Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-23T05:48:05.328Z Has data issue: false hasContentIssue false
  • English
  • Français

Animal breeding in the (post-) genomic era

Published online by Cambridge University Press:  18 August 2016

M. E. Goddardt
M. E. Goddardt*
Affiliation:
Institute of Land and Food Resources, University of Melbourne and Victorian Institute of Animal Science, Attwood, Victoria 3049, Australia
*
E-mail:[email protected]
Get access

Abstract

One of the benefits of the genomics revolution for animal production will be knowledge of genes that can be used to select more profitable livestock. Although it is possible to use genetic markers linked to genes of economic importance, tests for the genes themselves will be much more successful. Consequently finding genes of economic importance to livestock will be a major research aim for the future. Most traits of economic importance are quantitative traits affected by many genes. Mutations at many genes (e.g. 500) and at many positions within a gene (e.g. 1000 coding and non-coding bases) can affect a typical quantitative trait. The effect of these mutations on phenotype is usually small (e.g. 0·1 standard deviation) but occasionally large. Many mutations are lost from the population through genetic drift and selection, so that polymorphisms exist at only a subset of the relevant genes (e.g. 100 genes). Finding these genes, that have relatively small effects, is more difficult than finding genes for a classical Mendellian trait but, as the genomic tools become more powerful, it is becoming feasible and some successes have already occurred. The standard approach is to map a quantitative trait loci (QTL) to a chromosome region using linkage and linkage disequilibrium. Then test polymorphisms in positional candidate genes for an effect on the trait. Tools such as genomic sequence, EST collections and comparative maps make this approach feasible. Candidate genes can be selected based on functional data such as gene expression obtained from microarrays. At present the gain in rate of genetic improvement from use of DNA-based tests for QTL is small, because selection without them is already quite accurate, not enough QTL have been identified and genotyping is too expensive. However, in the future, with many QTL identified and inexpensive genotyping combined with decreased generation intervals, large gains are possible.

Keywords

genomicslivestockselection
Type
Invited paper
Information
Animal Science , Volume 76 , Issue 3 , June 2003 , pp. 353 - 365
Copyright
Copyright © British Society of Animal Science 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andersson, L. 2001. Genetic dissection of phenotypic diversity in farm animals. Nature Review Genetics 2: 130–138.Google Scholar
Band, M. R., Larson, J. H., Rebeiz, M., Green, C. A., Heyen, D. W., Donovan, J., Windish, R., Steining, C., Mahyuddin, P., Womack, J. E. and Lewin, H. A. 2000. An ordered comparative map of the cattle and human genomes. Genome Research 10: 1359–1368.CrossRefGoogle ScholarPubMed
Boland, M. and Boichard, D. 2002. Use of maternal information for QTL detection in a (grand) daughter design. Genetics, Selection, Evolution 34: 335–352.Google Scholar
Bussemaker, H. J., Li, H. and Siggia, E. D. 2001. Regulatory element detection using correlation with expression. Nature Genetics 27: 167–171.Google Scholar
Chamberlain, A., McParlan, H., Balasingham, T., Carrick, M., Bowman, P., Robinson, N. and Goddard, M. 2002. Mapping QTL affecting milk composition traits in dairy cattle using a complex pedigree. Proceedings of the seventh world congress on genetics applied to livestock production, Montpellier, vol. 33, pp. 35–38.Google Scholar
