scholarly journals Predicting the purebred-crossbred genetic correlation from the genetic variance components in the parental lines

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Pascal Duenk ◽  
Piter Bijma ◽  
Yvonne C. J. Wientjes ◽  
Mario P. L. Calus
Keyword(s):  
Genetic Correlation ◽  
Variance Components ◽  
Genetic Model ◽  
Allele Frequencies ◽  
Genetic Models ◽  
Upper And Lower Bounds ◽  
Additive Effects ◽  
Genetic Covariance ◽  
Parental Allele ◽  
Parental Lines

Abstract Background The genetic correlation between purebred and crossbred performance ($${r}_{pc}$$ r pc ) is an important parameter in pig and poultry breeding, because response to selection in crossbred performance depends on the value of $${r}_{pc}$$ r pc when selection is based on purebred (PB) performance. The value of $${r}_{pc}$$ r pc can be substantially lower than 1, which is partly due to differences in allele frequencies between parental lines when non-additive genetic effects are present. This relationship between $${r}_{pc}$$ r pc and parental allele frequencies suggests that $${r}_{pc}$$ r pc can be expressed as a function of genetic parameters for the trait in the parental lines. In this study, we derived expressions for $${r}_{pc}$$ r pc based on genetic variances within, and the genetic covariance between parental lines. It is important to note that the variance components used in our expressions are not the components that are typically estimated in empirical data. The expressions were derived for a genetic model with additive and dominance effects (D), and additive and epistatic additive-by-additive effects (EAA). We validated our expressions using simulations of purebred parental lines and their crosses, where the parental lines were either selected or not. Finally, using these simulations, we investigated the value of $${r}_{pc}$$ r pc for genetic models with both dominance and epistasis or with other types of epistasis, for which expressions could not be derived. Results Our simulations show that when non-additive effects are present, $${r}_{pc}$$ r pc decreases with increasing differences in allele frequencies between the parental lines. Genetic models that involve dominance result in lower values of $${r}_{pc}$$ r pc than genetic models that involve epistasis only. Using information of parental lines only, our expressions provide exact estimates of $${r}_{pc}$$ r pc for models D and EAA, and accurate upper and lower bounds of $${r}_{pc}$$ r pc for two other genetic models. Conclusion This work lays the foundation to enable estimation of $${r}_{pc}$$ r pc from information collected in PB parental lines only.

scholarly journals The Impact of Non-additive Effects on the Genetic Correlation Between Populations

2019 ◽  
Vol 10 (2) ◽  
pp. 783-795 ◽  
Author(s):  
Pascal Duenk ◽  
Piter Bijma ◽  
Mario P. L. Calus ◽  
Yvonne C. J. Wientjes ◽  
Julius H. J. van der Werf
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Genetic Model ◽  
Allele Frequencies ◽  
Additive Effects ◽  
Simulated Genotype ◽  
The Difference ◽  
Allelic Interaction ◽  
Additive Genetic Effects ◽  
The Impact

Average effects of alleles can show considerable differences between populations. The magnitude of these differences can be measured by the additive genetic correlation between populations (rg). This rg can be lower than one due to the presence of non-additive genetic effects together with differences in allele frequencies between populations. However, the relationship between the nature of non-additive effects, differences in allele frequencies, and the value of rg remains unclear, and was therefore the focus of this study. We simulated genotype data of two populations that have diverged under drift only, or under drift and selection, and we simulated traits where the genetic model and magnitude of non-additive effects were varied. Results showed that larger differences in allele frequencies and larger non-additive effects resulted in lower values of rg. In addition, we found that with epistasis, rg decreases with an increase of the number of interactions per locus. For both dominance and epistasis, we found that, when non-additive effects became extremely large, rg had a lower bound that was determined by the type of inter-allelic interaction, and the difference in allele frequencies between populations. Given that dominance variance is usually small, our results show that it is unlikely that true rg values lower than 0.80 are due to dominance effects alone. With realistic levels of epistasis, rg dropped as low as 0.45. These results may contribute to the understanding of differences in genetic expression of complex traits between populations, and may help in explaining the inefficiency of genomic trait prediction across populations.

