Identifying disease mutations in genomic medicine settings: current challenges and how to accelerate progress

One-pot DTECT enables rapid and efficient capture of genetic signatures for precision genome editing and clinical diagnostics

The detection of genomic sequences and their alterations is crucial for basic research and clinical diagnostics. However, current methodologies are costly and time-consuming and require outsourcing sample preparation, processing, and analysis to genomic companies. Here, we establish One-pot DTECT, a platform that expedites the detection of genetic signatures, only requiring a short incubation of a PCR product in an optimized one-pot mixture. One-pot DTECT enables qualitative, quantitative, and visual detection of biologically relevant variants, such as cancer mutations, and nucleotide changes introduced by prime editing and base editing into cancer cells and human primary T cells. Notably, One-pot DTECT achieves quantification accuracy for targeted genetic signatures comparable with Sanger and next-generation sequencing. Furthermore, its effectiveness as a diagnostic platform is demonstrated by successfully detecting sickle cell variants in blood and saliva samples. Altogether, One-pot DTECT offers an efficient, versatile, adaptable, and cost-effective alternative to traditional methods for detecting genomic signatures.

Understanding molecular signaling cascades in neural disease using multi-resolution models

If the genome defines the program for the operations of a cell, signaling networks execute it. These cascades of chemical, cell-biological, structural, and trafficking events span milliseconds (e.g., synaptic release) to potentially a lifetime (e.g., stabilization of dendritic spines). In principle almost every aspect of neuronal function, particularly at the synapse, depends on signaling. Thus dysfunction of these cascades, whether through mutations, local dysregulation, or infection, leads to disease. The sheer complexity of these pathways is matched by the range of diseases and the diversity of their phenotypes. In this review, we discuss how to build computational models, how these models are essential to tackle this complexity, and the benefits of using families of models at different levels of detail to understand signaling in health and disease.

HAP1, a new revolutionary cell model for gene editing using CRISPR-Cas9

The use of next-generation sequencing (NGS) technologies has been instrumental in the characterization of the mutational landscape of complex human diseases like cancer. But despite the enormous rise in the identification of disease candidate genetic variants, their functionality is yet to be fully elucidated in order to have a clear implication in patient care. Haploid human cell models have become the tool of choice for functional gene studies, since they only contain one copy of the genome and can therefore show the unmasked phenotype of genetic variants. Over the past few years, the human near-haploid cell line HAP1 has widely been consolidated as one of the favorite cell line models for functional genetic studies. Its rapid turnover coupled with the fact that only one allele needs to be modified in order to express the subsequent desired phenotype has made this human cell line a valuable tool for gene editing by CRISPR-Cas9 technologies. This review examines the recent uses of the HAP1 cell line model in functional genetic studies and high-throughput genetic screens using the CRISPR-Cas9 system. It covers its use in an attempt to develop new and relevant disease models to further elucidate gene function, and create new ways to understand the genetic basis of human diseases. We will cover the advantages and potential of the use of CRISPR-Cas9 technology on HAP1 to easily and efficiently study the functional interpretation of gene function and human single-nucleotide genetic variants of unknown significance identified through NGS technologies, and its implications for changes in clinical practice and patient care.

Genetics and"democracy"

BACKGROUND

The spread of knowledge on the important implications of a diagnosis of genetic disease does not correspond to a sharing of the knowledge and equal rights of children.

MAIN BODY

It is estimated that about 5% of newborns may have a rare disease that in some cases, if diagnosed early, could have specific treatments that may be able to modify the natural history of the disease. However, in most countries the diagnosis during the first hours of life is limited to a few diseases, due to the high costs and time required for genetic investigations with classical methods. Recently, experimental projects to subject all newborns to a complete DNA analysis, with Next Generation Sequencing techniques, to detect any genetic pathologies as early as possible, have been reported in some countries. The late diagnosis of some genetic diseases that have treatment plans, such as spinal muscular atrophy, can be a serious damage, for anyone who has seen and accompanied the life of a child with this disease and his/her family, before and after, the recent availability of therapies which, if started very early, can lead to an almost normal life. Rapid sequencing and genetic diagnosis are a crucial part of directing inpatient management and this resource should be accessible not only to academic medical centers but also in community settings.

CONCLUSIONS

It is time for a profound reflection that places in Italy, as in other countries, the use of genetic tests in neonatal and pediatric age based on principles of evidence, ethics, and democracy and on clear national guidelines, which also consider organizational aspects.

DYNC1H1 regulates NSCLC cell growth and metastasis by IFN-γ-JAK-STAT signaling and is associated with an aberrant immune response

It is urgent to identify new biomarkers and therapeutic targets to ameliorate the clinical prognosis of patients with lung cancer. The functional significance and molecular mechanism of dynein cytoplasmic 1 heavy chain 1 (DYNC1H1) in nonsmall cell lung cancer (NSCLC) progression is still elusive. In our current study, publicly available data and Western blotting experiments confirmed that DYNC1H1 expression was upregulated in lung cancer samples compared with noncancerous samples. Quantitative real-time PCR (qPCR) results indicated that high DYNC1H1 expression in lung cancer tissues was significantly associated with clinical tumor stage and distal metastasis; moreover, its high expression was negatively correlated with prognosis. Functional experiments demonstrated that DYNC1H1 loss of function caused a significant decrease in cell viability and cell proliferative ability, inhibition of the cell cycle, and promotion of both migration potential and invasion potential in vitro. Animal experiments by tail vein injection of lung cancer cells showed that DYNC1H1 knockdown significantly decreased lung cancer metastasis. Mechanistically, the results from a human protein array showed changes in the IFN-γ-JAK-STAT signaling pathway, and analysis of The Cancer Genome Atlas (TCGA) immune data demonstrated that disturbance of the immune microenvironment might be involved in the impaired growth and metastatic ability mediated by DYNC1H1 loss in NSCLC. DYNC1H1 might serve as a promising biological marker of prognosis and a potential clinical therapeutic target for patients with NSCLC.

Mechnetor: a web server for exploring protein mechanism and the functional context of genetic variants

Advances in DNA sequencing and proteomics mean that researchers must now regularly interrogate thousands of positional gene/protein changes in order to find those relevant for potential clinical application or biological insights. The abundance of already known information on protein interactions, mechanism, and tertiary structure provides the possible means to understand these changes rapidly, though a careful and systematic integration of these diverse datasets is first needed. For this purpose, we developed Mechnetor, a tool that allows users to quickly explore and visualize integrated mechanistic data for proteins or interactions of interest. Central to the system is a careful cataloguing of diverse sources of protein interaction mechanism, and an efficient means to visualize interactions between relevant and/or known protein regions. The result is a finer resolution interaction network that provides more immediate clues as to points of intervention or mechanistic understanding. Users can import protein, interactions, genetic variants or post-translational modifications and see these data in the best known mechanistic context. We demonstrate the tool with topical examples in human genetic diseases and cancer genomics. The tool is freely available at: mechnetor.russelllab.org.

How to study SARS-CoV-2 in soils?

