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Genetics, Vol. 178, 539-551, January 2008, Copyright © 2008
doi:10.1534/genetics.107.074245

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Genetic Design and Statistical Power of Nested Association Mapping in Maize

Jianming Yu*,1, James B. Holland{dagger}, Michael D. McMullen{ddagger} and Edward S. Buckler*,§,2

* Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, {dagger} United States Department of Agriculture–Agriculture Research Service (USDA–ARS) and Department of Crop Science, North Carolina State University, Raleigh, North Carolina 27695, {ddagger} USDA–ARS and the Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211 and § USDA–ARS and Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853

2 Corresponding author: USDA–ARS, Institute for Genomic Diversity, 159 Biotechnology Bldg., Cornell University, Ithaca, NY 14853-2703.
E-mail: esb33{at}cornell.edu

We investigated the genetic and statistical properties of the nested association mapping (NAM) design currently being implemented in maize (26 diverse founders and 5000 distinct immortal genotypes) to dissect the genetic basis of complex quantitative traits. The NAM design simultaneously exploits the advantages of both linkage analysis and association mapping. We demonstrated the power of NAM for high-power cost-effective genome scans through computer simulations based on empirical marker data and simulated traits with different complexities. With common-parent-specific (CPS) markers genotyped for the founders and the progenies, the inheritance of chromosome segments nested within two adjacent CPS markers was inferred through linkage. Genotyping the founders with additional high-density markers enabled the projection of genetic information, capturing linkage disequilibrium information, from founders to progenies. With 5000 genotypes, 30–79% of the simulated quantitative trait loci (QTL) were precisely identified. By integrating genetic design, natural diversity, and genomics technologies, this new complex trait dissection strategy should greatly facilitate endeavors to link molecular variation with phenotypic variation for various complex traits.


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