Publications by authors named "Gudmundur A Thorisson"

19 Publications

  • Page 1 of 1

A homozygous loss-of-function mutation leading to CYBC1 deficiency causes chronic granulomatous disease.

Nat Commun 2018 10 25;9(1):4447. Epub 2018 Oct 25.

deCODE Genetics/Amgen, Inc., Reykjavik, Iceland.

Mutations in genes encoding subunits of the phagocyte NADPH oxidase complex are recognized to cause chronic granulomatous disease (CGD), a severe primary immunodeficiency. Here we describe how deficiency of CYBC1, a previously uncharacterized protein in humans (C17orf62), leads to reduced expression of NADPH oxidase's main subunit (gp91) and results in CGD. Analyzing two brothers diagnosed with CGD we identify a homozygous loss-of-function mutation, p.Tyr2Ter, in CYBC1. Imputation of p.Tyr2Ter into 155K chip-genotyped Icelanders reveals six additional homozygotes, all with signs of CGD, manifesting as colitis, rare infections, or a severely impaired PMA-induced neutrophil oxidative burst. Homozygosity for p.Tyr2Ter consequently associates with inflammatory bowel disease (IBD) in Iceland (P = 8.3 × 10; OR = 67.6), as well as reduced height (P = 3.3 × 10; -8.5 cm). Overall, we find that CYBC1 deficiency results in CGD characterized by colitis and a distinct profile of infections indicative of macrophage dysfunction.
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http://dx.doi.org/10.1038/s41467-018-06964-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202333PMC
October 2018

COPA syndrome in an Icelandic family caused by a recurrent missense mutation in COPA.

BMC Med Genet 2017 11 14;18(1):129. Epub 2017 Nov 14.

deCODE Genetics/Amgen, Inc, Sturlugata 8, 101, Reykjavik, Iceland.

Background: Rare missense mutations in the gene encoding coatomer subunit alpha (COPA) have recently been shown to cause autoimmune interstitial lung, joint and kidney disease, also known as COPA syndrome, under a dominant mode of inheritance.

Case Presentation: Here we describe an Icelandic family with three affected individuals over two generations with a rare clinical presentation of lung and joint disease and a histological diagnosis of follicular bronchiolitis. We performed whole-genome sequencing (WGS) of the three affected as well as three unaffected members of the family, and searched for rare genotypes associated with disease using 30,067 sequenced Icelanders as a reference population. We assessed all coding and splicing variants, prioritizing variants in genes known to cause interstitial lung disease. We detected a heterozygous missense mutation, p.Glu241Lys, in the COPA gene, private to the affected family members. The mutation occurred de novo in the paternal germline of the index case and was absent from 30,067 Icelandic genomes and 141,353 individuals from the genome Aggregation Database (gnomAD). The mutation occurs within the conserved and functionally important WD40 domain of the COPA protein.

Conclusions: This is the second report of the p.Glu241Lys mutation in COPA, indicating the recurrent nature of the mutation. The mutation was reported to co-segregate with COPA syndrome in a large family from the USA with five affected members, and classified as pathogenic. The two separate occurrences of the p.Glu241Lys mutation in cases and its absence from a large number of sequenced genomes confirms its role in the pathogenesis of the COPA syndrome.
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http://dx.doi.org/10.1186/s12881-017-0490-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686906PMC
November 2017

Compound heterozygous mutations in UBA5 causing early-onset epileptic encephalopathy in two sisters.

BMC Med Genet 2017 10 2;18(1):103. Epub 2017 Oct 2.

deCODE Genetics/Amgen, Inc., Sturlugata 8, 101, Reykjavik, Iceland.

Background: Epileptic encephalopathies are a group of childhood epilepsies that display high phenotypic and genetic heterogeneity. The recent, extensive use of next-generation sequencing has identified a large number of genes in epileptic encephalopathies, including UBA5 in which biallelic mutations were first described as pathogenic in 2016 (Colin E et al., Am J Hum Genet 99(3):695-703, 2016. Muona M et al., Am J Hum Genet 99(3):683-694, 2016). UBA5 encodes an activating enzyme for a post-translational modification mechanism known as ufmylation, and is the first gene from the ufmylation pathway that is linked to disease.

Case Presentation: We sequenced the genomes of two sisters with early-onset epileptic encephalopathy along with their unaffected parents in an attempt to find a genetic cause for their condition. The sisters, born in 2004 and 2006, presented with infantile spasms at six months of age, which later progressed to recurrent, treatment-resistant seizures. We detected a compound heterozygous genotype in UBA5 in the sisters, a genotype not seen elsewhere in an Icelandic reference set of 30,067 individuals nor in public databases. One of the mutations, c.684G > A, is a paternally inherited exonic splicing mutation, occuring at the last nucleotide of exon 7 of UBA5. The mutation is predicted to disrupt the splice site, resulting in loss-of-function of one allele of UBA5. The second mutation is a maternally inherited missense mutation, p.Ala371Thr, previously reported as pathogenic when in compound heterozygosity with a loss-of-function mutation in UBA5 and is believed to produce a hypomorphic allele. Supportive of this, we have identified three adult Icelanders homozygous for the p.Ala371Thr mutation who show no signs of neurological disease.

