Evaluation of repeatability and pre-structured repeatability models for genetic analyses of repeated records of fat and protein contents of milk in Iranian Holstein cows

2018 ◽  
Vol 58 (11) ◽  
pp. 1983
Author(s):  
M. Asadi Fozi
Keyword(s):  
Genetic Analysis ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Holstein Cows ◽  
Phenotypic Correlation ◽  
Genetic Covariance ◽  
Future Studies ◽  
Phenotypic Correlations ◽  
Variance Structure

Fat and protein content of milk measurements from first to fifth lactations of Iranian Holstein cows were analysed using repeatability and several pre-structured repeatability models that varied in additive genetic variance structure and fitted heterogeneous residual co (variance). For this research, a total of 257 197 fat and 218 688 protein records were used. The records were measured on 116 531 cows born between 2010 and 2014. The animals originated from 2355 sires and 91 212 dams. Pre-structured repeatability models with heterogeneous residual co (variance) and the respective genetic variance structure were the best models for genetic analysis of the fat and protein data. The results derived from these models showed that heritability of both fat and protein are decreased from first to fifth lactations. Heritability of fat measured at first, second, third, fourth and fifth locations were between 0.10 and 0.19 and those for protein were between 0.07 and 0.24. Moderate to high phenotypic correlations were estimated between the repeated records of the fat and protein. Values of 0.13 and 0.16 were estimated for heritability of fat and protein using repeatability model. Phenotypic correlations among the repeated records of fat and protein were estimated to be 0.30 and 0.33, respectively when this model was applied. The results showed the genetic variance, heritability and phenotypic correlation of the fat and protein are changed over the lactations but the genetic parameters derived from the repeatability model are homogenous whereas in both models unity genetic correlations are assumed among the repeated records. The results of this study show that the repeatability model is not an appropriate model for genetic analysis of the repeated records of fat and protein in the population investigated and can be improved when pre-structured repeatability model is used. In the present study homogenous genetic covariance was assumed among the fat and protein taken at the different lactations which can be modelled in future studies for more improving the models.

A preliminary genetic analysis of breech and tail traits with the aim of improving the welfare of sheep

2007 ◽  
Vol 58 (2) ◽  
pp. 161 ◽  
Author(s):  
D. R. Scobie ◽  
D. O'Connell ◽  
C. A. Morris ◽  
S. M. Hickey
Keyword(s):  
New Zealand ◽  
Genetic Analysis ◽  
Genetic Correlation ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Linear Scale ◽  
Bare Area ◽  
Phenotypic Correlations ◽  
Tail Docking

The area of naturally bare skin around the perineum was scored at weaning in lambs (n = 2152) from a composite flock of New Zealand crossbred sheep. Breech bareness was scored on a range from 1, where wool was growing right to the edges of the anus, to 5, where a large bare area surrounded the perineum. Bareness on the under surface of the tail was measured on a linear scale at tail docking. Dag score (degree of breech soiling) was recorded at weaning, on a scale of 0–5, where an increasing score indicated more dags. Dag score was taken as a measure of the risk of flystrike in the breech. Female lambs tended to have slightly greater (P < 0.001) breech bareness score (mean score 2.7) than males (mean score 2.6), whereas mean dag score of females was lower than that of males (0.45 v. 0.53; P < 0.05). Breech bareness score had a heritability of 0.33 ± 0.06, and the length of bare skin under the tail had a heritability of 0.59 ± 0.06. The genetic correlation between breech bareness score at weaning and length of bare skin under the tail at docking was positive (0.35 ± 0.10). These 2 traits had phenotypic correlations with dag score of –0.17 ± 0.02 and –0.03 ± 0.03, respectively, and genetic correlations with dag score of –0.30 ± 0.13 and 0.03 ± 0.12, respectively; negative values indicated a favourable relationship. Tails were removed at docking, so the phenotypic correlation of about zero between tail data and dag score at weaning was of little utility. Our results suggest that selecting for these 2 bareness traits could reduce dag formation and the associated risk of breech strike.

scholarly journals Laboratory estimates of heritabilities and genetic correlations in nature.

