SECA: SNP effect concordance analysis using genome-wide association summary results

Nyholt, Dale R. (2014) SECA: SNP effect concordance analysis using genome-wide association summary results. Bioinformatics, 30(14), pp. 2086-2088.

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Abstract

The genomics era provides opportunities to assess the genetic overlap across phenotypes at the measured genotype level; however, current approaches require individual-level genome-wide association (GWA) single nucleotide polymorphism (SNP) genotype data in one or both of a pair of GWA samples. To facilitate the discovery of pleiotropic effects and examine genetic overlap across two phenotypes, I have developed a user-friendly web-based application called SECA to perform SNP effect concordance analysis using GWA summary results. The method is validated using publicly available summary data from the Psychiatric Genomics Consortium.

Impact and interest:

12 citations in Scopus
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13 citations in Web of Science®

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66 since deposited on 19 May 2015
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ID Code: 84317
Item Type: Journal Article
Refereed: Yes
Additional Information: Unmapped bibliographic data:
LA - English [Field not mapped to EPrints]
J2 - Bioinformatics [Field not mapped to EPrints]
C2 - 24695403 [Field not mapped to EPrints]
AD - Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane 4000, QLD, Australia [Field not mapped to EPrints]
DB - Scopus [Field not mapped to EPrints]
Keywords: article, computer program, genetic association, genomics, genotype, human, Internet, methodology, phenotype, single nucleotide polymorphism, Genome-Wide Association Study, Genomics, Genotype, Humans, Internet, Phenotype, Polymorphism, Single Nucleotide, Software
DOI: 10.1093/bioinformatics/btu171
ISSN: 1367-4803
Divisions: Current > Schools > School of Biomedical Sciences
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2014 Author
Deposited On: 19 May 2015 03:38
Last Modified: 27 Jan 2017 07:02

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