Genetic evaluation of growth using random regression models

R VENKATARAMANAN

doi:10.56093/ijans.v91i9.116456

Authors

R VENKATARAMANAN Tamil Nadu Veterinary and Animal Sciences University, Chennai, Tamil Nadu 600 051 India

https://doi.org/10.56093/ijans.v91i9.116456

Keywords:

Eigen value, Genetic parameters, Growth curve, Repeatable data

Abstract

The variability in growth traits provides enormous scope for improvement through selection and breeding. However, growth is a longitudinal trait measured repeatedly on the animal and random regression models (RRM) have been found to be suitable for modeling the trait as a growth curve. RRM accommodate repeated records for traits which change gradually and continually, over time, and do not require stringent assumptions about constancy of variances and correlations. RRM has the advantage that, variance components can be estimated for any point in the trajectory of the growth curve and genetic parameters could be estimated for any age class within the range of ages included in the study. RRM is suitable for group breeding schemes and field performance recording systems where the growth data will be uneven and for varying age points. Worldwide, several studies on use of the tool, RRM in growth of various livestock species are available, but literature on such studies is scanty from India. The methodology used, data requirement, assumptions, validity, software available and application of RRM in the field are discussed based on the earlier reports.

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References

Andersen S and Pedersen B. 1996. Growth and food intake curves for group-housed gilts and castrated male pigs. Animal Science 63(3): 457–64. DOI: https://doi.org/10.1017/S1357729800015356

Arango J A, Cundiff L V and Van Vleck L D. 2004. Covariance functions and random regression models for cow weight in beef cattle. Journal of Animal Science 82(1): 54–67. DOI: https://doi.org/10.2527/2004.82154x

Arthy V, Venkataramanan R, Sivaselvam S N, Sreekumar C and Balasubramanyam D. 2018. Genetic evaluation of growth in farmers’ flocks of Madras Red sheep under long-term selection in a group breeding scheme. Tropical animal health and production 50(7): 1463–71. DOI: https://doi.org/10.1007/s11250-018-1581-z

Arthy V, Venkataramanan R, Sivaselvam S N, Sreekumar C and Balasubramanyam D. 2020. Suitability of random regression models for growth of Madras Red sheep under a field performance recording system. Small Ruminant Research 193: 106260. DOI: https://doi.org/10.1016/j.smallrumres.2020.106260

Aziz M A, Nishida S, Suzuki K and Nishida A. 2005. Estimation of direct and maternal genetic and permanent environmental effects for weights from birth to 356 days of age in a herd of Japanese Black cattle using random regression. Journal of Animal Science 83(3): 519–30. DOI: https://doi.org/10.2527/2005.833519x

Baldi F, Albuquerque L G and Alencar M M. 2010. Random regression models on Legendre polynomials to estimate

genetic parameters for weights from birth to adult age in Canchim cattle. Journal of Animal Breeding and Genetics 127(4): 289–99.

Barazandeh A, Moghbeli S M, Hossein-Zadeh N G and Vatankhah M. 2012. Genetic evaluation of growth in Raini goat using random regression models. Livestock Science 145(1–3): 1–6. DOI: https://doi.org/10.1016/j.livsci.2011.12.004

Bhatia S and Arora R. 2005. Biodiversity and conservation of Indian sheep genetic resources-an overview. Asian- DOI: https://doi.org/10.5713/ajas.2005.1387

Australasian Journal of Animal Sciences 18(10): 1387–1402.

Bohlouli M, Mohammadi H and Alijani S. 2013. Genetic evaluation and genetic trend of growth traits of Zandi sheep

in semi-arid Iran using random regression models. Small Ruminant Research 114(2–3): 195–201.

Boligon A A, Mercadante M E Z, Baldi F, Lôbo R B and Albuquerque L G. 2009. Multi-trait and random regression

mature weight heritability and breeding value estimates in Nelore cattle. South African Journal of Animal Science 39 (sup-1): 145–48.

