Genomic prediction in plants: opportunities for machine learning-based approaches

Farooq, Muhammad; Dijk, Aalt D.J. van; Nijveen, Harm; Mansoor, Shahid; Ridder, Dick de

doi:10.21203/rs.3.rs-1315622/v1

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…Collinear features were detected using pearsonr from the python library scipy (Virtanen et al, 2020). To test for possible effects of population structure we integrated the top 10 principal components as additional features to the input data set (Farooq et al, 2022;Zhao et al, 2012).…”

Section: Model Selectionmentioning

confidence: 99%

Genomic basis of seed colour in quinoa inferred from variant patterns using extreme gradient boosting

Sandell,

Holzweber,

Street

et al. 2024

Plant Biotechnology Journal

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SummaryQuinoa is an agriculturally important crop species originally domesticated in the Andes of central South America. One of its most important phenotypic traits is seed colour. Seed colour variation is determined by contrasting abundance of betalains, a class of strong antioxidant and free radicals scavenging colour pigments only found in plants of the order Caryophyllales. However, the genetic basis for these pigments in seeds remains to be identified. Here we demonstrate the application of machine learning (extreme gradient boosting) to identify genetic variants predictive of seed colour. We show that extreme gradient boosting outperforms the classical genome‐wide association approach. We provide re‐sequencing and phenotypic data for 156 South American quinoa accessions and identify candidate genes potentially controlling betalain content in quinoa seeds. Genes identified include novel cytochrome P450 genes and known members of the betalain synthesis pathway, as well as genes annotated as being involved in seed development. Our work showcases the power of modern machine learning methods to extract biologically meaningful information from large sequencing data sets.

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Section: Model Selectionmentioning

confidence: 99%

Genomic basis of seed colour in quinoa inferred from variant patterns using extreme gradient boosting

Sandell,

Holzweber,

Street

et al. 2024

Plant Biotechnology Journal

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Improvements in Prediction Performance of Ensemble Approaches for Genomic Prediction in Crop Breeding

Tomura,

Wilkinson,

Cooper

et al. 2024

Preprint

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The refinement of prediction accuracy in genomic prediction is a key factor in accelerating genetic gain for crop breeding. The mainstream strategy for improving prediction performance has been to develop an individual genomic prediction model that performs better than others. However, this approach has limitations due to the absence of consistent outperformance by an individual genomic prediction model. This problem is aligned with the implication from the No Free Lunch Theorem, which states that the performance of an individual prediction model is expected to be equivalent to the others when averaged across all prediction scenarios. Hence, we applied an alternative method, combining multiple individual genomic prediction models into an ensemble, to investigate the potential for increased prediction performance. We developed a na&iumlve ensemble-average model, which averages the predicted phenotypes of individual genomic prediction models with equal weight. The prediction performance of the genomic prediction models was evaluated over two traits that influence crop yield, days to anthesis and tiller number, in a publicly available Teosinte Nested Association Mapping (TeoNAM) dataset. Here we show an improvement in prediction accuracy for the ensemble approach compared with the six individual genomic prediction models in more than 95% of prediction scenarios. The advantage of the ensemble was derived from a more comprehensive view of the genomic architecture of these complex traits provided by contrasting genomic marker effects from the individual genomic prediction models. These results indicate the potential for ensemble approaches to enhance the performance of genomic prediction and therefore genetic gain, in crop breeding programs.

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Genomic Prediction of Wheat Grain Yield Using Machine Learning

Sirsat¹,

Oblessuc²,

Ramiro³

2022

Agriculture

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Genomic Prediction (GP) is a powerful approach for inferring complex phenotypes from genetic markers. GP is critical for improving grain yield, particularly for staple crops such as wheat and rice, which are crucial to feeding the world. While machine learning (ML) models have recently started to be applied in GP, it is often unclear what are the best algorithms and how their results are affected by the feature selection (FS) methods. Here, we compared ML and deep learning (DL) algorithms with classical Bayesian approaches, across a range of different FS methods, for their performance in predicting wheat grain yield (in three datasets). Model performance was generally more affected by the prediction algorithm than the FS method. Among all models, the best performance was obtained for tree-based ML methods (random forests and gradient boosting) and for classical Bayesian methods. However, the latter was prone to fitting problems. This issue was also observed for models developed with features selected by BayesA, the only Bayesian FS method used here. Nonetheless, the three other FS methods led to models with no fitting problem but similar performance. Thus, our results indicate that the choice of prediction algorithm is more important than the choice of FS method for developing highly predictive models. Moreover, we concluded that random forests and gradient boosting algorithms generate highly predictive and robust wheat grain yield GP models.

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Genomic prediction in plants: opportunities for machine learning-based approaches

Cited by 3 publications

References 42 publications

Genomic basis of seed colour in quinoa inferred from variant patterns using extreme gradient boosting

Genomic basis of seed colour in quinoa inferred from variant patterns using extreme gradient boosting

Improvements in Prediction Performance of Ensemble Approaches for Genomic Prediction in Crop Breeding

Genomic Prediction of Wheat Grain Yield Using Machine Learning

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