Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Highly Accessed Research article

Comparison of mixed-model approaches for association mapping in rapeseed, potato, sugar beet, maize, and Arabidopsis

Benjamin Stich and Albrecht E Melchinger*

Author Affiliations

Department of Applied Genetics and Plant Breeding, Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany

For all author emails, please log on.

BMC Genomics 2009, 10:94  doi:10.1186/1471-2164-10-94

Published: 27 February 2009



In recent years, several attempts have been made in plant genetics to detect QTL by using association mapping methods. The objectives of this study were to (i) evaluate various methods for association mapping in five plant species and (ii) for three traits in each of the plant species compare the Topt, the restricted maximum likelihood (REML) estimate of the conditional probability that two genotypes carry at the same locus alleles that are identical in state but not identical by descent. In order to compare the association mapping methods based on scenarios with realistic estimates of population structure and familial relatedness, we analyzed phenotypic and genotypic data of rapeseed, potato, sugar beet, maize, and Arabidopsis. For the same reason, QTL effects were simulated on top of the observed phenotypic values when examining the adjusted power for QTL detection.


The correlation between the Topt values identified using REML deviance profiles and profiles of the mean of squared difference between observed and expected P values was 0.83.


The mixed-model association mapping approaches using a kinship matrix, which was based on Topt, were more appropriate for association mapping than the recently proposed QK method with respect to the adherence to the nominal α level and the adjusted power for QTL detection. Furthermore, we showed that Topt differs considerably among the five plant species but only marginally among different traits.