce a simple data-adaptive model for variant calling. This model automatically adjusts to specific factors such as alignment errors. To achieve this, several characteristics are sampled from sites with low mismatch rates, and these are used to estimate empirical log-likelihoods. These likelihoods are then combined to a score that typically gives rise to a mixture distribution. From these we determine a decision threshold to separate potentially variant sites from the noisy background. Conclusions: In simulations we show that our simple proposed model is competitive with frequently used much more complex SNV calling algorithms in terms of sensitivity and specificity. It performs specifically well in cases with low allele frequencies. The application to next-generation sequencing data reveals stark differences of the score distributions indicating a strong influence of data specific sources of noise. The proposed model is specifically designed to adjust to these differences.Comment: 19 pages, 6 figure

EXPLoRA-web: linkage analysis of quantitative trait loci using bulk segregant analysis

Identification of genomic regions associated with a phenotype of interest is a fundamental step toward solving questions in biology and improving industrial research. Bulk segregant analysis (BSA) combined with high-throughput sequencing is a technique to efficiently identify these genomic regions associated with a trait of interest. However, distinguishing true from spuriously linked genomic regions and accurately delineating the genomic positions of these truly linked regions requires the use of complex statistical models currently implemented in software tools that are generally difficult to operate for non-expert users. To facilitate the exploration and analysis of data generated by bulked segregant analysis, we present EXPLoRA-web, a web service wrapped around our previously published algorithm EXPLoRA, which exploits linkage disequilibrium to increase the power and accuracy of quantitative trait loci identification in BSA analysis. EXPLoRA-web provides a user friendly interface that enables easy data upload and parallel processing of different parameter configurations. Results are provided graphically and as BED file and/or text file and the input is expected in widely used formats, enabling straightforward BSA data analysis. The web server is available at http://bioinformatics.intec.ugent.be/explora-web/

Clinical exome performance for reporting secondary genetic findings.

BACKGROUND : Reporting clinically actionable incidental genetic findings in the course of clinical exome testing is recommended by the American College of Medical Genet- ics and Genomics (ACMG). However, the performance of clinical exome methods for reporting small subsets of genes has not been previously reported. METHODS : In this study, 57 exome data sets performed as clinical (n ! 12) or research (n ! 45) tests were retrospec- tively analyzed. Exome sequencing data was examined for adequacy in the detection of potentially pathogenic variant locations in the 56 genes described in the ACMG incidental findings recommendation. All exons of the 56 genes were examined for adequacy of sequencing coverage. In addition, nucleotide positions annotated in HGMD (Human Gene Mutation Database) were examined. RESULTS : The 56 ACMG genes have 18336 nucleotide variants annotated in HGMD. None of the 57 exome data sets possessed a HGMD variant. The clinical exome test had inadequate coverage for " 50% of HGMD vari- ant locations in 7 genes. Six exons from 6 different genes had consistent failure across all 3 test methods; these exons had high GC content (76%–84%). CONCLUSIONS : The use of clinical exome sequencing for the interpretation and reporting of subsets of genes requires recognition of the substantial possibility of inadequate depth and breadth of sequencing coverage at clinically relevant locations. Inadequate depth of coverage may contribute to false-negative clinical ex- ome results

Diagnostic applications of next generation sequencing: working towards quality standards

Over the past 6 years, next generation sequencing (NGS) has been established as a valuable high-throughput method for research in molecular genetics and has successfully been employed in the identification of rare and common genetic variations. All major NGS technology companies providing commercially available instruments (Roche 454, Illumina, Life Technologies) have recently marketed bench top sequencing instruments with lower throughput and shorter run times, thereby broadening the applications of NGS and opening the technology to the potential use for clinical diagnostics. Although the high expectations regarding the discovery of new diagnostic targets and an overall reduction of cost have been achieved, technological challenges in instrument handling, robustness of the chemistry and data analysis need to be overcome. To facilitate the implementation of NGS as a routine method in molecular diagnostics, consistent quality standards need to be developed. Here the authors give an overview of the current standards in protocols and workflows and discuss possible approaches to define quality criteria for NGS in molecular genetic diagnostics

SVIM: Structural Variant Identification using Mapped Long Reads

Motivation: Structural variants are defined as genomic variants larger than 50bp. They have been shown to affect more bases in any given genome than SNPs or small indels. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities. Results: We present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from PacBio and Nanopore sequencing machines. Availability and implementation: The source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package Index. Supplementary information: Supplementary data are available at Bioinformatics online

NGS Panels applied to Hereditary Cancer Syndromes

Cancer is among the leading causes of morbidity and mortality worldwide (Okur et al, 2017). Germline pathogenic variants for monogenic, highly penetrant cancer susceptibility genes are observed in 5%–10% of all cancers (Lu et al, 2014). Hereditary cancers due to monogenic causes are characterized by earlier age of onset, other associated cancers, and often a family history of specific cancers. From the clinical perspective, it is important to recognize the affected individuals to provide them the best clinical management (Hennessy et al, 2010; Ledermann et al, 2014; Pennington et al, 2014) and to identify at-risk family members who will benefit from predictive genetic testing and enhanced surveillance, including early detection and/or risk reduction measures (Kurian et al, 2010; Okur et al, 2017). Germline variants identified in major cancer susceptibility genes associated with hereditary breast or ovarian cancer (HBOC) or hereditary colorectal cancer (HCRC), also account for 5-10% of the patients with these cancers. In the last years, new susceptibility genes, with different penetrance degrees, have been identified. Variants in any of those genes are rare and classical methodologies (e.g. Sanger sequencing - SS) are time consuming and expensive. Next-generation sequencing (NGS) has several advantages compared to SS, including the simultaneous analysis of many samples and sequencing of a large set of genes, higher sensitivity (down to 1% vs 15-20% in SS), lower cost and faster turnaround time, reasons that make NGS the best approach for molecular diagnosis. It is possible nowadays to choose between whole-genome sequencing (WGS), whole-exome sequencing (WES) and NGS limited to a set of genes (NGS-Panel). In cases where a suspected genetic disease or condition has been identified, targeted sequencing of specific genes or genomic regions is preferred (Grada et al, 2013). For that reason, we use NGS-Panel approach using TruSight Cancer (Illumina) to sequence DNA extracted from blood samples of patients with personal and/or familiar history of cancer. This hereditary cancer gene panel sequences 94 genes associated with both common (e.g., breast, colorectal) and rare hereditary cancers and allows the creation of virtual gene panels according to each phenotype or disease under study. NGS workflow analysis (Figure 1) includes five steps: quality assessment of raw data, read alignment to a reference genome, variant identification/calling, variant annotation and data visualization (Pabinger et al, 2013). The establishment of the most appropriate bioinformatics pipeline is crucial in order to achieve the best results. NGS data allows the identification of several types of variants like single nucleotide variants (SNVs), small insertions/deletions, inversions and also copy number variants (CNVs).FCT - UID/BIM/0009/2016info:eu-repo/semantics/publishedVersio