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1.  The Genome of the Netherlands: design, and project goals 
PMCID: PMC3895638  PMID: 23714750
whole-genome sequence; trio-design; population genetics
PMCID: PMC3923238  PMID: 24484869
Harmonization; Obesity; Metabolic syndrome; Cardiovascular disease; Metabolically healthy
3.  Fine mapping of the celiac disease-associated LPP locus reveals a potential functional variant 
Human Molecular Genetics  2013;23(9):2481-2489.
Using the Immunochip for genotyping, we identified 39 non-human leukocyte antigen (non-HLA) loci associated to celiac disease (CeD), an immune-mediated disease with a worldwide frequency of ∼1%. The most significant non-HLA signal mapped to the intronic region of 70 kb in the LPP gene. Our aim was to fine map and identify possible functional variants in the LPP locus. We performed a meta-analysis in a cohort of 25 169 individuals from six different populations previously genotyped using Immunochip. Imputation using data from the Genome of the Netherlands and 1000 Genomes projects, followed by meta-analysis, confirmed the strong association signal on the LPP locus (rs2030519, P = 1.79 × 10−49), without any novel associations. The conditional analysis on this top SNP-indicated association to a single common haplotype. By performing haplotype analyses in each population separately, as well as in a combined group of the four populations that reach the significant threshold after correction (P < 0.008), we narrowed down the CeD-associated region from 70 to 2.8 kb (P = 1.35 × 10−44). By intersecting regulatory data from the ENCODE project, we found a functional SNP, rs4686484 (P = 3.12 × 10−49), that maps to several B-cell enhancer elements and a highly conserved region. This SNP was also predicted to change the binding motif of the transcription factors IRF4, IRF11, Nkx2.7 and Nkx2.9, suggesting its role in transcriptional regulation. We later found significantly low levels of LPP mRNA in CeD biopsies compared with controls, thus our results suggest that rs4686484 is the functional variant in this locus, while LPP expression is decreased in CeD.
PMCID: PMC3976328  PMID: 24334606
Nucleic Acids Research  2013;42(D1):D794-D801.
PMCID: PMC3965109  PMID: 24217915
5.  Consensus and conflict cards for metabolic pathway databases 
BMC Systems Biology  2013;7:50.
The metabolic network of H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial.
We introduce the concept of Consensus and Conflict Cards (C2Cards) to provide concise overviews of what the databases do or do not agree on. Each card is centered at a single gene, EC number or reaction. These three complementary perspectives make it possible to distinguish disagreements on the underlying biology of a metabolic process from differences that can be explained by different decisions on how and in what detail to represent knowledge. As a proof-of-concept, we implemented C2CardsHuman, as a web application, covering five human pathway databases.
C2Cards can contribute to ongoing reconciliation efforts by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute. Several case studies illustrate the potential of the C2Cards in identifying disagreements on the underlying biology of a metabolic process. The overviews may also point out controversial biological knowledge that should be subject of further research. Finally, the examples provided emphasize the importance of manual curation and the need for a broad community involvement.
PMCID: PMC3703255  PMID: 23803311
Metabolic network; Consensus; Community support; Human; Pathway database
6.  Deleterious Alleles in the Human Genome Are on Average Younger Than Neutral Alleles of the Same Frequency 
PLoS Genetics  2013;9(2):e1003301.
Large-scale population sequencing studies provide a complete picture of human genetic variation within the studied populations. A key challenge is to identify, among the myriad alleles, those variants that have an effect on molecular function, phenotypes, and reproductive fitness. Most non-neutral variation consists of deleterious alleles segregating at low population frequency due to incessant mutation. To date, studies characterizing selection against deleterious alleles have been based on allele frequency (testing for a relative excess of rare alleles) or ratio of polymorphism to divergence (testing for a relative increase in the number of polymorphic alleles). Here, starting from Maruyama's theoretical prediction (Maruyama T (1974), Am J Hum Genet USA 6:669–673) that a (slightly) deleterious allele is, on average, younger than a neutral allele segregating at the same frequency, we devised an approach to characterize selection based on allelic age. Unlike existing methods, it compares sets of neutral and deleterious sequence variants at the same allele frequency. When applied to human sequence data from the Genome of the Netherlands Project, our approach distinguishes low-frequency coding non-synonymous variants from synonymous and non-coding variants at the same allele frequency and discriminates between sets of variants independently predicted to be benign or damaging for protein structure and function. The results confirm the abundance of slightly deleterious coding variation in humans.
