Chapter 12 – Marker-<strong>assisted</strong> <strong>selection</strong> <strong>in</strong> dairy cattle 221MAS was a long way off for commercialanimal populations as there were very fewknown genetic <strong>marker</strong>s and methodologywas rudimentary. In the last 20 years therehave been huge advances <strong>in</strong> both DNAtechnology and statistical methodology,and it can now be stated with near certa<strong>in</strong>tythat the technology is available to detectand map accurately segregat<strong>in</strong>g QTL <strong>in</strong>dairy cattle. Furthermore, although manyeffects reported <strong>in</strong> the literature are “falsepositives”, there is a wealth of evidence thatseveral QTL are <strong>in</strong> fact real as a number ofeffects have been repeated across numerousexperiments, and the actual QTN havebeen identified for at least two QTL.The ma<strong>in</strong> limitation at this po<strong>in</strong>t todetect<strong>in</strong>g and mapp<strong>in</strong>g more QTL is thesample sizes available, especially the numberof progeny tested bulls per family. To mapQTL of smaller magnitude accurately, itwill be necessary to comb<strong>in</strong>e data acrossexperiments (e.g. Khatkar et al., 2004) orsignificantly <strong>in</strong>crease sample sizes. This canonly be done by genotyp<strong>in</strong>g cows, eventhough power per <strong>in</strong>dividual genotypedwill be lower.The fact that only two countries haveactually started MAS programmes highlightsthe current limitations to practicalapplication of MAS. To date, very few segregat<strong>in</strong>gQTL with economic impact havebeen identified <strong>in</strong> commercial dairy cattlepopulations. Of the two QTNs that havebeen detected, each has disadvantages withrespect to application <strong>in</strong> MAS. The alleleof DGAT1 that <strong>in</strong>creases fat productionand decreases water content <strong>in</strong> the milk,both desirable, also decreases prote<strong>in</strong> yield,which is undesirable (Weller et al., 2003).The allele of ABCG2 that decreases milkproduction and <strong>in</strong>creases prote<strong>in</strong> percentis clearly the favourable allele <strong>in</strong> nearly allcurrent <strong>selection</strong> <strong>in</strong>dices, but this allele isalready at a very high frequency <strong>in</strong> all majordairy cattle populations (Ron et al., 2006).In addition to the limitation of def<strong>in</strong>itivelyidentified QTL with economic value,suitable software for genetic evaluation<strong>in</strong>clud<strong>in</strong>g QTL effects is also a limit<strong>in</strong>gfactor. At present, those countries that areapply<strong>in</strong>g MAS are us<strong>in</strong>g two-step procedures,i.e. a prelim<strong>in</strong>ary analysis to computegenetic evaluations based only on pedigreeand phenotypic data, and then a secondanalysis <strong>in</strong> which the genetic evaluationsare “adjusted” for QTL effects. Ideally as<strong>in</strong>gle algorithm should be used to derivegenetic evaluations for the entire population<strong>in</strong>clud<strong>in</strong>g the effects of known QTL.AcknowledgementsI thank M. Ron, E. Seroussi and M. Ashwellfor their <strong>in</strong>put.ReferencesAshwell, M.S. & Van Tassell, C.P. 1999. Detection of putative loci affect<strong>in</strong>g milk, health, and typetraits <strong>in</strong> a US Holste<strong>in</strong> population us<strong>in</strong>g 70 microsatellite <strong>marker</strong>s <strong>in</strong> 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 affect<strong>in</strong>g economically important traits <strong>in</strong> a US Holste<strong>in</strong> population. J. Dairy Sci. 84:2535–2542.Ashwell, M.S., Rexroad, C.E., Miller, R.H. & VanRaden, P.M. 1996. Mapp<strong>in</strong>g economic trait loci forsomatic cell score <strong>in</strong> Holste<strong>in</strong> cattle us<strong>in</strong>g microsatellite <strong>marker</strong>s and selective genotyp<strong>in</strong>g. Anim.Genet. 27: 235–242.