Conclusions: We describe compound heterozygous mutations in the UBA5 gene in two sisters with early-onset epileptic encephalopathy. To our knowledge, this is the first description of mutations in UBA5 since the initial discovery that pathogenic biallelic variants in the gene cause early-onset epileptic encephalopathy. We further provide confirmatory evidence that p.Ala371Thr is a hypomorphic mutation, by presenting three adult homozygotes who show no signs of neurological disease.
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http://dx.doi.org/10.1186/s12881-017-0466-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5623963PMC
October 2017

Quantifying the use of bioresources for promoting their sharing in scientific research.

Gigascience 2013 May 1;2(1). Epub 2013 May 1.

Epidémiologie et analyses en santé publique, Faculté de médecine, UMR1027 INSERM-Université de Toulouse III, 37 allées Jules Guesde, Toulouse Cedex 7, F-31073, France.

An increasing portion of biomedical research relies on the use of biobanks and databases. Sharing of such resources is essential for optimizing knowledge production. A major obstacle for sharing bioresources is the lack of recognition for the efforts involved in establishing, maintaining and sharing them, due to, in particular, the absence of adequate tools. Increasing demands on biobanks and databases to improve access should be complemented with efforts of end-users to recognize and acknowledge these resources. An appropriate set of tools must be developed and implemented to measure this impact.To address this issue we propose to measure the use in research of such bioresources as a value of their impact, leading to create an indicator: Bioresource Research Impact Factor (BRIF). Key elements to be assessed are: defining obstacles to sharing samples and data, choosing adequate identifier for bioresources, identifying and weighing parameters to be considered in the metrics, analyzing the role of journal guidelines and policies for resource citing and referencing, assessing policies for resource access and sharing and their influence on bioresource use. This work allows us to propose a framework and foundations for the operational development of BRIF that still requires input from stakeholders within the biomedical community.
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http://dx.doi.org/10.1186/2047-217X-2-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3655103PMC
May 2013

Semantically enabling a genome-wide association study database.

J Biomed Semantics 2012 Dec 17;3(1). Epub 2012 Dec 17.

Department of Genetics, University of Leicester, University Road, Leicester, UK.

Unlabelled:

Background: The amount of data generated from genome-wide association studies (GWAS) has grown rapidly, but considerations for GWAS phenotype data reuse and interchange have not kept pace. This impacts on the work of GWAS Central - a free and open access resource for the advanced querying and comparison of summary-level genetic association data. The benefits of employing ontologies for standardising and structuring data are widely accepted. The complex spectrum of observed human phenotypes (and traits), and the requirement for cross-species phenotype comparisons, calls for reflection on the most appropriate solution for the organisation of human phenotype data. The Semantic Web provides standards for the possibility of further integration of GWAS data and the ability to contribute to the web of Linked Data.

Results: A pragmatic consideration when applying phenotype ontologies to GWAS data is the ability to retrieve all data, at the most granular level possible, from querying a single ontology graph. We found the Medical Subject Headings (MeSH) terminology suitable for describing all traits (diseases and medical signs and symptoms) at various levels of granularity and the Human Phenotype Ontology (HPO) most suitable for describing phenotypic abnormalities (medical signs and symptoms) at the most granular level. Diseases within MeSH are mapped to HPO to infer the phenotypic abnormalities associated with diseases. Building on the rich semantic phenotype annotation layer, we are able to make cross-species phenotype comparisons and publish a core subset of GWAS data as RDF nanopublications.

Conclusions: We present a methodology for applying phenotype annotations to a comprehensive genome-wide association dataset and for ensuring compatibility with the Semantic Web. The annotations are used to assist with cross-species genotype and phenotype comparisons. However, further processing and deconstructions of terms may be required to facilitate automatic phenotype comparisons. The provision of GWAS nanopublications enables a new dimension for exploring GWAS data, by way of intrinsic links to related data resources within the Linked Data web. The value of such annotation and integration will grow as more biomedical resources adopt the standards of the Semantic Web.
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http://dx.doi.org/10.1186/2041-1480-3-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3579732PMC
December 2012

VarioML framework for comprehensive variation data representation and exchange.

BMC Bioinformatics 2012 Oct 3;13:254. Epub 2012 Oct 3.

Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland.

Background: Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement.

Results: The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components.

Conclusions: VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.
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http://dx.doi.org/10.1186/1471-2105-13-254DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3507772PMC
October 2012

Observ-OM and Observ-TAB: Universal syntax solutions for the integration, search, and exchange of phenotype and genotype information.