Author Summary
A key challenge in human genetics is to identify, among the multitude of genetic differences between individuals, those that have an effect on traits. Even though new genetic variants arise through mutation in each generation, most are present only in a small proportion of individuals because they have slightly negative effects on fitness. Detecting such slightly deleterious variants is a key challenge in analyzing how genetics influence human characteristics. In this paper, we test a theoretical prediction by Takeo Maruyama from 1974 that a slightly deleterious variant is, on average, younger than a neutral (non affecting fitness) variant present at the same population frequency. Thus our method detects selection by using estimated age of variants. We applied our method to human data from the Genome of the Netherlands Project, and we show that it distinguishes low-frequency protein-modifying variants from silent variants at the same population frequency and discriminates between sets of variants predicted to be benign or damaging for protein structure and function. Our results confirm the abundance of slightly deleterious protein-coding variation in humans.
PMCID: PMC3585140  PMID: 23468643
7.  WormQTL—public archive and analysis web portal for natural variation data in Caenorhabditis spp 
Nucleic Acids Research  2012;41(D1):D738-D743.
PMCID: PMC3531126  PMID: 23180786
8.  VarioML framework for comprehensive variation data representation and exchange 
BMC Bioinformatics  2012;13:254.
PMCID: PMC3507772  PMID: 23031277
LSDB; Variation database curation; Data collection; Distribution
9.  xQTL workbench: a scalable web environment for multi-level QTL analysis 
Bioinformatics  2012;28(7):1042-1044.
PMCID: PMC3315722  PMID: 22308096
10.  Modifiers of mutant huntingtin aggregation 
PLoS Currents  2011;3:RRN1255.
Protein aggregation is a common hallmark of a number of age-related neurodegenerative diseases, including Alzheimer’s, Parkinson’s, and polyglutamine-expansion disorders such as Huntington’s disease, but how aggregation-prone proteins lead to pathology is not known. Using a genome-wide RNAi screen in a C. elegans-model for polyglutamine aggregation, we previously identified 186 genes that suppress aggregation. Using an RNAi screen for human orthologs of these genes, we here present 26 human genes that suppress aggregation of mutant huntingtin in a human cell line. Among these are genes that have not been previously linked to mutant huntingtin aggregation. They include those encoding eukaryotic translation initiation, elongation and translation factors, and genes that have been previously associated with other neurodegenerative diseases, like the ATP-ase family gene 3-like 2 (AFG3L2) and ubiquitin-like modifier activating enzyme 1 (UBA1). Unravelling the role of these genes will broaden our understanding of the pathogenesis of Huntington’s disease.
PMCID: PMC3166184  PMID: 21915392
11.  Bioinformatics tools and database resources for systems genetics analysis in mice—a short review and an evaluation of future needs 
Briefings in Bioinformatics  2011;13(2):135-142.
During a meeting of the SYSGENET working group ‘Bioinformatics’, currently available software tools and databases for systems genetics in mice were reviewed and the needs for future developments discussed. The group evaluated interoperability and performed initial feasibility studies. To aid future compatibility of software and exchange of already developed software modules, a strong recommendation was made by the group to integrate HAPPY and R/qtl analysis toolboxes, GeneNetwork and XGAP database platforms, and TIQS and xQTL processing platforms. R should be used as the principal computer language for QTL data analysis in all platforms and a ‘cloud’ should be used for software dissemination to the community. Furthermore, the working group recommended that all data models and software source code should be made visible in public repositories to allow a coordinated effort on the use of common data structures and file formats.
PMCID: PMC3294237  PMID: 22396485
QTL mapping; database; mouse; systems genetics
12.  OntoCAT -- simple ontology search and integration in Java, R and REST/JavaScript 
BMC Bioinformatics  2011;12:218.
