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.

Genetic variance components and genetic effects among eleven diverse upland cotton lines and their F2 hybrids

Euphytica ◽  
2009 ◽  
Vol 167 (3) ◽  
pp. 397-408 ◽  
Author(s):  
Johnie N. Jenkins ◽  
Jack C. McCarty ◽  
Jixiang Wu ◽  
Osman A. Gutierrez
Keyword(s):  
Variance Components ◽  
Upland Cotton ◽  
Genetic Variance ◽  
Genetic Effects ◽  
F2 Hybrids

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 A classical setting for associations between markers and loci affecting quantitative traits

1999 ◽  
Vol 74 (3) ◽  
pp. 271-277 ◽  
Author(s):  
DAHLIA M. NIELSEN ◽  
B. S. WEIR
Keyword(s):  
Genetic Marker ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Genetic Model ◽  
Marker Locus ◽  
Multiple Alleles ◽  
Classical Setting ◽  
Tests Of Association ◽  
Trait Locus

We examine the relationships between a genetic marker and a locus affecting a quantitative trait by decomposing the genetic effects of the marker locus into additive and dominance effects under a classical genetic model. We discuss the structure of the associations between the marker and the trait locus, paying attention to non-random union of gametes, multiple alleles at the marker and trait loci, and non-additivity of allelic effects at the trait locus. We consider that this greater-than-usual level of generality leads to additional insights, in a way reminiscent of Cockerham's decomposition of genetic variance into five terms: three terms in addition to the usual additive and dominance terms. Using our framework, we examine several common tests of association between a marker and a trait.

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 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 Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing among full-sibs

2021 ◽  
Author(s):  
Antoine Fraimout ◽  
Zitong Li ◽  
Mikko J. Sillanpää ◽  
Pasi Rastas ◽  
Juha Merilä
Keyword(s):  
Natural Selection ◽  
Variance Components ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Identity By Descent ◽  
Quantitative Genetic ◽  
Genome Wide ◽  
Quantitative Genetic Parameters

ABSTRACTAdditive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most wild animal populations. Using estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci, we estimated quantitative genetic parameters for traits known to be under directional natural selection in nine-spined sticklebacks (Pungitius pungitius) and compared these to traditional pedigree-based estimators. Using four different datasets, with varying sample sizes and pedigree complexity, we further assessed the performance of different Genomic Relationship Matrices (GRM) to estimate additive and dominance variance components. Large variance in IBD relationships allowed accurate estimation of genetic variance components, and revealed significant heritability for all measured traits, with negligible dominance contributions. Genome-partitioning analyses revealed that all traits have a polygenic basis and are controlled by genes at multiple chromosomes. The results demonstrate how large full-sib families of highly fecund vertebrates can be used to obtain accurate estimates quantitative genetic parameters to provide insights on genetic architecture of quantitative traits in non-model organisms from the wild. This approach should be particularly useful for studies requiring estimates of genetic variance components from multiple populations as for instance when aiming to infer the role of natural selection as a cause for population differentiation in quantitative traits.

scholarly journals Genetic Variance Components in Cotton by Generation Mean Analysis

2021 ◽  
Vol 3 (2) ◽  
pp. 72-85
Author(s):  
A. Isong ◽  
A. Balu ◽  
A. Ahmed ◽  
J. O. Mbe ◽  
I. G. Mohammed ◽  
...  
Keyword(s):  
Variance Components ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Gene Action ◽  
Dominance Effect ◽  
Generation Mean Analysis ◽  
Seed Cotton ◽  
Gene Effects ◽  
Main Effect ◽  
Additive Gene

The mode of gene action for the expression of quantitative traits is decided by the predominance of variances due to additive, dominance and epistasis gene effects. In this experiment, involving four F1 crosses (TCH1716 x TCB37, TCH1705-101 x TCB209, KC2 x TCB26 and TSH0250 x DB3) of upland cotton, inheritance of major yield components by Generation Mean Analysis was investigated. The investigation revealed that both additive and dominance gene effects were involved in the expression of most of the yield contributing traits. One or more types of epistatic interaction effects were prevalent for all the characters and thus played a major role in the control of the characters. The inheritance of the traits was found to be complex in lieu of the low heritability estimates and genetic advance over mean. For seed cotton yield per plant, the dominance x dominance interaction effect was positively significant for all the crosses, the additive x dominance effect was positively significant only in cross 1 and the dominance main effect showed negative significant in all crosses. The dominance (h) and dominance x dominance (l) effects were of opposite signs in all the crosses indicating the presence of duplicate epistasis in all the crosses. To harness additive gene effects for improvement of some of the traits, breeding methods with postponement of selection to later generation should be adopted.

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