Properties and power of the Drosophila Synthetic Population Resource for the routine dissection of complex traits

Varying recombination landscapes between individuals are driven by polymorphic transposable elements

Meiotic recombination is a prominent force shaping genome evolution, and understanding the causes for varying recombination landscapes within and between species has remained a central, though challenging, question. Recombination rates are widely observed to negatively associate with the abundance of transposable elements (TEs), selfish genetic elements that move between genomic locations. While such associations are usually interpreted as recombination influencing the efficacy of selection at removing TEs, accumulating findings suggest that TEs could instead be the cause rather than the consequence. To test this prediction, we formally investigated the influence of polymorphic, putatively active TEs on recombination rates. We developed and benchmarked a novel approach that uses PacBio long-read sequencing to efficiently, accurately, and cost-effectively identify crossovers (COs), a key recombination product, among large numbers of pooled recombinant individuals. By applying this approach to Drosophila strains with distinct TE insertion profiles, we found that polymorphic TEs, especially RNA-based TEs and TEs with local enrichment of repressive marks, reduce the occurrence of COs. Such an effect leads to different CO frequencies between homologous sequences with and without TEs, contributing to varying CO maps between individuals. The suppressive effect of TEs on CO is further supported by two orthogonal approaches-analyzing the distributions of COs in panels of recombinant inbred lines in relation to TE polymorphism and applying marker-assisted estimations of CO frequencies to isogenic strains with and without transgenically inserted TEs. Our investigations reveal how the constantly changing mobilome can actively modify recombination landscapes, shaping genome evolution within and between species.

Powerful QTL mapping and favorable allele mining in an all-in-one population: a case study of heading date

The multiparent advanced generation intercross (MAGIC) population is characterized with great potentials in power and resolution of quantitative trait locus (QTL) mapping, but single nucleotide polymorphism (SNP)-based GWAS does not fully reach its potential. In this study, a MAGIC population of 1021 lines was developed from four Xian and four Geng varieties from five subgroups of rice. A total of 44 000 genes showed functional polymorphisms among eight parents, including frameshift variations or premature stop codon variations, which provides the potential to map almost all genes of the MAGIC population. Principal component analysis results showed that the MAGIC population had a weak population structure. A high-density bin map of 24 414 bins was constructed. Segregation distortion occurred in the regions possessing the genes underlying genetic incompatibility and gamete development. SNP-based association analysis and bin-based linkage analysis identified 25 significant loci and 47 QTLs for heading date, including 14 known heading date genes. The mapping resolution of genes is dependent on genetic effects with offset distances of <55 kb for major effect genes and <123 kb for moderate effect genes. Four causal variants and noncoding structure variants were identified to be associated with heading date. Three to four types of alleles with strong, intermediate, weak, and no genetic effects were identified from eight parents, providing flexibility for the improvement of rice heading date. In most cases, japonica rice carries weak alleles, and indica rice carries strong alleles and nonfunctional alleles. These results confirm that the MAGIC population provides the exceptional opportunity to detect QTLs, and its use is encouraged for mapping genes and mining favorable alleles for breeding.

Naturally segregating genetic variants contribute to thermal tolerance in a Drosophila melanogaster model system

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants within the genes that control this trait is of high importance if we want to better comprehend thermal physiology. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource as a model system. First, we used quantitative genetics and Quantitative Trait Loci mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to (1) alter tissue-specific gene expression and (2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

From sub-Saharan Africa to China: Evolutionary history and adaptation of Drosophila melanogaster revealed by population genomics

Drosophila melanogaster is a widely used model organism for studying environmental adaptation. However, the genetic diversity of populations in Asia is poorly understood, leaving a notable gap in our knowledge of the global evolution and adaptation of this species. We sequenced genomes of 292 D. melanogaster strains from various ecological settings in China and analyzed them along with previously published genome sequences. We have identified six global genetic ancestry groups, despite the presence of widespread genetic admixture. The strains from China represent a unique ancestry group, although detectable differentiation exists among populations within China. We deciphered the global migration and demography of D. melanogaster, and identified widespread signals of adaptation, including genetic changes in response to insecticides. We validated the effects of insecticide resistance variants using population cage trials and deep sequencing. This work highlights the importance of population genomics in understanding the genetic underpinnings of adaptation, an effort that is particularly relevant given the deterioration of ecosystems.