Wastewater based epidemiology is increasingly being considered as a potentially useful tool for early warning about eventual new COVID-19 outbreaks. In addition, some authors are investigating on the detection and quantification of SARS-CoV-2 in sewage sludge. However, no paper has been published up to date indicating how this virus could be quantified in soil samples. In view of that, we review available data searching for methodological approaches that could guide on the quantification of SARS-CoV-2 (and even other pathogenic microorganisms) in soils.

Inherited Kidney Complement Diseases

In the past 20 years, we have witnessed tremendous advances in our ability to diagnose and treat genetic diseases of the kidney caused by complement dysregulation. Staggering progress was realized toward a better understanding of the genetic underpinnings and pathophysiology of many forms of atypical hemolytic uremic syndrome (aHUS) and C3-dominant glomerulopathies that are driven by complement system abnormalities. Many of these seminal discoveries paved the way for the design and characterization of several innovative therapies, some of which have already radically improved patients' outcomes. This review offers a broad overview of the exciting developments that have occurred in the recent past, with a particular focus on single-gene (or Mendelian), complement-driven aHUS and C3-dominant glomerulopathies that should be of interest to both nephrologists and kidney researchers. The discussion is restricted to genes with robust associations with both aHUS and C3-dominant glomerulopathies (complement factor H, complement component 3, complement factor H-related proteins) or only aHUS (complement factor B, complement factor I, and membrane cofactor protein). Key questions and challenges are highlighted, along with potential avenues for future directions.

Inherited Kidney Complement Diseases

In the past 20 years, we have witnessed tremendous advances in our ability to diagnose and treat genetic diseases of the kidney caused by complement dysregulation. Staggering progress was realized toward a better understanding of the genetic underpinnings and pathophysiology of many forms of atypical hemolytic uremic syndrome (aHUS) and C3-dominant glomerulopathies that are driven by complement system abnormalities. Many of these seminal discoveries paved the way for the design and characterization of several innovative therapies, some of which have already radically improved patients' outcomes. This review offers a broad overview of the exciting developments that have occurred in the recent past, with a particular focus on single-gene (or Mendelian), complement-driven aHUS and C3-dominant glomerulopathies that should be of interest to both nephrologists and kidney researchers. The discussion is restricted to genes with robust associations with both aHUS and C3-dominant glomerulopathies (complement factor H, complement component 3, complement factor H-related proteins) or only aHUS (complement factor B, complement factor I, and membrane cofactor protein). Key questions and challenges are highlighted, along with potential avenues for future directions.

Next-Generation Sequencing in Newborn Screening: A Review of Current State

Newborn screening was first introduced at the beginning of the 1960s with the successful implementation of the first phenylketonuria screening programs. Early expansion of the included disorders was slow because each additional disorder screened required a separate test. Subsequently, the technological advancements of biochemical methodology enabled the scaling-up of newborn screening, most notably with the implementation of tandem mass spectrometry. In recent years, we have witnessed a remarkable progression of high-throughput sequencing technologies, which has resulted in a continuous decrease of both cost and time required for genetic analysis. This has enabled more widespread use of the massive multiparallel sequencing. Genomic sequencing is now frequently used in clinical applications, and its implementation in newborn screening has been intensively advocated. The expansion of newborn screening has raised many clinical, ethical, legal, psychological, sociological, and technological concerns over time. This review provides an overview of the current state of next-generation sequencing regarding newborn screening including current recommendations and potential challenges for the use of such technologies in newborn screening.

SARS-CoV-2 and other pathogens could be determined in liquid samples from soils

In the current COVID-19 pandemic, SARS-CoV-2 has been quantified in wastewater in various countries, and wastewater based epidemiology has been proposed as a potential early warning tool for new outbreaks. However, even taking into account that poorly treated wastewater and sewage sludge may be spread on soils, there is no published paper dealing with the quantification of the virus in soil-related liquid samples, as could be runoff, leachates, or soil solution. To fill this gap, the authors of this piece propose reflections on the development of a methodological approach for the quantification of SARS-CoV-2 (and eventually other pathogens) in soil-related liquid samples.

Differences in substrate use linked to divergent carbon flow during litter decomposition

Discovering widespread microbial processes that create variation in soil carbon (C) cycling within ecosystems may improve soil C modeling. Toward this end, we screened 206 soil communities decomposing plant litter in a common garden microcosm environment and examined features linked to divergent patterns of C flow. C flow was measured as carbon dioxide (CO2) and dissolved organic carbon (DOC) from 44-days of litter decomposition. Two large groups of microbial communities representing ‘high’ and ‘low’ DOC phenotypes from original soil and 44-day microcosm samples were down-selected for fungal and bacterial profiling. Metatranscriptomes were also sequenced from a smaller subset of communities in each group. The two groups exhibited differences in average rate of CO2 production, demonstrating that the divergent patterns of C flow arose from innate functional constraints on C metabolism, not a time-dependent artefact. To infer functional constraints, we identified features – traits at the organism, pathway or gene level – linked to the high and low DOC phenotypes using RNA-Seq approaches and machine learning approaches. Substrate use differed across the high and low DOC phenotypes. Additional features suggested that divergent patterns of C flow may be driven in part by differences in organism interactions that affect DOC abundance directly or indirectly by controlling community structure.

Technologies and Computational Analysis Strategies for CRISPR Applications

The CRISPR-Cas system offers a programmable platform for eukaryotic genome and epigenome editing. The ability to perform targeted genetic and epigenetic perturbations enables researchers to perform a variety of tasks, ranging from investigating questions in basic biology to potentially developing novel therapeutics for the treatment of disease. While CRISPR systems have been engineered to target DNA and RNA with increased precision, efficiency, and flexibility, assays to identify off-target editing are becoming more comprehensive and sensitive. Furthermore, techniques to perform high-throughput genome and epigenome editing can be paired with a variety of readouts and are uncovering important cellular functions and mechanisms. These technological advances drive and are driven by accompanying computational approaches. Here, we briefly present available CRISPR technologies and review key computational advances and considerations for various CRISPR applications. In particular, we focus on the analysis of on- and off-target editing and CRISPR pooled screen data.

Identifying the tissues-of-origin of circulating cell-free DNAs is a promising way in noninvasive diagnostics

Advances in sequencing technologies facilitate personalized disease-risk profiling and clinical diagnosis. In recent years, some great progress has been made in noninvasive diagnoses based on cell-free DNAs (cfDNAs). It exploits the fact that dead cells release DNA fragments into the circulation, and some DNA fragments carry information that indicates their tissues-of-origin (TOOs). Based on the signals used for identifying the TOOs of cfDNAs, the existing methods can be classified into three categories: cfDNA mutation-based methods, methylation pattern-based methods and cfDNA fragmentation pattern-based methods. In cfDNA mutation-based methods, the SNP information or the detected mutations in driven genes of certain diseases are employed to identify the TOOs of cfDNAs. Methylation pattern-based methods are developed to identify the TOOs of cfDNAs based on the tissue-specific methylation patterns. In cfDNA fragmentation pattern-based methods, cfDNA fragmentation patterns, such as nucleosome positioning or preferred end coordinates of cfDNAs, are used to predict the TOOs of cfDNAs. In this paper, the strategies and challenges in each category are reviewed. Furthermore, the representative applications based on the TOOs of cfDNAs, including noninvasive prenatal testing, noninvasive cancer screening, transplantation rejection monitoring and parasitic infection detection, are also reviewed. Moreover, the challenges and future work in identifying the TOOs of cfDNAs are discussed. Our research provides a comprehensive picture of the development and challenges in identifying the TOOs of cfDNAs, which may benefit bioinformatics researchers to develop new methods to improve the identification of the TOOs of cfDNAs.

