scholarly journals Genetic components of birth weight of texel sheep reared in extensive system

2017 ◽  
Vol 40 ◽  
pp. 36481
Author(s):  
Fernando Amarilho-Silveira ◽  
Nelson José Laurino Dionello ◽  
Gilson De Mendonça ◽  
Jaqueline Freitas Motta ◽  
Tiago Albandes Fernandes ◽  
...  
Keyword(s):  
Birth Weight ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Likelihood Method ◽  
Phenotypic Variance ◽  
Genetic Trend ◽  
Extensive System ◽  
Genetic Components ◽  
Best Fit ◽  
Variance Explained

This study aimed to estimate the components of (co)variance, genetic and phenotypic parameters and trends for birth weight. We used 783 birth weight records, between 2012 to 2016, of Texel sheep reared in extensive system. The components of (co)variance and the genetic parameters were estimated using six different animal models, using the restricted maximum likelihood method (REML). The model that best fit the data was Model 3, with estimates of direct additive genetic variance of 0.004, maternal permanent environment variance of 0.164, heritability coefficient of 0.011 and phenotypic variation attributed to the maternal permanent environment of 0.394. For the genetic trend, we observed a genetic gain of 0.413% and for the phenotypic trend, a phenotypic gain of 0.159 kg, between 2012 and 2016 were found. Estimates of direct heritability and proportion of the phenotypic variance explained by the maternal permanent environment presented lower and higher values, respectively, in comparison to other studies. For trends, both genetic and phenotypic, there were gains in birth weight between 2012 and 2016. 

scholarly journals Combining ability for yield and yield attributes in rice (Oryza sativa l.) genotypes using CMS system

2014 ◽  
Vol 11 (1) ◽  
pp. 23-33 ◽  
Author(s):  
CL Sharma ◽  
NK Singh ◽  
AK Mall ◽  
K Kumar ◽  
ON Singh
Keyword(s):  
Combining Ability ◽  
Genetic Variance ◽  
Gene Action ◽  
Oryza Sativa L ◽  
Additive Genetic Variance ◽  
Hybrid Breeding ◽  
Male Sterile ◽  
Yield Attributes ◽  
Genetic Components ◽  
Per Se

Seventy five hybrids generated from crossing three cytoplasmic male sterile lines with 25 testers were studied along with parents for combining ability and gene action involved in expression of characters in rice. The GCA and SCA effects were significant for all the characters except seedling height, indicating the importance of both additive and non additive genetic components. The ratio of gca and sca variance was less than unity for all the characters also indicated preponderance of non additive genetic variance and suggested good prospects of the exploitation of variation through hybrid breeding. Amongst the parental lines, UPR-2080-24-1-R, IR-60076-1-R, PNR-165-10-6-R and IR-58025- A were found to be good general combiners which can be taken up to generate desirable segregates for further selection. None of the crosses showed significant sca effects for all the characters. On the basis of per se performance and high sca effects, IR-58025-A x CSRC-50-2-1-4-BR, PMS-10-A x IR-42688-2-118-6-3, RPMS-100-A x UPRI-92-79-R and NMS-4-A x IR-32419-28-3-1-3-R were good specific combiners for grain yield plant-1 and their components which could be used for exploitation of heterosis for yield. DOI: http://dx.doi.org/10.3329/sja.v11i1.18372 SAARC J. Agri., 11(1): 23-33 (2013)

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.

Genetic and environmental causes of variation in birth weight of Black Bengal goats

1970 ◽  
Vol 74 (3) ◽  
pp. 409-414 ◽  
Author(s):  
S. K. Moulick ◽  
O. Syrstad
Keyword(s):  
Body Weight ◽  
Birth Weight ◽  
Litter Size ◽  
Partial Correlation ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Maternal Environment ◽  
Breeding Station ◽  
Environmental Causes ◽  
Partial Correlation Coefficient

SUMMARYAn investigation on the different environmental and genetic causes of variation in the birth weight of Black Bengal goats was conducted at the Central Livestock Research-cum-Breeding Station, Haringhata, India. The data consisted of 1375 birth weight records of kids from 284 does and 20 bucks during the period from 1955 to 1961. The goats were maintained under standard farm management throughout the period.Year had significant effect on birth weight, while the effect of season was insignificant. The interaction was, however, significant. Male kids were significantly heavier at birth than the females. Age of dam and litter size also caused significant variation in birth weight of kids.From paternal half-sib analysis the heritability of birth weight was estimated to be 0·01. Full sib and maternal half sib analyses estimated the maternal environment common to litter mates to account for 60 % of the variance, out of which 25 % were due to permanent differences between dams. The remaining 39 % were attributed to individual environment, including most of the non-additive genetic variance. The heritability of maternal environment was estimated to be 0·2.The partial correlation coefficient between birth weight of kids and post-kidding body weight of their dam, independent of litter size and age of dam, was 0·175. Thus, body size of dam, as indicated by post kidding body weight, did not reveal much information about maternal environment.

