Originally published as Genetics Published Articles Ahead of Print on February 3, 2008.

Genetics, Vol. 178, 1745-1754, March 2008, Copyright © 2008
doi:10.1534/genetics.107.079707

Comparison of Mixed-Model Approaches for Association Mapping

Benjamin Stich^*, Jens Möhring, Hans-Peter Piepho, Martin Heckenberger^*, Edward S. Buckler^,,** and Albrecht E. Melchinger^*,1

^* Institute for Plant Breeding, Seed Science, and Population Genetics and Institute for Crop Production and Grassland Research, University of Hohenheim, 70593 Stuttgart, Germany, Institute for Genomic Diversity and Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853 and ^** United States Department of Agriculture–Agricultural Research Service, Ithaca, New York 14853

¹ Corresponding author: Institute for Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Fruwirthstrasse 21, 70599 Stuttgart, Germany. 
E-mail: melchinger{at}uni-hohenheim.de

Association-mapping methods promise to overcome the limitations of linkage-mapping methods. The main objectives of this study were to (i) evaluate various methods for association mapping in the autogamous species wheat using an empirical data set, (ii) determine a marker-based kinship matrix using a restricted maximum-likelihood (REML) estimate of the probability of two alleles at the same locus being identical in state but not identical by descent, and (iii) compare the results of association-mapping approaches based on adjusted entry means (two-step approaches) with the results of approaches in which the phenotypic data analysis and the association analysis were performed in one step (one-step approaches). On the basis of the phenotypic and genotypic data of 303 soft winter wheat (Triticum aestivum L.) inbreds, various association-mapping methods were evaluated. Spearman's rank correlation between P-values calculated on the basis of one- and two-stage association-mapping methods ranged from 0.63 to 0.93. The mixed-model association-mapping approaches using a kinship matrix estimated by REML are more appropriate for association mapping than the recently proposed QK method with respect to (i) the adherence to the nominal -level and (ii) the adjusted power for detection of quantitative trait loci. Furthermore, we showed that our data set could be analyzed by using two-step approaches of the proposed association-mapping method without substantially increasing the empirical type I error rate in comparison to the corresponding one-step approaches.

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