|Buckler, Edward - Ed|
Submitted to: Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/28/2008
Publication Date: 3/2/2008
Citation: Stich, B., Mohring, J., Piepho, H., Heckenberger, M., Buckler Iv, E.S., Melchinger, A.E. 2008. Comparison of Mixed-Model Approaches for Association Mapping. Genetics. 178(3):1748-1754. Interpretive Summary: Hundreds or thousands of genes control important agronomic traits, and the goal of complex trait genetics is to identify these genes. To identify the key genes is a two part process in evaluating the correct gene while controlling for the genetic history of the entire study population. The approaches for controlling the genetic history of a population are rapidly developing, and here we provide an extension of powerful method introduced a couple years ago by our group. This will allow these methods to be applied powerfully to a wide range of crops.
Technical Abstract: 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 a-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.