Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/20/1996
Publication Date: N/A
Interpretive Summary: Until now, the methods used by breeders to produce improved malting barleys have largely involved interbreeding barleys containing good genes and hoping that those genes would be combined in the new barley in such a way that it's malting quality was better than that of either parent. While use of this technique has led to the production of greatly improved malting barleys, it is not a very efficient method. This paper reports the locations, on the barley chromosome, of genes for various malting quality traits that need to be improved. These quality traits are notoriously hard to breed for, because they are almost all controlled by several or many different individual genes. This paper reports where some of these malt quality genes are located relative to other easily detected parts of the barley chromosome. With this knowledge, it is now possible for researchers to select barleys having the easily detected characters, knowing that the hard to measure good genes will also be present, because they occupy adjacent areas of the chromosomes. Breeders can now more quickly, easily and efficiently develop improved barleys for the brewing and other industries.
Technical Abstract: Grain and malt quality traits (kernel plumpness, kernel weight, grain protein, fine-grind extract, fine-coarse difference, soluble protein, extract beta-glucan, extract viscosity, diastatic power, and alpha-amylase activity) were measured on grain produced in six field environments, from parents and 145 doubled-haploid progeny of two-row barley cross, 'Harrington'/'TR306'. Quantitative trait loci (QTL) and QTL by environment interactions were detected using 127 mapped markers (mostly RFLP) and two methods of QTL analysis: simple interval mapping (SIM) and simplified composite interval mapping (sCIM). Each trait was affected by two to four primary QTL (those detected using both SIM and sCIM) and similar numbers of secondary QTL (those detected by only one of SIM or sCIM). Together, these QTL explained 21 to 67 percent 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. Most QTL interacted with environment, but many showed effects consistent enough that they might serve as useful targets for marker-assisted selection. All chromosomes, except chromosome 2, contained regions with at least one important QTL. Several genomic regions that affected multiple traits could be targets for identification and cloning of specific genes.