A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study

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dc.contributor.authorLiu, Qingzhong
dc.contributor.authorChen, Zhongxue
dc.contributor.authorYang, William
dc.contributor.authorYang, Jack Y
dc.contributor.authorLi, Jing
dc.contributor.authorYang, Mary Qu
dc.date.accessioned2022-01-21T17:21:34Z
dc.date.available2022-01-21T17:21:34Z
dc.date.issued2014-12-16
dc.descriptionArticle originally published in BMC Bioinformatics
dc.description.abstractBackground: Combining information from different studies is an important and useful practice in bioinformatics, including genome-wide association study, rare variant data analysis and other set-based analyses. Many statistical methods have been proposed to combine p-values from independent studies. However, it is known that there is no uniformly most powerful test under all conditions; therefore, finding a powerful test in specific situation is important and desirable. Results: In this paper, we propose a new statistical approach to combining p-values based on gamma distribution, which uses the inverse of the p-value as the shape parameter in the gamma distribution. Conclusions: Simulation study and real data application demonstrate that the proposed method has good performance under some situations.
dc.description.sponsorshipIndiana University Bloomington institutional research grant and NIH/NIGMS 5P20GM10342913 and ASTA Award # 15-B-23.
dc.description.subjectp-value
dc.identifier.citationChen Z, Yang W, Liu Q, Yang JY, Li J and Yang MQ (2014). A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study. BMC Bioinformatics, 2014, 15(Suppl 17):S3 (IF:2448).
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3254
dc.language.isoen_US
dc.publisherBMC Bioinformatics
dc.subjectgenome-wide
dc.subjectbioinformatics
dc.subjectrare variant data analysis
dc.subjectgamma distribution
dc.titleA new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study
dc.typeArticle

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