DNA Microsystems for Biodiagnosis

Researchers are continuously making progress towards diagnosis and treatment of numerous diseases. However, there are still major issues that are presenting many challenges for current medical diagnosis. On the other hand, DNA nanotechnology has evolved significantly over the last three decades and is highly interdisciplinary. With many potential technologies derived from the field, it is natural to begin exploring and incorporating its knowledge to develop DNA microsystems for biodiagnosis in order to help address current obstacles, such as disease detection and drug resistance. Here, current challenges in disease detection are presented along with standard methods for diagnosis. Then, a brief overview of DNA nanotechnology is introduced along with its main attractive features for constructing biodiagnostic microsystems. Lastly, suggested DNA-based microsystems are discussed through proof-of-concept demonstrations with improvement strategies for standard diagnostic approaches.

Inherited Renal Tubulopathies-Challenges and Controversies

Electrolyte homeostasis is maintained by the kidney through a complex transport function mostly performed by specialized proteins distributed along the renal tubules. Pathogenic variants in the genes encoding these proteins impair this function and have consequences on the whole organism. Establishing a genetic diagnosis in patients with renal tubular dysfunction is a challenging task given the genetic and phenotypic heterogeneity, functional characteristics of the genes involved and the number of yet unknown causes. Part of these difficulties can be overcome by gathering large patient cohorts and applying high-throughput sequencing techniques combined with experimental work to prove functional impact. This approach has led to the identification of a number of genes but also generated controversies about proper interpretation of variants. In this article, we will highlight these challenges and controversies.

History of DNA Helicases

Since the discovery of the DNA double helix, there has been a fascination in understanding the molecular mechanisms and cellular processes that account for: (i) the transmission of genetic information from one generation to the next and (ii) the remarkable stability of the genome. Nucleic acid biologists have endeavored to unravel the mysteries of DNA not only to understand the processes of DNA replication, repair, recombination, and transcription but to also characterize the underlying basis of genetic diseases characterized by chromosomal instability. Perhaps unexpectedly at first, DNA helicases have arisen as a key class of enzymes to study in this latter capacity. From the first discovery of ATP-dependent DNA unwinding enzymes in the mid 1970's to the burgeoning of helicase-dependent pathways found to be prevalent in all kingdoms of life, the story of scientific discovery in helicase research is rich and informative. Over four decades after their discovery, we take this opportunity to provide a history of DNA helicases. No doubt, many chapters are left to be written. Nonetheless, at this juncture we are privileged to share our perspective on the DNA helicase field - where it has been, its current state, and where it is headed.

Development of the variant calling algorithm, ADIScan, and its use to estimate discordant sequences between monozygotic twins

Calling variants from next-generation sequencing (NGS) data or discovering discordant sequences between two NGS data sets is challenging. We developed a computer algorithm, ADIScan1, to call variants by comparing the fractions of allelic reads in a tester to the universal reference genome. We then created ADIScan2 by modifying the algorithm to directly compare two sets of NGS data and predict discordant sequences between two testers. ADIScan1 detected >99.7% of variants called by GATK with an additional 724 393 SNVs. ADIScan2 identified ∼500 candidates of discordant sequences in each of two pairs of the monozygotic twins. About 200 of these candidates were included in the ∼2800 predicted by VarScan2. We verified 66 true discordant sequences among the candidates that ADIScan2 and VarScan2 exclusively predicted. ADIScan2 detected many discordant sequences overlooked by VarScan2 and Mutect, which specialize in detecting low frequency mutations in genetically heterogeneous cancerous tissues. Numbers of verified sequences alone were >5 times more than expected based on recently estimated mutation rates from whole genome sequences. Estimated post-zygotic mutation rates were 1.68 × 10⁻⁷ in this study. ADIScan1 and 2 would complement existing tools in screening causative mutations of diverse genetic diseases and comparing two sets of genome sequences, respectively.

PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data

BACKGROUND

Large-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy.

RESULTS

We present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility.

CONCLUSIONS

PanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org .

VarCards: an integrated genetic and clinical database for coding variants in the human genome

A growing number of genomic tools and databases were developed to facilitate the interpretation of genomic variants, particularly in coding regions. However, these tools are separately available in different online websites or databases, making it challenging for general clinicians, geneticists and biologists to obtain the first-hand information regarding some particular variants and genes of interest. Starting with coding regions and splice sties, we artificially generated all possible single nucleotide variants (n = 110 154 363) and cataloged all reported insertion and deletions (n = 1 223 370). We then annotated these variants with respect to functional consequences from more than 60 genomic data sources to develop a database, named VarCards (http://varcards.biols.ac.cn/), by which users can conveniently search, browse and annotate the variant- and gene-level implications of given variants, including the following information: (i) functional effects; (ii) functional consequences through different in silico algorithms; (iii) allele frequencies in different populations; (iv) disease- and phenotype-related knowledge; (v) general meaningful gene-level information; and (vi) drug-gene interactions. As a case study, we successfully employed VarCards in interpretation of de novo mutations in autism spectrum disorders. In conclusion, VarCards provides an intuitive interface of necessary information for researchers to prioritize candidate variations and genes.

KBG syndrome involving a single-nucleotide duplication in ANKRD11

KBG syndrome is a rare autosomal dominant genetic condition characterized by neurological involvement and distinct facial, hand, and skeletal features. More than 70 cases have been reported; however, it is likely that KBG syndrome is underdiagnosed because of lack of comprehensive characterization of the heterogeneous phenotypic features. We describe the clinical manifestations in a male currently 13 years of age, who exhibited symptoms including epilepsy, severe developmental delay, distinct facial features, and hand anomalies, without a positive genetic diagnosis. Subsequent exome sequencing identified a novel de novo heterozygous single base pair duplication (c.6015dupA) in ANKRD11, which was validated by Sanger sequencing. This single-nucleotide duplication is predicted to lead to a premature stop codon and loss of function in ANKRD11, thereby implicating it as contributing to the proband's symptoms and yielding a molecular diagnosis of KBG syndrome. Before molecular diagnosis, this syndrome was not recognized in the proband, as several key features of the disorder were mild and were not recognized by clinicians, further supporting the concept of variable expressivity in many disorders. Although a diagnosis of cerebral folate deficiency has also been given, its significance for the proband's condition remains uncertain.