PMCID: PMC3129328  PMID: 21619703
13.  The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button 
BMC Bioinformatics  2010;11(Suppl 12):S12.
There is a huge demand on bioinformaticians to provide their biologists with user friendly and scalable software infrastructures to capture, exchange, and exploit the unprecedented amounts of new *omics data. We here present MOLGENIS, a generic, open source, software toolkit to quickly produce the bespoke MOLecular GENetics Information Systems needed.
The MOLGENIS toolkit provides bioinformaticians with a simple language to model biological data structures and user interfaces. At the push of a button, MOLGENIS’ generator suite automatically translates these models into a feature-rich, ready-to-use web application including database, user interfaces, exchange formats, and scriptable interfaces. Each generator is a template of SQL, JAVA, R, or HTML code that would require much effort to write by hand. This ‘model-driven’ method ensures reuse of best practices and improves quality because the modeling language and generators are shared between all MOLGENIS applications, so that errors are found quickly and improvements are shared easily by a re-generation. A plug-in mechanism ensures that both the generator suite and generated product can be customized just as much as hand-written software.
In recent years we have successfully evaluated the MOLGENIS toolkit for the rapid prototyping of many types of biomedical applications, including next-generation sequencing, GWAS, QTL, proteomics and biobanking. Writing 500 lines of model XML typically replaces 15,000 lines of hand-written programming code, which allows for quick adaptation if the information system is not yet to the biologist’s satisfaction. Each application generated with MOLGENIS comes with an optimized database back-end, user interfaces for biologists to manage and exploit their data, programming interfaces for bioinformaticians to script analysis tools in R, Java, SOAP, REST/JSON and RDF, a tab-delimited file format to ease upload and exchange of data, and detailed technical documentation. Existing databases can be quickly enhanced with MOLGENIS generated interfaces using the ‘ExtractModel’ procedure.
The MOLGENIS toolkit provides bioinformaticians with a simple model to quickly generate flexible web platforms for all possible genomic, molecular and phenotypic experiments with a richness of interfaces not provided by other tools. All the software and manuals are available free as LGPLv3 open source at
PMCID: PMC3040526  PMID: 21210979
14.  Finding and sharing: new approaches to registries of databases and services for the biomedical sciences 
The recent explosion of biological data and the concomitant proliferation of distributed databases make it challenging for biologists and bioinformaticians to discover the best data resources for their needs, and the most efficient way to access and use them. Despite a rapid acceleration in uptake of syntactic and semantic standards for interoperability, it is still difficult for users to find which databases support the standards and interfaces that they need. To solve these problems, several groups are developing registries of databases that capture key metadata describing the biological scope, utility, accessibility, ease-of-use and existence of web services allowing interoperability between resources. Here, we describe some of these initiatives including a novel formalism, the Database Description Framework, for describing database operations and functionality and encouraging good database practise. We expect such approaches will result in improved discovery, uptake and utilization of data resources.
Database URL:
PMCID: PMC2911849  PMID: 20627863
15.  Mouse Resource Browser—a database of mouse databases 
The laboratory mouse has become the organism of choice for discovering gene function and unravelling pathogenetic mechanisms of human diseases through the application of various functional genomic approaches. The resulting deluge of data has led to the deployment of numerous online resources and the concomitant need for formalized experimental descriptions, data standardization, database interoperability and integration, a need that has yet to be met. We present here the Mouse Resource Browser (MRB), a database of mouse databases that indexes 217 publicly available mouse resources under 22 categories and uses a standardised database description framework (the CASIMIR DDF) to provide information on their controlled vocabularies (ontologies and minimum information standards), and technical information on programmatic access and data availability. Focusing on interoperability and integration, MRB offers automatic generation of downloadable and re-distributable SOAP application-programming interfaces for resources that provide direct database access. MRB aims to provide useful information to both bench scientists, who can easily navigate and find all mouse related resources in one place, and bioinformaticians, who will be provided with interoperable resources containing data which can be mined and integrated.
