Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-29T19:00:26.520Z Has data issue: false hasContentIssue false
  • English
  • Français

Merging and characterising phenotypic data on conventional and rare traits from dairy cattle experimental resources in three countries

Published online by Cambridge University Press:  04 January 2012

G. Banos ,
M. P. Coffey ,
R. F. Veerkamp ,
D. P. Berry  and
E. Wall
G. Banos*
Affiliation:
Department of Animal Production, Faculty of Veterinary Medicine, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece Sustainable Livestock Systems Group, Scottish Agricultural College, Roslin Institute Building, Easter Bush, Midlothian EH25 9RG, Scotland, UK
M. P. Coffey
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, Roslin Institute Building, Easter Bush, Midlothian EH25 9RG, Scotland, UK
R. F. Veerkamp
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands
D. P. Berry
Affiliation:
Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Republic of Ireland
E. Wall
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, Roslin Institute Building, Easter Bush, Midlothian EH25 9RG, Scotland, UK
*
†E-mail: [email protected]
Get access

Abstract

This study set out to demonstrate the feasibility of merging data from different experimental resource dairy populations for joint genetic analyses. Data from four experimental herds located in three different countries (Scotland, Ireland and the Netherlands) were used for this purpose. Animals were first lactation Holstein cows that participated in ongoing or previously completed selection and feeding experiments. Data included a total of 60 058 weekly records from 1630 cows across the four herds; number of cows per herd ranged from 90 to 563. Weekly records were extracted from the individual herd databases and included seven traits: milk, fat and protein yield, milk somatic cell count, liveweight, dry matter intake and energy intake. Missing records were predicted with the use of random regression models, so that at the end there were 44 weekly records, corresponding to the typical 305-day lactation, for each cow. A total of 23 different lactation traits were derived from these records: total milk, fat and protein yield, average fat and protein percentage, average fat-to-protein ratio, total dry matter and energy intake and average dry matter intake-to-milk yield ratio in lactation weeks 1 to 44 and 1 to 15; average milk somatic cell count in lactation weeks 1 to 15 and 16 to 44; average liveweight in lactation weeks 1 to 44; and average energy balance in lactation weeks 1 to 44 and 1 to 15. Data were subsequently merged across the four herds into a single dataset, which was analysed with mixed linear models. Genetic variance and heritability estimates were greater (P < 0.05) than zero for all traits except for average milk somatic cell count in weeks 16 to 44. Proportion of total phenotypic variance due to genotype-by-environment (sire-by-herd) interaction was not different (P > 0.05) from zero. When estimable, the genetic correlation between herds ranged from 0.85 to 0.99. Results suggested that merging experimental herd data into a single dataset is both feasible and sensible, despite potential differences in management and recording of the animals in the four herds. Merging experimental data will increase power of detection in a genetic analysis and augment the potential reference population in genome-wide association studies, especially of difficult-to-record traits.

Keywords

merging phenotypesdairy geneticsexperimental resourcesdry matter intake
Type
Full Paper
Information
animal , Volume 6 , Issue 7 , 28 May 2012 , pp. 1040 - 1048
Copyright
Copyright © The Animal Consortium 2012

