Nature Genetics 38, 203 - 208 (2005)
Published online: 25 December 2005; | doi:10.1038/ng1702
A unified mixed-model method for association mapping that accounts for multiple levels of relatednessJianming Yu1, 9, Gael Pressoir1, 9, William H Briggs2, Irie Vroh Bi1, Masanori Yamasaki3, John F Doebley2, Michael D McMullen3, 4, Brandon S Gaut5, Dahlia M Nielsen6, James B Holland4, 7, Stephen Kresovich1, 8
& Edward S Buckler1, 4, 81
Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA. 2
Department of Genetics, University of Wisconsin, Madison, Wisconsin 53706, USA. 3
Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211, USA. 4
United States Department of Agriculture-Agricultural Research Service (USDA-ARS). 5
Department of Ecology and Evolutionary Biology, University of California, Irvine, California 92697, USA. 6
Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, USA. 7
Department of Crop Science, North Carolina State University, Raleigh, North Carolina 27695, USA. 8
Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853, USA. 9
These authors contributed equally to this work.
Correspondence should be addressed to Edward S Buckler esb33@cornell.edu 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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