Bayesian Genomic-Enabled Prediction Models for Ordinal and Count Data

Montesinos‐López, Osval A.; Montesinos‐López, Abelardo; Crossa, José

doi:10.1007/978-3-319-63170-7_4

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…Since the seminal work of ( Meuwissen et al, 2001 ), genomic selection (GS) models harnessed the genomic marker information combined with observed phenotypic data to improve the prediction of unobserved phenotypic values. Most of the GS methods proposed over the last 2 decades were developed for continuous phenotypic traits, which were assumed to be normally distributed ( Kizilkaya et al, 2014 ; Montesinos-López et al, 2015a ; Montesinos-López et al, 2017 ; Silveira et al, 2019 ). However, several crops have categorical traits that have agronomic importance ( Iwata et al, 2013 ; Martínez-García et al, 2017 ; Sousa et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…Following this idea, Montesinos-López et al (2015a) extended the genomic best linear unbiased predictor (GBLUP) model ( Burgueño et al, 2012 ; Jarquín et al, 2014 ) for ordered categorical data using a probit link function. They also introduced a logit link based model for categorical traits that included interaction effects ( Montesinos-López et al, 2015b ; Montesinos-López et al, 2017 ). While some of the models above were developed to predict ordinal categorical traits based on genomic information, none of the models had provisions to integrate multi-type data to improve prediction.…”

Section: Introductionmentioning

confidence: 99%

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Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification

2023

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Modern plant breeding programs collect several data types such as weather, images, and secondary or associated traits besides the main trait (e.g., grain yield). Genomic data is high-dimensional and often over-crowds smaller data types when naively combined to explain the response variable. There is a need to develop methods able to effectively combine different data types of differing sizes to improve predictions. Additionally, in the face of changing climate conditions, there is a need to develop methods able to effectively combine weather information with genotype data to predict the performance of lines better. In this work, we develop a novel three-stage classifier to predict multi-class traits by combining three data types—genomic, weather, and secondary trait. The method addressed various challenges in this problem, such as confounding, differing sizes of data types, and threshold optimization. The method was examined in different settings, including binary and multi-class responses, various penalization schemes, and class balances. Then, our method was compared to standard machine learning methods such as random forests and support vector machines using various classification accuracy metrics and using model size to evaluate the sparsity of the model. The results showed that our method performed similarly to or better than machine learning methods across various settings. More importantly, the classifiers obtained were highly sparse, allowing for a straightforward interpretation of relationships between the response and the selected predictors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification

2023

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“…To address these issues, breeders and biometricians have used various strategies. The simplest approach is ignoring the lack of normality, under the argument that large sample sizes follow the central limit theorem -which states that treatment means have an approximately normal distribution when sample sizes are large enough (Montesinos-López, Montesinos-López, and Crossa, 2017). Another common alternative is transforming the phenotypes, which can stabilize the residual variation, and hence help fulfill the assumptions required by linear modeling.…”

Section: Introductionmentioning

confidence: 99%

Using visual scores and categorical data for genomic prediction of complex traits in breeding programs

Azevedo

Ferrão

Benevenuto

et al. 2023

Preprint

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Most genomic prediction methods are based on assumptions of normality due to their simplicity, robustness, and ease of implementation. However, in plant and animal breeding, target traits are often collected as categorical data, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of crucial genetic parameters. In this study, we examined the main challenges of categorical phenotypes in genomic prediction and genetic parameter estimation using mixed models, Bayesian approaches, and machine learning techniques. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1 to 3 and 1 to 5 scores) is the best strategy, even considering errors and subjectivity associated with visual scores; (ii) in the context of genomic prediction, Linear Mixed Models and Bayesian Linear Regression Models are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification technique; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry, which can guide breeders' decisions; and (v) a comparison of continuous and categorical phenotypes testing for complex traits with low heritability, found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using categorical traits to explore genetic information in breeding programs, and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping.

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Using visual scores for genomic prediction of complex traits in breeding programs

Azevedo,

Ferrão,

Benevenuto

et al. 2023

Theor Appl Genet

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Bayesian Genomic-Enabled Prediction Models for Ordinal and Count Data

Cited by 4 publications

References 45 publications

Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification

Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification

Using visual scores and categorical data for genomic prediction of complex traits in breeding programs

Using visual scores for genomic prediction of complex traits in breeding programs

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