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Research data is accumulating rapidly, and with it the challenge of irreproducible science. As a consequence, implementation of high quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data, however, processing data using workflows - systematic executions of a series computational tools - is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

MotivationWe are witnessing an enormous growth in the amount of molecular profiling (-omics) data. The integration of multi-omics data is challenging. Moreover, human multi-omics data may be privacy-sensitive and misused to de-anonymize and (re-)identify individuals. Hence, most data is kept in secure and protected silos. Therefore, it remains a challenge to reuse these data without infringing the privacy of the individuals from which the data were derived. Federated analysis of FAIR data is a privacy-preserving solution to make optimal use of these multi-omics data and transform them into actionable knowledge.ResultsThe Netherlands X-omics Initiative is a National Roadmap Large-Scale Research Infrastructure aiming for efficient integration of data generated within X-omics and external datasets. To facilitate this, we developed the FAIR Data Cube (FDCube), which adopts and applies the FAIR principles and helps researchers to create FAIR data and metadata, facilitate reuse of their data, and make their data analysis workflows transparent. The FDCube also meets security-by-design and privacy-by-design principles.Availabilityhttps://github.com/Xomics/FAIRDataCubeContactXiaofeng.Liao@radboudumc.nl,Peter-Bram.tHoen@radboudumc.nlSupplementary informationSupplementary data are available atBioinformaticsonline.

Background: In the molecular genetic diagnostics of Mendelian disorders, solutions are needed for the major challenge of dealing with the large number of variants of uncertain significance (VUSs) identified using next-generation sequencing (NGS). Recently, promising approaches using constraint metrics to calculate case excess scores (CE), etiological fractions (EF), and gnomAD-derived constraint scores have been reported that estimate the likelihood of rare variants in specific genes or regions that are pathogenic. Our objective is to study the usability of these constraint data into variant interpretation in a diagnostic setting, using our cardiomyopathy cohort.Methods and Results: Patients (N = 2002) referred for clinical genetic diagnostics underwent NGS testing of 55–61 genes associated with cardiomyopathies. Previously classified likely pathogenic (LP) and pathogenic (P) variants were used to validate the use of data from CE, EF, and gnomAD constraint analyses for (re)classification of associated variant types in specific cardiomyopathy subtype-related genes. The classifications corroborated in 94% (354/378) of cases. Next, we reclassified 23 unique VUSs to LP, increasing the diagnostic yield by 1.2%. In addition, 106 unique VUSs (5.3% of patients) were prioritized for co-segregation or functional analyses.Conclusions: Our analysis confirms that the use of constraint metrics data can improve variant interpretation, and we, therefore, recommend using constraint scores on other cohorts and disorders and its inclusion in variant interpretation protocols.

Introduction Rare disease patient data are typically sensitive, present in multiple registries controlled by different custodians, and non-interoperable. Making these data Findable, Accessible, Interoperable, and Reusable (FAIR) for humans and machines at source enables federated discovery and analysis across data custodians. This facilitates accurate diagnosis, optimal clinical management, and personalised treatments. In Europe, twenty-four European Reference Networks (ERNs) work on rare disease registries in different clinical domains. The process and the implementation choices for making data FAIR (‘FAIRification’) differ among ERN registries. For example, registries use different software systems and are subject to different legal regulations. To support the ERNs in making informed decisions and to harmonise FAIRification, the FAIRification steward team was established to work as liaisons between ERNs and researchers from the European Joint Programme on Rare Diseases. Results The FAIRification steward team inventoried the FAIRification challenges of the ERN registries and proposed solutions collectively with involved stakeholders to address them. Ninety-eight FAIRification challenges from 24 ERNs’ registries were collected and categorised into “training” (31), “community” (9), “modelling” (12), “implementation” (26), and “legal” (20). After curating and aggregating highly similar challenges, 41 unique FAIRification challenges remained. The two categories with the most challenges were “training” (15) and “implementation” (9), followed by “community” (7), and then “modelling” (5) and “legal” (5). To address all challenges, eleven types of solutions were proposed. Among them, the provision of guidelines and the organisation of training activities resolved the “training” challenges, which ranged from less-technical “coffee-rounds” to technical workshops, from informal FAIR Games to formal hackathons. Obtaining implementation support from technical experts was the solution type for tackling the “implementation” challenges. Conclusion This work shows that a dedicated team of FAIR data stewards is an asset for harmonising the various processes of making data FAIR in a large organisation with multiple stakeholders. Additionally, multi-levelled training activities are required to accommodate the diverse needs of the ERNs. Finally, the lessons learned from the experience of the FAIRification steward team described in this paper may help to increase FAIR awareness and provide insights into FAIRification challenges and solutions of rare disease registries.

