scholarly journals Simultane Zuchtwertschätzung mit Reinzucht- und Kreuzungsleistungen unter Dominanz

2000 ◽  
Vol 43 (1) ◽  
pp. 87-98
Author(s):  
N. Mielenz ◽  
L. Schüler ◽  
E. Groeneveld
Keyword(s):  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Selection Index ◽  
Index Theory ◽  
Genetic Evaluation ◽  
Sources Of Information ◽  
Dominance Variance ◽  
The Cross ◽  
Efficiency Of Selection ◽  
Additive Genetic Effects

Abstract. Title of the paper: Joint genetic evaluation of purebred and crossbred data under dominance Currently available Software packages are not able to handle the Joint genetic evaluation of pure and crossbred data correctly. Instead, the same traits originating in the two purebreds and the crossbred are treated as three separate traits and analysed in a three trait animal model. Using selection index theory it can be demonstrated that this procedure contains model violations even if only additive genetic effects are considered. The effectiveness of the correct model which allows different contributions of the parental lines to the genetic variance of the cross including animal specific dominance effects is assessed on the basis of different selection indices. The sources of information chosen reflect those available in typical chicken breeding programs. The genetic parameters required for index construction – in particular the dominance variance and the genetic correlation between the pure and cross breds – were derived using a two locus model. If the purebred contribute extremely differently to the crossbred variance large losses in efficiency can be observed. With contributions of the sire line of 11, 19, 37 and 40% to the additive genetic variance of the cross corresponding losses in efficiency of selection where 13.1, 6.5, 0.6 and 0.2% respectively. Not considering the dominance variance – depending on its proportion relative to the total genetic variance – lead to a moderate loss of efficiency from 0.1 to 2.6%.

scholarly journals The limits to artificial selection for body weight in the mouse: IV. Sources of new genetic variance—irradiation and outcrossing

1967 ◽  
Vol 9 (1) ◽  
pp. 87-98 ◽  
Author(s):  
R. C. Roberts
Keyword(s):  
Body Weight ◽  
Artificial Selection ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Negative Results ◽  
Selection For ◽  
The Cross

1. Two methods are examined of introducing new genetic variance into a line of mice selected for high 6-week weight which, at its limit, displayed no additive genetic variance.2. The first method—irradiation—gave largely negative results. Any further gain under selection that was achieved could not be clearly distinguished from a possible environmental trend.3. The second method—outcrossing to an unselected strain and then selecting from the cross—resulted in a clear gain over the original limit, but nine generations were required even to recover the original limit.4. Various methods of transcending selection limits are evaluated in terms of their application to livestock improvement.

scholarly journals Variance component analysis for viability in an isolated population of Drosophila melanogaster

1983 ◽  
Vol 42 (2) ◽  
pp. 207-217 ◽  
Author(s):  
Hidenori Tachida ◽  
Muneo Matsuda ◽  
Shin-Ichi Kusakabe ◽  
Terumi Mukai
Keyword(s):  
Drosophila Melanogaster ◽  
Genetic Variance ◽  
Balancing Selection ◽  
Additive Genetic Variance ◽  
Diallel Cross ◽  
Variance Component Analysis ◽  
Logarithmic Scale ◽  
Dominance Variance ◽  
Diversifying Selection ◽  
Partial Diallel

SUMMARYUsing the 602 second chromosome lines extracted from the Ishigakijima population of Drosophila melanogaster in Japan, partial diallel cross experiments (Design II of Comstock & Robinson, 1952) were carried out, and the additive genetic variance and the dominance variance of viability were estimated. The estimated value of the additive genetic variance is 0·01754±0·00608, and the dominance variance 0·00151±0·00114, using a logarithmic scale. Since the value of the additive genetic variance is much larger than expected under mutation–selection balance although the dominance variance is compatible with it, we speculate that in the Ishigakijima population some type of balancing selection must be operating to maintain the genetic variability with respect to viability at a minority of loci. As candidates for such selection, overdominance, frequency-dependent selection, and diversifying selection are considered, and it is suggested that diversifying selection is the most probable candidate for increasing the additive genetic 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 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.

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.

scholarly journals Genomic Prediction and the Practical Breeding of 12 Quantitative-Inherited Traits in Cucumber (Cucumis sativus L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Ce Liu ◽  
Xiaoxiao Liu ◽  
Yike Han ◽  
Xi'ao Wang ◽  
Yuanyuan Ding ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Complex Traits ◽  
Variance Components ◽  
Genetic Variance ◽  
Cross Validation ◽  
Additive Genetic Variance ◽  
Inbred Lines ◽  
Cucumis Sativus L ◽  
The Cross ◽  
Two Populations

