Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding
Genomic selection (GS) is a predictive methodology that is changing plant breeding. Genomic selection trains a statistical machine-learning model using available phenotypic and genotypic data with which predictions are performed for individuals that were only genotyped. For this reason, some statistical machine-learning methods are being implemented in GS, but in order to improve the selection of new genotypes early in the prediction process, the exploration of new statistical machine-learning algorithms must continue. In this paper, we performed a benchmarking study between the Bayesian threshold genomic best linear unbiased predictor model (TGBLUP; popular in GS) and the gradient boosting machine (GBM). This comparison was done using four real wheat (Triticum aestivum L.) data sets with categorical traits measured in terms of two metrics: the proportion of cases correctly classified (PCCC) and the Kappa coefficient in the testing set. Under 10 random partitions with four different sizes of testing proportions (20, 40, 60, and 80%), we compared the two algorithms and found that in three of the four data sets, the GBM outperformed the TGBLUP model in terms of both metrics (PCCC and Kappa coefficient). In the larger data sets (Data Sets 3 and 4), the gain in terms of prediction accuracy of the GBM was considerably significant. For this reason, we encourage more research using the GBM in GS to evaluate its virtues in terms of prediction performance in the context of GS.
Main Authors: | , , , , , , |
---|---|
Format: | Article biblioteca |
Language: | English |
Published: |
Wiley
2022
|
Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Best Linear Unbiased Prediction, MARKER-ASSISTED SELECTION, WHEAT, BREEDING, RESEARCH, BEST LINEAR UNBIASED PREDICTOR, |
Online Access: | https://hdl.handle.net/10883/22081 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
dig-cimmyt-10883-22081 |
---|---|
record_format |
koha |
spelling |
dig-cimmyt-10883-220812023-11-15T15:01:50Z Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding Montesinos-Lopez, O.A. Gonzalez, H.N. Montesinos-Lopez, A. Daza-Torres, M. Lillemo, M. Montesinos-Lopez, J.C. Crossa, J. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION WHEAT BREEDING RESEARCH BEST LINEAR UNBIASED PREDICTOR Genomic selection (GS) is a predictive methodology that is changing plant breeding. Genomic selection trains a statistical machine-learning model using available phenotypic and genotypic data with which predictions are performed for individuals that were only genotyped. For this reason, some statistical machine-learning methods are being implemented in GS, but in order to improve the selection of new genotypes early in the prediction process, the exploration of new statistical machine-learning algorithms must continue. In this paper, we performed a benchmarking study between the Bayesian threshold genomic best linear unbiased predictor model (TGBLUP; popular in GS) and the gradient boosting machine (GBM). This comparison was done using four real wheat (Triticum aestivum L.) data sets with categorical traits measured in terms of two metrics: the proportion of cases correctly classified (PCCC) and the Kappa coefficient in the testing set. Under 10 random partitions with four different sizes of testing proportions (20, 40, 60, and 80%), we compared the two algorithms and found that in three of the four data sets, the GBM outperformed the TGBLUP model in terms of both metrics (PCCC and Kappa coefficient). In the larger data sets (Data Sets 3 and 4), the gain in terms of prediction accuracy of the GBM was considerably significant. For this reason, we encourage more research using the GBM in GS to evaluate its virtues in terms of prediction performance in the context of GS. 2022-05-24T00:10:13Z 2022-05-24T00:10:13Z 2022 Article Published Version https://hdl.handle.net/10883/22081 10.1002/tpg2.20214 English https://acsess.onlinelibrary.wiley.com/doi/10.1002/tpg2.20214#support-information-section https://hdl.handle.net/11529/10548140 CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose Open Access Madison, WI (USA) Wiley 3 15 20214 1940-3372 Plant Genome |
institution |
CIMMYT |
collection |
DSpace |
country |
México |
countrycode |