Charlesworth, B. and Hughes, K. A. 2000. The maintenance of genetic variation in life history traits. In Evolutionary genetics (ed. Singh, R. S. and Krimbas, C. B.), pp. 363–392. Cambridge University Press.Google Scholar
Davis, G. H., Galloway, S. M., Ross, I. K., Gregan, S. M., Ward, J., Nimbkar, B. V., Ghalsasi, P. M., Nimbkar, C., Gray, G. D., Subandriyo Inounu, I., Tiesnamurti, B., Martyniuk, E., Eythorsdottir, E., Mulsant, P., Lecerf, F., Hanrahan, J. P., Bradford, G. E. and Wilson, T. 2002. DNA tests in prolific sheep from eight countries provide new evidence on origin of the Booroola (Fecb) mutation. Biology of Reproduction 66: 1869–1874.Google Scholar
Eddy, S. R. 2002. Computational genomics of non-coding RNA genes. Cell 109: 137–140.Google Scholar
Falconer, D. S. and MacKay, T. F. C. 1996. Introduction to quantitative genetics, fourth edition. Longman, Harlow, Essex.Google Scholar
Fay, J. C., Wycoft, G. J. and Wu, C. -I. 2002. Testing the neutral theory of molecular evolution with genome data in Drosophila . Nature 415: 1024–1026.Google Scholar
Fernando, R. L. and Grossman, M. 1989. Marker assisted selection using best linear unbiased prediction. Genetics, Selection, Evolution 21: 467–477.Google Scholar
Galloway, S. M., McNatty, K. P., Cambridge, L. M., Laitinen, M. P. E., Juengal, J. L., Jokiranta, T. S., McLaren, R. J., Luiro, K., Dodds, K. G., Montgomery, G. W., Beattie, A. E., Davis, G. H. and Ritvos, O. 2000. Mutations in the oocyte growth factor gene (BMP15) cause increased ovulation rate and infertility in a dosage-sensitive manner. Nature Genetics 25: 279–283.Google Scholar
Garcia-Dorado, A. and Caballero, A. 2000. On the average coefficient of dominance of deleterious, spontaneous mutants. Genetics 155: 1991–2001.Google Scholar
Georges, M. 2001. Recent progress in livestock genomics and potential impact on breeding programs. Theriogenology 55: 15–21.Google Scholar
Georges, M. and Massey, J. M. 1991. Velogenetics, or the synergistic use of marker assisted selection and germ-line manipulation. Theriogenology 35: 151–159.Google Scholar
Georges, M., Nielsen, D., MacKinnon, M., Mishra, A., Okimoto, R., Pasquino, A. T., Sargeant, A., Sorensen, A., Steele, M. R., Zao, X., Womck, J. E. and Hoeschele, I. 1995. Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing. Genetics 139: 907–920.Google Scholar
Goddard, M. E. 1991. Mapping genes for quantitative traits using linkage disequilibrium. Genetics, Selection, Evolution 23: N (suppl. 1) 131s–134s.Google Scholar
Goddard, M. E. and Hayes, B. 2002. Optimisation of response using molecular data. Proceedings of the seventh world congress on genetics applied to livestock production, Montpellier, vol. 33, pp. 3–10.Google Scholar
Grignola, F. E., Zhang, Q. and Hoeschele, I. 1997. Mapping linked quantitative trait loci via restricted maximum likelihood. Genetics, Selection, Evolution 29: 529–544.Google Scholar
Grisart, B., Coppieters, W., Farnir, F., Karim, L., Ford, C., Berzi, P., Cambisano, N., Mni, M., Reid, S., Simon, P., Spelman, R., Georges, M. and Snell, R. 2001. 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.Google Scholar
Gulcher, J. R., Kong, A. and Stefansson, K. 2001. The role of linkage studies for common diseases. Current Opinion in Genetics and Development 11: 264–267.Google Scholar
Haley, C. S. and Visscher, P. M. 1998. Strategies to utilize marker-quantitative trait loci associations. Journal of Dairy Science 81: 85–97.Google Scholar
