scholarly journals Generalizable approaches for genomic prediction of metabolites in plants

2021 ◽  
Author(s):  
Lauren J Brzozowski ◽  
Malachy T Campbell ◽  
Haixiao Hu ◽  
Melanie Caffe ◽  
Lucia Guterrez ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Group Model ◽  
Plant Metabolites ◽  
Plant Populations ◽  
Breeding Programs ◽  
Breeding Lines ◽  
Consistent Model ◽  
Selection For

Plant metabolites are important for plant breeders to improve nutrition and agronomic performance, yet integrating selection for metabolomic traits is limited by phenotyping expense and limited genetic characterization, especially of uncommon metabolites. As such, developing biologically-based and generalizable genomic selection methods for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for more than 600 metabolites measured by GC-MS and LC-MS in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite GWAS (mGWAS) and selected loci to use in multi-kernel models that encompassed metabolome-wide mGWAS results, or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel, consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We tested if similar metabolites had consistent model ranks and found that, while different metrics of similarity had different results, using annotation-free methods to group metabolites led to consistent within-group model rankings. Overall, testing biological rationales for developing kernels for genomic prediction across populations, contributes to developing frameworks for plant breeding for metabolite traits.

scholarly journals Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery

2017 ◽  
Vol 49 (9) ◽  
pp. 1297-1303 ◽  
Author(s):  
John M Hickey ◽  
◽  
Tinashe Chiurugwi ◽  
Ian Mackay ◽  
Wayne Powell
Keyword(s):  
Plant Breeding ◽  
Genomic Prediction ◽  
Breeding Programs ◽  
Biological Discovery

scholarly journals How Population Structure Impacts Genomic Selection Accuracy in Cross-Validation: Implications for Practical Breeding

2020 ◽  
Vol 11 ◽  
Author(s):  
Christian R. Werner ◽  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
John M. Hickey ◽  
Tobias Kox ◽  
...  
Keyword(s):  
Family Structure ◽  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Careful Analysis ◽  
Critical Approach ◽  
Crop Species ◽  
Breeding Programs ◽  
Mendelian Sampling

Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation. However, genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many breeding populations. An understanding of the effect of population and family structure on prediction accuracy is essential for the successful application of genomic selection in plant breeding programs. The objective of this study was to make this effect and its implications for practical breeding programs comprehensible for breeders and scientists with a limited background in quantitative genetics and genomic selection theory. We, therefore, compared genomic prediction accuracies obtained from different random cross validation approaches and within-family prediction in three different prediction scenarios. We used a highly structured population of 940 Brassica napus hybrids coming from 46 testcross families and two subpopulations. Our demonstrations show how genomic prediction accuracies obtained from among-family predictions in random cross validation and within-family predictions capture different measures of prediction accuracy. While among-family prediction accuracy measures prediction accuracy of both the parent average component and the Mendelian sampling term, within-family prediction only measures how accurately the Mendelian sampling term can be predicted. With this paper we aim to foster a critical approach to different measures of genomic prediction accuracy and a careful analysis of values observed in genomic selection experiments and reported in literature.

scholarly journals Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

2019 ◽  
Vol 10 (2) ◽  
pp. 581-590 ◽  
Author(s):  
Smaragda Tsairidou ◽  
Alastair Hamilton ◽  
Diego Robledo ◽  
James E. Bron ◽  
Ross D. Houston
Keyword(s):  
Atlantic Salmon ◽  
Genomic Selection ◽  
Environmental Sustainability ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Imputation Accuracy ◽  
Cost Effective ◽  
High Density ◽  
Genotype Imputation ◽  
Breeding Programs

Genomic selection enables cumulative genetic gains in key production traits such as disease resistance, playing an important role in the economic and environmental sustainability of aquaculture production. However, it requires genome-wide genetic marker data on large populations, which can be prohibitively expensive. Genotype imputation is a cost-effective method for obtaining high-density genotypes, but its value in aquaculture breeding programs which are characterized by large full-sibling families has yet to be fully assessed. The aim of this study was to optimize the use of low-density genotypes and evaluate genotype imputation strategies for cost-effective genomic prediction. Phenotypes and genotypes (78,362 SNPs) were obtained for 610 individuals from a Scottish Atlantic salmon breeding program population (Landcatch, UK) challenged with sea lice, Lepeophtheirus salmonis. The genomic prediction accuracy of genomic selection was calculated using GBLUP approaches and compared across SNP panels of varying densities and composition, with and without imputation. Imputation was tested when parents were genotyped for the optimal SNP panel, and offspring were genotyped for a range of lower density imputation panels. Reducing SNP density had little impact on prediction accuracy until 5,000 SNPs, below which the accuracy dropped. Imputation accuracy increased with increasing imputation panel density. Genomic prediction accuracy when offspring were genotyped for just 200 SNPs, and parents for 5,000 SNPs, was 0.53. This accuracy was similar to the full high density and optimal density dataset, and markedly higher than using 200 SNPs without imputation. These results suggest that imputation from very low to medium density can be a cost-effective tool for genomic selection in Atlantic salmon breeding programs.

