marker-assisted selection in wheat - ictsd

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Chapter 12 – Marker-assisted selection in dairy cattle 221MAS was a long way off for commercialanimal populations as there were very fewknown genetic markers and methodologywas rudimentary. In the last 20 years therehave been huge advances in both DNAtechnology and statistical methodology,and it can now be stated with near certaintythat the technology is available to detectand map accurately segregating QTL indairy cattle. Furthermore, although manyeffects reported in the literature are “falsepositives”, there is a wealth of evidence thatseveral QTL are in fact real as a number ofeffects have been repeated across numerousexperiments, and the actual QTN havebeen identified for at least two QTL.The main limitation at this point todetecting and mapping more QTL is thesample sizes available, especially the numberof progeny tested bulls per family. To mapQTL of smaller magnitude accurately, itwill be necessary to combine data acrossexperiments (e.g. Khatkar et al., 2004) orsignificantly increase sample sizes. This canonly be done by genotyping cows, eventhough power per individual genotypedwill be lower.The fact that only two countries haveactually started MAS programmes highlightsthe current limitations to practicalapplication of MAS. To date, very few segregatingQTL with economic impact havebeen identified in commercial dairy cattlepopulations. Of the two QTNs that havebeen detected, each has disadvantages withrespect to application in MAS. The alleleof DGAT1 that increases fat productionand decreases water content in the milk,both desirable, also decreases protein yield,which is undesirable (Weller et al., 2003).The allele of ABCG2 that decreases milkproduction and increases protein percentis clearly the favourable allele in nearly allcurrent selection indices, but this allele isalready at a very high frequency in all majordairy cattle populations (Ron et al., 2006).In addition to the limitation of definitivelyidentified QTL with economic value,suitable software for genetic evaluationincluding QTL effects is also a limitingfactor. At present, those countries that areapplying MAS are using two-step procedures,i.e. a preliminary analysis to computegenetic evaluations based only on pedigreeand phenotypic data, and then a secondanalysis in which the genetic evaluationsare “adjusted” for QTL effects. Ideally asingle algorithm should be used to derivegenetic evaluations for the entire populationincluding the effects of known QTL.AcknowledgementsI thank M. Ron, E. Seroussi and M. Ashwellfor their input.ReferencesAshwell, M.S. & Van Tassell, C.P. 1999. Detection of putative loci affecting milk, health, and typetraits in a US Holstein population using 70 microsatellite markers in a genome scan. J. Dairy Sci.82: 2497–2502.Ashwell, M.S., Van Tassell, C.P. & Sonstegard, T.S. 2001. A genome scan to identify quantitativetrait loci affecting economically important traits in a US Holstein population. J. Dairy Sci. 84:2535–2542.Ashwell, M.S., Rexroad, C.E., Miller, R.H. & VanRaden, P.M. 1996. Mapping economic trait loci forsomatic cell score in Holstein cattle using microsatellite markers and selective genotyping. Anim.Genet. 27: 235–242.
222Marker-assisted selection – Current status and future perspectives in crops, livestock, forestry and fishAshwell, M.S., Rexroad, C.E., Miller, R.H., VanRaden, P.M. & Da, Y. 1997. Detection of lociaffecting milk production and health traits in an elite US Holstein population using microsatellitemarkers. Anim. Genet. 28: 216–222.Ashwell, M.S., Da, Y., VanRaden, P.M., Rexroad, C.E. & Miller, R.H. 1998a. Detection of putativeloci affecting conformational type traits in an elite population of United States Holsteins using microsatellitemarkers. J. Dairy Sci. 81: 1120–1125.Ashwell, M.S., Da, Y., Van Tassell, C.P., VanRaden, P.M., Miller, R.H. & Rexroad, C.E. 1998b.Detection of putative loci affecting milk production and composition, health, and type traits in aUnited States Holstein population. J. Dairy Sci. 81: 3309–3314.Ashwell, M.S., Heyen, D.W., Sonstegard, T.S., Van Tassell, C.P., DA, Y., VanRaden, P.M., Ron,M., Weller, J.I. & Lewin, H.A. 2004. Detection of quantitative trait loci affecting milk production,health, and reproductive traits in Holstein cattle. J. Dairy Sci. 87: 468–475.Bennewitz, J., Reinsch, N. & Kalm, E. 2003. Comparison of several bootstrap methods for biasreduction of QTL effect estimates. J. Anim. Breed. Genet. 