Capturing all disease-causing mutations for clinical and research use: Toward an effortless system for the Human Variome Project

International Porphyria Molecular Diagnostic Collaborative: an evidence-based database of verified pathogenic and benign variants for the porphyrias

With the advent of precision and genomic medicine, a critical issue is whether a disease gene variant is pathogenic or benign. Such is the case for the three autosomal dominant acute hepatic porphyrias (AHPs), including acute intermittent porphyria, hereditary coproporphyria, and variegate porphyria, each resulting from the half-normal enzymatic activities of hydroxymethylbilane synthase, coproporphyrinogen oxidase, and protoporphyrinogen oxidase, respectively. To date, there is no public database that documents the likely pathogenicity of variants causing the porphyrias, and more specifically, the AHPs with biochemically and clinically verified information. Therefore, an international collaborative with the European Porphyria Network and the National Institutes of Health/National Center for Advancing Translational Sciences/National Institute of Diabetes and Digestive and Kidney Diseases (NIH/NCATS/NIDDK)-sponsored Porphyrias Consortium of porphyria diagnostic experts is establishing an online database that will collate biochemical and clinical evidence verifying the pathogenicity of the published and newly identified variants in the AHP-causing genes. The overall goal of the International Porphyria Molecular Diagnostic Collaborative is to determine the pathogenic and benign variants for all eight porphyrias. Here we describe the overall objectives and the initial efforts to validate pathogenic and benign variants in the respective heme biosynthetic genes causing the AHPs.

CFTR-France, a national relational patient database for sharing genetic and phenotypic data associated with rare CFTR variants

Most of the 2,000 variants identified in the CFTR (cystic fibrosis transmembrane regulator) gene are rare or private. Their interpretation is hampered by the lack of available data and resources, making patient care and genetic counseling challenging. We developed a patient-based database dedicated to the annotations of rare CFTR variants in the context of their cis- and trans-allelic combinations. Based on almost 30 years of experience of CFTR testing, CFTR-France (https://cftr.iurc.montp.inserm.fr/cftr) currently compiles 16,819 variant records from 4,615 individuals with cystic fibrosis (CF) or CFTR-RD (related disorders), fetuses with ultrasound bowel anomalies, newborns awaiting clinical diagnosis, and asymptomatic compound heterozygotes. For each of the 736 different variants reported in the database, patient characteristics and genetic information (other variations in cis or in trans) have been thoroughly checked by a dedicated curator. Combining updated clinical, epidemiological, in silico, or in vitro functional data helps to the interpretation of unclassified and the reassessment of misclassified variants. This comprehensive CFTR database is now an invaluable tool for diagnostic laboratories gathering information on rare variants, especially in the context of genetic counseling, prenatal and preimplantation genetic diagnosis. CFTR-France is thus highly complementary to the international database CFTR2 focused so far on the most common CF-causing alleles.

Public variant databases: liability?

Public variant databases support the curation, clinical interpretation, and sharing of genomic data, thus reducing harmful errors or delays in diagnosis. As variant databases are increasingly relied on in the clinical context, there is concern that negligent variant interpretation will harm patients and attract liability. This article explores the evolving legal duties of laboratories, public variant databases, and physicians in clinical genomics and recommends a governance framework for databases to promote responsible data sharing.

Genet Med advance online publication 15 December 2016

Establishment of an international database for genetic variants in esophageal cancer

The establishment of a database has been suggested in order to collect, organize, and distribute genetic information about esophageal cancer. The World Organization for Specialized Studies on Diseases of the Esophagus and the Human Variome Project will be in charge of a central database of information about esophageal cancer-related variations from publications, databases, and laboratories; in addition to genetic details, clinical parameters will also be included. The aim will be to get all the central players in research, clinical, and commercial laboratories to contribute. The database will follow established recommendations and guidelines. The database will require a team of dedicated curators with different backgrounds. Numerous layers of systematics will be applied to facilitate computational analyses. The data items will be extensively integrated with other information sources. The database will be distributed as open access to ensure exchange of the data with other databases. Variations will be reported in relation to reference sequences on three levels--DNA, RNA, and protein-whenever applicable. In the first phase, the database will concentrate on genetic variations including both somatic and germline variations for susceptibility genes. Additional types of information can be integrated at a later stage.