Database URL:
PMCID: PMC2911845  PMID: 20627861
Genome Biology  2010;11(3):R27.
PMCID: PMC2864567  PMID: 20214801
BMC Research Notes  2010;3:16.
The integration of information present in many disparate biological databases represents a major challenge in biomedical research. To define the problems and needs, and to explore strategies for database integration in mouse functional genomics, we consulted the biologist user community and implemented solutions to two user-defined use-cases.
We organised workshops, meetings and used a questionnaire to identify the needs of biologist database users in mouse functional genomics. As a result, two use-cases were developed that can be used to drive future designs or extensions of mouse databases. Here, we present the use-cases and describe some initial computational solutions for them. The application for the gene-centric use-case, "MUSIG-Gen" starts from a list of gene names and collects a wide range of data types from several distributed databases in a "shopping cart"-like manner. The iterative user-driven approach is a response to strongly articulated requests from users, especially those without computational biology backgrounds. The application for the phenotype-centric use-case, "MUSIG-Phen", is based on a similar concept and starting from phenotype descriptions retrieves information for associated genes.
The use-cases created, and their prototype software implementations should help to better define biologists' needs for database integration and may serve as a starting point for future bioinformatics solutions aimed at end-user biologists.
PMCID: PMC2826352  PMID: 20205870
18.  Neurodegenerative diseases: Lessons from genome-wide screens in small model organisms 
PMCID: PMC3378155  PMID: 20049741
19.  designGG: an R-package and web tool for the optimal design of genetical genomics experiments 
BMC Bioinformatics  2009;10:188.
High-dimensional biomolecular profiling of genetically different individuals in one or more environmental conditions is an increasingly popular strategy for exploring the functioning of complex biological systems. The optimal design of such genetical genomics experiments in a cost-efficient and effective way is not trivial.
This paper presents designGG, an R package for designing optimal genetical genomics experiments. A web implementation for designGG is available at . All software, including source code and documentation, is freely available.
DesignGG allows users to intelligently select and allocate individuals to experimental units and conditions such as drug treatment. The user can maximize the power and resolution of detecting genetic, environmental and interaction effects in a genome-wide or local mode by giving more weight to genome regions of special interest, such as previously detected phenotypic quantitative trait loci. This will help to achieve high power and more accurate estimates of the effects of interesting factors, and thus yield a more reliable biological interpretation of data. DesignGG is applicable to linkage analysis of experimental crosses, e.g. recombinant inbred lines, as well as to association analysis of natural populations.
PMCID: PMC2706229  PMID: 19538731
20.  Complex nature of SNP genotype effects on gene expression in primary human leucocytes 
Genome wide association studies have been hugely successful in identifying disease risk variants, yet most variants do not lead to coding changes and how variants influence biological function is usually unknown.
We correlated gene expression and genetic variation in untouched primary leucocytes (n = 110) from individuals with celiac disease – a common condition with multiple risk variants identified. We compared our observations with an EBV-transformed HapMap B cell line dataset (n = 90), and performed a meta-analysis to increase power to detect non-tissue specific effects.
In celiac peripheral blood, 2,315 SNP variants influenced gene expression at 765 different transcripts (< 250 kb from SNP, at FDR = 0.05, cis expression quantitative trait loci, eQTLs). 135 of the detected SNP-probe effects (reflecting 51 unique probes) were also detected in a HapMap B cell line published dataset, all with effects in the same allelic direction. Overall gene expression differences within the two datasets predominantly explain the limited overlap in observed cis-eQTLs. Celiac associated risk variants from two regions, containing genes IL18RAP and CCR3, showed significant cis genotype-expression correlations in the peripheral blood but not in the B cell line datasets. We identified 14 genes where a SNP affected the expression of different probes within the same gene, but in opposite allelic directions. By incorporating genetic variation in co-expression analyses, functional relationships between genes can be more significantly detected.
In conclusion, the complex nature of genotypic effects in human populations makes the use of a relevant tissue, large datasets, and analysis of different exons essential to enable the identification of the function for many genetic risk variants in common diseases.
PMCID: PMC2628677  PMID: 19128478

Results 1-20 (20)