Epidermolysis bullosa is a group of genetic skin conditions characterized by abnormal skin (and mucosal) fragility caused by pathogenic variants in various genes. The disease severity ranges from early childhood mortality in the most severe types to occasional acral blistering in the mildest types. The subtype and severity of EB is linked to the gene involved and the specific variants in that gene, which also determine its mode of inheritance. Current treatment is mainly focused on symptomatic relief such as wound care and blister prevention, because truly curative treatment options are still at the preclinical stage. Given the current level of understanding, the broad spectrum of genes and variants underlying EB makes it impossible to develop a single treatment strategy for all patients. It is likely that many different variant-specific treatment strategies will be needed to ultimately treat all patients. Antisense-oligonucleotide (ASO)-mediated exon skipping aims to counteract pathogenic sequence variants by restoring the open reading frame through the removal of the mutant exon from the pre-messenger RNA. This should lead to the restored production of the protein absent in the affected skin and, consequently, improvement of the phenotype. Several preclinical studies have demonstrated that exon skipping can restore protein production in vitro, in skin equivalents, and in skin grafts derived from EB-patient skin cells, indicating that ASO-mediated exon skipping could be a viable strategy as a topical or systemic treatment. The potential value of exon skipping for EB is supported by a study showing reduced phenotypic severity in patients who carry variants that result in natural exon skipping. In this article, we review the substantial progress made on exon skipping for EB in the past 15 years and highlight the opportunities and current challenges of this RNA-based therapy approach. In addition, we present a prioritization strategy for the development of exon skipping based on genomic information of all EB-involved genes.

Background: Genetic disorders are a substantial cause of infant morbidity and mortality and are frequently suspected in neonatal intensive care units. Non-specific clinical presentation or limitations to physical examination can result in a plethora of genetic testing techniques, without clear strategies on test ordering. Here, we review our 2-years experiences of rapid genetic testing of NICU patients in order to provide such recommendations.Methods: We retrospectively included all patients admitted to the NICU who received clinical genetic consultation and genetic testing in our University hospital. We documented reasons for referral for genetic consultation, presenting phenotypes, differential diagnoses, genetic testing requested and their outcomes, as well as the consequences of each (rapid) genetic diagnostic approach. We calculated diagnostic yield and turnaround times (TATs).Results: Of 171 included infants that received genetic consultation 140 underwent genetic testing. As a result of testing as first tier, 13/14 patients received a genetic diagnosis from QF-PCR; 14/115 from SNP-array; 12/89 from NGS testing, of whom 4/46 were diagnosed with a small gene panel and 8/43 with a large OMIM-morbid based gene panel. Subsequent secondary or tertiary analysis and/or additional testing resulted in five more diagnoses. TATs ranged from 1 day (QF-PCR) to a median of 14 for NGS and SNP-array testing, with increasing TAT in particular when many consecutive tests were performed. Incidental findings were detected in 5/140 tested patients (3.6%).Conclusion: We recommend implementing a broad NGS gene panel in combination with CNV calling as the first tier of genetic testing for NICU patients given the often unspecific phenotypes of ill infants and the high yield of this large panel.

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.

Item does not contain fulltext Information exists when it is being copied—a rather important notion—think of bits, DNA and lottery. This concept is highlighted by Shannon’s classical paper on Information theory which describes a mathematical theory of communication, communication being equal to the copying of information. The second leading notion is that the copying process only works when agreements have been made. Actually, everything we do with computers is based on agreements among people— often in the form of standards.

Background: Allele-specific expression (ASE) refers to divergent abundances of allelic copies of a transcript and is quantified by RNA sequencing. Multiple studies have shown that ASE likely plays a role in many diseases by modulating the pathogenesis or phenotypes. However, routine genome diagnostics is based on DNA sequencing and therefore neglects the regulation of gene expression such as ASE. To take advantage of ASE information in absence of RNA sequencing, ASE must be predicted using only DNA variation. Results: We have constructed ASE models from BIOS and GTEx that predict local ASE using DNA features. These models are highly stable as shown by performance metrics, cross-validation, and feature importance. Many different types of features are involved in these predictions, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a role in disease penetrance or modulation: BRCA2, RET, and NF1. This resulted in predicted ASE effects for 27 out of 8,957 variants. Ten of these were known pathogenic variants, while the rest are interesting candidates for further elucidation of disease etiology. Conclusions: We demonstrated that ASE can be stably and reliably predicted from DNA features using machine-learning models. Future efforts may further improve sensitivity and translate these models into a new class of genome diagnostic in silico prediction tools to prioritize candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing.