Genomic prediction is an effective way for predicting complex traits, and it is becoming more essential in horticultural crop breeding. In this study, we applied genomic prediction in the breeding of cucumber plants. Eighty-one cucumber inbred lines were genotyped and 16,662 markers were identified to represent the genetic background of cucumber. Two populations, namely, diallel cross population and North Carolina II population, having 268 combinations in total were constructed from 81 inbred lines. Twelve cucumber commercial traits of these two populations in autumn 2018, spring 2019, and spring 2020 were collected for model training. General combining ability (GCA) models under five-fold cross-validation and cross-population validation were applied to model validation. Finally, the GCA performance of 81 inbred lines was estimated. Our results showed that the predictive ability for 12 traits ranged from 0.38 to 0.95 under the cross-validation strategy and ranged from −0.38 to 0.88 under the cross-population strategy. Besides, GCA models containing non-additive effects had significantly better performance than the pure additive GCA model for most of the investigated traits. Furthermore, there were a relatively higher proportion of additive-by-additive genetic variance components estimated by the full GCA model, especially for lower heritability traits, but the proportion of dominant genetic variance components was relatively small and stable. Our findings concluded that a genomic prediction protocol based on the GCA model theoretical framework can be applied to cucumber breeding, and it can also provide a reference for the single-cross breeding system of other crops.

QUANTITATIVE INHERITANCE IN CROSSES AMONG FOUR TETRAPLOID WHEATS

1968 ◽  
Vol 10 (1) ◽  
pp. 33-43 ◽  
Author(s):  
B. R. Jackson ◽  
V. A. Dirks ◽  
L. A. Snyder
Keyword(s):  
Genetic Diversity ◽  
Genetic Relationship ◽  
North American ◽  
Triticum Durum ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Geographic Origin ◽  
Quantitative Inheritance ◽  
Dominance Variance ◽  
Tetraploid Wheats

Four lines of Triticum durum, representing diverse levels of genetic relationship and geographic origin, were crossed to produce six segregating populations. Additive and dominance components of genetic variance were estimated for five metric characters in the F2 and F4 generations grown in separate years. Dominance variance predominated in two out of the six populations in F2, but was minimal in the F4 generation. Additive genetic variance generally constituted the major source of genetic variance and was significantly greater than zero in all populations for every character investigated in F4. Populations based on crosses involving either of the two Ethiopian selections with either of the two North American varieties used as parents, in general showed larger amounts of genetic variance than the comparable intercross of the two North American varieties. Considerable genetic variance also existed in the populations derived from intercrossing the two Ethiopian wheats. The results of the investigation tend to support the hypothesized increase in genetic variance associated with genetic diversity, and the preponderance of additive genetic variance in naturally self-fertilizing species.

scholarly journals Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects

2021 ◽  
Author(s):  
Seema Yadav ◽  
Xianming Wei ◽  
Priya Joyce ◽  
Felicity Atkin ◽  
Emily Deomano ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Complex Traits ◽  
Genetic Variance ◽  
Prediction Models ◽  
Additive Genetic Variance ◽  
Genetic Effects ◽  
Additive Effects ◽  
Genetic Progress ◽  
Accurate Identification ◽  
Additive Genetic Effects

AbstractKey messageNon-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance.AbstractIn the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.

scholarly journals Additive and Dominance Genomic Analysis for Litter Size in Purebred and Crossbred Iberian Pigs

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 12
Author(s):  
Houssemeddine Srihi ◽  
José Luis Noguera ◽  
Victoria Topayan ◽  
Melani Martín de Hijas ◽  
Noelia Ibañez-Escriche ◽  
...  
Keyword(s):  
Litter Size ◽  
Genetic Variance ◽  
Mixed Model ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Genomic Analysis ◽  
Gxe Interaction ◽  
Genomic Regions ◽  
Additive Genetic Effects ◽  
Selection Of

INGA FOOD S. A., as a Spanish company that produces and commercializes fattened pigs, has produced a hybrid Iberian sow called CASTÚA by crossing the Retinto and Entrepelado varieties. The selection of the parental populations is based on selection criteria calculated from purebred information, under the assumption that the genetic correlation between purebred and crossbred performance is high; however, these correlations can be less than one because of a GxE interaction or the presence of non-additive genetic effects. This study estimated the additive and dominance variances of the purebred and crossbred populations for litter size, and calculated the additive genetic correlations between the purebred and crossbred performances. The dataset consisted of 2030 litters from the Entrepelado population, 1977 litters from the Retinto population, and 1958 litters from the crossbred population. The individuals were genotyped with a GeneSeek® GGP Porcine70K HDchip. The model of analysis was a ‘biological’ multivariate mixed model that included additive and dominance SNP effects. The estimates of the additive genotypic variance for the total number born (TNB) were 0.248, 0.282 and 0.546 for the Entrepelado, Retinto and Crossbred populations, respectively. The estimates of the dominance genotypic variances were 0.177, 0.172 and 0.262 for the Entrepelado, Retinto and Crossbred populations. The results for the number born alive (NBA) were similar. The genetic correlations between the purebred and crossbred performance for TNB and NBA—between the brackets—were 0.663 in the Entrepelado and 0.881 in Retinto poplulations. After backsolving to obtain estimates of the SNP effects, the additive genetic variance associated with genomic regions containing 30 SNPs was estimated, and we identified four genomic regions that each explained > 2% of the additive genetic variance in chromosomes (SSC) 6, 8 and 12: one region in SSC6, two regions in SSC8, and one region in SSC12.

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