Genetics ◽  
1989 ◽  
Vol 123 (4) ◽  
pp. 865-871 ◽  
Author(s):  
B Riska ◽  
T Prout ◽  
M Turelli
Keyword(s):  
Lower Bound ◽  
Genetic Correlation ◽  
Lower Bounds ◽  
Natural Environment ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Phenotypic Correlations ◽  
Significant Genetic Correlation ◽  
Genetic And Phenotypic Correlations

Abstract A lower bound on heritability in a natural environment can be determined from the regression of offspring raised in the laboratory on parents raised in nature. An estimate of additive genetic variance in the laboratory is also required. The estimated lower bounds on heritabilities can sometimes be used to demonstrate a significant genetic correlation between two traits in nature, if their genetic and phenotypic correlations in nature have the same sign, and if sample sizes are large, and heritabilities and phenotypic and genetic correlations are high.

scholarly journals Age-Specific Patterns of Genetic Variance in Drosophila melanogaster. II. Fecundity and Its Genetic Covariance With Age-Specific Mortality

Genetics ◽  
1996 ◽  
Vol 143 (2) ◽  
pp. 849-858 ◽  
Author(s):  
Marc Tatar ◽  
Daniel E L Promislow ◽  
Aziz A Khazaeli ◽  
James W Curtsinger
Keyword(s):  
Drosophila Melanogaster ◽  
Variance Components ◽  
Life History Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Mutation Accumulation ◽  
Age Classes ◽  
Genetic Covariance ◽  
Specific Mortality

Abstract Under the mutation accumulation model of senescence, it was predicted that the additive genetic variance (VA) for fitness traits will increase with age. We measured age-specific mortality and fecundity from 65,134 Drosophila melanogaster and estimated genetic variance components, based on reciprocal crosses of extracted second chromosome lines. Elsewhere we report the results for mortality. Here, for fecundity, we report a bimodal pattern for VA with peaks at 3 days and at 17–31 days. Under the antagonistic pleiotropy model of senescence, it was predicted that negative correlations will exist between early and late life history traits. For fecundity itself we find positive genetic correlations among age classes &gt;3 days but negative nonsignificant correlations between fecundity at 3 days and at older age classes. For fecundity vs. age-specific mortality, we find positive fitness correlations (negative genetic correlations) among the traits at all ages &gt;3 days but a negative fitness correlation between fecundity at 3 days and mortality at the oldest ages (positive genetic correlations). For age-specific mortality itself we find overwhelmingly positive genetic correlations among all age classes. The data suggest that mutation accumulation may be a major source of standing genetic variance for senescence.

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 Genetic variance in fitness and its cross-sex covariance predict adaptation during experimental evolution

2020 ◽  
Author(s):  
Eva L. Koch ◽  
Sonja H. Sbilordo ◽  
Frédéric Guillaume
Keyword(s):  
Experimental Evolution ◽  
Genetic Variance ◽  
Environmental Changes ◽  
Additive Genetic Variance ◽  
Relative Fitness ◽  
Male Fitness ◽  
Adaptive Potential ◽  
Genetic Covariance ◽  
Flour Beetles ◽  
Single Stress

AbstractIn presence of rapid environmental changes, it is of particular importance to assess the adaptive potential of populations, which is mostly determined by the additive genetic variation (VA) in fitness. In this study we used Tribolium castaneum (red flour beetles) to investigate its adaptive potential in three new environmental conditions (Dry, Hot, Hot-Dry). We tested for potential constraints that might limit adaptation, including negative genetic covariance between female and male fitness. Based on VA estimates for fitness, we expected the highest relative fitness increase in the most stressful condition Hot-Dry and similar increases in single stress conditions Dry and Hot. High adaptive potential in females in Hot was reduced by a negative covariance with male fitness. We tested adaptation to the three conditions after 20 generations of experimental evolution and found that observed adaptation mainly matched our predictions. Given that body size is commonly used as a proxy for fitness, we also tested how this trait and its genetic variance (including non-additive genetic variance) were impacted by environmental stress. In both traits, variances were sex and condition dependent, but they differed in their variance composition, cross-sex and cross-environment genetic covariances, as well as in the environmental impact on VA.