The quantitative genetics of gene expression in Mimulus guttatus

Gene expression can be influenced by genetic variants that are closely linked to the expressed gene (cis eQTLs) and variants in other parts of the genome (trans eQTLs). We created a multiparental mapping population by sampling genotypes from a single natural population of Mimulus guttatus and scored gene expression in the leaves of 1,588 plants. We find that nearly every measured gene exhibits cis regulatory variation (91% have FDR < 0.05). cis eQTLs are usually allelic series with three or more functionally distinct alleles. The cis locus explains about two thirds of the standing genetic variance (on average) but varies among genes and tends to be greatest when there is high indel variation in the upstream regulatory region and high nucleotide diversity in the coding sequence. Despite mapping over 10,000 trans eQTL / affected gene pairs, most of the genetic variance generated by trans acting loci remains unexplained. This implies a large reservoir of trans acting genes with subtle or diffuse effects. Mapped trans eQTLs show lower allelic diversity but much higher genetic dominance than cis eQTLs. Several analyses also indicate that trans eQTLs make a substantial contribution to the genetic correlations in expression among different genes. They may thus be essential determinants of "gene expression modules," which has important implications for the evolution of gene expression and how it is studied by geneticists.

Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster

Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.

The quantitative genetics of gene expression in Mimulus guttatus

Gene expression can be influenced by genetic variants that are closely linked to the expressed gene (cis eQTLs) and variants in other parts of the genome (trans eQTLs). We created a multiparental mapping population by sampling genotypes from a single natural population of Mimulus guttatus and scored gene expression in the leaves of 1,588 plants. We find that nearly every measured gene exhibits cis regulatory variation (91% have FDR < 0.05) and that cis eQTLs are usually allelic series with three or more functionally distinct alleles. The cis locus explains about two thirds of the standing genetic variance (on average) but varies among genes and tends to be greatest when there is high indel variation in the upstream regulatory region and high nucleotide diversity in the coding sequence. Despite mapping over 10,000 trans eQTL / affected gene pairs, most of the genetic variance generated by trans acting loci remains unexplained. This implies a large reservoir of trans acting genes with subtle or diffuse effects. Mapped trans eQTLs show lower allelic diversity but much higher genetic dominance than cis eQTLs. Several analyses also indicate that trans eQTL make a substantial contribution to the genetic correlations in expression among different genes. They may thus be essential determinants of "gene expression modules", which has important implications for the evolution of gene expression and also how it is studied by geneticists.

Paralog transcriptional differentiation in the D. melanogaster-specific gene family Sdic across populations and spermatogenesis stages

How recently originated gene copies become stable genomic components remains uncertain as high sequence similarity of young duplicates precludes their functional characterization. The tandem multigene family Sdic is specific to Drosophila melanogaster and has been annotated across multiple reference-quality genome assemblies. Here we show the existence of a positive correlation between Sdic copy number and total expression, plus vast intrastrain differences in mRNA abundance among paralogs, using RNA-sequencing from testis of four strains with variable paralog composition. Single cell and nucleus RNA-sequencing data expose paralog expression differentiation in meiotic cell types within testis from third instar larva and adults. Additional RNA-sequencing across synthetic strains only differing in their Y chromosomes reveal a tissue-dependent trans-regulatory effect on Sdic: upregulation in testis and downregulation in male accessory gland. By leveraging paralog-specific expression information from tissue- and cell-specific data, our results elucidate the intraspecific functional diversification of a recently expanded tandem gene family.

An investigation of the sex-specific genetic architecture of fitness in Drosophila melanogaster

In dioecious populations, the sexes employ divergent reproductive strategies to maximize fitness and, as a result, genetic variants can affect fitness differently in males and females. Moreover, recent studies have highlighted an important role of the mating environment in shaping the strength and direction of sex-specific selection. Here, we measure adult fitness for each sex of 357 lines from the Drosophila Synthetic Population Resource in two different mating environments. We analyze the data using three different approaches to gain insight into the sex-specific genetic architecture for fitness: classical quantitative genetics, genomic associations, and a mutational burden approach. The quantitative genetics analysis finds that on average segregating genetic variation in this population has concordant fitness effects both across the sexes and across mating environments. We do not find specific genomic regions with strong associations with either sexually antagonistic (SA) or sexually concordant (SC) fitness effects, yet there is modest evidence of an excess of genomic regions with weak associations, with both SA and SC fitness effects. Our examination of mutational burden indicates stronger selection against indels and loss-of-function variants in females than in males.

Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster

Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper quickly results in cell and tissue damage that can range in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes also respond to other non-essential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the response to heavy metal stress. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource (DSPR) using a combination of differential expression analysis and expression quantitative trait locus (eQTL) mapping. Differential expression analysis revealed clear patterns of tissue-specific expression, primarily driven by a more pronounced gene expression response in gut tissue. eQTL mapping of gene expression under control and copper conditions as well as for the change in gene expression following copper exposure (copper response eQTL) revealed hundreds of genes with tissue-specific local cis-eQTL and many distant trans-eQTL. eQTL associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited genotype by environment effects on gene expression under copper stress, illuminating several tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight many candidate genes for future functional characterization.

Naturally segregating genetic variants contribute to thermal tolerance in a D. melanogaster model system

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation, are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants of the genes that control this trait is of high importance if we want to better comprehend how this trait evolves in natural populations. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource (DSPR) as a model system. First, we used quantitative genetics and Quantitative Trait Loci (QTL) mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to 1) alter tissue-specific gene expression and 2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

Complexities of recapitulating polygenic effects in natural populations: replication of genetic effects on wing shape in artificially selected and wild-caught populations of Drosophila melanogaster

Identifying the genetic architecture of complex traits is important to many geneticists, including those interested in human disease, plant and animal breeding, and evolutionary genetics. Advances in sequencing technology and statistical methods for genome-wide association studies have allowed for the identification of more variants with smaller effect sizes, however, many of these identified polymorphisms fail to be replicated in subsequent studies. In addition to sampling variation, this failure to replicate reflects the complexities introduced by factors including environmental variation, genetic background, and differences in allele frequencies among populations. Using Drosophila melanogaster wing shape, we ask if we can replicate allelic effects of polymorphisms first identified in a genome-wide association studies in three genes: dachsous, extra-macrochaete, and neuralized, using artificial selection in the lab, and bulk segregant mapping in natural populations. We demonstrate that multivariate wing shape changes associated with these genes are aligned with major axes of phenotypic and genetic variation in natural populations. Following seven generations of artificial selection along the dachsous shape change vector, we observe genetic differentiation of variants in dachsous and genomic regions containing other genes in the hippo signaling pathway. This suggests a shared direction of effects within a developmental network. We also performed artificial selection with the extra-macrochaete shape change vector, which is not a part of the hippo signaling network, but showed a largely shared direction of effects. The response to selection along the emc vector was similar to that of dachsous, suggesting that the available genetic diversity of a population, summarized by the genetic (co)variance matrix (G), influenced alleles captured by selection. Despite the success with artificial selection, bulk segregant analysis using natural populations did not detect these same variants, likely due to the contribution of environmental variation and low minor allele frequencies, coupled with small effect sizes of the contributing variants.

Genetic variation in chromatin state across multiple tissues in Drosophila melanogaster

We use ATAC-seq to examine chromatin accessibility for four different tissues in Drosophila melanogaster: adult female brain, ovaries, and both wing and eye-antennal imaginal discs from males. Each tissue is assayed in eight different inbred strain genetic backgrounds, seven associated with a reference quality genome assembly. We develop a method for the quantile normalization of ATAC-seq fragments and test for differences in coverage among genotypes, tissues, and their interaction at 44099 peaks throughout the euchromatic genome. For the strains with reference quality genome assemblies, we correct ATAC-seq profiles for read mis-mapping due to nearby polymorphic structural variants (SVs). Comparing coverage among genotypes without accounting for SVs results in a highly elevated rate (55%) of identifying false positive differences in chromatin state between genotypes. After SV correction, we identify 1050, 30383, and 4508 regions whose peak heights are polymorphic among genotypes, among tissues, or exhibit genotype-by-tissue interactions, respectively. Finally, we identify 3988 candidate causative variants that explain at least 80% of the variance in chromatin state at nearby ATAC-seq peaks.

