Mutations in many genes affect aggressive behavior in Drosophila melanogaster

Background Aggressive behavior in animals is important for survival and reproduction. Identifying the underlying genes and environmental contexts that affect aggressive behavior is important for understanding the evolutionary forces that maintain variation for aggressive behavior in natural populations, and to develop therapeutic interventions to modulate extreme levels of aggressive behavior in humans. While the role of neurotransmitters and a few other molecules in mediating and modulating levels of aggression is well established, it is likely that many additional genetic pathways remain undiscovered. Drosophila melanogaster has recently been established as an excellent model organism for studying the genetic basis of aggressive behavior. Here, we present the results of a screen of 170 Drosophila P-element insertional mutations for quantitative differences in aggressive behavior from their co-isogenic control line. Results We identified 59 mutations in 57 genes that affect aggressive behavior, none of which had been previously implicated to affect aggression. Thirty-two of these mutants exhibited increased aggression, while 27 lines were less aggressive than the control. Many of the genes affect the development and function of the nervous system, and are thus plausibly relevant to the execution of complex behaviors. Others affect basic cellular and metabolic processes, or are mutations in computationally predicted genes for which aggressive behavior is the first biological annotation. Most of the mutations had pleiotropic effects on other complex traits. We characterized nine of these mutations in greater detail by assessing transcript levels throughout development, morphological changes in the mushroom bodies, and restoration of control levels of aggression in revertant alleles. All of the P-element insertions affected the tagged genes, and had pleiotropic effects on brain morphology. Conclusion This study reveals that many more genes than previously suspected affect aggressive behavior, and that these genes have widespread pleiotropic effects. Given the conservation of aggressive behavior among different animal species, these are novel candidate genes for future study in other animals, including humans

Functional Validation of Candidate Genes Detected by Genomic Feature Models

Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) categories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach

Supplemental Material for Morgante et al., 2020

Supplemental_Figures contains all the supplemental figures for the paper. Supplementary_Table_1 contains information about the three most predictive GO terms for SNPs. Supplementary_Table_2 contains information about the three most predictive GO terms for genes

Supplemental Material for Parker et al., 2020

Supplementary Table 1 gives the raw data for the four week productivity assay of the pure and reciprocal corsses between the O and B lines. Supplementary Table 2 gives the analyses of variance (ANOVAs) of O and B line productivity. Supplementary Table 3 shows the genomic regions containing genetically divergent SNPS. Supplementary Table 4 gives the list of 57 candidate genes tested, including their FlyBase ID, gene name, gene, symbol, and the Vienna stock number used in the RNAi assay. Supplementary Table 5 gives the raw lifespan data for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 6 gives the raw lifetime productivity data for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 7 gives the raw weekly productivity data for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 8 gives the analyses of variance (ANOVAs) of lifespan for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 9 gives the analyses of variance (ANOVAs) of lifetime productivity for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 10 gives the analyses of variance (ANOVAs) of weekly productivity for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 11 gives the analyses of variance (ANOVAs) of ovary qPCR for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 12 gives the analyses of variance (ANOVAs) of accessory gland qPCR for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 13 is a summary of genes for which RNAi affects lifespan and/or productivity. Supplementary Figure 1 shows the results of ovary qPCR for the difference in expression between GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Figure 2 show the results of accessory gland qPCR for the difference in expression between GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies

Supplemental Material for Yanagawa et al., 2020

File S1. Video of high grooming DGRP line.File S2. Video of low grooming DGRP line.Table S1. ANOVA of spontaneous grooming behavior.Table S2. GWA analyses of spontaneous grooming behavior.Table S3. GWA analysis candidate gene list.Table S4. RNAi functional assessment results

Supplemental Material for Parker et al., 2020

Supplementary Table 1 gives the raw data for the four week productivity assay of the pure and reciprocal corsses between the O and B lines. Supplementary Table 2 gives the analyses of variance (ANOVAs) of O and B line productivity. Supplementary Table 3 shows the genomic regions containing genetically divergent SNPS. Supplementary Table 4 gives the list of 57 candidate genes tested, including their FlyBase ID, gene name, gene, symbol, and the Vienna stock number used in the RNAi assay. Supplementary Table 5 gives the raw lifespan data for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 6 gives the raw lifetime productivity data for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 7 gives the raw weekly productivity data for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 8 gives the analyses of variance (ANOVAs) of lifespan for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 9 gives the analyses of variance (ANOVAs) of lifetime productivity for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 10 gives the analyses of variance (ANOVAs) of weekly productivity for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 11 gives the analyses of variance (ANOVAs) of ovary qPCR for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 12 gives the analyses of variance (ANOVAs) of accessory gland qPCR for GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Table 13 is a summary of genes for which RNAi affects lifespan and/or productivity. Supplementary Figure 1 shows the results of ovary qPCR for the difference in expression between GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies. Supplementary Figure 2 show the results of accessory gland qPCR for the difference in expression between GAL4-c825 × UAS RNAi and GAL4-c825 × control F1 flies

Supplemental Material for Matute et al., 2020

Supplementary tables for Matute et al. Rapid and predictable evolution of admixed populations between two Drosophila species pair