{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li Q"],"funding":["NIAMS NIH HHS"],"pagination":["284-95"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC3944971"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(2)"],"pubmed_abstract":["A classical approach to combine independent test statistics is Fisher's combination of $p$-values, which follows the $\\chi ^2$ distribution. When the test statistics are dependent, the gamma distribution (GD) is commonly used for the Fisher's combination test (FCT). We propose to use two generalizations of the GD: the generalized and the exponentiated GDs. We study some properties of mis-using the GD for the FCT to combine dependent statistics when one of the two proposed distributions are true. Our results show that both generalizations have better control of type I error rates than the GD, which tends to have inflated type I error rates at more extreme tails. In practice, common model selection criteria (e.g. Akaike information criterion/Bayesian information criterion) can be used to help select a better distribution to use for the FCT. A simple strategy of the two generalizations of the GD in genome-wide association studies is discussed. Applications of the results to genetic pleiotrophic associations are described, where multiple traits are tested for association with a single marker."],"journal":["Biostatistics (Oxford, England)"],"pubmed_title":["Fisher's method of combining dependent statistics using generalizations of the gamma distribution with applications to genetic pleiotropic associations."],"pmcid":["PMC3944971"],"funding_grant_id":["N01-AR-2-2263","R01-AR-44422"],"pubmed_authors":["Hu J","Li Q","Ding J","Zheng G"],"additional_accession":[]},"is_claimable":false,"name":"Fisher's method of combining dependent statistics using generalizations of the gamma distribution with applications to genetic pleiotropic associations.","description":"A classical approach to combine independent test statistics is Fisher's combination of $p$-values, which follows the $\\chi ^2$ distribution. When the test statistics are dependent, the gamma distribution (GD) is commonly used for the Fisher's combination test (FCT). We propose to use two generalizations of the GD: the generalized and the exponentiated GDs. We study some properties of mis-using the GD for the FCT to combine dependent statistics when one of the two proposed distributions are true. Our results show that both generalizations have better control of type I error rates than the GD, which tends to have inflated type I error rates at more extreme tails. In practice, common model selection criteria (e.g. Akaike information criterion/Bayesian information criterion) can be used to help select a better distribution to use for the FCT. A simple strategy of the two generalizations of the GD in genome-wide association studies is discussed. Applications of the results to genetic pleiotrophic associations are described, where multiple traits are tested for association with a single marker.","dates":{"release":"2014-01-01T00:00:00Z","publication":"2014 Apr","modification":"2024-11-08T19:08:12.574Z","creation":"2019-03-27T01:22:50Z"},"accession":"S-EPMC3944971","cross_references":{"pubmed":["24174580"],"doi":["10.1093/biostatistics/kxt045"]}}