Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium

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.

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

The Journal of Agricultural Science ◽

10.1017/s0021859618000497 ◽

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.

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

Animals ◽

10.3390/ani11020481 ◽

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.

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Selection Under Inbreeding

10.1093/oso/9780198830870.003.0023 ◽

2018 ◽

Author(s):

Bruce Walsh ◽

Michael Lynch

Keyword(s):

Linkage Disequilibrium ◽

Recombination Rate ◽

Variance Components ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Selection Response ◽

Selfing Rate ◽

Evolutionary Forces ◽

Additional Variance

When dominance is presence, the selection response equations under inbreeding can become rather complex, they require additional variance components beyond the additive-genetic variance. Further, both transient and permanent components of response can arise. This chapter examines the theory of both the covariance of relatives under general inbreeding, as well as the expected selection response under inbreeding. Due to the decrease in the effective recombination rate under selfing, the Bulmer effect can be rather dramatic, as any linkage disequilibrium generated by selection is only weakly removed by recombination. Finally, this chapter also examines the evolutionary forces that interact to determine the selfing rate for a given population.

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

Genetics Selection Evolution ◽

10.1186/s12711-019-0498-y ◽

2019 ◽

Vol 51 (1) ◽

Cited By ~ 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.

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Linkage disequilibrium and genetic variances under mutation-selection balance.

Genetics ◽

10.1093/genetics/121.4.857 ◽

1989 ◽

Vol 121 (4) ◽

pp. 857-860 ◽

Cited By ~ 1

Author(s):

A Hastings

Keyword(s):

Linkage Disequilibrium ◽

Mutation Rate ◽

Genetic Variance ◽

Harmonic Mean ◽

Recombination Rates ◽

Genetic Variances ◽

Locus Selection

Abstract I determine the contribution of linkage disequilibrium to genetic variances using results for two loci and for induced or marginal systems. The analysis allows epistasis and dominance, but assumes that mutation is weak relative to selection. The linkage disequilibrium component of genetic variance is shown to be unimportant for unlinked loci if the gametic mutation rate divided by the harmonic mean of the pairwise recombination rates is much less than one. For tightly linked loci, linkage disequilibrium is unimportant if the gametic mutation rate divided by the (induced) per locus selection is much less than one.

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

BMC Genomics ◽

10.1186/s12864-021-07792-y ◽

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.

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Selection Response in Finite Populations

Genetics ◽

10.1093/genetics/144.4.1961 ◽

1996 ◽

Vol 144 (4) ◽

pp. 1961-1974 ◽

Cited By ~ 2

Author(s):

Ming Wei ◽

Armando Caballero ◽

William G Hill

Keyword(s):

Linkage Disequilibrium ◽

Inbreeding Depression ◽

Genetic Variance ◽

Relative Rate ◽

Selection Response ◽

Rate Of Change ◽

Effective Population ◽

Short Term ◽

Model Account

Formulae were derived to predict genetic response under various selection schemes assuming an infinitesimal model. Account was taken of genetic drift, gametic (linkage) disequilibrium (Bulmer effect), inbreeding depression, common environmental variance, and both initial segregating variance within families (σAW02) and mutational (σM2) variance. The cumulative response to selection until generation t(CRt) can be approximated asCRt≈R0[t−β(1−σAW∞2σAW02)t24Ne]−Dt2Ne,where Ne is the effective population size, σAW∞2=NeσM2 is the genetic variance within families at the steady state (or one-half the genic variance, which is unaffected by selection), and D is the inbreeding depression per unit of inbreeding. R 0 is the selection response at generation 0 assuming preselection so that the linkage disequilibrium effect has stabilized. β is the derivative of the logarithm of the asymptotic response with respect to the logarithm of the within-family genetic variance, i.e., their relative rate of change. R 0 is the major determinant of the short term selection response, but σM2, Ne and β are also important for the long term. A selection method of high accuracy using family information gives a small Ne and will lead to a larger response in the short term and a smaller response in the long term, utilizing mutation less efficiently.

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