The genetic basis of adaptation to copper pollution in Drosophila melanogaster

Introduction: Heavy metal pollutants can have long lasting negative impacts on ecosystem health and can shape the evolution of species. The persistent and ubiquitous nature of heavy metal pollution provides an opportunity to characterize the genetic mechanisms that contribute to metal resistance in natural populations. Methods: We examined variation in resistance to copper, a common heavy metal contaminant, using wild collections of the model organism Drosophila melanogaster. Flies were collected from multiple sites that varied in copper contamination risk. We characterized phenotypic variation in copper resistance within and among populations using bulked segregant analysis to identify regions of the genome that contribute to copper resistance. Results and Discussion: Copper resistance varied among wild populations with a clear correspondence between resistance level and historical exposure to copper. We identified 288 SNPs distributed across the genome associated with copper resistance. Many SNPs had population-specific effects, but some had consistent effects on copper resistance in all populations. Significant SNPs map to several novel candidate genes involved in refolding disrupted proteins, energy production, and mitochondrial function. We also identified one SNP with consistent effects on copper resistance in all populations near CG11825, a gene involved in copper homeostasis and copper resistance. We compared the genetic signatures of copper resistance in the wild-derived populations to genetic control of copper resistance in the Drosophila Synthetic Population Resource (DSPR) and the Drosophila Genetic Reference Panel (DGRP), two copper-naïve laboratory populations. In addition to CG11825, which was identified as a candidate gene in the wild-derived populations and previously in the DSPR, there was modest overlap of copper-associated SNPs between the wild-derived populations and laboratory populations. Thirty-one SNPs associated with copper resistance in wild-derived populations fell within regions of the genome that were associated with copper resistance in the DSPR in a prior study. Collectively, our results demonstrate that the genetic control of copper resistance is highly polygenic, and that several loci can be clearly linked to genes involved in heavy metal toxicity response. The mixture of parallel and population-specific SNPs points to a complex interplay between genetic background and the selection regime that modifies the effects of genetic variation on copper resistance.

Discovery of malathion resistance QTL in Drosophila melanogaster using a bulked phenotyping approach

Drosophila melanogaster has proved an effective system with which to understand the evolutionary genetics and molecular mechanisms of insecticide resistance. Insecticide use has left signatures of selection in the fly genome, and both functional and quantitative genetic studies in the system have identified genes and variants associated with resistance. Here, we use D. melanogaster and leverage a bulk phenotyping and pooled sequencing "extreme quantitative trait loci" approach to genetically dissect variation in resistance to malathion, an organophosphate insecticide. We resolve 2 quantitative trait loci, one of which implicates allelic variation at the cytochrome P450 gene Cyp6g1, a strong candidate based on previous work. The second shows no overlap with hits from a previous genome-wide association study for malathion resistance, recapitulating other studies showing that different strategies for complex trait dissection in flies can yield apparently different architectures. Notably, we see no genetic signal at the Ace gene. Ace encodes the target of organophosphate insecticide inhibition, and genome-wide association studies have identified strong Ace-linked associations with resistance in flies. The absence of quantitative trait locus implicating Ace here is most likely because our mapping population does not segregate for several of the known functional polymorphisms impacting resistance at Ace, perhaps because our population is derived from flies collected prior to the widespread use of organophosphate insecticides. Our fundamental approach can be an efficient, powerful strategy to dissect genetic variation in resistance traits. Nonetheless, studies seeking to interrogate contemporary insecticide resistance variation may benefit from deriving mapping populations from more recently collected strains.

Genetic dissection of innate immune memory in Drosophila melanogaster

Current studies have demonstrated that innate immunity possesses memory characteristics. Although the molecular mechanisms underlying innate immune memory have been addressed by numerous studies, genetic variations in innate immune memory and the associated genes remain unclear. Here, we explored innate immune memory in 163 lines of Drosophila melanogaster from the Drosophila Synthetic Population Resource. In our assay system, prior training with low pathogenic bacteria (Micrococcus luteus) increased the survival rate of flies after subsequent challenge with highly pathogenic bacteria (Staphylococcus aureus). This positive training effect was observed in most lines, but some lines exhibited negative training effects. Survival rates under training and control conditions were poorly correlated, suggesting that distinct genetic factors regulate training effects and normal immune responses. Subsequent quantitative trait loci analysis suggested that four loci containing 80 genes may be involved in regulating innate immune memory. Among them, Adgf-A, which encodes an extracellular adenosine deaminase-related growth factor, was shown to be associated with training effects. Our study findings help to elucidate the genetic architecture of innate immune memory in Drosophila and may provide insight for new therapeutic treatments aimed at boosting immunity.

