An Evaluation of Copy Number Variation Detection Tools from Whole‐Exome Sequencing Data

Copy number variation (CNV) has been found to play an important role in human disease. Next‐generation sequencing technology, including whole‐genome sequencing (WGS) and whole‐exome sequencing (WES), has become a primary strategy for studying the genetic basis of human disease. Several CNV calling tools have recently been developed on the basis of WES data. However, the comparative performance of these tools using real data remains unclear. An objective evaluation study of these tools in practical research situations would be beneficial. Here, we evaluated four well‐known WES‐based CNV detection tools (XHMM, CoNIFER, ExomeDepth, and CONTRA) using real data generated in house. After evaluation using six metrics, we found that the sensitive and accurate detection of CNVs in WES data remains challenging despite the many algorithms available. Each algorithm has its own strengths and weaknesses. None of the exome‐based CNV calling methods performed well in all situations; in particular, compared with CNVs identified from high coverage WGS data from the same samples, all tools suffered from limited power. Our evaluation provides a comprehensive and objective comparison of several well‐known detection tools designed for WES data, which will assist researchers in choosing the most suitable tools for their research needs.     

Detection of Clinically Relevant Copy Number Variants with Whole‐Exome Sequencing

Copy number variation (CNV) is a common source of genetic variation that has been implicated in many genomic disorders. This has resulted in the widespread application of genomic microarrays as a first‐tier diagnostic tool for CNV detection. More recently, whole‐exome sequencing (WES) has been proven successful for the detection of clinically relevant point mutations and small insertion–deletions exome wide. We evaluate the utility of short‐read WES (SOLiD 5500xl) to detect clinically relevant CNVs in DNA from 10 patients with intellectual disability and compare these results to data from two independent high‐resolution microarrays. Eleven of the 12 clinically relevant CNVs were detected via read‐depth analysis of WES data; a heterozygous single‐exon deletion remained undetected by all algorithms evaluated. Although the detection power of WES for small CNVs currently does not match that of high‐resolution microarray platforms, we show that the majority (88%) of rare coding CNVs containing three or more exons are successfully identified by WES. These results show that the CNV detection resolution of WES is comparable to that of medium‐resolution genomic microarrays commonly used as clinical assays. The combined detection of point mutations, indels, and CNVs makes WES a very attractive first‐tier diagnostic test for genetically heterogeneous disorders.     

Comparison of Exome and Genome Sequencing Technologies for the Complete Capture of Protein‐Coding Regions

For next‐generation sequencing technologies, sufficient base‐pair coverage is the foremost requirement for the reliable detection of genomic variants. We investigated whether whole‐genome sequencing (WGS) platforms offer improved coverage of coding regions compared with whole‐exome sequencing (WES) platforms, and compared single‐base coverage for a large set of exome and genome samples. We find that WES platforms have improved considerably in the last years, but at comparable sequencing depth, WGS outperforms WES in terms of covered coding regions. At higher sequencing depth (95x–160x), WES successfully captures 95% of the coding regions with a minimal coverage of 20x, compared with 98% for WGS at 87‐fold coverage. Three different assessments of sequence coverage bias showed consistent biases for WES but not for WGS. We found no clear differences for the technologies concerning their ability to achieve complete coverage of 2,759 clinically relevant genes. We show that WES performs comparable to WGS in terms of covered bases if sequenced at two to three times higher coverage. This does, however, go at the cost of substantially more sequencing biases in WES approaches. Our findings will guide laboratories to make an informed decision on which sequencing platform and coverage to choose.     

