Quantitative Trait Loci for Locomotor Behavior in<i>Drosophila melanogaster</i>

Jordan, Katherine; Morgan, Theodore J.; Mackay, Trudy F. C.

doi:10.1534/genetics.106.058099

Cited by 70 publications

(72 citation statements)

References 82 publications

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“…Only three of these genes (Beadex, Sema-5c, and neur) had previously been documented to affect locomotion (19)(20)(21). Many of these candidate genes affect nervous system development and function, as well as other aspects of development, the cell cycle, and oogenesis and spermatogenesis, consistent with previous observations that genes affecting startle-induced locomotion are highly pleiotropic (5,11,12).…”

supporting

confidence: 84%

“…The flies were subjected to a gentle mechanical disturbance by tapping the vial lightly, then placing it horizontally under a fluorescent lamp. Locomotor behavior was quantified as the number of seconds each fly was active during an observation period immediately following the disturbance (5,11). The observation period lasted 30 s for the homozygous mutation screen and 45 s for the epistasis analysis.…”

Section: Methodsmentioning

confidence: 99%

“…Previously, we developed a simple, high-throughput and highly repeatable assay to quantify startle-induced locomotion in Drosophila, and used it to map 13 positional candidate genes corresponding to quantitative trait loci for this trait in a population of recombinant inbred lines; these include genes associated with bioamine synthesis as well as genes that encode transcription factors implicated in development of the nervous system (11). Transcriptional analysis of lines selected for low or high startle-induced locomotion showed 1,790 probe sets with different expression levels between the selection lines at a false discovery rate of 0.001, suggesting that a large fraction of the genome affects this behavior (5).…”

mentioning

confidence: 99%

“…Transcriptional analysis of lines selected for low or high startle-induced locomotion showed 1,790 probe sets with different expression levels between the selection lines at a false discovery rate of 0.001, suggesting that a large fraction of the genome affects this behavior (5). In addition, association studies found polymorphisms associated with variation in startle-induced locomotion in Catsup, which encodes a negative regulator of tyrosine hydroxylase, the rate-limiting enzyme in the synthesis of dopamine (12), and in Ddc, which encodes dopamine decarboxylase (11). Additional studies in Drosophila have implicated serotonin, octopamine, and ␥-aminobutyric acid as critical mediators of locomotion (13)(14)(15)(16).…”

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confidence: 99%

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Neurogenetic networks for startle-induced locomotion in Drosophila melanogaster

Yamamoto

Zwarts

Callaerts

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

105

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Understanding how the genome empowers the nervous system to express behaviors remains a critical challenge in behavioral genetics. The startle response is an attractive behavioral model for studies on the relationship between genes, brain, and behavior, as the ability to respond rapidly to harmful changes in the environment is a universal survival trait. Drosophila melanogaster provides a powerful system in which genetic studies on individuals with controlled genetic backgrounds and reared under controlled environmental conditions can be combined with neuroanatomical studies to analyze behaviors. In a screen of 720 lines of D. melanogaster, carrying single P[GT1] transposon insertions, we found 267 lines that showed significant changes in startle-induced locomotor behavior. Excision of the transposon reversed this effect in five lines out of six tested. We infer that most of the 267 lines show mutant effects on startle-induced locomotion that are caused by the transposon insertions. We selected a subset of 15 insertions in the same genetic background in autosomal genes with strong mutant effects and crossed them to generate all 105 possible nonreciprocal double heterozygotes. These hybrids revealed an extensive network of epistatic interactions on the behavioral trait. In addition, we observed changes in neuroanatomy that were caused by these 15 mutations, individually and in their double heterozygotes. We find that behavioral and neuroanatomical phenotypes are determined by a common set of genes that are organized as partially overlapping genetic networks.behavioral genetics ͉ epistasis ͉ sensorimotor integration ͉ startle behavior ͉ P-element insertional mutagenesis A major goal of behavioral genetics is to understand the relationship between the genome and the nervous system. From a neuroscience perspective, behaviors represent the ultimate expression of the nervous system. From a genetics perspective, behaviors are complex traits for which natural variation is caused by many interacting genetic variants, with allelic effects that depend on social and external environments, sex, and genetic background (1, 2). To date, most studies that have attempted to relate genetic variation to the neural regulation of behavior have adopted a ''one gene at a time'' approach. Such studies have made important contributions and generated significant insights. However, recent genomic approaches, in which candidate genes affecting behaviors are identified by comparing whole-genome expression profiles of genetically divergent strains, have implicated large numbers of coregulated genes affecting behaviors that have pleiotropic effects on other traits, and that would not have been a priori predicted to affect behavior (3-10). In the current study, we used quantitative genetic approaches to analyze the genes-brain-behavior relationship at the level of genetic networks rather than at the level of single genes.We used startle-induced locomotion to a mechanical disturbance in Drosophila melanogaster as a model behavior. Previously, we...