Genetic parameter estimates (full sib and half sib analysis) of fertility and hatchability in two strains of Rhode Island chickens

2021 ◽  
Vol 38 (1) ◽  
pp. 14-22
Author(s):  
M. Orunmuyi ◽  
I. A. Adeyinka ◽  
O.O Oni
Keyword(s):  
Rhode Island ◽  
Additive Genetic Variance ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Reproductive Traits ◽  
Parameter Estimates ◽  
Phenotypic Correlations ◽  
Poultry Breeding ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

A study was conducted to estimate the genetic parameters of fertility and hatchability in two strains of Rhode Island Red (RIR) Chickens denoted as Strain A and Strain B respectively using the full-sib (sire +dam variance) and maternal half-sib (dam variance) components. The birds were obtained from the selected populations of RIR Chickens kept at the poultry breeding programme of National Animal Production Research Institute, Shika, Zaria, Nigeria. Settable eggs were collected from mating 28 cocks to 252 hens in a ratio of 1cock:9 hens from each strain. Eggs were pedigreed according to sire and dam. Results showed that values obtained for number of egg set (EGGSET), number of fertile eggs (NFERT), number of hatched chicks (NHATCH), percentage of chicks hatched from total eggs set (PHATCH) and percentage of chicks hatched from fertile eggs (PHATCHBL) were all higher in strain A than strain B. Heritability estimates obtained from the full-sib and maternal half-sib analysis ranged from medium to high for the two strains (0.24-0.96). The maternal half sib estimates were higher (0.40-0.96) than the estimates obtained from full sibs (0.24- 0.48). Genetic and phenotypic correlations obtained for both strains were positive and similar regardless of method of estimation. Genetic correlations between EGGSET and PFERT were low in strain A using both full-sib and maternal half-sib analyses (0.09-0.14). Phenotypic correlations between EGGSET and PFERT, PHATCH and PHATCHBL were also low in both strains and regardless of method of analyses. Moderate to high heritability estimates suggest that genetic improvement can be obtained by selection of these reproductive traits. The full-sib analysis for estimating heritability will be preferred since it is assumed that only additive genetic variance contributes to the covariance between family members.

scholarly journals Genetic variance of metabolomic features and their relationship with body weight and body weight gain in Holstein cattle1

2019 ◽  
Vol 97 (9) ◽  
pp. 3832-3844 ◽  
Author(s):  
Amir Aliakbari ◽  
Alireza Ehsani ◽  
Rasoul Vaez Torshizi ◽  
Peter Løvendahl ◽  
Hadi Esfandyari ◽  
...  
Keyword(s):  
Body Weight ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Body Weight Gain ◽  
Genetic Correlations ◽  
Alternative Methods ◽  
Data Set ◽  
Phenotypic Correlations ◽  
Binned Data ◽  
Univariate Analyses

Abstract In recent years, metabolomics has been used to clarify the biology underlying biological samples. In the field of animal breeding, investigating the magnitude of genetic control on the metabolomic profiles of animals and their relationships with quantitative traits adds valuable information to animal improvement schemes. In this study, we analyzed metabolomic features (MFs) extracted from the metabolomic profiles of 843 male Holstein calves. The metabolomic profiles were obtained using nuclear magnetic resonance (NMR) spectroscopy. We investigated 2 alternative methods to control for peak shifts in the NMR spectra, binning and aligning, to determine which approach was the most efficient for assessing genetic variance. Series of univariate analyses were implemented to elucidate the heritability of each MF. Furthermore, records on BW and ADG from 154 to 294 d of age (ADG154–294), 294 to 336 d of age (ADG294–336), and 154 to 336 d of age (ADG154–336) were used in a series of bivariate analyses to establish the genetic and phenotypic correlations with MFs. Bivariate analyses were only performed for MFs that had a heritability significantly different from zero. The heritabilities obtained in the univariate analyses for the MFs in the binned data set were low (<0.2). In contrast, in the aligned data set, we obtained moderate heritability (0.2 to 0.5) for 3.5% of MFs and high heritability (more than 0.5) for 1% of MFs. The bivariate analyses showed that ~12%, ~3%, ~9%, and ~9% of MFs had significant additive genetic correlations with BW, ADG154–294, ADG294–336, and ADG154–336, respectively. In all of the bivariate analyses, the percentage of significant additive genetic correlations was higher than the percentage of significant phenotypic correlations of the corresponding trait. Our results provided insights into the influence of the underlying genetic mechanisms on MFs. Further investigations in this field are needed for better understanding of the genetic relationship among the MFs and quantitative traits.

scholarly journals Evolution of the additive genetic variance–covariance matrix under continuous directional selection on a complex behavioural phenotype

2015 ◽  
Vol 282 (1819) ◽  
pp. 20151119 ◽  
Author(s):  
Vincent Careau ◽  
Matthew E. Wolak ◽  
Patrick A. Carter ◽  
Theodore Garland
Keyword(s):  
Covariance Matrix ◽  
Adaptive Response ◽  
Genetic Variance ◽  
Environmental Changes ◽  
Wheel Running ◽  
Additive Genetic Variance ◽  
Phenotypic Selection ◽  
Directional Selection ◽  
Genetic Covariance ◽  
Variance Covariance Matrix

Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance–covariance matrix ( G ). Yet knowledge of G in a population experiencing new or altered selection is not sufficient to predict selection response because G itself evolves in ways that are poorly understood. We experimentally evaluated changes in G when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset ( n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change.

scholarly journals Effects on phenotypic variability of directional selection arising through genetic differences in residual variability

2004 ◽  
Vol 83 (2) ◽  
pp. 121-132 ◽  
Author(s):  
WILLIAM G. HILL ◽  
XU-SHENG ZHANG
Keyword(s):  
Genetic Variance ◽  
Environmental Variability ◽  
Phenotypic Variability ◽  
Additive Genetic Variance ◽  
Directional Selection ◽  
Frequency Change ◽  
Phenotypic Variance ◽  
Additive Effects ◽  
Genetic Covariance ◽  
Mean And Variance

In standard models of quantitative traits, genotypes are assumed to differ in mean but not variance of the trait. Here we consider directional selection for a quantitative trait for which genotypes also confer differences in variability, viewed either as differences in residual phenotypic variance when individual loci are concerned or as differences in environmental variability when the whole genome is considered. At an individual locus with additive effects, the selective value of the increasing allele is given by ia/σ+½ixb/σ2, where i is the selection intensity, x is the standardized truncation point, σ2 is the phenotypic variance, and a/σ and b/σ2 are the standardized differences in mean and variance respectively between genotypes at the locus. Assuming additive effects on mean and variance across loci, the response to selection on phenotype in mean is iσAm2/σ+½ixcovAmv/σ2 and in variance is icovAmv/σ+½ixσ2Av/σ2, where σAm2 is the (usual) additive genetic variance of effects of genes on the mean, σ2Av is the corresponding additive genetic variance of their effects on the variance, and covAmv is the additive genetic covariance of their effects. Changes in variance also have to be corrected for any changes due to gene frequency change and for the Bulmer effect, and relevant formulae are given. It is shown that effects on variance are likely to be greatest when selection is intense and when selection is on individual phenotype or within family deviation rather than on family mean performance. The evidence for and implications of such variability in variance are discussed.

scholarly journals Estimation of genetic parameters and relationships between type traits and milk yield of Holstein cows in Turkey

2021 ◽  
Vol 73 (6) ◽  
pp. 1371-1380
Author(s):  
O. Ermetin ◽  
B. Dağ
Keyword(s):  
Milk Yield ◽  
Selection Criteria ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Scoring Systems ◽  
Holstein Cows ◽  
Phenotypic Correlations ◽  
Type Traits ◽  
Final Score ◽  
Selection For

ABSTRACT In this study, milk yield, reproductive yield, and type traits of 533 Holstein cows in the first lactation raised in 54 farms were examined. In the three-year study, phenotypic (rP) and genetic (rG) correlations between type traits and milk yield were estimated based on the variance elements and heritability of the type traits of Holstein cows in the first lactation. Linear identification and scoring systems have been applied to classify the cows according to type traits. Heritability and correlations were estimated with ASREML models. The type traits included stature, angularity, rump width, hocks, rear udder height, central ligament, teat length, body capacity, feet and legs, udder composite and final score for genetic correlations with 305-day milk yield were estimated as -0.49, -0.14, -0.93, 0.35, 0.40, 0.11, -0.65, 0.70, 0.31, 0.54, and 0.70, for phenotypic correlations were estimated as 0.28, 0.28, 0.30, 0.21, 0.35, 0.39, -0.06, 0.46, 0.48, 0.56, and 0.58 respectively. Among the phenotypic correlations between the type traits, especially the phenotypic correlations between the final score and various type traits were found to be high and significant. The fact that these traits are in high correlation with other traits and milk yield may enable these to be used as indirect selection criteria in the selection for milk yield.

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