Literature DB >> 19208106

Robust tests for single-marker analysis in case-control genetic association studies.

Qizhai Li1, Gang Zheng, Xueying Liang, Kai Yu.   

Abstract

Choosing an appropriate single-marker association test is critical to the success of case-control genetic association studies.An ideal single-marker analysis should have robust performance across a wide range of potential disease risk models. MAX was designed specifically to achieve such robustness. In this work, we derived the power calculation formula for MAX and conducted a comprehensive power comparison between MAX and two other commonly used single-marker tests,the one-degree-of-freedom (1-df) Cochran-Armitage trend test and the 2-df Pearson chi2 test. We used a single-marker disease risk model and a two-marker haplotype risk model to explore the performances of the above three tests. We found that each test has its own "sweet" spots. Among the three tests considered, MAX appears to have the most robust performance.

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Year:  2009        PMID: 19208106      PMCID: PMC5742554          DOI: 10.1111/j.1469-1809.2009.00506.x

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


  13 in total

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

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Journal:  Nature       Date:  2007-02-11       Impact factor: 49.962

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Journal:  Nat Genet       Date:  2007-05-27       Impact factor: 38.330

5.  So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests.

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6.  MAX-rank: a simple and robust genome-wide scan for case-control association studies.

Authors:  Qizhai Li; Kai Yu; Zhaohai Li; Gang Zheng
Journal:  Hum Genet       Date:  2008-05-20       Impact factor: 4.132

7.  What's the best statistic for a simple test of genetic association in a case-control study?

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Journal:  Genet Epidemiol       Date:  2010-04       Impact factor: 2.135

8.  Genome-wide association study of prostate cancer identifies a second risk locus at 8q24.

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Journal:  Nat Genet       Date:  2007-04-01       Impact factor: 38.330

9.  Power and sample size calculations in the presence of phenotype errors for case/control genetic association studies.

Authors:  Brian J Edwards; Chad Haynes; Mark A Levenstien; Stephen J Finch; Derek Gordon
Journal:  BMC Genet       Date:  2005-04-08       Impact factor: 2.797

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Authors: 
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  11 in total

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Journal:  Genet Epidemiol       Date:  2012-05-30       Impact factor: 2.135

2.  Incorporating parental information into family-based association tests.

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Journal:  Biostatistics       Date:  2012-12-23       Impact factor: 5.899

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

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Journal:  Ann Hum Genet       Date:  2017-06-27       Impact factor: 1.670

4.  Fine-mapping additive and dominant SNP effects using group-LASSO and fractional resample model averaging.

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5.  Robust joint analysis allowing for model uncertainty in two-stage genetic association studies.

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6.  Response to interferon-beta treatment in multiple sclerosis patients: a genome-wide association study.

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

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8.  The role of microRNA-binding site polymorphisms in DNA repair genes as risk factors for bladder cancer and breast cancer and their impact on radiotherapy outcomes.

Authors:  Mark T W Teo; Debora Landi; Claire F Taylor; Faye Elliott; Laurence Vaslin; David G Cox; Janet Hall; Stefano Landi; D Timothy Bishop; Anne E Kiltie
Journal:  Carcinogenesis       Date:  2011-12-12       Impact factor: 4.944

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

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10.  Robust joint analysis with data fusion in two-stage quantitative trait genome-wide association studies.

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