Brendan McEwen
Fly Sociality
You are a sociobiologist investigating the genetic basis of sociability in fruit flies. A genetic screening study has identified several ‘candidate genes’ that may play a role in regulating social behaviour in Drosophila melanogaster. You decide to investigate the candidate gene Sec5, by performing a knockdown experiment. Male and female cohorts of flies were subjected to RNA Interference Gene Silencing (RNAi), effectively eliminating Sec5 gene activity in those individuals. Another genetically un-altered cohort of males and females were kept as controls. You assembled groups of same-sex flies into sociability arenas, and tracked their aggregation behaviour over time. A ‘sociability index’ score was calculated for each group, where higher scores indicate that the flies were more closely aggregated towards one another – a stronger signal of social grouping.
Analyze this dataset to see whether males and females differ in their levels of sociality, and whether the silencing of the Sec5 gene affected sociality in either males or females
- Load in the data and use the head() command to preview the top of the dataframe
- Create a new column called condition, that represents the factorial combinations of Sex and Gene Treatment
- In GGplot, produce a set of sociality index histograms of the four condition combinations. Facet the panel by condition, such that all four conditions’ distributions are laid out on the plot at once.
- Using the lmer() function from the lme4 package, create a linear mixed model to determine whether Sex, Treatment, or their interaction have a significant effect on sociability index scores on the flies in your experiment. Use a Type III Sum-of-Squares approach to analyze your model, using the Anova() function from the car package in R. After constructing your model, check its diagnostics using the check_model() function from the performance package
- For information on linear mixed modeling, see: This Blog Post
- Also see: This Instructional Video
- Hint: the ‘Arena’, ‘Time’, and ‘Day’ variables should be present in your random effects.
- For a primer on Type I / II / III Sums-of-Squares ANOVA analyses, see: This Blog Post
- Using GGplot, produce a rainplot showing the sociability scores of each of the four condition combinations
- For a primer on rainplots, see This Blog Post/Tutorial
- Use a color palette that incorporates accessibility for differences in color vision. For more information on accessible GGplot palettes, see: http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/
Files to download:
To download, right-click and press “Save File As” or “Download Linked File”
Laboratory and Institution or PI
Cognitive Ecology Lab, Dr. Reuven Dukas, McMaster University Department of Psychology, Neuroscience, & Behaviour https://psych.mcmaster.ca/dukas/index.htm
References and Further Reading
Torabi-Marashi, A. (2023). Investigating the genetic basis of natural variation in sociability within Drosophila melanogaster (Doctoral dissertation)
Scott, A. M., Dworkin, I., & Dukas, R. (2022). Evolution of sociability by artificial selection. Evolution, 76(3), 541-553
