Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-06-03T07:52:52.857Z Has data issue: false hasContentIssue false
  • English
  • Français

Inferring genetic parameters on latent variables underlying milk yield and quality, protein composition, curd firmness and cheese-making traits in dairy cattle

Published online by Cambridge University Press:  17 July 2017

C. Dadousis ,
C. Cipolat-Gotet ,
G. Bittante  and
A. Cecchinato
C. Dadousis
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, PD, Italy
C. Cipolat-Gotet
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, PD, Italy
G. Bittante
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, PD, Italy
A. Cecchinato*
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, PD, Italy
*
†E-mail: alessio.cecchinato@unipd.it
Get access

Abstract

We studied the genetics of cheese-related latent variables (factors; Fs) for application in dairy cattle breeding. In total, 26 traits, recorded in 1264 Brown Swiss cows, were analyzed through multivariate factor analysis (MFA). Traits analyzed were descriptors of milk quality and yield (including protein fractions) and measures of coagulation, curd firmness (CF), cheese yields (%CY) and nutrient recoveries in the curd (REC). A total of 10 Fs (mutual orthogonal with a varimax rotation) were obtained. To assess the practical use of the Fs into breeding, we inferred their genetic parameters using single and bivariate animal models under a Bayesian framework. Heritability estimates (intra-herd) varied between 0.11 and 0.72 (F3: Yield and F7: κ-β-CN, respectively). The Fs underlined basic characteristics of the cheese-making process, milk components and udder health, while retaining 74% of the original variability. The first two Fs were indicators of the CY percentage (F1: %CY) and the CF process (F2: CFt), and presented similar heritability estimates: 0.268 and 0.295, respectively. The third factor was associated with the yield of milk and solids (F3: Yield) characterized by a low heritability (0.108) and the fourth with the cheese nitrogen (N) (F4: Cheese N) that conversely appeared to be characterized by a high heritability (0.618). Three Fs were associated with the proportion of the basic milk caseins on total milk protein (F5: as1-β-CN, F7: κ-β-CN, F8: as2-CN), also highly heritable (0.565, 0.723 and 0.397, respectively) and 1 factor with the phosphorylated form of the as1-CN (F9: as1-CN-Ph; 0.318). Moreover, 1 factor was linked to the whey protein α-LA (F10: α-LA; 0.147). An indicator factor of a cow’s udder health (F6: Udder health) was also obtained and showed a moderate heritability (0.204). Although the Fs were phenotypically uncorrelated, considerable additive genetic correlations existed among them, with highest values observed between F10: α-LA and F6: Udder health (−0.67) as well as between F9: as1-CN-Ph and F3: Yield (−0.60). Our results show the usefulness of MFA in dairy cattle breeding. The ability to replace a large number of variables with a few latent indicators of the same biological meaning marks MFA as a valuable tool for developing breeding strategies to improve cow’s cheese-related traits.

Keywords

multivariate factor analysismilk proteincoagulation and curd firmnessindividual cheese yield
Type
Research Article
Information
animal , Volume 12 , Issue 2 , February 2018 , pp. 224 - 231
Copyright
© The Animal Consortium 2017 