Chaudhary, Rajni, Ved Prakash, Lalrengpuii Sailo, Akansha Singh, A Karthikeyan, Aamir Bashir, S K Mondal, N R Sahoo and Amit Kumar. 2019. Estimation of genetic parameters and breeding values for growth traits using random regression model in Landrace × desi crossbred pigs. Indian Journal of Animal Sciences 89(10): 1104–08.

Chiaia H L J, De Lemos M V A, Venturini G C, Aboujaoude C, Berton M P, Feitosa F B, Carvalheiro R, Albuquerque L G, De Oliveira H N and Baldi F. 2015. Genotype× environment interaction for age at first calving, scrotal circumference, and yearling weight in Nellore cattle using reaction norms in multitrait random regression models. Journal of Animal Science 93(4): 1503–10. DOI: https://doi.org/10.2527/jas.2014-8217

Coutinho de Barros I, Souza Carneiro P L, Reis Mota R, Pinheiro da Silva L, Martins Filho R and Mendes Malhado C H. 2017. Genetic parameters estimation of growth in Polled Nellore cattle via random regression models. Livestock Research for Rural Development 29(12).

Druet T, Jaffrézic F, Boichard D and Ducrocq V. 2003. Modeling lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows. Journal of Dairy Science 86(7): 2480–90. DOI: https://doi.org/10.3168/jds.S0022-0302(03)73842-9

Fischer T M and Van der Werf J H J. 2002, August. Effect of data structure on the estimation of genetic parameters using random regression. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France 19–23.

Fischer T M, Van der Werf J H J, Banks R G and Ball A J. 2004. Description of lamb growth using random regression on field data. Livestock Production Science 89(2–3): 175–85. DOI: https://doi.org/10.1016/j.livprodsci.2004.02.004

Fogarty N M, Brash L D and Gilmour A R. 1994. Genetic parameters for reproduction and lamb production and their

components and liveweight, fat depth and wool production in Hyfer sheep. Australian Journal of Agricultural Research 45: 443–57.

Foulley J L and Robert-Granié C. 2002. Heteroskedastic random coefficient models. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France.

Freitas L A, Savegnago R P, Oliveira E J, Paz C C P and Munari D P. 2019. Inheritance, genetic correlation and cluster analyses of fecal egg count, packed cell volume and body weight in different ages using random regression model in Santa Ines sheep. Small Ruminant Research 174: 57–61. DOI: https://doi.org/10.1016/j.smallrumres.2019.03.011

Galyca and Takmac. 2019. Estimates of genetic parameters for body weight in Turkish Holstein bulls using random regression model. Journal of Agricultural Sciences 25(3): 328–33. DOI: https://doi.org/10.15832/ankutbd.417422

Ghafouri-Kesbi F and Eskandarinasab M. 2021. Genetic analysis of growth curve of Afshari lambs by legendre polynomials based random regression models. Songklanakarin Journal of Science and Technology 43(1).

Ghafouri-Kesbi F and Gholizadeh M. 2018. Random regression models to explore genetic variation and genetic variability in the growth curve of Baluchi lambs. Meta Gene 18: 195–201. DOI: https://doi.org/10.1016/j.mgene.2018.09.011

Ghafouri-Kesbi F. 2018. Determination of the genetic and nongenetic variations in growth curve of Zandi lambs by random regression models. Journal of Livestock Science and Technologies 6(2): 57–66.

Ghahri B, Alijani S, Rafat S A, Nabavi R and Kia H D. 2019. Genetic appraisal of growth traits in Iranian native Ghezel

sheep using random regression models. Turkish Journal of Veterinary and Animal Sciences 43(3): 372–79.

Hill W G. 1998. Inferences from evolutionary biology to livestock breeding. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, 23, Armidale, Australia 32–39.