Variation within and between F2-derived families for grain yield and barley malting quality

2001 ◽  
Vol 52 (1) ◽  
pp. 85 ◽  
Author(s):  
M. Q. Lu ◽  
L. O'Brien ◽  
I. M. Stuart
Keyword(s):  
Genetic Variation ◽  
Grain Yield ◽  
Near Infrared ◽  
Malting Quality ◽  
Breeding Programs ◽  
Breeding Lines ◽  
Selection Strategies ◽  
Infrared Transmittance ◽  
Selection For ◽  
F2 Generation

Variation within and between F2-derived families for grain yield and malting quality was investigated using F4 breeding lines derived from F2 families of 4 barley crosses. The variation between F2-derived families was greater than within F2-derived families for grain yield and all malting quality attributes. Superior segregates almost exclusively came from the best performing families. The greater similarity of lines eventually drawn from an F2-derived family has significant implications for selection strategies in barley breeding programs as it facilitates the early discard of F2-derived families. To maximise the exploitation of genetic variation as early as possible, selection for malting quality could start in the F2 generation using near infrared transmittance (NIT) spectroscopy and for grain yield in the F3 generation.

scholarly journals Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Owen M. Powell ◽  
Kai P. Voss-Fels ◽  
David R. Jordan ◽  
Graeme Hammer ◽  
Mark Cooper
Keyword(s):  
Plant Breeding ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Predictive Ability ◽  
Complex Trait ◽  
Substitution Effects ◽  
Term Prediction ◽  
Gxe Interactions

Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (GxE) interactions generate allele substitution effects that are non-stationary across different contexts. Such non-stationary effects of alleles are either ignored or assumed to be implicitly captured by most gene-to-phenotype (G2P) maps used in genomic prediction. The implicit capture of non-stationary effects of alleles requires the G2P map to be re-estimated across different contexts. We discuss the development and application of hierarchical G2P maps that explicitly capture non-stationary effects of alleles and have successfully increased short-term prediction accuracy in plant breeding. These hierarchical G2P maps achieve increases in prediction accuracy by allowing intermediate processes such as other traits and environmental factors and their interactions to contribute to complex trait variation. However, long-term prediction remains a challenge. The plant breeding community should undertake complementary simulation and empirical experiments to interrogate various hierarchical G2P maps that connect GxG and GxE interactions simultaneously. The existing genetic correlation framework can be used to assess the magnitude of non-stationary effects of alleles and the predictive ability of these hierarchical G2P maps in long-term, multi-context genomic predictions of complex traits in plant breeding.

How Genetic Variance and Number of Genotypes and Markers Influence Estimates of Genomic Prediction Accuracy in Plant Breeding

Crop Science ◽  
2015 ◽  
Vol 55 (5) ◽  
pp. 1911-1924 ◽  
Author(s):  
Sidi Boubacar Ould Estaghvirou ◽  
Joseph O. Ogutu ◽  
Hans-Peter Piepho
Keyword(s):  
Plant Breeding ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Genetic Variance

scholarly journals Plant Breeding Integrated with Genomic-Enabled Prediction

OBM Genetics ◽  
2021 ◽  
Vol 05 (03) ◽  
pp. 1-1
Author(s):  
Siamak Shirani Bidabadi ◽  
◽  
Parisa Sharifi ◽  
S. Mohan Jain ◽  
◽  
...  
Keyword(s):  
Gene Flow ◽  
Plant Breeding ◽  
Genomic Prediction ◽  
Crop Production ◽  
Economic Benefits ◽  
Plant Genome ◽  
Plant Production ◽  
Breeding Methods ◽  
Breeding Programs ◽  
Conventional Breeding

Plant breeding programs have used conventional breeding methods, such as hybridization, induced mutations, and other methods to manipulate the plant genome within the species' natural genetic boundaries to improve crop varieties. However, repeatedly using conventional breeding methods might lead to the erosion of the gene reservoir, thereby rendering crops vulnerable to environmental stresses and hampering future progress in crop production, food and nutritional security, and socio-economic benefits. Integrating innovative technologies in breeding programs to accelerate gene flow is critical for sustaining global plant production. Genomic prediction is a promising tool to assist the rapid selection of premiere genotypes and accelerate breeding gains for climate-resilient plant varieties. This review surveys the annals and principles of genomic-enabled prediction. Based on the problem that is investigated through the prediction, as well as several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, the number of markers, sample size, and the interaction between genotype and environment, different levels of accuracy have been reported. Genomic prediction might play a decisive role and facilitate gene flow from gene bank accessions to elite lines in future breeding programs.

scholarly journals Multi-Species Genomics-Enabled Selection for Improving Agroecosystems Across Space and Time

