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

Lifecourse Nutrition & Health

2 Citations (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.

Original language	English
Journal	Human Heredity
Volume	81
Issue number	3
Pages (from-to)	150-172
Number of pages	23
ISSN	0001-5652
DOIs	https://doi.org/10.1159/000448477
Publication status	Published - 1 Feb 2017

Keywords

Faculty of Science
Genetic association
Generalized linear models
Pleiotropy
Simultaneous inference

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10.1159/000448477

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title = "A general framework for the evaluation of genetic association studies using multiple marginal models",

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.",

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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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Y1 - 2017/2/1

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.

KW - Faculty of Science

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KW - Generalized linear models

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JO - Human Heredity

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

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Keywords

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