Iwaisaki H, Tsuruta S, Misztal I and Bertrand J K. 2005. Genetic parameters estimated with multitrait and linear spline-random regression models using Gelbvieh early growth data. Journal of Animal Science 83(4): 757–63. DOI: https://doi.org/10.2527/2005.834757x

Jamrozik J and Schaeffer L R. 1997. Estimates of genetic parameters for a test day model with random regressions for

yield traits of first lactation Holsteins. Journal of Dairy Science. https://doi.org/10.3168/jds.S0022-0302(97)75996-4. DOI: https://doi.org/10.3168/jds.S0022-0302(97)75996-4

Jannoune A, Boujenane I, Falaki M and Derqaoui L. 2015. Genetic analysis of live weight of Sardi sheep using random regression and multi-trait animal models. Small Ruminant Research 130: 1–7. DOI: https://doi.org/10.1016/j.smallrumres.2015.06.015

Kariuki C M, Ilatsia E D, Wasike C B, Kosgey I S and Kahi A K. 2010. Genetic evaluation of growth of Dorper sheep in semiarid Kenya using random regression models. Small Ruminant Research 93(2–3): 126–34. DOI: https://doi.org/10.1016/j.smallrumres.2010.05.011

Kesbi F G, Eskandarinasab M and Shahir M H. 2008. Estimation of direct and maternal effects on body weight in Mehraban sheep using random regression models. Archives of Animal Breeding 51(3): 235–46. DOI: https://doi.org/10.5194/aab-51-235-2008

Kheirabadi K and Rashidi A. 2016. Genetic description of growth traits in Markhoz goat using random regression models. Small Ruminant Research 144: 305–12. DOI: https://doi.org/10.1016/j.smallrumres.2016.10.003

Kirkpatrick M and Heckman N. 1989. A quantitative genetic model for growth, shape, reaction norms, and other infinite-dimensional characters. Journal of Mathematical Biology 27: 429–50. DOI: https://doi.org/10.1007/BF00290638

Kirkpatrick M, Lofsvold D and Bulmer M. 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124(4): 979–93. DOI: https://doi.org/10.1093/genetics/124.4.979

Lewis R M and Brotherstone S. 2002. A genetic evaluation of growth in sheep using random regression techniques. Animal Science 74(1): 63–70. DOI: https://doi.org/10.1017/S1357729800052218

Mahala S, Saini S, Kumar A, Sharma R C and Gowane G R. 2020. Genotype × environment interaction affects sire ranking for live weights in Avikalin sheep. Small Ruminant Research 186: 106092. DOI: https://doi.org/10.1016/j.smallrumres.2020.106092

Masuda Y. 2018. Introduction to BLUPF90 suite programs. University of Georgia. http://nce.ads.uga.edu/wiki/doku.php?id=documentation

Meneìndez-Buxadera A, Carleos C, Baro J A, Villa A and Cañón J. 2008. Multi-trait and random regression approaches for

addressing the wide range of weaning ages in Asturiana de los Valles beef cattle for genetic parameter estimation. Journal of Animal Science 86(2): 278–86. DOI: https://doi.org/10.2527/jas.2007-0252

Menezes G R O, Torres R A, Torres Jr R A, Silva L O C, Gondo A and Euclydes R F. 2013. Estimation of genetic parameters for growth traits in Tabapua cattle using a multi-trait model. Brazilian Journal of Animal Science 42: 570–74. DOI: https://doi.org/10.1590/S1516-35982013000800006

Meyer B K. 1999. Estimates of genetic and phenotypic covariance functions for postweaning growth and mature weight of beef cows. Journal of Animal Breeding and Genetics 116(3): 181–205. DOI: https://doi.org/10.1046/j.1439-0388.1999.00193.x

Meyer K. 1998. Estimating covariance functions for longitudinal data using a random regression model. Genetics Selection Evolution 30(3): 221–40. DOI: https://doi.org/10.1186/1297-9686-30-3-221