A Post‐Hoc Comparison of the Utility of Sanger Sequencing and Exome Sequencing for the Diagnosis of Heterogeneous Diseases

The advent of massive parallel sequencing is rapidly changing the strategies employed for the genetic diagnosis and research of rare diseases that involve a large number of genes. So far it is not clear whether these approaches perform significantly better than conventional single gene testing as requested by clinicians. The current yield of this traditional diagnostic approach depends on a complex of factors that include gene‐specific phenotype traits, and the relative frequency of the involvement of specific genes. To gauge the impact of the paradigm shift that is occurring in molecular diagnostics, we assessed traditional Sanger‐based sequencing (in 2011) and exome sequencing followed by targeted bioinformatics analysis (in 2012) for five different conditions that are highly heterogeneous, and for which our center provides molecular diagnosis. We find that exome sequencing has a much higher diagnostic yield than Sanger sequencing for deafness, blindness, mitochondrial disease, and movement disorders. For microsatellite‐stable colorectal cancer, this was low under both strategies. Even if all genes that could have been ordered by physicians had been tested, the larger number of genes captured by the exome would still have led to a clearly superior diagnostic yield at a fraction of the cost.     

Targeted Next‐Generation Sequencing can Replace Sanger Sequencing in Clinical Diagnostics

Mutation detection through exome sequencing allows simultaneous analysis of all coding sequences of genes. However, it cannot yet replace Sanger sequencing (SS) in diagnostics because of incomplete representation and coverage of exons leading to missing clinically relevant mutations. Targeted next‐generation sequencing (NGS), in which a selected fraction of genes is sequenced, may circumvent these shortcomings. We aimed to determine whether the sensitivity and specificity of targeted NGS is equal to those of SS. We constructed a targeted enrichment kit that includes 48 genes associated with hereditary cardiomyopathies. In total, 84 individuals with cardiomyopathies were sequenced using 151 bp paired‐end reads on an Illumina MiSeq sequencer. The reproducibility was tested by repeating the entire procedure for five patients. The coverage of ≥30 reads per nucleotide, our major quality criterion, was 99% and in total ～21,000 variants were identified. Confirmation with SS was performed for 168 variants (155 substitutions, 13 indels). All were confirmed, including a deletion of 18 bp and an insertion of 6 bp. The reproducibility was nearly 100%. We demonstrate that targeted NGS of a disease‐specific subset of genes is equal to the quality of SS and it can therefore be reliably implemented as a stand‐alone diagnostic test.     

AmpliVar: Mutation Detection in High‐Throughput Sequence from Amplicon‐Based Libraries

Conventional means of identifying variants in high‐throughput sequencing align each read against a reference sequence, and then call variants at each position. Here, we demonstrate an orthogonal means of identifying sequence variation by grouping the reads as amplicons prior to any alignment. We used AmpliVar to make key‐value hashes of sequence reads and group reads as individual amplicons using a table of flanking sequences. Low‐abundance reads were removed according to a selectable threshold, and reads above this threshold were aligned as groups, rather than as individual reads, permitting the use of sensitive alignment tools. We show that this approach is more sensitive, more specific, and more computationally efficient than comparable methods for the analysis of amplicon‐based high‐throughput sequencing data. The method can be extended to enable alignment‐free confirmation of variants seen in hybridization capture target‐enrichment data.     

Network‐Informed Gene Ranking Tackles Genetic Heterogeneity in Exome‐Sequencing Studies of Monogenic Disease

Genetic heterogeneity presents a significant challenge for the identification of monogenic disease genes. Whole‐exome sequencing generates a large number of candidate disease‐causing variants and typical analyses rely on deleterious variants being observed in the same gene across several unrelated affected individuals. This is less likely to occur for genetically heterogeneous diseases, making more advanced analysis methods necessary. To address this need, we present HetRank, a flexible gene‐ranking method that incorporates interaction network data. We first show that different genes underlying the same monogenic disease are frequently connected in protein interaction networks. This motivates the central premise of HetRank: those genes carrying potentially pathogenic variants and whose network neighbors do so in other affected individuals are strong candidates for follow‐up study. By simulating 1,000 exome sequencing studies (20,000 exomes in total), we model varying degrees of genetic heterogeneity and show that HetRank consistently prioritizes more disease‐causing genes than existing analysis methods. We also demonstrate a proof‐of‐principle application of the method to prioritize genes causing Adams‐Oliver syndrome, a genetically heterogeneous rare disease. An implementation of HetRank in R is available via the Website http://sourceforge.net/p/hetrank/ .     

