[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126250-en":3,"doc-seo-126250-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126250,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Machine Learning for Prediction of Resistance Scores in Wheat (Triticum aestivum L.)","Machine learning approaches are evaluated for predicting wheat resistance scores, aiming to improve genomic prediction accuracy relative to RR-BLUP, especially when resistance is treated as an ordinal rather than purely metric response. A cross-validation study uses 361 wheat genotypes assessed across five fungal diseases to compare 19 genomic prediction approaches differing in prediction methods, marker predictors, and response transformations. RR-BLUP is among the top performers overall, but for P. triticina gradient boosting and random forests increase accuracy from 0.64 to 0.71.","Plant Breeding  \nORIGINAL ARTICLE  OPEN ACCESS   \nMachine Learning for Prediction of Resistance Scores in Wheat (Triticum aestivum L.)  \nPhilipp Georg Heilmann1 | Yohannes Fekadu Difabachew1 | Matthias Frisch1  | Anna Luise Moritz2 | Andreas Stahl3 | Benjamin Wittkop2 | Rod J. Snowdon2  | Michael Koch4 | Martin Kirchhoff5 | László Cselényi6 | Markus Wolf7,8 | Jutta Förster8 | Carola Zenke-Philippi1   \n1Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany | 2Institute of Agronomy and Plant Breeding I, Justus Liebig University, Gießen, Germany | 3Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany | 4Deutsche  \nSaatveredelung AG, Lippstadt, Germany | 5Nordsaat Saatzucht GmbH, Langenstein, Germany | 6W. von Borries-Eckendorf GmbH & Co. KG, Leopoldshöhe, Germany | 7German Seed Alliance GmbH, Holtsee, Germany | 8Saaten-Union Biotec GmbH, Leopoldshöhe, Germany Correspondence: Carola Zenke-Philippi (biometry.popgen@uni-giessen.de)  \nReceived: 7 February 2024 | Revised: 12 July 2024 | Accepted: 10 October 2024  \nFunding: This research was supported by the German Federal Ministry of Food and Agriculture, Grant/Award number: FKZ 2818403A18 .  \nKeywords: cross-validation | genomic prediction | machine learning | wheat  \nABSTRACT  \nMachine learning methods were shown to improve the prediction accuracies of genomic prediction of resistance scores compared to methods like RR-BLUP, which were originally designed for metric rather than ordinal response values. We conducted across-validation study with 361 wheat genotypes evaluated for five fungal diseases. Our objective was to compare the prediction accuracy and the ability to identify the most resistant genotypes of 19 genomic prediction approaches. Each approach consisted of a different combination of prediction method (RR-BLUP, an alternative method with heterogeneous marker variances, Bayesian generalized linear regression with an ordinal response, support vector machine, gradient boosting machine and random forest), predictor (single SNP markers, LD-based haplotype blocks, 250 variables generated with an autoencoder and SNPs identified with incremental feature selection) and response value (untransformed and logit-transformed resistance scores) . In our dataset, RR-BLUP was consistently among the methods with the largest prediction accuracies and the best abilities to identify resistant genotypes in four of five investigated traits. However, in P. triticina, using gradient boosting machine and random forest instead of RR-BLUP increased the prediction accuracy from 0.64 to 0.71, indicating that machine learning methods may have an advantage over linear models in genomic prediction. We also found that even though there was a positive correlation between the prediction accuracy and Cohen's 􀀟, a measure to judge how well the most resistant genotypes can be identified, the correlation isnot perfect and a large value for the prediction accuracy does not necessarily translate into an equally large 􀀟 value.  \n1 | Introduction  \nIn the last two decades, genomic prediction (Meuwissen, Hayes, and Goddard 2001), which aims at predicting the phenotypic  \nvalue of an individual from its genotypic data, has increasingly replaced phenotypic selection. The advantage is that only a part of all the genotypes in the breeding population have to be phenotyped or, even better, that phenotypic data that are already  \n\n| Abbreviations: BGLR, Bayesian generalized linear regression; GBLUP, genomic best linear unbiased prediction; GBM, gradient boosting machine; GWAS, genome-wide association study; LD, linkage disequilibrium; RF, random forest; RMLA, estimation of the error and genetic variance components with restricted maximum likelihood and partitioning according to ANOVA variance components; RMSE, square root of the mean square error; RR-BLUP, ridge-regression best linear unbiased prediction; SNP, single nucleotide polymorphism; S","cbCaie1CjS63VJbV","https://ap.wps.com/l/cbCaie1CjS63VJbV","pdf",1068172,9,1,14,"English","en",105,"# Introduction\n## Genomic prediction and resistance-trait evaluation\n## Predictors, response types, and statistical models\n## Standard methods vs alternatives (RR-BLUP and Bayesian models)\n# Study design and comparison framework\n## Cross-validation dataset and diseases tested\n## Prediction approaches and their components\n# Results and interpretation","[{\"question\":\"What problem does the study address in wheat breeding?\",\"answer\":\"It addresses how to predict phenotypic resistance scores from genotypic data more accurately, particularly when resistance scores behave as ordinal outcomes rather than metric values.\"},{\"question\":\"How was the predictive performance evaluated?\",\"answer\":\"Through an across-validation design using 361 wheat genotypes evaluated for five fungal diseases, comparing 19 prediction approaches based on accuracy and the ability to identify resistant genotypes.\"},{\"question\":\"Which machine learning methods improved predictions for specific diseases?\",\"answer\":\"For P. triticina, using gradient boosting machine and random forest instead of RR-BLUP increased prediction accuracy from 0.64 to 0.71.\"}]","Machine Learning for Prediction of Resistance Scores in Wheat (Triticum aestivum L.) | 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problem does the study address in wheat breeding?","Question",{"text":77,"@type":78},"It addresses how to predict phenotypic resistance scores from genotypic data more accurately, particularly when resistance scores behave as ordinal outcomes rather than metric values.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How was the predictive performance evaluated?",{"text":82,"@type":78},"Through an across-validation design using 361 wheat genotypes evaluated for five fungal diseases, comparing 19 prediction approaches based on accuracy and the ability to identify resistant genotypes.",{"name":84,"@type":75,"acceptedAnswer":85},"Which machine learning methods improved predictions for specific diseases?",{"text":86,"@type":78},"For P. triticina, using gradient boosting machine and random forest instead of RR-BLUP increased prediction accuracy from 0.64 to 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