Meyer K. 2000. Random regressions to model phenotypic variation in monthly weights of Australian beef cows. Livestock Production Science 65(1–2): 19–38. DOI: https://doi.org/10.1016/S0301-6226(99)00183-9

Meyer K. 2004. Scope for a random regression model in genetic evaluation of beef cattle for growth. Livestock Production Science 86(1–3): 69–83. DOI: https://doi.org/10.1016/S0301-6226(03)00142-8

Meyer K. 2005. Random regression analyses using B-splines to model growth of Australian Angus cattle. Genetics Selection Evolution 37(6): 1–28. DOI: https://doi.org/10.1186/1297-9686-37-6-473

Meyer K. 2007. WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University Science B 8(11): 815–21. DOI: https://doi.org/10.1631/jzus.2007.B0815

Misztal I, Strabel T, Jamrozik J, Mäntysaari E A and Meuwissen T H E. 2000. Strategies for estimating the parameters needed for different test-day models. Journal of Dairy Science 83(5): 1125–34. DOI: https://doi.org/10.3168/jds.S0022-0302(00)74978-2

Misztal I, Tsuruta S, Lourenco D A L, Masuda Y, Aguilar I, Legarra A and Vitezica Z. 2018. Manual for BLUPF90 family

programs. University of Georgia. http://nce.ads.uga.edu/wiki/doku.php?id=documentation Montpellier, France 28: 21–22

Mohammadi A and Farhadian M. 2017. Genetic evaluation of growth traits in Iranian Kordi Sheep using random regression model with homogeneous and heterogeneous residual variances. Genetika 49(2): 469–82. DOI: https://doi.org/10.2298/GENSR1702469M

Molina A, Menéndez-Buxadera A, Valera M and Serradilla J M. 2007. Random regression model of growth during the first three months of age in Spanish Merino sheep. Journal of Animal Science 85(11): 2830–39. DOI: https://doi.org/10.2527/jas.2006-647

Mota R R, Marques L F A, Lopes P S, Silva L P, Araujo Neto F R, Resende M D V, Torres R A. 2013. Genetic evaluation using multi-trait and random regression models in Simmental beef cattle. Genetics and Molecular Research 12: 2465–80. DOI: https://doi.org/10.4238/2013.July.24.2

Naderi Y. 2018. Genetic evaluation and genetic trend of growth in makouei sheep via random regression. Journal of Animal and Plant sciences 28(2): 388–95.

Nemutandani K, Snyman G, Olivier W and Visser C. 2018. Estimation of variance components and heritabilities for body weight from birth to six years of age in Merino sheep using random regression models. Proceedings of the World Congress on Genetics Applied to Livestock Production (Vol. 11).

Nobre P R C, Misztal I, Tsuruta S, Bertrand J K, Silva L O C and Lopes P S. 2003a. Analyses of growth curves of Nellore cattle by multiple-trait and random regression models. Journal of Animal Science 81(4): 918–26. DOI: https://doi.org/10.2527/2003.814918x

Nobre P R C, Lopes P S, Torres R A, Silva L O C, Regazzi A J, Torres Júnior R A A and Misztal I. 2003b. Analyses of growth curves of Nellore cattle by Bayesian method via Gibbs sampling. Arquivo Brasileiro de Medicina Veterinária e DOI: https://doi.org/10.1590/S0102-09352003000400015

Zootecnia 55: 480–90.

Oliveira H R, Brito L F, Lourenco D A L, Silva F F, Jamrozik J, Schaeffer L R and Schenkel F S. 2019. Invited review:

Advances and applications of random regression models: From quantitative genetics to genomics. Journal of Dairy Science 102(9): 7664–83. DOI: https://doi.org/10.3168/jds.2019-16265

Prakash V, Gupta A K, Singh M, Ambhore G S, Singh A and Gandhi R S. 2017. Random regression test-day milk yield

models as a suitable alternative to the traditional 305-day lactation model for genetic evaluation of Sahiwal cattle. Indian Journal of Animal Sciences 87(3): 340–44.

