Cauchy Combination Test: A Powerful Test With Analytic <i>p</i>-Value Calculation Under Arbitrary Dependency Structures

Liu, Yaowu; Xie, Jun

doi:10.1080/01621459.2018.1554485

Cited by 314 publications

(460 citation statements)

References 31 publications

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“…To utilize the power of predictions based on H3K27ac-ChIP-seq and DNase-seq ISD models, and TF ChIP-seq peak-only models (Fig. 1c-5), results are combined using the Cauchy combination test 38 (Fig. 1c-6).…”

Section: Resultsmentioning

confidence: 99%

“…For motif-based methods, DNase-seq and H3K27ac ΔRPs are combined to get the final inference of TRs. Both combination of statistics follows the Cauchy combination test 38 , in which the combined statistics for each TR is , j represents one TR, i represents i th method within ChIP-seq-based or motif-based methods, p i is the corresponding p-value, w i is set to 1/d where d is 3 for ChIP-seq-based method or 2 for the motif-based method. The combined p-value for a TR j is computed as p j = 1 / 2 − (arctan( t j )) / π .…”

Section: Methodsmentioning

confidence: 99%

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Inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data

Qin

Fan

Zheng

et al. 2019

Preprint

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21We developed Lisa (http://lisa.cistrome.org) to predict the transcriptional regulators (TRs) of 22 differentially expressed or co-expressed gene sets. Based on the input gene sets, Lisa first uses 23 compendia of public histone mark ChIP-seq and chromatin accessibility profiles to construct a 24 chromatin model related to the regulation of these genes. Then using TR ChIP-seq peaks or 25 imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the 26 most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa 27 boosted the performance of imputed TR cistromes, and outperformed alternative methods in 28 identifying the perturbed TRs. 29 30 Keywords 31 Transcription factors, gene regulation, chromatin accessibility, DNase-seq, H3K27ac ChIP-seq, 32 differential gene expression, gene set analysis 33 34 List of abbreviations 35 TF: transcription factor 36 CR: chromatin regulator 37 TR: transcriptional regulator 38 RP: regulatory potential 39 ISD: in silico deletion 40 ROC: receiver operator characteristic 41 AUC: area under curve 42 ChIP-seq: chromatin immunoprecipitation followed by DNA sequencing 43 DNase-seq: DNase I digestion followed by DNA sequencing 44 H3K27ac: histone H3 lysine 27 acetylation 45 AR: Androgen Receptor 46 ER: Estrogen Receptor 47 GR: Glucocorticoid Receptor 48 49 Introduction 50Transcriptional regulators (TRs), which include transcription factors (TFs) and chromatin 51 regulators (CRs), play essential roles in controlling normal biological processes and are frequently 52 implicated in disease [1][2][3][4] . The genomic landscape of TF binding sites and histone modifications 53 collectively shape the transcriptional regulatory environments of genes 5-8 . ChIP-seq has been 54 widely used to map the genome-wide set of cis-elements bound by trans-acting factors such as 55 TFs and CRs, which we henceforth refer to as "cistromes" 9 . There are approximately 1,500 56 transcription factors in human and mouse 10,11 , regulating a wide variety of biological processes in 57 constitutive or cell-type-specific manners, and tens of thousands of ChIP-seq and DNase-seq 58 experiments have been performed in human and mouse. We previously developed the Cistrome 59 Data Browser (DB) 12 , a collection of uniformly processed TF ChIP-seq (~11,000) and chromatin 60 profiles (~12,000 histone mark ChIP-seq and DNase-seq) in human and mouse. 62The question we address in this paper is how to effectively use these data to infer the TRs that 63 regulate a query gene set derived from differential or correlated gene expression analyses in 64 human or mouse. TR ChIP-seq data, when available, is the most accurate available data type 65 representing TR binding. ChIP-seq data availability, in terms of covered TRs and cell types, even 66 with large contributions from projects such as ENCODE 13 , is still sparse due to the limited 67 availability of specific antibodies. Although advances have been made in TR cistrome mapping 68 with the introduction of technolo...

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data

Qin

Fan

Zheng

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…To obviate the need for choosing between them, we have proposed an adaptive omnibus test (O-INT). O-INT combines the p-values from D-INT and I-INT via Cauchy combination(Liu and Xie, 2019;, and may easily be extended to incorporate p-values from complementary (e.g. non-parametric) association tests.…”

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confidence: 99%

Operating Characteristics of the Rank-Based Inverse Normal Transformation for Quantitative Trait Analysis in Genome-Wide Association Studies

McCaw

Lane

Saxena

et al. 2019

Preprint

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Quantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often non-normally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based Inverse Normal Transformation (INT) to non-normally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robustto model misspecification, protects the type I error, and is well powered against a wide range of non-normally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for non-normally distributed traits, INT-based tests outperform the standard untransformed association test (UAT), both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available on CRAN.

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“…Other choices of 1 and 2 , e.g., ( 1 , 2 ) = (0.5,0.5) can also be used. The p-value of could be approximated by29…”

mentioning

confidence: 99%

“…Instead of performing the usual Bonferroni correction, which could be quite conservative for multiple testing adjustment in this situation, we empirically calculate genome-wise/family-wise p-values by efficient Monte Carlo simulations. For each region, we calculate the region-based p-value using the score-based tests, such as burden and SKAT and the new omnibus tests that combine burden and SKAT and different weights using the Cauchy-based ACAT method 29. Calculations of these methods only require fitting the null model by regressing a phenotype on covariates once across the genome.…”

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confidence: 99%

Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole Genome Sequencing Studies

Liu

et al. 2019

Preprint

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1Whole genome sequencing (WGS) studies are being widely conducted to identify rare 2 variants associated with human diseases and disease-related traits. Classical single-3 marker association analyses for rare variants have limited power, and variant-set based 4analyses are commonly used to analyze rare variants. However, existing variant-set 5 based approaches need to pre-specify genetic regions for analysis, and hence are not 6 directly applicable to WGS data due to the large number of intergenic and intron regions 7 that consist of a massive number of non-coding variants. The commonly used sliding 8 window method requires pre-specifying fixed window sizes, which are often unknown as 9 a priori, are difficult to specify in practice and are subject to limitations given genetic 10 association region sizes are likely to vary across the genome and phenotypes. We 11 propose a computationally-efficient and dynamic scan statistic method (Scan the 12 Genome (SCANG)) for analyzing WGS data that flexibly detects the sizes and the 13 locations of rare-variants association regions without the need of specifying a prior fixed 14 window size. The proposed method controls the genome-wise type I error rate and 15 accounts for the linkage disequilibrium among genetic variants. It allows the detected 16 rare variants association region sizes to vary across the genome. Through extensive 17 simulated studies that consider a wide variety of scenarios, we show that SCANG 18 substantially outperforms several alternative rare-variant association detection methods 19 while controlling for the genome-wise type I error rates. We illustrate SCANG by 20 analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) 21 study. 22 23

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Cauchy Combination Test: A Powerful Test With Analytic p-Value Calculation Under Arbitrary Dependency Structures

Cited by 314 publications

References 31 publications

Inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data

Inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data

Operating Characteristics of the Rank-Based Inverse Normal Transformation for Quantitative Trait Analysis in Genome-Wide Association Studies

Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole Genome Sequencing Studies

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