Originally published as Genetics Published Articles Ahead of Print on October 18, 2007.

Genetics, Vol. 177, 1889-1913, November 2007, Copyright © 2007
doi:10.1534/genetics.107.078659

Association Analysis of Historical Bread Wheat Germplasm Using Additive Genetic Covariance of Relatives and Population Structure

José Crossa^*,1, Juan Burgueño^*, Susanne Dreisigacker^*, Mateo Vargas^*, Sybil A. Herrera-Foessel^*, Morten Lillemo, Ravi P. Singh^*, Richard Trethowan, Marilyn Warburton^*, Jorge Franco, Matthew Reynolds^*, Jonathan H. Crouch^* and Rodomiro Ortiz^*

^* International Maize and Wheat Improvement Center (CIMMYT), 06600 México, D.F., Mexico, Department of Plant and Environmental Sciences, Norwegian University of Life Sciences, N-1432 Ås, Norway, University of Sydney, Camden NSW 2570, Australia and Facultad de Agronomía, Universidad de la República del Uruguay, CP 12900, Montevideo, Uruguay

¹ Corresponding author: Biometrics and Statistics Unit, Crop Research Informatics Laboratory, CIMMYT, Apdo. Postal 6-641, 06600 México, D.F., Mexico.
E-mail: j.crossa{at}cgiar.org

Linkage disequilibrium can be used for identifying associations between traits of interest and genetic markers. This study used mapped diversity array technology (DArT) markers to find associations with resistance to stem rust, leaf rust, yellow rust, and powdery mildew, plus grain yield in five historical wheat international multienvironment trials from the International Maize and Wheat Improvement Center (CIMMYT). Two linear mixed models were used to assess marker–trait associations incorporating information on population structure and covariance between relatives. An integrated map containing 813 DArT markers and 831 other markers was constructed. Several linkage disequilibrium clusters bearing multiple host plant resistance genes were found. Most of the associated markers were found in genomic regions where previous reports had found genes or quantitative trait loci (QTL) influencing the same traits, providing an independent validation of this approach. In addition, many new chromosome regions for disease resistance and grain yield were identified in the wheat genome. Phenotyping across up to 60 environments and years allowed modeling of genotype x environment interaction, thereby making possible the identification of markers contributing to both additive and additive x additive interaction effects of traits.

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