The future of livestock breeding: Genomic selection for efficiency, reduced emissions intensity, and adaptation

Trends in Genetics

Volumn 29, Issue 4, 2013, Pages 206-214

  Hayes, Ben J ^a,b,c   Lewin, Harris A ^d   Goddard, Michael E ^a,b,e  

^a DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

^b Dairy Futures Cooperative Research Centre   (Australia)

^c LA TROBE UNIVERSITY   (Australia)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Breeding; Genomic selection; Livestock production; Methane emissions; Rumen microbiome

Indexed keywords

METHANE;

AIR POLLUTION CONTROL; BREEDING; CATTLE; CLIMATE CHANGE; GENETIC GAIN; GENETIC SELECTION; LIVESTOCK; MILK PRODUCTION; NONHUMAN; PRIORITY JOURNAL; REPRODUCTIVE PROCEDURE; REVIEW; SHEEP; SINGLE NUCLEOTIDE POLYMORPHISM;

ADAPTATION, PHYSIOLOGICAL; AGRICULTURE; ANIMAL HUSBANDRY; ANIMALS; BASE SEQUENCE; BREEDING; CATTLE; DAIRY PRODUCTS; GENOME; GENOMICS; LIVESTOCK; SEQUENCE ANALYSIS, DNA; SHEEP;

ANIMALIA; BOS; OVIS ARIES;

EID: 84879182023     PISSN: 01689525     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tig.2012.11.009     Document Type: Review

References (78)

