CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates

The additive genetic model as implemented in logistic regression has been widely used in genome-wide association studies (GWASs) for binary outcomes. Unfortunately, for many complex diseases, the underlying genetic models are generally unknown and a mis-specification of the genetic model can result...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhongxue Chen, Yong Zang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	MAX3 test genetic model score test risk allele genotype phenotype Genetics QH426-470
Acceso en línea:	https://doaj.org/article/53a6945da3574ec4aa94eeb74803d4d4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:53a6945da3574ec4aa94eeb74803d4d4
record_format	dspace
spelling	oai:doaj.org-article:53a6945da3574ec4aa94eeb74803d4d42021-11-25T17:41:15ZCMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates10.3390/genes121117232073-4425https://doaj.org/article/53a6945da3574ec4aa94eeb74803d4d42021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4425/12/11/1723https://doaj.org/toc/2073-4425The additive genetic model as implemented in logistic regression has been widely used in genome-wide association studies (GWASs) for binary outcomes. Unfortunately, for many complex diseases, the underlying genetic models are generally unknown and a mis-specification of the genetic model can result in a substantial loss of power. To address this issue, the MAX3 test (the maximum of three separate test statistics) has been proposed as a robust test that performs plausibly regardless of the underlying genetic model. However, the original implementation of MAX3 utilizes the trend test so it cannot adjust for any covariates such as age and gender. This drawback has significantly limited the application of the MAX3 in GWASs, as covariates account for a considerable amount of variability in these disorders. In this paper, we extended the MAX3 and proposed the CMAX3 (covariate-adjusted MAX3) based on logistic regression. The proposed test yielded a similar robust efficiency as the original MAX3 while easily adjusting for any covariate based on the likelihood framework. The asymptotic formula to calculate the <i>p</i>-value of the proposed test was also developed in this paper. The simulation results showed that the proposed test performed desirably under both the null and alternative hypotheses. For the purpose of illustration, we applied the proposed test to re-analyze a case-control GWAS dataset from the Collaborative Studies on Genetics of Alcoholism (COGA). The R code to implement the proposed test is also introduced in this paper and is available for free download.Zhongxue ChenYong ZangMDPI AGarticleMAX3 testgenetic modelscore testrisk allelegenotypephenotypeGeneticsQH426-470ENGenes, Vol 12, Iss 1723, p 1723 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MAX3 test genetic model score test risk allele genotype phenotype Genetics QH426-470
spellingShingle	MAX3 test genetic model score test risk allele genotype phenotype Genetics QH426-470 Zhongxue Chen Yong Zang CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates
description	The additive genetic model as implemented in logistic regression has been widely used in genome-wide association studies (GWASs) for binary outcomes. Unfortunately, for many complex diseases, the underlying genetic models are generally unknown and a mis-specification of the genetic model can result in a substantial loss of power. To address this issue, the MAX3 test (the maximum of three separate test statistics) has been proposed as a robust test that performs plausibly regardless of the underlying genetic model. However, the original implementation of MAX3 utilizes the trend test so it cannot adjust for any covariates such as age and gender. This drawback has significantly limited the application of the MAX3 in GWASs, as covariates account for a considerable amount of variability in these disorders. In this paper, we extended the MAX3 and proposed the CMAX3 (covariate-adjusted MAX3) based on logistic regression. The proposed test yielded a similar robust efficiency as the original MAX3 while easily adjusting for any covariate based on the likelihood framework. The asymptotic formula to calculate the <i>p</i>-value of the proposed test was also developed in this paper. The simulation results showed that the proposed test performed desirably under both the null and alternative hypotheses. For the purpose of illustration, we applied the proposed test to re-analyze a case-control GWAS dataset from the Collaborative Studies on Genetics of Alcoholism (COGA). The R code to implement the proposed test is also introduced in this paper and is available for free download.
format	article
author	Zhongxue Chen Yong Zang
author_facet	Zhongxue Chen Yong Zang
author_sort	Zhongxue Chen
title	CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates
title_short	CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates
title_full	CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates
title_fullStr	CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates
title_full_unstemmed	CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates
title_sort	cmax3: a robust statistical test for genetic association accounting for covariates
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/53a6945da3574ec4aa94eeb74803d4d4
work_keys_str_mv	AT zhongxuechen cmax3arobuststatisticaltestforgeneticassociationaccountingforcovariates AT yongzang cmax3arobuststatisticaltestforgeneticassociationaccountingforcovariates
_version_	1718412103418642432

CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates

Ejemplares similares