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{dagger}, Hans-Peter Piepho{dagger}, Martin Heckenberger*, Edward S. Buckler{ddagger},§,** and Albrecht E. Melchinger*,1

* Institute for Plant Breeding, Seed Science, and Population Genetics and {dagger} Institute for Crop Production and Grassland Research, University of Hohenheim, 70593 Stuttgart, Germany, {ddagger} 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 {alpha}-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.