Simple and Efficient Identification of Rare Recessive Pathologically Important Sequence Variants from Next Generation Exome Sequence Data

Massively parallel (“next generation”) DNA sequencing (NGS) has quickly become the method of choice for seeking pathogenic mutations in rare uncharacterized monogenic diseases. Typically, before DNA sequencing, protein‐coding regions are enriched from patient genomic DNA, representing either the entire genome (“exome sequencing”) or selected mapped candidate loci. Sequence variants, identified as differences between the patient's and the human genome reference sequences, are then filtered according to various quality parameters. Changes are screened against datasets of known polymorphisms, such as dbSNP and the 1000 Genomes Project, in the effort to narrow the list of candidate causative variants. An increasing number of commercial services now offer to both generate and align NGS data to a reference genome. This potentially allows small groups with limited computing infrastructure and informatics skills to utilize this technology. However, the capability to effectively filter and assess sequence variants is still an important bottleneck in the identification of deleterious sequence variants in both research and diagnostic settings. We have developed an approach to this problem comprising a user‐friendly suite of programs that can interactively analyze, filter and screen data from enrichment‐capture NGS data. These programs (“Agile Suite”) are particularly suitable for small‐scale gene discovery or for diagnostic analysis.     

Autozygosity Mapping with Exome Sequence Data

Autozygosity mapping is a powerful method for the identification of recessively inherited disease genes using small inbred families. Typically, microarray SNP genotype data are first used to identify autozygous regions as extended runs of homozygous genotypes. Next, candidate disease loci are found by defining regions that are autozygous in all affected patients. Finally, the disease gene is identified by sequencing the genes within the candidate disease loci. However, with the advent of massively parallel sequencing, it is now possible to sample or to completely sequence an individual's genome, or, more commonly, exome. This opens up the possibility of concurrently defining autozygous regions and identifying possibly deleterious sequence variants, using data from a single sequencing experiment. Consequently, we have developed a set of computer programs that identify autozygous regions using exome sequence data. These programs derive their genotyping data either by the ab initio detection of all sequence variants or by the assessment of 0.53 million known polymorphic positions within each exome dataset. Using genotype data derived solely from exome sequence data, it was possible to identify the majority of autozygous regions found by microarray SNP genotype data.     

Kullback–Leibler Distance Methods for Detecting Disease Association with Rare Variants from Sequencing Data

Because next generation sequencing technology that can rapidly genotype most genetic variations genome, there is considerable interest in investigating the effects of rare variants on complex diseases. In this paper, we propose four Kullback–Leibler distance‐based Tests (KLTs) for detecting genotypic differences between cases and controls. There are several features that set the proposed tests apart from existing ones. First, by explicitly considering and comparing the distributions of genotypes, existence of variants with opposite directional effects does not compromise the power of KLTs. Second, it is not necessary to set a threshold for rare variants as the KL definition makes it reasonable to consider rare and common variants together without worrying about the contribution from one type overshadowing the other. Third, KLTs are robust to null variants thanks to a built‐in noise fighting mechanism. Finally, correlation among variants is taken into account implicitly so the KLTs work well regardless of the underlying LD structure. Through extensive simulations, we demonstrated good performance of KLTs compared to the sum of squared score test (SSU) and optimal sequence kernel association test (SKAT‐O). Moreover, application to the Dallas Heart Study data illustrates the feasibility and performance of KLTs in a realistic setting.     

Lake Louise Mutation Detection Meeting 2013: Clinical Translation of Next‐Generation Sequencing Requires Optimization of Workflows and Interpretation of Variants

With the exponential reduction of the cost of next‐generation sequencing (NGS), it is no longer the generation of data but the analysis and interpretation of massive amounts of sequencing data that are seen as key challenges for the effective integration of these technologies into clinical practice. Clinical geneticists, informaticians, and scientists from 17 countries gathered for the 12th International Symposium on Mutation in the Genome at the Fairmont Chateau Lake Louise (Canada) to discuss technological advances and applications of NGS and consider possible approaches to the challenges of clinical translation. Here, we provide an overview of the main themes of the meeting that included development of innovative solutions for variant sharing, tools and resources for NGS analysis, novel technology and methodology development, NGS‐based discovery of disease pathogenesis, development of multigene NGS sequencing panels for clinical use, exploring diagnostic utility of whole‐exome and whole‐genome sequencing, and, finally, integration of genomic sequencing into the clinic.     

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.     