2021 ◽  
Vol 12 ◽  
Author(s):  
Marnin D. Wolfe ◽  
Jean-Luc Jannink ◽  
Michael B. Kantar ◽  
Nicholas Santantonio
Keyword(s):  
Food Security ◽  
Plant Breeding ◽  
Crop Yields ◽  
Unintended Consequences ◽  
Breeding Methods ◽  
Agricultural Systems ◽  
Breeding Programs ◽  
Space And Time ◽  
Selection Strategies ◽  
Selection For

Plant breeding has been central to global increases in crop yields. Breeding deserves praise for helping to establish better food security, but also shares the responsibility of unintended consequences. Much work has been done describing alternative agricultural systems that seek to alleviate these externalities, however, breeding methods and breeding programs have largely not focused on these systems. Here we explore breeding and selection strategies that better align with these more diverse spatial and temporal agricultural systems.

scholarly journals Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for dealing with G × E

2019 ◽  
Author(s):  
L.M. Souza ◽  
F.R. Francisco ◽  
P.S. Gonçalves ◽  
E.J. Scaloppi Junior ◽  
V. Le Guen ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Rubber Tree ◽  
Tree Breeding ◽  
Genotypic Effect ◽  
Breeding Programs ◽  
Genetic Breeding ◽  
Effect Model

AbstractSeveral genomic prediction models incorporating genotype × environment (G×E) interactions have recently been developed and used in genomic selection (GS) in plant breeding programs. G×E interactions decrease selection accuracy and limit genetic gains in plant breeding. Two genomic data sets were used to compare the prediction ability of multi-environment G×E genomic models and two kernel methods (a linear kernel (genomic best linear unbiased predictor, GBLUP) (GB) and a nonlinear kernel (Gaussian kernel, GK)) and prediction accuracy (PA) of five genomic prediction models: (1) one without environmental data (BSG); (2) a single-environment, main genotypic effect model (SM); (3) a multi-environment, main genotypic effect model (MM); (4) a multi-environment, single variance GxE deviation model (MDs); and (5) a multi-environment, environment-specific variance GxE deviation model (MDe). We evaluated the utility of GS with 435 rubber tree individuals in two sites and genotyped the individuals with genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were estimated for diameter (DAP) and height (AP) at different ages, with a heritability ranging from 0.59 to 0.75 for both traits. Applying the model (BSG, SM, MM, MDs, and MDe) and kernel method (GBLUP and GK) combinations to rubber tree data showed that models with the nonlinear GK and linear GBLUP kernel had similar PAs. Multi-environment models were superior to single-environment genomic models regardless the kernel (GBLUP or GK), suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. In the best scenario (well-watered (WW / GK), an increase of 6.7 and 8.7 fold of genetic gain can be obtained for AP and DAP, respectively, with multi-environment GS (MM, MDe and MDS) than by conventional genetic breeding model (CBM). Furthermore, GS resulted in a more balanced selection response in DAP and AP and if used in conjunction with traditional genetic breeding programs will contribute to a reduction in selection time. With the rapid advances in and declining costs of genotyping methods, balanced against the overall costs of managing large progeny trials and potential increased gains per unit time, we are hopeful that GS can be implemented in rubber tree breeding programs.

Sugarcane smut, caused by Sporisorium scitamineum, a major disease of sugarcane – a contemporary review

2021 ◽  
Author(s):  
Shamsul Arafin Bhuiyan ◽  
Robert Magarey ◽  
Meredith McNeil ◽  
Karen Aitken
Keyword(s):  
Perennial Crop ◽  
Breeding Programs ◽  
Breeding Lines ◽  
Sporisorium Scitamineum ◽  
Resistant Varieties ◽  
The World ◽  
Recent Developments ◽  
Dry Conditions ◽  
Selection For ◽  
Rapid Tool

Sugarcane smut caused by the fungus Sporisorium scitamineum is one of the major diseases of sugarcane worldwide, causing significant losses in productivity and profitability of this perennial crop. Teliospores of this fungus are air-borne, can travel long distances and remain viable in hot and dry conditions for more than six months. The disease is easily recognised by its long ‘whip’-like sorus produced on the apex or side shoots of sugarcane stalks. Each sorus can release up to 100 million teliospores in a day; the spores are relatively small (≤7.5 µ), light and can survive in harsh environmental conditions. The air-borne teliospores are the primary mode of smut spread around the world and across cane-growing regions. The most effective method of managing this disease is via resistant varieties. Due to the complex genomic makeup of sugarcane, selection for resistant traits is difficult in sugarcane breeding programs. In recent times, the application of molecular markers as a rapid tool of discarding susceptible genotypes early in the selection program has been investigated. Large effect resistance loci have been identified and have the potential to be utilised for marker-assisted selection to increase the frequency of resistant breeding lines in breeding programs. Recent developments in “omics” technologies (genomics, transcriptomics, proteomics, and metabolomics) have contributed to our understanding and provided insights into the mechanism of resistance and susceptibility. This knowledge will further our understanding of smut and its interactions with sugarcane genotypes, and aid in the development of durable resistant varieties.

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