Novel bioinformatic developments for exome sequencing

DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts. Here, we developed a deep convolutional neural network (CNN) DNM caller (DeNovoCNN), that encodes the alignment of sequence reads for a trio as 160\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$ \times$\end{document}164 resolution images. DeNovoCNN was trained on DNMs of 5616 whole exome sequencing (WES) trios achieving total 96.74% recall and 96.55% precision on the test dataset. We find that DeNovoCNN has increased recall/sensitivity and precision compared to existing DNM calling approaches (GATK, DeNovoGear, DeepTrio, Samtools) based on the Genome in a Bottle reference dataset and independent WES and WGS trios. Validations of DNMs based on Sanger and PacBio HiFi sequencing confirm that DeNovoCNN outperforms existing methods. Most importantly, our results suggest that DeNovoCNN is likely robust against different exome sequencing and analyses approaches, thereby allowing the application on other datasets. DeNovoCNN is freely available as a Docker container and can be run on existing alignment (BAM/CRAM) and variant calling (VCF) files from WES and WGS without a need for variant recalling.

Innovative in Silico Approaches for Characterization of Genes and Proteins

Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.

Clinical exome sequencing - mistakes and caveats

Massive parallel sequencing technology has become the predominant technique for genetic diagnostics and research. Many genetic laboratories have wrestled with the challenges of setting up genetic testing workflows based on a completely new technology. The learning curve we went through as a laboratory was accompanied by growing pains while we gained new knowledge and expertise. Here we discuss some important mistakes that have been made in our laboratory through 10 years of clinical exome sequencing but that have given us important new insights on how to adapt our working methods. We provide these examples and the lessons that we learned to help other laboratories avoid to make the same mistakes.

Utility of next-generation sequencing in genetic testing and counseling of disorders involving the musculoskeletal system—trends observed from a single genetic unit

Background

Disorders involving the musculoskeletal system are often identified with short stature and a range of orthopedic problems. The clinical and genetic heterogeneity of these diseases along with several characteristic overlaps makes definitive diagnosis difficult for clinicians. Hence, using molecular testing in addition to conventional tests becomes essential for appropriate diagnosis and management.

Methods

Comprehensive clinical examination, detailed pretest and posttest counseling, molecular diagnosis with next-generation sequencing (NGS), genotype–phenotype correlation and Sanger sequencing for targeted variant analysis.

Results

This manuscript reports a molecular spectrum of variants in 34 orthopedic cases referred to a single genetic unit attached to a tertiary care hospital. The diagnostic yield of NGS-based tests coupled with genetic counseling and segregation analysis was 79% which included 7 novel variants. In about 53% (i.e. 18/34 cases), molecular testing outcome was actionable since 8 of the 18 underwent prenatal diagnosis, as they were either in their early gestation or had planned a pregnancy subsequent to molecular testing, while ten cases were premaritally/prenatally counseled for the families to take informed decisions as they were in the reproductive age.

Conclusions

The report highlights the importance of NGS-based tests even in a low resource setting as it helps patients, families and healthcare providers in reducing the economic, social and emotional burden of these disorders.

VarGenius-HZD Allows Accurate Detection of Rare Homozygous or Hemizygous Deletions in Targeted Sequencing Leveraging Breadth of Coverage

Homozygous deletions (HDs) may be the cause of rare diseases and cancer, and their discovery in targeted sequencing is a challenging task. Different tools have been developed to disentangle HD discovery but a sensitive caller is still lacking. We present VarGenius-HZD, a sensitive and scalable algorithm that leverages breadth-of-coverage for the detection of rare homozygous and hemizygous single-exon deletions (HDs). To assess its effectiveness, we detected both real and synthetic rare HDs in fifty exomes from the 1000 Genomes Project obtaining higher sensitivity in comparison with state-of-the-art algorithms that each missed at least one event. We then applied our tool on targeted sequencing data from patients with Inherited Retinal Dystrophies and solved five cases that still lacked a genetic diagnosis. We provide VarGenius-HZD either stand-alone or integrated within our recently developed software, enabling the automated selection of samples using the internal database. Hence, it could be extremely useful for both diagnostic and research purposes.