Human Variome Project Quality Assessment Criteria for Variation Databases

Numerous databases containing information about DNA, RNA, and protein variations are available. Gene-specific variant databases (locus-specific variation databases, LSDBs) are typically curated and maintained for single genes or groups of genes for a certain disease(s). These databases are widely considered as the most reliable information source for a particular gene/protein/disease, but it should also be made clear they may have widely varying contents, infrastructure, and quality. Quality is very important to evaluate because these databases may affect health decision-making, research, and clinical practice. The Human Variome Project (HVP) established a Working Group for Variant Database Quality Assessment. The basic principle was to develop a simple system that nevertheless provides a good overview of the quality of a database. The HVP quality evaluation criteria that resulted are divided into four main components: data quality, technical quality, accessibility, and timeliness. This report elaborates on the developed quality criteria and how implementation of the quality scheme can be achieved. Examples are provided for the current status of the quality items in two different databases, BTKbase, an LSDB, and ClinVar, a central archive of submissions about variants and their clinical significance.

Medical genomics: The intricate path from genetic variant identification to clinical interpretation

The field of medical genomics involves translating high throughput genetic methods to the clinic, in order to improve diagnostic efficiency and treatment decision making. Technical questions related to sample enrichment, sequencing methodologies and variant identification and calling algorithms, still need careful investigation in order to validate the analytical step of next generation sequencing techniques for clinical applications. However, the main foreseeable challenge will be interpreting the clinical significance of the variants observed in a given patient, as well as their significance for family members and for other patients.

Every step in the variant interpretation process has limitations and difficulties, and its quote of contribution to false positive and false negative results. There is no single piece of evidence enough on its own to make firm conclusions on the pathogenicity and disease causality of a given variant.

A plethora of automated analysis software tools is being developed that will enhance efficiency and accuracy. However a risk of misinterpretation could derive from biased biorepository content, facilitated by annotation of variant functional consequences using previous datasets stored in the same or linked repositories. In order to improve variant interpretation and avoid an exponential accumulation of confounding noise in the medical literature, the use of terms in a standard way should be sought and requested when reporting genetic variants and their consequences. Generally, stepwise and linear interpretation processes are likely to overrate some pieces of evidence while underscoring others. Algorithms are needed that allow a multidimensional, parallel analysis of diverse lines of evidence to be carried out by expert teams for specific genes, cellular pathways or disorders.

DNA variant databases: current state and future directions

In this chapter we aim to provide an overview of DNA variant databases, commonly known as Locus-Specific Databases (LSDBs), or Gene-Disease Specific Databases (GDSDBs), but the term variant database will be used for simplicity. We restrict this overview to germ-line variants, particularly as related to Mendelian diseases, which are diseases caused by a variant in a single gene. Common difficulties associated with variant databases and some proposed solutions are reviewed. Finally, systems where technical solutions have been implemented are discussed. This work will be useful for anyone wishing to establish their own variant database, or to learn about the global picture of variant databases, and the technical challenges to be overcome.

Next-generation sequencing diagnostics for neurological diseases/disorders: from a clinical perspective

Neurological diseases encompass a broad, heterogeneous group of disorders ranging from pediatric neurodevelopmental diseases to late-onset neurodegenerative diseases, most of which are poorly understood and few of which are curable. Most of these diseases have a genetic basis and thus are expected to be amenable to genetic or genomic analysis by next-generation sequencing (NGS). While the advancement of contemporary technologies (such as NGS) is exciting, translating this tool into actual benefit for patients and clinicians can be challenging. In a clinical setting, a sequencing test that is fast, non-invasive, cheap and with perfect specificity would be ideal. However, in practice, there are several hurdles and caveats to consider even before a NGS diagnostic testing can be optimally applied. Proper definition of clinical phenotype, selection of the most appropriate subjects and the clinical setting, optimization of both sensitivity and specificity of the test, evaluation of the availability of the infrastructure and expertise, and consideration of economic, ethical and legal issues are vital in the final application of NGS diagnostic screening in the clinics.

