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 note on genetic parameters and accuracy of estimated breeding values in honey bees

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Evert W. Brascamp ◽  
Piter Bijma
Keyword(s):  
Honey Bees ◽  
Variance Components ◽  
Genetic Parameters ◽  
Additive Genetic Variance ◽  
Breeding Value ◽  
Phenotypic Variance ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Additive Genetic Relationship ◽  
The Relationship

Abstract Background In honey bees, observations are usually made on colonies. The phenotype of a colony is affected by the average breeding value for the worker effect of the thousands of workers in the colony (the worker group) and by the breeding value for the queen effect of the queen of the colony. Because the worker group consists of multiple individuals, interpretation of the variance components and heritabilities of phenotypes observed on the colony and of the accuracy of selection is not straightforward. The additive genetic variance among worker groups depends on the additive genetic relationship between the drone-producing queens (DPQ) that produce the drones that mate with the queen. Results Here, we clarify how the relatedness between DPQ affects phenotypic variance, heritability and accuracy of the estimated breeding values of replacement queens. Second, we use simulation to investigate the effect of assumptions about the relatedness between DPQ in the base population on estimates of genetic parameters. Relatedness between DPQ in the base generation may differ considerably between populations because of their history. Conclusions Our results show that estimates of (co)variance components and derived genetic parameters were seriously biased (25% too high or too low) when assumptions on the relationship between DPQ in the statistical analysis did not agree with reality.

scholarly journals Relationships among growth and different NOR phenotypes in a specific stock of rainbow trout (Oncorhynchus mykiss)

2007 ◽  
Vol 67 (2) ◽  
pp. 355-361 ◽  
Author(s):  
F. Porto-Foresti ◽  
C. Oliveira ◽  
YA. Tabata ◽  
MG. Rigolino ◽  
F. Foresti
Keyword(s):  
Rainbow Trout ◽  
Oncorhynchus Mykiss ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Statistical Test ◽  
Allometric Growth ◽  
Rainbow Trout Oncorhynchus Mykiss ◽  
Cultured Fish ◽  
The Relationship

Growth is one of the most important aspects in the genetic improvement of cultured fish species. Consequently, genetic parameters related to this feature and their response to selection have been the focus of most research in this area. Such research indicates that, in general, there is enough additive genetic variance related to growth, justifying the use of selection. Based on the usefulness of cytogenetic and molecular markers in the fish culture, the aim of the present work was to analyze the possible relationships among cytogenetic characteristics, specifically the NOR phenotypes, and the increase in length and weight in specimens of the rainbow trout (Oncorhynchus mykiss), resultant from directed mating between homozygous females and heterozygous males according to their NOR phenotypic patterns. The equations of the relationship between length and weight of the analyzed specimens followed the model Wt = a Lt b, showing b values higher than 3, determinant of a positive allometric growth. The results showed that the different NOR phenotypes were not related with the growth values for length and weight in any statistical test.

scholarly journals Sapling and coppice biomass heritabilities and potential gains from Eucalyptus polybractea progeny trials

2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Beren Spencer ◽  
Richard Mazanec ◽  
Mark Gibberd ◽  
Ayalsew Zerihun
Keyword(s):  
Western Australia ◽  
Growth Rates ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Short Rotation ◽  
Sapling Growth ◽  
Genetic And Phenotypic Correlations ◽  
Breeding Selections ◽  
Cross Site

AbstractEucalyptus polybractea has been planted as a short-rotation coppice crop for bioenergy in Western Australia. Historical breeding selections were based on sapling biomass and despite a long history as a coppice crop, the genetic parameters of coppicing are unknown. Here, we assessed sapling biomass at ages 3 and 6 from three progeny trials across southern Australia. After the second sapling assessment, all trees were harvested. Coppice biomass was assessed 3.5 years later. Mortality following harvest was between 1 and 2%. Additive genetic variance for the 6-sapling estimate at one site was not significant. Sapling heritabilities were between 0.06 and 0.36 at 3 years, and 0.18 and 0.20 at 6 years. The heritability for the coppice biomass was between 0.07 and 0.17. Within-site genetic and phenotypic correlations were strong between all biomass assessments. Cross-site correlations were not different from unity. Selections based on net breeding values revealed positive gains in sapling and coppice biomass. Lower or negative gains were estimated if 3-year sapling selections were applied to the coppice assessments (−7.1% to 3.4%) with useful families culled. Positive gains were obtained if 6-year sapling selections were applied to the coppice assessment (6.4% to 9.3%) but these were lower than those obtained by applying coppice selections to the coppice assessment (8.4% to 14.8%). Removal of poor performing families and families that displayed fast sapling growth rates but under-performed as coppice will benefit potential coppice production. These results indicate that selections should be made using coppice data.

scholarly journals Estimate of genetic parameters and predicted gains with early selection of physic nut families

