scholarly journals Genomic Analysis of Milk Protein Fractions in Brown Swiss Cattle

Animals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 336
Author(s):  
Lucio Flavio Macedo Mota ◽  
Sara Pegolo ◽  
Vittoria Bisutti ◽  
Giovanni Bittante ◽  
Alessio Cecchinato
Keyword(s):  
Milk Protein ◽  
Genetic Parameter ◽  
Whey Proteins ◽  
Prediction Method ◽  
Genomic Analysis ◽  
Protein Fractions ◽  
Parameter Estimates ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Β Lactoglobulin

Depending on whether milk protein fractions are evaluated qualitatively or quantitatively, different genetic outcomes may emerge. In this study, we compared the genetic parameters for the major milk protein fractions—caseins (αS1-, αS2-, β-, and к-CN), and whey proteins (β-lactoglobulin, β-LG; α-lactalbumin, α-LA)—estimated using the multi-trait genomic best linear unbiased prediction method and expressed variously as milk content (g/100g milk), percentage of milk nitrogen (%N) and daily yield per cow (g/d). The results showed that the genetic parameter estimates varied according to how the milk protein fractions were expressed. Heritability estimates for the caseins and whey protein fractions expressed as daily yields were lower than when they were expressed as proportions and contents, revealing important differences in genetic outcomes. The proportion and the content of β-CN were negatively correlated with the proportions and contents of αS1-CN, αS2-CN, and к-CN, while the daily yield of β–CN was negatively correlated with the daily yields of αS1-CN and αS2-CN. The Spearman’s rank correlations and the coincidence rates between the various predicted genomic breeding values (GEBV) for the milk protein fractions expressed in different ways indicated that these differences had a significant effect on the ranking of the animals. The results suggest that the way milk protein fractions are expressed has implications for breeding programs aimed at improving milk nutritional and technological characteristics.

Heterogeneity of variance between herds for milk production traits

1991 ◽  
Vol 1991 ◽  
pp. 28-28
Author(s):  
P.M. Visscher
Keyword(s):  
Prediction Method ◽  
Parameter Estimates ◽  
Production Traits ◽  
Milk Production Traits ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Heterogeneity Of Variance ◽  
Abundant Evidence ◽  
Best Linear Unbiased ◽  
Fat Yield

One assumption made by most users of the BLUP (Best Linear Unbiased Prediction) method to predict breeding values, is that variances are homogeneous over herds or herd-year-seasons (HYS). In dairy cattle there is abundant evidence, however, of heterogeneity of variance across herds or herd groups (see e.g. Hill et al. 1983 and Brotherstone and Hill, 1986, for U.K. studies). The effect of ignoring heterogenous variances between herds on prediction of breeding values for bulls may be small when using a sire model, if sires were tested across many herd-variance groups. Loss in efficiency may be larger when sires are tested in few herds, or, for cows, when the genetic evaluation is for bulls and cows simultaneously (with an animal model (AM)). The aim of this study was to calculate individual herd parameter estimates to investigate heterogeneity of within herd variance in the U.K. dairy population. The investigated trait was fat yield and the estimations were carried out using a REML (Restricted Maximum Likelihood; Patterson and Thompson, 1971) program written by Karin Meyer (Meyer, 1989).

Selection on lean growth in a nucleus of Landrace pigs: an analysis using Gibbs sampling

1996 ◽  
Vol 63 (2) ◽  
pp. 243-253 ◽  
Author(s):  
M. C. Rodriguez ◽  
M. Toro ◽  
L. Silió
Keyword(s):  
Gibbs Sampling ◽  
Variance Components ◽  
Fixed Effects ◽  
Prediction Method ◽  
Environmental Parameters ◽  
Linear Unbiased Prediction ◽  
Normal Distributions ◽  
Lean Growth ◽  
Best Linear Unbiased ◽  
Selection For

AbstractData from 4150 Landrace pigs tested during the period 1989-94 for backfat thickness and age at 100 kg in an open selection nucleus were analysed with the standard restricted maximum likelihood/best linear unbiased prediction method and with a Bayesian approach based on the marginal posterior distributions of parameters of interest achieved via Gibbs sampling. Breeding values and fixed effects were sampled from normal distributions and (co)variance components from inverted Wishart distributions. The Bayesian analysis indicated that the selection was effective for both traits. Assuming flat priors for the (co)variance components, the posterior means of the annual rates of response to selection for both traits were −0·473 days and −0·212 mm. The influence of informative priors constructed from (co)variances estimated in the French Landrace breed on inferences about genetic and common environmental parameters, genetic group effects and total and annual responses was also examined.

Study on Milk Protein Hydrolysis of Infant Formula

2014 ◽  
Vol 898 ◽  
pp. 326-329
Author(s):  
Li Fu ◽  
Xi Qing Yue ◽  
Jian Xin Song
Keyword(s):  
Infant Formula ◽  
Milk Protein ◽  
Whey Proteins ◽  
Enzyme Linked Immunosorbent Assay ◽  
Protein Hydrolysis ◽  
Step Process ◽  
Hydrolysis Process ◽  
One Step ◽  
Β Lactoglobulin ◽  
Hydrolysis Of

First of all, whey protein concentrat was added to milk adjusting the proportion of casein and whey proteins to 40:60. And then milk was hydrolyzed by trypsin and flavourzyme (TF) single respectively in one-step process or staged in two-step process. The Antigen contents of α-lactalbumin (α-LA) and β-lactoglobulin (β-LG) during the hydrolysis process were determined by indirect competitive enzyme-linked immunosorbent assay (ELISA) methods. The result showed that the most significant antigen reduction was observed in two-step hydrolysis process compared with one-step hydrolysis process

Sign in / Sign up

Export Citation Format

Share Document