As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure1,2. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.
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We thank past and present members of the Buckler lab for their assistance in phenotypic data collection. We thank P.J. Bradbury, Z. Zhang, R.L. Quass and E.J. Pollak for their insights and discussion regarding the mixed model. We thank N. Stevens for technical editing of the manuscript. This work was supported by the US National Science Foundation and the USDA-ARS. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture.
The authors declare no competing financial interests.
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Yu, J., Pressoir, G., Briggs, W. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38, 203–208 (2006). https://doi.org/10.1038/ng1702
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