MAPPIN: a method for annotating, predicting pathogenicity and mode of inheritance for nonsynonymous variants

Nonsynonymous single nucleotide variants (nsSNVs) constitute about 50% of known disease-causing mutations and understanding their functional impact is an area of active research. Existing algorithms predict pathogenicity of nsSNVs; however, they are unable to differentiate heterozygous, dominant disease-causing variants from heterozygous carrier variants that lead to disease only in the homozygous state. Here, we present MAPPIN (Method for Annotating, Predicting Pathogenicity, and mode of Inheritance for Nonsynonymous variants), a prediction method which utilizes a random forest algorithm to distinguish between nsSNVs with dominant, recessive, and benign effects. We apply MAPPIN to a set of Mendelian disease-causing mutations and accurately predict pathogenicity for all mutations. Furthermore, MAPPIN predicts mode of inheritance correctly for 70.3% of nsSNVs. MAPPIN also correctly predicts pathogenicity for 87.3% of mutations from the Deciphering Developmental Disorders Study with a 78.5% accuracy for mode of inheritance. When tested on a larger collection of mutations from the Human Gene Mutation Database, MAPPIN is able to significantly discriminate between mutations in known dominant and recessive genes. Finally, we demonstrate that MAPPIN outperforms CADD and Eigen in predicting disease inheritance modes for all validation datasets. To our knowledge, MAPPIN is the first nsSNV pathogenicity prediction algorithm that provides mode of inheritance predictions, adding another layer of information for variant prioritization.

Prevalence of Rare Genetic Variations and Their Implications in NGS-data Interpretation

Next-generation sequencing (NGS) technology has improved enough to discover mutations associated with genetic diseases. Our study evaluated the feasibility of targeted NGS as a primary screening tool to detect causal variants and subsequently predict genetic diseases. We performed parallel computations on 3.7-megabase-targeted regions to detect disease-causing mutations in 103 participants consisting of 81 patients and 22 controls. Data analysis of the participants took about 6 hours using local databases and 200 nodes of a supercomputer. All variants in the selected genes led on average to 3.6 putative diseases for each patient while variants restricted to disease-causing genes identified the correct disease. Notably, only 12% of predicted causal variants were recorded as causal mutations in public databases: 88% had no or insufficient records. In this study, most genetic diseases were caused by rare mutations and public records were inadequate. Most rare variants, however, were not associated with genetic diseases. These data implied that novel, rare variants should not be ignored but interpreted in conjunction with additional clinical data. This step is needed so appropriate advice can be given to primary doctors and parents, thus fulfilling the purpose of this method as a primary screen for rare genetic diseases.

Development of pathogenicity predictors specific for variants that do not comply with clinical guidelines for the use of computational evidence

Background

Strict guidelines delimit the use of computational information in the clinical setting, due to the still moderate accuracy of in silico tools. These guidelines indicate that several tools should always be used and that full coincidence between them is required if we want to consider their results as supporting evidence in medical decision processes. Application of this simple rule certainly decreases the error rate of in silico pathogenicity assignments. However, when predictors disagree this rule results in the rejection of potentially valuable information for a number of variants. In this work, we focus on these variants of the protein sequence and develop specific predictors to help improve the success rate of their annotation.

Results

We have used a set of 59,442 protein sequence variants (15,723 pathological and 43,719 neutral) from 228 proteins to identify those cases for which pathogenicity predictors disagree. We have repeated this process for all the possible combinations of five known methods (SIFT, PolyPhen-2, PON-P2, CADD and MutationTaster2). For each resulting subset we have trained a specific pathogenicity predictor. We find that these specific predictors are able to discriminate between neutral and pathogenic variants, with a success rate different from random. They tend to outperform the constitutive methods but this trend decreases as the performance of the constitutive predictor improves (e.g. with PON-P2 and PolyPhen-2). We also find that specific methods outperform standard consensus methods (Condel and CAROL).

Conclusion

Focusing development efforts on the case of variants for which known methods disagree we may obtain pathogenicity predictors with improved performances. Although we have not yet reached the success rate that allows the use of this computational evidence in a clinical setting, the simplicity of the approach indicates that more advanced methods may reach this goal in a close future.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3914-0) contains supplementary material, which is available to authorized users.

InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines

In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published updated standards and guidelines for the clinical interpretation of sequence variants with respect to human diseases on the basis of 28 criteria. However, variability between individual interpreters can be extensive because of reasons such as the different understandings of these guidelines and the lack of standard algorithms for implementing them, yet computational tools for semi-automated variant interpretation are not available. To address these problems, we propose a suite of methods for implementing these criteria and have developed a tool called InterVar to help human reviewers interpret the clinical significance of variants. InterVar can take a pre-annotated or VCF file as input and generate automated interpretation on 18 criteria. Furthermore, we have developed a companion web server, wInterVar, to enable user-friendly variant interpretation with an automated interpretation step and a manual adjustment step. These tools are especially useful for addressing severe congenital or very early-onset developmental disorders with high penetrance. Using results from a few published sequencing studies, we demonstrate the utility of InterVar in significantly reducing the time to interpret the clinical significance of sequence variants.

Novel human mutation and CRISPR/Cas genome-edited mice reveal the importance of C-terminal domain of MSX1 in tooth and palate development

Several mutations, located mainly in the MSX1 homeodomain, have been identified in non-syndromic tooth agenesis predominantly affecting premolars and third molars. We identified a novel frameshift mutation of the highly conserved C-terminal domain of MSX1, known as Msx homology domain 6 (MH6), in a Japanese family with non-syndromic tooth agenesis. To investigate the importance of MH6 in tooth development, Msx1 was targeted in mice with CRISPR/Cas system. Although heterozygous MH6 disruption did not alter craniofacial development, homozygous mice exhibited agenesis of lower incisors with or without cleft palate at E16.5. In addition, agenesis of the upper third molars and the lower second and third molars were observed in 4-week-old mutant mice. Although the upper second molars were present, they were abnormally small. These results suggest that the C-terminal domain of MSX1 is important for tooth and palate development, and demonstrate that that CRISPR/Cas system can be used as a tool to assess causality of human disorders in vivo and to study the importance of conserved domains in genes.

Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data

BACKGROUND

Detecting somatic mutations in whole exome sequencing data of cancer samples has become a popular approach for profiling cancer development, progression and chemotherapy resistance. Several studies have proposed software packages, filters and parametrizations. However, many research groups reported low concordance among different methods. We aimed to develop a pipeline which detects a wide range of single nucleotide mutations with high validation rates. We combined two standard tools - Genome Analysis Toolkit (GATK) and MuTect - to create the GATK-LODN method. As proof of principle, we applied our pipeline to exome sequencing data of hematological (Acute Myeloid and Acute Lymphoblastic Leukemias) and solid (Gastrointestinal Stromal Tumor and Lung Adenocarcinoma) tumors. We performed experiments on simulated data to test the sensitivity and specificity of our pipeline.

