Genetic parameters for milk yield, casein percentage, subclinical mastitis incidence and sexual precocity using Bayesian linear and threshold models
Saditt Rocio Robles Colonia A , Andréia do Carmo Oliveira A , Fabrício Pilonetto B , Brayan Dias Dauria B , Gerson Barreto Mourão B , Paulo Fernando Machado B , Denismar Alves Nogueira A , Luiz Alberto Beijo A and Juliana Petrini
A C *
+ Author Affiliations
- Author Affiliations
A Department of Statistics, Institute of Exact Sciences, Federal University of Alfenas, Alfenas, 37130-001, Brazil.
B Department of Animal Science, University of São Paulo, 13418-900, Piracicaba, Brazil.
C Present address: Instituto Clínica do Leite, Piracicaba 13414-157, Brazil.
Animal Production Science - https://doi.org/10.1071/AN20313
Submitted: 23 May 2020 Accepted: 8 March 2022 Published online: 13 April 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: The economic efficiency of a dairy system is associated with the animal’s productive and reproductive abilities. Therefore, selection criteria should include milk production and quality traits as well as traits related to health and fertility. Since such phenotypes can present non-normal distributions, the use of threshold models is appropriate to study the genetic variation of such traits.
Aim: To estimate variance components for dairy production and functional traits in a Brazilian Holstein cattle population using linear and threshold models under a Bayesian approach.
Methods: Data comprised 64 657 test-day records for milk yield (MY, kg/day), casein percentage (CP, % of milk) and subclinical mastitis incidence (SCM), along with 4460 records for sexual precocity (PREC) from 5439 cows. Both SCM and PREC were defined as binary traits. Genetic analyses were based on linear (for MY and CP) and threshold (for SCM and PREC) models using Bayesian estimation. Non-informative and informative priors were considered for variance components, and these models were compared using the deviance information criterion (DIC) and the absolute difference between DIC (Δ).
Key results: Posterior means of heritability for MY, CP, SCM and PREC were 0.14, 0.39, 0.13 and 0.38 (based on non-informative priors) and 0.13, 0.27, 0.13 and 0.44 (considering informative priors), respectively. The model based on non-informative priors was better (lower DIC) for CP, whereas for PREC, the best model used informative priors. No differences between priors (Δ < 5) were observed for MY and SCM.
Conclusions: Threshold models were adequate for the analysis of non-normally distributed traits. The use of informative priors can be beneficial if specification is based on results from similar databases and models. Due to their high genetic variation, CP and PREC can be considered as selection criteria in animal breeding programs. In turn, accurate genetic evaluation for MY and SCM will depend on the pedigree and the information from genetically correlated traits.
Implications: Our study contributes to the understanding of the variation under important dairy production traits in a tropical Holstein population and provides information on the use of Bayesian threshold models as an appropriate method for the evaluation of non-normally distributed phenotypes.
Keywords: animal breeding, binary trait, dairy cattle, heritability, Holstein, informative prior, milk quality, repeatability model, somatic cell count.
References
Abdel-Rady A, Sayed M (2009) Epidemiological studies on subclinical mastitis in dairy cows in Assiut Governorate. Veterinary World 2, 373–380.Ayalew W, Aliy M, Negussie E (2017) Estimation of genetic parameters of the productive and reproductive traits in Ethiopian Holstein using multi-trait models. Asian-Australasian Journal of Animal Sciences 30, 1550–1556.
| Estimation of genetic parameters of the productive and reproductive traits in Ethiopian Holstein using multi-trait models.Crossref | GoogleScholarGoogle Scholar | 28728362PubMed |
Banks BD, Mao IL, Walter JP (1985) Robustness of the restricted maximum likelihood estimator derived under normality as applied to data with skewed distributions. Journal of Dairy Science 68, 1785–1792.
| Robustness of the restricted maximum likelihood estimator derived under normality as applied to data with skewed distributions.Crossref | GoogleScholarGoogle Scholar |
Becerril CM, Wilcox CJ, Wiggans GR, Sigmon KN (1994) Transformation of measurements percentage of White coat color for Holsteins and estimation of heritability. Journal of Dairy Science 77, 2651–2657.
| Transformation of measurements percentage of White coat color for Holsteins and estimation of heritability.Crossref | GoogleScholarGoogle Scholar | 7814735PubMed |
Ben Zaabza H, Ben Gara A, Rekik B (2018) Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials. Asian-Australasian Journal of Animal Sciences 31, 636–642.
| Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials.Crossref | GoogleScholarGoogle Scholar | 28823122PubMed |
Bignardi AB, El Faro L, Santana ML, Rosa GJM, Cardoso VL, Machado PF, Albuquerque LG (2012) Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil. Livestock Science 150, 401–406.
| Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil.Crossref | GoogleScholarGoogle Scholar |
Bittante G, Cipolat-Gotet C, Cecchinato A (2013) Genetic parameters of different measures of cheese yield and milk nutrient recovery from an individual model cheese-manufacturing process. Journal of Dairy Science 96, 7966–7979.
| Genetic parameters of different measures of cheese yield and milk nutrient recovery from an individual model cheese-manufacturing process.Crossref | GoogleScholarGoogle Scholar | 24094539PubMed |
Blasco A (2001) The Bayesian controversy in animal breeding. Journal of Animal Science 79, 2023–2046.
| The Bayesian controversy in animal breeding.Crossref | GoogleScholarGoogle Scholar | 11518211PubMed |
Blasco A, Sorensen D, Bidanel JP (1998) Bayesian inference of genetic parameters and selection response for litter size components in pigs. Genetics 149, 301–306.
| Bayesian inference of genetic parameters and selection response for litter size components in pigs.Crossref | GoogleScholarGoogle Scholar | 9584104PubMed |
Box GEP, Tiao GC (1973) ‘Bayesian inference in statistical analysis.’ (Addison-Wesley Publishing Co.: Reading, MA, USA)
Carneiro Júnior JM, Assis GMLd, Euclydes RF, Lopes PS (2005) Influência da informação a priori na avaliação genética animal utilizando dados simulados. Revista Brasileira de Zootecnia 34, 1905–1913.
| Influência da informação a priori na avaliação genética animal utilizando dados simulados.Crossref | GoogleScholarGoogle Scholar |
Carriquiry AL, Gianola D, Fernando RL (1987) Mixed-model analysis of a censored normal distribution with reference to animal breeding. Biometrics 43, 929–939.
| Mixed-model analysis of a censored normal distribution with reference to animal breeding.Crossref | GoogleScholarGoogle Scholar | 3509964PubMed |
Cecchinato A, Penasa M, De Marchi M, Gallo L, Bittante G, Carnier P (2011) Genetic parameters of coagulation properties, milk yield, quality, and acidity estimated using coagulating and noncoagulating milk information in Brown Swiss and Holstein-Friesian cows. Journal of Dairy Science 94, 4205–4213.
| Genetic parameters of coagulation properties, milk yield, quality, and acidity estimated using coagulating and noncoagulating milk information in Brown Swiss and Holstein-Friesian cows.Crossref | GoogleScholarGoogle Scholar | 21787956PubMed |
Chandan R (1997) Properties of milk and its components. In ‘Dairy-based ingredients’. (Ed. R Chandan) pp. 1–10. (American Association of Cereal Chemists: MN, USA)
Cheruiyot EK, Nguyen TTT, Haile-Mariam M, Cocks BG, Abdelsayed M, Pryce JE (2020) Genotype-by-environment (temperature-humidity) interaction of milk production traits in Australian Holstein cattle. Journal of Dairy Science 103, 2460–2476.
| Genotype-by-environment (temperature-humidity) interaction of milk production traits in Australian Holstein cattle.Crossref | GoogleScholarGoogle Scholar | 31864748PubMed |
Faraji-Arough H, Aslaminejad AA, Tahmoorespur M, Rokouei M, Shariati MM (2015) Bayesian inference of (co)variance components and genetic parameters for economic traits in Iranian Holsteins via Gibbs sampling. Iranian Journal of Applied Animal Science 5, 51–60.