1. Darwin C. On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life 1859, John Murray.
2. Havenstein G.B., et al. Growth, livability, and feed conversion of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poult. Sci. 2003, 82:1500-1508.
3. Havenstein G.B., et al. Carcass composition and yield of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poult. Sci. 2003, 82:1509-1518.
4. Gerosa S., Skoet J. Milk Availability: Trends in Production and Demand and Medium-Term Outlook 2012, FAO.
5. Miglior F., et al. Selection indices in Holstein cattle of various countries. J. Dairy Sci. 2005, 88:1255-1263.
6. Pavlidis H.O., et al. Divergent selection for ascites incidence in chickens. Poult. Sci. 2007, 86:2517-2529.
7. Charlier C., et al. Highly effective SNP-based association mapping and management of recessive defects in livestock. Nat. Genet. 2008, 40:449-454.
8. Pryce J.E., et al. Genetic relationships between calving interval and body condition score conditional on milk yield. J. Dairy Sci. 2002, 85:1590-1595.
9. Hegarty R.S., et al. Nutritional and flock management options to reduce methane output and methane per unit product from sheep enterprises. Anim. Prod. Sci. 2010, 50:1026-1033.
10. Hegarty R.S. Livestock nutrition: a perspective on future needs in a resource-challenged planet. Anim. Prod. Sci. 2012, 52:406-415.
11. Nkrumah J.D., et al. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 2006, 84:145-153.
12. Hegarty R.S. Cattle selected for lower residual feed intake have reduced daily methane production. J. Anim. Sci. 2007, 85:1479-1486.
13. de Haas Y., et al. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci. 2011, 94:6122-6134.
14. Parry M.L., et al. Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007, Cambridge University Press.
15. Hayes B.J., et al. Genotype x environment interaction for milk production of daughters of Australian dairy sires from test-day records. J. Dairy Sci. 2003, 86:3736-3744.
16. Haile-Mariam M., et al. Genotype by environment interaction for fertility, survival, and milk production traits in Australian dairy cattle. J. Dairy Sci. 2008, 91:4840-4853.
17. Ravagnolo O., Misztal I. Genetic component of heat stress in dairy cattle, parameter estimation. J. Dairy Sci. 2000, 83:2126-3210.
18. Aguilar I., et al. Short communication: genetic trends of milk yield under heat stress for US Holsteins. J. Dairy Sci. 2010, 93:1754-1758.
19. Brügemann K., et al. Genetic analyses of protein yield in dairy cows applying random regression models with time-dependent and temperature x humidity-dependent covariates. J. Dairy Sci. 2011, 94:4129-4139.
20. Bloemhof S., et al. Sow line differences in heat stress tolerance expressed in reproductive performance traits. J. Anim. Sci. 2008, 86:3330-3337.
21. Zumbach B., et al. Genetic components of heat stress in finishing pigs: parameter estimation. J. Anim. Sci. 2008, 86:2076-2081.
22. Hansen P.J. Physiological and cellular adaptations of zebu cattle to thermal stress. Anim. Reprod. Sci. 2004, 83:349-360.
23. Godfray H.C.J., et al. Food security: the challenge of feeding 9 billion people. Science 2010, 327:812-818.
24. Macdonald K.A., et al. A comparison of three strains of Holstein-Friesian grazed on pasture and managed under different feed allowances. J. Dairy Sci. 2008, 91:1693-1707.
25. Lillehammer M., et al. Gene by environment interactions for production traits in Australian dairy cattle. J. Dairy Sci. 2009, 92:4008-4017.
26. Boichard D., Brochard M. New phenotypes for new breeding goals in dairy cattle. Animal 2012, 6:544-550.
27. Meuwissen T.H., et al. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001, 157:1819-1829.
28. Cole J.B., et al. Distribution and location of genetic effects for dairy traits. J. Dairy Sci. 2009, 92:2931-2946.
29. Kemper K.E., et al. Genetic architecture of body size in mammals. Genome Biol. 2012, 13:244.
30. Schaeffer L.R. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet. 2006, 123:218-223.
31. Pryce J.E., Daetwyler H.D. Designing dairy cattle breeding schemes under genomic selection: a review of international research. Anim. Prod. Sci. 2011, 52:107-114.
32. Wickham B. An information infrastructure for facilitating the delivery of improved profits on Irish cattle farms and improving the commercial viability of the Irish breeding industry. Proceedings International Committee for Animal Recording conference, June 2012 2012, 1-6.
33. Van Eenennaam A.L., et al. The value of using DNA markers for beef bull selection in the seedstock sector. J. Anim. Sci. 2011, 89:307-320.
34. Pimentel E.C., König S. Genomic selection for the improvement of meat quality in beef. J. Anim. Sci. 2012, 90:3418-3426.
35. Weber K.L., et al. Accuracy of genomic breeding values in multi-breed beef cattle populations derived from deregressed breeding values and phenotypes. J. Anim. Sci. 2012, 10.2527/jas.2011-4586.
36. Saatchi M., et al. Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet. Sel. Evol. 2011, 43:40.
37. Mujibi F.D., et al. Associations of marker panel scores with feed intake and efficiency traits in beef cattle using preselected single nucleotide polymorphisms. J. Anim. Sci. 2011, 89:3362-3371.
38. Erbe M., et al. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. J. Dairy Sci. 2012, 95:4114-4129.
39. Daetwyler H.D., et al. Components of the accuracy of genomic prediction in a multi-breed sheep population. J. Anim. Sci. 2012, 90:3375-3384.
