Quantitative trait locus (QTL) and QTL x environment (E) interaction effects for agronomic and malting quality traits were measured using a 123-point linkage map and multi-environment phenotype data from an F1-derived doubled haploid population of barley (Hordeum vulgare). The QTL × E interactions were due to differences in magnitude of QTL effects. Highly significant QTL effects were found for all traits at multiple sites in the genome. Yield QTL peaks and support intervals often coincided with plant height and lodging QTL peaks and support intervals. QTL were detected in the vicinity of a previously mapped Mendelian maturity locus and known function probes forα- andβ-amylase genes. The average map density (9.6 cM) should be adequate for molecular marker-assisted selection, particularly since there were few cases of alternative favorable alleles for different traits mapping to the same or adjacent intervals.

Genomic mapping has been used to identify a region of the host genome that determines resistance to fusiform rust disease in loblolly pine where no discrete, simply inherited resistance factors had been previously found by conventional genetic analysis over four decades. A resistance locus, behaving as a single dominant gene, was mapped by association with genetic markers, even though the disease phenotype deviated from the expected Mendelian ratio. The complexity of forest pathosystems and the limitations of genetic analysis, based solely on phenotype, had led to an assumption that effective long-term disease resistance in trees should be polygenic. However, our data show that effective long-term resistance can be obtained from a single qualitative resistance gene, despite the presence of virulence in the pathogen population. Therefore, disease resistance in this endemic coevolved forest pathosystem is not exclusively polygenic. Genomic mapping now provides a powerful tool for characterizing the genetic basis of host pathogen interactions in forest trees and other undomesticated, organisms, where conventional genetic analysis often is limited or not feasible.