Deep phenotyping for precision medicine

In medical contexts, the word "phenotype" is used to refer to some deviation from normal morphology, physiology, or behavior. The analysis of phenotype plays a key role in clinical practice and medical research, and yet phenotypic descriptions in clinical notes and medical publications are often imprecise. Deep phenotyping can be defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The emerging field of precision medicine aims to provide the best available care for each patient based on stratification into disease subclasses with a common biological basis of disease. The comprehensive discovery of such subclasses, as well as the translation of this knowledge into clinical care, will depend critically upon computational resources to capture, store, and exchange phenotypic data, and upon sophisticated algorithms to integrate it with genomic variation, omics profiles, and other clinical information. This special issue of Human Mutation offers a number of articles describing computational solutions for current challenges in deep phenotyping, including semantic and technical standards for phenotype and disease data, digital imaging for facial phenotype analysis, model organism phenotypes, and databases for correlating phenotypes with genomic variation.

Microattribution and nanopublication as means to incentivize the placement of human genome variation data into the public domain

The advances in bioinformatics required to annotate human genomic variants and to place them in public data repositories have not kept pace with their discovery. Moreover, a law of diminishing returns has begun to operate both in terms of data publication and submission. Although the continued deposition of such data in the public domain is essential to maximize both their scientific and clinical utility, rewards for data sharing are few, representing a serious practical impediment to data submission. To date, two main strategies have been adopted as a means to encourage the submission of human genomic variant data: (1) database journal linkups involving the affiliation of a scientific journal with a publicly available database and (2) microattribution, involving the unambiguous linkage of data to their contributors via a unique identifier. The latter could in principle lead to the establishment of a microcitation-tracking system that acknowledges individual endeavor and achievement. Both approaches could incentivize potential data contributors, thereby encouraging them to share their data with the scientific community. Here, we summarize and critically evaluate approaches that have been proposed to address current deficiencies in data attribution and discuss ways in which they could become more widely adopted as novel scientific publication modalities.

Human Variome Project country nodes: documenting genetic information within a country

The Human Variome Project (http://www.humanvariomeproject.org) is an international effort aiming to systematically collect and share information on all human genetic variation. The two main pillars of this effort are gene/disease-specific databases and a network of Human Variome Project Country Nodes. The latter are nationwide efforts to document the genomic variation reported within a specific population. The development and successful operation of the Human Variome Project Country Nodes are of utmost importance to the success of Human Variome Project's aims and goals because they not only allow the genetic burden of disease to be quantified in different countries, but also provide diagnosticians and researchers access to an up-to-date resource that will assist them in their daily clinical practice and biomedical research, respectively. Here, we report the discussions and recommendations that resulted from the inaugural meeting of the International Confederation of Countries Advisory Council, held on 12th December 2011, during the 2011 Human Variome Project Beijing Meeting. We discuss the steps necessary to maximize the impact of the Country Node effort for developing regional and country-specific clinical genetics resources and summarize a few well-coordinated genetic data collection initiatives that would serve as paradigms for similar projects.

How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis

Background

Prediction methods are increasingly used in biosciences to forecast diverse features and characteristics. Binary two-state classifiers are the most common applications. They are usually based on machine learning approaches. For the end user it is often problematic to evaluate the true performance and applicability of computational tools as some knowledge about computer science and statistics would be needed.

Results

Instructions are given on how to interpret and compare method evaluation results. For systematic method performance analysis is needed established benchmark datasets which contain cases with known outcome, and suitable evaluation measures. The criteria for benchmark datasets are discussed along with their implementation in VariBench, benchmark database for variations. There is no single measure that alone could describe all the aspects of method performance. Predictions of genetic variation effects on DNA, RNA and protein level are important as information about variants can be produced much faster than their disease relevance can be experimentally verified. Therefore numerous prediction tools have been developed, however, systematic analyses of their performance and comparison have just started to emerge.

