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Powdery mildew (Blumeria graminis f. sp. tritici) is one of the major diseases of wheat (Triticum aestivum). The objective of the present study was to describe the quantitative resistance to powdery mildew of the winter wheat line RE714 at the seedling stage and to identify microsatellite markers tightly linked to the RE714 resistance QTL, which could be used in marker-assisted selection. A population of 160 recombinant inbred lines obtained from the cross between RE714 (resistant) and Hardi (susceptible) was genotyped with microsatellite and AFLP markers. Fifteen powdery mildew isolates were used to test the resistance of these lines at the seedling stage. QTL analysis enabled us to identify three major QTLs controlling powdery mildew resistance in RE714: a QTL located on chromosome 2A, corresponding to the Pm4b gene, explaining 76–93% of the phenotypic variance for resistance to six isolates; two QTLs located on chromosomes 5D and 6A, each explaining 20–67% of the phenotypic variance for resistance to five isolates. A minor QTL for resistance to four of the six isolates revealing Pm4b was detected in the same region as the 5D QTL. Other minor QTLs were detected on chromosomes 2A and 6B, explaining, respectively, 10.9 and 11.5% of the phenotypic variance for resistance to isolate 96-27. The maps around the three major QTLs were enriched with microsatellite markers that could be used in marker-assisted selection of these QTLs.

Selective genotyping, i.e. increasing the size of the population phenotyped and genotyping only individuals from the high and low tails of the population, can considerably improve the efficiency of experiments aimed at detecting and locating quantitative trait loci (QTLs) affecting a single trait. In this paper we study how selective genotyping can increase the efficiency of multitrait QTL experiments. By selecting on an index combining the variables of interest and having the maximum correlation with each variable, the efficiency of QTL detection is increased for each trait. The efficiency of selective genotyping relative to random selection strongly depends on the correlation between the index and each variable. The optimum selection rate that minimizes costs for a given experimental power depends also on this correlation and on the genotyping costs relative to phenotyping costs. When the population segregating for the quantitative traits and the markers is not as simple as a backcross or an F2 population, but is composed of several connected or unconnected families, selective genotyping can be used to improve the efficiency of the QTL study. In this case, the extreme individuals should be selected within each family. A method is provided to choose the selection rates within each family in order to optimize the global power of the experiment when the family sizes are unequal.