Resource Allocation for Maximizing Prediction Accuracy and Genetic Gain of Genomic Selection in Plant Breeding: A Simulation Experiment

Abstract Some of the most economically important traits in plant breeding show highly polygenic inheritance. Genetic variation is a key determinant of the rates of genetic improvement in selective breeding programs. Rapid progress in genetic improvement comes at the cost of a rapid loss of genetic variation. Germplasm available through expired Plant Variety Protection (exPVP) lines is a potential resource of variation previously lost in elite breeding programs. Introgression for polygenic traits is challenging, as many genes have a small effect on the trait of interest. Here we propose a way to overcome these challenges with a multi-part pre-breeding program that has feedback pathways to optimise recurrent genomic selection. The multi-part breeding program consists of three components, namely a bridging component, population improvement, and product development. Parameters influencing the multi-part program were optimised with the use of a grid search. Haploblock effect and origin were investigated. Results showed that the introgression of exPVP germplasm using an optimised multi-part breeding strategy resulted in 1.53 times higher genetic gain compared to a two-part breeding program. Higher gain was achieved through reducing the performance gap between exPVP and elite germplasm and breaking down linkage drag. Both first and subsequent introgression events showed to be successful. In conclusion, the multi-part breeding strategy has a potential to improve long-term genetic gain for polygenic traits and therefore, potential to contribute to global food security.

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Training Population Design and Resource Allocation for Genomic Selection in Plant Breeding

Genomic Selection for Crop Improvement ◽

10.1007/978-3-319-63170-7_2 ◽

2017 ◽

pp. 7-22 ◽

Cited By ~ 5

Author(s):

Aaron Lorenz ◽

Liana Nice

Keyword(s):

Resource Allocation ◽

Plant Breeding ◽

Genomic Selection ◽

Training Population ◽

Population Design

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Genomic Selection: Current Status, Opportunities and Challenges

Bhartiya Krishi Anusandhan Patrika ◽

10.18805/bkap340 ◽

2021 ◽

Author(s):

Neeraj Budhlakoti ◽

Dwijesh Chandra Mishra ◽

Anil Rai ◽

K.K. Chaturvedi ◽

Anu Sharma ◽

...

Keyword(s):

Plant Breeding ◽

Genomic Selection ◽

Genetic Gain ◽

Early Stage ◽

Breeding Value ◽

Current Status ◽

Breeding Research ◽

Potential Benefits ◽

Genetic Interval

Now a days, Genomic Selection (GS) became a preferable choice for selection of appropriate candidate for animal and plant breeding research. Various studies related to GS has been done recently where it has shown potential benefits and advantages over traditional and conventional plant breeding methods. GS has been successfully implemented in various animal and plant breeding programs. It reduces the total costs by selecting the animals at early stage hence shorten the generation interval. Genomic selection is the future of livestock and plant breeding as it improves the genetic gain by decreasing genetic interval and improving reliability. Although there is a need of further investigation to improve the accuracy of genomic estimated breeding value and manage long-term genetic gain. This article provides a brief review what we have achieved through GS till yet and what is future scope and perspective in the GS research.

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A hybrid optimal contribution approach to drive short-term gains while maintaining long-term sustainability in a modern plant breeding program

10.1101/2020.01.08.899039 ◽

2020 ◽

Cited By ~ 2

Author(s):

Nicholas Santantonio ◽

Kelly Robbins

Keyword(s):

Plant Breeding ◽

Genomic Selection ◽

Genetic Gain ◽

Genetic Variance ◽

Hybrid Approach ◽

Breeding Program ◽

Short Term ◽

Breeding Programs ◽

Plant Breeding Program

1AbstractPlant breeding programs must adapt genomic selection to an already complex system. Inbred or hybrid plant breeding programs must make crosses, produce inbred individuals, and phenotype inbred lines or their hybrid test-crosses to select and validate superior material for product release. These products are few, and while it is clear that population improvement is necessary for continued genetic gain, it may not be sufficient to generate superior products. Rapid-cycle recurrent truncation genomic selection has been proposed to increase genetic gain by reducing generation time. This strategy has been shown to increase short-term gains, but can quickly lead to loss of genetic variance through inbreeding as relationships drive prediction. The optimal contribution of each individual can be determined to maximize gain in the following generation while limiting inbreeding. While optimal contribution strategies can maintain genetic variance in later generations, they suffer from a lack of short-term gains in doing so. We present a hybrid approach that branches out yearly to push the genetic value of potential varietal materials while maintaining genetic variance in the recurrent population, such that a breeding program can achieve short-term success without exhausting long-term potential. Because branching increases the genetic distance between the phenotyping pipeline and the recurrent population, this method requires sacrificing some trial plots to phenotype materials directly out of the recurrent population. We envision the phenotypic pipeline not only for selection and validation, but as an information generator to build predictive models and develop new products.

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Genomic selection and genetic gain for nut yield in an Australian macadamia breeding population

BMC Genomics ◽

10.1186/s12864-021-07694-z ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Katie M. O’Connor ◽

Ben J. Hayes ◽

Craig M. Hardner ◽

Mobashwer Alam ◽

Robert J. Henry ◽

...

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Prediction Accuracy ◽

Breeding Population ◽

Yield Stability ◽

Breeding Program ◽

Breeding Methods ◽

Selection Efficiency ◽

Generation Length ◽

Traditional Breeding

Abstract Background Improving yield prediction and selection efficiency is critical for tree breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia breeding program with comparison to traditional breeding methods. This study evaluated the prediction accuracy of GS in a macadamia breeding population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8 years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8 years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related populations and prediction across unrelated populations. Results Narrow-sense heritability of yield and yield stability was low (h2 = 0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related populations and 0.14 when predicted across unrelated populations. Accuracy of prediction of yield stability was high (r = 0.79) for predictions across related populations. Predicted genetic gain of yield using GS in related populations was 474 g/year, more than double that of traditional breeding methods (226 g/year), due to the halving of generation length from 8 to 4 years. Conclusions The results of this study indicate that the incorporation of GS for yield into the Australian macadamia breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present.

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The look ahead trace back optimizer for genomic selection under transparent and opaque simulators

Scientific Reports ◽

10.1038/s41598-021-83567-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fatemeh Amini ◽

Felipe Restrepo Franco ◽

Guiping Hu ◽

Lizhi Wang

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Genomic Prediction ◽

In Planta ◽

Data Set ◽

Look Ahead ◽

Trace Back ◽

New Variant ◽

The Look ◽

Partially Observable

AbstractRecent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.

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Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

Genes ◽

10.3390/genes12020210 ◽

2021 ◽

Vol 12 (2) ◽

pp. 210

Author(s):

Sang V. Vu ◽

Cedric Gondro ◽

Ngoc T. H. Nguyen ◽

Arthur R. Gilmour ◽

Rick Tearle ◽

...

Keyword(s):

Moisture Content ◽

Genomic Selection ◽

Shell Length ◽

Prediction Accuracy ◽

Genomic Information ◽

Morphometric Traits ◽

Shell Weight ◽

Shell Width ◽

Crassostrea Angulata ◽

Selection For

Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to (i) further evaluate the use of genomic information to improve prediction accuracies of breeding values from, (ii) compare different prediction methods (BayesA, BayesCπ and GBLUP) on prediction accuracies in our field data, and (iii) investigate the effects of different SNP marker densities on prediction accuracies of traits in the Portuguese oyster (Crassostrea angulata). The traits studied are all of economic importance and included morphometric traits (shell length, shell width, shell depth, shell weight), edibility traits (tenderness, taste, moisture content), and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58–0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35–0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240–0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.

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