Conclusions

The end users of prediction tools should be able to understand how evaluation is done and how to interpret the results. Six main performance evaluation measures are introduced. These include sensitivity, specificity, positive predictive value, negative predictive value, accuracy and Matthews correlation coefficient. Together with receiver operating characteristics (ROC) analysis they provide a good picture about the performance of methods and allow their objective and quantitative comparison. A checklist of items to look at is provided. Comparisons of methods for missense variant tolerance, protein stability changes due to amino acid substitutions, and effects of variations on mRNA splicing are presented.

Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting

Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for uncertain or novel gene variants in an objective manner. Here, complementary predictors for missense gene variants were incorporated into a weighted Consensus framework that includes calculated reference intervals from known disease outcomes. Data visualization for clinical reporting is also discussed.

DRUMS: a human disease related unique gene mutation search engine

With the completion of the human genome project and the development of new methods for gene variant detection, the integration of mutation data and its phenotypic consequences has become more important than ever. Among all available resources, locus-specific databases (LSDBs) curate one or more specific genes' mutation data along with high-quality phenotypes. Although some genotype-phenotype data from LSDB have been integrated into central databases little effort has been made to integrate all these data by a search engine approach. In this work, we have developed disease related unique gene mutation search engine (DRUMS), a search engine for human disease related unique gene mutation as a convenient tool for biologists or physicians to retrieve gene variant and related phenotype information. Gene variant and phenotype information were stored in a gene-centred relational database. Moreover, the relationships between mutations and diseases were indexed by the uniform resource identifier from LSDB, or another central database. By querying DRUMS, users can access the most popular mutation databases under one interface. DRUMS could be treated as a domain specific search engine. By using web crawling, indexing, and searching technologies, it provides a competitively efficient interface for searching and retrieving mutation data and their relationships to diseases. The present system is freely accessible at http://www.scbit.org/glif/new/drums/index.html.

Recommendations for genetic variation data capture in developing countries to ensure a comprehensive worldwide data collection

Developing countries have significantly contributed to the elucidation of the genetic basis of both common and rare disorders, providing an invaluable resource of cases due to large family sizes, consanguinity, and potential founder effects. Moreover, the recognized depth of genomic variation in indigenous African populations, reflecting the ancient origins of humanity on the African continent, and the effect of selection pressures on the genome, will be valuable in understanding the range of both pathological and nonpathological variations. The involvement of these populations in accurately documenting the extant genetic heterogeneity is more than essential. Developing nations are regarded as key contributors to the Human Variome Project (HVP; http://www.humanvariomeproject.org), a major effort to systematically collect mutations that contribute to or cause human disease and create a cyber infrastructure to tie databases together. However, biomedical research has not been the primary focus in these countries even though such activities are likely to produce economic and health benefits for all. Here, we propose several recommendations and guidelines to facilitate participation of developing countries in genetic variation data documentation, ensuring an accurate and comprehensive worldwide data collection. We also summarize a few well-coordinated genetic data collection initiatives that would serve as paradigms for similar projects. Hum Mutat 31:1–8, 2010. © 2010 Wiley-Liss, Inc.

Guidelines for establishing locus specific databases

Information about genetic variation has been collected for some 20 years into registries, known as locus specific databases (LSDBs), which nowadays often contain information in addition to the actual genetic variation. Several issues have to be taken into account when considering establishing and maintaining LSDBs and these have been discussed previously in a number of articles describing guidelines and recommendations. This information is widely scattered and, for a newcomer, it would be difficult to obtain the latest information and guidance. Here, a sequence of steps essential for establishing an LSDB is discussed together with guidelines for each step. Curators need to collect information from various sources, code it in systematic way, and distribute to the research and clinical communities. In doing this, ethical issues have to be taken into account. To facilitate integration of information to, for example, analyze genotype-phenotype correlations, systematic data representation using established nomenclatures, data models, and ontologies is essential. LSDB curation and maintenance comprises a number of tasks that can be managed by following logical steps. These resources are becoming ever more important and new curators are essential to ensure that we will have expertly curated databases for all disease-related genes in the near future.

