A powerful truncated tail strength method for testing multiple null hypotheses in one dataset

Jiang, Bo; Zhang, Xiao; Zuo, Yijun; Kang, Guolian

doi:10.1016/j.jtbi.2011.01.029

Cited by 17 publications

(17 citation statements)

References 24 publications

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“…We can calculate the pairwise correlation matrix between these marker-based interaction test statistics, Σ, using Equation (2) or Equation (3), depending on whether genotype information is available. Using Σ, we are able extend four P value combining methods to four equivalent tests of GGG, GG_minP [32], GG_GATES [31], GG_tTS [47], and GG_tProd [48] as described in the following sections.…”

Section: Methodsmentioning

confidence: 99%

“…Jiang et al extended the original tail strength method to a truncated version which only combines P values less than a predefined cutoff value, and demonstrated its superior power through simulations [47]. We derived the GG_tTS statistic for GGG as,where I(.)…”

Section: Methodsmentioning

confidence: 99%

“…While it is imperative to account for the correlation between tests of different markers that is due to LD, this can be achieved using estimates from an external reference panel if genotype information is not available. Here, we propose four gene-based gene-gene interaction (GGG) tests of quantitative traits by extending four existing methods of combining P values: (i) minimum p value [32], (ii) extended Simes procedure (GATES) [31], (iii) truncated tail strength [47], and (iv) truncated-product P value [48]. Our tests employ these methods to combine P values of interaction tests between all pairs of individual SNPs to obtain a P value for a GGG test, while accounting for the correlation between the individual P values (Figure 1B).…”

Section: Introductionmentioning

confidence: 99%

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Gene-Based Testing of Interactions in Association Studies of Quantitative Traits

Clark

Keinan

2013

PLoS Genet

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Various methods have been developed for identifying gene–gene interactions in genome-wide association studies (GWAS). However, most methods focus on individual markers as the testing unit, and the large number of such tests drastically erodes statistical power. In this study, we propose novel interaction tests of quantitative traits that are gene-based and that confer advantage in both statistical power and biological interpretation. The framework of gene-based gene–gene interaction (GGG) tests combine marker-based interaction tests between all pairs of markers in two genes to produce a gene-level test for interaction between the two. The tests are based on an analytical formula we derive for the correlation between marker-based interaction tests due to linkage disequilibrium. We propose four GGG tests that extend the following P value combining methods: minimum P value, extended Simes procedure, truncated tail strength, and truncated P value product. Extensive simulations point to correct type I error rates of all tests and show that the two truncated tests are more powerful than the other tests in cases of markers involved in the underlying interaction not being directly genotyped and in cases of multiple underlying interactions. We applied our tests to pairs of genes that exhibit a protein–protein interaction to test for gene-level interactions underlying lipid levels using genotype data from the Atherosclerosis Risk in Communities study. We identified five novel interactions that are not evident from marker-based interaction testing and successfully replicated one of these interactions, between SMAD3 and NEDD9, in an independent sample from the Multi-Ethnic Study of Atherosclerosis. We conclude that our GGG tests show improved power to identify gene-level interactions in existing, as well as emerging, association studies.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gene-Based Testing of Interactions in Association Studies of Quantitative Traits

Clark

Keinan

2013

PLoS Genet

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“…To consider information from beyond only the most significant SNP in a gene, we also implemented the truncated tail strength [40] and the truncated product methods [41] to combine p-values in each gene in replacement of the minimum p-value, and followed a similar procedure for assigning new gene-level p-values. For the analyses presented in the following, results from all methods were similar though results with the minimum p-value approach clusters similar diseases better (Figure S2, S3).…”

Section: Methodsmentioning

confidence: 99%

Principal Component Analysis Characterizes Shared Pathogenetics from Genome-Wide Association Studies

Chang

Keinan

2014

PLoS Comput Biol

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Genome-wide association studies (GWASs) have recently revealed many genetic associations that are shared between different diseases. We propose a method, disPCA, for genome-wide characterization of shared and distinct risk factors between and within disease classes. It flips the conventional GWAS paradigm by analyzing the diseases themselves, across GWAS datasets, to explore their “shared pathogenetics”. The method applies principal component analysis (PCA) to gene-level significance scores across all genes and across GWASs, thereby revealing shared pathogenetics between diseases in an unsupervised fashion. Importantly, it adjusts for potential sources of heterogeneity present between GWAS which can confound investigation of shared disease etiology. We applied disPCA to 31 GWASs, including autoimmune diseases, cancers, psychiatric disorders, and neurological disorders. The leading principal components separate these disease classes, as well as inflammatory bowel diseases from other autoimmune diseases. Generally, distinct diseases from the same class tend to be less separated, which is in line with their increased shared etiology. Enrichment analysis of genes contributing to leading principal components revealed pathways that are implicated in the immune system, while also pointing to pathways that have yet to be explored before in this context. Our results point to the potential of disPCA in going beyond epidemiological findings of the co-occurrence of distinct diseases, to highlighting novel genes and pathways that unsupervised learning suggest to be key players in the variability across diseases.

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“…However, the value of the statistic can be small when effect is truly present in only a few partial tests, therefore potentially reducing power. By analogy to the Zaykin method, Jiang et al (2011) proposed to compute the tail strength using only partial tests with p-values smaller than a certain level α . The method is called truncated tail strength ( tts ).…”

Section: Tippettmentioning

confidence: 99%

Non‐parametric combination and related permutation tests for neuroimaging

Winkler

Webster

Brooks

et al. 2016

Human Brain Mapping

236

263

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In this work we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well-known definition of union-intersection tests and closed testing procedures, we use synchronised permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume-based representations of the brain, including non-imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced Non-Parametric Combination (NPC) methodology, such that instead of a two-phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one-way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction.

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A powerful truncated tail strength method for testing multiple null hypotheses in one dataset

Cited by 17 publications

References 24 publications

Gene-Based Testing of Interactions in Association Studies of Quantitative Traits

Gene-Based Testing of Interactions in Association Studies of Quantitative Traits

Principal Component Analysis Characterizes Shared Pathogenetics from Genome-Wide Association Studies

Non‐parametric combination and related permutation tests for neuroimaging

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