Machine Learning models identify gene predictors of waggle dance behaviour in honeybees

Marcell Veiner* (Corresponding Author), Juliano Morimoto Borges, Ellouise Leadbeater, Fabio Manfredini* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The molecular characterisation of complex behaviours is a challenging task as a range of different factors are often involved to produce the observed phenotype. An established approach is to look at the overall levels of expression of brain genes – or ‘neurogenomics’ – to select the best candidates that associate with patterns of interest. However, traditional neurogenomic analyses have some well-known limitations; above all, the usually limited number of biological replicates compared to the number of genes tested – known as “curse of dimensionality”. In this study we implemented a Machine Learning (ML) approach that can be used as a complement to more established methods of transcriptomic analyses. We tested three supervised learning algorithms (Random Forests, Lasso and Elastic net Regularized Generalized Linear Model, and Support Vector Machine) for their performance in the characterization of transcriptomic patterns and identification of genes associated with honeybee waggle dance. We then intersected the results of these analyses with traditional outputs of differential gene expression analyses and identified two promising candidates for the neural regulation of the waggle dance: boss and hnRNP A1. Overall, our study demonstrates the application of Machine Learning to analyse transcriptomics data and identify candidate genes underlying social behaviour. This approach has great potential for application to a wide range of different scenarios in evolutionary ecology, when investigating the genomic basis for complex phenotypic traits and can present some clear advantages compared to the established tools of gene expression analysis, making it a valuable complement for future studies.

Original languageEnglish
JournalMolecular Ecology Resources
Early online date18 Apr 2022
DOIs
Publication statusE-pub ahead of print - 18 Apr 2022

Keywords

  • bioinfomatics
  • feature selection
  • genomics
  • gene structure and function
  • insects
  • social evolution

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