scholarly journals The Heritability of Type D Personality by an Extended Twin-Pedigree Analysis in the Netherlands Twin Register

2020 ◽  
Author(s):  
Ruifang Li-Gao ◽  
Dorret I. Boomsma ◽  
Eco J. C. de Geus ◽  
Johan Denollet ◽  
Nina Kupper
Keyword(s):  
The Netherlands ◽  
Variance Components ◽  
Genetic Model ◽  
Additive Genetic Variance ◽  
Type D Personality ◽  
Continuous Measure ◽  
Genetic Covariance ◽  
Type D ◽  
Genetic Components ◽  
Increased Risk

Abstract Type D (Distressed) personality combines negative affectivity (NA) and social inhibition (SI) and is associated with an increased risk of cardiovascular disease. We aimed to (1) validate a new proxy based on the Achenbach System of Empirically Based Assessment (ASEBA) for Type D personality and its NA and SI subcomponents and (2) estimate the heritability of the Type D proxy in an extended twin-pedigree design in the Netherlands Twin Register (NTR). Proxies for the dichotomous Type D classification, and continuous NA, SI, and NAxSI (the continuous measure of Type D) scales were created based on 12 ASEBA items for 30,433 NTR participants (16,449 twins and 13,984 relatives from 11,106 pedigrees) and sources of variation were analyzed in the ‘Mendel’ software package. We estimated additive and non-additive genetic variance components, shared household and unique environmental variance components and ran bivariate models to estimate the genetic and non-genetic covariance between NA and SI. The Type D proxy showed good reliability and construct validity. The best fitting genetic model included additive and non-additive genetic effects with broad-sense heritabilities for NA, SI and NAxSI estimated at 49%, 50% and 49%, respectively. Household effects showed small contributions (4–9%) to the total phenotypic variation. The genetic correlation between NA and SI was .66 (reflecting both additive and non-additive genetic components). Thus, Type D personality and its NA and SI subcomponents are heritable, with a shared genetic basis for the two subcomponents.

scholarly journals Analysis of conditional genetic effects and variance components in developmental genetics.

Genetics ◽  
1995 ◽  
Vol 141 (4) ◽  
pp. 1633-1639 ◽  
Author(s):  
J Zhu
Keyword(s):  
Variance Components ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Genetic Model ◽  
Genetic Effects ◽  
Unbiased Estimation ◽  
Developmental Genetics ◽  
Additive Effects ◽  
Genotype X Environment ◽  
Worked Example

Abstract A genetic model with additive-dominance effects and genotype x environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t-1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects.

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 Using Genomics to Measure Phenomics: Repeatability of Bull Prolificacy in Multiple-bull Pastures

2021 ◽  
Author(s):  
Gary Bennett ◽  
John Keele ◽  
Larry Kuehn ◽  
Warren Snelling ◽  
Aaron Dickey ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Variance Components ◽  
Lower Cost ◽  
Predictive Ability ◽  
Allele Frequencies ◽  
Beef Production ◽  
Age Class ◽  
Scrotal Circumference ◽  
Beef Producers

Phenotypes are necessary for genomic evaluations and management. Sometimes genomics can be used to measure phenotypes when other methods are difficult or expensive. Prolificacy of bulls used in multiple-bull pastures for commercial beef production is an example. A retrospective study of 79 bulls aged 2-year-old and older used 141 times in 4-5 pastures across 4 years was used to estimate repeatability from variance components. Traits available before each season’s use were tested for predictive ability. Sires were matched to calves using individual genotypes and evaluating exclusions. A lower cost method of measuring prolificacy was simulated for 5 pastures using the bulls’ genotypes and pooled genotypes to estimate average allele frequencies of calves and of cows. Repeatability of prolificacy was 0.62 ± 0.09. A combination of age-class and scrotal circumference accounted for less than 5 % of variation. Simulated estimation of prolificacy by pooling DNA of calves was accurate. Adding pooling of cow DNA or actual genotypes both increased accuracy about the same. Knowing a bull’s prior prolificacy would help predict future prolificacy for management purposes and could be used in genomic evaluations and research with coordination of breeders and commercial beef producers.

