Marker-Assisted
Selection |
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Most economically important traits of crop plants follow
a continuous distribution caused by the action and interaction of many
genes and various environmental factors. Phenotypic selection (PS) for
such "quantitative traits" is the more effective the higher
their heritability (h2). For complex characters, such as grain yield,
h2 can be improved considerably by increasing the number of test environments
and replicates, when evaluating the respective progenies. Molecular marker
analysis allows the identification of genome segments, so-called quantitative
trait loci (QTL), contributing to the genetic variance of a trait and
thus to select superior genotypes at these loci without uncertainties
due to genotype by environment interaction and experimental error. Selecting
for favorable QTL effects based on marker data (marker-assisted selection,
MAS) therefore has great potential for improving quantitative traits.
In evaluating the possible impact of MAS it is important to know that
in general a quantitative trait is controlled by quite a large number
of genes. Evidence for this is provided by long-term selection experiments.
Presently, MAS can significantly accelerate the improvement of quantitative traits in back cross (BC) programs. It may also be useful for selecting among progenies in advanced generations of a mapping population. In both cases, only verified, important QTL effects should be included in the selection index. Overestimation of environment-specific QTL may reduce the efficacy of the selection index or may even be counter-productive. Including QTL with minor effects adds little to the gain from selection but increases the overall linkage drag (fixation of non-target genes) and thus reduces the potential progress achievable by progeny selection (PS). However, application of MAS to recurrent population improvement or to selection among experimental hybrids or synthetics would require universally (i.e. for a whole population) valid QTL/marker associations. Future progress in functional genomics is expected to provide new approaches.
Prepared by M. Abhary (mohammedjabri@hotmail.com) |
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