Literature DB >> 25750439

Analytic P-value calculation for the higher criticism test in finite d problems.

Ian J Barnett1, Xihong Lin1.   

Abstract

The higher criticism is effective for testing a joint null hypothesis against a sparse alternative, e.g., for testing the effect of a gene or a genetic pathway that consists of d genetic markers. Accurate p-value calculations for the higher criticism based on the asymptotic distribution require a very large d, which is not the case for the number of genetic variants in a gene or a pathway. In this paper we propose an analytic method that accurately computes the p-value of the higher criticism test for finite d problems. Unlike previous treatments, this method does not rely on asymptotics in d or simulation, and is exact for arbitrary d when the test statistics are normally distributed. The method is particularly computationally advantageous when d is not large. We illustrate the proposed method with a case-control genome-wide association study of lung cancer and compare its power to competing methods through simulations.

Entities:  

Keywords:  Empirical process; Genome-wide association studies; Higher criticism; Multiple hypothesis testing; Signal detection

Year:  2014        PMID: 25750439      PMCID: PMC4350786          DOI: 10.1093/biomet/asu033

Source DB:  PubMed          Journal:  Biometrika        ISSN: 0006-3444            Impact factor:   2.445


  7 in total

1.  Principal components analysis corrects for stratification in genome-wide association studies.

Authors:  Alkes L Price; Nick J Patterson; Robert M Plenge; Michael E Weinblatt; Nancy A Shadick; David Reich
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2.  Higher criticism thresholding: Optimal feature selection when useful features are rare and weak.

Authors:  David Donoho; Jiashun Jin
Journal:  Proc Natl Acad Sci U S A       Date:  2008-09-24       Impact factor: 11.205

3.  Haplotype-based association analysis via variance-components score test.

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Journal:  Am J Hum Genet       Date:  2007-10-03       Impact factor: 11.025

4.  Rare-variant association testing for sequencing data with the sequence kernel association test.

Authors:  Michael C Wu; Seunggeun Lee; Tianxi Cai; Yun Li; Michael Boehnke; Xihong Lin
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5.  The CHRNA5-A3 region on chromosome 15q24-25.1 is a risk factor both for nicotine dependence and for lung cancer.

Authors:  Margaret R Spitz; Christopher I Amos; Qiong Dong; Jie Lin; Xifeng Wu
Journal:  J Natl Cancer Inst       Date:  2008-10-28       Impact factor: 13.506

6.  Quantitative proteomics reveals regulation of karyopherin subunit alpha-2 (KPNA2) and its potential novel cargo proteins in nonsmall cell lung cancer.

Authors:  Chun-I Wang; Kun-Yi Chien; Chih-Liang Wang; Hao-Ping Liu; Chia-Chen Cheng; Yu-Sun Chang; Jau-Song Yu; Chia-Jung Yu
Journal:  Mol Cell Proteomics       Date:  2012-07-25       Impact factor: 5.911

7.  The TERT-CLPTM1L lung cancer susceptibility variant associates with higher DNA adduct formation in the lung.

Authors:  Shanbeh Zienolddiny; Vidar Skaug; Nina E Landvik; David Ryberg; David H Phillips; Richard Houlston; Aage Haugen
Journal:  Carcinogenesis       Date:  2009-05-22       Impact factor: 4.944

  7 in total
  7 in total

1.  The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies.

Authors:  Ian Barnett; Rajarshi Mukherjee; Xihong Lin
Journal:  J Am Stat Assoc       Date:  2017-05-03       Impact factor: 5.033

2.  Optimal detection of weak positive latent dependence between two sequences of multiple tests.

Authors:  Sihai Dave Zhao; T Tony Cai; Hongzhe Li
Journal:  J Multivar Anal       Date:  2017-07-14       Impact factor: 1.473

3.  A powerful microbial group association test based on the higher criticism analysis for sparse microbial association signals.

Authors:  Hyunwook Koh; Ni Zhao
Journal:  Microbiome       Date:  2020-05-11       Impact factor: 14.650

4.  Effective SNP ranking improves the performance of eQTL mapping.

Authors:  X Jessie Jeng; Jacob Rhyne; Teng Zhang; Jung-Ying Tzeng
Journal:  Genet Epidemiol       Date:  2020-03-26       Impact factor: 2.135

5.  An Omnibus Test for Detecting Multiple Phenotype Associations Based on GWAS Summary Level Data.

Authors:  Wei Liu; Yunshan Guo; Zhonghua Liu
Journal:  Front Genet       Date:  2021-03-17       Impact factor: 4.599

6.  p-Value Histograms: Inference and Diagnostics.

Authors:  Patrick Breheny; Arnold Stromberg; Joshua Lambert
Journal:  High Throughput       Date:  2018-08-31

7.  Adaptive Fisher method detects dense and sparse signals in association analysis of SNV sets.

Authors:  Xiaoyu Cai; Lo-Bin Chang; Jordan Potter; Chi Song
Journal:  BMC Med Genomics       Date:  2020-04-03       Impact factor: 3.063

  7 in total

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