Geweke J (1992) Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In ‘Bayesian statistics’. (Eds JM Bernardo, JO Berger, AP Dawid, AFM Smith) pp. 625–631. (Oxford University Press: Oxford)
Gianola D, Fernando RL (1986) Bayesian methods in animal breeding theory. Journal of Animal Science 63, 217–244.
| Bayesian methods in animal breeding theory.Crossref | GoogleScholarGoogle Scholar |
Gianola D, Foulley JL (1990) Variance estimation from integrated likelihoods (VEIL. Genetics Selection Evolution 22, 403–417
| Variance estimation from integrated likelihoods (VEIL.Crossref | GoogleScholarGoogle Scholar |
Gianola D, Foulley JL, Fernando RL (1986) Prediction of breeding values when variances are not known. Genetics Selection Evolution 18, 485–498.
| Prediction of breeding values when variances are not known.Crossref | GoogleScholarGoogle Scholar |
Hadfield JD, Nakagawa S (2010) General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. Journal of Evolutionary Biology 23, 494–508.
| General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters.Crossref | GoogleScholarGoogle Scholar | 20070460PubMed |
Harville DA (1977) Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association 72, 320–338.
| Maximum likelihood approaches to variance component estimation and to related problems.Crossref | GoogleScholarGoogle Scholar |
Heidelberger P, Welch PD (1983) Simulation run length control in the presence of an initial transient. Operations Research 31, 1109–1144.
| Simulation run length control in the presence of an initial transient.Crossref | GoogleScholarGoogle Scholar |
Hofer A (1998) Variance component estimation in animal breeding: a review. Journal of Animal Breeding and Genetics 115, 247–265.
| Variance component estimation in animal breeding: a review.Crossref | GoogleScholarGoogle Scholar |
Hossein-Zadeh NG, Ardalan M (2011) Bayesian estimates of genetic parameters for metritis, retained placenta, milk fever, and clinical mastitis in Holstein dairy cows via Gibbs sampling. Research in Veterinary Science 90, 146–149.
| Bayesian estimates of genetic parameters for metritis, retained placenta, milk fever, and clinical mastitis in Holstein dairy cows via Gibbs sampling.Crossref | GoogleScholarGoogle Scholar | 20462620PubMed |
Hutchison JL, VanRaden PM, Null DJ, Cole JB, Bickhart DM (2017) Genomic evaluation of age at first calving. Journal of Dairy Science 100, 6853–6861.
| Genomic evaluation of age at first calving.Crossref | GoogleScholarGoogle Scholar | 28624286PubMed |
Ikonen T, Morri S, Tyrisevä A-M, Ruottinen O, Ojala M (2004) Genetic and phenotypic correlations between milk coagulation properties, milk production traits, somatic cell count, casein content, and pH of milk. Journal of Dairy Science 87, 458–467.
| Genetic and phenotypic correlations between milk coagulation properties, milk production traits, somatic cell count, casein content, and pH of milk.Crossref | GoogleScholarGoogle Scholar | 14762089PubMed |
Kluska S, Olivieri BF, Bonamy M, Chiaia HLJ, Feitosa FLB, Berton MP, Peripolli E, Lemos MVA, Tonussi RL, Lôbo RB, Magnabosco CdU, Di Croce F, Osterstock J, Pereira ASC, Munari DP, Bezerra LA, Lopes FB, Baldi F (2018) Estimates of genetic parameters for growth, reproductive, and carcass traits in Nelore cattle using the single step genomic BLUP procedure. Livestock Science 216, 203–209.
| Estimates of genetic parameters for growth, reproductive, and carcass traits in Nelore cattle using the single step genomic BLUP procedure.Crossref | GoogleScholarGoogle Scholar |
Koeck A, Heringstad B, Egger-Danner C, Fuerst C, Winter P, Fuerst-Waltl B (2010) Genetic analysis of clinical mastitis and somatic cell count traits in Austrian Fleckvieh cows. Journal of Dairy Science 93, 5987–5995.
| Genetic analysis of clinical mastitis and somatic cell count traits in Austrian Fleckvieh cows.Crossref | GoogleScholarGoogle Scholar | 21094773PubMed |
Kruschke JK (2015) ‘Doing Bayesian data analysis.’ (Academic Press: Amsterdam, The Netherlands)
Kruuk LEB, Hadfield JD (2007) How to separate genetic and environmental causes of similarity between relatives. Journal of Evolutionary Biology 20, 1890–1903.
| How to separate genetic and environmental causes of similarity between relatives.Crossref | GoogleScholarGoogle Scholar |
Lesaffre E, Lawson AB (2012) ‘Bayesian biostatistics.’ (John Wiley & Sons: Chichester, UK)
Miglior F, Fleming A, Malchiodi F, Brito LF, Martin P, Baes CF (2017) A 100-year review: identification and genetic selection of economically important traits in dairy cattle. Journal of Dairy Science 100, 10251–10271.
