C B Peterson1, M Bogomolov2, Y Benjamini3, C Sabatti4. 1. Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA. 2. Faculty of Industrial Engineering and Management, Technion, Haifa 32000, Israel. 3. Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 6997801, Israel. 4. Departments of Biomedical Data Science and Statistics, Stanford University, Stanford, CA 94305, USA.
Abstract
UNLABELLED: : Commonly used multiplicity adjustments fail to control the error rate for reported findings in many expression quantitative trait loci (eQTL) studies. TreeQTL implements a hierarchical multiple testing procedure which allows control of appropriate error rates defined relative to a grouping of the eQTL hypotheses. AVAILABILITY AND IMPLEMENTATION: The R package TreeQTL is available for download at http://bioinformatics.org/treeqtl CONTACT: sabatti@stanford.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: : Commonly used multiplicity adjustments fail to control the error rate for reported findings in many expression quantitative trait loci (eQTL) studies. TreeQTL implements a hierarchical multiple testing procedure which allows control of appropriate error rates defined relative to a grouping of the eQTL hypotheses. AVAILABILITY AND IMPLEMENTATION: The R package TreeQTL is available for download at http://bioinformatics.org/treeqtl CONTACT: sabatti@stanford.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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