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      Prediction of near‐term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding

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          Abstract

          Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed. Grain quality is paramount in determining its value and downstream use. While we know that climate change threatens global crop yields, a better understanding of impacts on wheat end‐use quality is also critical. Combining quantitative genetics with climate model outputs, we investigated UK‐wide trends in genotypic adaptation for wheat quality traits. In our approach, we augmented genomic prediction models with environmental characterisation of field trials to predict trait values and climate effects in historical field trial data between 2001 and 2020. Addition of environmental covariates, such as temperature and rainfall, successfully enabled prediction of genotype by environment interactions (G × E), and increased prediction accuracy of most traits for new genotypes in new year cross validation. We then extended predictions from these models to much larger numbers of simulated environments using climate scenarios projected under Representative Concentration Pathways 8.5 for 2050–2069. We found geographically varying climate change impacts on wheat quality due to contrasting associations between specific weather covariables and quality traits across the UK. Notably, negative impacts on quality traits were predicted in the East of the UK due to increased summer temperatures while the climate in the North and South‐west may become more favourable with increased summer temperatures. Furthermore, by projecting 167,040 simulated future genotype–environment combinations, we found only limited potential for breeding to exploit predictable G × E to mitigate year‐to‐year environmental variability for most traits except Hagberg falling number. This suggests low adaptability of current UK wheat germplasm across future UK climates. More generally, approaches demonstrated here will be critical to enable adaptation of global crops to near‐term climate change.

          Abstract

          Wheat bread‐making quality is strongly affected by the environment, both directly and through genotype by environment interaction (G × E) effects. Augmentation of genomic prediction models with environmental covariables enabled successful G × E prediction in a large historical field trial dataset. Prediction models were projected into future UK climate scenarios. Negative impacts due to increased summer temperatures were predicted in the main wheat growing area in the east, but the climate in the North and South‐west may become more favourable. For most traits, only limited potential was found for breeders to exploit predictable G × E and select stable genotypes to mitigate year‐to‐year climate variability.

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          Efficient methods to compute genomic predictions.

          P VanRaden (2008)
          Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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            MissForest--non-parametric missing value imputation for mixed-type data.

            Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch
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              Stochastic gradient boosting

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                Author and article information

                Contributors
                nick.fradgley@niab.com
                Journal
                Glob Chang Biol
                Glob Chang Biol
                10.1111/(ISSN)1365-2486
                GCB
                Global Change Biology
                John Wiley and Sons Inc. (Hoboken )
                1354-1013
                1365-2486
                23 December 2022
                March 2023
                : 29
                : 5 ( doiID: 10.1111/gcb.v29.5 )
                : 1296-1313
                Affiliations
                [ 1 ] NIAB Cambridge UK
                [ 2 ] Met Office Exeter UK
                [ 3 ] International Maize and Wheat Improvement Center (CIMMYT) Carretera México‐Veracruz Mexico
                [ 4 ] Institute for Genomics Diversity, Cornell University Ithaca NY USA
                [ 5 ] Universidad Autonoma del Estado de Quintana Roo Chetumal Quintana Roo Mexico
                [ 6 ] DSVUK, Top Dawkins Barn Banbury UK
                Author notes
                [*] [* ] Correspondence

                Nick S. Fradgley, NIAB, 93 Lawrence Weaver Road, Cambridge CB3 0LE, UK.

                Email: nick.fradgley@ 123456niab.com

                Author information
                https://orcid.org/0000-0002-7868-8795
                https://orcid.org/0000-0001-7142-5418
                https://orcid.org/0000-0001-5519-4357
                https://orcid.org/0000-0003-1137-6786
                https://orcid.org/0000-0002-7526-2991
                https://orcid.org/0000-0001-9429-5855
                https://orcid.org/0000-0002-0685-2867
                https://orcid.org/0000-0002-9587-8523
                https://orcid.org/0000-0002-8295-2667
                https://orcid.org/0000-0001-8355-7354
                https://orcid.org/0000-0002-4890-301X
                Article
                GCB16552 GCB-22-1550.R1
                10.1111/gcb.16552
                10108302
                36482280
                84b0b014-b579-4a78-acc4-dbb9becb49f9
                © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 November 2022
                : 12 July 2022
                : 29 November 2022
                Page count
                Figures: 7, Tables: 2, Pages: 18, Words: 12690
                Funding
                Funded by: Biotechnology and Biological Sciences Research Council , doi 10.13039/501100000268;
                Award ID: BB/M011194/1
                Funded by: Agriculture and Horticulture Development Board , doi 10.13039/501100007981;
                Funded by: British Society of Plant Breeders Limited
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                March 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:17.04.2023

                adaptation,climate change impacts,genomic prediction,grain quality,wheat breeding

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