Influence of maternal and additive genetic effects on offspring growth traits in Beetal goat

2021 ◽  
Author(s):  
Ankit Magotra ◽  
Yogesh C. Bangar ◽  
Ashish Chauhan ◽  
B.S. Malik ◽  
Z.S. Malik
Keyword(s):  
Growth Traits ◽  
Genetic Effects ◽  
Offspring Growth ◽  
Additive Genetic Effects ◽  
Beetal Goat

Estimates of genetic parameters for growth traits of Gobra cattle

1998 ◽  
Vol 66 (2) ◽  
pp. 349-355 ◽  
Author(s):  
M. Diop ◽  
L. D. Van Vleck
Keyword(s):  
Animal Models ◽  
Maternal Effects ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Random Effect ◽  
Random Effect Model ◽  
Genetic Effects ◽  
Breeding Value ◽  
Effect Model ◽  
Additive Genetic Effects

AbstractEstimates of (co)variance components and genetic parameters were obtained for birth (no. = 3909), weaning (no. = 3425), yearling (no. = 2763), and final weight (no. = 2142) for Gobra cattle at the Centre de Recherches Zootechniques de Dahra (Senegal), using single trait animal models. Data were analysed by restricted maximum likelihood. Four different animal models were fitted for each trait. Model 1 considered the animal as the only random effect. Model 2 included in addition to the additive direct effect of the animal, the environmental effect due to the dam. Model 3 added the maternal additive genetic effects and allowed a covariance between the direct and maternal genetic effects. Model 4 fitted both maternal genetic and permanent environmental effects. Inclusion of both types of maternal effects (genetic and environmental) provided a better fit for birth and weaning weights than models with one maternal effect only. For yearling and final weights, the improvement was not significant. Important maternal effects werefound for all traits. Estimates of direct heritabilities were substantially higher when maternal effects were ignored. Estimates of direct and maternal heritabilities with model 4 were 0·07 (s.e. 0·03) and 0·04 (s.e. 0·02), 0·20 (s.e. 0·05) and 0·21 (s.e. 0.05), 0·24 (s.e. 0·07) and 0·21 (s.e. 0·06), and 0·14 (s.e. 0·06) and 0.16 (s.e. 0·06) for birth, weaning, yearling and final weights, respectively. Correlations between direct and maternal genetic effects were negative for all traits, and large for weaning and yearling weights with estimates of -0·61 (s.e. 0·33) and -0·50 (s.e. 0·31), respectively. There was a significant positive linear phenotypic trend for weaning and yearling weights. Linear trends for additive direct and maternal breeding values were not significant for any trait except maternal breeding value for yearling weight.

32 A Bayesian Hierarchical Model to Integrate Growth Models into Genomic Evaluation of Pigs

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 18-19
Author(s):  
Haipeng Yu ◽  
Jaap Milgen ◽  
Egbert Knol ◽  
Rohan Fernando ◽  
Jack C Dekkers
Keyword(s):  
Genomic Prediction ◽  
Genetic Improvement ◽  
Growth Traits ◽  
Average Daily Gain ◽  
Genetic Effects ◽  
Latent Growth ◽  
Bayesian Hierarchical ◽  
Additive Genetic Effects ◽  
Daily Gain ◽  
Genomic Predictions

Abstract Genomic prediction has advanced genetic improvement by enabling more accurate estimates of breeding values at an early age. Although genomic prediction is efficient in predicting traits dominated by additive genetic effects within common settings, prediction in the presence of non-additive genetic effects and genotype by environmental interactions (GxE) remains a challenge. Previous studies have attempted to address these challenges by statistical modeling, while the augmentation of statistical models with biological information has received relatively little attention. A pig growth model assumes growth performance is a nonlinear functional interaction between the animal’s genetic potential for underlying latent growth traits and environmental factors and has the potential to capture GxE and non-additive genetic effects. The objective of this study was to integrate a nonlinear stable Gompertz function of three latent growth traits and age into genomic prediction models using Bayesian hierarchical modeling. The three latent growth traits were modeled as a linear combination of systematic environmental, marker, and residual effects. The model was applied to daily body weight data from ~83 to ~186 days of age on 4,039 purebred boars that were genotyped for 24K markers. Bias and prediction accuracy of genomic predictions of selection candidates were assessed by extending the linear regression method of predictions based on part and whole data to a non-linear setting. The accuracy (bias) of genomic predictions was 0.58 (0.82), 0.46 (0.90), 0.54 (0.78), and 0.60 (0.84) for the three latent growth traits and average daily gain derived from integrated nonlinear model, respectively, compared to 0.58 (0.87) for genomic predictions of average daily gain using standard linear models. In subsequent work, the growth model will be extended to include daily feed intake and carcass composition data. Resulting models are expected to substantially advance genetic improvement in pigs across environments. Funded by USDA-NIFA grant # 2020-67015-31031.