2012 ◽  
Vol 36 (2) ◽  
pp. 163-170 ◽  
Author(s):  
Bruno Galvêas Laviola ◽  
Alexandre Alonso Alves ◽  
Fábio de Lima Gurgel ◽  
Tatiana Barbosa Rosado ◽  
Rhayanne Dias Costa ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Biodiesel Production ◽  
Early Selection ◽  
Physic Nut ◽  
Genetic Gains ◽  
Canopy Volume ◽  
Selection Of

An initial evaluation of early selection of physic nut genotypes based on phenotypic data is presented. In order to predict the genetic gains with early selection, genetic parameters, e.g. additive genetic variance, were first obtained for grain yield along with other numerous traits. The results demonstrated that additive genetic variance exists not only for grain yield, which is considered to be the most important trait for oil and biodiesel production, but also for numerous other traits. The predicted genetic gains for grain yield, considering the selection of the 30, 20, 10 and 5 best families in the second crop year are respectively, 40.47, 48.43, 61.78 and 70.28%. With the selection of highly yielding physic nut genotypes indirectly genotypes with enhanced volume would be also selected, because yield exhibits moderate to high genetic correlations with height e canopy volume. The results here presented demonstrate the potential of the population gathered in the Brazilian physic nut germplasm bank for genetic breeding purposes and that superior physic nut families can be selected with high accuracy based on the evaluation of its second crop.

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 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.

scholarly journals Growth curves and genetic parameters in Nelore animals estimated by random regression models

2019 ◽  
Vol 40 (2) ◽  
pp. 935
Author(s):  
Maurício Vargas da Silveira ◽  
Júlio César de Souza ◽  
Tássia Souza Bertipaglia ◽  
Paulo Bahiense Ferraz Filho ◽  
Mariana Alencar Pereira ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Growth Curves ◽  
Random Regression ◽  
Birth Season ◽  
Average Growth ◽  
Mato Grosso ◽  
Production Region ◽  
Quadratic Effects

The objective of this work was to estimate growth curves and genetic parameters from birth to 650 days of age of Nelore cattle raised in pasture in two production regions of the Mato Grosso do Sul State, Brazil (233,835 weight records from 47,459 cattle were analyzed). Genetic parameters were determined by random regression using Legendre orthogonal polynomials of cubic order, and age at weighing was considered in the model as a fixed effect to model the average growth trajectory. In the models, the effects of the contemporary group were considered as fixed and, as covariates, the animal age at weighing and the cow age at calving were nested in the animal age class (linear and quadratic effects), forming eight age classes. All models included the direct genetic additive, maternal genetic, and animal permanent environment as random effects, and the most appropriate model to describe the studied effects was defined according to the AIC and BIC criteria. Heritability estimates for birth weight varied between the two production regions, Campo Grande-Dourados (R1) and Alto Taquari-Bolsão (R2) and R1 (0.36 ± 0.02) and R2 (0.28 ± 0.03), and there were variations in the estimates at advanced ages. In both regions, the highest heritability values at 650 days of age were 0.47 ± 0.03 and 0.65 ± 0.02 for R1 and R2, respectively, with high heritability reflecting the high values of additive genetic variance. The random regression methodology was efficient in estimating growth curves and genetic parameters. Growth curves were different when they were estimated separately by sex, birth season, and production region. Genetic parameters estimated separately by region indicate differences in additive genetic variance, maternal additive, and animal permanent environment for weights up to 650 days of age.

QUANTITATIVE GENETICS OF DROSOPHILA MELANOGASTER. I. SEXUAL DIMORPHISM IN GENETIC PARAMETERS FOR WING TRAITS

Genetics ◽  
1986 ◽  
Vol 114 (2) ◽  
pp. 549-566
Author(s):  
David E Cowley ◽  
William R Atchley ◽  
J J Rutledge
Keyword(s):  
Drosophila Melanogaster ◽  
Sexual Dimorphism ◽  
Dosage Compensation ◽  
Genetic Parameters ◽  
Genetic Model ◽  
Genetic Correlations ◽  
Error Variance ◽  
Minor Effect ◽  
Phenotypic Variance ◽  
Environmental Variance

ABSTRACT Sexual dimorphism in genetic parameters is examined for wing dimensions of Drosophila melanogaster. Data are fit to a quantitative genetic model where phenotypic variance is a linear function of additive genetic autosomal variance (common to both sexes), additive genetic X-linked variances distinct for each sex, variance due to common rearing environment of families, residual environmental variance, random error variance due to replication, and variance due to measurement error and developmental asymmetry (left vs. right sides). Polygenic dosage compensation and its effect on genetic variances and covariances between sexes is discussed. Variance estimates for wing length and other wing dimensions highly correlated with length support the hypothesis that the Drosophila system of dosage compensation will cause male X-linked genetic variance to be substantially larger than female X-linked variance. Results for various wing dimensions differ, suggesting that the level of dosage compensation may differ for different traits. Genetic correlations between sexes for the same trait are presented. Total additive genetic correlations are near unity for most wing traits; this indicates that selection in the same direction in both sexes would have a minor effect on changing the magnitude of difference between sexes. Additive X-linked correlations suggest some genotype × sex interactions for X-linked effects.

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.

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