R package implementing hierarchical error control procedures. In the context of eQTL studies, TreeQTL provides methods allowing control of the false discovery rate or family wise error rate for the discovery of eSNPs or eGenes, as well as control of the expected average proportion of false discoveries for eAssociations involving the identified eSNPs or eGenes. In the multi-tissue eQTL setting, TreeQTL implements selection procedures which control error rates relevant to the discovery of eSNPs and eGenes which may be active in multiple tissues. In the context of multi-trait association studies, TreeQTL can be used to control the error rate for the discovery of variants associated to any phenotypes and the average false discovery rate of phenotypes influenced by such variants.
For a more general approach to error control for tree-structured hypotheses, please see the R package TreeBH, available at http://odin.mdacc.tmc.edu/~cbpeterson/software.html, which implements the methods proposed in Bogomolov et al. (2021).
|Author:||Christine B. Peterson|
|Version info:||Version 2.0 includes multi-tissue eQTL procedures|
|Installation:||To install the package from the source given above, run the commmand install.packages("TreeQTL_2.0.tgz", repos = NULL, type = "source"). Please ensure that you have the most recent version of the data.table package installed.|
|References:||Peterson CB, Bogomolov M, Benjamini Y, Sabatti C. (2016) TreeQTL: hierarchical error control for eQTL findings. Bioinformatics. 32(16): 2556-2558. Peterson CB, Bogomolov M, Benjamini Y, Sabatti C. (2016) Many phenotypes without many false discoveries: Error controlling strategies for multi-trait association studies. Genetic Epidemiology. 40(1): 45-56. Bogomolov M*, Peterson CB*, Benjamini Y, Sabatti C. (2021) Hypotheses on a tree: new error rates and testing strategies. Biometrika. 108(3): 575-590. *Authors contributed equally.|