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Nat Genet. 2014 Feb;46(2):100-6. doi: 10.1038/ng.2876.

Advantages and pitfalls in the application of mixed-model association methods.

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1] Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. [2] University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland, Australia. [3].
1] Department of Medicine, Lung Biology Center, University of California, San Francisco, San Francisco, California, USA. [2].
1] Faculty of Land and Food Resources, University of Melbourne, Parkville, Victoria, Australia. [2].
1] Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. [2] Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. [3] Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [4].


Mixed linear models are emerging as a method of choice for conducting genetic association studies in humans and other organisms. The advantages of the mixed-linear-model association (MLMA) method include the prevention of false positive associations due to population or relatedness structure and an increase in power obtained through the application of a correction that is specific to this structure. An underappreciated point is that MLMA can also increase power in studies without sample structure by implicitly conditioning on associated loci other than the candidate locus. Numerous variations on the standard MLMA approach have recently been published, with a focus on reducing computational cost. These advances provide researchers applying MLMA methods with many options to choose from, but we caution that MLMA methods are still subject to potential pitfalls. Here we describe and quantify the advantages and pitfalls of MLMA methods as a function of study design and provide recommendations for the application of these methods in practical settings.

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