Safari E, Fogarty N M and Gilmour A R. 2005. A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep. Livestock Production Science 92(3): 271–89. DOI: https://doi.org/10.1016/j.livprodsci.2004.09.003

Saghi D A, Shahdadi A R, Borzelabad F K and Mohammadi K. 2018. Estimates of covariance functions for growth of Kordi sheep in Iran using random regression models. Small Ruminant Research 162: 69–76. DOI: https://doi.org/10.1016/j.smallrumres.2018.03.007

Sallam A M, Ibrahim A H and Alsheikh S M. 2019. Genetic evaluation of growth in Barki sheep using random regression models. Tropical Animal Health and Production 51(7): 1893–1901. DOI: https://doi.org/10.1007/s11250-019-01885-3

Sarmento J L R, Torres R A, Sousa W H, Lôbo R N B, Albuquerque L G, Lopes P S, Santos N P S and Bignard A B. 2016. Random regression models for the estimation of genetic and environmental covariance functions for growth traits in Santa Ines sheep. Genetics and Molecular Research 15(2). DOI: https://doi.org/10.4238/gmr.15025749

Sarti F M, Lasagna E, Giontella A, Panella F and Pieramati C. 2015. The use of a random regression model on the estimation of genetic parameters for weight at performance test in Appenninica sheep breed. Italian Journal of Animal Science 14(3): 3892. DOI: https://doi.org/10.4081/ijas.2015.3892

Scalez D C B, Fragomeni B D O, Santos D C C D, Passafaro T L, Alencar M M D and Toral F L B. 2018. Random regression models with B-splines to estimate genetic parameters for body weight of young bulls in performance tests. Revista Brasileira de Zootecnia 47(0). DOI: https://doi.org/10.1590/rbz4720150300

Schaeffer L R and Dekkers J C. 1994. Random regressions in animal models for test-day production in dairy cattle.

Proceedings of 5th World Congress Genetics Applied Livestock. Production, Guelph ON, Canada. University of Guelph, Guelph, ON, Canada. pp 443–446.

Silva F G, Torres R A, Brito L F, Euclydes R F, Melo A L P, Souza N O, Ribeiro Jr J I and Rodrigues M T. 2013. Random

regression models using Legendre orthogonal polynomials to evaluate the milk production of Alpine goats. Genetics and Molecular Research 12(4): 6502–11. DOI: https://doi.org/10.4238/2013.December.11.1

Strucken E M, Laurenson Y C and Brockmann G A. 2015. Go with the flow—biology and genetics of the lactation DOI: https://doi.org/10.3389/fgene.2015.00118

cycle. Frontiers in Genetics 6: 118.

Van der Werf J H J. 2001. Random regression in animal breeding. Course Notes. Jaboticabal, SP Brazil (http://www-personal.une.edu.au/~jvanderw/CFcoursenotes.pdf downloaded on 25/06/2014).

Vatankhah M. 2013. Genetic analysis of ewe body weight in Lori-Bakhtiari sheep using random regression models. Journal of Livestock Science and Technologies 1(1): 44–49.

Venkataramanan R. 2016. Random regressions to model growth in Nilagiri sheep of South India. Small Ruminant DOI: https://doi.org/10.1016/j.smallrumres.2016.10.002

Research 144: 242–47.

Wolc A, Barczak E, Wójtowski J, OElósarz P and Szwaczkowski T. 2011. Genetic parameters of body weight in sheep estimated via random regression and multi-trait animal models. Small Ruminant Research 100(1): 15–18. DOI: https://doi.org/10.1016/j.smallrumres.2011.05.009

Zamani P, Moradi M R, Alipour D and Ghafouri-Kesbi F. 2016. Combination of B-Spline and Legendre functions in random regression models to fit growth curve of Moghani sheep. Small Ruminant Research 145: 115–22. DOI: https://doi.org/10.1016/j.smallrumres.2016.11.006