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.

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 dissection of maternal, additive and non-additive genetic effects for growth and carcass traits in Nile tilapia

2019 ◽  
Author(s):  
Rajesh Joshi ◽  
John Woolliams ◽  
Theodorus Meuwissen ◽  
Hans Magnus Gjøen
Keyword(s):  
Inbreeding Depression ◽  
Nile Tilapia ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genomic Data ◽  
Genetic Effects ◽  
Phenotypic Variance ◽  
Breeding Schemes ◽  
Additive Genetic Effects ◽  
The Impact

AbstractBackgroundThe availability of both pedigree and genomic sources of information for animal breeding and genetics has created new challenges in understanding how best they may be utilized and how they may be interpreted. This study computed the variance components obtained using genomic information and compared these to the variances obtained using pedigree in a population generated to estimate non-additive genetic variance. Further, the impact of assumptions concerning Hardy-Weinberg Equilibrium (HWE) on the component estimates was examined. The magnitude of inbreeding depression for important commercial traits in Nile tilapia was estimated for the first time, here using genomic data.ResultsThe non-additive genetic variance in a Nile tilapia population was estimated from fullsib families and, where present, was found to be almost entirely additive by additive epistatic variance, although in pedigree studies this source is commonly assumed to arise from dominance. For body depth (BD) and body weight at harvest (BWH), the estimates of the additive by additive epistatic ratio (P<0.05) were found to be 0.15 and 0.17 in the current breeding population using genomic data. In addition, we found maternal variance (P<0.05) for BD, BWH, body length (BL) and fillet weight (FW), explaining approximately 10% of the observed phenotypic variance, which are comparable to the pedigree-based estimates. This study also disclosed detrimental effects of inbreeding in commercial traits of tilapia, which were estimated to cause 1.1%, 0.9%, 0.4% and 0.3% decrease in the trait value with 1% increase in the individual homozygosity for FW, BWH, BD and BL, respectively. The inbreeding depression and lack of dominance variance was consistent with an infinitesimal dominance modelConclusionsAn eventual utilisation of non-additive genetic effects in breeding schemes is not evident or straightforward from our findings, but inbreeding depression suggests for cross-breeding, although commercially this conclusion will depend on cost structures. However, the creation of maternal lines in Tilapia breeding schemes may be a possibility if this variation is found to be heritable.

ESTIMATING HERITABILITY OF FAMILIES DERIVED FROM SINGLE PLANTS AT AN ADVANCED GENERATION OF SELF-FERTILIZING SPECIES: DEVELOPING GENERAL FORMULAS AND ESTIMATING SPRING WHEAT TRAITS

1998 ◽  
Vol 46 (3) ◽  
pp. 209-212
Author(s):  
Alex Beharav ◽  
Moshe Pinthus J. ◽  
Avigdor Cahaner
Keyword(s):  
Spring Wheat ◽  
Variance Components ◽  
Genetic Variance ◽  
Heading Date ◽  
Phenotypic Variance ◽  
Additive Variance ◽  
Dominance Variance ◽  
Total Genetic Variance ◽  
Two Populations ◽  
Advanced Generation

Genetic expectations of total genetic variance, and between-family and within-family variance components were developed for any given generation (Fn) derived from single selfed plants of an earlier generation (Fk). A formula to estimate the heritability (h2) in any desired generation (Fn) was developed on the basis of these expectations. This formula estimates the value of the genetic variance from the phenotypic variance adjusted to the F2 generation. Heritability estimates of culm length, heading date, and mean grain weight from two populations of F6 families, each derived from a single F5 plant, were computed using this formula, and a formula which estimates the value of the genetic variance from the phenotypic variance in the Fn generation (“Fn estimates”). The FN h2 estimates at F6 were always higher than those adjusted to F2 variance, due to the increase in additive variance and the reduction in dominance variance.

scholarly journals SNP-based mate allocation strategies to maximize total genetic value in pigs

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
David González-Diéguez ◽  
Llibertat Tusell ◽  
Céline Carillier-Jacquin ◽  
Alban Bouquet ◽  
Zulma G. Vitezica
Keyword(s):  
Inbreeding Depression ◽  
Genetic Gain ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Effects ◽  
Breeding Values ◽  
Genetic Merit ◽  
Additive Genetic Effects ◽  
Allocation Strategies

Abstract Background Mate allocation strategies that account for non-additive genetic effects can be used to maximize the overall genetic merit of future offspring. Accounting for dominance effects in genetic evaluations is easier in a genomic context, than in a classical pedigree-based context because the combinations of alleles at loci are known. The objective of our study was two-fold. First, dominance variance components were estimated for age at 100 kg (AGE), backfat depth (BD) at 140 days, and for average piglet weight at birth within litter (APWL). Second, the efficiency of mate allocation strategies that account for dominance and inbreeding depression to maximize the overall genetic merit of future offspring was explored. Results Genetic variance components were estimated using genomic models that included inbreeding depression with and without non-additive genetic effects (dominance). Models that included dominance effects did not fit the data better than the genomic additive model. Estimates of dominance variances, expressed as a percentage of additive genetic variance, were 20, 11, and 12% for AGE, BD, and APWL, respectively. Estimates of additive and dominance single nucleotide polymorphism effects were retrieved from the genetic variance component estimates and used to predict the outcome of matings in terms of total genetic and breeding values. Maximizing total genetic values instead of breeding values in matings gave the progeny an average advantage of − 0.79 days, − 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE. Conclusions Genomic mate allocation accounting for non-additive genetic effects is a feasible and potential strategy to improve the performance of the offspring without dramatically compromising additive genetic gain.

