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Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates

The association of genetic variants with outcomes is usually assessed under an additive model, for example by the trend test. However, misspecification of the genetic model will lead to a reduction in power. More robust tests for association might therefore be preferred. A useful approach is to cons...

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Detalles Bibliográficos
Autores principales: So, Hon-Cheong, Sham, Pak C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3162964/
https://www.ncbi.nlm.nih.gov/pubmed/21305351
http://dx.doi.org/10.1007/s10519-011-9450-9
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author So, Hon-Cheong
Sham, Pak C.
author_facet So, Hon-Cheong
Sham, Pak C.
author_sort So, Hon-Cheong
collection PubMed
description The association of genetic variants with outcomes is usually assessed under an additive model, for example by the trend test. However, misspecification of the genetic model will lead to a reduction in power. More robust tests for association might therefore be preferred. A useful approach is to consider the maximum of the three test statistics under additive, dominant and recessive models (MAX3). The p-value however has to be adjusted to maintain the type I error rate. Previous studies and software on robust association tests have focused on binary traits without covariates. In this study we developed an analytic approach to robust association tests using MAX3, allowing for quantitative or binary traits as well as covariates. The p-values from our theoretical calculations match very well with those from a bootstrap resampling procedure. The methodology is implemented in the R package RobustSNP which is able to handle both small-scale studies and GWAS. The package and documentation are available at http://sites.google.com/site/honcheongso/software/robustsnp.
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spelling pubmed-31629642011-09-26 Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates So, Hon-Cheong Sham, Pak C. Behav Genet Original Research The association of genetic variants with outcomes is usually assessed under an additive model, for example by the trend test. However, misspecification of the genetic model will lead to a reduction in power. More robust tests for association might therefore be preferred. A useful approach is to consider the maximum of the three test statistics under additive, dominant and recessive models (MAX3). The p-value however has to be adjusted to maintain the type I error rate. Previous studies and software on robust association tests have focused on binary traits without covariates. In this study we developed an analytic approach to robust association tests using MAX3, allowing for quantitative or binary traits as well as covariates. The p-values from our theoretical calculations match very well with those from a bootstrap resampling procedure. The methodology is implemented in the R package RobustSNP which is able to handle both small-scale studies and GWAS. The package and documentation are available at http://sites.google.com/site/honcheongso/software/robustsnp. Springer US 2011-02-09 2011 /pmc/articles/PMC3162964/ /pubmed/21305351 http://dx.doi.org/10.1007/s10519-011-9450-9 Text en © The Author(s) 2011 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Original Research
So, Hon-Cheong
Sham, Pak C.
Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates
title Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates
title_full Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates
title_fullStr Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates
title_full_unstemmed Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates
title_short Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates
title_sort robust association tests under different genetic models, allowing for binary or quantitative traits and covariates
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3162964/
https://www.ncbi.nlm.nih.gov/pubmed/21305351
http://dx.doi.org/10.1007/s10519-011-9450-9
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