![]() ![]() ![]() Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM’s constant-residual variance assumption. Linear mixed models ( LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.Ĭontrol for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed ModelsĬhen, Han Wang, Chaolong Conomos, Matthew P. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi- model inference in ecology. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The use of linear mixed effects models ( LMMs) is increasingly common in the analysis of biological data. Harrison, Xavier A Donaldson, Lynda Correa-Cano, Maria Eugenia Evans, Julian Fisher, David N Goodwin, Cecily E D Robinson, Beth S Hodgson, David J Inger, Richard PMID:29844961Ī brief introduction to mixed effects modelling and multi- model inference in ecology. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions. ![]() ![]() A brief introduction to mixed effects modelling and multi- model inference in ecologyĭonaldson, Lynda Correa-Cano, Maria Eugenia Goodwin, Cecily E.D. ![]()
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