There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.

/pdf/genome-wide-association-study-of-14-000-cases-of-seven-5ay7q0q3vy.pdf

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Human Protein Reference Database (HPRD--http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system--Human Proteinpedia (http://www.humanproteinpedia.org/)--through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15,000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome.

/pdf/human-protein-reference-database-2009-update-2hsuvdywil.pdf

Human Protein Reference Database—2009 update

The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.

http://csg.sph.umich.edu/abecasis/publications/pdf/Nat.Rev.Genet.vol.9-pp.356.pdf

Genome-wide association studies for complex traits: consensus, uncertainty and challenges

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(13)60591-7.pdf

Type 1 Diabetes

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.

/pdf/bayesian-test-for-colocalisation-between-pairs-of-genetic-1wem7o953e.pdf

Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.

The Wellcome Trust Case Control Consortium (WTCCC) primary genome-wide association (GWA) scan on seven diseases, including the multifactorial autoimmune disease type 1 diabetes (T1D), shows associations at P < 5 x 10(-7) between T1D and six chromosome regions: 12q24, 12q13, 16p13, 18p11, 12p13 and 4q27. Here, we attempted to validate these and six other top findings in 4,000 individuals with T1D, 5,000 controls and 2,997 family trios independent of the WTCCC study. We confirmed unequivocally the associations of 12q24, 12q13, 16p13 and 18p11 (P(follow-up) <or= 1.35 x 10(-9); P(overall) <or= 1.15 x 10(-14)), leaving eight regions with small effects or false-positive associations. We also obtained evidence for chromosome 18q22 (P(overall) = 1.38 x 10(-8)) from a GWA study of nonsynonymous SNPs. Several regions, including 18q22 and 18p11, showed association with autoimmune thyroid disease. This study increases the number of T1D loci with compelling evidence from six to at least ten.

Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes.

T1DBase (http://www.t1dbase.org) is web platform, which supports the type 1 diabetes (T1D) community. It integrates genetic, genomic and expression data relevant to T1D research across mouse, rat and human and presents this to the user as a set of web pages and tools. This update describes the incorporation of new data sets, tools and curation efforts as well as a new website design to simplify site use. New data sets include curated summary data from four genome-wide association studies relevant to T1D, HaemAtlas-a data set and tool to query gene expression levels in haematopoietic cells and a manually curated table of human T1D susceptibility loci, incorporating genetic overlap with other related diseases. These developments will continue to support T1D research and allow easy access to large and complex T1D relevant data sets.

T1DBase: update 2011, organization and presentation of large-scale data sets for type 1 diabetes research.

T1DBase (http://T1DBase.org) [Smink et al. (2005) Nucleic Acids Res., 33, D544-D549; Burren et al. (2004) Hum. Genomics, 1, 98-109] is a public website and database that supports the type 1 diabetes (T1D) research community. T1DBase provides a consolidated T1D-oriented view of the complex data world that now confronts medical researchers and enables scientists to navigate from information they know to information that is new to them. Overview pages for genes and markers summarize information for these elements. The Gene Dossier summarizes information for a list of genes. GBrowse [Stein et al. (2002) Genome Res., 10, 1599-1610] displays genes and other features in their genomic context, and Cytoscape [Shannon et al. (2003) Genome Res., 13, 2498-2504] shows genes in the context of interacting proteins and genes. The Beta Cell Gene Atlas shows gene expression in beta cells, islets, and related cell types and lines, and the Tissue Expression Viewer shows expression across other tissues. The Microarray Viewer shows expression from more than 20 array experiments. The Beta Cell Gene Expression Bank contains manually curated gene and pathway annotations for genes expressed in beta cells. T1DMart is a query tool for markers and genotypes. PosterPages are 'home pages' about specific topics or datasets. The key challenge, now and in the future, is to provide powerful informatics capabilities to T1D scientists in a form they can use to enhance their research.

https://biblio.ugent.be/publication/847091/file/854718.pdf

T1DBase: integration and presentation of complex data for type 1 diabetes research.

https://biblio.ugent.be/publication/847124/file/854796.pdf

Ellen C. Adlem

Papers

Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes.

T1DBase: update 2011, organization and presentation of large-scale data sets for type 1 diabetes research.

T1DBase: integration and presentation of complex data for type 1 diabetes research.

Erratum: T1DBase: Integration and presentation of complex data for type 1 diabetes research (Nucleic Acids Research (2007) vol. 35 (D742-D746))