Semi‐automated cancer genome analysis using high‐performance computing

Next‐generation sequencing (NGS) has turned from a new and experimental technology into a standard procedure for cancer genome studies and clinical investigation. While a multitude of software packages for cancer genome data analysis have been made available, these need to be combined into efficient analytical workflows that cover multiple aspects relevant to a clinical environment and that deliver handy results within a reasonable time frame. Here, we introduce QuickNGS Cancer as a new suite of bioinformatics pipelines that is focused on cancer genomics and significantly reduces the analytical hurdles that still limit a broader applicability of NGS technology, particularly to clinically driven research. QuickNGS Cancer allows a highly efficient analysis of a broad variety of NGS data types, specifically considering cancer‐specific issues, such as biases introduced by tumor impurity and aneuploidy or the assessment of genomic variations regarding their biomedical relevance. It delivers highly reproducible analysis results ready for interpretation within only a few days after sequencing, as shown by a reanalysis of 140 tumor/normal pairs from The Cancer Genome Atlas (TCGA) in which QuickNGS Cancer detected a significant number of mutations in key cancer genes missed by a well‐established mutation calling pipeline. Finally, QuickNGS Cancer obtained several unexpected mutations in leukemias that could be confirmed by Sanger sequencing.     

GEnomes Management Application (GEM.app): A New Software Tool for Large‐Scale Collaborative Genome Analysis

Novel genes are now identified at a rapid pace for many Mendelian disorders, and increasingly, for genetically complex phenotypes. However, new challenges have also become evident: (1) effectively managing larger exome and/or genome datasets, especially for smaller labs; (2) direct hands‐on analysis and contextual interpretation of variant data in large genomic datasets; and (3) many small and medium‐sized clinical and research‐based investigative teams around the world are generating data that, if combined and shared, will significantly increase the opportunities for the entire community to identify new genes. To address these challenges, we have developed GEnomes Management Application (GEM.app), a software tool to annotate, manage, visualize, and analyze large genomic datasets ( https://genomics.med.miami.edu/">https://genomics.med.miami.edu/">https://genomics.med.miami.edu/ ). GEM.app currently contains ～1,600 whole exomes from 50 different phenotypes studied by 40 principal investigators from 15 different countries. The focus of GEM.app is on user‐friendly analysis for nonbioinformaticians to make next‐generation sequencing data directly accessible. Yet, GEM.app provides powerful and flexible filter options, including single family filtering, across family/phenotype queries, nested filtering, and evaluation of segregation in families. In addition, the system is fast, obtaining results within 4 sec across ～1,200 exomes. We believe that this system will further enhance identification of genetic causes of human disease.     

Exome Sequencing as a Diagnostic Tool for Pediatric‐Onset Ataxia

Ataxia demonstrates substantial phenotypic and genetic heterogeneity. We set out to determine the diagnostic yield of exome sequencing in pediatric patients with ataxia without a molecular diagnosis after standard‐of‐care assessment in Canada. FORGE (Finding Of Rare disease GEnes) Canada is a nation‐wide project focused on identifying novel disease genes for rare pediatric diseases using whole‐exome sequencing. We retrospectively selected all FORGE Canada projects that included cerebellar ataxia as a feature. We identified 28 such families and a molecular diagnosis was made in 13; a success rate of 46%. In 11 families, we identified mutations in genes associated with known neurological syndromes and in two we identified novel disease genes. Exome analysis of sib pairs and/or patients born to consanguineous parents was more likely to be successful (9/13) than simplex cases (4/15). Our data suggest that exome sequencing is an effective first line test for pediatric patients with ataxia where a specific single gene is not immediately suspected to be causative.     