Hum Mutat 2012 May 4;33(5):867-73. Epub 2012 Apr 4.

EU-GEN2PHEN, and European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom.

Genetic and epidemiological research increasingly employs large collections of phenotypic and molecular observation data from high quality human and model organism samples. Standardization efforts have produced a few simple formats for exchange of these various data, but a lightweight and convenient data representation scheme for all data modalities does not exist, hindering successful data integration, such as assignment of mouse models to orphan diseases and phenotypic clustering for pathways. We report a unified system to integrate and compare observation data across experimental projects, disease databases, and clinical biobanks. The core object model (Observ-OM) comprises only four basic concepts to represent any kind of observation: Targets, Features, Protocols (and their Applications), and Values. An easy-to-use file format (Observ-TAB) employs Excel to represent individual and aggregate data in straightforward spreadsheets. The systems have been tested successfully on human biobank, genome-wide association studies, quantitative trait loci, model organism, and patient registry data using the MOLGENIS platform to quickly setup custom data portals. Our system will dramatically lower the barrier for future data sharing and facilitate integrated search across panels and species. All models, formats, documentation, and software are available for free and open source (LGPLv3) at http://www.observ-om.org.
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http://dx.doi.org/10.1002/humu.22070DOI Listing
May 2012

An informatics project and online "Knowledge Centre" supporting modern genotype-to-phenotype research.

Hum Mutat 2011 May 22;32(5):543-50. Epub 2011 Mar 22.

Department of Genetics, University of Leicester, University Road, Leicester, United Kingdom.

Explosive growth in the generation of genotype-to-phenotype (G2P) data necessitates a concerted effort to tackle the logistical and informatics challenges this presents. The GEN2PHEN Project represents one such effort, with a broad strategy of uniting disparate G2P resources into a hybrid centralized-federated network. This is achieved through a holistic strategy focussed on three overlapping areas: data input standards and pipelines through which to submit and collect data (data in); federated, independent, extendable, yet interoperable database platforms on which to store and curate widely diverse datasets (data storage); and data formats and mechanisms with which to exchange, combine, and extract data (data exchange and output). To fully leverage this data network, we have constructed the "G2P Knowledge Centre" (http://www.gen2phen.org). This central platform provides holistic searching of the G2P data domain allied with facilities for data annotation and user feedback, access to extensive G2P and informatics resources, and tools for constructing online working communities centered on the G2P domain. Through the efforts of GEN2PHEN, and through combining data with broader community-derived knowledge, the Knowledge Centre opens up exciting possibilities for organizing, integrating, sharing, and interpreting new waves of G2P data in a collaborative fashion.
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http://dx.doi.org/10.1002/humu.21469DOI Listing
May 2011

The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button.

BMC Bioinformatics 2010 Dec 21;11 Suppl 12:S12. Epub 2010 Dec 21.

Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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 http://www.molgenis.org.
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http://dx.doi.org/10.1186/1471-2105-11-S12-S12DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3040526PMC
December 2010

Finding and sharing: new approaches to registries of databases and services for the biomedical sciences.

Database (Oxford) 2010 Jul 6;2010:baq014. Epub 2010 Jul 6.

European Bioinformatics Institute, Genome Campus, Hinxton, Cambridgeshire, CB10 1SA.

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: http://www.casimir.org.uk/casimir_ddf.
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http://dx.doi.org/10.1093/database/baq014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2911849PMC
July 2010

Accreditation and attribution in data sharing.

Nat Biotechnol 2009 Nov;27(11):984-5

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http://dx.doi.org/10.1038/nbt1109-984bDOI Listing
November 2009

The phenotype and genotype experiment object model (PaGE-OM): a robust data structure for information related to DNA variation.

Hum Mutat 2009 Jun;30(6):968-77

University of Leicester, Department of Genetics, Leicester, UK.

Torrents of genotype-phenotype data are being generated, all of which must be captured, processed, integrated, and exploited. To do this optimally requires the use of standard and interoperable "object models," providing a description of how to partition the total spectrum of information being dealt with into elemental "objects" (such as "alleles," "genotypes," "phenotype values," "methods") with precisely stated logical interrelationships (such as "A objects are made up from one or more B objects"). We herein propose the Phenotype and Genotype Experiment Object Model (PaGE-OM; www.pageom.org), which has been tested and implemented in conjunction with several major databases, and approved as a standard by the Object Management Group (OMG). PaGE-OM is open-source, ready for use by the wider community, and can be further developed as needs arise. It will help to improve information management, assist data integration, and simplify the task of informatics resource design and construction for genotype and phenotype data projects.
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http://dx.doi.org/10.1002/humu.20973DOI Listing
June 2009

Genotype-phenotype databases: challenges and solutions for the post-genomic era.

