Dataset Information

The PhenoGen informatics website: tools for analyses of complex traits.

ABSTRACT:

Background

With the advent of "omics" (e.g. genomics, transcriptomics, proteomics and phenomics), studies can produce enormous amounts of data. Managing this diverse data and integrating with other biological data are major challenges for the bioinformatics community. Comprehensive new tools are needed to store, integrate and analyze the data efficiently.

Description

The PhenoGen Informatics website http://phenogen.uchsc.edu is a comprehensive toolbox for storing, analyzing and integrating microarray data and related genotype and phenotype data. The site is particularly suited for combining QTL and microarray data to search for "candidate" genes contributing to complex traits. In addition, the site allows, if desired by the investigators, sharing of the data. Investigators can conduct "in-silico" microarray experiments using their own and/or "shared" data.

Conclusion

The PhenoGen website provides access to tools that can be used for high-throughput data storage, analyses and interpretation of the results. Some of the advantages of the architecture of the website are that, in the future, the present set of tools can be adapted for the analyses of any type of high-throughput "omics" data, and that access to new tools, available in the public domain or developed at PhenoGen, can be easily provided.

SUBMITTER: Bhave SV

PROVIDER: S-EPMC2034588 | biostudies-literature | 2007 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The PhenoGen informatics website: tools for analyses of complex traits.

Bhave Sanjiv V SV Hornbaker Cheryl C Phang Tzu L TL Saba Laura L Lapadat Razvan R Kechris Katherina K Gaydos Jeanette J McGoldrick Daniel D Dolbey Andrew A Leach Sonia S Soriano Brian B Ellington Allison A Ellington Eric E Jones Kendra K Mangion Jonathan J Belknap John K JK Williams Robert W RW Hunter Lawrence E LE Hoffman Paula L PL Tabakoff Boris B

BMC genetics 20070830

<h4>Background</h4>With the advent of "omics" (e.g. genomics, transcriptomics, proteomics and phenomics), studies can produce enormous amounts of data. Managing this diverse data and integrating with other biological data are major challenges for the bioinformatics community. Comprehensive new tools are needed to store, integrate and analyze the data efficiently.<h4>Description</h4>The PhenoGen Informatics website http://phenogen.uchsc.edu is a comprehensive toolbox for storing, analyzing and in ...[more]

PMID: 17760997

Similar Datasets

Project description:Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

Dataset Information

The PhenoGen informatics website: tools for analyses of complex traits.

Background

Description

Conclusion

Publications

The PhenoGen informatics website: tools for analyses of complex traits.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets