scholarly journals Estimation of quantitative genetic parameters

2005 ◽  
Vol 360 (1459) ◽  
pp. 1469-1477 ◽  
Author(s):  
Robin Thompson ◽  
Sue Brotherstone ◽  
Ian M.S White
Keyword(s):  
Genetic Parameters ◽  
Genetic Parameter ◽  
Direct Methods ◽  
Short Review ◽  
Computationally Efficient ◽  
Likelihood Methods ◽  
Maximum Likelihood Methods ◽  
Best Linear Unbiased ◽  
Relevant Work ◽  
Quantitative Genetic Parameters

This paper gives a short review of the development of genetic parameter estimation over the last 40 years. This shows the development of more statistically and computationally efficient methods that allow the fitting of more biologically appropriate models. Methods have evolved from direct methods based on covariances between relatives to methods based on individual animal models. Maximum-likelihood methods have a natural interpretation in terms of best linear unbiased predictors. Improvements in iterative schemes to give estimates are discussed. As an example, a recent estimation of genetic parameters for a British population of dairy cattle is discussed. The development makes a connection to relevant work by Bill Hill.

scholarly journals Genetic parameters of production and reproduetive traits in on a farm tested Danish Large White and Landrace swine in Greece

2000 ◽  
Vol 43 (3) ◽  
pp. 287-298
Author(s):  
J. Bizelis ◽  
A. Kominakis ◽  
E. Rogdakis ◽  
F. Georgadopoulou
Keyword(s):  
Maximum Likelihood ◽  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Production Traits ◽  
Likelihood Methods ◽  
Large White ◽  
Reproduction Traits ◽  
Maximum Likelihood Methods ◽  
Genetic And Phenotypic Correlations

Abstract. Production and reproduetive traits in Danish Landrace (LD) and Large White (LW) swine were analysed by restricted maximum likelihood methods to obtain heritabilities as well as genetic and phenotypic correlations. Production traits were: age, backfat thickness (BT), muscle depth (MD) and the ratio BT/MD, adjusted to Standard bodyweight of 85 kg. Reproduction traits were: number of pigs born (NB) and number of pigs weaned (NW) per sow and parity. Heritabilities for age, BT, MD and BT/MD were 0.60, 0.44, 0.51 and 0.42 for LD and 0.36, 0.44, 0.37 and 0.45 for LW, respectively. Genetic correlations between age and BT were −0.22 in LD and – 0.44 in LW. The genetic correlation between age and MD was close to zero in both breeds. Genetic correlation between BT and MD were −0.36 and −0.25 in LD and LW, respectively. Heritabilities for NB were 0.25 in LD and 0.13 in LW while heritabilities for NW were close to zero in both breeds. Genetic correlation between NB and NW was 0.46 and 0.70 in LD and LW, respectively.

scholarly journals Genetic Parameter Estimation and Genomic Prediction of Duroc Boars’ Sperm Morphology Abnormalities

Animals ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 710 ◽  
Author(s):  
Yunxiang Zhao ◽  
Ning Gao ◽  
Jian Cheng ◽  
Saeed El-Ashram ◽  
Lin Zhu ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Genetic Parameters ◽  
Sperm Morphology ◽  
Genetic Parameter ◽  
Predictive Ability ◽  
Single Step ◽  
Swine Production ◽  
Forward Prediction ◽  
Best Linear Unbiased ◽  
First Time

Artificial insemination (AI) has been used globally as a routine technology in the swine production industry. However, genetic parameters and genomic prediction accuracy of semen traits have seldom been reported. In this study, we estimated genetic parameters and conducted genomic prediction for five types of sperm morphology abnormalities in a large Duroc boar population. The estimated heritability of the studied traits ranged from 0.029 to 0.295. In the random cross-validation scenario, the predictive ability ranged from 0.212 to 0.417 for genomic best linear unbiased prediction (GBLUP) and from 0.249 to 0.565 for single-step GBLUP (ssGBLUP). In the forward prediction scenario, the predictive ability ranged from 0.069 to 0.389 for GBLUP and from 0.085 to 0.483 for ssGBLUP. In conclusion, the studied sperm morphology abnormalities showed moderate to low heritability. Both GBLUP and ssGBLUP showed comparative predictive abilities of breeding values, and ssGBLUP outperformed GBLUP under many circumstances in respect to predictive ability. To our knowledge, this is the first time that the genetic parameters and genomic predictive ability of these traits were reported in such a large Duroc boar population.