Hayes, B. and Goddard, M. E. 2001. The distribution of the effects of genes affecting quantitative traits in livestock. Genetics, Selection, Evolution 33: 209–229.CrossRefGoogle ScholarPubMed
Hayes, B. J., Visscher, P. E., McPartlan, H. and Goddard, M. E. 2003. A novel multi-locus measure of linkage disequilibrium to estimate past effective population size. Genome Research In press.Google Scholar
Henshall, J. M. and Goddard, M. E. 1997. Comparison of marker assisted selection using mixed model (BLUP) and mixed model with a test for the QTL. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 12: 217–221.Google Scholar
Henshall, J. M. and Goddard, M. E. 1998. Marker assisted selection in complex pedigrees using maximum likelihood. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, vol. 26, pp. 345–348.Google Scholar
Jeon, J. T., Carlborg, O., Torsten, A., Giuffra, E., Amarger, V., , Chardon, P., Andersson-Eklund, L., Andersson, K., Hansson, I., Lundstrom, K. and Andersson, L. 1999. A paternally expressed QTL affecting skeletal and cardiac muscle in pigs maps to the IGF2 locus. Nature Genetics 21: 157–158.Google Scholar
Jiang, C. and Zeng, Z. B. 1995. Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140: 1111–1127.Google Scholar
Keightley, P. D. and Eyre-Walker, A. 2000. Deleterious mutation and the evolution of sex. Science 290: 331–333.Google Scholar
Knight, J. and Abbott, A. 2002. Full house. Nature 417: 785.Google Scholar
Korstanje, R. and Paigen, B. 2002. From QTL to gene: the harvest begins. Nature Genetics 31: 235–236.Google Scholar
Larzul, C., Manfredi, E. and Elsen, J. M. 1997. Potential gain from including major gene information in breeding value estimation. Genetics, Selection, Evolution 29: 161–184.Google Scholar
Lockhart, D. J. and Winzeler, E. A. 2000. Genomics, gene expression and DNA arrays. Nature 405: 827–836.Google Scholar
Lum, L. S., Docv, P. and Medrano, J. F. 1997. Polymorphism of bovine beta-lactoglobulin promoter and differences in binding affinity of activator protein-2 transcription factor. Journal of Dairy Science 80: 1389–1397.Google Scholar
Lynch, M. and Walsh, B. 1998. Genetics and analysis of quantitative traits. Sinauer Assoc., Sunderland.Google Scholar
McEwan, J. C., Paterson, K. A., Zadissa, A., Stijn, T. van, Diez-Tascon, C. and Crawford, A. M. 2001. TIPS: a process for rapid fine mapping of QTLs using ESTs and comparative mapping. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 14: 103–106.Google Scholar
MacHugh, D. E., Shriver, M. D., Loftus, R. T., Cunningham, P. and Bradley, D. G. 1997. Microsatellite DNA variation and the evolution, domestication and phylogeography of taurine and zebu cattle (Bos taurus and Bos indicus). Genetics 146: 1071–1086.Google Scholar
MacKay, T. F. C. 2001. The genetic architecture of quantitative traits. Annual Review of Genetics 35: 303–339.Google Scholar
Master, S. R., Hartman, J. L., D’Cruz, C. M., Moody, S.E., Keiper, E. A., Ha, S. I., Cox, J. D., Belka, G. K. and Chodosh, L. A. 2002. Functional microarray analysis of mammary organogenesis reveals a developmental role in adaptive thermogenesis. Molecular Endocrinology 16: 1185–1203.Google Scholar
Mattick, J. S. and Gagen, M. J. 2001. The evolution of controlled multitasked gene networks: the role of introns and other non-coding RNAs in the development of complex organisms. Molecular Biology and Evolution 18: 1611–1630.Google Scholar
Meltzer, P. 2001. Spotting the target: microarrays for disease gene discovery. Current Opinion in Genetics and Development 11: 258–263.Google Scholar