RESULTS

The software MuTect presented the highest validation rate (90 %) for mutation detection, but limited number of somatic mutations detected. The GATK detected a high number of mutations but with low specificity. The GATK-LODN increased the performance of the GATK variant detection (from 5 of 14 to 3 of 4 confirmed variants), while preserving mutations not detected by MuTect. However, GATK-LODN filtered more variants in the hematological samples than in the solid tumors. Experiments in simulated data demonstrated that GATK-LODN increased both specificity and sensitivity of GATK results.

CONCLUSION

We presented a pipeline that detects a wide range of somatic single nucleotide variants, with good validation rates, from exome sequencing data of cancer samples. We also showed the advantage of combining standard algorithms to create the GATK-LODN method, that increased specificity and sensitivity of GATK results. This pipeline can be helpful in discovery studies aimed to profile the somatic mutational landscape of cancer genomes.

Unsolved challenges of clinical whole-exome sequencing: a systematic literature review of end-users' views

BACKGROUND

Whole-exome sequencing (WES) consists in the capture, sequencing and analysis of all exons in the human genome. Originally developed in the research context, this technology is now increasingly used clinically to inform patient care. The implementation of WES into healthcare poses significant organizational, regulatory, and ethical hurdles, which are widely discussed in the literature.

METHODS

In order to inform future policy decisions on the integration of WES into standard clinical practice, we performed a systematic literature review to identify the most important challenges directly reported by technology users.

RESULTS

Out of 2094 articles, we selected and analyzed 147 which reported a total of 23 different challenges linked to the production, analysis, reporting and sharing of patients' WES data. Interpretation of variants of unknown significance, incidental findings, and the cost and reimbursement of WES-based tests were the most reported challenges across all articles.

CONCLUSIONS

WES is already used in the clinical setting, and may soon be considered the standard of care for specific medical conditions. Yet, technology users are calling for certain standards and guidelines to be published before this technology replaces more focused approaches such as gene panels sequencing. In addition, a number of infrastructural adjustments will have to be made for clinics to store, process and analyze the amounts of data produced by WES.

DIDA: A curated and annotated digenic diseases database

DIDA (DIgenic diseases DAtabase) is a novel database that provides for the first time detailed information on genes and associated genetic variants involved in digenic diseases, the simplest form of oligogenic inheritance. The database is accessible via http://dida.ibsquare.be and currently includes 213 digenic combinations involved in 44 different digenic diseases. These combinations are composed of 364 distinct variants, which are distributed over 136 distinct genes. The web interface provides browsing and search functionalities, as well as documentation and help pages, general database statistics and references to the original publications from which the data have been collected. The possibility to submit novel digenic data to DIDA is also provided. Creating this new repository was essential as current databases do not allow one to retrieve detailed records regarding digenic combinations. Genes, variants, diseases and digenic combinations in DIDA are annotated with manually curated information and information mined from other online resources. Next to providing a unique resource for the development of new analysis methods, DIDA gives clinical and molecular geneticists a tool to find the most comprehensive information on the digenic nature of their diseases of interest.

Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR

Recent developments in sequencing techniques have enabled rapid and high-throughput generation of sequence data, democratizing the ability to compile information on large amounts of genetic variations in individual laboratories. However, there is a growing gap between the generation of raw sequencing data and the extraction of meaningful biological information. Here, we describe a protocol to use the ANNOVAR (ANNOtate VARiation) software to facilitate fast and easy variant annotations, including gene-based, region-based and filter-based annotations on a variant call format (VCF) file generated from human genomes. We further describe a protocol for gene-based annotation of a newly sequenced nonhuman species. Finally, we describe how to use a user-friendly and easily accessible web server called wANNOVAR to prioritize candidate genes for a Mendelian disease. The variant annotation protocols take 5-30 min of computer time, depending on the size of the variant file, and 5-10 min of hands-on time. In summary, through the command-line tool and the web server, these protocols provide a convenient means to analyze genetic variants generated in humans and other species.

Phenolyzer: phenotype-based prioritization of candidate genes for human diseases

Prior biological knowledge and phenotype information may help to identify disease genes from human whole-genome and whole-exome sequencing studies. We developed Phenolyzer (http://phenolyzer.usc.edu), a tool that uses prior information to implicate genes involved in diseases. Phenolyzer exhibits superior performance over competing methods for prioritizing Mendelian and complex disease genes, based on disease or phenotype terms entered as free text.

Genomics in the clinic: ethical and policy challenges in clinical next-generation sequencing programs at early adopter USA institutions

Next-generation sequencing (NGS) technologies are poised to revolutionize clinical diagnosis and treatment, but raise significant ethical and policy challenges. This review examines NGS program challenges through a synthesis of published literature, website and conference presentation content, and interviews at early-adopting institutions in the USA. Institutions are proactively addressing policy challenges related to the management and technical aspects of program development. However, ethical challenges related to patient-related aspects have not been fully addressed. These complex challenges present opportunities to develop comprehensive and standardized regulations across programs. Understanding the strengths, weaknesses and current practices of evolving NGS program approaches are important considerations for institutions developing NGS services, policymakers regulating or funding NGS programs and physicians and patients considering NGS services.

Genomic medicine and risk prediction across the disease spectrum

Genomic medicine is based on the knowledge that virtually every medical condition, disease susceptibility or response to treatment is caused, regulated or influenced by genes. Genetic testing may therefore add value across the disease spectrum, ranging from single-gene disorders with a Mendelian inheritance pattern to complex multi-factorial diseases. The critical factors for genomic risk prediction are to determine: (1) where the genomic footprint of a particular susceptibility or dysfunction resides within this continuum, and (2) to what extent the genetic determinants are modified by environmental exposures. Regarding the small subset of highly penetrant monogenic disorders, a positive family history and early disease onset are mostly sufficient to determine the appropriateness of genetic testing in the index case and to inform pre-symptomatic diagnosis in at-risk family members. In more prevalent polygenic non-communicable diseases (NCDs), the use of appropriate eligibility criteria is required to ensure a balance between benefit and risk. An additional screening step may therefore be necessary to identify individuals most likely to benefit from genetic testing. This need provided the stimulus for the development of a pathology-supported genetic testing (PSGT) service as a new model for the translational implementation of genomic medicine in clinical practice. PSGT is linked to the establishment of a research database proven to be an invaluable resource for the validation of novel and previously described gene-disease associations replicated in the South African population for a broad range of NCDs associated with increased cardio-metabolic risk. The clinical importance of inquiry concerning family history in determining eligibility for personalized genotyping was supported beyond its current limited role in diagnosing or screening for monogenic subtypes of NCDs. With the recent introduction of advanced microarray-based breast cancer subtyping, genetic testing has extended beyond the genome of the host to also include tumor gene expression profiling for chemotherapy selection. The decreasing cost of next generation sequencing over recent years, together with improvement of both laboratory and computational protocols, enables the mapping of rare genetic disorders and discovery of shared genetic risk factors as novel therapeutic targets across diagnostic boundaries. This article reviews the challenges, successes, increasing inter-disciplinary integration and evolving strategies for extending PSGT towards exome and whole genome sequencing (WGS) within a dynamic framework. Specific points of overlap are highlighted between the application of PSGT and exome or WGS, as the next logical step in genetically uncharacterized patients for whom a particular disease pattern and/or therapeutic failure are not adequately accounted for during the PSGT pre-screen. Discrepancies between different next generation sequencing platforms and low concordance among variant-calling pipelines caution against offering exome or WGS as a stand-alone diagnostic approach. The public reference human genome sequence (hg19) contains minor alleles at more than 1 million loci and variant calling using an advanced major allele reference genome sequence is crucial to ensure data integrity. Understanding that genomic risk prediction is not deterministic but rather probabilistic provides the opportunity for disease prevention and targeted treatment in a way that is unique to each individual patient.