Whole‐Exome Sequencing Identifies a Variant in TMEM132E Causing Autosomal‐Recessive Nonsyndromic Hearing Loss DFNB99

Autosomal‐recessive nonsyndromic hearing loss (ARNSHL) features a high degree of genetic heterogeneity. Many genes responsible for ARNSHL have been identified or mapped. We previously mapped an ARNSHL locus at 17q12, herein designated DFNB99, in a consanguineous Chinese family. In this study, whole‐exome sequencing revealed a homozygous missense mutation (c.1259G>A, p.Arg420Gln) in the gene‐encoding transmembrane protein 132E (TMEM132E) as the causative variant. Immunofluorescence staining of the Organ of Corti showed Tmem132e highly expressed in murine inner hair cells. Furthermore, knockdown of the tmem132e ortholog in zebrafish affected the mechanotransduction of hair cells. Finally, wild‐type human TMEM132E mRNA, but not the mRNA carrying the c.1259G>A mutation rescued the Tmem132e knockdown phenotype. We conclude that the variant in TMEM132E is the most likely cause of DFNB99.     

A Robust Approach for Blind Detection of Balanced Chromosomal Rearrangements with Whole‐Genome Low‐Coverage Sequencing

Balanced chromosomal rearrangement (or balanced chromosome abnormality, BCA) is a common chromosomal structural variation. Next‐generation sequencing has been reported to detect BCA‐associated breakpoints with the aid of karyotyping. However, the complications associated with this approach and the requirement for cytogenetics information has limited its application. Here, we provide a whole‐genome low‐coverage sequencing approach to detect BCA events independent of knowing the affected regions and with low false positives. First, six samples containing BCAs were used to establish a detection protocol and assess the efficacy of different library construction approaches. By clustering anomalous read pairs and filtering out the false‐positive results with a control cohort and the concomitant mapping information, we could directly detect BCA events for each sample. Through optimizing the read depth, BCAs in all samples could be blindly detected with only 120 million read pairs per sample for data from a small‐insert library and 30 million per sample for data from nonsize‐selected mate‐pair library. This approach was further validated using another 13 samples that contained BCAs. Our approach advances the application of high‐throughput whole‐genome low‐coverage analysis for robust BCA detection—especially for clinical samples—without the need for karyotyping.     

Clinical Applications of Next‐Generation Sequencing: The 2013 Human Genome Variation Society Scientific Meeting

Next‐generation sequencing (NGS) has significantly contributed to the transformation of genomic research by providing access to the genome for analysis, by significantly decreasing the sequencing costs and increasing the throughput. The next goal is to exploit this powerful technology in the clinic, namely for diagnostics and therapeutics. The 2013 annual meeting of the Human Genome Variation Society, held in Paris, France, provided a forum to discuss possible clinical applications of NGS, the potential of some of the current NGS systems to transition to the clinic, the identification of causative mutations for rare genetic disorders through whole‐genome or targeted genome resequencing, the application of NGS for family genomics, and NGS data analysis tools.     

Massive parallel sequencing of solid tumours – challenges and opportunities for pathologists

The role of pathologists is to provide diagnostic, prognostic and predictive data to enable clinical colleagues to manage patients optimally. Current histo/anatomical pathology is predominantly morphology‐based, with the addition of biomarkers, applied largely through immunohistochemistry, fluorescence in‐situ hybridization (FISH) and a limited range of polymerase chain reaction (PCR)‐based molecular tests. The desire to apply genomics to the clinical care of patients has been facilitated by the human genome project and subsequently by high‐throughput technologies known collectively as massive parallel sequencing (MPS, also referred to as next‐generation sequencing, NGS). The use of MPS to identify mutations/variants and tissue RNA expression profiles for diagnosis, prognostication and targeted therapy stratification is now a reality in many clinical specialities. If histopathologists are considered experts in solid tumour pathology, MPS potentially falls within their scope; however, it challenges our predominant morphology‐based paradigm. This review summarizes and comments on the current and future state of play of MPS for the practising histopathologist. It will focus on somatic mutations in solid tumours and will challenge histopathologists to take further leadership roles in this area.     