Missense3D-DB web catalogue: an atom-based analysis and repository of 4M human protein-coding genetic variants

The interpretation of human genetic variation is one of the greatest challenges of modern genetics. New approaches are urgently needed to prioritize variants, especially those that are rare or lack a definitive clinical interpretation. We examined 10,136,597 human missense genetic variants from GnomAD, ClinVar and UniProt. We were able to perform large-scale atom-based mapping and phenotype interpretation of 3,960,015 of these variants onto 18,874 experimental and 84,818 in house predicted three-dimensional coordinates of the human proteome. We demonstrate that 14% of amino acid substitutions from the GnomAD database that could be structurally analysed are predicted to affect protein structure (n = 568,548, of which 566,439 rare or extremely rare) and may, therefore, have a yet unknown disease-causing effect. The same is true for 19.0% (n = 6266) of variants of unknown clinical significance or conflicting interpretation reported in the ClinVar database. The results of the structural analysis are available in the dedicated web catalogue Missense3D-DB ( http://missense3d.bc.ic.ac.uk/ ). For each of the 4 M variants, the results of the structural analysis are presented in a friendly concise format that can be included in clinical genetic reports. A detailed report of the structural analysis is also available for the non-experts in structural biology. Population frequency and predictions from SIFT and PolyPhen are included for a more comprehensive variant interpretation. This is the first large-scale atom-based structural interpretation of human genetic variation and offers geneticists and the biomedical community a new approach to genetic variant interpretation.

Bioinformatics Analysis Revealed Novel 3'UTR Variants Associated with Intellectual Disability

MicroRNAs (or miRNAs) are short nucleotide sequences (~17–22 bp long) that play important roles in gene regulation through targeting genes in the 3′untranslated regions (UTRs). Variants located in genomic regions might have different biological consequences in changing gene expression. Exonic variants (e.g., coding variant and 3′UTR variant) are often causative of diseases due to their influence on gene product. Variants harbored in the 3′UTR region where miRNAs perform their targeting function could potentially alter the binding relationships for target pairs, which could relate to disease causation. We gathered miRNA–mRNA targeting pairs from published studies and then employed the database of microRNA Target Site single nucleotide variants (SNVs) (dbMTS) to discover novel SNVs within the selected pairs. We identified a total of 183 SNVs for the 114 pairs of accurate miRNA–mRNA targeting pairs selected. Detailed bioinformatics analysis of the three genes with identified variants that were exclusively located in the 3′UTR section indicated their association with intellectual disability (ID). Our result showed an exceptionally high expression of GPR88 in brain tissues based on GTEx gene expression data, while WNT7A expression data were relatively high in brain tissues when compared to other tissues. Motif analysis for the 3′UTR region of WNT7A showed that five identified variants were well-conserved across three species (human, mouse, and rat); the motif that contains the variant identified in GPR88 is significant at the level of the 3′UTR of the human genome. Studies of pathways, protein–protein interactions, and relations to diseases further suggest potential association with intellectual disability of our discovered SNVs. Our results demonstrated that 3′UTR variants could change target interactions of miRNA–mRNA pairs in the context of their association with ID. We plan to automate the methods through developing a bioinformatics pipeline for identifying novel 3′UTR SNVs harbored by miRNA-targeted genes in the future.

A survey of RNA viruses in mosquitoes from Mozambique reveals novel genetic lineages of flaviviruses and phenuiviruses, as well as frequent flavivirus-like viral DNA forms in Mansonia

BACKGROUND

Mosquito-borne diseases involving arboviruses represent expanding threats to sub-Saharan Africa imposing as considerable burden to human and veterinary public health. In Mozambique over one hundred species of potential arbovirus mosquito vectors have been identified, although their precise role in maintaining such viruses in circulation in the country remains to be elucidated. The aim of this study was to screen for the presence of flaviviruses, alphaviruses and bunyaviruses in mosquitoes from different regions of Mozambique.

RESULTS

Our survey analyzed 14,519 mosquitoes, and the results obtained revealed genetically distinct insect-specific flaviviruses, detected in multiple species of mosquitoes from different genera. In addition, smaller flavivirus-like NS5 sequences, frequently detected in Mansonia seemed to correspond to defective viral sequences, present as viral DNA forms. Furthermore, three lineages of putative members of the Phenuiviridae family were also detected, two of which apparently corresponding to novel viral genetic lineages.

CONCLUSION

This study reports for the first-time novel insect-specific flaviviruses and novel phenuiviruses, as well as frequent flavivirus-like viral DNA forms in several widely known vector species. This unique work represents recent investigation of virus screening conducted in mosquitoes from Mozambique and an important contribution to inform the establishment of a vector control program for arbovirus in the country and in the region.

Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis

PURPOSE

A primary barrier to improving exome sequencing diagnostic rates is the interpretation of variants of uncertain clinical significance. We aimed to determine the contribution of integrated untargeted metabolomics in the analysis of exome sequencing data by retrospective analysis of patients evaluated by both exome sequencing and untargeted metabolomics within the same clinical laboratory.

METHODS

Exome sequencing and untargeted metabolomic data were collected and analyzed for 170 patients. Pathogenic variants, likely pathogenic variants, and variants of uncertain significance in genes associated with a biochemical phenotype were extracted. Metabolomic data were evaluated to determine if these variants resulted in biochemical abnormalities that could be used to support their interpretation using current American College of Genetics and Genomics (ACMG) guidelines.

RESULTS

Metabolomic data contributed to the interpretation of variants in 74 individuals (43.5%) over 73 different genes. The data allowed for the reclassification of 9 variants as likely benign, 15 variants as likely pathogenic, and 3 variants as pathogenic. Metabolomic data confirmed a clinical diagnosis in 21 cases, for a diagnostic rate of 12.3% in this population.

CONCLUSION

Untargeted metabolomics can serve as a useful adjunct to exome sequencing by providing valuable functional data that may not otherwise be clinically available, resulting in improved variant classification.

Insights into the inhibitory potential of selective phytochemicals against Mpro of 2019-nCoV: a computer-aided study

At the end of December 2019, a novel strain of coronavirus, given the name of 2019-nCoV, emerged for exhibiting symptoms of severe acute respiratory syndrome. The virus is spreading rapidly in China and around the globe, affecting thousands of people leading to a pandemic. To control the mortality rate associated with the 2019-nCoV, prompt steps are needed. Until now there is no effective treatment or drug present to control its life-threatening effects in the humans. The scientist is struggling to find new inhibitors of this deadly virus. In this study, to identify the effective inhibitor candidates against the main protease (Mpro) of 2019-nCoV, computational approaches were adopted. Phytochemicals having immense medicinal properties as ligands were docked against the Mpro of 2019-nCoV to study their binding properties. ADMET and DFT analyses were also further carried out to analyze the potential of these phytochemicals as an effective inhibitor against Mpro of 2019-nCoV.

Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics

Clinical genetics has an important role in the healthcare system to provide a definitive diagnosis for many rare syndromes. It also can have an influence over genetics prevention, disease prognosis and assisting the selection of the best options of care/treatment for patients. Next-generation sequencing (NGS) has transformed clinical genetics making possible to analyze hundreds of genes at an unprecedented speed and at a lower price when comparing to conventional Sanger sequencing. Despite the growing literature concerning NGS in a clinical setting, this review aims to fill the gap that exists among (bio)informaticians, molecular geneticists and clinicians, by presenting a general overview of the NGS technology and workflow. First, we will review the current NGS platforms, focusing on the two main platforms Illumina and Ion Torrent, and discussing the major strong points and weaknesses intrinsic to each platform. Next, the NGS analytical bioinformatic pipelines are dissected, giving some emphasis to the algorithms commonly used to generate process data and to analyze sequence variants. Finally, the main challenges around NGS bioinformatics are placed in perspective for future developments. Even with the huge achievements made in NGS technology and bioinformatics, further improvements in bioinformatic algorithms are still required to deal with complex and genetically heterogeneous disorders.

Genetic Landscape of Rett Syndrome Spectrum: Improvements and Challenges

Rett syndrome (RTT) is an early-onset neurodevelopmental disorder that primarily affects females, resulting in severe cognitive and physical disabilities, and is one of the most prevalent causes of intellectual disability in females. More than fifty years after the first publication on Rett syndrome, and almost two decades since the first report linking RTT to the MECP2 gene, the research community's effort is focused on obtaining a better understanding of the genetics and the complex biology of RTT and Rett-like phenotypes without MECP2 mutations. Herein, we review the current molecular genetic studies, which investigate the genetic causes of RTT or Rett-like phenotypes which overlap with other genetic disorders and document the swift evolution of the techniques and methodologies employed. This review also underlines the clinical and genetic heterogeneity of the Rett syndrome spectrum and provides an overview of the RTT-related genes described to date, many of which are involved in epigenetic gene regulation, neurotransmitter action or RNA transcription/translation. Finally, it discusses the importance of including both phenotypic and genetic diagnosis to provide proper genetic counselling from a patient's perspective and the appropriate treatment.

Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data

Background

Whole exome sequencing (WES) is a cost-effective method that identifies clinical variants but it demands accurate variant caller tools. Currently available tools have variable accuracy in predicting specific clinical variants. But it may be possible to find the best combination of aligner-variant caller tools for detecting accurate single nucleotide variants (SNVs) and small insertion and deletion (InDels) separately. Moreover, many important aspects of InDel detection are overlooked while comparing the performance of tools, particularly its base pair length.

Results

We assessed the performance of variant calling pipelines using the combinations of four variant callers and five aligners on human NA12878 and simulated exome data. We used high confidence variant calls from Genome in a Bottle (GiaB) consortium for validation, and GRCh37 and GRCh38 as the human reference genome. Based on the performance metrics, both BWA and Novoalign aligners performed better with DeepVariant and SAMtools callers for detecting SNVs, and with DeepVariant and GATK for InDels. Furthermore, we obtained similar results on human NA24385 and NA24631 exome data from GiaB.

Conclusion

In this study, DeepVariant with BWA and Novoalign performed best for detecting accurate SNVs and InDels. The accuracy of variant calling was improved by merging the top performing pipelines. The results of our study provide useful recommendations for analysis of WES data in clinical genomics.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2928-9) contains supplementary material, which is available to authorized users.

Panel-Based Next-Generation Sequencing for the Diagnosis of Cholestatic Genetic Liver Diseases: Clinical Utility and Challenges

OBJECTIVE

To test the application of a target enrichment next-generation sequencing (NGS) jaundice panel in genetic diagnosis of pediatric liver diseases.

STUDY DESIGN

We developed a capture-based target enrichment NGS jaundice panel containing 42 known disease-causing genes associated with jaundice or cholestasis and 10 pathway-related genes. During 2015-2017, 102 pediatric patients with various forms of cholestasis or idiopathic liver diseases were tested, including patients with initial diagnosis of cholestasis in infancy, progressive familial intrahepatic cholestasis, syndromic cholestasis, Wilson disease, and others.

RESULTS

Of the 102 patients, 137 mutations/variants in 44 different genes were identified in 84 patients. The genetic disease diagnosis rate was 33 of 102 (32.4%). A total of 79 of 102 (77.5%) of patients had at least 1 heterozygous genetic variation. Those with progressive intrahepatic cholestasis or syndromic cholestasis in infancy had a diagnostic rate of 62.5%. Disease-causing mutations, including ATP8B1, ABCB11, ABCB4, ABCC2, TJP2, NR1H4 (FXR), JAG1, AKR1D1, CYP7B1, PKHD1, ATP7B, and SLC25A13, were identified. Nine patients had unpredicted genetic diagnosis with atypical phenotype or novel mutations in the investigational genes. We propose an NGS diagnosis classification categorizing patients into high (n = 24), moderate (n = 9), or weak (n = 25) levels of genotype-phenotype correlations to facilitate patient management.

CONCLUSIONS

This panel enabled high-throughput detection of genetic variants and disease diagnosis in patients with a long list of candidate causative genes. A NGS report with diagnosis classification may aid clinicians in data interpretation and patient management.

MECP2 variation in Rett syndrome-An overview of current coverage of genetic and phenotype data within existing databases

Rett syndrome (RTT) is a monogenic rare disorder that causes severe neurological problems. In most cases, it results from a loss-of-function mutation in the gene encoding methyl-CPG-binding protein 2 (MECP2). Currently, about 900 unique MECP2 variations (benign and pathogenic) have been identified and it is suspected that the different mutations contribute to different levels of disease severity. For researchers and clinicians, it is important that genotype-phenotype information is available to identify disease-causing mutations for diagnosis, to aid in clinical management of the disorder, and to provide counseling for parents. In this study, 13 genotype-phenotype databases were surveyed for their general functionality and availability of RTT-specific MECP2 variation data. For each database, we investigated findability and interoperability alongside practical user functionality, and type and amount of genetic and phenotype data. The main conclusions are that, as well as being challenging to find these databases and specific MECP2 variants held within, interoperability is as yet poorly developed and requires effort to search across databases. Nevertheless, we found several thousand online database entries for MECP2 variations and their associated phenotypes, diagnosis, or predicted variant effects, which is a good starting point for researchers and clinicians who want to provide, annotate, and use the data.

