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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)

Malting is an important end use of barley (Hordeum vulgare L.). The suitability of barley for malting depends on numerous quality characteristics, all of which are affected by genetic and environmental variation and many of which are inter-related. Here, our objective was to use genome mapping to improve knowledge about the genetic basis for variation and covariation in grain and malt quality characteristics. Kernel plumpness, kernel weight, grain protein, fine-grind extract, fine-coarse difference, soluble protein, extract p-glucan, extract viscosity, diastatic power, and a-amylase activity were measured on grain produced in six field environments, from parents and doubledhaploid progeny of a two-row barley cross, 'Harrington'/'TRJ06'. Quantitative trait loci and QTL x environment interactions were detected by means of 127 mapped markers and two methods of QTL analysis: simple interval mapping (81M) and simplified composite interval mapping (sCIM). Each trait was affected by two to four primary QTL (those detected using both 81M and sCIM) and similar numbers of secondary QTL (those detected by only one of 81M or sCIM). Together, these QTL explained 21 to 67% of the phenotypic variance per trait. The numbers, effects, and relative positions of these QTL were in concordance with the quantitative trait distributions and with correlations among traits. All chromosomes, except chromosome 2, contained regions with at least one important QTL. Several genomic regions affected multiple traits. Most QTL interacted with environment, but many showed effects consistent enough that they might serve as targets for marker-assisted selection. There was little similarity in the QTL positions detected here and those detected previously for the same traits in crosses representing other germ plasm groups. MALTINGis an important end use of barley. Barley grain suitable for malting normally commands a premium price. Malted barley (malt) is used predominantly for brewing beer, but some is used for distilling and in food products. The malting process involves steeping the grain in water, followed by germination in a controlled environment. The resulting "green malt" is then dried by kilning at gradually increasing temperatures. During steeping and germination, hydrolytic enzymes are synthesized and/or activated. Some of these enzymes are involved in the breakdown of endosperm cell walls. This breakdown, which opens up the cells to attack by starch-and protein-degrading enzymes, is often

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.