Infinitesimal Model y = Xb + Z1u + e 2 Var(u) = Asu Henderson, 1975 Systems Biology Dimension Reduction y = Xb + Wα + e W = Cov(X) = KΛΛT T KΛ X −½ Chiaromonte & Matinelli, 2002 (leukemia, humans) Fig. 5.1. Diagrammatic representation of data integration approaches. Precision <strong>Animal</strong> Breeding 77 Mixed-Inheritance Model y = Xb + Z1u + Z2q + e Fernando and Grossman, 1989 Many authors and species NB: Segregation Variance Issues Phenotype + Pedigree Gene (protein/metabolite) Expression ANOVA Model y = Xb + Z3g + e References Phenotype + Marker Cui and Churchill, 2003; Reverter et al., 2004, 2005a,b ANOVA Model y = Xb + Z 2q + e Many authors and species Segregation Analysis IBD Probabilities Henshall et al., 2001 Genetical Genomics y = Xb + Z2q + e Schadt et al., 2003 (mice) Andersson, L. (2001) Genetic dissection of phenotypic diversity in farm animals. Nature Reviews Genetics 2, 130–138. Andersson, L. and Georges, M. (2004) Domestic-animal genomics: deciphering the genetics of complex traits. Nature Reviews Genetics 5, 202–212. Barendse, W. (2005a) DNA markers for tick resistance. Provisional patent application. Barendse, W. (2005b) The transition from quantitative trait loci to diagnostic test in cattle and other livestock. Australian Journal of Experimental <strong>Agriculture</strong> 45, 831–836. Barendse, W., Armitage, S.M., Kossarek, L.M., Shalom, A., Kirkpatrick, B.W., Ryan, A.M., Clayton, D., Li, L., Neiberg, H.L., Zhang, N., Grosse, W.M., Weiss, J., Creighton, P., McCarthy, F., Ron, M., Teale, A.J., Fries, R., McGraw, R.A., Moore, S.S., Georges, M., Womack, J.E. and Hetzel, D.J.S. (1994) A genetic linkage map of the bovine genome. Nature Genetics 6, 227–238. Barendse, W., Vaiman, D., Kemp, S.J., Sugimoto, Y., Armitage, S.M., Williams, J.L., Sun, H.S., Eggen, A., Agaba, M., Aleyasin, S.A., Band, M., Bishop, M.D., Buitkamp, J., Byrne, K., Collins, F., Cooper, L., Coppettiers, W., Denys, B., Drinkwater, R.D., Easterday, K., Elduque, C., Ennis, S., Erhardt, G., Ferretti, L., Flavin, N., Gao, Q., Georges, M., Gurung, R., Harlizius, B., Hawkins, G., Hetzel, J., Hirano, T., Hulme, D., Jorgensen, C., Kessler, M., Kirkpatrick, B.W., Konfortov, B., Kostia, S., Kuhn, C., Martin Burriel, I., McGraw, R.A., Miller, J.R., Moody, D.E., Moore, S.S., Nakane, S., Nijman, I.J., Olsaker, I., Pomp, D., Rando, A., Ron, M., Shalom, A., Teale, A.J., Thieven, U., Urquhart, B.G.D., Vage, D.I., Van de Weghe, A., Varvio, S., Velmala, R., Vilkki, J., Weikard, R., Woodside, C., Womack, J.E., Zanotti, M. and Zaragoza, P. (1997) A medium density genetic linkage map of the bovine Genome. Mammalian Genome 8, 21–28. Beckman, J.S. and Soller, M. (1983) Restriction fragment length polymorphisms in genetic improvement: methodologies, mapping and costs. Theoretical and Applied Genetics 67, 35–43. Beckman, J.S. and Soller, M. (1988) Detection of linkage between marker loci and loci affecting quantitative traits in crosses between segregating populations. Theoretical and Applied Genetics 76, 228–236. Bishop, M.D., Kappes, S.M., Keele, J.W., Stone, R.T., Sunden, S.L.F., Hawkins, G.A., Salinas Toldo, S., Fries, R., Grosz, M.D., Yoo, J. and Beattie, C.W. (1994) A genetic linkage map for cattle. Genetics 136, 619–639.