Utility of gene-specific algorithms for predicting pathogenicity of uncertain gene variants

The rapid advance of gene sequencing technologies has produced an unprecedented rate of discovery of genome variation in humans. A growing number of authoritative clinical repositories archive gene variants and disease phenotypes, yet there are currently many more gene variants that lack clear annotation or disease association. To date, there has been very limited coverage of gene-specific predictors in the literature. Here the evaluation is presented of "gene-specific" predictor models based on a naïve Bayesian classifier for 20 gene-disease datasets, containing 3986 variants with clinically characterized patient conditions. The utility of gene-specific prediction is then compared with "all-gene" generalized prediction and also with existing popular predictors. Gene-specific computational prediction models derived from clinically curated gene variant disease datasets often outperform established generalized algorithms for novel and uncertain gene variants.

Call for participation in the neurogenetics consortium within the Human Variome Project

The rate of DNA variation discovery has accelerated the need to collate, store and interpret the data in a standardised coherent way and is becoming a critical step in maximising the impact of discovery on the understanding and treatment of human disease. This particularly applies to the field of neurology as neurological function is impaired in many human disorders. Furthermore, the field of neurogenetics has been proven to show remarkably complex genotype-to-phenotype relationships. To facilitate the collection of DNA sequence variation pertaining to neurogenetic disorders, we have initiated the "Neurogenetics Consortium" under the umbrella of the Human Variome Project. The Consortium's founding group consisted of basic researchers, clinicians, informaticians and database creators. This report outlines the strategic aims established at the preliminary meetings of the Neurogenetics Consortium and calls for the involvement of the wider neurogenetic community in enabling the development of this important resource.

Locus-specific database domain and data content analysis: evolution and content maturation toward clinical use

Genetic variation databases have become indispensable in many areas of health care. In addition, more and more experts are depositing published and unpublished disease-causing variants of particular genes into locus-specific databases (LSDBs). Some of these databases contain such extensive information that they have become known as knowledge bases. Here, we analyzed 1,188 LSDBs and their content for the presence or absence of 44 content criteria related to database features (general presentation, locus-specific information, database structure) and data content (data collection, summary table of variants, database querying). Our analyses revealed that several elements have helped to advance the field and reduce data heterogeneity, such as the development of specialized database management systems and the creation of data querying tools. We also identified a number of deficiencies, namely, the lack of detailed disease and phenotypic descriptions for each genetic variant and links to relevant patient organizations, which, if addressed, would allow LSDBs to better serve the clinical genetics community. We propose a structure, based on LSDBs and closely related repositories (namely, clinical genetics databases), which would contribute to a federated genetic variation browser and also allow the maintenance of variation data.

Development of NIPBL locus-specific database using LOVD: from novel mutations to further genotype-phenotype correlations in Cornelia de Lange Syndrome

The establishment of Locus Specific Databases (LSDB) is a crucial aspect for the Human Genetics field and one of the aims of the Human Variation Project. We report the development of a publicly accessible LSDB for the NIPBL gene (http://www.lovd.nl/NIPBL) implicated in Cornelia de Lange Syndrome (CdLS). This rare disorder is characterized by developmental and growth retardation, typical facial features, limb anomalies, and multiple organ involvement. Mutations in the NIPBL gene, the product of which is involved in control of the cohesion complex, account for over half of the patients currently characterized. The NIPBL LSDB adopted the Leiden Open Variation database (LOVD) software platform, which enables the comprehensive Web-based listing and curation of sequence variations and associated phenotypical information. The NIPBL-LOVD database contains 199 unique mutations reported in 246 patients (last accessed April 2010). Information on phenotypic characteristics included in the database enabled further genotype-phenotype correlations, the most evident being the severe form of CdLS associated with premature termination codons in the NIPBL gene. In addition to the NIPBL LSDB, 50 novel mutations are described in detail, resulting from a collaborative multicenter study.