scholarly journals Prediction of subgenome additive and interaction effects in allohexaploid wheat

2018 ◽  
Author(s):  
Nicholas Santantonio ◽  
Jean-Luc Jannink ◽  
Mark E. Sorrells
Keyword(s):  
Genomic Prediction ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Predictive Ability ◽  
Breeding Population ◽  
Wheat Breeding ◽  
Covariance Matrices ◽  
Genome Duplications ◽  
Increase In Accuracy ◽  
Allohexaploid Wheat

1AbstractWhole genome duplications have played an important role in the evolution of angiosperms. These events often occur through hybridization between closely related species, resulting in an allopolyploid with multiple subgenomes. With the availability of affordable genotyping and a reference genome to locate markers, breeders of allopolyploids now have the opportunity to manipulate subgenomes independently. This also presents a unique opportunity to investigate epistatic interactions between homeologous orthologs across subgenomes. We present a statistical framework for partitioning genetic variance to the subgenomes of an allopolyploid, predicting breeding values for each subgenome, and determining the importance of inter-genomic epistasis. We demonstrate using an allohexaploid wheat breeding population evaluated in Ithaca, NY and an important wheat dataset previously shown to demonstrate non-additive genetic variance. Subgenome covariance matrices were constructed and used to calculate subgenome interaction covariance matrices across subgenomes for variance component estimation and genomic prediction. We propose a method to extract population structure from all subgenomes at once before covariances are calculated to reduce collinearity between subgenome estimates. Variance parameter estimation was shown to be reliable for additive subgenome effects, but was less reliable for subgenome interaction components. Predictive ability was equivalent to current genomic prediction methods. Including only inter-genomic interactions resulted in the same increase in accuracy as modeling all pairwise marker interactions. Thus, we provide a new tool for breeders of allopolyploid crops to characterize the genetic architecture of existing populations, determine breeding goals, and develop new strategies for selection of additive effects and fixation of inter-genomic epistasis.

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.

scholarly journals Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Animals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1055 ◽  
Author(s):  
Ying Liu ◽  
Lei Xu ◽  
Zezhao Wang ◽  
Ling Xu ◽  
Yan Chen ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Genetic Variance ◽  
Association Studies ◽  
Additive Genetic Variance ◽  
Additive Models ◽  
Carcass Weight ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies

Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

Breeding and selection for high early season sugar content in a sugarcane (Saccharum spp. hybrids) improvement program

1994 ◽  
Vol 45 (7) ◽  
pp. 1569 ◽  
Author(s):  
MC Cox ◽  
DM Hogarth ◽  
PB Hansen
Keyword(s):  
Genetic Variance ◽  
Recurrent Selection ◽  
Sugar Content ◽  
Additive Genetic Variance ◽  
Breeding Population ◽  
Cane Sugar ◽  
Improvement Program ◽  
Narrow Sense Heritability ◽  
Saccharum Spp ◽  
Early Season

A recurrent selection program for high early season commercial cane sugar (CCS) in sugarcane (Saccharum spp. hybrids) commenced in 1988. About 20 families (first clonal stage) and their parents are grown in two regions each year. Elite clones with high early CCS are selected and these are recycled into the breeding population with a short generation interval. Estimates of narrow-sense heritability from midparent-offspring regressions were moderate to high (0.6 or greater). Thus breeding for this trait using a large database of information available on early CCS characteristics of parental clones should be effective. Additive genetic variance (=2A) was more important than non-additive genetic variance.

scholarly journals Comparing G Matrices: Are Common Principal Components Informative?

Genetics ◽  
2003 ◽  
Vol 165 (1) ◽  
pp. 411-425
Author(s):  
Jason G Mezey ◽  
David Houle
Keyword(s):  
Principal Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Covariance Matrices ◽  
Allele Frequencies ◽  
Modular Organization ◽  
Common Principal Components ◽  
Different Populations ◽  
G Matrices ◽  
Allele Model

Abstract Common principal components (CPC) analysis is a technique for assessing whether variance-covariance matrices from different populations have similar structure. One potential application is to compare additive genetic variance-covariance matrices, G. In this article, the conditions under which G matrices are expected to have common PCs are derived for a two-locus, two-allele model and the model of constrained pleiotropy. The theory demonstrates that whether G matrices are expected to have common PCs is largely determined by whether pleiotropic effects have a modular organization. If two (or more) populations have modules and these modules have the same direction, the G matrices have a common PC, regardless of allele frequencies. In the absence of modules, common PCs exist only for very restricted combinations of allele frequencies. Together, these two results imply that, when populations are evolving, common PCs are expected only when the populations have modules in common. These results have two implications: (1) In general, G matrices will not have common PCs, and (2) when they do, these PCs indicate common modular organization. The interpretation of common PCs identified for estimates of G matrices is discussed in light of these results.

