Literature DB >> 19183346

Pearson's test, trend test, and MAX are all trend tests with different types of scores.

Gang Zheng1, Jungnam Joo, Yaning Yang.   

Abstract

Pearson's test is one of the most commonly used statistics for testing genetic association of case-control data. The trend test is another one which assumes a dose-response model between the risk of the disease and genotypes. To apply the trend test, a set of ordered scores is assigned a priori based on the underlying genetic model. Pearson's test is model-free and robust, but is less powerful for common genetic models. MAX is another robust test statistic, which takes the maximum of the trend tests over a family of scientifically plausible genetic models. We show that the three test statistics are all trend tests but with different types of scores; whether the scores are prespecified or data-driven, or whether the scores are ordered (restricted) or not ordered (unrestricted). We then provide insights into power performance of the three tests when the underlying genetic model is unknown and discuss which test to use for the analyses of case-control genetic association studies.

Entities:  

Mesh:

Year:  2009        PMID: 19183346      PMCID: PMC2679501          DOI: 10.1111/j.1469-1809.2008.00500.x

Source DB:  PubMed          Journal:  Ann Hum Genet        ISSN: 0003-4800            Impact factor:   1.670


  16 in total

1.  Trend tests for case-control studies of genetic markers: power, sample size and robustness.

Authors:  B Freidlin; G Zheng; Z Li; J L Gastwirth
Journal:  Hum Hered       Date:  2002       Impact factor: 0.444

2.  Comparison of maximum statistics for hypothesis testing when a nuisance parameter is present only under the alternative.

Authors:  Gang Zheng; Zehua Chen
Journal:  Biometrics       Date:  2005-03       Impact factor: 2.571

3.  A constrained-likelihood approach to marker-trait association studies.

Authors:  Kai Wang; Val C Sheffield
Journal:  Am J Hum Genet       Date:  2005-09-14       Impact factor: 11.025

4.  A genome-wide association study identifies novel risk loci for type 2 diabetes.

Authors:  Robert Sladek; Ghislain Rocheleau; Johan Rung; Christian Dina; Lishuang Shen; David Serre; Philippe Boutin; Daniel Vincent; Alexandre Belisle; Samy Hadjadj; Beverley Balkau; Barbara Heude; Guillaume Charpentier; Thomas J Hudson; Alexandre Montpetit; Alexey V Pshezhetsky; Marc Prentki; Barry I Posner; David J Balding; David Meyre; Constantin Polychronakos; Philippe Froguel
Journal:  Nature       Date:  2007-02-11       Impact factor: 49.962

5.  Genetic model testing and statistical power in population-based association studies of quantitative traits.

Authors:  Guillaume Lettre; Christoph Lange; Joel N Hirschhorn
Journal:  Genet Epidemiol       Date:  2007-05       Impact factor: 2.135

6.  Efficient approximation of P-value of the maximum of correlated tests, with applications to genome-wide association studies.

Authors:  Qizhai Li; Gang Zheng; Zhaohai Li; Kai Yu
Journal:  Ann Hum Genet       Date:  2008-03-03       Impact factor: 1.670

7.  Maximizing association statistics over genetic models.

Authors:  Juan R González; Josep L Carrasco; Frank Dudbridge; Lluís Armengol; Xavier Estivill; Victor Moreno
Journal:  Genet Epidemiol       Date:  2008-04       Impact factor: 2.135

8.  From genotypes to genes: doubling the sample size.

Authors:  P D Sasieni
Journal:  Biometrics       Date:  1997-12       Impact factor: 2.571

Review 9.  A tutorial on statistical methods for population association studies.

Authors:  David J Balding
Journal:  Nat Rev Genet       Date:  2006-10       Impact factor: 53.242

10.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.

Authors: 
Journal:  Nature       Date:  2007-06-07       Impact factor: 49.962

View more
  6 in total

1.  Impact on modes of inheritance and relative risks of using extreme sampling when designing genetic association studies.

Authors:  Gang Zheng; Xu Jinfeng; Ao Yuan; O Wu Colin
Journal:  Ann Hum Genet       Date:  2012-11-20       Impact factor: 1.670

2.  Scalable privacy-preserving data sharing methodology for genome-wide association studies.

Authors:  Fei Yu; Stephen E Fienberg; Aleksandra B Slavković; Caroline Uhler
Journal:  J Biomed Inform       Date:  2014-02-06       Impact factor: 6.317

3.  Differentiating the Cochran-Armitage Trend Test and Pearson's χ2 Test: Location and Dispersion.

Authors:  Zhengyang Zhou; Hung-Chih Ku; Zhipeng Huang; Guan Xing; Chao Xing
Journal:  Ann Hum Genet       Date:  2017-06-27       Impact factor: 1.670

4.  A unifying framework for robust association testing, estimation, and genetic model selection using the generalized linear model.

Authors:  Christina Loley; Inke R König; Ludwig Hothorn; Andreas Ziegler
Journal:  Eur J Hum Genet       Date:  2013-04-10       Impact factor: 4.246

5.  Systematic detection of epistatic interactions based on allele pair frequencies.

Authors:  Marit Ackermann; Andreas Beyer
Journal:  PLoS Genet       Date:  2012-02-09       Impact factor: 5.917

6.  Decomposing Pearson's χ2 test: A linear regression and its departure from linearity.

Authors:  Zhengyang Zhou; Hung-Chih Ku; Guan Xing; Chao Xing
Journal:  Ann Hum Genet       Date:  2018-05-31       Impact factor: 2.180

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.