Meuwissen, T. H. E. and Goddard, M. E. 1996. The use of marker haplotypes in animal breeding. Genetics, Selection, Evolution 28: 161–176.Google Scholar
Meuwissen, T. H. E., Karlsen, A., Lien, S., Olsaker, I. and Goddard, M. E. 2002. Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping. Genetics 161: 373–379.Google Scholar
Milan, D., Jeon, J. T., Looft, C., Amarger, V., Robic, A., Thelander, M., Rogel-Gaillard, C., Paul, S., Iannuccelli, N., Rask, L., Ronne, H., Lundstrom, K., Reinsch, N., Gellin, J., Kalm, E., Roy, P. L., Chardon, P. and Andersson, L. 2000. A mutation in PRKAG3 associated with excess glycogen content in pig skeletal muscle. Science 288: 1248–1251.Google Scholar
Moore, K. J. and Nagle, D. L. 2000. Complex trait analysis in the mouse: the strengths, the limitations and the promise yet to come. Annual Review of Genetics 34: 653–686.Google Scholar
Mosig, M. O., Lipkin, E., Khutoreskaya, G., Tchouryna, E., Soller, M. and Friedman, A. 2001. A whole genome scan for quantitative trait loci affecting milk protein percentage in Israeli Holstein cattle, by means of selective milk DNA pooling in a daughter design, using an adjusted false discovery rate criterion. Genetics 157: 1683–1698.Google Scholar
Nadeau, J. H. and Frankel, W. N. 2000. The road from phenotypic variation to the gene discovery: mutagenesis versus QTLs. Nature Genetics 25: 381–384.Google Scholar
Nerez, C., Moreau, L., Brouwers, B., Coppieters, W., Detilleux, J., Hanset, R., Karim, L., Kvasz, A., Leroy, P. and Georges, M. 1999. An imprinted QTL with major effect on muscle mass and fat deposition maps to the IGF2 locus in pigs. Nature Genetics 21: 155–156.Google Scholar
Ng, P. C. and Henikoff, S. 2001. Predicting deleterious amino acid substitutions. Genome Research 11: 863–874.Google Scholar
Ollivier, L. and Colleau, J. J. 1998. The accuracy of marker-assisted selection in situations of linkage equilibrium. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, vol. 26, pp. 337–340.Google Scholar
Pandey, A. and Mann, M. 2000. Proteomics to study genes and genomes. Nature 405: 837–846.CrossRefGoogle ScholarPubMed
Remington, D. L., Ungerer, M. C. and Purugganan, M. D. 2001. Map-based cloning of quantitative trait loci: progress and prospects Genetics Research 78: 213–218.Google Scholar
Risch, N. J. 2000. Searching for genetic determinants in the new millenium. Nature 405: 847–856.Google Scholar
Rothschild, M., Jacobson, C., Vaske, D., Tuggle, C., Wang, L., Short, T., Eckardt, G., Sasaki, S., Vincent, A., McLaren, D., Southwood, O., Steen, H. van der, Mileham, A. and Plastow, G. 1996. The estrogen receptor locus is associated with a major gene influencing liter size in pigs. Proceedings of the National Academy of Sciences of the United States of America 93: 201–205.Google Scholar
Santure, A., Dodds, K. G. and McEwan, J. C. 2001. SNP detection using overlapping bovine ESTs. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 14: 87–90.Google Scholar
Shillingford, J. M. and Hennighausen, L. 2001. Experimental mouse genetics – answering fundamental questions about mammary biology. Tr ends in Endocrinology and Metabolism 12: 402–408.Google Scholar
Shrimpton, A. E. and Robertson, A. 1988. The isolation of polygenic factors controlling Bristle score in Drosophila melanogaster. II. Distribution of third chromosome Bristle effects within chromosome sections. Genetics 118: 445–459.Google Scholar
Smith, S. J., Cases, S., Jensen, D. R., Chen, H. C., Sarde, E., Tow, B., Sanan, D. A., Raber, J., Eckel, R. H., JrFarese, R. V. 2000. Obesity resistance and multiple mechanisms of triglyceride synthesis in mice lacking DGAT1. Nature Genetics 25: 87–90.Google Scholar