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Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) athttps://github.com/molgenis/capice.

Despite an explosive growth of next-generation sequencing data, genome diagnostics only provides a molecular diagnosis to a minority of patients. Software tools that prioritize genes based on patient symptoms using known gene-disease associations may complement variant filtering and interpretation to increase chances of success. However, many of these tools cannot be used in practice because they are embedded within variant prioritization algorithms, or exist as remote services that cannot be relied upon or are unacceptable because of legal/ethical barriers. In addition, many tools are not designed for command-line usage, closed-source, abandoned, or unavailable. We present Variant Interpretation using Biomedical literature Evidence (VIBE), a tool to prioritize disease genes based on Human Phenotype Ontology codes. VIBE is a locally installed executable that ensures operational availability and is built upon DisGeNET-RDF, a comprehensive knowledge platform containing gene-disease associations mostly from literature and variant-disease associations mostly from curated source databases. VIBE's command-line interface and output are designed for easy incorporation into bioinformatic pipelines that annotate and prioritize variants for further clinical interpretation. We evaluate VIBE in a benchmark based on 305 patient cases alongside seven other tools. Our results demonstrate that VIBE offers consistent performance with few cases missed, but we also find high complementarity among all tested tools. VIBE is a powerful, free, open source and locally installable solution for prioritizing genes based on patient symptoms. Project source code, documentation, benchmark and executables are available at https://github.com/molgenis/vibe.

The diagnostic yield of exome and genome sequencing remains low (8–70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases., A genetic diagnosis remains unattainable for many individuals with a rare disease because of incomplete knowledge about the genetic basis of many diseases. Here, the authors present the web-based tool GADO (GeneNetwork Assisted Diagnostic Optimization) that uses public RNA-seq data for prioritization of candidate genes.

Motivation: While the size and number of biobanks, patient registries and other data collections are increasing, biomedical researchers still often need to pool data for statistical power, a task that requires time-intensive retrospective integration. Results: To address this challenge, we developed MOLGENIS/connect, a semi-automatic system to find, match and pool data from different sources. The system shortlists relevant source attributes from thousands of candidates using ontology-based query expansion to overcome variations in terminology. Then it generates algorithms that transform source attributes to a common target DataSchema. These include unit conversion, categorical value matching and complex conversion patterns (e.g. calculation of BMI). In comparison to human-experts, MOLGENIS/connect was able to auto-generate 27% of the algorithms perfectly, with an additional 46% needing only minor editing, representing a reduction in the human effort and expertise needed to pool data. Availability and Implementation: Source code, binaries and documentation are available as open-source under LGPLv3 from http://github.com/molgenis/molgenis and www.molgenis.org/connect. Contact: m.a.swertz@rug.nl Supplementary information: Supplementary data are available at Bioinformatics online.

The rapid advances in the genomic study have made genetics testing common in today's diagnostic practices [1]. Next-generation sequencing provides researchers with a huge amount of genomic data, yet the interpretation still in its infancy [5].The diagnostics yield is still around 30% [1, 3, 8]. Interpretation tools for analyzing the genomic data is needed.

Clinical interpretation of exome and genome sequencing data remains challenging and time consuming, with many variants with unknown effects found in genes with unknown functions. Automated prioritization of these variants can improve the speed of current diagnostics and identify previously unknown disease genes. Here, we used 31,499 RNA-seq samples to predict the phenotypic consequences of variants in genes. We developed GeneNetwork Assisted Diagnostic Optimization (GADO), a tool that uses these predictions in combination with a patient’s phenotype, denoted using HPO terms, to prioritize identified variants and ease interpretation. GADO is unique because it does not rely on existing knowledge of a gene and can therefore prioritize variants missed by tools that rely on existing annotations or pathway membership. In a validation trial on patients with a known genetic diagnosis, GADO prioritized the causative gene within the top 3 for 41% of the cases. Applying GADO to a cohort of 38 patients without genetic diagnosis, yielded new candidate genes for seven cases. Our results highlight the added value of GADO (www.genenetwork.nl) for increasing diagnostic yield and for implicating previously unknown disease-causing genes.

Motivation The volume and complexity of biological data increases rapidly. Many clinical professionals and biomedical researchers without a bioinformatics background are generating big ’-omics’ data, but do not always have the tools to manage, process or publicly share these data. Results Here we present MOLGENIS Research, an open-source web-application to collect, manage, analyze, visualize and share large and complex biomedical datasets, without the need for advanced bioinformatics skills. Availability and implementation MOLGENIS Research is freely available (open source software). It can be installed from source code (see http://github.com/molgenis), downloaded as a precompiled WAR file (for your own server), setup inside a Docker container (see http://molgenis.github.io), or requested as a Software-as-a-Service subscription. For a public demo instance and complete installation instructions see http://molgenis.org/research.