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.

What contributes to differences in phenotypic variation between generations?

2009 ◽  
Vol 2009 ◽  
pp. 200-200
Author(s):  
A Wolc ◽  
I White ◽  
M Lisowski ◽  
W G Hill
Keyword(s):  
Animal Model ◽  
Environmental Effects ◽  
Environmental Conditions ◽  
Phenotypic Variation ◽  
Variance Components ◽  
Genetic Variance ◽  
Phenotypic Variance ◽  
Base Population ◽  
Substantial Heterogeneity ◽  
Over Time

Under the animal model genetic variance is estimated in the base population taking into account inbreeding and is otherwise assumed to remain unchanged over generations. In practice, phenotypic variation differs randomly or systematically over time. Intuitively, such changes would be attributed mostly to environmental effects, and so lower heritability would be expected when variation is inflated. Studies in dairy cattle show contradictory results (e.g. Boldman and Freeman, 1990). Laying hens are kept under environmental conditions intended to be constant, but show substantial heterogeneity in phenotypic variance (VP) over generations. The aim was to investigate how variance components 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.

Comparison of F2 and F∞ diallel analyses in barley

Genome ◽  
1988 ◽  
Vol 30 (6) ◽  
pp. 865-869 ◽  
Author(s):  
T. M. Choo ◽  
E. Reinbergs ◽  
P. Y. Jui
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Plant Height ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Heading Date ◽  
Diallel Analysis ◽  
Hordeum Vulgare L ◽  
Hill Plots

A study was conducted in barley (Hordeum vulgare L.) to compare the relative magnitudes of heterosis to additive × additive epistasis and to compare F2 and F∞, diallel analyses. Both F2 and F∞, progenies were derived from 7 × 7 diallel crosses. Progenies and their parents were evaluated for grain yield, heading date, plant height, and the number of spikes per hill in hill plots with five replications at Elora (Ontario) in 1978. Results suggested that additive × additive epistasis were present for these traits and its magnitude was similar to that of heterosis estimated in F2. Both F2 and F∞ analyses detected the presence of epistasis. Both analyses provided similar estimates of the additive genetic variance for heading date and the number of spikes per hill, but the F2 analysis provided higher estimates than the F∞ analysis for grain yield and plant height. The estimate for grain yield and plant height obtained from the F2 analysis could be biased upward because of the invalid assumption of no epistasis. Estimates of other genetic variance components from the F2 analysis could be biased also. The F∞ diallel analysis not only provided estimates of additive × additive genetic variance for the four traits, it also allowed detection of nonindependent gene distribution in the parents for three of the four traits. Therefore, the limitations of the F2 diallel analysis in the presence of epistasis were apparent in the study. The F2 diallel analysis, however, could be used to detect dominance and maternal effects and thus to complement the F∞ diallel analysisKey words: barley, Hordeum vulgare, diallels, haploids, epistasis, heterosis.

scholarly journals Estimation of additive, dominance and epistatic variance components using finite locus models implemented with a single-site Gibbs and a descent graph sampler

2000 ◽  
Vol 76 (2) ◽  
pp. 187-198 ◽  
Author(s):  
F.-X. DU ◽  
I. HOESCHELE
Keyword(s):  
Variance Components ◽  
Additive Genetic Variance ◽  
Genetic Parameter ◽  
Genetic Effects ◽  
Single Site ◽  
Phenotypic Data ◽  
Variance Components Estimation ◽  
Locus Number ◽  
Variance Estimates ◽  
Finite Locus

In a previous contribution, we implemented a finite locus model (FLM) for estimating additive and dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we extended the FLM to include two-locus epistasis, and implemented the analysis with two genotype samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and variable across loci, uniform and constant across loci, and normal). Phenotypic data were simulated for two pedigrees with 6300 and 12300 individuals in closed populations, using several different, non-additive genetic models. Replications of these data were analysed with FLMs differing in the number of loci. Simulation results indicate that the dependency of non-additive genetic variance estimates on locus number persisted in all implementation strategies we investigated. However, this dependency was considerably diminished with normal priors for genetic effects as compared with uniform priors (constant or variable across loci). Descent graph sampling of genotypes modestly improved variance components estimation compared with Gibbs sampling. Moreover, a larger pedigree produced considerably better variance components estimation, suggesting this dependency might originate from data insufficiency. As the FLM represents an appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion of polygenic background variation in QTL mapping analyses, further improvements are warranted and might be achieved via improvement of the sampler or treatment of the number of loci as an unknown.

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