scholarly journals ESTİMATİON OF GENETİC AND ENVİRONMENTAL PARAMETERS AFFECTİNG PRODUCTİVİTY İN MORKARAMAN SHEEP AND ECONOMİC EVALUATİON OF PARAMETERS

2021 ◽  
pp. 155-161
Author(s):  
Ufuk Karadavut ◽  
Burhan Bahadır ◽  
Volkan Karadavut ◽  
Galip Şimşek ◽  
Hakan İnci
Keyword(s):  
Genetic Parameters ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Environmental Parameters ◽  
Error Variance ◽  
Economic Importance ◽  
Phenotypic Variance ◽  
Economic Aspects ◽  
Evaluation Of Parameters ◽  
The Relationship

This study was carried out to protect the continuity of productivity in morkaraman sheep raised in Turkey and determine their economic importance. Morkaraman sheep are concentrated in the Eastern Regions of the country. The province of Bingöl, where the study was conducted, is located in this region and has an important morkaraman population. The study was carried out between 2008-2018. Sixty-eight morkaraman sheep were used during the study period out of 317 lambing lambs. In the study, the total number of lambs born per sheep (TNLBS), the number of weaned lambs (NWL), the weights of the lambs weaned per sheep (WLWS) and the total weight of the lambs weaned in the first period (TWLWFP) were determined. In addition, Additive genetic variance, Error variance, Phenotypic variance, Heritability and Ratio of error variation were determined for these variables. As a result, the correlation between the examined variables was significant and positive, except for the relationship between TNLBS and TWLWFP. The relationship between these two variables was significant but negative. Significant changes were also observed in terms of genetic parameters. It was concluded that the economic aspects of the examined variables should not be ignored in terms of sustainability. Keywords: Sheep, morkaraman, sustainability, genotypic and phenotypic variance.

scholarly journals A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance

2018 ◽  
Author(s):  
Caroline E. Thomson ◽  
Isabel S. Winney ◽  
Oceane C. Salles ◽  
Benoit Pujol
Keyword(s):  
Animal Model ◽  
Genetic Variance ◽  
Genetic Relatedness ◽  
Additive Genetic Variance ◽  
Phenotypic Traits ◽  
Phenotypic Variance ◽  
Data Types ◽  
Evolutionary Potential ◽  
Recent Developments

AbstractNon-genetic influences on phenotypic traits can affect our interpretation of genetic variance and the evolutionary potential of populations to respond to selection, with consequences for our ability to predict the outcomes of selection. Long-term population surveys and experiments have shown that quantitative genetic estimates are influenced by nongenetic effects, including shared environmental effects, epigenetic effects, and social interactions. Recent developments to the “animal model” of quantitative genetics can now allow us to calculate precise individual-based measures of non-genetic phenotypic variance. These models can be applied to a much broader range of contexts and data types than used previously, with the potential to greatly expand our understanding of nongenetic effects on evolutionary potential. Here, we provide the first practical guide for researchers interested in distinguishing between genetic and nongenetic causes of phenotypic variation in the animal model. The methods use matrices describing individual similarity in nongenetic effects, analogous to the additive genetic relatedness matrix. In a simulation of various phenotypic traits, accounting for environmental, epigenetic, or cultural resemblance between individuals reduced estimates of additive genetic variance, changing the interpretation of evolutionary potential. These variances were estimable for both direct and parental nongenetic variances. Our tutorial outlines an easy way to account for these effects in both wild and experimental populations. These models have the potential to add to our understanding of the effects of genetic and nongenetic effects on evolutionary potential. This should be of interest both to those studying heritability, and those who wish to understand nongenetic variance.

Estimation of additive and dominance genetic variance components for female fertility traits in Iranian Holstein cows

2018 ◽  
Vol 156 (4) ◽  
pp. 565-569
Author(s):  
H. Ghiasi ◽  
R. Abdollahi-Arpanahi ◽  
M. Razmkabir ◽  
M. Khaldari ◽  
R. Taherkhani
Keyword(s):  
Dairy Cows ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Effects ◽  
Information Criteria ◽  
Phenotypic Variance ◽  
Dominance Variance ◽  
Dominance Genetic Variance ◽  
Estimated Heritability

AbstractThe aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike's information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman's rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.

scholarly journals Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Animals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1055 ◽  
Author(s):  
Ying Liu ◽  
Lei Xu ◽  
Zezhao Wang ◽  
Ling Xu ◽  
Yan Chen ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Genetic Variance ◽  
Association Studies ◽  
Additive Genetic Variance ◽  
Additive Models ◽  
Carcass Weight ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies

Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

scholarly journals Combining Ability and Gene Action in Cucumber (Cucumis Sativus L.)