Symptom-driven idiopathic disease gene identification

Purpose

Rare genetic variants are the major cause of Mendelian disorders, yet only half of described genetic diseases have been causally linked to a gene. In addition, the total number of rare genetic diseases is projected to be far greater than that of those already described. Whole-genome sequencing of patients with subsequent genetic and functional analysis is a powerful way to describe these gene anomalies. However, this approach results in tens to hundreds of candidate disease-causative genes, and the identification of additional individuals suffering from the same disorder can be difficult because of rarity and phenotypic heterogeneity.

Methods

We describe a genetic network–based method to rank candidate genes identified in family-based sequencing studies, termed phenotype informed network (PIN) ranking. Furthermore, we present a case study as an extension of the PIN ranking method in which disease symptoms drive the network ranking and identification of the disease-causative gene.

Results

We demonstrate, through simulation, that our method is capable of identifying the correct disease-causative gene in a majority of cases. PIN-rank is available at https://genomics.scripps.edu/pin-rank/.

Conclusion

We have developed a method to prioritize candidate disease-causative genes based on symptoms that would be useful for both the prioritization of candidates and the identification of additional subjects.

Accounting for uncertainty in DNA sequencing data

Science is defined in part by an honest exposition of the uncertainties that arise in measurements and propagate through calculations and inferences, so that the reliabilities of its conclusions are made apparent. The recent rapid development of high-throughput DNA sequencing technologies has dramatically increased the number of measurements made at the biochemical and molecular level. These data come from many different DNA-sequencing technologies, each with their own platform-specific errors and biases, which vary widely. Several statistical studies have tried to measure error rates for basic determinations, but there are no general schemes to project these uncertainties so as to assess the surety of the conclusions drawn about genetic, epigenetic, and more general biological questions. We review here the state of uncertainty quantification in DNA sequencing applications, describe sources of error, and propose methods that can be used for accounting and propagating these errors and their uncertainties through subsequent calculations.

Evolutionary and structural annotation of disease-associated mutations in human aminoacyl-tRNA synthetases

Background

Mutation(s) in proteins are a natural byproduct of evolution but can also cause serious diseases. Aminoacyl-tRNA synthetases (aaRSs) are indispensable components of all cellular protein translational machineries, and in humans they drive translation in both cytoplasm and mitochondria. Mutations in aaRSs have been implicated in a plethora of diseases including neurological conditions, metabolic disorders and cancer.

Results

We have developed an algorithmic approach for genome-wide analyses of sequence substitutions that combines evolutionary, structural and functional information. This pipeline enabled us to super-annotate human aaRS mutations and analyze their linkage to health disorders. Our data suggest that in some but not all cases, aaRS mutations occur in functional and structural sectors where they can manifest their pathological effects by altering enzyme activity or causing structural instability. Further, mutations appear in both solvent exposed and buried regions of aaRSs indicating that these alterations could lead to dysfunctional enzymes resulting in abnormal protein translation routines by affecting inter-molecular interactions or by disruption of non-bonded interactions. Overall, the prevalence of mutations is much higher in mitochondrial aaRSs, and the two most often mutated aaRSs are mitochondrial glutamyl-tRNA synthetase and dual localized glycyl-tRNA synthetase. Out of 63 mutations annotated in this work, only 12 (~20%) were observed in regions that could directly affect aminoacylation activity via either binding to ATP/amino-acid, tRNA or by involvement in dimerization. Mutations in structural cores or at potential biomolecular interfaces account for ~55% mutations while remaining mutations (~25%) remain structurally un-annotated.

Conclusion

This work provides a comprehensive structural framework within which most defective human aaRSs have been structurally analyzed. The methodology described here could be employed to annotate mutations in other protein families in a high-throughput manner.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1063) contains supplementary material, which is available to authorized users.

"Genotype-first" approaches on a curious case of idiopathic progressive cognitive decline

BACKGROUND

In developing countries, many cases with rare neurological diseases remain undiagnosed due to limited diagnostic experience. We encountered a case in China where two siblings both began to develop idiopathic progressive cognitive decline starting from age six, and were suspected to have an undiagnosed neurological disease.

METHODS

Initial clinical assessments included review of medical history, comprehensive physical examination, genetic testing for metabolic diseases, blood tests and brain imaging. We performed exome sequencing with Agilent SureSelect exon capture and Illumina HiSeq2000 platform, followed by variant annotation and selection of rare, shared mutations that fit a recessive model of inheritance. To assess functional impacts of candidate variants, we performed extensive biochemical tests in blood and urine, and examined their possible roles by protein structure modeling.

RESULTS

Exome sequencing identified NAGLU as the most likely candidate gene with compound heterozygous mutations (chr17:40695717C > T and chr17:40693129A > G in hg19 coordinate), which were documented to be pathogenic. Sanger sequencing confirmed the recessive patterns of inheritance, leading to a genetic diagnosis of Sanfilippo syndrome (mucopolysaccharidosis IIIB). Biochemical tests confirmed the complete loss of activity of alpha-N-acetylglucosaminidase (encoded by NAGLU) in blood, as well as significantly elevated dermatan sulfate and heparan sulfate in urine. Structure modeling revealed the mechanism on how the two variants affect protein structural stability.

CONCLUSIONS

Successful diagnosis of a rare genetic disorder with an atypical phenotypic presentation confirmed that such "genotype-first" approaches can particularly succeed in areas of the world with insufficient medical genetics expertise and with cost-prohibitive in-depth phenotyping.

Reducing INDEL calling errors in whole genome and exome sequencing data

Background

INDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts.

Methods

We characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low-quality INDELs (7% vs. 51%).

Results

Simulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (53%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (84% vs. 57%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data.

Conclusions

Overall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (for example, capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-014-0089-z) contains supplementary material, which is available to authorized users.

The humankind genome: from genetic diversity to the origin of human diseases

Genome-wide association studies have failed to establish common variant risk for the majority of common human diseases. The underlying reasons for this failure are explained by recent studies of resequencing and comparison of over 1200 human genomes and 10 000 exomes, together with the delineation of DNA methylation patterns (epigenome) and full characterization of coding and noncoding RNAs (transcriptome) being transcribed. These studies have provided the most comprehensive catalogues of functional elements and genetic variants that are now available for global integrative analysis and experimental validation in prospective cohort studies. With these datasets, researchers will have unparalleled opportunities for the alignment, mining, and testing of hypotheses for the roles of specific genetic variants, including copy number variations, single nucleotide polymorphisms, and indels as the cause of specific phenotypes and diseases. Through the use of next-generation sequencing technologies for genotyping and standardized ontological annotation to systematically analyze the effects of genomic variation on humans and model organism phenotypes, we will be able to find candidate genes and new clues for disease's etiology and treatment. This article describes essential concepts in genetics and genomic technologies as well as the emerging computational framework to comprehensively search websites and platforms available for the analysis and interpretation of genomic data.

