Comparison of Mixed-Model Approaches for Association Mapping

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). Based on the phenotypic and genotypic data of 303 soft winter wheat (Triticum aestivum L.) inbreds, various association mapping methods were evaluated. Spearman rank correlation between P values calculated based on one- and two-stage association mapping methods ranged from 0.63 and 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.

Key Words: Association mapping, Population structure, Relatedness

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