Literature DB >> 33205213

A multiple-phenotype imputation procedure as a method for prediction of cheese-making efficiency in Spanish Assaf sheep.

Héctor Marina1, Antonio Reverter2, Beatriz Gutiérrez-Gil1, Pamela Almeida Alexandre2, Rocío Pelayo1, Aroa Suárez-Vega1, Cristina Esteban-Blanco1, Juan José Arranz1.   

Abstract

Sheep milk is mainly intended to manufacture a wide variety of high-quality cheeses. The ovine cheese industry would benefit from an improvement, through genetic selection, of traits related to the milk coagulation properties (MCPs) and cheese yield-related traits, broadly denoted as "cheese-making traits." Considering that routine measurements of these traits needed for genetic selection are expensive and time-consuming, this study aimed to evaluate the accuracy of a cheese-making phenotype imputation method based on the information from official milk control records combined with the pH of the milk. For this study, we analyzed records of milk production traits, milk composition traits, and measurements of cheese-making traits available from a total of 1,145 dairy ewes of the Spanish Assaf sheep breed. Cheese-making traits included five related to the MCPs and two cheese yield-related traits. The milk and cheese-making phenotypes were adjusted for significant effects based on a general linear model. The adjusted phenotypes were used to define a multiple-phenotype imputation procedure for the cheese-making traits based on multivariate normality and Markov chain Monte Carlo sampling. Five of the seven cheese-making traits considered in this study achieved a prediction accuracy of 0.60 computed as the correlation between the adjusted phenotypes and the imputed phenotypes. Particularly the logarithm of curd-firming time since rennet addition (logK20) (0.68), which has been previously suggested as a potential candidate trait to improve the cheese ability in this breed, and the logarithm of the ratio between the rennet clotting time and the curd firmness at 60 min (logRCT/A60) (0.65), which has been defined by other studies as an indicator trait of milk coagulation efficiency. This study represents a first step toward the possible use of the phenotype imputation of cheese-making traits to develop a practical methodology for the dairy sheep industry to impute cheese-making traits only based on the analysis of a milk sample without the need of pedigree information. This information could be also used in future planning of specific breeding programs considering the importance of the cheese-making efficiency in dairy sheep and highlights the potential of phenotype imputation to leverage sample size on expensive, hard-to-measure phenotypes.
© The Author(s) 2020. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  cheese-making traits; dairy sheep; milk traits; phenotype imputation

Year:  2020        PMID: 33205213      PMCID: PMC7751019          DOI: 10.1093/jas/skaa370

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  14 in total

1.  Phenotypic factors affecting coagulation properties of milk from Sarda ewes.

Authors:  M Pazzola; M L Dettori; C Cipolat-Gotet; A Cecchinato; G Bittante; G M Vacca
Journal:  J Dairy Sci       Date:  2014-08-22       Impact factor: 4.034

2.  Genetic parameters of udder traits, somatic cell score, and milk yield in Latxa sheep.

Authors:  A Legarra; E Ugarte
Journal:  J Dairy Sci       Date:  2005-06       Impact factor: 4.034

3.  Genetic analysis of coagulation properties, curd firming modeling, milk yield, composition, and acidity in Sarda dairy sheep.

Authors:  G Bittante; C Cipolat-Gotet; M Pazzola; M L Dettori; G M Vacca; A Cecchinato
Journal:  J Dairy Sci       Date:  2016-10-27       Impact factor: 4.034

4.  Phenotypic and genetic relationships between indicators of the mammary gland health status and milk composition, coagulation, and curd firming in dairy sheep.

Authors:  Michele Pazzola; Claudio Cipolat-Gotet; Giovanni Bittante; Alessio Cecchinato; Maria L Dettori; Giuseppe M Vacca
Journal:  J Dairy Sci       Date:  2018-02-07       Impact factor: 4.034

5.  Derivation of multivariate indices of milk composition, coagulation properties, and individual cheese yield in dairy sheep.

Authors:  M G Manca; J Serdino; G Gaspa; P Urgeghe; I Ibba; M Contu; P Fresi; N P P Macciotta
Journal:  J Dairy Sci       Date:  2016-04-06       Impact factor: 4.034

6.  Multivariate analysis of the milk coagulation process in ovine breeds from Spain.

Authors:  J Caballero-Villalobos; A Figueroa; K Xibrraku; E Angón; J M Perea; A Garzón
Journal:  J Dairy Sci       Date:  2018-10-11       Impact factor: 4.034

7.  Mediterranean dairy sheep and goat products and their quality. A critical review.

Authors:  J Boyazoglu; P Morand-Fehr
Journal:  Small Rumin Res       Date:  2001-04       Impact factor: 1.611

8.  Estimates of heritability and genetic correlations for milk coagulation properties and individual laboratory cheese yield in Sarda ewes.

Authors:  A Puledda; G Gaspa; M G Manca; J Serdino; P P Urgeghe; C Dimauro; R Negrini; N P P Macciotta
Journal:  Animal       Date:  2016-11-02       Impact factor: 3.240

9.  Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows.

Authors:  Marie-Pierre Sanchez; Yuliaxis Ramayo-Caldas; Valérie Wolf; Cécile Laithier; Mohammed El Jabri; Alexis Michenet; Mekki Boussaha; Sébastien Taussat; Sébastien Fritz; Agnès Delacroix-Buchet; Mickaël Brochard; Didier Boichard
Journal:  Genet Sel Evol       Date:  2019-07-01       Impact factor: 4.297

10.  Prediction of Milk Coagulation Properties and Individual Cheese Yield in Sheep Using Partial Least Squares Regression.

Authors:  Massimo Cellesi; Fabio Correddu; Maria Grazia Manca; Jessica Serdino; Giustino Gaspa; Corrado Dimauro; Nicolò Pietro Paolo Macciotta
Journal:  Animals (Basel)       Date:  2019-09-07       Impact factor: 2.752

View more
  1 in total

1.  Assessing the statistical training in animal science graduate programs in the United States: survey on statistical training.

Authors:  Nick V L Serão; Amy L Petry; Leticia P Sanglard; Mariana C Rossoni-Serão; Jennifer M Bundy
Journal:  J Anim Sci       Date:  2021-05-01       Impact factor: 3.159

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.