Whole-Exome Sequencing Reveals Uncaptured Variation and Distinct Ancestry in the Southern African Population of Botswana

Large-scale, population-based genomic studies have provided a context for modern medical genetics. Among such studies, however, African populations have remained relatively underrepresented. The breadth of genetic diversity across the African continent argues for an exploration of local genomic context to facilitate burgeoning disease mapping studies in Africa. We sought to characterize genetic variation and to assess population substructure within a cohort of HIV-positive children from Botswana-a Southern African country that is regionally underrepresented in genomic databases. Using whole-exome sequencing data from 164 Batswana and comparisons with 150 similarly sequenced HIV-positive Ugandan children, we found that 13%-25% of variation observed among Batswana was not captured by public databases. Uncaptured variants were significantly enriched (p = 2.2 × 10^-16) for coding variants with minor allele frequencies between 1% and 5% and included predicted-damaging non-synonymous variants. Among variants found in public databases, corresponding allele frequencies varied widely, with Botswana having significantly higher allele frequencies among rare (<1%) pathogenic and damaging variants. Batswana clustered with other Southern African populations, but distinctly from 1000 Genomes African populations, and had limited evidence for admixture with extra-continental ancestries. We also observed a surprising lack of genetic substructure in Botswana, despite multiple tribal ethnicities and language groups, alongside a higher degree of relatedness than purported founder populations from the 1000 Genomes project. Our observations reveal a complex, but distinct, ancestral history and genomic architecture among Batswana and suggest that disease mapping within similar Southern African populations will require a deeper repository of genetic variation and allelic dependencies than presently exists.

MoBiDiC Prioritization Algorithm, a Free, Accessible, and Efficient Pipeline for Single-Nucleotide Variant Annotation and Prioritization for Next-Generation Sequencing Routine Molecular Diagnosis

Interpretation of next-generation sequencing constitutes the main limitation of molecular diagnostics. In diagnosing myopathies and muscular dystrophies, another issue is efficiency in predicting the pathogenicity of variants identified in large genes, especially TTN; current in silico prediction tools show limitations in predicting and ranking the numerous variants of such genes. We propose a variant-prioritization tool, the MoBiDiCprioritization algorithm (MPA). MPA is based on curated interpretation of data on previously reported variants, biological assumptions, and splice and missense predictors, and is used to prioritize all types of single-nucleotide variants. MPA was validated by comparing its sensitivity and specificity to those of dbNSFP database prediction tools, using a data set composed of DYSF, DMD, LMNA, NEB, and TTN variants extracted from expert-reviewed and ExAC databases. MPA obtained the best annotation rates for missense and splice variants. As MPA aggregates the results from several predictors, individual predictor errors are counterweighted, improving the sensitivity and specificity of missense and splice variant predictions. We propose a sequential use of MPA, beginning with the selection of variants with higher scores and followed by, in the absence of candidate pathologic variants, consideration of variants with lower scores. We provide scripts and documentation for free academic use and a validated annotation pipeline scaled for panel and exome sequencing to prioritize single-nucleotide variants from a VCF file.

A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing

BACKGROUND

Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and software tools, the majority of variants called using NGS alone are in fact accurate and reliable. However, a small subset of difficult-to-call variants that still do require orthogonal confirmation exist. For this reason, many clinical laboratories confirm NGS results using orthogonal technologies such as Sanger sequencing. Here, we report the development of a deterministic machine-learning-based model to differentiate between these two types of variant calls: those that do not require confirmation using an orthogonal technology (high confidence), and those that require additional quality testing (low confidence). This approach allows reliable NGS-based calling in a clinical setting by identifying the few important variant calls that require orthogonal confirmation.

RESULTS

We developed and tested the model using a set of 7179 variants identified by a targeted NGS panel and re-tested by Sanger sequencing. The model incorporated several signals of sequence characteristics and call quality to determine if a variant was identified at high or low confidence. The model was tuned to eliminate false positives, defined as variants that were called by NGS but not confirmed by Sanger sequencing. The model achieved very high accuracy: 99.4% (95% confidence interval: +/- 0.03%). It categorized 92.2% (6622/7179) of the variants as high confidence, and 100% of these were confirmed to be present by Sanger sequencing. Among the variants that were categorized as low confidence, defined as NGS calls of low quality that are likely to be artifacts, 92.1% (513/557) were found to be not present by Sanger sequencing.

