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marker-assisted selection in wheat

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272Marker-<strong>assisted</strong> <strong>selection</strong> – Current status and future perspectives <strong>in</strong> crops, livestock, forestry and fishFigure 7Proposed scheme for early <strong>marker</strong>-<strong>assisted</strong> <strong>selection</strong> of plus trees to be used as clonesMAS STAGEXF 1F 1Mat<strong>in</strong>g between plus hybrid trees tomaximize segregation for several traits<strong>in</strong> the outbred F 2 Deployment of a large number (> 1000) of F 2progeny <strong>in</strong>dividuals toQTL MAPPING STAGEQTL mapp<strong>in</strong>g for wood properties traitssuch as lign<strong>in</strong>, fibreand wood densityembra_622*embra_28*embra_94*embra_627*EG_62*embra_372*embra_32*embra_31*embra_233*embra_50*embra_8*embra_328*embra_324*embra_173*embra_51*embra_25*embra_106*embra_248*embra_258*embra_105*embra_646*embra_175*ES_157*embra_277*embra_362*embra_189embra_115EG_94embra_227embra_122*EG_98*embra_125*embra_114embra_361F 2embra_189*EG_94*embra_227*embra_350*embra_321*EG-98*embra_125*embra_629*EN_14*embra_109embra_197embra_57EN_1embra_53EG_67embra_103embra_624ES_140embra_18embra_131embra_210.1*embra_210embra_204*embra_217*embra_365*embra_701*embra_19embra_393embra_66*embra_78*embra_137*ES_54*embra_645*QTL mapp<strong>in</strong>g <strong>in</strong>formationto be used <strong>in</strong> MASF 2maximize probabilityof generat<strong>in</strong>ga recomb<strong>in</strong>ant <strong>in</strong>dividual with asuperior multipleQTL allele contentGenotyp<strong>in</strong>g > 1000 seedl<strong>in</strong>gs witha small (~ 6 to 10) set of flank<strong>in</strong>g<strong>marker</strong>s for a targetednumber of QTL for wood qualitytraitsEarly MAS. Selection <strong>in</strong>tensity is <strong>in</strong>creased by MAS for late express<strong>in</strong>g traitsbut number of trees commonly deployed <strong>in</strong> progeny (~ 100) test iskept the same, thus allow<strong>in</strong>g large variation to select for othertraits such as volume growth, form and branch<strong>in</strong>g habitQTL mapp<strong>in</strong>g is carried out and flank<strong>in</strong>g <strong>marker</strong>s <strong>in</strong> l<strong>in</strong>kage disequilibrium with favourable alleles at major effect QTLalleles are identified. These <strong>marker</strong>s are then used for early with<strong>in</strong>-family <strong>selection</strong> <strong>in</strong> expanded progeny sizes at veryearly age to select for late expression traits such as wood density and lign<strong>in</strong> content.deploy<strong>in</strong>g clones to capture non-additivegenetic variation, it is reasonable to state thateucalypt is the forest tree crop for whichMAS has the best prospects of application.Quantitative theory as well as commonsense suggest that MAS <strong>in</strong> forest treesshould help, particularly <strong>in</strong> situations wheretrait heritability is low and <strong>selection</strong> occursat the level of the <strong>in</strong>dividual tree. However,implement<strong>in</strong>g MAS for such traits is a challeng<strong>in</strong>gtask as extremely precise QTLmapp<strong>in</strong>g <strong>in</strong>formation is required and thiscan only be derived from experiments withlarge progeny sizes (<strong>in</strong> the order of severalhundred <strong>in</strong>dividuals), clonal replicatesfor <strong>in</strong>creased precision, representative andmultiple genetic backgrounds and environments.To date, mapped QTL <strong>in</strong> foresttrees still do not fall <strong>in</strong>to this descriptionalthough improved experiments are underway (Grattapaglia, 2004). Most experimentshave mapped QTL for traits thatdisplay <strong>in</strong>termediate to high heritabilityand probably did not tag the top alleles thatexist <strong>in</strong> the breed<strong>in</strong>g populations as only avery limited sample of crosses were conducted.Furthermore, given the relativelysmall progeny sizes used for QTL detection(around 100 to 200 <strong>in</strong>dividuals), theestimated magnitude of the effects werelargely overestimated follow<strong>in</strong>g the wellknown “Beavis effect” (Beavis, 1998).

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