Genomic Selection in Wheat

Sweeney, Daniel W.; Sun, Jin; Taagen, Ella; Sorrells, Mark E.

doi:10.1016/b978-0-08-102163-7.00013-2

Cited by 11 publications

(5 citation statements)

References 88 publications

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“…There are many recent published examples of selection on relatively small scale data sets in crop and animal breeding (Gorjanc et al, 2018;Dimitrijevic and Horn, 2018;Ozimati et al, 2019;Rio et al, 2019;Sweeney et al, 2019). Linear methods such as GBLUP typically work quite well for building predictions for almost all applications.…”

Section: Paradox Of Choice: Many Methods One Gebvmentioning

confidence: 99%

On to the Next Chapter for Crop Breeding: Convergence with Data Science

Ersoz

Martín

Stapleton

2019

Preprint

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Crop breeding is as ancient as the invention of cultivation.  In essence, the objective of crop breeding is to improve plant fitness under human cultivation conditions, making crops more productive while maintaining consistency in life cycle and quality. The applications of predictive breeding has been gaining momentum in agricultural industry and public breeding programs for the last decade, in the aftermath of genomic selection being recognized and widely applied for accelerating genetic gain in breeding programs. The massive amounts of data that has been generated by industry and farmers year after year through several decades has finally been recognized as an asset. A wide range of analytical methods such as machine learning, deep learning and artificial intelligence that were initially developed for diverse quantitative disciplines are now being adopted to crop breeding decision making processes. New technologies are currently being developed that would enable integration of data from various domains such as geospatial variables and a multitude of phenotypic responses as well as genetic information, in order to identify, develop and improve crop faster via partial or full automation of the decisions that pertain to variety development. Here we will discuss and summarize efforts from public and private domains for predictive analytics, and its applications to crop breeding and agricultural product development, and provide suggestions for future research.

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Section: Paradox Of Choice: Many Methods One Gebvmentioning

confidence: 99%

On to the Next Chapter for Crop Breeding: Convergence with Data Science

Ersoz

Martín

Stapleton

2019

Preprint

View full text Add to dashboard Cite

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“…Breeding has contributed significantly to the improvement of wheat yields in the past ( e.g. , Mackay et al 2011 ; Rife et al 2019 ), and the recent adoption of genomic selection could enable further significant improvements ( Gaynor et al 2017 ; Belamkar et al 2018 ; Sweeney et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge

et al. 2021

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We propose a novel Bayesian approach that robustifies genomic modelling by leveraging expert knowledge through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and non-additive genetic variation, which leads to an intuitive model parameterization that can be visualised as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which expert knowledge is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates expert knowledge through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of expert knowledge in the context of plant breeding. A simulation study shows that the proposed priors implementing expert knowledge improve the robustness of genomic modelling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study expert knowledge increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of expert knowledge priors for genomic modelling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modelling.

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“…, Brinton et al 2020 ; Scott et al 2021 ), and implement genome-assisted breeding schemes using marker-assisted and/or genomic selection ( e.g. , Rasheed and Xia 2019 ; Sweeney et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Trend, population structure, and trait mapping from 15 years of national varietal trials of UK winter wheat

Shorinola

Simmonds

Wingen

et al. 2021

G3 Genes|Genomes|Genetics

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There are now a rich variety of genomic and genotypic resources available to wheat researchers and breeders. However, the generation of high-quality and field-relevant phenotyping data which is required to capture the complexities of gene x environment interactions remains a major bottleneck. Historical datasets from national variety performance trials (NVPT) provide sufficient dimensions, in terms of numbers of years and locations, to examine phenotypic trends and study gene x environment interactions. Using NVPT for winter wheat varieties grown in the UK between 2002 – 2017, we examined temporal trends for eight traits related to yield, adaptation, and grain quality performance. We show a non-stationary linear trend for yield, grain protein content, HFN and days to ripening. Our data also show high environmental stability for yield, grain protein content and specific weight in UK winter wheat varieties and high environmental sensitivity for Hagberg Falling Number. We also show that UK varieties released within this period cluster into four main population groups. Using the historical NVPT data in a genome-wide association analysis, we uncovered a significant marker-trait association peak on wheat chromosome 6A spanning the NAM-A1 gene that have been previously associated with early senescence. Together our results show the value of utilizing the data routinely collected during national variety evaluation process for examining breeding progress and the genetic architecture of important traits.

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Genomic Selection in Wheat

Cited by 11 publications

References 88 publications

On to the Next Chapter for Crop Breeding: Convergence with Data Science

On to the Next Chapter for Crop Breeding: Convergence with Data Science

Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge

Trend, population structure, and trait mapping from 15 years of national varietal trials of UK winter wheat

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