120: 403–416.Bennewitz, J., Reinsch, N., Grohs, C., Leveziel, H., Malafosse, A., Thomsen, H., Xu, N.Y., Looft,C., Kuhn, C., Brockmann, G.A., Schwerin, M., Weimann, C., Hiendleder, S., Erhardt, G.,Medjugorac, I., Russ, I., Forster., M., Brenig, B., Reinhardt, F., Reents, R., Averdunk, G.,Blumel, J., Boichard, D. & Kalm, E. 2003a. Combined analysis of data from two granddaughterdesigns: A simple strategy for QTL confirmation and increasing experimental power in dairy cattle.Genet. Sel. Evol. 35: 319–38.Bennewitz, J., Reinsch, N., Thomsen, H., Szyda, J., Reinhart, F., Kuhn, C., Schwerin, M., Erhardt,G., Weimann, C. & Kalm, E. 2003b. Marker assisted selection in German Holstein dairy cattlebreeding: outline of the program and marker assisted breeding value estimation. Ann. Meet. Europ.Ass. Anim. Prod. 54th Session G1.9. Rome, Italy.Bennewitz, J., Reinsch, N., Paul, S., Looft, C., Kaupe, B., Weimann, C., Erhardt, G., Thaller, G.,Kuhn, C., Schwerin, M., Thomsen, H., Reinhardt, F., Reents, R. & Kalm, E. 2004a. The DGAT1K232A mutation is not solely responsible for the milk production quantitative trait locus on thebovine chromosome 14. J. Dairy Sci. 87: 431–442.Bennewitz, J., Reinsch, N., Reinhardt, F., Liu, Z. & Kalm, E. 2004b. Top down preselection usingmarker assisted estimates of breeding values in dairy cattle. J. Anim. Breed. Genet. 121: 307–318.Blott, S., Kim, J., Moisio, S., Schmidt-Kuntzel, A., Cornet, A., Berzi, P., Cambisano, N., Ford, C.,Grisart, B., Johnson, D., Karim, L., Simon, P., Snell, R., Spelman, R., Wong, J., Vilkki, J., Georges,M., Farnir, F. & Coppieters, W. 2003. Molecular dissection of a quantitative trait locus: A phenylalanine-to-tyrosinesubstitution in the transmembrane domain of the bovine growth hormonereceptor is associated with a major effect on milk yield and composition. Genetics 163: 253–266.Boichard, D., Fritz, S., Rossignol, M.N., Boscher, M.Y., Malafosse, A. & Colleau, J.J. 2002.Implementation of marker-assisted selection in French dairy cattle. Proc. 7th World Cong. Genet.Appl. Livest. Prod. 33: 19–22. Montpellier, France.Boichard, D., Fritz, S., Rossignol, M.N., Guillaume, F., Colleau, J.J. & Druet, T. 2006.Implementation of marker-assisted selection: practical lessons from dairy cattle. Proc. 8th WorldCong. Genet. Appl. Livest. Prod. 22: 11. 13–18 August 2006, Belo Horizonte, MG, Brazil.Boichard, D., Grohs, C., Bourgeois, F., Cerqueira, F., Faugeras, R., Neau, A., Rupp, R., Amigues,Y., Boscher, M.Y. & Leveziel, H. 2003. Detection of genes influencing economic traits in threeFrench dairy cattle breeds. Genet. Sel. Evol. 35: 77–101.
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iiiContentsAcknowledgementsForeword
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Section v - marker-assisted selecti
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viiForewordSince almost the beginni
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ixAbbreviations and acronymsAATFAB-
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xiFIVIMSFNPFSCFSILGABIGASGCAGCPGDPG
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xiiiOBMOECDOIEOPVPAGEPBRsPCRPGRFAPI
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xvContributorsAmalia BaroneProfesso
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xviiElcio Perpétuo GuimarãesSenio
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xixAndrea SonninoSenior Agricultura
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Chapter 1Marker-assisted selection
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Chapter 1 - An overview of the issu
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Chapter 3Molecular markers for use
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This book provides a comprehensive
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