Breeding for Grain Quality Traits

Abstract

Breeding for complex multigenic phenotypic quality characters in cereals by chemical analyses and functional pilot tests is traditionally a slow and expensive process. The development of new instrumental screening methods for complex quality traits evaluated by multivariate data analysis has during the last decades revolutionised the economy and scale in breeding for quality. The traditional explorative plant breeding view is pragmatically oriented to manipulate the whole plant and its environment by “top down” observation and selection to improve complex traits, such as yield and baking quality. The new molecular and biochemical techniques are promising in increasing the genetic variation by breaking the barriers of species and in explaining the chemical and genetic basis of quality. In molecular biology traits are seen “bottom up” from the genome perspective, for example, to find genetic markers by quantitative trait loci (QTL). To improve efficiency the plant breeder can now complement his classical tools of observation by overviewing the whole physical–chemical composition of the seed by near infrared spectroscopy (NIRS) from a Principal Component Analysis (PCA) score plot to connect to genetic, (bio)chemical, and technological data through pattern recognition data analysis (chemometrics). Genes and genotypes can also be directly evaluated as imprints in NIR spectra. Recent applications in NIR technology by ”data breeding” demonstrate manual selection for complex high-quality traits and seed genotypes directly from a PCA score plot. New equipment makes automatic analysis and sorting for complex quality traits possible both in bulk and on single seed basis. Seed sorting can be used directly in seed production and to speed up selection for quality traits in early generations of plant breeding and to document genetic diversity in gene banks.