A principal objective of the NABGMP is to identify quantitative trait loci controlling agronomic and quality traits in two model systems: Steptoe/Morex (six-rowed) and Harrington/TR-306(two-rowed). These crosses are perhaps wider than one would normally make in the course of a breeding program, but they at least approach the realm of agronomic reason. Our expectation is that if key QTLs can be identified in these two crosses, breeders will be able to target certain chromosome regions in their own germplasm. The catalog of markers developed by the NABGMP will provide an excellent resource for initiating a search for usable flanking markers.
Work remains to be done in making markers "Breeder Friendly", but the technology is already being applied to breeding in some labs. For example, at Oregon State University, P. M. Hayes is identifying markers to use in selecting for cold tolerance and resistance to barley stripe rust, race 24. T. K. Blake at Montana State University and S. E. Ullrich at Washington State University are identifying markers to use in selecting against dormancy.
Considerable work remains to be done, however, in the mechanics of QTL analysis and the implications for real world issues, such as genotype × environment interaction (GXE). A QTL is, after all, a statistical entity rather than a biological entity and our ability to find a QTL is a function of the estimation procedure. Our strategy is to develop extensive field performance data sets for the two crosses as represented by doubled haploid populations and, by integrating these with the linkage maps, to identify QTLs for the full spectrum of agronomic and quality traits that concern the North American barley community. Knapp, Liu, and Hayes at Oregon State are currently analyzing the 1991 agronomic data and documenting the software so that analyses can be somewhat de-centralized for analysis of the 1992 data. We plan to complete analysis of the agronomic data by mid-spring, 1992 and the quality data by early summer, 1992. These data will be summarized in companion papers. Our initial objectives are to (i) identify QTLs using a non-linear model procedure, (ii) assess the role of GXE in QTL analyses, and (iii) compare QTL and classical quantitative genetics parameters. Initial analyses of the 1991 data reveal significant QTL effects, significant GXE, and significant "common sense" loci. For example, yield QTLs found on chromosome 7 also correspond to height QTL. Our forefathers recognized that a tall barley that lodges early in the season will not yield well. Our task is to move beyond the obvious and focus on those loci that will allow for selection response.