Comparison of mixed-model approaches for association mapping - PubMed (original) (raw)

Comparative Study

Comparison of mixed-model approaches for association mapping

Benjamin Stich et al. Genetics. 2008 Mar.

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 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.

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Figures

F<sc>igure</sc> 1.—

Figure 1.—

Principal coordinate analysis of the 303 entries as well as five wheat cultivars, which are unrelated by pedigree to the 303 entries, based on Rogers' distance estimates. Percentages in parentheses refer to the proportion of variance explained by the principal coordinate.

F<sc>igure</sc> 2.—

Figure 2.—

Mean of the squared differences (MSD) between observed and expected _P_-values as well as deviance for different two-stage association-mapping methods depending on threshold T.

F<sc>igure</sc> 3.—

Figure 3.—

Plot of observed vs. expected _P_-values for the 10 two-stage association-mapping methods.

F<sc>igure</sc> 4.—

Figure 4.—

Adjusted power to detect quantitative trait loci (QTL) for the 10 two-stage association-mapping methods depending on the size of the QTL effect Gr. The percentage of phenotypic variation explained by a QTL was calculated for an allele frequency of 0.2.

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