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


METHODS
http://neurogenetics.qimrberghofer.edu.au/SECA.
Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane 4000, Queensland, Australia.
Dale R
DR
Nyholt
D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D005838:Genotype; D006801:Humans; D020407:Internet; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012984:Software