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

Ali, A and Shook, G 1980. An optimum transformation for somatic cell concentration in milk. Journal of Dairy Science 63, 487–490.CrossRefGoogle Scholar
Bittante, G, Cipolat-Gotet, C and 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.CrossRefGoogle ScholarPubMed
Bobbo, T, Cipolat-Gotet, C, Bittante, G and Cecchinato, A 2016. The nonlinear effect of somatic cell count on milk composition, coagulation properties, curd firmness modeling, cheese yield, and curd nutrient recovery. Journal of Dairy Science 99, 5104–5119.CrossRefGoogle ScholarPubMed
Bobe, G, Beitz, D, Freeman, A and Lindberg, G 1999. Effect of milk protein genotypes on milk protein composition and its genetic parameter estimates. Journal of Dairy Science 82, 2797–2804.CrossRefGoogle ScholarPubMed
Bollen, KA 2014. Structural equations with latent variables. John Wiley & Sons, New York.Google Scholar
Bonfatti, V, Cecchinato, A, Gallo, L, Blasco, A and Carnier, P 2011. Genetic analysis of detailed milk protein composition and coagulation properties in simmental cattle. Journal of Dairy Science 94, 5183–5193.CrossRefGoogle ScholarPubMed
Bonfatti, V, Grigoletto, L, Cecchinato, A, Gallo, L and Carnier, P 2008. Validation of a new reversed-phase high-performance liquid chromatography method for separation and quantification of bovine milk protein genetic variants. Journal of Chromatography A 1195, 101–106.CrossRefGoogle ScholarPubMed
Caffin, JP, Poutrel, B and Rainard, P 1985. Physiological and pathological factors influencing bovine α-lactalbumin and β-lactoglobulin concentrations in milk. Journal of Dairy Science 68, 1087–1094.CrossRefGoogle ScholarPubMed
Cecchinato, A and Bittante, G 2016. Genetic and environmental relationships of different measures of individual cheese yield and curd nutrients recovery with coagulation properties of bovine milk. Journal of Dairy Science 99, 1975–1989.CrossRefGoogle ScholarPubMed
Cecchinato, A, Chessa, S, Ribeca, C, Cipolat-Gotet, C, Bobbo, T, Casellas, J and Bittante, G 2015. Genetic variation and effects of candidate-gene polymorphisms on coagulation properties, curd firmness modeling and acidity in milk from brown swiss cows. Animal 9, 1104–1112.CrossRefGoogle ScholarPubMed
Cipolat-Gotet, C, Cecchinato, A, De Marchi, M and Bittante, G 2013. Factors affecting variation of different measures of cheese yield and milk nutrient recovery from an individual model cheese-manufacturing process. Journal of Dairy Science 96, 7952–7965.CrossRefGoogle ScholarPubMed
Conte, G, Serra, A, Cremonesi, P, Chessa, S, Castiglioni, B, Cappucci, A, Bulleri, E and Mele, M 2016. Investigating mutual relationship among milk fatty acids by multivariate factor analysis in dairy cows. Livestock Science 188, 124–132.CrossRefGoogle Scholar
Dziuban, CD and Shirkey, EC 1974. When is a correlation matrix appropriate for factor analysis? some decision rules. Psychological Bulletin 81, 358–361.CrossRefGoogle Scholar
Food and Agriculture Organization of the United Nations 2015. FAOSTAT. Retrieved on 27 March 2015 from http://www.milb.com/index. jsp?sid=t564.Google Scholar
Gebreyesus, G, Lund, M, Janss, L, Poulsen, N, Larsen, L, Bovenhuis, H and Buitenhuis, A 2016. Short communication: multi-trait estimation of genetic parameters for milk protein composition in the Danish Holstein. Journal of Dairy Science 99, 2863–2866.CrossRefGoogle ScholarPubMed
Jõudu, I, Henno, M, Kaart, T, Püssa, T and Kärt, O 2008. The effect of milk protein contents on the rennet coagulation properties of milk from individual dairy cows. International Dairy Journal 18, 964–967.CrossRefGoogle Scholar
Kaiser, HF and Rice, J 1974. Little Jiffy, Mark IV. Educational and Psychological Measurement 34, 111–117.CrossRefGoogle Scholar
Macciotta, NPP, Cecchinato, A, Mele, M and Bittante, G 2012. Use of multivariate factor analysis to define new indicator variables for milk composition and coagulation properties in brown swiss cows. Journal of Dairy Science 95, 7346–7354.CrossRefGoogle ScholarPubMed
Manca, M, Serdino, J, Gaspa, G, Urgeghe, P, Ibba, I, Contu, M, Fresi, P and Macciotta, N 2016. Derivation of multivariate indices of milk composition, coagulation properties, and individual cheese yield in dairy sheep. Journal of Dairy Science 99, 4547–4557.CrossRefGoogle ScholarPubMed
Mele, M., Macciotta, N, Cecchinato, A, Conte, G, Schiavon, S and Bittante, G 2016. Multivariate factor analysis of detailed milk fatty acid profile: Effects of dairy system, feeding, herd, parity, and stage of lactation. Journal of Dairy Science 99, 9820–9833.CrossRefGoogle ScholarPubMed
NRC 2001. Nutrient requirements of dairy cattle (7th revised edition). National Academies Press, Washington, DC.Google Scholar
Ostersen, S, Foldager, J and Hermansen, JE 1997. Effects of stage of lactation, milk protein genotype and body condition at calving on protein composition and renneting properties of bovine milk. Journal of Dairy Research 64, 207–219.CrossRefGoogle ScholarPubMed
R Core Team 2013. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Revelle, W 2014. Psych: procedures for personality and psychological research, R package version, 1(1). Northwestern University, Evanston.Google Scholar
Schopen, GCB, Heck, JML, Bovenhuis, H, Visker, MHPW, van Valenberg, HJF and van Arendonk, JAM 2009. Genetic parameters for major milk proteins in Dutch Holstein-Friesians. Journal of Dairy Science 92, 1182–1191.CrossRefGoogle ScholarPubMed
Stocco, G, Cipolat-Gotet, C, Bobbo, T, Cecchinato, A and Bittante, G 2016. Herd productivity and breed of cows affect milk composition, coagulation properties and curd firming and syneresis modeling. Journal of Dairy Science 100, 129–145.CrossRefGoogle ScholarPubMed
Todaro, M, Scatassa, ML and Giaccone, P 2005. Multivariate factor analysis of girgentana goat milk composition. Italian Journal of Animal Science 4, 403–410.CrossRefGoogle Scholar
Vacca, G, Paschino, P, Dettori, M, Bergamaschi, M, Cipolat-Gotet, C, Bittante, G and Pazzola, M 2016. Environmental, morphological, and productive characterization of sardinian goats and use of latent explanatory factors for population analysis. Journal of Animal Science 94, 3947–3957.CrossRefGoogle ScholarPubMed
Walstra, P, Wouters, JTM and Geurts, TJ 2006. Dairy science and technology. Taylor & Francis, New York.Google Scholar
Supplementary material: File

Dadousis supplementary material

Dadousis supplementary material

Download Dadousis supplementary material(File)
File 1.1 MB