MX |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-cimmyt |
tag |
biblioteca |
region |
America del Norte |
libraryname |
CIMMYT Library |
language |
English |
topic |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION WHEAT BREEDING RESEARCH BEST LINEAR UNBIASED PREDICTOR AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION WHEAT BREEDING RESEARCH BEST LINEAR UNBIASED PREDICTOR |
spellingShingle |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION WHEAT BREEDING RESEARCH BEST LINEAR UNBIASED PREDICTOR AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION WHEAT BREEDING RESEARCH BEST LINEAR UNBIASED PREDICTOR Montesinos-Lopez, O.A. Gonzalez, H.N. Montesinos-Lopez, A. Daza-Torres, M. Lillemo, M. Montesinos-Lopez, J.C. Crossa, J. Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding |
description |
Genomic selection (GS) is a predictive methodology that is changing plant breeding. Genomic selection trains a statistical machine-learning model using available phenotypic and genotypic data with which predictions are performed for individuals that were only genotyped. For this reason, some statistical machine-learning methods are being implemented in GS, but in order to improve the selection of new genotypes early in the prediction process, the exploration of new statistical machine-learning algorithms must continue. In this paper, we performed a benchmarking study between the Bayesian threshold genomic best linear unbiased predictor model (TGBLUP; popular in GS) and the gradient boosting machine (GBM). This comparison was done using four real wheat (Triticum aestivum L.) data sets with categorical traits measured in terms of two metrics: the proportion of cases correctly classified (PCCC) and the Kappa coefficient in the testing set. Under 10 random partitions with four different sizes of testing proportions (20, 40, 60, and 80%), we compared the two algorithms and found that in three of the four data sets, the GBM outperformed the TGBLUP model in terms of both metrics (PCCC and Kappa coefficient). In the larger data sets (Data Sets 3 and 4), the gain in terms of prediction accuracy of the GBM was considerably significant. For this reason, we encourage more research using the GBM in GS to evaluate its virtues in terms of prediction performance in the context of GS. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION WHEAT BREEDING RESEARCH BEST LINEAR UNBIASED PREDICTOR |
author |
Montesinos-Lopez, O.A. Gonzalez, H.N. Montesinos-Lopez, A. Daza-Torres, M. Lillemo, M. Montesinos-Lopez, J.C. Crossa, J. |
author_facet |
Montesinos-Lopez, O.A. Gonzalez, H.N. Montesinos-Lopez, A. Daza-Torres, M. Lillemo, M. Montesinos-Lopez, J.C. Crossa, J. |
author_sort |
Montesinos-Lopez, O.A. |
title |
Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding |
title_short |
Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding |
title_full |
Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding |
title_fullStr |
Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding |
title_full_unstemmed |
Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding |
title_sort |
comparing gradient boosting machine and bayesian threshold blup for genome-based prediction of categorical traits in wheat breeding |
publisher |
Wiley |
publishDate |
2022 |
url |
https://hdl.handle.net/10883/22081 |
work_keys_str_mv |
AT montesinoslopezoa comparinggradientboostingmachineandbayesianthresholdblupforgenomebasedpredictionofcategoricaltraitsinwheatbreeding AT gonzalezhn comparinggradientboostingmachineandbayesianthresholdblupforgenomebasedpredictionofcategoricaltraitsinwheatbreeding AT montesinoslopeza comparinggradientboostingmachineandbayesianthresholdblupforgenomebasedpredictionofcategoricaltraitsinwheatbreeding AT dazatorresm comparinggradientboostingmachineandbayesianthresholdblupforgenomebasedpredictionofcategoricaltraitsinwheatbreeding AT lillemom comparinggradientboostingmachineandbayesianthresholdblupforgenomebasedpredictionofcategoricaltraitsinwheatbreeding AT montesinoslopezjc comparinggradientboostingmachineandbayesianthresholdblupforgenomebasedpredictionofcategoricaltraitsinwheatbreeding AT crossaj comparinggradientboostingmachineandbayesianthresholdblupforgenomebasedpredictionofcategoricaltraitsinwheatbreeding |
_version_ |
1787233006961819648 |