scholarly journals A variance component estimation approach to infer associations between Mendelian polledness and quantitative production and female fertility traits in German Simmental cattle

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Carsten Scheper ◽  
Reiner Emmerling ◽  
Kay-Uwe Götz ◽  
Sven König
Keyword(s):  
Variance Component ◽  
Female Fertility ◽  
Variance Component Estimation ◽  
Production Traits ◽  
Breeding Values ◽  
Breeding Goal ◽  
Component Estimation ◽  
Simmental Cattle ◽  
Fertility Traits ◽  
Qtl Effects

Abstract Background Managing beneficial Mendelian characteristics in dairy cattle breeding programs implies that the correlated genetic effects are considered to avoid possible adverse effects in selection processes. The Mendelian trait polledness in cattle is traditionally associated with the belief that the polled locus has unfavorable effects on breeding goal traits. This may be due to the inferior breeding values of former polled bulls and cows in cattle breeds, such as German Simmental, or to pleiotropic or linkage effects of the polled locus. Methods We focused on a variance component estimation approach that uses a marker-based numerator relationship matrix reflecting gametic relationships at the polled locus to test for direct pleiotropic or linked quantitative trait loci (QTL) effects of the polled locus on relevant traits. We applied the approach to performance, health, and female fertility traits in German Simmental cattle. Results Our results showed no evidence for any pleiotropic QTL effects of the polled locus on test-day production traits milk yield and fat percentage, on the mastitis indicator ‘somatic cell score’, and on several female fertility traits, i.e. 56 days non return rate, days open and days to first service. We detected a significant and unfavorable QTL effect accounting for 6.6% of the genetic variance for protein percentage only. Conclusions Pleiotropy does not explain the lower breeding values and phenotypic inferiority of polled German Simmental sires and cows relative to the horned population in the breed. Thus, intensified selection in the polled population will contribute to increased selection response in breeding goal traits and genetic merit and will narrow the deficit in breeding values for production traits.

VARIANCE COMPONENT ESTIMATION AND PREDICTION OF BREEDING VALUES

1981 ◽  
Vol 23 (4) ◽  
pp. 565-578 ◽  
Author(s):  
B. W. Kennedy
Keyword(s):  
Maximum Likelihood ◽  
Analysis Of Variance ◽  
Variance Component ◽  
Unbiased Estimation ◽  
Minimum Norm ◽  
Variance Component Estimation ◽  
Components Of Variance ◽  
Breeding Values ◽  
Estimation And Prediction ◽  
Component Estimation

A brief review is given of prediction of breeding values when the genetic and phenotypic variances of the trait are known. In many situations, however, these variances are not known and must be estimated from the data. A review is given of the traditionally used analysis of variance (ANOVA) type methods of variance component estimation and of the more recently developed methods of maximum likelihood (ML), restricted maximum likelihood (REML), minimum norm quadratic unbiased estimation (MINQUE) and some variations on MINQUE. Criteria for choosing among methods are discussed and the consequences of applying estimates obtained by these methods in place of known variances to the prediction of breeding values are considered. The methods are illustrated for estimation of paternal half-sib heritability and prediction of breeding values of sires using a small numerical example. Lastly, the consequences of selection on prediction of breeding values and estimation of components of variance for populations under selection are discussed.

GPS-BDS-Galileo double-differenced stochastic model refinement based on least-squares variance component estimation

2021 ◽  
pp. 1-16
Author(s):  
Hong Hu ◽  
Xuefeng Xie ◽  
Jingxiang Gao ◽  
Shuanggen Jin ◽  
Peng Jiang
Keyword(s):  
Stochastic Model ◽  
Least Squares ◽  
Stochastic Models ◽  
Variance Component ◽  
Satellite System ◽  
Variance Component Estimation ◽  
Short Baseline ◽  
Component Estimation ◽  
Zero Baseline ◽  
Global Navigation Satellite

Abstract Stochastic models are essential for precise navigation and positioning of the global navigation satellite system (GNSS). A stochastic model can influence the resolution of ambiguity, which is a key step in GNSS positioning. Most of the existing multi-GNSS stochastic models are based on the GPS empirical model, while differences in the precision of observations among different systems are not considered. In this paper, three refined stochastic models, namely the variance components between systems (RSM1), the variances of different types of observations (RSM2) and the variances of observations for each satellite (RSM3) are proposed based on the least-squares variance component estimation (LS-VCE). Zero-baseline and short-baseline GNSS experimental data were used to verify the proposed three refined stochastic models. The results show that, compared with the traditional elevation-dependent model (EDM), though the proposed models do not significantly improve the ambiguity resolution success rate, the positioning precision of the three proposed models has been improved. RSM3, which is more realistic for the data itself, performs the best, and the precision at elevation mask angles 20°, 30°, 40°, 50° can be improved by 4⋅6%, 7⋅6%, 13⋅2%, 73⋅0% for L1-B1-E1 and 1⋅1%, 4⋅8%, 16⋅3%, 64⋅5% for L2-B2-E5a, respectively.

scholarly journals An overview of variance component estimation

Metrika ◽  
1995 ◽  
Vol 42 (1) ◽  
pp. 215-230 ◽  
Author(s):  
Shayle R. Searle
Keyword(s):  
Variance Component ◽  
Variance Component Estimation ◽  
Component Estimation
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