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

Research output: Contribution to journalJournal articleResearchpeer-review

Andreas Kitsche, Christian Ritz, Ludwig A. Hothorn

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 languageEnglish
JournalHuman Heredity
Issue number3
Pages (from-to)150-172
Number of pages23
Publication statusPublished - 22 Dec 2016

    Research areas

  • The Faculty of Science - Genetic association, Generalized linear models, Pleiotropy, Simultaneous inference

ID: 170799986