78 K. Prayaga and A. Reverter Burrow, H.M. (2001) Variances and covariances between productive and adaptive traits and temperament in a composite breed of tropical beef cattle. Livestock Production Science 70, 213–233. Burrow, H.M. (2006) Utilization of diverse breed resources for tropical beef production. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production. Ed: Organizing Committee WCGALP, Brazil, Communication No: 32–01. Chiaromonte, F. and Martinelli, J. (2002) Dimension reduction strategies for analyzing global gene expression data with a response. Mathematical Biosciences 176, 123–144. Cui, X. and Churchill, G.A. (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biology 4, 210. Cundiff, L.V. (2006) The impact of quantitative genetics on productive, reproductive and adaptive traits in beef cattle. Proceedings of the Conference Australian Beef – the Leader! Beef CRC, Armidale, NSW, pp. 29–46. Dalrymple, B.P. (2005) Harnessing the bovine genome sequence for the Australian cattle and sheep industries. Australian Journal of Experimental <strong>Agriculture</strong> 45, 1011–1016. Donaldson L., Vuocolo, T., Gray, C., Strandberg, Y., Reverter, A., McWilliam, S.M., Wang, Y.H., Byrne, K.A and Tellam, R. (2005) Construction and validation of a bovine innate immune microarray. BMC Genomics 6, 135. Elsen, J.M., Mangin, B., Goffinet, B., Boichard, D. and Le Roy, P. (1999) Alternative models for QTL detection in livestock I. General introduction. Genetics Selection Evolution 31, 213–224. Falconer, D.S. and Mackay, T.F.C. (1996) Introduction to Quantitative Genetics. Pearson Education, Harlow, Essex. Fernando, R.L. and Grossman, M. (1989) Marker-assisted selection using best linear unbiased prediction. Genetics Selection Evolution 21, 467–477. Franklin, I.R. (1980) Evolutionary change in small populations. In: Soule, M.E. and Wilcox, B.A. (eds) Conservation Biology: an Evolutionary–Ecological Perspective. Sinauer, Sunderland, pp. 135–150. Franklin, I.R. and Frankham, R. (1998) How large must populations be to retain evolutionary potential? <strong>Animal</strong> Conservation 1, 69–70. George, A.W., Visscher, P.M. and Haley, C.S. (2000) Mapping quantitative trait loci in complex pedigrees: a two step variance component approach. Genetics 156, 2081–2092. Goddard, M.E. (1991) Mapping genes for quantitative traits using linkage disequilibrium. Genetics Selection Evolution 23 suppl. 1, 1315–1345. Goddard, M.E. and Meuwissen, T.H.E. (2005) The use of linkage disequilibrium to map quantitative trait loci. Australian Journal of Experimental <strong>Agriculture</strong> 45, 837–845. Gregory, K.E., Cundiff, L.V. and Koch, R.M. (1999) Composite breeds to use heterosis and breed differences to improve efficiency of beef production. USDA-ARS, Clay Center, USA. Grisart, B., Coppieters, W., Farnir, F., Karim, L., Ford, C., Berzi, P., Cambisano, N., Mini, M., Reid, S., Simon, P., Spelman, R., Georges, M. and Snell, R. (2002) Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Research 12, 222–231. Grisart, B., Farnir, F., Karim, L., Cambisano, N., Kim, J.J., Kvasv, A., Mini, M., Simon, P., Frere, J.M., Copieters, W. and Georges, M. (2004) Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleatide in affecting milk yield and composition. Proceedings of the National Academy of Sciences of the United States of America 101, 2398–2403. Haley, C.S., Knott, S.A. and Elsen, J.M. (1994) Mapping quantitative trait loci in crosses between outbred lines using least squares. Genetics 136, 1195–1207. Henderson, C.R. (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31, 423–447. Henderson, C.R. (1977) Best linear unbiased prediction of breeding values not in the model for records. Journal of Dairy Science 60, 783–787. Henshall, J.M., Tier, B. and Kerr, R.J. (2001) Estimating genotypes with independently sampled descent graphs. Genetical Research 78, 281–288. Hwang, D., Rust, A.G., Ramsey, S., Smith, J.J., Leslie, D.M., Weston, A.D., Atauri, P. de, Aitchison, J.D., Hood, L., Siegel, A.F. and Bolouri, H. (2005a) A data integration methodology for systems biology. Proceedings of the National Academy of Sciences of the United States of America 102, 17296–17301. Hwang, D., Smith, J.J., Leslie, D.M., Weston, A.D., Rust, A.G., Ramsey, S., Atauri, P. de, Siegel, A.F., Bolouri, H., Aitchison, J.D. and Hood, L. (2005b) A data integration methodology for systems biology: experimental verification. Proceedings of the National Academy of Sciences of the United States of America 102, 17302–17307. Itoh, T., Watanabe, T., Ihara, N., Mariani, P., Beattie, C.W., Sugimoto, Y. and Takasuga, A. (2005) A comprehensive rediation hybrid map of the bovine genome comprising 5593 loci. Genomics 85, 413–424.
