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

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

doi:10.12688/f1000research.122437.2

Cited by 3 publications

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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: Methodsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

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

View full text Add to dashboard Cite

show abstract

Trait genetic architecture and population structure determine model selection for genomic prediction in naturalArabidopsis thalianapopulations

Gibbs,

Paril,

Fournier-level

2024

Preprint

View full text Add to dashboard Cite

In plants, genomic prediction is employed to predict agronomically relevant traits, such as crop yield, phenology, and the concentration of minerals and metabolites, which possess varying genetic bases. Selecting the most appropriate model for a trait's genetic effects distribution and the population's allele frequencies is crucial. Linear regression models are typically preferred for genomic prediction given the number of genome-wide markers are usually much greater than the number of observations. However, additive models may not suit all genetic architectures and ancestral populations. Machine learning (ML) is suggested to enhance genomic prediction by modelling complex biology, such as epistasis, though has underperformed in past studies. This study evaluates (1) genomic prediction model sensitivity to trait ontology and (2) the impact of ancestral population-specific selection on prediction and model choice. Examining 35 quantitative traits in Arabidopsis thaliana with ~1000 observations across Europe, we assessed penalised regression, random forest, and multilayer perceptron performance. Regression models were generally most accurate, except in some biochemical traits where random forest excelled. We link this result to the biochemical traits having more simple genetic architecture, than macroscopic traits. Moreover, macroscopic traits, especially related to flowering and yield were strongly correlated with population structure and demography, while molecular traits required individual marker resolution. The study thus highlights the relevance of ensemble approaches for simple molecular traits in crop breeding and underscores the need to consider differential ancestral selection when designing association panels and training populations.

show abstract

Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits

Kelly,

McLaughlin

2024

PLoS ONE

View full text Add to dashboard Cite

We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidopsis Genomes Project. An existing body of evidence suggests that linear models may be impeded by their inability to make use of non-additive effects to explain phenotypic variation at the population level. The results presented here use a nested cross-validation approach to confirm that some machine learning methods have the ability to statistically outperform linear prediction models, with the optimal model dependent on availability of training data and genetic architecture of the trait in question. Linear models were competitive in their performance as per previous work, though the neural network class of predictors was observed to be the most accurate and robust for traits with high heritability. The extent to which non-linear models exploit interaction effects will require further investigation of the causal pathways that lay behind their predictions. Future work utilizing more traits and larger sample sizes, combined with an improved understanding of their respective genetic architectures, may lead to improvements in prediction accuracy.

show abstract

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

Cited by 3 publications

References 55 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

Trait genetic architecture and population structure determine model selection for genomic prediction in naturalArabidopsis thalianapopulations

Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits

Contact Info

Product

Resources

About