Accurately Identifying Low‐Allelic Fraction Variants in Single Samples with Next‐Generation Sequencing: Applications in Tumor Subclone Resolution

Current methods for resolving genetically distinct subclones in tumor samples require somatic mutations to be clustered by allelic frequencies, which are determined by applying a variant calling program to next‐generation sequencing data. Such programs were developed to accurately distinguish true polymorphisms and somatic mutations from the artifactual nonreference alleles introduced during library preparation and sequencing. However, numerous variant callers exist with no clear indication of the best performer for subclonal analysis, in which the accuracy of the assigned variant frequency is as important as correctly indicating whether the variant is present or not. Furthermore, sequencing depth (the number of times that a genomic position is sequenced) affects the ability to detect low‐allelic fraction variants and accurately assign their allele frequencies. We created two synthetic sequencing datasets, and sequenced real KRAS amplicons, with variants spiked in at specific ratios, to assess which caller performs best in terms of both variant detection and assignment of allelic frequencies. We also assessed the sequencing depths required to detect low‐allelic fraction variants. We found that VarScan2 performed best overall with sequencing depths of 100×, 250×, 500×, and 1,000× required to accurately identify variants present at 10%, 5%, 2.5%, and 1%, respectively.     

Evaluation of Hybridization Capture Versus Amplicon‐Based Methods for Whole‐Exome Sequencing

Next‐generation sequencing has aided characterization of genomic variation. While whole‐genome sequencing may capture all possible mutations, whole‐exome sequencing remains cost‐effective and captures most phenotype‐altering mutations. Initial strategies for exome enrichment utilized a hybridization‐based capture approach. Recently, amplicon‐based methods were designed to simplify preparation and utilize smaller DNA inputs. We evaluated two hybridization capture‐based and two amplicon‐based whole‐exome sequencing approaches, utilizing both Illumina and Ion Torrent sequencers, comparing on‐target alignment, uniformity, and variant calling. While the amplicon methods had higher on‐target rates, the hybridization capture‐based approaches demonstrated better uniformity. All methods identified many of the same single‐nucleotide variants, but each amplicon‐based method missed variants detected by the other three methods and reported additional variants discordant with all three other technologies. Many of these potential false positives or negatives appear to result from limited coverage, low variant frequency, vicinity to read starts/ends, or the need for platform‐specific variant calling algorithms. All methods demonstrated effective copy‐number variant calling when evaluated against a single‐nucleotide polymorphism array. This study illustrates some differences between whole‐exome sequencing approaches, highlights the need for selecting appropriate variant calling based on capture method, and will aid laboratories in selecting their preferred approach.     

Analysis of Human Triallelic SNPs by Next‐Generation Sequencing

Although single‐nucleotide polymorphisms (SNPs) have become extremely useful in the study of geneticvariation, triallelic SNPs are still not fully understood. Next‐generation sequencing (NGS) is a promising approach to identify triallelic sites in large populations. In this study, we explored exome sequencing data from 221 Chinese individuals, with an average depth of 70‐fold. We identified 382,901 SNPs in the study samples, including 2,002 (0.52%) triallelic sites. Among the triallelic SNPs, 17.3% were coding SNPs (cSNPs) and 78.3% were novel. Comparison and analysis revealed that the variant alleles were more likely to result in nonsynonymous variation at triallelic sites. In addition, natural selection seemed to influence triallelic SNPs. However, with the limited sample size assessed, more studies will be required in order to fully characterize the features of triallelic SNPs.     

The Clinical Next‐Generation Sequencing Database: A Tool for the Unified Management of Clinical Information and Genetic Variants to Accelerate Variant Pathogenicity Classification

Recent advances in next‐generation sequencing (NGS) have given rise to new challenges due to the difficulties in variant pathogenicity interpretation and large dataset management, including many kinds of public population databases as well as public or commercial disease‐specific databases. Here, we report a new database development tool, named the “Clinical NGS Database,” for improving clinical NGS workflow through the unified management of variant information and clinical information. This database software offers a two‐feature approach to variant pathogenicity classification. The first of these approaches is a phenotype similarity‐based approach. This database allows the easy comparison of the detailed phenotype of each patient with the average phenotype of the same gene mutation at the variant or gene level. It is also possible to browse patients with the same gene mutation quickly. The other approach is a statistical approach to variant pathogenicity classification based on the use of the odds ratio for comparisons between the case and the control for each inheritance mode (families with apparently autosomal dominant inheritance vs. control, and families with apparently autosomal recessive inheritance vs. control). A number of case studies are also presented to illustrate the utility of this database.     

Targeted Next‐Generation Sequencing Analysis of 1,000 Individuals with Intellectual Disability

To identify genetic causes of intellectual disability (ID), we screened a cohort of 986 individuals with moderate to severe ID for variants in 565 known or candidate ID‐associated genes using targeted next‐generation sequencing. Likely pathogenic rare variants were found in ～11% of the cases (113 variants in 107/986 individuals: ～8% of the individuals had a likely pathogenic loss‐of‐function [LoF] variant, whereas ～3% had a known pathogenic missense variant). Variants in SETD5, ATRX, CUL4B, MECP2, and ARID1B were the most common causes of ID. This study assessed the value of sequencing a cohort of probands to provide a molecular diagnosis of ID, without the availability of DNA from both parents for de novo sequence analysis. This modeling is clinically relevant as 28% of all UK families with dependent children are single parent households. In conclusion, to diagnose patients with ID in the absence of parental DNA, we recommend investigation of all LoF variants in known genes that cause ID and assessment of a limited list of proven pathogenic missense variants in these genes. This will provide 11% additional diagnostic yield beyond the 10%–15% yield from array CGH alone.     

