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

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Abstract

Summary:

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.

Availability and implementation:

http://neurogenetics.qimrberghofer.edu.au/SECA.

Contact:

dale.nyholt@qimrberghofer.edu.au

Supplementary information:

Supplementary data are available at Bioinformatics online.

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