Quantitative trait locus (QTL) main effects and QTL by environment (QTL × E) interactions for seven agronomic traits (grain yield, days to heading, days to maturity, plant height, lodging severity, kernel weight, and test weight) were investigated in a two-row barley (Hordeura vulgare L.) cross, Harrington/TR306. A 127-point base map was constructed from markers (mostly RFLP) scored in 146 random double-haploid (DH) lines from the Harrington/TR306 cross. Field experiments involving the two parents and 145 random DH lines were grown in 1992 and/or 1993 at 17 locations in North America. Analysis of QTL was based on simple and composite interval mapping. Primary QTL were declared at positions where both methods gave evidence for QTL. The number of primary QTL ranged from three to six per trait, collectively explaining 34 to 52% of the genetic variance. None of these primary QTL showed major effects, but many showed effects that were consistent across environments. The addition of secondary QTL gave models that explained 39 to 80% of the genetic variance. The QTL were dispersed throughout the barley genome and some were detected in regions where QTL have been found in previous studies. Eight chromosome regions contained pleiotropic loci and/or linked clusters of loci that affected multiple traits. One region on chromosome 7 affected all traits except days to heading. This study was an intensive effort to evaluate QTL in a narrow-base population grown in a large set of environments. The results reveal the types and distributions of QTL effects manipulated by plant breeders and provide opportunities for future testing of marker-assisted selection. M OLECULAR MAPS of plant genomes, used in conjunction with phenotypic measurements, can provide information about chromosome regions that affect quantitative traits. Although knowing whether such regions represent individual quantitative trait loci (QTL)

Using field-scored data of disease severity under natural infestation, we mapped loci affecting resistance to powdery mildew (Blumeria graminis DC f. sp. hordei ~m. Marchal), leaf rust (Puccinia hordei Otth.), stem rust (Puccinia graminis f. sp. tritici Eriks. & E. Henn.), scald [Rhynchosporium secalis (Oudem.) J.J. Davis], and net blotch (Pyrenophora teres Drechs.). The mapping population included parents and doubled-haploid progeny of the two-row barley cross Harrington/TR306. Resistance was affected by two to five loci, explaining 8 to 45% of the phenotypic variance, per disease. All chromosomes, except chromosome 5 (1H), contained regions with at least one disease resistance locus. One region on chromosome 4 (4H) contributed to resistance to stem rust, scald, and net blotch. This region has previously been reported to affect days to heading and maturity. Two known resistance genes in the population, Rpgl and Mlg, were mapped to within 3 centimorgans (cM) of their previously estimated genomic locations by simple interval mapping of the field-scored data. This indicates that the genomic positions of disease resistance genes can be estimated accurately with simple interval mapping, even on the basis of field-scored data. G ENETIC RESISTANCE is an ecologically and economically sound approach to disease control in crops and is a common and important objective of barley (Hordeum vulgare L.) breeding. Breeders and pathologists select plants or lines with complete or partial disease resistance. They commonly make selections based on the results of artificially inoculated trials, on visual assessments of naturally occurring disease symptoms, or both. Plants or lines may be qualitatively classified as either resistant or susceptible. The examination of disease response may also involve the quantitative assessment of continuous variation in plant response to disease infestation. Both qualitative and quantitative data may be used to map resistance loci relative to molecular and/or morphological markers in plant genomes. With qualitative data, classical linkage mapping may be used to map genes with major effects on disease resistance. With quantitative data, quantitative trait locus (QTL) analysis may be used to detect chromosome regions that contribute to disease resistance.

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