A general framework for the evaluation of genetic association studies using multiple marginal models
Research output: Contribution to journal › Journal article › Research › peer-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.
|Number of pages||23|
|Publication status||Published - 22 Dec 2016|
- The Faculty of Science - Genetic association, Generalized linear models, Pleiotropy, Simultaneous inference