Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior

Abstract

Human psychiatric disorders such as schizophrenia, bipolar disorder and attention-deficit/hyper-activity disorder often include adverse behaviors including increased aggressiveness. Individuals with psychiatric disorders often exhibit social withdrawal, which can further increase the probability of conducting a violent act. Here, we used the inbred, sequenced lines of the Drosophila Genetic Reference Panel (DGRP) to investigate the genetic basis of variation in male aggressive behavior for flies reared in a socialized and socially isolated environment. We identified genetic variation for aggressive behavior, as well as significant genotype by social environmental interaction (GSEI); i.e., variation among DGRP genotypes in the degree to which social isolation affected aggression. We performed genome-wide association (GWA) analyses to identify genetic variants associated with aggression within each environment. We used genomic prediction to partition genetic variants into gene ontology (GO) terms and constituent genes, and identified GO terms and genes with high prediction accuracies in both social environments and for GSEI. The top predictive GO terms significantly increased the proportion of variance explained, compared to prediction models based on all segregating variants. We performed genomic prediction across environments, and identified genes in common between the social environments which turned to be enriched for genome-wide associated variants.A large proportion of the associated genes have previously been associated with aggressive behavior in Drosophila and mice.Further, many of these genes have human orthologs that have been associated with neurological disorders, indicating partially shared genetic mechanisms underlying aggression in animal models and human psychiatric disorders.

Polygenicity and Epistasis Underlie Fitness-Proximal Traits in theCaenorhabditis elegansMultiparental Experimental Evolution (CeMEE) Panel

“…Microscopic nematodes and macroscopic mammals vary in many ways, and major conveniences of the C. elegans system such as parental zygosity, inbreeding by selfing without a high burden of lethality, advanced pedigree, cryopreservation, and small genome size, have facilitated the generation and characterization of a powerful and stable resource that is relatively representative of its founders. The comparison with Drosophila is perhaps more fair, where the Drosophila Genetic Reference Panel (DGRP) and Drosophila Synthetic Population Resource (DSPR) have been widely used to study the genetic basis of complex organismal and molecular traits ( King et al 2012b , 2014 ; Swarup et al 2013 ; Marriage et al 2014 ; Dembeck et al 2015 ; Huang et al 2015 ; Najarro et al 2015 ; Rohde et al 2017 ). The DGRP GWAS panel preserves a sample of genetic variation from an ancestral population of large effective size in a relatively small number of RILs (185 with coefficient of coancestry < 0.25), and therefore is highly diverse, with an allele frequency spectrum skewed toward rare alleles.…”

Identification of a genetic network for an ecologically relevant behavioural phenotype in Drosophila melanogaster

“…Natural populations of D. melanogaster harbour a wealth of genetic variation, which has been proven to constitute a powerful resource to detect the genetic architecture for a wide range of phenotypes (Huang et al, 2014;King, Macdonald, & Long, 2012;MacKay et al, 2012). The Drosophila Genetic Reference Panel (DGRP), a population consisting of more than 200 highly inbred D. melanogaster strains derived from the Raleigh, US population (Huang et al, 2014;MacKay et al, 2012), has been successfully exploited to identify the association between a broad range of phenotypes and their underlying genetic basis, including several behavioural traits (Lee et al, 2017;Rohde, Gaertner, Ward, Sørensen, & Mackay, 2017;Shorter et al, 2015;Xue, Wang, & Zhu, 2017). The highly inbred nature of these DGRP strains allows accurate association mapping, through repeatedly sampling for individuals with the same genotype.…”

Prediction of complex phenotypes using theDrosophilametabolome

“…grouping genetic variants into functional pathways, can increase the prediction accuracy markedly. [33][34][35][36][37][38] Therefore, we investigated if similar benefits could be achieved by partitioning the metabolome.…”