scholarly journals TreeQTL: hierarchical error control for eQTL findings

2015 ◽  
Author(s):  
Christine Peterson ◽  
Marina Bogomolov ◽  
Yoav Benjamini ◽  
Chiara Sabatti
Keyword(s):  
Quantitative Trait Loci ◽  
Error Rate ◽  
Quantitative Trait ◽  
Multiple Testing ◽  
Error Control ◽  
R Package ◽  
Testing Procedure ◽  
Error Rates ◽  
Multiple Testing Procedure ◽  
Hierarchical Grouping

Commonly used multiplicity adjustments fail to control the error rate for reported findings in many expression quantitative trait loci (eQTL) studies. TreeQTL implements a stage-wise multiple testing procedure which allows control of appropriate error rates defined relative to a hierarchical grouping of the eQTL hypotheses. The R package TreeQTL is available for download at http://bioinformatics.org/treeqtl.

scholarly journals A region-based multiple testing method for hypotheses ordered in space or time

2015 ◽  
Vol 14 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Rosa J. Meijer ◽  
Thijmen J.P. Krebs ◽  
Jelle J. Goeman
Keyword(s):  
Error Rate ◽  
Null Hypothesis ◽  
Multiple Testing ◽  
Testing Procedure ◽  
Familywise Error Rate ◽  
Expected Number ◽  
Multiple Testing Procedure ◽  
Exact Location ◽  
Testing Method

AbstractWe present a multiple testing method for hypotheses that are ordered in space or time. Given such hypotheses, the elementary hypotheses as well as regions of consecutive hypotheses are of interest. These region hypotheses not only have intrinsic meaning but testing them also has the advantage that (potentially small) signals across a region are combined in one test. Because the expected number and length of potentially interesting regions are usually not available beforehand, we propose a method that tests all possible region hypotheses as well as all individual hypotheses in a single multiple testing procedure that controls the familywise error rate. We start at testing the global null-hypothesis and when this hypothesis can be rejected we continue with further specifying the exact location/locations of the effect present. The method is implemented in the

Application of the False Discovery Rate to Quantitative Trait Loci Interval Mapping With Multiple Traits

Genetics ◽  
2002 ◽  
Vol 161 (2) ◽  
pp. 905-914 ◽  
Author(s):  
Hakkyo Lee ◽  
Jack C M Dekkers ◽  
M Soller ◽  
Massoud Malek ◽  
Rohan L Fernando ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
False Discovery Rate ◽  
Error Rate ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Error Rates ◽  
Multiple Traits ◽  
Test Statistic ◽  
False Discovery ◽  
Trait Loci

Abstract Controlling the false discovery rate (FDR) has been proposed as an alternative to controlling the genomewise error rate (GWER) for detecting quantitative trait loci (QTL) in genome scans. The objective here was to implement FDR in the context of regression interval mapping for multiple traits. Data on five traits from an F2 swine breed cross were used. FDR was implemented using tests at every 1 cM (FDR1) and using tests with the highest test statistic for each marker interval (FDRm). For the latter, a method was developed to predict comparison-wise error rates. At low error rates, FDR1 behaved erratically; FDRm was more stable but gave similar significance thresholds and number of QTL detected. At the same error rate, methods to control FDR gave less stringent significance thresholds and more QTL detected than methods to control GWER. Although testing across traits had limited impact on FDR, single-trait testing was recommended because there is no theoretical reason to pool tests across traits for FDR. FDR based on FDRm was recommended for QTL detection in interval mapping because it provides significance tests that are meaningful, yet not overly stringent, such that a more complete picture of QTL is revealed.

The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses

2012 ◽  
Vol 11 (1) ◽  
Author(s):  
Jelle J. Goeman ◽  
Livio Finos
Keyword(s):  
Multiple Testing ◽  
Error Control ◽  
Testing Procedure ◽  
Tree Structure ◽  
Graph Structure ◽  
Multiple Testing Procedure ◽  
Natural Way ◽  
Chromosome Level

Hypotheses tests in bioinformatics can often be set in a tree structure in a very natural way, e.g. when tests are performed at probe, gene, and chromosome level. Exploiting this graph structure in a multiple testing procedure may result in a gain in power or increased interpretability of the results.We present the inheritance procedure, a method of familywise error control for hypotheses structured in a tree. The method starts testing at the top of the tree, following up on those branches in which it finds significant results, and following up on leaf nodes in the neighborhood of those leaves. The method is a uniform improvement over a recently proposed method by Meinshausen. The inheritance procedure has been implemented in the globaltest package which is available on www.bioconductor.org.

Hypotheses on a tree: new error rates and testing strategies

Biometrika ◽  
2020 ◽  
Author(s):  
Marina Bogomolov ◽  
Christine B Peterson ◽  
Yoav Benjamini ◽  
Chiara Sabatti
Keyword(s):  
Multiple Testing ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Testing Procedure ◽  
Error Rates ◽  
Alternative Methods ◽  
Multiple Testing Procedure ◽  
Testing Strategies ◽  
Dependency Structures ◽  
Selection Of

Abstract We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We describe a fast algorithm and prove that it controls relevant error rates given certain assumptions on the dependence between the p-values. Through simulations, we demonstrate that the proposed procedure provides the desired guarantees under a range of dependency structures and that it has the potential to gain power over alternative methods. Finally, we apply the method to studies on the genetic regulation of gene expression across multiple tissues and on the relation between the gut microbiome and colorectal cancer.

scholarly journals An Alpha-Exhaustive Multiple Testing Procedure

2016 ◽  
Vol 6 (2) ◽  
pp. 30-41
Author(s):  
Mark Chang ◽  
Xuan Deng ◽  
John Balser
Keyword(s):  
Multiple Testing ◽  
Testing Procedure ◽  
Multiple Testing Procedure

A factor-adjusted multiple testing procedure for ERP data analysis

2012 ◽  
Vol 44 (3) ◽  
pp. 635-643 ◽  
Author(s):  
David Causeur ◽  
Mei-Chen Chu ◽  
Shulan Hsieh ◽  
Ching-Fan Sheu
Keyword(s):  
Data Analysis ◽  
Multiple Testing ◽  
Testing Procedure ◽  
Multiple Testing Procedure
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