CONCLUSIONS

This work shows that NGS data contains sufficient characteristics for a machine-learning-based model to differentiate low from high confidence variants. Additionally, it reveals the importance of incorporating site-specific features as well as variant call features in such a model.

A Screening Approach to Identify Clinically Actionable Variants Causing Congenital Heart Disease in Exome Data

BACKGROUND

Congenital heart disease (CHD)-structural abnormalities of the heart that arise during embryonic development-is the most common inborn malformation, affecting ≤1% of the population. However, currently, only a minority of cases can be explained by genetic abnormalities. The goal of this study was to identify disease-causal genetic variants in 30 families affected by CHD.

METHODS

Whole-exome sequencing was performed with the DNA of multiple family members. We utilized a 2-tiered whole-exome variant screening and interpretation procedure. First, we manually curated a high-confidence list of 90 genes known to cause CHD in humans, identified predicted damaging variants in genes on this list, and rated their pathogenicity using American College of Medical Genetics and Genomics-Association for Molecular Pathology guidelines.

RESULTS

In 3 families (10%), we found pathogenic variants in known CHD genes TBX5, TFAP2B, and PTPN11, explaining the cardiac lesions. Second, exomes were comprehensively analyzed to identify additional predicted damaging variants that segregate with disease in CHD candidate genes. In 10 additional families (33%), likely disease-causal variants were uncovered in PBX1, CNOT1, ZFP36L2, TEK, USP34, UPF2, KDM5A, KMT2C, TIE1, TEAD2, and FLT4.

CONCLUSIONS

The pathogenesis of CHD could be explained using our high-confidence CHD gene list for variant filtering in a subset of cases. Furthermore, our unbiased screening procedure of family exomes implicates additional genes and variants in the pathogenesis of CHD, which suggest themselves for functional validation. This 2-tiered approach provides a means of (1) identifying clinically actionable variants and (2) identifying additional disease-causal genes, both of which are essential for improving the molecular diagnosis of CHD.

Current developments in the genetics of Rett and Rett-like syndrome

PURPOSE OF REVIEW

This article reviews the current molecular genetic studies, which investigate the genetic causes of Rett syndrome or Rett-like phenotypes without a MECP2 mutation.

RECENT FINDINGS

As next generation sequencing becomes broadly available, especially whole exome sequencing is used in clinical diagnosis of the genetic causes of a wide spectrum of intellectual disability, autism, and encephalopathies. Patients who were diagnosed with Rett syndrome or Rett-like syndrome because of their phenotype but were negative for mutations in the MECP2, CDKL5 or FOXG1 genes were subjected to whole exome sequencing and the results of the last few years revealed yet 69 different genes. Many of these genes are involved in epigenetic gene regulation, chromatin shaping, neurotransmitter action or RNA transcription/translation. Genetic data also allows to investigate the individual genetic background of an individual patient, which can modify the severity of a genetic disorder.

SUMMARY

We conclude that the Rett syndrome phenotype has a much broader underlying genetic cause and the typical phenotype overlap with other genetic disorders. For proper genetic counselling, patient perspective and treatment it is important to include both phenotype and genetic information.

Leveraging splice-affecting variant predictors and a minigene validation system to identify Mendelian disease-causing variants among exon-captured variants of uncertain significance

The genetic heterogeneity of Mendelian disorders results in a significant proportion of patients that are unable to be assigned a confident molecular diagnosis after conventional exon sequencing and variant interpretation. Here, we evaluated how many patients with an inherited retinal disease (IRD) have variants of uncertain significance (VUS) that are disrupting splicing in a known IRD gene by means other than affecting the canonical dinucleotide splice site. Three in silico splice-affecting variant predictors were leveraged to annotate and prioritize variants for splicing functional validation. An in vitro minigene system was used to assay each variant's effect on splicing. Starting with 745 IRD patients lacking a confident molecular diagnosis, we validated 23 VUS as splicing variants that likely explain disease in 26 patients. Using our results, we optimized in silico score cutoffs to guide future variant interpretation. Variants that alter base pairs other than the canonical GT-AG dinucleotide are often not considered for their potential effect on RNA splicing but in silico tools and a minigene system can be utilized for the prioritization and validation of such splice-disrupting variants. These variants can be overlooked causes of human disease but can be identified using conventional exon sequencing with proper interpretation guidelines.

