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).

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.

scholarly journals The relation of GH1, GHR and DGAT1 polymorphisms with estimated breeding values for milk production traits of German Holstein sires

2008 ◽  
Vol 53 (No. 6) ◽  
pp. 238-246 ◽  
Author(s):  
E. Hradecká ◽  
J. Čítek ◽  
L. Panicke ◽  
V. Řehout ◽  
L. Hanusová
Keyword(s):  
Growth Hormone ◽  
Milk Production ◽  
Milk Fat ◽  
Milk Protein ◽  
Growth Hormone Gene ◽  
Production Traits ◽  
Milk Production Traits ◽  
Breeding Values ◽  
Fat Yield ◽  
Estimated Breeding Values

: We analysed the relations of estimated breeding values (EBV) of 315 German Holstein sires to their genotypes in growth hormone gene (<i>GH1</i>), growth hormone receptor gene (<i>GHR</i>) and acylCoA-diacylglycerol acyltransferase 1 (<i>DGAT1</i>). The strong relation of <i>DGAT1 K232A</i> to the estimated breeding values for milk production traits has been confirmed, when allele <i>DGAT1<sup>K</sup></i> was connected with higher milk fat yield, milk fat and milk protein content, while allele <i>DGAT1<sup>A</sup></i> increased milk yield and milk protein yield. The effect of <i>DGAT1</i> genotype explained from 4.70% of variability of EBVs for fat yield to 31.90% of variability of EBVs for fat content. The evaluation of <i>GH1</i> 127 Leu/Val and <i>GHR</i> 257 SNP polymorphisms did not reveal an association of their polymorphism with EBVs for milk production traits, except the EBVs of <i>GHR<sup>G</sup>/GHR<sup>G</sup></i> homozygotes for fat yield, which were significantly lower. The effect of <i>GH1</i> or <i>GHR genotype explained only a negligible portion of variability of EBVs (<i>R</i><sup>2</sup> < 1.00% in most cases).

scholarly journals Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle

Animals ◽  
2022 ◽  
Vol 12 (2) ◽  
pp. 136
Author(s):  
Menghua Zhang ◽  
Hanpeng Luo ◽  
Lei Xu ◽  
Yuangang Shi ◽  
Jinghang Zhou ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Bayesian Methods ◽  
Breeding Value ◽  
Production Traits ◽  
Milk Production Traits ◽  
Linear Unbiased Prediction ◽  
Brown Cattle ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Better Than

One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value.

scholarly journals Estimating Breeding values for milk production and mastitis traits for Holstein cattle in Egypt

2019 ◽  
Vol 23 (4) ◽  
pp. 159
Author(s):  
Eslam Faid-Allah
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Holstein Cattle ◽  
Phenotypic Variance ◽  
Production Traits ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Time Parity ◽  
Best Linear Unbiased ◽  
Selection Of

<p class="abstrak2">This study was carried out to evaluate the sires and dams genetically for milk production and mastitis traits in Egyptian 12 herds of Holstein cattle using Best Linear Unbiased Prediction via MTDFREML program. The data was obtained from a commercial farm called Dena, located in Cairo-Alex Desert Road (80 Km), Menofia, Egypt. Data included 4791 cows, 4227 dams and 248 sires that represented the period from 2007 to 2014. Estimating breeding values for milk production traits as cumulative milk yield at 90 days (90-DM), cumulative milk yield at 180 days (180-DM), cumulative milk yield at 270 days (270-DM), cumulative milk yield at 305 days (305-DM), and number of mastitis infection around the season of lactation (MAST). The averages of the 90-DM, 180-DM, 270-DM, 305-DM and MAST were 3026.3±655.1 kg, 5873.3±1081.1 kg, 7891.1±2692.2 kg, 9611.2±1897.9 kg, and 0.712±1.2 time/parity, respectively. Estimates of heritability for the previous traits were 0.11±0.016, 0.15±0.014, 0.18±0.012, 0.22±0.015, and 0.09±0.029, respectively; genetic variance were 47206.2 kg, 175300.6 kg, 1304654.4 kg, 792411.6 kg and 0.12 time/parity, respectively; and phenotypic variance were 429147.6 kg, 1168670.6 kg, 7248079.9 kg, 3601870.9 kg, and 1.35 time/parity, respectively. The EBV values as average, SD, (Min: Max) for sires were 0.0±0.179 (-0.4: 0.66) for MAST, 0.0±86.176 (-263.1: 245.4) for 90-DM, 0.0±227.523 (-600.3: 800.3) for 180-DM, 0.0±413.48 (-323.3: 1277.7) for 270-DM and 0.0±440.26 (-1280.9: 1565.1) for 305-DM. Also, The EBVs for dams were 0.0±0.055 (-0.14: 0.45) for MAST, 0.033±26.24 (-142.8: 103.0) for 90-DM, 0.074±76.81 (-360.2: 289.6) for 180-DM, -0.045±139.66 (-591.9: 529.2) for 270-DM and 0.266±154.1 (-666.3: 617.6) for 305-DM. These results provide that the selection of sires and dams will improve the traits of milk production and mastitis in this herd because of the wide differences in genetic potential among sires and dams.</p>

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.

Prospects for genetic improvement of milk production traits of Sahiwal cattle in Kenya

2007 ◽  
Vol 2007 ◽  
pp. 69-69
Author(s):  
E.D. Ilatsia ◽  
T. K. Muasya ◽  
W. B. Muhuyi ◽  
A. K. Kahi
Keyword(s):  
Animal Model ◽  
Milk Production ◽  
Genetic Parameters ◽  
Genetic Relationships ◽  
Additive Genetic Variance ◽  
Parameter Estimates ◽  
Production Traits ◽  
Milk Production Traits ◽  
Phenotypic Parameter ◽  
Sahiwal Cattle

The primary emphasis of the long-term Sahiwal cattle breeding programme is to increase milk yield by selecting cows based on their performance in first three lactations. It is therefore important to have knowledge on the extend of additive genetic variance and genetic parameters for these traits. Genetic and phenotypic parameter estimates normally apply directly to the specific population and environment from which the data were collected. In the Sahiwal cattle in Kenya, very little is known about the genetic variation of milk production traits and their genetic relationships. Furthermore, genetic and phenotypic parameter estimates for the Sahiwal cattle based on multivariate animal model are scarce. This paper presents estimates of variance components and genetic parameters for milk production traits using trivariate animal model.

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