Energy allocation strategies of young temperate fish: an eco-genetic modeling approach

2015 ◽  
Vol 72 (8) ◽  
pp. 1243-1258 ◽  
Author(s):  
Lori N. Ivan ◽  
Tomas O. Höök
Keyword(s):  
Genetic Model ◽  
Energy Allocation ◽  
Genetic Models ◽  
Stabilizing Selection ◽  
Density Dependent ◽  
Size Dependent ◽  
Selective Pressures ◽  
Adaptive Processes ◽  
Overwinter Mortality ◽  
Temperate Fish

We use an individual-based eco-genetic model to explore the relative selective pressures of size-dependent predation, overwinter mortality, and density-dependent energy acquisition in structuring plastic and adaptive energy allocation during the first year of life of a temperate fish population. While several patterns emerging from a suite of eco-genetic model simulations were consistent with past theoretical models and empirical evaluations of energy allocation by young fishes, results also highlight the utility of eco-genetic models for simultaneous consideration of plastic and adaptive processes. Across simulations, variation in genetic control of energy allocation was limited during very early ontogeny when size-dependent predation pressure was particularly high. While this stabilizing selection on energy allocation diminished later in the growing season, predation, overwinter mortality, and density-dependent processes simultaneously structured energy partitioning later in ontogeny through the interactive influence of plastic and adaptive processes. Specifically, high risk of overwinter mortality and low predation selected for high prioritization of energy storage. We suggest that simulations demonstrate the utility of eco-genetic models for generating null predictions of how selective pressures may structure expression of life history traits, such as early life energy allocation.

Animal x testing environment interaction on postweaning liveweight gains of young bulls

1998 ◽  
Vol 49 (4) ◽  
pp. 607 ◽  
Author(s):  
S. J. Schoeman ◽  
G. G. Jordaan
Keyword(s):  
Maximum Likelihood ◽  
Genetic Correlation ◽  
Variance Components ◽  
Restricted Maximum Likelihood ◽  
Animal Breeding ◽  
Environment Interaction ◽  
Maximum Likelihood Analysis ◽  
Breeding Values ◽  
Testing Environment ◽  
Likelihood Analysis

Postweaning liveweight gain records of 1610 young bulls obtained both in feedlot and under pasture were used to estimate (co)variance components using a multivariate restricted maximum likelihood analysis. The pedigree file included 3477 animals. Heritability estimates for liveweights and gain in both environments correspond to most previously reported estimates. The genetic correlation of gain between the 2 environments was -0·12, suggesting a large genotype testing environment interaction and re-ranking of animal breeding values across environments. Results of this analysis suggest the need for environment-specific breeding values for postweaning gain.

Amplifiers and the handicap principle in sexual selection: a different emphasis

1989 ◽  
Vol 235 (1281) ◽  
pp. 383-406 ◽  
Keyword(s):  
Sexual Selection ◽  
Genetic Association ◽  
Female Choice ◽  
Mating Success ◽  
Population Genetic ◽  
Genetic Model ◽  
Genetic Models ◽  
Handicap Principle ◽  
Resolution Power ◽  
Male Display

Population genetic models have shown that female choice is a potential cause of the evolution of male display. In these models the display is assumed to be the immediate object of female choice. Here I present an explicit genetic model that shows that male display can evolve as a consequence of female choice even when the display is not the immediate object of choice. When females initially base their preferences on the existence of variance in a cue that is correlated with male viability, a rare display can evolve to fixation if it amplifies the previously recognized differences in males, (i. e. if it increases the resolution power of females with respect to the original cue). By definition, amplifying displays (or amplifiers) increase mating success of the more viable males and decrease mating success of the less viable males. Therefore, the higher the frequency of the preferred, more viable males, the more likely it is that amplifiers will evolve to fixation. The evolution of an amplifier is further facilitated by a genetic association that is built up between the amplifier allele and the more viable allele. If the expression of the amplifier is limited to the more viable males, the amplifier will evolve to fixation provided only that the change in total fitness to the more viable males (higher mating success, lower viability), is positive.

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.

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