scholarly journals Independent Validation of Genomic Prediction in Strawberry Over Multiple Cycles

2021 ◽  
Vol 11 ◽  
Author(s):  
Luis F. Osorio ◽  
Salvador A. Gezan ◽  
Sujeet Verma ◽  
Vance M. Whitaker
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Predictive Ability ◽  
Practical Implementation ◽  
Breeding Cycle ◽  
Effective Population ◽  
Independent Validation ◽  
Breeding Cycles ◽  
Multiple Cycles ◽  
Strawberry Breeding

The University of Florida strawberry (Fragaria × ananassa) breeding program has implemented genomic prediction (GP) as a tool for choosing outstanding parents for crosses over the last five seasons. This has allowed the use of some parents 1 year earlier than with traditional methods, thus reducing the duration of the breeding cycle. However, as the number of breeding cycles increases over time, greater knowledge is needed on how multiple cycles can be used in the practical implementation of GP in strawberry breeding. Advanced selections and cultivars totaling 1,558 unique individuals were tested in field trials for yield and fruit quality traits over five consecutive years and genotyped for 9,908 SNP markers. Prediction of breeding values was carried out using Bayes B models. Independent validation was carried out using separate trials/years as training (TRN) and testing (TST) populations. Single-trial predictive abilities for five polygenic traits averaged 0.35, which was reduced to 0.24 when individuals common across trials were excluded, emphasizing the importance of relatedness among training and testing populations. Training populations including up to four previous breeding cycles increased predictive abilities, likely due to increases in both training population size and relatedness. Predictive ability was also strongly influenced by heritability, but less so by changes in linkage disequilibrium and effective population size. Genotype by year interactions were minimal. A strategy for practical implementation of GP in strawberry breeding is outlined that uses multiple cycles to predict parental performance and accounts for traits not included in GP models when constructing crosses. Given the importance of relatedness to the success of GP in strawberry, future work could focus on the optimization of relatedness in the design of TRN and TST populations to increase predictive ability in the short-term without compromising long-term genetic gains.

scholarly journals Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

2021 ◽  
Author(s):  
Xabi Cazenave ◽  
Bernard Petit ◽  
Marc Lateur ◽  
Hilde Nybom ◽  
Jiri Sedlak ◽  
...  
Keyword(s):  
Genetic Resources ◽  
Genomic Selection ◽  
Predictive Ability ◽  
Practical Implementation ◽  
Specific Marker ◽  
Training Set ◽  
High Genetic Diversity ◽  
Breeding Programs ◽  
Breeding Cycles ◽  
Two Populations

Abstract Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e. genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.

scholarly journals Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

2021 ◽  
Author(s):  
Xabi Cazenave ◽  
Bernard Petit ◽  
Francois Laurens ◽  
Charles-Eric Durel ◽  
Helene Muranty
Keyword(s):  
Genetic Resources ◽  
Genomic Selection ◽  
Predictive Ability ◽  
Practical Implementation ◽  
Specific Marker ◽  
Training Set ◽  
High Genetic Diversity ◽  
Breeding Programs ◽  
Breeding Cycles ◽  
Two Populations

Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e. genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and were always highest when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.

scholarly journals Genomic prediction for malting quality traits in practical barley breeding programs

2020 ◽  
Author(s):  
Pernille Sarup ◽  
Vahid Edriss ◽  
Nanna Hellum Kristensen ◽  
Jens Due Jensen ◽  
Jihad Orabi ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Spring Barley ◽  
Malting Quality ◽  
Breeding Cycle ◽  
Quality Traits ◽  
Training Population ◽  
Barley Breeding ◽  
Breeding Cycles

AbstractGenomic prediction can be advantageous in barley breeding for traits such as yield and malting quality to increase selection accuracy and minimize expensive phenotyping. In this paper, we investigate the possibilities of genomic selection for malting quality traits using a limited training population. The size of the training population is an important factor in determining the prediction accuracy of a trait. We investigated the potential for genomic prediction of malting quality within breeding cycles with leave one out (LOO) cross-validation, and across breeding cycles with leave set out (LSO) cross-validation. In addition, we investigated the effect of training population size on prediction accuracy by random two, four, and ten-fold cross-validation. The material used in this study was a population of 1329 spring barley lines from four breeding cycles. We found medium to high narrow sense heritabilities of the malting traits (0.31 to 0.65). Accuracies of predicting breeding values from LOO tests ranged from 0.6 to 0.9 making it worth the effort to use genomic prediction within breeding cycles. Accuracies from LSO tests ranged from 0.39 to 0.70 showing that genomic prediction across the breeding cycles were possible as well. Accuracy of prediction increased when the size of the training population increased. Therefore, prediction accuracy might be increased both within and across breeding cycle by increasing size of the training population

scholarly journals The effect of tree (and cambium) age on genomic prediction for solid wood properties in Norway spruce

2020 ◽  
Author(s):  
Linghua Zhou ◽  
Zhiqiang Chen ◽  
Lars Olsson ◽  
Thomas Grahn ◽  
Bo Karlsson ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Genomic Prediction ◽  
Microfibril Angle ◽  
Predictive Ability ◽  
Tree Breeding ◽  
Wood Properties ◽  
Solid Wood ◽  
Breeding Cycle ◽  
Old Trees ◽  
The Impact

Abstract Genomic selection (GS) or genomic prediction is considered as a promising approach to accelerate tree breeding and increase genetic gain by shortening breeding cycle. We investigated the predictive ability (PA) of GS based on 484 progeny trees from 62 half-sib families in Norway spruce ( Picea abies (L.) Karst.) for wood density, modulus of elasticity (MOE) and microfibril angle (MFA) measured with SilviScan, as well as for measurements on standing trees by Pilodyn and Hitman instruments. GS predictive abilities (PA) were comparable with those based on pedigree-based selection. The highest PAs were reached with at least 80-90% of the dataset used as training set. Use of different statistical methods had no significant impact on the estimated PAs. We also compared the abilities to predict density, MFA and MOE of 19 year old trees with use of models trained on data from coring at different ages and to different depths into the stem. The comparison indicated that close to the maximal PAs can be reached at age 10-12 by drilling only half way (ringwise) towards the pith, thereby reducing the impact on the tree.

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.

Evolutionary genetics of small populations

2017 ◽  
Author(s):  
Richard Frankham ◽  
Jonathan D. Ballou ◽  
Katherine Ralls ◽  
Mark D. B. Eldridge ◽  
Michele R. Dudash ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Population Size ◽  
Evolutionary Genetics ◽  
Small Population ◽  
Small Populations ◽  
Effective Population ◽  
Genetic Management ◽  
Evolutionary Genetic ◽  
Loss Of Genetic Diversity ◽  
Number Of Individuals

Genetic management of fragmented populations involves the application of evolutionary genetic theory and knowledge to alleviate problems due to inbreeding and loss of genetic diversity in small population fragments. Populations evolve through the effects of mutation, natural selection, chance (genetic drift) and gene flow (migration). Large outbreeding, sexually reproducing populations typically contain substantial genetic diversity, while small populations typically contain reduced levels. Genetic impacts of small population size on inbreeding, loss of genetic diversity and population differentiation are determined by the genetically effective population size, which is usually much smaller than the number of individuals.

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