Whole‐exome sequencing is a valuable diagnostic tool for inherited peripheral neuropathies: Outcomes from a cohort of 50 families

The inherited peripheral neuropathies (IPNs) are characterized by marked clinical and genetic heterogeneity and include relatively frequent presentations such as Charcot‐Marie‐Tooth disease and hereditary motor neuropathy, as well as more rare conditions where peripheral neuropathy is associated with additional features. There are over 250 genes known to cause IPN‐related disorders but it is estimated that in approximately 50% of affected individuals a molecular diagnosis is not achieved. In this study, we examine the diagnostic utility of whole‐exome sequencing (WES) in a cohort of 50 families with 1 or more affected individuals with a molecularly undiagnosed IPN with or without additional features. Pathogenic or likely pathogenic variants in genes known to cause IPN were identified in 24% (12/50) of the families. A further 22% (11/50) of families carried sequence variants in IPN genes in which the significance remains unclear. An additional 12% (6/50) of families had variants in novel IPN candidate genes, 3 of which have been published thus far as novel discoveries (KIF1A, TBCK, and MCM3AP). This study highlights the use of WES in the molecular diagnostic approach of highly heterogeneous disorders, such as IPNs, places it in context of other published neuropathy cohorts, while further highlighting associated benefits for discovery.     

mit‐o‐matic: A Comprehensive Computational Pipeline for Clinical Evaluation of Mitochondrial Variations from Next‐Generation Sequencing Datasets

The human mitochondrial genome has been reported to have a very high mutation rate as compared with the nuclear genome. A large number of mitochondrial mutations show significant phenotypic association and are involved in a broad spectrum of diseases. In recent years, there has been a remarkable progress in the understanding of mitochondrial genetics. The availability of next‐generation sequencing (NGS) technologies have not only reduced sequencing cost by orders of magnitude but has also provided us good quality mitochondrial genome sequences with high coverage, thereby enabling decoding of a number of human mitochondrial diseases. In this study, we report a computational and experimental pipeline to decipher the human mitochondrial DNA variations and examine them for their clinical correlation. As a proof of principle, we also present a clinical study of a patient with Leigh disease and confirmed maternal inheritance of the causative allele. The pipeline is made available as a user‐friendly online tool to annotate variants and find haplogroup, disease association, and heteroplasmic sites. The “mit‐o‐matic” computational pipeline represents a comprehensive cloud‐based tool for clinical evaluation of mitochondrial genomic variations from NGS datasets. The tool is freely available at http://genome.igib.res.in/mitomatic/ .     

Next‐generation sequencing: a change of paradigm in molecular diagnostic validation

Next‐generation sequencing (NGS) is beginning to show its full potential for diagnostic and therapeutic applications. In particular, it is enunciating its capacity to contribute to a molecular taxonomy of cancer, to be used as a standard approach for diagnostic mutation detection, and to open new treatment options that are not exclusively organ‐specific. If this is the case, how much validation is necessary and what should be the validation strategy, when bringing NGS into the diagnostic/clinical practice? This validation strategy should address key issues such as: what is the overall extent of the validation? Should essential indicators of test performance such as sensitivity of specificity be calculated for every target or sample type? Should bioinformatic interpretation approaches be validated with the same rigour? What is a competitive clinical turnaround time for a NGS‐based test, and when does it become a cost‐effective testing proposition? While we address these and other related topics in this commentary, we also suggest that a single set of international guidelines for the validation and use of NGS technology in routine diagnostics may allow us all to make a much more effective use of resources. Copyright © 2014 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.     

Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data

Autozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of exome sequencing. Although these methods have simplified the generation of experimental data, its analysis, particularly when disparate data types must be integrated, remains time consuming. Moreover, the huge volume of sequence variant data generated from next generation sequencing experiments opens up the possibility of using these data instead of microarray genotype data to identify disease loci. To allow these two types of data to be used in an integrated fashion, we have developed AgileVCFMapper, a program that performs both the mapping of disease loci by SNP genotyping and the analysis of potentially deleterious variants using exome sequence variant data, in a single step. This method does not require microarray SNP genotype data, although analysis with a combination of microarray and exome genotype data enables more precise delineation of disease loci, due to superior marker density and distribution.