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.

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 Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

2021 ◽  
Author(s):  
Valentin Hivert ◽  
Julia Sidorenko ◽  
Florian Rohart ◽  
Michael E. Goddard ◽  
Jian Yang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Large Sample ◽  
Human Complex

Comparison of F2 and F∞ diallel analyses in barley

Genome ◽  
1988 ◽  
Vol 30 (6) ◽  
pp. 865-869 ◽  
Author(s):  
T. M. Choo ◽  
E. Reinbergs ◽  
P. Y. Jui
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Plant Height ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Heading Date ◽  
Diallel Analysis ◽  
Hordeum Vulgare L ◽  
Hill Plots

A study was conducted in barley (Hordeum vulgare L.) to compare the relative magnitudes of heterosis to additive × additive epistasis and to compare F2 and F∞, diallel analyses. Both F2 and F∞, progenies were derived from 7 × 7 diallel crosses. Progenies and their parents were evaluated for grain yield, heading date, plant height, and the number of spikes per hill in hill plots with five replications at Elora (Ontario) in 1978. Results suggested that additive × additive epistasis were present for these traits and its magnitude was similar to that of heterosis estimated in F2. Both F2 and F∞ analyses detected the presence of epistasis. Both analyses provided similar estimates of the additive genetic variance for heading date and the number of spikes per hill, but the F2 analysis provided higher estimates than the F∞ analysis for grain yield and plant height. The estimate for grain yield and plant height obtained from the F2 analysis could be biased upward because of the invalid assumption of no epistasis. Estimates of other genetic variance components from the F2 analysis could be biased also. The F∞ diallel analysis not only provided estimates of additive × additive genetic variance for the four traits, it also allowed detection of nonindependent gene distribution in the parents for three of the four traits. Therefore, the limitations of the F2 diallel analysis in the presence of epistasis were apparent in the study. The F2 diallel analysis, however, could be used to detect dominance and maternal effects and thus to complement the F∞ diallel analysisKey words: barley, Hordeum vulgare, diallels, haploids, epistasis, heterosis.

Quantitative inheritance studies in sugar-cane. I. Estimation of variance components

1971 ◽  
Vol 22 (1) ◽  
pp. 93 ◽  
Author(s):  
DM Hogarth
Keyword(s):  
Factorial Design ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
The Other ◽  
Quantitative Inheritance ◽  
Genetic Determination ◽  
Nested Design ◽  
Dominance Genetic Variance ◽  
Estimation Of Variance

Two experiments in quantitative genetics were conducted, one based on a nested design in lattice squares and the other on a factorial design in a balanced lattice. Lattice designs were found to be suitable for genetic experiments if a large number of crosses was involved, but posed some problems in partitioning the sum of squares for treatments. The factorial design was considered preferable to the nested design, although neither design permitted estimation of epistatic variances which, therefore, were assumed to be negligible. Additive genetic variance was found to be more important than dominance genetic variance for most characters. However, most estimates of genetic variance lacked precision in spite of the use of large, precise experiments, which illustrated the difficulty in obtaining estimates of variance components with adequate precision. The validity of assumptions made for these analyses is discussed. The effect of competition was studied and estimates of heritability and degree of genetic determination were determined.

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 Estimation of variance components for bodyweight of grasscutters using expectation maximization algorithm of restricted maximum likelihood

2020 ◽  
Vol 44 (5) ◽  
pp. 5-8
Author(s):  
I. Udeh
Keyword(s):  
Body Weight ◽  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Variance Components ◽  
Genetic Variance ◽  
Phenotypic Variability ◽  
Additive Genetic Variance ◽  
Expectation Maximization Algorithm ◽  
Estimation Of Variance ◽  
Selection Of

The objective of this study was to estimate the variance components and heritability of bodyweight of grasscutters at 4, 6 and 8 months of age using EM algorithm of REML procedures. The data used for the study were obtained from the bodyweight records of 20 grasscutters from four families at 4, 6 and 8 months of age. The heritability of bodyweight of grasscutters at 4, 6 and 8 months of age were 0.14, 0.10 and 0.12 respectively. This implies that about 10 – 14 % of the phenotypic variability of body weight in this grasscutter population was accounted by additive genetic variance while environmental and gene combination variance made a larger contribution. The implication is that selection of grasscutters in this population should not be based on the information on the animals alone but also information fromits relatives.

scholarly journals Genetic Mapping And Genomic Prediction For Northern Corn Leaf Blight (Exserohilum Turcicum (Pass.) Leonard And Suggs) Resistance

2021 ◽  
Author(s):  
D.C. Balasundara ◽  
H. C. Lohithaswa ◽  
M. Rahul ◽  
R. L. Ravikumar ◽  
Anand Pandravada ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Combining Ability ◽  
Inbred Lines ◽  
Leaf Blight ◽  
Environment Interaction ◽  
Exserohilum Turcicum ◽  
Northern Corn Leaf Blight ◽  
Polymorphic Snps ◽  
Corn Leaf Blight ◽  
Two Populations

Abstract Background: Northern corn leaf blight (NCLB) of maize caused by Exserohilum turcicum is a serious foliar disease. Resistance to NCLB is complexly inherited and the highly significant genotype x environment interaction effect makes selection of resistant genotypes difficult through conventional breeding methods. Hence an attempt was made to identify the genomic regions associated with NCLB resistance and perform genomic selection (GS) in two F2:3 populations derived from the crosses CM212 × MAI172 (Population-1) and CM202 × SKV50 (Population-2). Results: Two populations, each comprising of 366 progenies, were phenotyped at three different locations in the disease screening nurseries. Linkage analysis using 297 polymorphic SNPs in Population-1 and 290 polymorphic SNPs in Population-2 revealed 10 linkage groups spanning 3623.88cM and 4261.92cM with an average distance of 12.40 cM and 14.9 cM, respectively. Location-wise and pooled data across locations indicated that QTL expression was population and environment specific. The genomic prediction accuracies of 0.83 and 0.79 were achieved for NCLB Population 1 and Population 2, respectively. The resistant progenies from both populations were advanced to derive inbred lines and crossed with four different testers in line x tester mating design to test for their combining ability. High overall general combining ability was exhibited by 21 inbred lines. Among crosses 48 % were assigned high overall specific combining ability status. Out of 136 single crosses, seven recorded significant positive standard heterosis over the best check for grain yield. The clustering pattern of inbred lines developed from the two populations revealed high molecular diversity. Conclusions: In this study, comparatively better genomic prediction accuracies were achieved for NCLB and the worth of F3 progenies with high genomic predictions was proved by advancing them to derive inbred lines and establishing their higher combining ability for yield and yield related traits.

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.

Sign in / Sign up

Export Citation Format

Share Document