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marker-assisted selection in wheat - ictsd

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Chapter 14 – Marker-<strong>assisted</strong> <strong>selection</strong> <strong>in</strong> Eucalyptus 269Figure 5Classification of three different types of <strong>marker</strong>-trait associations relevant to Eucalyptus MAS(see text for details)QTLLE <strong>marker</strong>s – L<strong>in</strong>kage equilibriumEx. microsatellite <strong>marker</strong>s flank<strong>in</strong>g a QTL mapped <strong>in</strong> a highLD pedigree - Centimorgan resolution, ~ 10 5 a 10 7 bpQTL or candidate geneResolutionLD <strong>marker</strong>s – L<strong>in</strong>kage disequilibriumEx. SNPs strongly associated with the QTL or candidategene - Subcentimorgan resolution ~ 10 2 a 10 4 bpGene and exact polymorphism (QTN) identifiedDirect <strong>marker</strong>sEx. causal SNPs ( QTNs ) of quantitative variationMaximum resolutionand identification of exact allelemore precise understand<strong>in</strong>g of genetic architectureof quantitative traits; the absence ofsimply <strong>in</strong>herited traits that could be immediatelyand more easily targeted; and f<strong>in</strong>allyto the very limited number of scientistsactually work<strong>in</strong>g on forest trees.If apply<strong>in</strong>g MAS <strong>in</strong> other <strong>in</strong>tensivelystudied crops besides maize is already asignificant challenge; this challenge is evenmore difficult and complex for Eucalyptusand forest trees <strong>in</strong> general as it pre-supposes:(i) the manipulation of polygenic traits withvariable heritabilities <strong>in</strong> breed<strong>in</strong>g populationswith a heterogeneous genetic baseand <strong>in</strong> l<strong>in</strong>kage equilibrium; (ii) its <strong>in</strong>corporation<strong>in</strong> breed<strong>in</strong>g schemes that <strong>in</strong>volvealter<strong>in</strong>g the frequencies of favourable allelesthrough recurrent <strong>selection</strong> <strong>in</strong> large populations;and (iii) deal<strong>in</strong>g with age x agetrait correlations, and late express<strong>in</strong>g phenotypes(Grattapaglia, 2000). In apply<strong>in</strong>gMAS for forest trees, more will likelybe learned from experiences <strong>in</strong> livestock(Dekkers, 2004; Chapter 10) than fromannual crop plants, with the added advantagethat ga<strong>in</strong>s can be quickly realized bylarge-scale clon<strong>in</strong>g of selected <strong>in</strong>dividuals.In this context, the categorization of threedifferent levels of <strong>marker</strong>-trait associationdescribed by Dekkers (2004) are relevant totrees: (a) direct <strong>marker</strong>s, i.e. loci that codefor the functional mutation; (b) l<strong>in</strong>kage disequilibrium(LD) <strong>marker</strong>s: loci that are <strong>in</strong>population-wide LD with the functionalmutation; (c) l<strong>in</strong>kage equilibrium (LE)<strong>marker</strong>s: loci that are <strong>in</strong> population-wideLE with the functional mutation <strong>in</strong> outbredpopulations (Figure 5). In forest trees,besides the recent encourag<strong>in</strong>g discoveryof an LD <strong>marker</strong> for MFA <strong>in</strong> Eucalyptus(Thumma et al., 2005), only LE <strong>marker</strong>traitassociations have been described. LE<strong>marker</strong>s have been readily detected ona genome-wide basis by analys<strong>in</strong>g largefull-sib families with sparse <strong>marker</strong> mapsallow<strong>in</strong>g the detection of most QTL ofmoderate to large effects. For the othertwo types of <strong>marker</strong>-trait association, it isonly now that the first association mapp<strong>in</strong>gexperiments are be<strong>in</strong>g started to uncover

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