Genetic parameters of test day records of British Holstein-Friesian heifers

1992 ◽  
Vol 55 (1) ◽  
pp. 11-21 ◽  
Author(s):  
B. L. Pander ◽  
W. G. Hill ◽  
R. Thompson
Keyword(s):  
Maximum Likelihood ◽  
Milk Yield ◽  
Milk Fat ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Lactation Performance ◽  
Likelihood Methods ◽  
Holstein Friesian ◽  
Maximum Likelihood Methods ◽  
Heritability Estimates

AbstractEstimates of genetic parameters for test day records of yields of milk, fat and protein and concentrations of fat and protein were obtained on 47 736 British Holstein-Friesian heifers in 7973 herds, progeny of 40 proven (to improve connectedness) and 707 young sires (comprising about one-fifth of the progeny), using multivariate restricted maximum likelihood methods with a sire model.Heritability estimates for lactation yields of milk, fat and protein and concentrations of fat and protein were 0·49, 0·39, 0·43, 0·63 and 0·47, respectively. Estimates for individual test day records of these traits ranged from 0·27 to 0·43, 0·16 to 0·34, 0·22 to 0·33, 0·11 to 0·48 and 0·21 to 0·43, respectively. Generally, heritability estimates for test day records were lowest at start and highest in mid lactation.Estimates of genetic correlations among yields of a trait on different test days ranged from 0·57 to 0·99, and for fat and protein concentrations from 0·34 to 0·99, the correlations being highest for adjacent tests. Phenotypic correlations were lower than genetic correlations. Genetic correlations of test day records with corresponding lactation traits were high (0·76 to 0·99), being highest in mid lactation.Genetic correlations of test day milk yield with test day yields and concentrations of fat and protein throughout the lactation were similar to those for complete lactation.The high heritabilities of test day yields and their high genetic correlations with complete lactation, except for the first 1 or 2 test days, suggest that lactation performance may be predicted from test days in early and mid lactation.

scholarly journals Realized Sampling Variances of Estimates of Genetic Parameters and the Difference Between Genetic and Phenotypic Correlations

Genetics ◽  
1996 ◽  
Vol 143 (3) ◽  
pp. 1409-1416 ◽  
Author(s):  
Kenneth R Koots ◽  
John P Gibson
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Parameter Estimates ◽  
Sampling Variance ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

Abstract A data set of 1572 heritability estimates and 1015 pairs of genetic and phenotypic correlation estimates, constructed from a survey of published beef cattle genetic parameter estimates, provided a rare opportunity to study realized sampling variances of genetic parameter estimates. The distribution of both heritability estimates and genetic correlation estimates, when plotted against estimated accuracy, was consistent with random error variance being some three times the sampling variance predicted from standard formulae. This result was consistent with the observation that the variance of estimates of heritabilities and genetic correlations between populations were about four times the predicted sampling variance, suggesting few real differences in genetic parameters between populations. Except where there was a strong biological or statistical expectation of a difference, there was little evidence for differences between genetic and phenotypic correlations for most trait combinations or for differences in genetic correlations between populations. These results suggest that, even for controlled populations, estimating genetic parameters specific to a given population is less useful than commonly believed. A serendipitous discovery was that, in the standard formula for theoretical standard error of a genetic correlation estimate, the heritabilities refer to the estimated values and not, as seems generally assumed, the true population values.

scholarly journals Selecting between causal and noncausal models with quantile autoregressions

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alain Hecq ◽  
Li Sun
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Latin American ◽  
Financial Time Series ◽  
Selection Criterion ◽  
Likelihood Methods ◽  
Approximate Maximum Likelihood ◽  
Financial Time ◽  
Maximum Likelihood Methods ◽  
Latin American Countries

AbstractWe propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

scholarly journals A Generalized Rayleigh Family of Distributions Based on the Modified Slash Model

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1226
Author(s):  
Inmaculada Barranco-Chamorro ◽  
Yuri A. Iriarte ◽  
Yolanda M. Gómez ◽  
Juan M. Astorga ◽  
Héctor W. Gómez
Keyword(s):  
Heavy Tails ◽  
Real Data ◽  
Rate Function ◽  
Data Sets ◽  
Estimation Of Parameters ◽  
Stochastic Representation ◽  
Likelihood Methods ◽  
Chi Square ◽  
Maximum Likelihood Methods ◽  
Family Of Distributions

Specifying a proper statistical model to represent asymmetric lifetime data with high kurtosis is an open problem. In this paper, the three-parameter, modified, slashed, generalized Rayleigh family of distributions is proposed. Its structural properties are studied: stochastic representation, probability density function, hazard rate function, moments and estimation of parameters via maximum likelihood methods. As merits of our proposal, we highlight as particular cases a plethora of lifetime models, such as Rayleigh, Maxwell, half-normal and chi-square, among others, which are able to accommodate heavy tails. A simulation study and applications to real data sets are included to illustrate the use of our results.

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