222Marker-<strong>assisted</strong> <strong>selection</strong> – Current status and future perspectives <strong>in</strong> crops, livestock, forestry and fishAshwell, M.S., Rexroad, C.E., Miller, R.H., VanRaden, P.M. & Da, Y. 1997. Detection of lociaffect<strong>in</strong>g milk production and health traits <strong>in</strong> an elite US Holste<strong>in</strong> population us<strong>in</strong>g microsatellite<strong>marker</strong>s. Anim. Genet. 28: 216–222.Ashwell, M.S., Da, Y., VanRaden, P.M., Rexroad, C.E. & Miller, R.H. 1998a. Detection of putativeloci affect<strong>in</strong>g conformational type traits <strong>in</strong> an elite population of United States Holste<strong>in</strong>s us<strong>in</strong>g microsatellite<strong>marker</strong>s. 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 affect<strong>in</strong>g milk production and composition, health, and type traits <strong>in</strong> aUnited States Holste<strong>in</strong> 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. & Lew<strong>in</strong>, H.A. 2004. Detection of quantitative trait loci affect<strong>in</strong>g milk production,health, and reproductive traits <strong>in</strong> Holste<strong>in</strong> cattle. J. Dairy Sci. 87: 468–475.Bennewitz, J., Re<strong>in</strong>sch, N. & Kalm, E. 2003. Comparison of several bootstrap methods for biasreduction of QTL effect estimates. J. Anim. Breed. Genet. 120: 403–416.Bennewitz, J., Re<strong>in</strong>sch, N., Grohs, C., Leveziel, H., Malafosse, A., Thomsen, H., Xu, N.Y., Looft,C., Kuhn, C., Brockmann, G.A., Schwer<strong>in</strong>, M., Weimann, C., Hiendleder, S., Erhardt, G.,Medjugorac, I., Russ, I., Forster., M., Brenig, B., Re<strong>in</strong>hardt, F., Reents, R., Averdunk, G.,Blumel, J., Boichard, D. & Kalm, E. 2003a. Comb<strong>in</strong>ed analysis of data from two granddaughterdesigns: A simple strategy for QTL confirmation and <strong>in</strong>creas<strong>in</strong>g experimental power <strong>in</strong> dairy cattle.Genet. Sel. Evol. 35: 319–38.Bennewitz, J., Re<strong>in</strong>sch, N., Thomsen, H., Szyda, J., Re<strong>in</strong>hart, F., Kuhn, C., Schwer<strong>in</strong>, M., Erhardt,G., Weimann, C. & Kalm, E. 2003b. Marker <strong>assisted</strong> <strong>selection</strong> <strong>in</strong> German Holste<strong>in</strong> dairy cattlebreed<strong>in</strong>g: outl<strong>in</strong>e of the program and <strong>marker</strong> <strong>assisted</strong> breed<strong>in</strong>g value estimation. Ann. Meet. Europ.Ass. Anim. Prod. 54th Session G1.9. Rome, Italy.Bennewitz, J., Re<strong>in</strong>sch, N., Paul, S., Looft, C., Kaupe, B., Weimann, C., Erhardt, G., Thaller, G.,Kuhn, C., Schwer<strong>in</strong>, M., Thomsen, H., Re<strong>in</strong>hardt, F., Reents, R. & Kalm, E. 2004a. The DGAT1K232A mutation is not solely responsible for the milk production quantitative trait locus on thebov<strong>in</strong>e chromosome 14. J. Dairy Sci. 87: 431–442.Bennewitz, J., Re<strong>in</strong>sch, N., Re<strong>in</strong>hardt, F., Liu, Z. & Kalm, E. 2004b. Top down pre<strong>selection</strong> us<strong>in</strong>g<strong>marker</strong> <strong>assisted</strong> estimates of breed<strong>in</strong>g values <strong>in</strong> 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 phenylalan<strong>in</strong>e-to-tyros<strong>in</strong>esubstitution <strong>in</strong> the transmembrane doma<strong>in</strong> of the bov<strong>in</strong>e 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 <strong>marker</strong>-<strong>assisted</strong> <strong>selection</strong> <strong>in</strong> 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 <strong>marker</strong>-<strong>assisted</strong> <strong>selection</strong>: 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 <strong>in</strong>fluenc<strong>in</strong>g economic traits <strong>in</strong> 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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This book provides a comprehensive