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Machine Learning models identify gene predictors of waggle dance behaviour in honeybees
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  • Marcell Veiner,
  • Juliano Morimoto,
  • Elli Leadbeater,
  • Fabio Manfredini
Marcell Veiner
University of Aberdeen

Corresponding Author:[email protected]

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Juliano Morimoto
University of Aberdeen
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Elli Leadbeater
Royal Holloway University of London Faculty of Science
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Fabio Manfredini
University of Aberdeen
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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 – known as ‘neurogenomics’ – to select the best candidates that associate with patterns of interest. This approach has relied so far on a set of powerful statistical tools capable to provide a snapshot of the expression of many thousands of genes that are present in an organism’s genome. However, traditional neurogenomic analyses have some well-known limitations; above all, the limited number of biological replicates compared to the number of genes tested – often referred to as “curse of dimensionality”. Here we implemented a new Machine Learning (ML) approach that can be used as a complement to established methods of transcriptomic analyses. We tested three types of ML models for their performance in the 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: the G-protein coupled receptor boss and hnRNP A1, a gene involved in alternative splicing. Overall, our study demonstrates the application of Machine Learning to analyse transcriptomics data and identify 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.
23 Aug 2021Submitted to Molecular Ecology Resources
06 Sep 2021Submission Checks Completed
06 Sep 2021Assigned to Editor
14 Sep 2021Reviewer(s) Assigned
23 Nov 2021Review(s) Completed, Editorial Evaluation Pending
07 Dec 2021Editorial Decision: Revise Minor
20 Jan 2022Review(s) Completed, Editorial Evaluation Pending
20 Jan 20221st Revision Received
27 Jan 2022Reviewer(s) Assigned
22 Feb 2022Editorial Decision: Revise Minor
02 Mar 2022Review(s) Completed, Editorial Evaluation Pending
02 Mar 20222nd Revision Received
21 Mar 2022Editorial Decision: Accept
Aug 2022Published in Molecular Ecology Resources volume 22 issue 6 on pages 2248-2261. 10.1111/1755-0998.13611