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

Akaike, H 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723.CrossRefGoogle Scholar
Banos, G, Coffey, MP, Brotherstone, S 2005. Modeling daily energy balance of dairy cows in the first three lactations. Journal of Dairy Science 88, 2226–2237.CrossRefGoogle ScholarPubMed
Beerda, B, Ouweltjes, W, Sebek, LBJ, Windig, JJ, Veerkamp, RF 2007. Effects of genotype by environment interactions on milk yield, energy balance, and protein balance. Journal of Dairy Science 90, 219–228.CrossRefGoogle ScholarPubMed
Buckley, F, Dillon, P, Rath, M, Veerkamp, RF 2000. The relationship between genetic merit for yield and live weight, condition score and energy balance of spring calving Holstein–Friesian dairy cows on grass based systems of milk production. Journal of Dairy Science 83, 1878–1886.CrossRefGoogle ScholarPubMed
Buttchereit, N, Stamer, E, Junge, W, Thaller, G 2011. Genetic relationships among daily energy balance, feed intake, body condition score, and fat to protein ratio of milk in dairy cows. Journal of Dairy Science 94, 1586–1591.CrossRefGoogle ScholarPubMed
Coffey, MP, Emmans, GC, Brotherstone, S 2001. Genetic evaluation of dairy bulls for energy balance traits using random regression. Animal Science 73, 29–40.CrossRefGoogle Scholar
Emmans, GC 1994. Effective energy: a concept of energy utilization applied across species. British Journal of Nutrition 71, 801–821.CrossRefGoogle ScholarPubMed
Erdfelder, E, Faul, F, Buchner, A 1996. GPOWER: a general power analysis program. Behavior Research Methods, Instruments and Computers 28, 1–11.CrossRefGoogle Scholar
Friggens, NC, Ingvartsen, KL, Emmans, GC 2003. Prediction of body lipid change in pregnancy and lactation. Journal of Dairy Science 87, 988–1000.CrossRefGoogle Scholar
Gilmour, AR, Gogel, BJ, Cullis, BR, Welham, SJ, Thompson, R 2006. ASREML user guide, release 2.0. VSN International Ltd, Hemel Hempstead, UK.Google Scholar
Horan, B, Faverdin, P, Delaby, L, Rath, M, Dillon, P 2006. The effect of strain of Holstein–Friesian dairy cows and pasture-based system on grass intake and milk production. Animal Science 82, 435–444.CrossRefGoogle Scholar
International Committee for Animal Recording 2011. www.icar.org, Rome, Italy.Google Scholar
Kennedy, E, O'Donovan, M, Murphy, JP, O'Mara, FP, Delaby, L 2006. The effect of initial grazing date and subsequent stocking rate on the grazing management, grass dry matter intake and milk production of dairy cows in summer. Grass Forage Science 61, 375–384.CrossRefGoogle Scholar
Kennedy, J, Dillon, P, Faverdin, P, Delaby, L, Stakelum, G, Rath, M 2003. Effect of genetic merit and concentrate supplementation on grass intake and milk production with Holstein–Friesian dairy cows. Journal of Dairy Science 86, 610–621.CrossRefGoogle ScholarPubMed
McCarthy, S, Berry, DP, Dillon, P, Rath, M, Horan, B 2007. Effect of strain of Holstein–Friesian and feed system on udder health and milking characteristics. Livestock Science 107, 1–28.CrossRefGoogle Scholar
McEvoy, M, O'Donovan, M, Murphy, JP, O'Mara, F, Rath, M, Delaby, L 2007. Effect of concentrate supplementation and herbage allowance on milk production performance of spring calving dairy cows in early lactation. Proceedings of the Irish Agricultural Research Forum, Tullamore, Ireland, 15–16 March 2007, p. 46.Google Scholar
Mrode, RA, Swanson, GJT 2003. Estimation of genetic parameters for somatic cell count in the first three lactations using random regression. Livestock Production Science 86, 253–260.CrossRefGoogle Scholar
O'Donovan, M, Delaby, L 2005. A comparison of perennial ryegrass cultivars differing in heading date and grass ploidy with spring calving dairy cows grazed at two different stocking rates. Animal Research 54, 337–350.CrossRefGoogle Scholar
Ordway, RS, Boucher, SE, Whitehouse, NL, Schwab, CG, Sloan, BK 2009. Effects of providing two forms of supplemental methionine to periparturient Holstein dairy cows on feed intake and lactational performance. Journal of Dairy Science 92, 5154–5166.CrossRefGoogle ScholarPubMed
Pryce, JE, Nielson, BL, Veerkamp, RF, Simm, G 1999. Genotype and feeding system effects and interactions for health and fertility traits in dairy cattle. Livestock Production Science 57, 193–201.CrossRefGoogle Scholar
Toshniwal, JK, Dechow, CD, Cassell, BG, Appuhamy, J A D R N, Varga, GA 2008. Heritability of electronically recorded daily body weight and correlations with yield, dry matter intake, and body condition score. Journal of Dairy Science 91, 3201–3210.CrossRefGoogle ScholarPubMed
Urioste, JI, Franzén, J, Strandberg, E 2010. Phenotypic and genetic characterization of novel somatic cell count traits from weekly or monthly observations. Journal of Dairy Science 93, 5930–5941.CrossRefGoogle ScholarPubMed
Vallimont, JE, Dechow, CD, Daubert, JM, Dekleva, MW, Blum, JW, Barlieb, CM, Liu, W, Varga, GA, Heinrichs, AJ, Baumrucker, CR 2010. Genetic parameters of feed intake, production, body weight, body condition score, and selected type traits of Holstein cows in commercial tie-stall barns. Journal of Dairy Science 93, 4892–4901.CrossRefGoogle ScholarPubMed
Van Es, AJH 1978. Feed evaluation for ruminants. I. The systems in use from May 1977–onwards in the Netherlands. Livestock Production Science 5, 331–345.CrossRefGoogle Scholar
Veerkamp, RF, Simm, G, Oldham, JD 1995. Genotype by environment interaction – experience from Langhill. In Breeding and feeding the high genetic merit dairy cow (ed. TLJ Lawrence, FJ Gordon and A Carson), vol. 19. pp. 59–66. British Society of Animal Science (Occasional Publication), Midlothian, Scotland.Google Scholar
Veerkamp, RF, Oldenbroek, JK, van der Gaast, HJ, van der Werf, JHJ 2000. Genetic correlation between days until start of luteal activity and milk yield, energy balance and live weights. Journal of Dairy Science 83, 577–583.CrossRefGoogle ScholarPubMed
Windig, JJ, Beerda, B, Veerkamp, RF 2008. Relationship between milk progesterone profiles and genetic merit for milk production, milking frequency, and feeding regimen in dairy cattle. Journal of Dairy Science 91, 2874–2884.CrossRefGoogle ScholarPubMed