Journal article
Advantages and pitfalls in the application of mixed-model association methods
J Yang, NA Zaitlen, ME Goddard, PM Visscher, AL Price
Nature Genetics | Published : 2014
DOI: 10.1038/ng.2876
Abstract
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 c..
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Awarded by National Institutes of Health
Funding Acknowledgements
We are grateful to N. Patterson, D. Heckerman, J. Listgarten, C. Lippert, E. Eskin, B. Vilhjalmsson, P. Loh, T. Hayeck, T. Frayling, A. McRae, L. Ronnegard, O. Weissbrod, G. Tucker and the GIANT Consortium for helpful discussions and to A. Gusev and S. Pollack for assistance with the multiple sclerosis and ulcerative colitis data sets. We are grateful to two anonymous referees for their helpful comments. This study makes use of data generated by the Wellcome Trust Case Control Consortium and data from the database of Genotypes and Phenotypes (dbGaP) under accessions phs000090.v2.p1 and phs000091.v2.p1 (see the Supplementary Note for the full set of acknowledgments for these data). This research was supported by US National Institutes of Health (NIH) grants R01 HG006399, P01 GM099568 and R01 GM075091, by the Australian Research Council (DP130102666) and by the Australian National Health and Medical Research Council (APP1011506 and APP1052684).