The winged insects of the orderDipteraare colloquially named for their most recognizable phenotype: flight. These insects rely on flight for a number of important life history traits, such as dispersal, foraging, and courtship. Despite the importance of flight, relatively little is known about the genetic architecture of flight performance. Accordingly, we sought to uncover the genetic modifiers of flight using a measure of flies’ reaction and response to an abrupt drop in a vertical flight column. We conducted a genome wide association study (GWAS) using 197 of theDrosophilaGenetic Reference Panel (DGRP) lines, and identified a combination of additive and marginal variants, epistatic interactions, whole genes, and enrichment across interaction networks.Egfr, a highly pleiotropic developmental gene, was among the most significant additive variants identified. We functionally validated 13 of the additive candidate genes’ (Adgf-A/Adgf-A2/CG32181,bru1,CadN,flapper(CG11073),CG15236,flippy(CG9766),CREG,Dscam4,form3,fry,Lasp/CG9692,Pde6,Snoo), and introduce a novel approach to whole gene significance screens:PEGASUS_flies. Additionally, we identifiedppk23, an Acid Sensing Ion Channel (ASIC) homolog, as an important hub for epistatic interactions. We propose a model that suggests genetic modifiers of wing and muscle morphology, nervous system development and function, BMP signaling, sexually dimorphic neural wiring, and gene regulation are all important for the observed differences flight performance in a natural population. Additionally, these results represent a snapshot of the genetic modifiers affecting drop-response flight performance inDrosophila, with implications for other insects.

Negative geotaxis (climbing) performance is a useful metric for quantifying Drosophila health. Manual methods to quantify climbing performance are tedious and often biased, while many available computational methods have challenging hardware or software requirements. We present an alternative: FreeClimber. This open source, Python-based platform subtracts a video's static background to improve detection for flies moving across heterogeneous backgrounds. FreeClimber calculates a cohort's velocity as the slope of the most linear portion of a mean-vertical position vs. time curve. It can run from a graphical user interface for optimization or a command line interface for high-throughput and automated batch processing, improving accessibility for users with different expertise. FreeClimber outputs calculated slopes, spot locations for follow up analyses (e.g. tracking), and several visualizations and plots. We demonstrate FreeClimber's utility in a longitudinal study for endurance exercise performance in Drosophila mitonuclear genotypes using six distinct mitochondrial haplotypes paired with a common w1118 nuclear background.

The winged insects of the order Diptera are colloquially named for their most recognizable phenotype: flight. These insects rely on flight for a number of important life history traits, like dispersal, foraging, and courtship. Despite the importance of flight, relatively little is known about the genetic architecture of variation for flight performance. Accordingly, we sought to uncover the genetic modifiers of flight using a measure of flies’ reaction and response to an abrupt drop in a vertical flight column. We conducted an association study using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, and identified a combination of additive and marginal variants, epistatic interactions, whole genes, and enrichment across interaction networks. We functionally validated 13 of these candidate genes’ ( Adgf-A/Adgf-A2/CG32181, bru1, CadN, CG11073, CG15236, CG9766, CREG, Dscam4, form3, fry, Lasp/CG9692, Pde6, Snoo ) contribution to flight, two of which ( fry and Snoo ) also validate a whole gene analysis we introduce for the DGRP: PEGASUS_flies . Overall, our results suggest modifiers of muscle and wing morphology, and peripheral and central nervous system assembly and function are all important for flight performance. Additionally, we identified ppk23 , an Acid Sensing Ion Channel (ASIC) homolog, as an important hub for epistatic interactions. These results represent a snapshot of the genetic modifiers affecting drop-response flight performance in Drosophila , with implications for other insects. It also draws connections between genetic modifiers of performance and BMP signaling and ASICs as targets for treating neurodegeneration and neurodysfunction. Author summary: Insect flight is a widely recognizable phenotype of winged insects, hence the name: flies. While fruit flies, or Drosophila melanogaster , are a genetically tractable model, flight performance is a highly integrative phenotype, making it challenging to comprehensively identify the genetic modifiers that contribute to its genetic architecture. Accordingly, we screened 197 Drosophila Genetic Reference Panel lines for their ability to react and respond to an abrupt drop. Using several computational tools, we successfully identified several additive, marginal, and epistatic variants, as well as whole genes and altered sub-networks of gene-gene and protein-protein interaction networks, demonstrating the benefits of using multiple methodologies to elucidate the genetic architecture of complex traits more generally. Many of these significant genes and variants mapped to regions of the genome that affect development of sensory and motor neurons, wing and muscle development, and regulation of transcription factors. We also introduce PEGASUS_flies, a Drosophila -adapted version of the PEGASUS platform first used in human studies, to infer gene-level significance of association based on the distribution of individual variant P -values. Our results contribute to the debate over the relative importance of individual, additive factors and epistatic, or higher order, interactions, in the mapping of genotype to phenotype.

Negative geotaxis (climbing) performance is a useful metric for quantifyingDrosophilahealth and vigor. Manual methods to quantify climbing performance are slow, tedious, and may be systematically biased, while available computational methods have inflexible hardware or software requirements. We present an alternative: FreeClimber. This open source, Python-based pipeline subtracts a video’s static background to improve spot detection for moving flies in heterogeneous backgrounds. FreeClimber calculates a cohort’s velocity as the slope of the most linear portion of a mean-vertical position vs. time plot. It can run from a graphical user interface for parameter optimization or a command line interface for high-throughput and automated batch processing. It outputs calculated slopes, spot locations for follow up analyses such as tracking, and several visualizations and diagnostic plots. We demonstrate FreeClimber’s utility in a longitudinal study for endurance exercise performance inDrosophilausing six distinct mitochondrial haplotypes paired with a commonw¹¹¹⁸nuclear background.Summary statement: FreeClimber quantifies the climbing velocity for a group of flies, eliminating systematic biases associated with traditional manual methods in a high throughput and automated (graphical and/or command line-based) platform.

A central challenge of quantitative genetics is partitioning phenotypic variation into genetic and non-genetic components. These non-genetic components are usually interpreted as environmental effects; however, variation between genetically identical individuals in a common environment can still exhibit phenotypic variation. A trait’s resistance to variation is called robustness, though the genetics underlying it are poorly understood. Accordingly, we performed an association study on a previously studied, whole organism trait: flight performance. Using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, we surveyed variation at the level of single nucleotide polymorphisms and whole genes using additive, marginal, and epistatic analyses that associated with robustness for flight performance. Many genes had developmental and neurodevelopmental annotations, and many more were identified from associations that differed between sexes. Additionally, many genes were pleiotropic, with several annotated for fitness-associated traits (e.g. gametogenesis and courtship). Our results corroborate a previous study for genetic modifiers of micro-environmental variation, and have sizable overlap with studies for modifiers of wing morphology and courtship behavior. These results point to an important and shared role for genetic modifiers of robustness of flight performance affecting development, neurodevelopment, and behavior.