|Tinker, N. - MCGILL UNIV. CANADA|
|Mather, D. - MCGILL UNIV. CANADA|
|Rossnagel, B. - UNIV. OF SASKATCHEWAN, CA|
|Kasha, K. - UNIV. OF GUELPH, CANADA|
|Kleinhofs, A. - WASHINGTON, STATE UNIV.|
|Hayes, P. - OREGON SU, CORVALLIS, OR|
|Falk, D. - UNIV. OF GUELPH, CANADA|
|Ferguson, T. - ALBERTA WHEAT, CANADA|
|Shugar, L. - W.G. THOMPSON, ON. CANADA|
Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 20, 1995
Publication Date: N/A
Interpretive Summary: Many important agronomic traits in barley, such as yield, are controlled by many genes. Expression of these genes can be affected by the environment where the barley is grown. To find locations on the barley chromosomes that are important for agronomic traits, 150 barley lines were grown at 17 sites in North America. These same 150 lines had been used to develop a molecular marker map for barley. Field measurements of grain yield, days to heading and maturity, plant height, lodging severity, single kernel weight, and grain test weight were made at each growing site. These data, along with the molecular marker map data, were analyzed using statistics. The number of chromosomal locations having effects ranged from three to six for each trait. These additional locations were more affected by the environment where the plants were grown. Knowledge of the number and location of genes controlling these agronomic traits will help barley breeders develop better yielding barley cultivars.
Technical Abstract: Quantitative trait locus (QTL) main effects and QTL-by-environment (QTLxE) interactions for seven agronomic traits (grain yield, days to heading, days to maturity, plant height, lodging severity, single kernel weight, and grain test weight) were detected in a two-row barley (Hordeum vulgare L.) cross, Harrington/TR306. 150 random doubled-haploid lines and parents were grown in 1992 and(or) 1993 at 17 sites in North America. A 127 marker Harrington/TR306 base map was constructed from a more extensive set of polymorphic markers. The statistical methods used for QTL analysis were similar to previously described methods of simple and composite interval mapping, but with an additional test for QTLxE interaction. The number of primary QTL estimates per trait ranged from three to six. These explained 34% to 52% of the genetic variance. None of these QTL showed a major effect, but many showed effects that were very consistent across environments. With the addition of secondary QTL inferences, 39% to 80% of the genetic variance was explained. Many of the secondary inferences showed a greater amount of QTLxE interaction. The positions of QTL were dispersed throughout the barley genome, including locations where QTL have been previously detected. 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 represents an intensive effort to evaluate QTL in a very large set of environments within a population that has a relatively narrow genetic base. These results provide opportunities to test marker assisted selection, and they give a glimpse of the types and distributions of QTL effects that are manipulated by plant breeders.