ICO Amplicon NGS Data Analysis: A Web Tool for Variant Detection in Common High‐Risk Hereditary Cancer Genes Analyzed by Amplicon GS Junior Next‐Generation Sequencing

Next‐generation sequencing (NGS) has revolutionized genomic research and is set to have a major impact on genetic diagnostics thanks to the advent of benchtop sequencers and flexible kits for targeted libraries. Among the main hurdles in NGS are the difficulty of performing bioinformatic analysis of the huge volume of data generated and the high number of false positive calls that could be obtained, depending on the NGS technology and the analysis pipeline. Here, we present the development of a free and user‐friendly Web data analysis tool that detects and filters sequence variants, provides coverage information, and allows the user to customize some basic parameters. The tool has been developed to provide accurate genetic analysis of targeted sequencing of common high‐risk hereditary cancer genes using amplicon libraries run in a GS Junior System. The Web resource is linked to our own mutation database, to assist in the clinical classification of identified variants. We believe that this tool will greatly facilitate the use of the NGS approach in routine laboratories.     

mirVAFC: A Web Server for Prioritizations of Pathogenic Sequence Variants from Exome Sequencing Data via Classifications

Exome sequencing has been widely used to identify the genetic variants underlying human genetic disorders for clinical diagnoses, but the identification of pathogenic sequence variants among the huge amounts of benign ones is complicated and challenging. Here, we describe a new Web server named mirVAFC for pathogenic sequence variants prioritizations from clinical exome sequencing (CES) variant data of single individual or family. The mirVAFC is able to comprehensively annotate sequence variants, filter out most irrelevant variants using custom criteria, classify variants into different categories as for estimated pathogenicity, and lastly provide pathogenic variants prioritizations based on classifications and mutation effects. Case studies using different types of datasets for different diseases from publication and our in‐house data have revealed that mirVAFC can efficiently identify the right pathogenic candidates as in original work in each case. Overall, the Web server mirVAFC is specifically developed for pathogenic sequence variant identifications from family‐based CES variants using classification‐based prioritizations. The mirVAFC Web server is freely accessible at https://www.wzgenomics.cn/mirVAFC/ .     

Mutation Detection by Clonal Sequencing of PCR Amplicons and Grouped Read Typing is Applicable to Clinical Diagnostics

We describe a sensitive technique for mutation detection using clonal sequencing. We analyzed DNA extracted from 13 cancer cell lines and 35 tumor samples and applied a novel approach to identify disease‐associated somatic mutations. By matching reads against an index of known variants, noise can be dramatically reduced, enabling the detection and quantification of those variants, even when they are present at less than 1% of the total sequenced population; this is comparable to, or better than, current diagnostic methods. Following the identification or exclusion of known variants, unmatched reads are grouped for BLAST searching to identify novel variants or contaminants. Known variants, novel variants, and contaminants were readily identified in tumor tissue using this approach. Our approach also enables an estimation of the per‐base sequencing error rate, providing a confidence threshold for interpretation of the results in the clinic. This novel approach has immediate applicability to clinical testing for disease‐associated genetic variants.     

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 an activated JAK3 variant and a Xq26.3 microdeletion causing loss of PHF6 and miR-424 expression in myelodysplastic syndromes by combined targeted next generation sequencing and SNP array analysis

Myelodysplastic syndromes (MDS) are hematopoietic disorders characterized by ineffective hematopoiesis and progression to acute leukemia. In patients ineligible for hematopoietic stem cell transplantation, azacitidine is the only treatment shown to prolong survival. However, with the availability of a growing compendium of cancer biomarkers and related drugs, analysis of relevant genetic alterations for individual MDS patients might become part of routine evaluation.     

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.     

Impact of new genomic tools on the practice of clinical genetics in consanguineous populations: the Saudi experience

Consanguinity is practiced by around one tenth of the world population but its global distribution is far from uniform. In countries where consanguinity is common, a corresponding increase in the frequency of autosomal recessive diseases is usually observed owing to increased risk of homozygosity for ancestral haplotypes (autozygosity or identity by descent) that harbor pathogenic alleles. The burden of these diseases becomes more apparent as the healthcare system makes gains in its fight against communicable diseases in these countries. Recent advances in molecular genetics make it possible to leverage the mechanism by which consanguinity predisposes to the occurrence of autosomal recessive diseases in order to uncover the causal mutations at an efficient and cost‐effective way compared to outbred populations. The identification of these mutations at an unprecedented scale has the potential to significantly reshape the practice of clinical genetics in these populations and to offer opportunities for innovative public health policies. This review discusses the impact that new genomic tools have had on a sample patient population and how they can inform future public health policies in ways that might be relevant to other consanguineous populations.     

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/ .     

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.