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Redesigning Animal Agriculture The
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Redesigning Animal Agriculture The
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Contents Contributors vii Acknowled
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Contributors Margaret Alston, Direc
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Acknowledgements “The editors gra
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Introduction to Redesigning Animal
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Introduction xiii factors). In rede
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1 Redesigning Animal Agriculture: a
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ing to the confusions and complexit
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such as environmental integrity alo
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The Systems Idea in Agriculture Sys
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These are all matters that are enti
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was intended to capture this vital
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wants and needs to be comprehensive
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people who can journey together tow
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A Systemic Perspective 17 Gunderson
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and telecommunications infrastructu
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y a move from a more intensive indu
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from 10 to 40% higher than urban Au
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in rural areas - environmental degr
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- Page 62 and 63: plasticity of the genome and the po
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- Page 66 and 67: immune response genes in both speci
- Page 68 and 69: annotate these regions. The Herefor
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- Page 78 and 79: The Impact of Genomics 63 Mattick,
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- Page 94 and 95: Precision Animal Breeding 79 Jansen
- Page 96 and 97: 6 Germ Cell Transplantation - a Nov
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- Page 110 and 111: own maternal chromosomes. This reco
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- Page 116 and 117: lentivectors and RNA interference (
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- Page 120 and 121: Rather than attempting to manipulat
- Page 122 and 123: strated in transgenic mice over-exp
- Page 124 and 125: Regulatory Issues The regulatory re
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- Page 128 and 129: goals. More specifically, it remain
- Page 130 and 131: Cloning and Transgenesis 115 Gama,
- Page 132 and 133: Cloning and Transgenesis 117 McCrea
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- Page 136 and 137: 8 Transforming Livestock with Trans
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protection of the genome against mo
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(Hammond et al., 2000). Binding of
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There are a number of ways to produ
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ules that determine siRNA activity
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known influenza A virus H subtypes
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will need to be balanced against th
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Transforming Livestock with Transge
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Transforming Livestock with Transge
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Transforming Livestock with Transge
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Introduction A key difficulty faced
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dation to probabilistic (e.g. a giv
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Similarly, the probability that a d
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One approach to the development of
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1 1 nb − ( m + mI ) m XS = ( m +
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1.0 0.8 0.6 0.4 0.2 value of the st
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that shown in (9.9). In the sequel
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Metropolis-Hastings algorithm descr
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30 20 10 logging system composed of
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0.09 0.08 0.07 0.06 0.05 0.04 0e+00
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confronting models with data in thi
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This is especially surprising since
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Stochastic Process-based Modelling
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10 Reef Safe Beef: Environmentally
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and most beef is produced under ext
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Mean NDVI Burdekin Mean NDVI Fitzro
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High biomass/cover Also, the increa
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Good or ‘A’ condition has the f
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Value ($) Grazier disinclination of
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Reef Safe Beef 183 Ludwig, J.A., Wi
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11 Meeting Ecological Restoration T
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evidence of only slight degradation
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Throughout the UK, there are spatia
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Quarterly flow weighted mean nitrat
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suggest that the majority of inland
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Table 11.3. Current problems in dif
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of diffuse nutrient loading on wate
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● Introducing full nutrient budge
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following scenario 1, and scenario
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Ecological Restoration Targets 203
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This chapter draws on two examples
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(Firbank, 2005; Potter and Tilzey,
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growth in the export market has com
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the use of larger amounts of agroch
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need to establish partnerships with
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Social, Environmental and Economic
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adaptation to environment genetic b
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sequencing of 10, 000 non-redundant
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transfer of nutrients and micro-org
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genotypic value, and EBV 67-68 germ
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livestock transgenics for agricultu
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quantitative genetics use 66-69 sys
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speciesism 35 sperm-mediated gene t
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water quality EU Water Framework Di