Literature DB >> 18854360

GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies.

Marit Holden1, Shiwei Deng, Leszek Wojnowski, Bettina Kulle.   

Abstract

The power of genome-wide SNP association studies is limited, among others, by the large number of false positive test results. To provide a remedy, we combined SNP association analysis with the pathway-driven gene set enrichment analysis (GSEA), recently developed to facilitate handling of genome-wide gene expression data. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype are enriched in genes constituting a signaling pathway or those with a common regulation. Besides improving power for association mapping, GSEA-SNP may facilitate the identification of disease-associated SNPs and pathways, as well as the understanding of the underlying biological mechanisms. GSEA-SNP may also help to identify markers with weak effects, undetectable in association studies without pathway consideration. The program is freely available and can be downloaded from our website.

Entities:  

Mesh:

Year:  2008        PMID: 18854360     DOI: 10.1093/bioinformatics/btn516

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  103 in total

1.  Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets.

Authors:  Qing Xiong; Nicola Ancona; Elizabeth R Hauser; Sayan Mukherjee; Terrence S Furey
Journal:  Genome Res       Date:  2011-09-22       Impact factor: 9.043

2.  Disease and phenotype gene set analysis of disease-based gene expression in mouse and human.

Authors:  Supriyo De; Yongqing Zhang; John R Garner; S Alex Wang; Kevin G Becker
Journal:  Physiol Genomics       Date:  2010-08-03       Impact factor: 3.107

3.  Testing SNPs and sets of SNPs for importance in association studies.

Authors:  Holger Schwender; Ingo Ruczinski; Katja Ickstadt
Journal:  Biostatistics       Date:  2010-07-02       Impact factor: 5.899

Review 4.  Analysing biological pathways in genome-wide association studies.

Authors:  Kai Wang; Mingyao Li; Hakon Hakonarson
Journal:  Nat Rev Genet       Date:  2010-12       Impact factor: 53.242

Review 5.  Integration of biological networks and pathways with genetic association studies.

Authors:  Yan V Sun
Journal:  Hum Genet       Date:  2012-07-10       Impact factor: 4.132

Review 6.  Application of computational methods in genetic study of inflammatory bowel disease.

Authors:  Jin Li; Zhi Wei; Hakon Hakonarson
Journal:  World J Gastroenterol       Date:  2016-01-21       Impact factor: 5.742

7.  A FAST ALGORITHM FOR DETECTING GENE-GENE INTERACTIONS IN GENOME-WIDE ASSOCIATION STUDIES.

Authors:  Jiahan Li; Wei Zhong; Runze Li; Rongling Wu
Journal:  Ann Appl Stat       Date:  2014       Impact factor: 2.083

8.  Identification of additional loci associated with antibody response to Mycobacterium avium ssp. Paratuberculosis in cattle by GSEA-SNP analysis.

Authors:  Marcello Del Corvo; Mario Luini; Alessandra Stella; Giulio Pagnacco; Paolo Ajmone-Marsan; John L Williams; Giulietta Minozzi
Journal:  Mamm Genome       Date:  2017-09-01       Impact factor: 2.957

Review 9.  Systems analysis of high-throughput data.

Authors:  Rosemary Braun
Journal:  Adv Exp Med Biol       Date:  2014       Impact factor: 2.622

Review 10.  The genetic basis for interindividual immune response variation to measles vaccine: new understanding and new vaccine approaches.

Authors:  Iana H Haralambieva; Inna G Ovsyannikova; V Shane Pankratz; Richard B Kennedy; Robert M Jacobson; Gregory A Poland
Journal:  Expert Rev Vaccines       Date:  2013-01       Impact factor: 5.217

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.