Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-27T18:27:00.011Z Has data issue: false hasContentIssue false
  • English
  • Français

Heterogeneous variances and genetics by environment interactions in genetic evaluation of crossbred lambs

Published online by Cambridge University Press:  19 November 2014

G. C. Márquez ,
W. Haresign ,
M. H. Davies ,
R. Roehe ,
L. Bünger ,
G. Simm  and
R. M. Lewis
G. C. Márquez
Affiliation:
Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA 24060, USA
W. Haresign
Affiliation:
Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3FG, UK
M. H. Davies
Affiliation:
ADAS Rosemaund, Preston Wynne, HR1 3PG, UK
R. Roehe
Affiliation:
Animal & Veterinary Sciences Group, SRUC, Edinburgh, EH9 3JG, UK
L. Bünger
Affiliation:
Animal & Veterinary Sciences Group, SRUC, Edinburgh, EH9 3JG, UK
G. Simm
Affiliation:
Animal & Veterinary Sciences Group, SRUC, Edinburgh, EH9 3JG, UK
R. M. Lewis*
Affiliation:
Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA 24060, USA
*
E-mail: [email protected]
Get access

Abstract

Accounting for environmental heteroscedasticity and genetics by environment interaction (G×E) in genetic evaluation is important because animals may not perform predictably across environments. The objectives of this study were to evaluate the presence and consequences of heteroscedasticity and G×E on genetic evaluation. The population considered was crossbred lambs sired by terminal sires and reared under commercial conditions in the UK. Data on 6325 lambs sired by Charollais, Suffolk and Texel rams were obtained. The experiment was conducted between 1999 and 2002 on three farms located in England, Scotland and Wales. There were 2322, 2137 and 1866 lambs in England, Scotland and Wales, respectively. A total of 89 sires were mated to 1984 ewes of two types (Welsh and Scottish Mules). Most rams were used for two breeding seasons with some rotated among farms to create genetic links. Lambs were reared on pasture and had their parentage, birth, 5 week, 10 week, and slaughter weights recorded. Lambs were slaughtered at a constant fatness, at which they were ultrasonically scanned for fat and muscle depth. Heteroscedasticity was evaluated in two ways. First, data were separated into three subsets by farm. Within-farm variance component estimates were then compared with those derived from the complete data (Model 1). Second, the combined data were fitted, but with a heterogeneous (by farm) environmental variance structure (Model 2). To investigate G×E, a model with a random farm by sire (F×S) interaction was used (Model 3). The ratio of the F×S variance to total variance was a measure of the level of G×E in the population. Heterogeneity in environmental variability across farm was identified for all traits (P<0.01). Rank correlations of sire estimated breeding value between farms differed for Model 1 for all traits. However, sires ranked similarly (rank correlation of 0.99) for weight traits with Model 2, but less so for ultrasonic measures. Including the F×S interaction (Model 3) improved model fit for all traits. However, the F×S term explained a small proportion of variation in weights (<2%) although more in ultrasonic traits (at least 10%). In conclusion, heteroscedasticity and G×E were not large for these data, and can be ignored in genetic evaluation of weight but, perhaps, not ultrasonic traits. Still, before incorporating heteroscedasticity and G×E into routine evaluations of even ultrasonic traits, their consequences on selection response in the breeding goal should be evaluated.

Keywords

crossbred lambsgenetics by environment interactionheterogeneous variancessheep
Type
Research Article
Information
animal , Volume 9 , Issue 3 , March 2015 , pp. 380 - 387
Copyright
© The Animal Consortium 2014 