Disease variants in genomes of 44 centenarians

To identify previously reported disease mutations that are compatible with extraordinary longevity, we screened the coding regions of the genomes of 44 Ashkenazi Jewish centenarians. Individual genome sequences were generated with 30× coverage on the Illumina HiSeq 2000 and single-nucleotide variants were called with the genome analysis toolkit (GATK). We identified 130 coding variants that were annotated as “pathogenic” or “likely pathogenic” based on the ClinVar database and that are infrequent in the general population. These variants were previously reported to cause a wide range of degenerative, neoplastic, and cardiac diseases with autosomal dominant, autosomal recessive, and X-linked inheritance. Several of these variants are located in genes that harbor actionable incidental findings, according to the recommendations of the American College of Medical Genetics. In addition, we found risk variants for late-onset neurodegenerative diseases, such as the APOE ε4 allele that was even present in a homozygous state in one centenarian who did not develop Alzheimer's disease. Our data demonstrate that the incidental finding of certain reported disease variants in an individual genome may not preclude an extraordinarily long life. When the observed variants are encountered in the context of clinical sequencing, it is thus important to exercise caution in justifying clinical decisions. In genome sequences of 44 Ashkenazi centenarians, we identified many coding variants that were annotated as “pathogenic” or “likely pathogenic” based on the ClinVar database. Our data demonstrate that the incidental finding of certain reported disease variants in an individual genome may not preclude an extraordinarily long life. When the observed variants are encountered in the context of clinical sequencing, it is thus important to exercise caution in justifying clinical decisions.

Identification of a novel C-terminal extension mutation in EPHA2 in a family affected with congenital cataract

PURPOSE

Congenital cataracts occur in 3-4 per 10,000 live births and account for 5% to 20% of pediatric blindness worldwide. With more than 37 genes known to be associated with isolated congenital cataract, whole exome sequencing (WES) was recently introduced as an efficient method for screening all known factors.

METHODS

Whole exome analysis in two members of a four-generation pedigree affected with dominant congenital cataract and glaucoma was performed by WES; co-segregation analysis of identified variants in all pedigree members was completed by Sanger sequencing.

RESULTS

Analysis of the WES data identified a novel pathogenic variant in EPHA2, c.2925dupC, p.(Ile976Hisfs*37), that demonstrated complete cosegregation with the phenotype in the pedigree. The mutation occurs in the final amino acid before the stop codon of the normal EPHA2 protein and is predicted to produce a mutant protein with an erroneous C-terminal extension of 35 amino acids. Nine other families have been previously reported with dominant congenital/juvenile cataracts and mutations in EPHA2. Two additional likely loss-of-function variants in genes known to cause dominant congenital cataract were considered and excluded based on control data and cosegregation analysis: a nonsense variant in CYRBB3, c.547G>T, p.(Glu183*), and a splicing variant in CRYBA2, c.446+1G>A.

CONCLUSIONS

Identification of a novel pathogenic EPHA2 allele further implicates this gene in congenital cataract. This is only the second EPHA2 mutation that specifically affects the most C-terminal PSD95/Dlg/ZO1 (PDZ)-binding motif and the third pathogenic allele associated with an erroneous C-terminal extension beyond the normal stop codon.

Next-generation sequencing applied to rare diseases genomics

Genomics has revolutionized the study of rare diseases. In this review, we overview the latest technological development, rare disease discoveries, implementation obstacles and bioethical challenges. First, we discuss the technology of genome and exome sequencing, including the different next-generation platforms and exome enrichment technologies. Second, we survey the pioneering centers and discoveries for rare diseases, including few of the research institutions that have contributed to the field, as well as an overview survey of different types of rare diseases that have had new discoveries due to next-generation sequencing. Third, we discuss the obstacles and challenges that allow for clinical implementation, including returning of results, informed consent and privacy. Last, we discuss possible outlook as clinical genomics receives wider adoption, as third-generation sequencing is coming onto the horizon, and some needs in informatics and software to further advance the field.

FamAnn: an automated variant annotation pipeline to facilitate target discovery for family-based sequencing studies

FamAnn is an automated variant annotation pipeline designed for facilitating target discovery for family-based sequencing studies. It can apply a different inheritance pattern or a de novo mutations discovery model to each family and select single nucleotide variants and small insertions and deletions segregating in each family or shared by multiple families. It also provides a variety of variant annotations and retains and annotates all transcripts hit by a single variant. Excel-compatible outputs including all annotated variants segregating in each family or shared by multiple families will be provided for users to prioritize variants based on their customized thresholds. A list of genes that harbor the segregating variants will be provided as well for possible pathway/network analyses. FamAnn uses the de facto community standard Variant Call Format as the input format and can be applied to whole exome, genome or targeted resequencing data.

AVAILABILITY

https://sites.google.com/site/famannotation/home CONTACT: jianchaoyao@gmail.com, kelvinzhang@mednet.ucla.edu, mccombie@cshl.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Single-nucleotide evolution quantifies the importance of each site along the structure of mitochondrial carriers

Mitochondrial carriers are membrane-embedded proteins consisting of a tripartite structure, a three-fold pseudo-symmetry, related sequences, and similar folding whose main function is to catalyze the transport of various metabolites, nucleotides, and coenzymes across the inner mitochondrial membrane. In this study, the evolutionary rate in vertebrates was screened at each of the approximately 50,000 nucleotides corresponding to the amino acids of the 53 human mitochondrial carriers. Using this information as a starting point, a scoring system was developed to quantify the evolutionary pressure acting on each site of the common mitochondrial carrier structure and estimate its functional or structural relevance. The degree of evolutionary selection varied greatly among all sites, but it was highly similar among the three symmetric positions in the tripartite structure, known as symmetry-related sites or triplets, suggesting that each triplet constitutes an evolutionary unit. Based on evolutionary selection, 111 structural sites (37 triplets) were found to be important. These sites play a key role in structure/function of mitochondrial carriers and are involved in either conformational changes (sites of the gates, proline-glycine levels, and aromatic belts) or in binding and specificity of the transported substrates (sites of the substrate-binding area in between the two gates). Furthermore, the evolutionary pressure analysis revealed that the matrix short helix sites underwent different degrees of selection with high inter-paralog variability. Evidence is presented that these sites form a new sequence motif in a subset of mitochondrial carriers, including the ADP/ATP translocator, and play a regulatory function by interacting with ligands and/or proteins of the mitochondrial matrix.