Objective Pooling data across biobanks is necessary to increase statistical power, reveal more subtle associations, and synergize the value of data sources. However, searching for desired data elements among the thousands of available elements and harmonizing differences in terminology, data collection, and structure, is arduous and time consuming. Materials and methods To speed up biobank data pooling we developed BiobankConnect, a system to semi-automatically match desired data elements to available elements by: (1) annotating the desired elements with ontology terms using BioPortal; (2) automatically expanding the query for these elements with synonyms and subclass information using OntoCAT; (3) automatically searching available elements for these expanded terms using Lucene lexical matching; and (4) shortlisting relevant matches sorted by matching score. Results We evaluated BiobankConnect using human curated matches from EU-BioSHaRE, searching for 32 desired data elements in 7461 available elements from six biobanks. We found 0.75 precision at rank 1 and 0.74 recall at rank 10 compared to a manually curated set of relevant matches. In addition, best matches chosen by BioSHaRE experts ranked first in 63.0% and in the top 10 in 98.4% of cases, indicating that our system has the potential to significantly reduce manual matching work. Conclusions BiobankConnect provides an easy user interface to significantly speed up the biobank harmonization process. It may also prove useful for other forms of biomedical data integration. All the software can be downloaded as a MOLGENIS open source app from http://www.github.com/molgenis, with a demo available at http://www.biobankconnect.org.

Here, we present GAVIN, a new method that delivers accurate classification of variants for next-generation sequencing molecular diagnostics. It is based on gene-specific calibrations of allele frequencies (from the ExAC database), effect impact (using SnpEff) and estimated deleteriousness (CADD scores) for >3,000 genes. In a benchmark on 18 clinical gene sets, we achieved a sensitivity of 91.6%, with a specificity of 78.2%. This accuracy was unmatched by 12 other tools we tested. We provide GAVIN as an online MOLGENIS service to annotate VCF files, and as open source executable for use in bioinformatic pipelines. It can be found athttp://molgenis.org/gavin.

Here, we present WormQTL (http://www.wormqtl.org), an easily accessible database enabling search, comparative analysis and meta-analysis of all data on variation in Caenorhabditis spp. Over the past decade, Caenorhabditis elegans has become instrumental for molecular quantitative genetics and the systems biology of natural variation. These efforts have resulted in a valuable amount of phenotypic, high-throughput molecular and genotypic data across different developmental worm stages and environments in hundreds of C. elegans strains. WormQTL provides a workbench of analysis tools for genotype-phenotype linkage and association mapping based on but not limited to R/qtl (http://www.rqtl.org). All data can be uploaded and downloaded using simple delimited text or Excel formats and are accessible via a public web user interface for biologists and R statistic and web service interfaces for bioinformaticians, based on open source MOLGENIS and xQTL workbench software. WormQTL welcomes data submissions from other worm researchers.

Next-generation sequencing in clinical diagnostics is providing valuable genomic variant data, which can be used to support healthcare decisions. In silico tools to predict pathogenicity are crucial to assess such variants and we have evaluated a new tool, Combined Annotation Dependent Depletion (CADD), and its classification of gene variants in Lynch syndrome by using a set of 2,210 DNA mismatch repair gene variants. These had already been classified by experts from InSiGHT's Variant Interpretation Committee. Overall, we found CADD scores do predict pathogenicity (Spearman's = 0.595, P

Background RNA-sequencing (RNA-seq) is a powerful technique for the identification of genetic variants that affect gene-expression levels, either through expression quantitative trait locus (eQTL) mapping or through allele-specific expression (ASE) analysis. Given increasing numbers of RNA-seq samples in the public domain, we here studied to what extent eQTLs and ASE effects can be identified when using public RNA-seq data while deriving the genotypes from the RNA-sequencing reads themselves. Methods We downloaded the raw reads for all available human RNA-seq datasets. Using these reads we performed gene expression quantification. All samples were jointly normalized and subjected to a strict quality control. We also derived genotypes using the RNA-seq reads and used imputation to infer non-coding variants. This allowed us to perform eQTL mapping and ASE analyses jointly on all samples that passed quality control. Our results were validated using samples for which DNA-seq genotypes were available. Results 4,978 public human RNA-seq runs, representing many different tissues and cell-types, passed quality control. Even though these data originated from many different laboratories, samples reflecting the same cell type clustered together, suggesting that technical biases due to different sequencing protocols are limited. In a joint analysis on the 1,262 samples with high quality genotypes, we identified cis-eQTLs effects for 8,034 unique genes (at a false discovery rate ≤0.05). eQTL mapping on individual tissues revealed that a limited number of samples already suffice to identify tissue-specific eQTLs for known disease-associated genetic variants. Additionally, we observed strong ASE effects for 34 rare pathogenic variants, corroborating previously observed effects on the corresponding protein levels. Conclusions By deriving and imputing genotypes from RNA-seq data, it is possible to identify both eQTLs and ASE effects. Given the exponential growth of the number of publicly available RNA-seq samples, we expect this approach will become especially relevant for studying the effects of tissue-specific and rare pathogenic genetic variants to aid clinical interpretation of exome and genome sequencing. Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0152-4) contains supplementary material, which is available to authorized users.