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.

Heritabilities and genetic and phenotypic correlations for height and diameter in coastal Douglas-fir

1982 ◽  
Vol 12 (2) ◽  
pp. 181-185 ◽  
Author(s):  
Francis C. Yeh ◽  
Chris Heaman
Keyword(s):  
Genetic Correlation ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Growing Season ◽  
Breeding Population ◽  
Douglas Fir ◽  
Single Tree ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

Factorial crosses of 22 seed trees and 4 pollen parents from a breeding population of 445 coastal Douglas-firs were tested at two sites. Analyses of heights and diameters after the sixth growing season indicated only the significance of additive genetic variance. The single tree heritability estimates for height and diameter were 0.10 ± 0.07 and 0.12 ± 0.08, respectively. The genetic correlation between height and diameter was 0.81 ± 0.64.

scholarly journals GENETIC VARIANCE FOR RESISTANCE TO SWEETPOTATO WEEVIL

HortScience ◽  
1991 ◽  
Vol 26 (5) ◽  
pp. 493d-493
Author(s):  
Paul G. Thompson ◽  
John Schneider ◽  
Boyett Graves
Keyword(s):  
Genetic Variability ◽  
Field Experiment ◽  
High Resistance ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Breeding Population ◽  
Sweetpotato Weevil ◽  
Naturally Occurring

A breeding population including parents with known moderate levels of weevil resistance was evaluated for resistance in a field experiment with few naturally occurring weevils. Weevils were collected from several Mississippi locations and intermated for increase. One male and one female were applied to the crown of each plant 73 days after transplanting. Seventeen parents and their progenies plus 6 additional clones or families were grown in 5 plant plots replicated 8 times. Genetic variability for resistance to weevil injury was observed. Mean percentage injured roots ranged from 25 to 85 and there were differences in resistance to injury among genotypes. Additive genetic variance was moderate so increased resistance levels should result from selection and intermating for high resistance.

scholarly journals Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis

2022 ◽  
Author(s):  
Miguel Angel Raffo ◽  
Pernille Sarup ◽  
Xiangyu Guo ◽  
Huiming Liu ◽  
Jeppe Reitan Andersen ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Prediction ◽  
Cross Validation ◽  
Predictive Ability ◽  
Wheat Breeding ◽  
Breeding Cycle ◽  
Wheat Grain ◽  
Additive Variance ◽  
Genetic Merit ◽  
Genetic Variances

Abstract Key message Including additive and additive-by-additive epistasis in a NOIA parametrization did not yield orthogonal partitioning of genetic variances, nevertheless, it improved predictive ability in a leave-one-out cross-validation for wheat grain yield. Abstract Additive-by-additive epistasis is the principal non-additive genetic effect in inbred wheat lines and is potentially useful for developing cultivars based on total genetic merit; nevertheless, its practical benefits have been highly debated. In this article, we aimed to (i) evaluate the performance of models including additive and additive-by-additive epistatic effects for variance components (VC) estimation of grain yield in a wheat-breeding population, and (ii) to investigate whether including additive-by-additive epistasis in genomic prediction enhance wheat grain yield predictive ability (PA). In total, 2060 sixth-generation (F6) lines from Nordic Seed A/S breeding company were phenotyped in 21 year-location combinations in Denmark, and genotyped using a 15 K-Illumina-BeadChip. Three models were used to estimate VC and heritability at plot level: (i) “I-model” (baseline), (ii) “I + GA-model”, extending I-model with an additive genomic effect, and (iii) “I + GA + GAA-model”, extending I + GA-model with an additive-by-additive genomic effects. The I + GA-model and I + GA + GAA-model were based on the Natural and Orthogonal Interactions Approach (NOIA) parametrization. The I + GA + GAA-model failed to achieve orthogonal partition of genetic variances, as revealed by a change in estimated additive variance of I + GA-model when epistasis was included in the I + GA + GAA-model. The PA was studied using leave-one-line-out and leave-one-breeding-cycle-out cross-validations. The I + GA + GAA-model increased PA significantly (16.5%) compared to the I + GA-model in leave-one-line-out cross-validation. However, the improvement due to including epistasis was not observed in leave-one-breeding-cycle-out cross-validation. We conclude that epistatic models can be useful to enhance predictions of total genetic merit. However, even though we used the NOIA parameterization, the variance partition into orthogonal genetic effects was not possible.

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