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

Canavesi, F, Schaeffer, LR, Burnside, EB, Jansen, GB and Rozzi, P 1995. Sire-by-herd interaction effect when variances across herds are heterogeneous. I. Expected genetic progress. Journal of Animal Breeding and Genetics 112, 95–106.Google Scholar
Dickerson, GE 1962. Implications of genetic-environmental interaction in animal breeding. Animal Production 4, 47–63.Google Scholar
Emenheiser, JC, Greiner, SP, Lewis, RM and Notter, DR 2010. Validation of live animal ultrasonic measurements of body composition in market lambs. Journal of Animal Science 88, 2932–2939.CrossRefGoogle ScholarPubMed
Garrick, DJ and Van Vleck, LD 1987. Aspects of selection for performance in several environments with heterogeneous variances. Journal of Animal Science 65, 409–421.CrossRefGoogle Scholar
Gianola, D 1986. On selection criteria and estimation of parameters when the variance is heterogeneous. Theoretical and Applied Genetics 72, 671–677.CrossRefGoogle ScholarPubMed
Gilmour, AR, Gogel, BJ, Cullis, BR and Thompson, R 2009. ASReml User Guide Release 3.0. VSN International Ltd, Hemel, Hempstead, UK.Google Scholar
Hagger, C 1998. Litter, permanent environmental, ram-flock, and genetic effects on early weight gain of lambs. Journal of Animal Science 76, 452–457.Google Scholar
Hill, WG 1984. On selection among groups with heterogeneous variance. Animal Production 39, 473–477.Google Scholar
Hill, WG and Zhang, XS 2004. Effects on phenotypic variability of directional selection arising through genetic differences in residual variability. Genetical Research 83, 121–132.Google Scholar
Kempster, AJ, Cook, GL and Grantley-Smith, M 1986. National estimates of the body composition of British cattle, sheep and pigs with special reference to trends in fatness. A review. Meat Science 17, 107–138.Google Scholar
Kuehn, LA, Lewis, RM and Notter, DR 2007. Managing the risk of comparing estimated breeding values across flocks or herds through connectedness: a review and application. Genetics Selection Evolution 39, 225–247.Google ScholarPubMed
Kuehn, LA, Notter, DR, Nieuwhof, GJ and Lewis, RM 2008. Changes in connectedness over time in alternative sheep sire referencing schemes. Journal of Animal Science 86, 536–544.Google Scholar
Lewis, RM, Crump, RE, Kuehn, LA, Simm, G and Thompson, R 2005. Assessing connectedness in across-flock genetic evaluations. Journal of Animal Science 83 (suppl. 1), 101.Google Scholar
Maniatis, N and Pollott, GE 2002. Genotype by environment interactions in lamb weight and carcass composition traits. Animal Science 75, 3–14.CrossRefGoogle Scholar
Márquez, GC, Haresign, W, Davies, MH, Roehe, R, Bünger, L, Simm, G and Lewis, RM 2013. Index selection in terminal sires improves lamb performance at finishing. Journal of Animal Science 91, 38–43.CrossRefGoogle ScholarPubMed
Márquez, GC, Haresign, W, Davies, MH, Emmans, GC, Roehe, R, Bünger, L, Simm, G and Lewis, RM 2012. Index selection in terminal sires improves early lamb growth. Journal of Animal Science 90, 142–151.Google Scholar
Mekkawy, W, Roehe, R, Lewis, RM, Davies, MH, Bünger, L, Simm, G and Haresign, W 2009. Genetic relationship between longevity and objectively or subjectively assessed performance traits in sheep using linear censored models. Journal of Animal Science 87, 3482–3489.Google Scholar
Meyer, K 1987. Estimates of variances due to sirexherd interactions and environmental covariances between paternal half-sibs for first lactation dairy production. Livestock Production Science 17, 95–115.CrossRefGoogle Scholar
Misztal, I 1990. Restricted maximum likelihood estimation of variance components in animal model using sparse matrix inversion and a supercomputer. Journal of Dairy Science 73, 163–172.CrossRefGoogle Scholar
Mulder, HA and Bijma, P 2005. Effects of genotype x environment interaction on genetic gain in breeding programs. Journal of Animal Science 83, 49–61.Google Scholar
Mulder, HA and Bijma, P 2006. Benefits of cooperation between breeding programs in the presence of genotype by environment interaction. Journal of Dairy Science 89, 1727–1739.Google Scholar
Nakaoka, H, Narita, A, Ibi, T, Sasae, Y, Miyake, T, Yamada, T and Sasaki, Y 2007. Effectiveness of adjusting for heterogeneity of variance in genetic evaluation of Japanese Black cattle. Journal of Animal Science 85, 2429–2436.Google Scholar
Notter, DR, Tier, B and Meyer, K 1992. Sire x herd interactions for weaning weight in beef cattle. Journal of Animal Science 70, 2359–2365.CrossRefGoogle ScholarPubMed
Pollott, GE and Greeff, JC 2004. Genotype x environment interactions and genetic parameters for fecal egg count and production traits of Merino sheep. Journal of Animal Science 82, 2840–2851.CrossRefGoogle ScholarPubMed
Pollott, GE and Stone, DG 2004. Mating structure of the sheep industry. In The breeding structure of the British sheep industry 2003 (ed. RD Eglin, A Ortiz Pelaez and CJ Cook), pp. 18–22. Department for Environment Food and Rural Affairs, London, UK.Google Scholar
Robert-Graniè, C, Bonaôti, B, Boichard, D and Barbat, A 1999. Accounting for variance heterogeneity in French dairy cattle genetic evaluation. Livestock Production Science 60, 343–357.CrossRefGoogle Scholar
Rowe, SJ, White, IMS, Avendaño, S and Hill, WG 2006. Genetic heterogeneity of residual variance in broiler chickens. Genetics Selection Evolution 38, 617–635.CrossRefGoogle ScholarPubMed
SanCristobal-Gaudy, M, Bodin, L, Elsen, J-M and Chevalet, C 2001. Genetic components of litter size variability in sheep. Genetics Selection Evolution 33, 249–271.Google Scholar
Simm, G and Dingwall, WS 1989. Selection indices for lean meat production in sheep. Livestock Production Science 21, 223–233.Google Scholar
Steinheim, G, Ødegård, J, Ådnøy, T and Klemetsdal, G 2008. Genotype by environment interaction for lamb weaning weight in two Norwegian sheep breeds. Journal of Animal Science 86, 33–39.Google Scholar
Tosh, JJ and Kemp, RA 1994. Estimation of variance components for lamb weights in three sheep populations. Journal of Animal Science 72, 1184–1190.CrossRefGoogle ScholarPubMed
van Heelsum, AM, Lewis, RM, Davies, MH and Haresign, W 2003. Growth and carcass characteristics in wether lambs of a crossbred dam line. Animal Science 76, 45–53.Google Scholar
Van Vleck, LD 1990. Breeding value prediction with maternal genetic groups. Journal of Animal Science 68, 3998–4013.Google Scholar
Wiggans, GR and VanRaden, PM 1991. Method and effect of adjustment for heterogeneous variance. Journal of Dairy Science 74, 4350–4357.CrossRefGoogle ScholarPubMed
Winkelman, A and Schaeffer, LR 1988. Effect of heterogeneity of variance on dairy sire evaluation. Journal of Dairy Science 71, 3033–3039.Google Scholar