marker-assisted selection in wheat - ictsd

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Chapter 3 – Molecular markers for use in plant molecular breeding and germplasm evaluation 39Table 3Examples of SSR markers available across different plant speciesCommon name Species Number ofSSRsReferenceRice Oryza sativa 2240 McCouch et al., 2002Maize Zea mays 1669 MapPairs (mp.invitrogen.com)Soybean Glycine max 597 MapPairs (mp.invitrogen.com)Cassava Manihot esculenta 318 MapPairs (mp.invitrogen.com)Arabidopsis Arabidopsis thaliana 290 MapPairs (mp.invitrogen.com)Cotton Gossypium spp. 217 MapPairs (mp.invitrogen.com)Sugar cane Saccharum spp. 200 www.intl-pag.org/pag/9/abstracts/W30_04.htmlWheat Triticum aestivum 193 MapPairs (mp.invitrogen.com)Grape Vitis vinifera 152 noGroundnut Arachis hypogaea 110 Ferguson et al., 2004Cucumber Cucumis sativus 110 Fazio, Staub and Chung, 2002Peach Prunus persica 109 Aranzana et al., 2004Kiwifruit Actinidia spp. 105 Testolin et al., 2001Barley Hordeum vulgare 44 MapPairs (mp.invitrogen.com)Potato Solanum tuberosum 31 Ghislain et al., 2004Pine trees Pinus spp. 28 MapPairs (mp.invitrogen.com)Banana Musa spp. 28 MapPairs (mp.invitrogen.com)Sweet potato Ipomoea batatas 26 MapPairs (mp.invitrogen.com)Sugar beet Beta vulgaris 25 www.intl-pag.org/pag/10/abstracts/PAGX_W306.htmlEggplant Solanum melongena 23 www.intl-pag.org/pag/11/abstracts/P3b_P181_XI.htmlFrom: Thomson, Septiningsih and Sutrisno, 2003 (reprinted with permission of author)number of ways. For example, theyprovide:• the ability to screen in the juvenile stagefor traits that are expressed late in the lifeof the organism (i.e. grain or fruit quality,male sterility, photoperiod sensitivity);• the ability to screen for traits that areextremely difficult, expensive or timeconsuming to score phenotypically (i.e.quantitatively inherited or environmentallysensitive traits such as rootmorphology, resistance to quarantinedpests or to specific races or biotypes ofdiseases or insects, tolerance to certainabiotic stresses such as drought, salt andmineral deficiencies or toxicities);• the ability to distinguish the homozygousfrom the heterozygous conditionof many loci in a single generationwithout the need for progeny testing (asmolecular markers are co-dominant);• the ability to perform simultaneousMAS for several characters at one time(or to combine MAS with phenotypic orbiochemical evaluation).This section provides examples ofhow molecular markers are being used inbreeding and germplasm evaluation. Whilethese examples are drawn mostly from rice,they illustrate applications of MAS techniquesthat are used in other species.Before molecular markers can be usedfor selection purposes, their associationwith genes or traits of interest must befirmly established. While the number ofeconomically important genetic loci thathave been cloned or tagged via linkage tomolecular markers is still limited in mostspecies, work towards this end is acceleratingrapidly. This is particularly true inrice, due to the availability of completegenome sequence information.Nonetheless, a great deal of time andeffort is required to identify the geneticloci and specific allelic variants that areresponsible for the tremendous array of
40Marker-assisted selection – Current status and future perspectives in crops, livestock, forestry and fishcharacters that breeders are concernedabout in population or variety improvementprogrammes. Given the complexity ofquantitative traits, many different lines orcrosses must be carefully analysed overdifferent years and environments to unravelimportant components of gene interaction.In a breeding context, understanding thegenetic basis of genotype by genotypeinteraction (G x G) and genotype byenvironment interaction (G x E) is criticalas the basis for predicting how QTL arelikely to behave. Information from a largenumber of studies addressing each of thesepoints must then be assembled into adatabase that offers easy access to users andallows many different kinds of data to beintegrated with a simple query.The Gramene database represents abeginning in the quest to serve this usercommunity. Gramene is a comparativegenome database for grasses and currentlyoffers a complete inventory of all publishedQTL that have been identified in rice (www.gramene.org/qtl/index.html), allowing usersto find information about where along thechromosome a QTL is located, what phenotypeis associated with the QTL, how itwas measured, what germplasm was used,what molecular markers reside nearby, whatthe corresponding position is on a comparativemap of another grass species and withwhat statistical significance the QTL wasdetected. The database also provides a linkto the published article so that users canreadily find more information on the subject.Similar inventories and databases arebeing assembled for other families of plantsand are critical to the implementation ofeffective molecular breeding strategies.Comparative genome methods takeadvantage of the fact that some specieshave more developed genetic systems thanothers. Examples of well studied “model”organisms with available genomic sequenceinclude species such as Arabidopsis andrice for plants, Populus (Taylor, 2002) andEucalyptus (Poke et al., 2005) specificallyfor forestry, and Fugu (Aparicio et al.,2002) and zebrafish (Guryev et al., 2006)for fisheries. Relying heavily on the useof comparative maps and comparativesequence analysis, genome databases allowresearchers to make predictions about thelocation and phenotypic consequences ofhomologous genes in related species. Thus,understanding how a gene or QTL behavesin one species can potentially shortcutthe process of identifying a related geneor QTL in the genetic system of anotherspecies. This approach underscores thesearch for QTL associated with abioticstress tolerance in cereals. A global effortto identify loci associated with droughttolerance has recently been initiated underthe umbrella of the Generation ChallengeProgramme (www.generationcp.org).Markers associated with tolerance fora variety of environmental stresses rank asimportant targets for molecular MAS incereal breeding because these complex traitsare often prohibitively difficult to screenusing classical selection techniques. Effortsto identify QTL associated with tolerance todrought, salt and mineral deficiencies or toxicities(Champoux et al., 1995; Flowers et al.,2000; Nguyen et al., 2002; Kamoshita et al.,2002; Price et al., 2002; Gregorio, 2002) ina number of genetic backgrounds representan important first step towards achievingthis goal. Additional studies have specificallyaddressed the problems associated withG x G and G x E (Zheng et al., 2000; Li etal., 2003; Hittalmani et al., 2003).In the area of biotic stress, several geneshave been cloned and characterized forresistance to major diseases such as bacterialblight and blast (Song et al., 1995;
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iiiContentsAcknowledgementsForeword
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Section v - marker-assisted selecti
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viiForewordSince almost the beginni
Page 10 and 11: ixAbbreviations and acronymsAATFAB-
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Page 14 and 15: xiiiOBMOECDOIEOPVPAGEPBRsPCRPGRFAPI
Page 16 and 17: xvContributorsAmalia BaroneProfesso
Page 18 and 19: xviiElcio Perpétuo GuimarãesSenio
Page 20: xixAndrea SonninoSenior Agricultura
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Section VMarker-assisted selectioni
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Chapter 19Technical, economic and p
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This book provides a comprehensive
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