Summary: xQTL workbench is a scalable web platform for the mapping of quantitative trait loci (QTLs) at multiple levels: for example gene expression (eQTL), protein abundance (pQTL), metabolite abundance (mQTL) and phenotype (phQTL) data. Popular QTL mapping methods for model organism and human populations are accessible via the web user interface. Large calculations scale easily on to multi-core computers, clusters and Cloud. All data involved can be uploaded and queried online: markers, genotypes, microarrays, NGS, LC-MS, GC-MS, NMR, etc. When new data types come available, xQTL workbench is quickly customized using the Molgenis software generator. Availability: xQTL workbench runs on all common platforms, including Linux, Mac OS X and Windows. An online demo system, installation guide, tutorials, software and source code are available under the LGPL3 license from http://www.xqtl.org. Contact: m.a.swertz@rug.nl

Contains fulltext : 137213.pdf (Publisher’s version ) (Closed access) Whole-genome sequencing enables complete characterization of genetic variation, but geographic clustering of rare alleles demands many diverse populations be studied. Here we describe the Genome of the Netherlands (GoNL) Project, in which we sequenced the whole genomes of 250 Dutch parent-offspring families and constructed a haplotype map of 20.4 million single-nucleotide variants and 1.2 million insertions and deletions. The intermediate coverage ( approximately 13x) and trio design enabled extensive characterization of structural variation, including midsize events (30-500 bp) previously poorly catalogued and de novo mutations. We demonstrate that the quality of the haplotypes boosts imputation accuracy in independent samples, especially for lower frequency alleles. Population genetic analyses demonstrate fine-scale structure across the country and support multiple ancient migrations, consistent with historical changes in sea level and flooding. The GoNL Project illustrates how single-population whole-genome sequencing can provide detailed characterization of genetic variation and may guide the design of future population studies.

In Caenorhabditis elegans, the recent advances in high-throughput quantitative analyses of natural genetic and phenotypic variation have led to a wealth of data on genotype phenotype relations. This data has resulted in the discovery of genes with major allelic effects and insights in the effect of natural genetic variation on a whole range of complex traits as well as how this variation is distributed across the genome. Regardless of the advances presented in specific studies, the majority of the data generated in these studies had yet to be made easily accessible, allowing for meta-analysis. Not only data in figures or tables but meta-data should be accessible for further investigation and comparison between studies. A platform was created where all the data, phenotypic measurements, genotypes, and mappings can be stored, compared, and new linkages within and between published studies can be discovered. WormQTL focuses on quantitative genetics in Caenorhabditis and other nematode species, whereas WormQTL(HD) quantitatively links gene expression quantitative trait loci (eQTL) in C. elegans to gene-disease associations in humans.

Interactions between proteins are highly conserved across species. As a result, the molecular basis of multiple diseases affecting humans can be studied in model organisms that offer many alternative experimental opportunities. One such organism-Caenorhabditis elegans-has been used to produce much molecular quantitative genetics and systems biology data over the past decade. We present WormQTL(HD) (Human Disease), a database that quantitatively and systematically links expression Quantitative Trait Loci (eQTL) findings in C. elegans to gene-disease associations in man. WormQTL(HD), available online at http://www.wormqtl-hd.org, is a user-friendly set of tools to reveal functionally coherent, evolutionary conserved gene networks. These can be used to predict novel gene-to-gene associations and the functions of genes underlying the disease of interest. We created a new database that links C. elegans eQTL data sets to human diseases (34 337 gene-disease associations from OMIM, DGA, GWAS Central and NHGRI GWAS Catalogue) based on overlapping sets of orthologous genes associated to phenotypes in these two species. We utilized QTL results, high-throughput molecular phenotypes, classical phenotypes and genotype data covering different developmental stages and environments from WormQTL database. All software is available as open source, built on MOLGENIS and xQTL workbench.