Gene and Variant Annotation for Mendelian Disorders in the Era of Advanced Sequencing Technologies

Comprehensive annotations of genetic and noncoding regions and corresponding accurate variant classification for Mendelian diseases are the next big challenge in the new genomic era of personalized medicine. Progress in the development of faster and more accurate pipelines for genome annotation and variant classification will lead to the discovery of more novel disease associations and candidate therapeutic targets. This ultimately will facilitate better patient recruitment in clinical trials. In this review, we describe the trends in research at the intersection of basic and clinical genomics that aims to increase understanding of overall genomic complexity, complex inheritance patterns of disease, and patient-phenotype-specific genomic associations. We describe the emerging field of translational functional genomics, which integrates other functional "-omics" approaches that support next-generation sequencing genomic data in order to facilitate personalized diagnostics, disease management, biomarker discovery, and medicine. We also discuss the utility of this integrated approach for diagnostic clinics and medical databases and its role in the future of personalized medicine.

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 whole-exome 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.

Big Data Analytics for Genomic Medicine

Genomic medicine attempts to build individualized strategies for diagnostic or therapeutic decision-making by utilizing patients’ genomic information. Big Data analytics uncovers hidden patterns, unknown correlations, and other insights through examining large-scale various data sets. While integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a Big Data infrastructure exhibit challenges, they also provide a feasible opportunity to develop an efficient and effective approach to identify clinically actionable genetic variants for individualized diagnosis and therapy. In this paper, we review the challenges of manipulating large-scale next-generation sequencing (NGS) data and diverse clinical data derived from the EHRs for genomic medicine. We introduce possible solutions for different challenges in manipulating, managing, and analyzing genomic and clinical data to implement genomic medicine. Additionally, we also present a practical Big Data toolset for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN

Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, ‘exon CNVs’) in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs. 

Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA). 

Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%. 

Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon.

Integrating next-generation sequencing into clinical oncology: strategies, promises and pitfalls

We live in an era of genomic medicine. The past five years brought about many significant achievements in the field of cancer genetics, driven by rapidly evolving technologies and plummeting costs of next-generation sequencing (NGS). The official completion of the Cancer Genome Project in 2014 led many to envision the clinical implementation of cancer genomic data as the next logical step in cancer therapy. Stemming from this vision, the term 'precision oncology' was coined to illustrate the novelty of this individualised approach. The basic assumption of precision oncology is that molecular markers detected by NGS will predict response to targeted therapies independently from tumour histology. However, along with a ubiquitous availability of NGS, the complexity and heterogeneity at the individual patient level had to be acknowledged. Not only does the latter present challenges to clinical decision-making based on sequencing data, it is also an obstacle to the rational design of clinical trials. Novel tissue-agnostic trial designs were quickly developed to overcome these challenges. Results from some of these trials have recently demonstrated the feasibility and efficacy of this approach. On the other hand, there is an increasing amount of whole-exome and whole-genome NGS data which allows us to assess ever smaller differences between individual patients with cancer. In this review, we highlight different tumour sequencing strategies currently used for precision oncology, describe their individual strengths and weaknesses, and emphasise their feasibility in different clinical settings. Further, we evaluate the possibility of NGS implementation in current and future clinical trials, and point to the significance of NGS for translational research.

OpEx - a validated, automated pipeline optimised for clinical exome sequence analysis

We present an easy-to-use, open-source Optimised Exome analysis tool, OpEx (http://icr.ac.uk/opex) that accurately detects small-scale variation, including indels, to clinical standards. We evaluated OpEx performance with an experimentally validated dataset (the ICR142 NGS validation series), a large 1000 exome dataset (the ICR1000 UK exome series), and a clinical proband-parent trio dataset. The performance of OpEx for high-quality base substitutions and short indels in both small and large datasets is excellent, with overall sensitivity of 95%, specificity of 97% and low false detection rate (FDR) of 3%. Depending on the individual performance requirements the OpEx output allows one to optimise the inevitable trade-offs between sensitivity and specificity. For example, in the clinical setting one could permit a higher FDR and lower specificity to maximise sensitivity. In contexts where experimental validation is not possible, minimising the FDR and improving specificity may be a preferable trade-off for slightly lower sensitivity. OpEx is simple to install and use; the whole pipeline is run from a single command. OpEx is therefore well suited to the increasing research and clinical laboratories undertaking exome sequencing, particularly those without in-house dedicated bioinformatics expertise.