Literature DB >> 12925518

Combining dependent tests for linkage or association across multiple phenotypic traits.

Xin Xu1, Lu Tian, L J Wei.   

Abstract

A robust statistical method to detect linkage or association between a genetic marker and a set of distinct phenotypic traits is to combine univariate trait-specific test statistics for a more powerful overall test. This procedure does not need complex modeling assumptions, can easily handle the problem with partially missing trait values, and is applicable to the case with a mixture of qualitative and quantitative traits. In this note, we propose a simple test procedure along this line, and show its advantages over the standard combination tests for linkage or association in the literature through a data set from Genetic Analysis Workshop 12 (GAW12) and an extensive simulation study.

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Year:  2003        PMID: 12925518     DOI: 10.1093/biostatistics/4.2.223

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  35 in total

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3.  A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.

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5.  Exact and efficient inference procedure for meta-analysis and its application to the analysis of independent 2 x 2 tables with all available data but without artificial continuity correction.

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Journal:  Biostatistics       Date:  2008-10-14       Impact factor: 5.899

6.  Power analysis of principal components regression in genetic association studies.

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Journal:  J Zhejiang Univ Sci B       Date:  2009-10       Impact factor: 3.066

7.  Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.

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Journal:  Am J Hum Genet       Date:  2014-12-11       Impact factor: 11.025

8.  Statistical tests for detecting associations with groups of genetic variants: generalization, evaluation, and implementation.

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9.  Quantitative and Qualitative Role of Antagonistic Heterogeneity in Genetics of Blood Lipids.

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10.  Omnibus risk assessment via accelerated failure time kernel machine modeling.

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Journal:  Biometrics       Date:  2013-11-06       Impact factor: 2.571

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