A primer for disease gene prioritization using next-generation sequencing data

High-throughput next-generation sequencing (NGS) technology produces a tremendous amount of raw sequence data. The challenges for researchers are to process the raw data, to map the sequences to genome, to discover variants that are different from the reference genome, and to prioritize/rank the variants for the question of interest. The recent development of many computational algorithms and programs has vastly improved the ability to translate sequence data into valuable information for disease gene identification. However, the NGS data analysis is complex and could be overwhelming for researchers who are not familiar with the process. Here, we outline the analysis pipeline and describe some of the most commonly used principles and tools for analyzing NGS data for disease gene identification.

Integrating precision medicine in the study and clinical treatment of a severely mentally ill person

Background. In recent years, there has been an explosion in the number of technical and medical diagnostic platforms being developed. This has greatly improved our ability to more accurately, and more comprehensively, explore and characterize human biological systems on the individual level. Large quantities of biomedical data are now being generated and archived in many separate research and clinical activities, but there exists a paucity of studies that integrate the areas of clinical neuropsychiatry, personal genomics and brain-machine interfaces. Methods. A single person with severe mental illness was implanted with the Medtronic Reclaim(®) Deep Brain Stimulation (DBS) Therapy device for Obsessive Compulsive Disorder (OCD), targeting his nucleus accumbens/anterior limb of the internal capsule. Programming of the device and psychiatric assessments occurred in an outpatient setting for over two years. His genome was sequenced and variants were detected in the Illumina Whole Genome Sequencing Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. Results. We report here the detailed phenotypic characterization, clinical-grade whole genome sequencing (WGS), and two-year outcome of a man with severe OCD treated with DBS. Since implantation, this man has reported steady improvement, highlighted by a steady decline in his Yale-Brown Obsessive Compulsive Scale (YBOCS) score from ∼38 to a score of ∼25. A rechargeable Activa RC neurostimulator battery has been of major benefit in terms of facilitating a degree of stability and control over the stimulation. His psychiatric symptoms reliably worsen within hours of the battery becoming depleted, thus providing confirmatory evidence for the efficacy of DBS for OCD in this person. WGS revealed that he is a heterozygote for the p.Val66Met variant in BDNF, encoding a member of the nerve growth factor family, and which has been found to predispose carriers to various psychiatric illnesses. He carries the p.Glu429Ala allele in methylenetetrahydrofolate reductase (MTHFR) and the p.Asp7Asn allele in ChAT, encoding choline O-acetyltransferase, with both alleles having been shown to confer an elevated susceptibility to psychoses. We have found thousands of other variants in his genome, including pharmacogenetic and copy number variants. This information has been archived and offered to this person alongside the clinical sequencing data, so that he and others can re-analyze his genome for years to come. Conclusions. To our knowledge, this is the first study in the clinical neurosciences that integrates detailed neuropsychiatric phenotyping, deep brain stimulation for OCD and clinical-grade WGS with management of genetic results in the medical treatment of one person with severe mental illness. We offer this as an example of precision medicine in neuropsychiatry including brain-implantable devices and genomics-guided preventive health care.

Whole-genome DNA/RNA sequencing identifies truncating mutations in RBCK1 in a novel Mendelian disease with neuromuscular and cardiac involvement

Background

Whole-exome sequencing has identified the causes of several Mendelian diseases by analyzing multiple unrelated cases, but it is more challenging to resolve the cause of extremely rare and suspected Mendelian diseases from individual families. We identified a family quartet with two children, both affected with a previously unreported disease, characterized by progressive muscular weakness and cardiomyopathy, with normal intelligence. During the course of the study, we identified one additional unrelated patient with a comparable phenotype.

Methods

We performed whole-genome sequencing (Complete Genomics platform), whole-exome sequencing (Agilent SureSelect exon capture and Illumina Genome Analyzer II platform), SNP genotyping (Illumina HumanHap550 SNP array) and Sanger sequencing on blood samples, as well as RNA-Seq (Illumina HiSeq platform) on transformed lymphoblastoid cell lines.

Results

From whole-genome sequence data, we identified RBCK1, a gene encoding an E3 ubiquitin-protein ligase, as the most likely candidate gene, with two protein-truncating mutations in probands in the first family. However, exome data failed to nominate RBCK1 as a candidate gene, due to poor regional coverage. Sanger sequencing identified a private homozygous splice variant in RBCK1 in the proband in the second family, yet SNP genotyping revealed a 1.2Mb copy-neutral region of homozygosity covering RBCK1. RNA-Seq confirmed aberrant splicing of RBCK1 transcripts, resulting in truncated protein products.

Conclusions

While the exact mechanism by which these mutations cause disease is unknown, our study represents an example of how the combined use of whole-genome DNA and RNA sequencing can identify a disease-predisposing gene for a novel and extremely rare Mendelian disease.

Exome sequencing reveals new causal mutations in children with epileptic encephalopathies

PURPOSE

The management of epilepsy in children is particularly challenging when seizures are resistant to antiepileptic medications, or undergo many changes in seizure type over time, or have comorbid cognitive, behavioral, or motor deficits. Despite efforts to classify such epilepsies based on clinical and electroencephalographic criteria, many children never receive a definitive etiologic diagnosis. Whole exome sequencing (WES) is proving to be a highly effective method for identifying de novo variants that cause neurologic disorders, especially those associated with abnormal brain development. Herein we explore the utility of WES for identifying candidate causal de novo variants in a cohort of children with heterogeneous sporadic epilepsies without etiologic diagnoses.

METHODS

We performed WES (mean coverage approximately 40×) on 10 trios comprised of unaffected parents and a child with sporadic epilepsy characterized by difficult-to-control seizures and some combination of developmental delay, epileptic encephalopathy, autistic features, cognitive impairment, or motor deficits. Sequence processing and variant calling were performed using standard bioinformatics tools. A custom filtering system was used to prioritize de novo variants of possible functional significance for validation by Sanger sequencing.

KEY FINDINGS

In 9 of 10 probands, we identified one or more de novo variants predicted to alter protein function, for a total of 15. Four probands had de novo mutations in genes previously shown to harbor heterozygous mutations in patients with severe, early onset epilepsies (two in SCN1A, and one each in CDKL5 and EEF1A2). In three children, the de novo variants were in genes with functional roles that are plausibly relevant to epilepsy (KCNH5, CLCN4, and ARHGEF15). The variant in KCNH5 alters one of the highly conserved arginine residues of the voltage sensor of the encoded voltage-gated potassium channel. In vitro analyses using cell-based assays revealed that the CLCN4 mutation greatly impaired ion transport by the ClC-4 2Cl(-) /H(+) -exchanger and that the mutation in ARHGEF15 reduced GEF exchange activity of the gene product, Ephexin5, by about 50%. Of interest, these seven probands all presented with seizures within the first 6 months of life, and six of these have intractable seizures.

SIGNIFICANCE

The finding that 7 of 10 children carried de novo mutations in genes of known or plausible clinical significance to neuronal excitability suggests that WES will be of use for the molecular genetic diagnosis of sporadic epilepsies in children, especially when seizures are of early onset and difficult to control.