GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation
Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation. PLOS Genetics 2014, 10: e1004787. PMID: 25393678, PMCID: PMC4230845, DOI: 10.1371/journal.pgen.1004787.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesFunctional annotationGWAS datasetsAnnotation informationStatistical approachMultiple GWAS datasetsGenome-wide markersPowerful statistical methodsSingle-phenotype analysisCentral nervous system genesRisk variantsNervous system genesGenotype-Tissue Expression (GTEx) databaseComplex diseasesGWAS data setsSignificant pleiotropic effectsCommon risk basisDifferent complex diseasesDNase-seq dataCell linesStatistical inferenceGenetic architectureGWAS hitsGWAS resultsNovel statistical approach