A general framework for the evaluation of genetic association studies using multiple marginal models

Andreas Kitsche; Christian Ritz; Ludwig A. Hothorn

doi:10.1159/000448477

A general framework for the evaluation of genetic association studies using multiple marginal models

Andreas Kitsche, Christian Ritz, Ludwig A. Hothorn

Ernæring og Sundhed hos børn og voksne

2 Citationer (Scopus)

Abstract

OBJECTIVE: In this study, we present a simultaneous inference procedure as a unified analysis framework for genetic association studies.

METHODS: The method is based on the formulation of multiple marginal models that reflect different modes of inheritance. The basic advantage of this methodology is that no explicit formulation of the correlation between the test statistics is required. Moreover, the genotype scores are considered as a quantitative explanatory variable, i.e., regression models are used.

RESULTS: The proposed approach covers a wide variety of endpoints (binary, count, quantitative, and time-to-event data). In addition, multiple endpoints of different types can be assessed simultaneously. This allows the detection of pleiotropic effects while taking the mode of inheritance into account. Moreover, multiple loci can be assessed simultaneously.

CONCLUSION: The flexibility of the proposed approach is demonstrated while analyzing a variety of data examples.

Originalsprog	Engelsk
Tidsskrift	Human Heredity
Vol/bind	81
Udgave nummer	3
Sider (fra-til)	150-172
Antal sider	23
ISSN	0001-5652
DOI	https://doi.org/10.1159/000448477
Status	Udgivet - 1 feb. 2017

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Det Natur- og Biovidenskabelige Fakultet

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10.1159/000448477

Citationsformater

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abstract = "OBJECTIVE: In this study, we present a simultaneous inference procedure as a unified analysis framework for genetic association studies.METHODS: The method is based on the formulation of multiple marginal models that reflect different modes of inheritance. The basic advantage of this methodology is that no explicit formulation of the correlation between the test statistics is required. Moreover, the genotype scores are considered as a quantitative explanatory variable, i.e., regression models are used.RESULTS: The proposed approach covers a wide variety of endpoints (binary, count, quantitative, and time-to-event data). In addition, multiple endpoints of different types can be assessed simultaneously. This allows the detection of pleiotropic effects while taking the mode of inheritance into account. Moreover, multiple loci can be assessed simultaneously.CONCLUSION: The flexibility of the proposed approach is demonstrated while analyzing a variety of data examples.",

keywords = "Faculty of Science, Genetic association, Generalized linear models, Pleiotropy, Simultaneous inference",

author = "Andreas Kitsche and Christian Ritz and Hothorn, {Ludwig A.}",

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T1 - A general framework for the evaluation of genetic association studies using multiple marginal models

AU - Kitsche, Andreas

AU - Ritz, Christian

AU - Hothorn, Ludwig A.

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N2 - OBJECTIVE: In this study, we present a simultaneous inference procedure as a unified analysis framework for genetic association studies.METHODS: The method is based on the formulation of multiple marginal models that reflect different modes of inheritance. The basic advantage of this methodology is that no explicit formulation of the correlation between the test statistics is required. Moreover, the genotype scores are considered as a quantitative explanatory variable, i.e., regression models are used.RESULTS: The proposed approach covers a wide variety of endpoints (binary, count, quantitative, and time-to-event data). In addition, multiple endpoints of different types can be assessed simultaneously. This allows the detection of pleiotropic effects while taking the mode of inheritance into account. Moreover, multiple loci can be assessed simultaneously.CONCLUSION: The flexibility of the proposed approach is demonstrated while analyzing a variety of data examples.

AB - OBJECTIVE: In this study, we present a simultaneous inference procedure as a unified analysis framework for genetic association studies.METHODS: The method is based on the formulation of multiple marginal models that reflect different modes of inheritance. The basic advantage of this methodology is that no explicit formulation of the correlation between the test statistics is required. Moreover, the genotype scores are considered as a quantitative explanatory variable, i.e., regression models are used.RESULTS: The proposed approach covers a wide variety of endpoints (binary, count, quantitative, and time-to-event data). In addition, multiple endpoints of different types can be assessed simultaneously. This allows the detection of pleiotropic effects while taking the mode of inheritance into account. Moreover, multiple loci can be assessed simultaneously.CONCLUSION: The flexibility of the proposed approach is demonstrated while analyzing a variety of data examples.

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KW - Generalized linear models

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KW - Simultaneous inference

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A general framework for the evaluation of genetic association studies using multiple marginal models

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