Recognition of environmental and genetic effects on barley phenolic fingerprints by neural networks.

Through computational analysis of high-performance liquid chromatography (HPLC) traces we find correlations between secondary metabolites and growth conditions of six varieties of barley. Using artificial neural networks, it was possible to classify chromatograms for which the varieties were fertilized by nitrogen and treated by fungicide. For each variety of barley we could also differentiate it from the others. Surprisingly, all these classification tasks could be solved successfully by a simple network with no hidden units. When adding to the methodology pruning of the network weights, we were able to reduce the set of peaks in the chromatograms and obtain a necessary subset from which the growth conditions and differentiation may be decided. In some instances, more complex networks with hidden units could lead to a further reduction of the number of peaks used. In most cases, far more than half of the peaks are redundant. We find that it requires fewer information-rich peaks to perform the variety differentiation tasks than to recognize any of the growth conditions. Analysis of the network weights reveals correlations between weighted combinations of peaks.

Expression of individual HMW glutenin subunit genes of wheat (Triticum aestivum L.) in relation to differences in the number and type of homoeologous subunits and differences in genetic background.

The amount of individual high-molecular-weight (HMW) glutenin subunits of bread-wheat has been studied in relation to variation at homoeologous loci and in the general genetic background. The relationships between Glu-1 loci have been studied using nearisogenic lines (NILs) of the variety Sicco and in the progenies of two crosses. Substitution of the Sicco Glu-D1 allele by a null-allele resulted in higher amounts of the homoeologous subunits. The presence of a Glu-A1 nullallele did not have a noticeable effect on the amounts of homoeologous subunits. In three out of four NILs and in the sister-lines of two crosses, the amounts of HMW-subunits did not depend on the allele make-up at homoeologous loci. Only in the NIL which contains the Glu-D1 allele, encoding subunits 1Dx2.2 and 1Dy12, was the amount of homoeologous subunits lower than the amount of these subunits in Sicco. This study suggests a relation between the amount of HMW-subunits encoded by an allele and its contribution to bread-making quality. The effect of genetic background has been studied using F4 and F5 lines of two crosses. The total amounts of subunits, relative to the total amount of kernel proteins, showed a considerable variation between lines. The ratio between individual subunits did not differ between genetic backgrounds. Because this ratio is also largely independent of differences in environmental conditions, it is concluded that the relative amount of a subunit is a valuable measure for the detection of genetically-determined differences in the expression of HMW-subunit genes.