40. Duchemin S.I., et al. Genomic selection in the French Lacaune dairy sheep breed. J. Dairy Sci. 2012, 95:2723-2733.
41. Daetwyler H.D., et al. Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population. Anim. Prod. Sci. 2011, 50:1004-1010.
42. Daetwyler H.D., et al. The accuracy from cross-validation of pedigree and genomic predictions of carcass and novel meat quality traits in multi-breed sheep data. Gen. Sel. Evol. 2012, 44:33.
43. González-Recio O., et al. Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens. Genet. Sel. Evol. 2009, 41:3.
44. Ostersen T., et al. Deregressed EBV as the response variable yield more reliable genomic predictions than traditional EBV in pure-bred pigs. Genet. Sel. Evol. 2011, 43:38.
45. Pryce J.E., et al. Accuracy of genomic predictions of residual feed intake and 250-day body weight in growing heifers using 625,000 single nucleotide polymorphism markers. J. Dairy Sci. 2012, 95:2108-2119.
46. Wiggans G.R., et al. The genomic evaluation system in the United States: past, present, future. J. Dairy Sci. 2011, 94:3202-3211.
47. de Haas Y., et al. Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. J. Dairy Sci. 2012, 95:6103-6112.
48. Hayes B.J., et al. A validated genome wide association study to breed cattle adapted to an environment altered by climate change. PLoS ONE 2009, 4:e6676.
49. Haynes F.E., et al. Myostatin allelic status interacts with level of nutrition to affect growth, composition, and myofiber characteristics of lambs. J. Anim. Sci. 2012, 90:456-465.
50. Daetwyler H.D., et al. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 2008, 3:e3395.
51. Goddard M.E. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 2008, 10.1007/s10709-008-9308-0.
52. Garnsworthy P.C., et al. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J. Dairy Sci. 2012, 95:3166-3180.
53. Grainger C., et al. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 2007, 90:2755-2766.
54. McGinn S.M., et al. Methane emissions from grazing cattle using point-source dispersion. J. Environ. Qual. 2011, 40:22-27.
55. Habier D., et al. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet. Sel. Evol. 2010, 42:5.
56. Olson K.M., et al. Multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss. J. Dairy Sci. 2012, 95:5378-5383.
57. Meuwissen T-H.E., Goddard M.E. Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 2010, 185:623-631.
58. de Roos A.P., et al. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics 2008, 179:1503-1512.
59. Haile-Mariam M., et al. Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations. J. Anim. Breed. Genet. 2012, 10.1111/j.1439-0388.2012.01001.
60. Jensen J., et al. Partitioning additive genetic variance into genomic and remaining polygenic components for complex traits in dairy cattle. BMC Genet. 2012, 13:44.
61. Larkin D.M., et al. Whole-genome resequencing of two elite sires for the detection of haplotypes under selection in dairy cattle. Proc. Natl. Acad. Sci. U.S.A. 2012, 109:7693-7698.
62. Hayes B.J., et al. Towards genomic prediction from genome sequence data and the 1000 bull genomes project. Proceedings 4th International Conference on Quantiative Genetics, Edinburgh, June 2012. 2012, O-54.
63. Georges M., Massey J.M. Velogenetics, or the synergistic use of marker assisted selection and germ-line manipulation. Theriogenology 1991, 35:151-159.
64. Hayley C.S., Visscher P.M. Strategies to utilize marker-quantitative trait loci associations. J. Dairy Sci. 1998, 81:85-97.
65. Gou K.M., et al. Field evaluation of juvenile in vitro embryo transfer (JIVET) in sheep. Anim. Reprod. Sci. 2009, 112:316-324.
66. Armstrong D.T., et al. Advances in production of embryos in vitro from juvenile and prepubertal oocytes from the calf and lamb. Reprod. Fertil. Dev. 1997, 9:333-339.
67. Brulc J.M., et al. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:1948-1953.
68. Hess M., et al. Metagenomic discovery of biomass-degrading genes and genomes from cow rumen. Science 2011, 331:463-467.
69. Ross E.M., et al. High throughput whole rumen metagenome profiling using untargeted massively parallel sequencing. BMC Genet. 2012, 13:53.
70. Benson A.K., et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc. Natl. Acad. Sci. U.S.A. 2010, 107:18933-18938.
71. VanRaden P.M., et al. Harmful recessive effects on fertility detected by absence of homozygous haplotypes. J. Dairy Sci. 2011, 94:6153-6161.
72. Adams H.A., et al. Identification of a nonsense mutation in APAF1 that is causal for a decrease in reproductive efficiency in dairy cattle. International Plant and Animal Genome Conference 2012 2012.
73. Su G., et al. Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS ONE 2012, 7:e45293.
74. de Los Campos G. Whole genome regression and prediction methods applied to plant and animal breeding. Genetics 2012, 10.1534/genetics.112.143313.
75. Habier D., et al. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 2011, 12:186.
76. Verbyla K.L., et al. Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle. Genet. Res. 2009, 91:307-311.
77. Gianola D., et al. Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 2006, 173:1761-1776.
78. Wittenburg D. Including non-additive genetic effects in Bayesian methods for the prediction of genetic values based on genome-wide markers. BMC Genet. 2011, 12:74.