Spelman, R. J. and Arendonk, J. A. M. van. 1997. Effect of inaccurate parameter estimates on genetic response to marker-assisted selection in an outbred population. Journal of Dairy Science 80: 3399–3410.Google Scholar
Spelman, R. J., Garrick, D. J. and Arendonk, J. A. M. van. 1999. Utilisation of genetic variation by marker assisted selection in commercial dairy cattle populations. Livestock Production Science 59: 51–60.CrossRefGoogle Scholar
Sved, J. 1971. Linkage disequilibrium and homozygosity of chromosome segments in finite populations. Theoretical Population Biology 2: 125–141.Google Scholar
Tishkoff, S. A., Varkonyi, R., Cahinhinian, N., Abbes, S., Argyropoulous, G., Destro-Bisol, G., Droustiotou, A., Dangerfield, B., Lefranc, G., Loiselet, J., Piro, A., Stonaking, M., Targarelli, A., Targarelli, G., Touma, E. H., Williams, S. M. and Clarke, A. G. 2001. Haplotype diversity and linkage disequilibrium at the human G6PD: recent origin of alleles that confer malarial resistance. Science 293: 455–462.Google Scholar
Urrutia, A. O. and Hurst, L. D. 2001. Codon usage bias covaries with expression breadth and rate of synonymous evolution in humans, but this is not evidence for selection. Genetics 59: 1191.Google Scholar
Vignal, A., Milan, D., SanCristobal, M. and Eggen, A. 2002. A review of SNP and other types of molecular markers and their use in animal genetics. Genetics, Selection, Evolution 34: 275.Google Scholar
Visscher, P. M., Beek, S. van der and Haley, C. S. 1998. Marker assisted selection. In Animal breeding: technology for the 21st century (ed. Clarke, A. J.), pp. 119–136. Harwood Academic Publishers, Amsterdam.Google Scholar
Walling, G. A., Visscher, P. M., Andersson, L., Rothschild, M. F., Wang, L., Moser, G., Groenen, M. A. M., Bidanel, J.-P., Cepica, S., Archibald, A. L., Geldermann, H., deKoning, D. J., Milan, D. and Haley, C. S. 2000. Combined analysis of data from quantitative trait mapping studies. Chromosome 4 effects on porcine growth and fatness. Genetics 155: 1369–1378.Google Scholar
Walsh, B. 2001. Quantitative genetics in the age of genomics. Theoretical Population Biology 59: 175–184.Google Scholar
Weller, J. L., Kashi, Y. and Soller, M. 1990. Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy cattle. Journal of Dairy Science 73: 2525–2537.Google Scholar
Wilson, T., Wu, X. Y., Juengel, J. L., Ross, I. K., Lumsden, J. M., Lord, E. A., Dodds, K. G., Walling, G. A., McEwan, J. C., O’Connell, A. R., McNatty, K. P. and Montgomery, G. W. 2001. Highly prolific Booroola sheep have a mutation in the intracellular kinase domain of bone morphogenetic protein IB receptor (ALK-6) that is expressed in both oocytes and granulosa cells. Biology of Reproduction 64: 1225–1235.Google Scholar
Winter, A., Kramer, W., Werner, F. A., Kollers, S., Kata, S., Durstewitz, G., Buitkamp, J., Womack, J. E., Thaller, G. and Fries, R. 2002. Association of a lysine-232/alanine polymorphism in a bovine gene encoding acyl-CoA: diacylglycerol acyltransferase (DGAT1) with variation at a quantitative trait locus for milk fat content. Proceedings of the National Academy of Sciences of the United States of America 99: 9300–9305.Google Scholar
Wyrick, J. J. and Young, R. A. 2002. Deciphering gene expression regulatory networks. Review. Current Opinion in Genetics and Develoment 12: 130–136.Google Scholar
Zhu, H. and Snyder, M. 2001. ‘Omic’ approaches for unravelling signalling networks. Current Opinion in Cell Biology 14: 173–179.Google Scholar