Genome-wide scan reveals genetic divergence in Italian Holstein cows bred within PDO cheese production chains

AbstractDairy cattle breeds have been exposed to intense artificial selection for milk production traits over the last fifty years. In Italy, where over 80% of milk is processed into cheese, selection has also focused on cheese-making traits. Due to a deep-rooted tradition in cheese-making, currently fifty Italian cheeses are marked with the Protected Designation of Origin (PDO) label as they proved traditional land of origin and procedures for milk transformation. This study aimed to explore from a genetic point of view if the presence of such diverse productive contexts in Italy have shaped in a different manner the genome of animals originally belonging to a same breed. We analyzed high density genotype data from 1000 Italian Holstein cows born between 2014 and 2018. Those animals were either farmed in one of four Italian PDO consortia or used for drinkable milk production only. Runs of Homozygosity, Bayesian Information Criterion and Discriminant Analysis of Principal Components were used to evaluate potential signs of genetic divergence within the breed. We showed that the analyzed Italian Holstein cows have genomic inbreeding level above 5% in all subgroups, reflecting the presence of ongoing artificial selection in the breed. Our study provided a comprehensive representation of the genetic structure of the Italian Holstein breed, highlighting the presence of potential genetic subgroups due to divergent dairy farming systems. This study can be used to further investigate genetic variants underlying adaptation traits in these subgroups, which in turn might be used to design more specialized breeding programs.

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Factors influencing milk osteopontin concentration based on measurements from Danish Holstein cows

Journal of Dairy Research ◽

10.1017/s0022029921000054 ◽

2021 ◽

pp. 1-6

Author(s):

Brian Christensen ◽

Elias D. Zachariae ◽

Nina A. Poulsen ◽

Albert J. Buitenhuis ◽

Lotte B. Larsen ◽

...

Keyword(s):

Milk Production ◽

Bovine Milk ◽

Farm Management ◽

Holstein Cows ◽

Enzyme Linked Immunosorbent Assay ◽

Production Traits ◽

Milk Production Traits ◽

Factors Influencing ◽

Significant Interest ◽

Variation Explained

Abstract Our objective was to determine the content of the bioactive protein osteopontin (OPN) in bovine milk and identify factors influencing its concentration. OPN is expressed in many tissues and body fluids, with by far the highest concentrations in milk. OPN plays a role in immunological and developmental processes and it has been associated with several milk production traits and lactation persistency in cows. In the present study, we report the development of an enzyme linked immunosorbent assay (ELISA) for measurement of OPN in bovine milk. The method was used to determine the concentration of OPN in milk from 661 individual Danish Holstein cows. The median OPN level was determined to 21.9 mg/l with a pronounced level of individual variation ranging from 0.4 mg/l to 67.8 mg/l. Breeding for increased OPN in cow's milk is of significant interest, however, the heritability of OPN in milk was found to be relatively low, with an estimated value of 0.19 in the current dataset. The variation explained by the herd was also found to be low suggesting that OPN levels are not affected by farm management or feeding. Interestingly, the concentration of OPN was found to increase with days in milk and to decrease with parity.

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Association between a polymorphism of the α-lactalbumin gene and milk production traits in Chinese Holstein cows

Genetics and Molecular Research ◽

10.4238/2013.september.4.3 ◽

2013 ◽

Vol 12 (3) ◽

pp. 3375-3382 ◽

Cited By ~ 5

Author(s):

J.P. Zhou ◽

C.H. Dong

Keyword(s):

Milk Production ◽

Holstein Cows ◽

Production Traits ◽

Milk Production Traits ◽

Chinese Holstein ◽

Chinese Holstein Cows

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Genetic parameters for female fertility and milk production traits in first-parity Czech Holstein cows

Czech Journal of Animal Science ◽

10.17221/5562-cjas ◽

2012 ◽

Vol 57 (No. 3) ◽

pp. 108-114 ◽

Cited By ~ 7

Author(s):

V. Zink ◽

J. Lassen ◽

M. Štípková

Keyword(s):

Milk Production ◽

Genetic Parameters ◽

Selection Index ◽

Female Fertility ◽

Holstein Cows ◽

Production Traits ◽

Milk Production Traits ◽

Clear Trend ◽

Genetic Potential ◽

Fertility Traits

The aim of this study was to estimate genetic parameters for female fertility and production traits in first-parity Czech Holstein cows and to quantify the effect of using this information on the accuracy of a selection index in seven different scenarios. In order to estimate genetic (co)variance components, the DMU software running an AI-REML algorithm was used. The analyses were made using a series of bivariate animal models. The pedigree included 164 125 animals and it was set up using a pruned animal model design. The present study included the following female fertility traits for the first lactations: calving to the first insemination (CF), days open (DO), calving from the first to the last insemination (FL), and milk production traits: milk production (MLK), kg of fat (FAT), and kg of protein (PROT). The heritability for all the investigated fertility traits was low and close to 0. Moderate heritabilities for production traits ranging from 0.20 (MLK) to 0.23 (PROT) were estimated. The strongest unfavourable correlation was found between PROT and DO (0.49). Other estimated correlations between fertility traits and production traits were moderate, ranging from 0.26 to 0.41. The results of this study evidence that cows with the poorest genetic potential for reproductive performance are those having high genetic potential for milk production and milk components. The results also show that the number of days from calving to new pregnancy depends on the production level. Seven investigated scenarios using selection index theory show a clear trend for increasing accuracy when more fertility traits were added as well as when higher numbers of daughters with information on reproduction traits per sire were available.  