2018 ◽  
Vol 5 (4) ◽  
pp. 145
Author(s):  
S. Ahammed ◽  
M. M. Hossain ◽  
M. Zakaria ◽  
B. Ahmed ◽  
M. A. K. Mian
Keyword(s):  
Combining Ability ◽  
Genetic Variance ◽  
Gene Action ◽  
Additive Genetic Variance ◽  
Cucumis Sativus L ◽  
Effective Utilization ◽  
Qualitative And Quantitative ◽  
Genetic Components ◽  
Additive Gene

Combining ability and genetic components of eleven inbred line of cucumber were estimated following line x tester mating design for qualitative and quantitative characters. Three inbred lines were used as tester. Variance within the treatments, parents, parent vs crosses, crosses, testers and line x tester interaction were highly significant for all the characters. Considering the gca effects the lines CS08, CS16, CS040, CS07 and CS51 were best for their earliness and other horticulture traits. The hybrids CS07×CS08, CS16×CS44, CS51×CS44, CS40×CS08, CS17×CS39 were superior in terms of yield per plant and its component characters. The magnitude of σ2SCA was high in all characters compared to σ2GCA and dominance variance (σ2D) was higher than the additive genetic variance (σ2A) indicating that the predominance role of non-additive gene action. The results indicated the importance of heterosis breeding for effective utilization of non-additive genetic variance in cucumber.

The Changes in Genetic and Environmental Variance With Inbreeding in Drosophila melanogaster

Genetics ◽  
1999 ◽  
Vol 152 (1) ◽  
pp. 345-353 ◽  
Author(s):  
Michael C Whitlock ◽  
Kevin Fowler
Keyword(s):  
Drosophila Melanogaster ◽  
Large Scale ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Inbred Lines ◽  
Residual Variance ◽  
Phenotypic Variance ◽  
Population Bottlenecks ◽  
Environmental Variance ◽  
Components Of Variance

Abstract We performed a large-scale experiment on the effects of inbreeding and population bottlenecks on the additive genetic and environmental variance for morphological traits in Drosophila melanogaster. Fifty-two inbred lines were created from the progeny of single pairs, and 90 parent-offspring families on average were measured in each of these lines for six wing size and shape traits, as well as 1945 families from the outbred population from which the lines were derived. The amount of additive genetic variance has been observed to increase after such population bottlenecks in other studies; in contrast here the mean change in additive genetic variance was in very good agreement with classical additive theory, decreasing proportionally to the inbreeding coefficient of the lines. The residual, probably environmental, variance increased on average after inbreeding. Both components of variance were highly variable among inbred lines, with increases and decreases recorded for both. The variance among lines in the residual variance provides some evidence for a genetic basis of developmental stability. Changes in the phenotypic variance of these traits are largely due to changes in the genetic variance.

scholarly journals Estimation of Additive and Dominance Genetic Effects on Body Weight, Carcass and Ham Quality Traits in Heavy Pigs

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 481
Author(s):  
Valentina Bonfatti ◽  
Roberta Rostellato ◽  
Paolo Carnier
Keyword(s):  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Model Fitting ◽  
Genetic Effects ◽  
Phenotypic Variance ◽  
Genetic Response ◽  
Dominance Variance ◽  
Dry Cured Ham ◽  
Genetic Evaluations

Neglecting dominance effects in genetic evaluations may overestimate the predicted genetic response achievable by a breeding program. Additive and dominance genetic effects were estimated by pedigree-based models for growth, carcass, fresh ham and dry-cured ham seasoning traits in 13,295 crossbred heavy pigs. Variance components estimated by models including litter effects, dominance effects, or both, were compared. Across traits, dominance variance contributed up to 26% of the phenotypic variance and was, on average, 22% of the additive genetic variance. The inclusion of litter, dominance, or both these effects in models reduced the estimated heritability by 9% on average. Confounding was observed among litter, additive genetic and dominance effects. Model fitting improved for models including either the litter or dominance effects, but it did not benefit from the inclusion of both. For 15 traits, model fitting slightly improved when dominance effects were included in place of litter effects, but no effects on animal ranking and accuracy of breeding values were detected. Accounting for litter effects in the models for genetic evaluations would be sufficient to prevent the overestimation of the genetic variance while ensuring computational efficiency.

Sign in / Sign up

Export Citation Format

Share Document