Combining Ability of Plant Height and Yield components in Indian Mustard (Brassica junceaL.Czern & Coss.) under Salt affected Soil using Line×Tester Analysis

2016 ◽  
Vol 3 (2) ◽  
Author(s):  
SHAILESH CHAND GAUTAM ◽  
MP Chauhan
Keyword(s):  
Plant Height ◽  
Seed Yield ◽  
Combining Ability ◽  
Yield Components ◽  
Indian Mustard ◽  
Heritability Estimate ◽  
Genetic Effects ◽  
Narrow Sense ◽  
Narrow Sense Heritability ◽  
Additive Genetic Effects

Line × tester analysis of twenty lines and three testers of Indian mustard (Brassica juncea L. Czern & Coss.) cultivars were used to estimate general combining ability (GCA), specific combining ability (SCA) effects, high parent heterosis and narrow-sense heritability estimate for plant height, yield components and seed yield. Significant variance of line x tester for the traits like pods per plant and seed yield indicating non additive genetic effects have important role for controlling these traits. Significant mean squares of parents v/s crosses which are indicating significant average heterosis were also significant for all the traits except seeds per pod. High narrow-sense heritability estimates for all the traits except seeds per pod exhibited the prime importance of additive genetic effects for these traits except seeds per pod. Most of the crosses with negative SCA effect for plant height had at least one parent with significant negative or negative GCA effect for this trait. For most of the traits except pods per plant, the efficiency of high parent heterosis effect was more than SCA effect for determining superior cross combinations.

scholarly journals Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Akio Onogi ◽  
Toshio Watanabe ◽  
Atsushi Ogino ◽  
Kazuhito Kurogi ◽  
Kenji Togashi
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Variance Components ◽  
Variance Component ◽  
Predictive Accuracy ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Additive Effects ◽  
Additive Variance ◽  
Additive Genetic Effects

Abstract Background Genomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes. Results Records of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6–19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size. Conclusions The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.

scholarly journals The expression of additive and nonadditive genetic variation under stress.

Genetics ◽  
1995 ◽  
Vol 140 (3) ◽  
pp. 1149-1159
Author(s):  
M W Blows ◽  
M B Sokolowski
Keyword(s):  
Genetic Variation ◽  
Development Time ◽  
Genetic Effects ◽  
Hybrid Breakdown ◽  
Bristle Number ◽  
Gene Complexes ◽  
General Expectation ◽  
Faster Development ◽  
Seven Generations ◽  
Additive Genetic Effects

Abstract Experimental lines of Drosophila melanogaster derived from a natural population, which had been isolated in the laboratory for approximately 70 generations, were crossed to determine if the expression of additive, dominance and epistatic genetic variation in development time and viability was associated with the environment. No association was found between the level of additive genetic effects and environmental value for either trait, but nonadditive genetic effects increased at both extremes of the environmental range for development time. The expression of high levels of dominance and epistatic genetic variation at environmental extremes may be a general expectation for some traits. The disruption of the epistatic gene complexes in the parental lines resulted in hybrid breakdown toward faster development and there was some indication of hybrid breakdown toward higher viability. A combination of genetic drift and natural selection had therefore resulted in different epistatic gene complexes being selected after approximately 70 generations from a common genetic base. After crossing, the hybrid populations were observed for 10 generations. Epistasis contributed on average 12 hr in development time. Fluctuating asymmetry in sternopleural bristle number also evolved in the hybrid populations, decreasing by > 18% in the first seven generations after hybridization.

scholarly journals Evidence of Shared Genome-Wide Additive Genetic Effects on Interpersonal Trauma Exposure and Generalized Vulnerability to Drug Dependence in a Population of Substance Users

2016 ◽  
Vol 29 (3) ◽  
pp. 197-204 ◽  
Author(s):  
Rohan H. C. Palmer ◽  
Nicole R. Nugent ◽  
Leslie A. Brick ◽  
Cinnamon L. Bidwell ◽  
John E. McGeary ◽  
...  
Keyword(s):  
Drug Dependence ◽  
Trauma Exposure ◽  
Interpersonal Trauma ◽  
Genetic Effects ◽  
Substance Users ◽  
Genome Wide ◽  
Additive Genetic Effects
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