| A 100-year review: identification and genetic selection of economically important traits in dairy cattle.Crossref | GoogleScholarGoogle Scholar | 29153164PubMed |
Misztal I (2007) Computational techniques in animal breeding. Available at http://nce.ads.uga.edu/~ignacy/course2002/notes.pdfhttp://nce.ads.uga.edu/~ignacy/course11.pdf [Verified 24 March 2022]
Misztal I (2008) Reliable computing in estimation of variance components. Journal of Animal Breeding and Genetics 125, 363–370.
| Reliable computing in estimation of variance components.Crossref | GoogleScholarGoogle Scholar | 19134071PubMed |
Misztal I, Tsuruta S, Lourenco D, Masuda Y, Aguilar I, Legarra A, Vitezica Z (2018) Manual for BLUPF90 family of programs. Available at http://nce.ads.uga.edu/wiki/lib/exe/fetch.php?media=blupf90_all7.pdf [Verified 13 November 2020]
Mrode RA (2014) ‘Linear models for the prediction of animal breeding values.’ (CABI: Oxfordshire, UK)
Nilforooshan MA, Edriss MA (2004) Effect of age at first calving on some productive and longevity traits in Iranian Holsteins of the Isfahan province. Journal of Dairy Science 87, 2130–2135.
| Effect of age at first calving on some productive and longevity traits in Iranian Holsteins of the Isfahan province.Crossref | GoogleScholarGoogle Scholar | 15328226PubMed |
Ntzoufras I (2011) ‘Bayesian modeling using WinBUGS.’ (John Wiley and Sons: New York, NY, USA)
O’Hagan A (1976) On posterior joint and marginal modes. Biometrika 63, 329–333.
| On posterior joint and marginal modes.Crossref | GoogleScholarGoogle Scholar |
Oliveira EJ, Bignardi AB, Santana Junior ML, Paz CCPd, Zadra LEF (2015) Associação genética entre ocorrência de mastite clínica e produção de leite em vacas Holandesas. Ciência Rural 45, 2187–2192.
| Associação genética entre ocorrência de mastite clínica e produção de leite em vacas Holandesas.Crossref | GoogleScholarGoogle Scholar |
Penasa M, Cecchinato A, Battagin M, De Marchi M, Pretto D, Cassandro M (2010) Bayesian inference of genetic parameters for test-day milk yield, milk quality traits, and somatic cell score in Burlina cows. Journal of Applied Genetics 51, 489–495.
| Bayesian inference of genetic parameters for test-day milk yield, milk quality traits, and somatic cell score in Burlina cows.Crossref | GoogleScholarGoogle Scholar | 21063066PubMed |
Penasa M, Tiezzi F, Gottardo P, Cassandro M, De Marchi M (2015) Genetics of milk fatty acid groups predicted during routine data recording in Holstein dairy cattle. Livestock Science 173, 9–13.
| Genetics of milk fatty acid groups predicted during routine data recording in Holstein dairy cattle.Crossref | GoogleScholarGoogle Scholar |
Pereira E, Oliveira HN, Eler JP, Silva JA, Van Melis MH (2007) Comparison among three approaches for evaluation of sexual precocity in Nellore cattle. Animal 1, 411–418.
| Comparison among three approaches for evaluation of sexual precocity in Nellore cattle.Crossref | GoogleScholarGoogle Scholar | 22444339PubMed |
Pérez-Cabal MA, de los Campos G, Vazquez AI, Gianola D, Rosa GJM, Weigel KA, Alenda R (2009) Genetic evaluation of susceptibility to clinical mastitis in Spanish Holstein cows. Journal of Dairy Science 92, 3472–3480.
| Genetic evaluation of susceptibility to clinical mastitis in Spanish Holstein cows.Crossref | GoogleScholarGoogle Scholar | 19528625PubMed |
Petrini J, Iung LHS, Rodriguez MAP, Salvian M, Pértille F, Rovadoscki GA, Cassoli LD, Coutinho LL, Machado PF, Wiggans GR, Mourão GB (2016) Genetic parameters for milk fatty acids, milk yield and quality traits of a Holstein cattle population reared under tropical conditions. Journal of Animal Breeding and Genetics 133, 384–395.