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supporting

confidence: 84%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Neurogenetic networks for startle-induced locomotion in Drosophila melanogaster

Yamamoto

Zwarts

Callaerts

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

105

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“…However, in all studies reported to date, both common and rare QTNs have been associated with quantitative traits and occur in presumed regulatory regions, as well as nonsynonymous polymorphisms in coding regions (Mackay and Langley 1990;Long et al 2000;Robin et al 2002;Palsson and Gibson 2004;Jordan et al 2006). Although the total number of studies is small, there is a tendency for less common polymorphisms associated with quantitative traits to Effect sizes (expressed as percentage of total variation explained) for six categories of phenotype.…”

Section: How Much Does Genetic Architecture Vary Between Phenotypes?mentioning

confidence: 99%

Genetic architecture of quantitative traits in mice, flies, and humans

Flint¹,

Mackay²

2009

Genome Res.

409

364

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We compare and contrast the genetic architecture of quantitative phenotypes in two genetically well-characterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, with that found in our own species from recent successes in genome-wide association studies. We show that the current model of large numbers of loci, each of small effect, is true for all species examined, and that discrepancies can be largely explained by differences in the experimental designs used. We argue that the distribution of effect size of common variants is the same for all phenotypes regardless of species, and we discuss the importance of epistasis, pleiotropy, and gene by environment interactions. Despite substantial advances in mapping quantitative trait loci, the identification of the quantitative trait genes and ultimately the sequence variants has proved more difficult, so that our information on the molecular basis of quantitative variation remains limited. Nevertheless, available data indicate that many variants lie outside genes, presumably in regulatory regions of the genome, where they act by altering gene expression. As yet there are very few instances where homologous quantitative trait loci, or quantitative trait genes, have been identified in multiple species, but the availability of high-resolution mapping data will soon make it possible to test the degree of overlap between species.Recent successes in mapping quantitative trait loci (QTLs) that contribute to phenotypic variation in humans and model organisms make it possible to address important questions about the genetic architecture of quantitative phenotypes, such as the likely number of loci, their mode of genetic action, and interaction with each other and with the environment. An understanding of the genetic architecture of quantitative traits is not only important in its own right, it will also inform studies that seek to proceed to the identification of the quantitative trait genes (QTGs) and the quantitative trait nucleotide (QTN) variants, the functional changes in DNA sequence that contribute to trait variation.In this work we use examples from two genetically wellcharacterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, to discuss how an understanding of the genetic architecture of quantitative phenotypes in model organisms informs mapping experiments in human genetics. In our discussion of the latter, we draw upon the recent successful application of genome-wide association studies (GWAS) to both quantitative phenotypes (such as height and weight) and disease phenotypes (assuming that the genetic architecture of common disease is similar, if not identical, to that of quantitative phenotypes).Genetic architecture has been the subject of a number of recent reviews, most of which deal with information from a single model organism (Mackay 2004) or review a small number of phenotypes (Kendler and Greenspan 2006). Here, our purpose is different. We tackle three relate...

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Identifying genes that interact with Drosophila presenilin and amyloid precursor protein

Hoef

Hughes

Livne‐Bar

et al. 2009

Genesis

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The gamma-secretase complex is involved in cleaving transmembrane proteins such as Notch and one of the genes targeted in Alzheimer's disease known as amyloid precursor protein (APP). Presenilins function within the catalytic core of gamma-secretase, and mutated forms of presenilins were identified as causative factors in familial Alzheimer's disease. Recent studies show that in addition to Notch and APP, numerous signal transduction pathways are modulated by presenilins, including intracellular calcium signaling. Thus, presenilins appear to have diverse roles. To further understand presenilin function, we searched for Presenilin-interacting genes in Drosophila by performing a genetic modifier screen for enhancers and suppressors of Presenilin-dependent Notch-related phenotypes. We identified 177 modifiers, including known members of the Notch pathway and genes involved in intracellular calcium homeostasis. We further demonstrate that 53 of these modifiers genetically interacted with APP. Characterization of these genes may provide valuable insights into Presenilin function in development and disease.

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Quantitative Trait Loci for Locomotor Behavior inDrosophila melanogaster

Cited by 70 publications

References 82 publications

Neurogenetic networks for startle-induced locomotion in Drosophila melanogaster

Neurogenetic networks for startle-induced locomotion in Drosophila melanogaster

Genetic architecture of quantitative traits in mice, flies, and humans

Identifying genes that interact with Drosophila presenilin and amyloid precursor protein

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