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Fitting mathematical models to lactation curves from holstein cows in the southwestern region of the state of Parana, Brazil

Anais da Academia Brasileira de Ciências ◽

10.1590/0001-3765201520130514 ◽

2015 ◽

Vol 87 (1) ◽

pp. 503-517 ◽

Cited By ~ 4

Author(s):

Abílio G.T. Ferreira ◽

Douglas S. Henrique ◽

Ricardo A.M. Vieira ◽

Emilyn M. Maeda ◽

Altair A. Valotto

Keyword(s):

Mathematical Models ◽

Milk Production ◽

Information Criterion ◽

The State ◽

Holstein Cows ◽

Suitable Model ◽

Almost All ◽

Quality Of Fit ◽

Wood Model

The objective of this study was to evaluate four mathematical models with regards to their fit to lactation curves of Holstein cows from herds raised in the southwestern region of the state of Parana, Brazil. Initially, 42,281 milk production records from 2005 to 2011 were obtained from "Associação Paranaense de Criadores de Bovinos da Raça Holandesa (APCBRH)". Data lacking dates of drying and total milk production at 305 days of lactation were excluded, resulting in a remaining 15,142 records corresponding to 2,441 Holstein cows. Data were sorted according to the parity order (ranging from one to six), and within each parity order the animals were divided into quartiles (Q25%, Q50%, Q75% and Q100%) corresponding to 305-day lactation yield. Within each parity order, for each quartile, four mathematical models were adjusted, two of which were predominantly empirical (Brody and Wood) whereas the other two presented more mechanistic characteristics (models Dijkstra and Pollott). The quality of fit was evaluated by the corrected Akaike information criterion. The Wood model showed the best fit in almost all evaluated situations and, therefore, may be considered as the most suitable model to describe, at least empirically, the lactation curves of Holstein cows raised in Southwestern Parana.

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Introducing a sinusoidal equation to describe lactation curves for cumulative milk yield and composition in Holstein cows

Journal of Dairy Research ◽

10.1017/s0022029920000254 ◽

2020 ◽

Vol 87 (2) ◽

pp. 220-225

Author(s):

Navid Ghavi Hossein-Zadeh ◽

Hassan Darmani Kuhi ◽

James France ◽

Secundino López

Keyword(s):

Linear Model ◽

Milk Yield ◽

Milk Fat ◽

Goodness Of Fit ◽

Growth Models ◽

Information Criterion ◽

Holstein Cows ◽

Coefficient Of Determination ◽

Production Traits ◽

Lactation Curve

AbstractThe aim of the work reported here was to investigate the appropriateness of a sinusoidal function by applying it to model the cumulative lactation curves for milk yield and composition in primiparous Holstein cows, and to compare it with three conventional growth models (linear, Richards and Morgan). Data used in this study were 911 144 test-day records for milk, fat and protein yields, which were recorded on 834 dairy herds from 2000 to 2011 by the Animal Breeding Centre and Promotion of Animal Products of Iran. Each function was fitted to the test-day production records using appropriate procedures in SAS (PROC REG for the linear model and PROC NLIN for the Richards, Morgan and sinusoidal equations) and the parameters were estimated. The models were tested for goodness of fit using adjusted coefficient of determination $\lpar {R_{{\rm adj}}^2 } \rpar $, root mean square error (RMSE), Akaike's information criterion (AIC) and the Bayesian information criterion (BIC). $R_{{\rm adj}}^2 $ values were generally high (>0.999), implying suitable fits to the data, and showed little differences among the models for cumulative yields. The sinusoidal equation provided the lowest values of RMSE, AIC and BIC, and therefore the best fit to the lactation curve for cumulative milk, fat and protein yields. The linear model gave the poorest fit to the cumulative lactation curve for all production traits. The current results show that classical growth functions can be fitted accurately to cumulative lactation curves for production traits, but the new sinusoidal equation introduced herein, by providing best goodness of fit, can be considered a useful alternative to conventional models in dairy research.

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Using quantile regression for fitting lactation curve in dairy cows

Journal of Dairy Research ◽

10.1017/s0022029919000013 ◽

2019 ◽

Vol 86 (1) ◽

pp. 19-24

Author(s):

Hossein Naeemipour Younesi ◽

Mohammad Mahdi Shariati ◽

Saeed Zerehdaran ◽

Mehdi Jabbari Nooghabi ◽

Peter Løvendahl

Keyword(s):

Quantile Regression ◽

Dairy Cows ◽

Milk Production ◽

Milk Yield ◽

Information Criterion ◽

Production Traits ◽

Milk Production Traits ◽

Fat Percentage ◽

Protein Percentage ◽

Breeding Center

AbstractThe main objective of this study was to compare the performance of different ‘nonlinear quantile regression’ models evaluated at theτth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali & Schaeffer) functions were implemented in the quantile regression. Residual mean square, Akaike information criterion and log-likelihood from different models and quantiles indicated that in the same quantile, the best models were Wilmink for milk yield, Dijkstra for fat percentage and Ali & Schaeffer for protein percentage. Over all models the best model fit occurred at quantile 0·50 for milk yield, fat and protein percentage, whereas, for SCS the 0·25th quantile was best. The best model to describe SCS was Dijkstra at quantiles 0·25 and 0·50, and Ali & Schaeffer at quantile 0·75. Wood function had the worst performance amongst all traits. Quantile regression is specifically appropriate for SCS which has a mixed multimodal distribution.

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