| Genetic parameters for milk fatty acids, milk yield and quality traits of a Holstein cattle population reared under tropical conditions.Crossref | GoogleScholarGoogle Scholar | 26968150PubMed |
Pirlo G, Miglior F, Speroni M (2000) Effect of age at first calving on production traits and on difference between milk yield returns and rearing costs in Italian Holsteins. Journal of Dairy Science 83, 603–608.
| Effect of age at first calving on production traits and on difference between milk yield returns and rearing costs in Italian Holsteins.Crossref | GoogleScholarGoogle Scholar | 10750118PubMed |
Raftery AE, Lewis SM (1992) [Practical Markov chain Monte Carlo]: Comment: one long run with diagnostics: implementation strategies for Markov chain Monte Carlo. Statistical Science 7, 493–497.
| [Practical Markov chain Monte Carlo]: Comment: one long run with diagnostics: implementation strategies for Markov chain Monte Carlo.Crossref | GoogleScholarGoogle Scholar |
R Core Team (2019) R: a language and environment for statistical computing. Available at https://www.r-project.org
Rodriguez MAP, Petrini J, Ferreira EM, Mourão LRMB, Salvian M, Cassoli LD, Pires AV, Machado PF, Mourão GB (2014) Concordance analysis between estimation methods of milk fatty acid content. Food Chemistry 156, 170–175.
| Concordance analysis between estimation methods of milk fatty acid content.Crossref | GoogleScholarGoogle Scholar |
Sargolzaei M, Iwaisaki HJ, Colleau J (2006) CFC: a tool for monitoring genetic diversity. In ‘Proceedings of 8th world congress on genetics applied to livestock production’. CD-ROM communication 27–28. (Instituto Prociência: Belo Horizonte, Brazil)
Schukken YH, Wilson DJ, Welcome F, Garrison-Tikofsky L, Gonzalez RN (2003) Monitoring udder health and milk quality using somatic cell counts. Veterinary Research 34, 579–596.
| Monitoring udder health and milk quality using somatic cell counts.Crossref | GoogleScholarGoogle Scholar | 14556696PubMed |
Singh K, Molenaar AJ, Swanson KM, Gudex B, Arias JA, Erdman RA, Stelwagen K (2012) Epigenetics: a possible role in acute and transgenerational regulation of dairy cow milk production. Animal 6, 375–381.
| Epigenetics: a possible role in acute and transgenerational regulation of dairy cow milk production.Crossref | GoogleScholarGoogle Scholar | 22436216PubMed |
Smith BJ (2007) BOA: an R package for MCMC output convergence assessment and posterior inference. Journal of Statistical Software 21, 1–37.
| BOA: an R package for MCMC output convergence assessment and posterior inference.Crossref | GoogleScholarGoogle Scholar |
Sorensen D, Gianola D (2007) ‘Likelihood, Bayesian, and MCMC methods in quantitative genetics.’ (Springer: New York, NY, USA)
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64, 583–639.
| Bayesian measures of model complexity and fit.Crossref | GoogleScholarGoogle Scholar |
Tiezzi F, Pretto D, De Marchi M, Penasa M, Cassandro M (2013) Heritability and repeatability of milk coagulation properties predicted by mid-infrared spectroscopy during routine data recording, and their relationships with milk yield and quality traits. Animal 7, 1592–1599.
| Heritability and repeatability of milk coagulation properties predicted by mid-infrared spectroscopy during routine data recording, and their relationships with milk yield and quality traits.Crossref | GoogleScholarGoogle Scholar | 23816365PubMed |
Tozer PR, Heinrichs AJ (2001) What affects the costs of raising replacement dairy heifers: a multiple-component analysis. Journal of Dairy Science 84, 1836–1844.
| What affects the costs of raising replacement dairy heifers: a multiple-component analysis.Crossref | GoogleScholarGoogle Scholar | 11518308PubMed |
Urioste JI, Franzén J, Windig JJ, Strandberg E (2012) Genetic relationships among mastitis and alternative somatic cell count traits in the first 3 lactations of Swedish Holsteins. Journal of Dairy Science 95, 3428–3434.
| Genetic relationships among mastitis and alternative somatic cell count traits in the first 3 lactations of Swedish Holsteins.Crossref | GoogleScholarGoogle Scholar | 22612977PubMed |