Nat Rev Genet 2009 Jan;10(1):9-18

Department of Genetics, University of Leicester, University Road, Leicester LE1 7RH, UK.

The flow of research data concerning the genetic basis of health and disease is rapidly increasing in speed and complexity. In response, many projects are seeking to ensure that there are appropriate informatics tools, systems and databases available to manage and exploit this flood of information. Previous solutions, such as central databases, journal-based publication and manually intensive data curation, are now being enhanced with new systems for federated databases, database publication, and more automated management of data flows and quality control. Along with emerging technologies that enhance connectivity and data retrieval, these advances should help to create a powerful knowledge environment for genotype-phenotype information.
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http://dx.doi.org/10.1038/nrg2483DOI Listing
January 2009

HGVbaseG2P: a central genetic association database.

Nucleic Acids Res 2009 Jan 23;37(Database issue):D797-802. Epub 2008 Oct 23.

Department of Genetics, University of Leicester, University Road, Leicester, LE1 7RH, UK.

The Human Genome Variation database of Genotype to Phenotype information (HGVbaseG2P) is a new central database for summary-level findings produced by human genetic association studies, both large and small. Such a database is needed so that researchers have an easy way to access all the available association study data relevant to their genes, genome regions or diseases of interest. Such a depository will allow true positive signals to be more readily distinguished from false positives (type I error) that fail to consistently replicate. In this paper we describe how HGVbaseG2P has been constructed, and how its data are gathered and organized. We present a range of user-friendly but powerful website tools for searching, browsing and visualizing G2P study findings. HGVbaseG2P is available at http://www.hgvbaseg2p.org.
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http://dx.doi.org/10.1093/nar/gkn748DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2686551PMC
January 2009

Genome-wide detection and characterization of positive selection in human populations.

Nature 2007 Oct;449(7164):913-8

Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02139, USA.

With the advent of dense maps of human genetic variation, it is now possible to detect positive natural selection across the human genome. Here we report an analysis of over 3 million polymorphisms from the International HapMap Project Phase 2 (HapMap2). We used 'long-range haplotype' methods, which were developed to identify alleles segregating in a population that have undergone recent selection, and we also developed new methods that are based on cross-population comparisons to discover alleles that have swept to near-fixation within a population. The analysis reveals more than 300 strong candidate regions. Focusing on the strongest 22 regions, we develop a heuristic for scrutinizing these regions to identify candidate targets of selection. In a complementary analysis, we identify 26 non-synonymous, coding, single nucleotide polymorphisms showing regional evidence of positive selection. Examination of these candidates highlights three cases in which two genes in a common biological process have apparently undergone positive selection in the same population:LARGE and DMD, both related to infection by the Lassa virus, in West Africa;SLC24A5 and SLC45A2, both involved in skin pigmentation, in Europe; and EDAR and EDA2R, both involved in development of hair follicles, in Asia.
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http://dx.doi.org/10.1038/nature06250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2687721PMC
October 2007

A second generation human haplotype map of over 3.1 million SNPs.

Nature 2007 Oct;449(7164):851-61

The Scripps Research Institute, 10550 North Torrey Pines Road MEM275, La Jolla, California 92037, USA.

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.
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http://dx.doi.org/10.1038/nature06258DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2689609PMC
October 2007

The International HapMap Project Web site.

Genome Res 2005 Nov;15(11):1592-3

Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA.

The HapMap Web site at http://www.hapmap.org is the primary portal to genotype data produced as part of the International Haplotype Map Project. In phase I of the project, >1.1 million SNPs were genotyped in 270 individuals from four worldwide populations. The HapMap Web site provides researchers with a number of tools that allow them to analyze the data as well as download data for local analyses. This paper presents step-by-step guides to using those tools, including guides for retrieving genotype and frequency data, picking tag-SNPs for use in association studies, viewing haplotypes graphically, and examining marker-to-marker LD patterns.
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http://dx.doi.org/10.1101/gr.4413105DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1310647PMC
November 2005

The SNP Consortium website: past, present and future.

Nucleic Acids Res 2003 Jan;31(1):124-7

Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA.

The SNP Consortium website (http://snp.cshl.org) has undergone many changes since its initial conception three years ago. The database back end has been changed from the venerable ACeDB to the more scalable MySQL engine. Users can access the data via gene or single nucleotide polymorphism (SNP) keyword searches and browse or dump SNP data to textfiles. A graphical genome browsing interface shows SNPs mapped onto the genome assembly in the context of externally available gene predictions and other features. SNP allele frequency and genotype data are available via FTP-download and on individual SNP report web pages. SNP linkage maps are available for download and for browsing in a comparative map viewer. All software components of the data coordinating center (DCC) website (http://snp.cshl.org) are open source.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC165499PMC
http://dx.doi.org/10.1093/nar/gkg052DOI Listing
January 2003