The structure of behavioral variation within a genotype
Abstract
Individual animals vary in their behaviors. This is true even when they share the same genotype and were reared in the same environment. Clusters of covarying behaviors constitute behavioral syndromes, and an individual's position along such axes of covariation is a representation of their personality. Despite these conceptual frameworks, the structure of behavioral covariation within a genotype is essentially uncharacterized and its mechanistic origins unknown. Passing hundreds of inbred Drosophila individuals through an experimental pipeline that captured hundreds of behavioral measures, we found sparse but significant correlations among small sets of behaviors. Thus, the space of behavioral variation has many independent dimensions. Manipulating the physiology of the brain, and specific neural populations, altered specific correlations. We also observed that variation in gene expression can predict an individual's position on some behavioral axes. This work represents the first steps in understanding the biological mechanisms determining the structure of behavioral variation within a genotype.
Additional Information
© 2021 Werkhoven et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Preprinted: 23 September 2019; Received: 18 November 2020; Accepted: 14 September 2021; Published: 19 October 2021. We thank Ed Soucy, Brett Graham, Adam Bercu, and Joel Greenwood of Harvard's CBS Neuroengineering core for help fabricating our instruments. Julie Peng helped with the genomic experiments. Gordon Berman generously consulted on the analysis and sharing data. Timothy Sackton provided insightful guidance on the RNAseq modeling and KEGG enrichment analysis. Joshua Shaevitz kindly shared code and expertise for the unsupervised analysis imaging rig. Tanya Wolff and Gerry Rubin kindly shared most of the Gal4 lines from the circuit screen. James Crall, Jennifer Erickson, Danylo Lavrentovich, and Shradda Lall provided helpful feedback on the manuscript. ZW and KSK were supported by NSF Graduate Research Fellowships DGE-1144152 and #2013170544; JFA was supported by the NIH under grant no. GM124881. BdB was supported by a Sloan Research Fellowship, a Klingenstein-Simons Fellowship Award, a Smith Family Odyssey Award, a Harvard/MIT Basic Neuroscience Grant, the NSF under grant no. IOS-1557913, and the NIH under grant no. MH119092. We also thank the reviewers for providing thorough, helpful feedback on a hefty manuscript in the midst of a pandemic. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Author contributions: Zachary Werkhoven, Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing; Alyssa Bravin, Investigation; Kyobi Skutt-Kakaria, Pablo Reimers, Luisa F Pallares, Data curation, Investigation; Julien Ayroles, Investigation, Supervision; Benjamin L de Bivort, Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review and editing. Data availability: All analysis data and code generated are linked in the manuscript and supporting files at: https:// zenodo.org/ record/4081667#. X8U1tohKiHs Our raw tracking data from the assays covers ~600 flies over 12 days of continuous tracking between 3 and 60Hz and are greater than 50GB in size. Our unsupervised classification videos comprise ~144 million frames of uncompressed video data and are greater than 8TB in size. For these reasons, we have not posted the rawest forms of the data to online repositories, and are instead offering them upon request by external hard drive.Attached Files
Published - elife-64988-v1.pdf
Submitted - 779363v2.full.pdf
Supplemental Material - elife-64988-supp1-v1.xlsx
Supplemental Material - elife-64988-supp2-v1.xlsx
Supplemental Material - elife-64988-supp3-v1.xlsx
Supplemental Material - elife-64988-supp4-v1.xlsx
Supplemental Material - elife-64988-supp5-v1.xlsx
Supplemental Material - elife-64988-transrepform1-v1.pdf
Supplemental Material - elife-64988-video1.mp4
Supplemental Material - elife-64988-video2.mp4
Supplemental Material - elife-64988-video3.mp4
Supplemental Material - elife-64988-video4.mp4
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Additional details
- PMCID
- PMC8526060
- Eprint ID
- 98799
- Resolver ID
- CaltechAUTHORS:20190923-110251459
- DGE-1144152
- NSF Graduate Research Fellowship
- 2013170544
- NSF Graduate Research Fellowship
- GM124881
- NIH
- Alfred P. Sloan Foundation
- Klingenstein-Simons Foundation
- Smith Family Odyssey Award
- Harvard University
- Massachusetts Institute of Technology (MIT)
- IOS-1557913
- NSF
- MH119092
- NIH
- Created
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2019-09-23Created from EPrint's datestamp field
- Updated
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2023-09-08Created from EPrint's last_modified field