Evaluating the power and limitations of genome-wide association studies in Caenorhabditis elegans

Quantitative genetics in Caenorhabditis elegans seeks to identify naturally segregating genetic variants that underlie complex traits. Genome-wide association studies scan the genome for individual genetic variants that are significantly correlated with phenotypic variation in a population, or quantitative trait loci. Genome-wide association studies are a popular choice for quantitative genetic analyses because the quantitative trait loci that are discovered segregate in natural populations. Despite numerous successful mapping experiments, the empirical performance of genome-wide association study has not, to date, been formally evaluated in C. elegans. We developed an open-source genome-wide association study pipeline called NemaScan and used a simulation-based approach to provide benchmarks of mapping performance in collections of wild C. elegans strains. Simulated trait heritability and complexity determined the spectrum of quantitative trait loci detected by genome-wide association studies. Power to detect smaller-effect quantitative trait loci increased with the number of strains sampled from the C. elegans Natural Diversity Resource. Population structure was a major driver of variation in mapping performance, with populations shaped by recent selection exhibiting significantly lower false discovery rates than populations composed of more divergent strains. We also recapitulated previous genome-wide association studies of experimentally validated quantitative trait variants. Our simulation-based evaluation of performance provides the community with critical context to pursue quantitative genetic studies using the C. elegans Natural Diversity Resource to elucidate the genetic basis of complex traits in C. elegans natural populations.

Pre-adult exposure to three heavy metals leads to changes in the head transcriptome of adult flies

We examined the effect of developmental exposure to three heavy metals - cadmium, copper, and lead - on gene expression in adult head tissue in the model organism Drosophila melanogaster . All metals affected development time and/or gene expression level. While variation in the response to each metal was apparent, two differentially-expressed genes were upregulated in response to all three metal treatments, and 11 genes were downregulated in two of the three treatments. Our work reveals that developmental metal exposure has the potential to have long-lasting, metal-specific effects on gene expression in adults, even after the metal stress has been removed.

The utility of a closed breeding colony of Peromyscus leucopus for dissecting complex traits

Deermice of the genus Peromyscus are well suited for addressing several questions of biologist interest, including the genetic bases of longevity, behavior, physiology, adaptation, and their ability to serve as disease vectors. Here, we explore a diversity outbred approach for dissecting complex traits in Peromyscus leucopus, a nontraditional genetic model system. We take advantage of a closed colony of deer-mice founded from 38 individuals and subsequently maintained for ∼40-60 generations. From 405 low-pass short-read sequenced deermice we accurate impute genotypes at 16 million single nucleotide polymorphisms. Conditional on observed genotypes simulations were conducted in which three different sized quantitative trait loci contribute to a complex trait under three different genetic models. Using a stringent significance threshold power was modest, largely a function of the percent variation attributable to the simulated quantitative trait loci, with the underlying genetic model having only a subtle impact. We additionally simulated 2,000 pseudo-individuals, whose genotypes were consistent with those observed in the genotyped cohort and carried out additional power simulations. In experiments employing more than 1,000 mice power is high to detect quantitative trait loci contributing greater than 2.5% to a complex trait, with a localization ability of ∼100 kb. We finally carried out a Genome-Wide Association Study on two demonstration traits, bleeding time and body weight, and uncovered one significant region. Our work suggests that complex traits can be dissected in founders-unknown P. leucopus colony mice and similar colonies in other systems using easily obtained genotypes from low-pass sequencing.

Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing

Despite the value of recombinant inbred lines for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to recombinant inbred lines for many traits leverages selecting phenotypically extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here, we describe such an extreme quantitative trait locus, or extreme quantitative trait loci, mapping strategy that builds on an existing multiparental population, the Drosophila Synthetic Population Resource, and involves phenotyping and genotyping a population derived by mixing hundreds of Drosophila Synthetic Population Resource recombinant inbred lines. Simulations demonstrate that challenging, yet experimentally tractable extreme quantitative trait loci designs (≥4 replicates, ≥5,000 individuals/replicate, and selecting the 5-10% most extreme animals) yield at least the same power as traditional recombinant inbred line-based quantitative trait loci mapping and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated extreme quantitative trait loci experiment that identifies 7 quantitative trait loci for caffeine resistance. Two mapped extreme quantitative trait loci factors replicate loci previously identified in recombinant inbred lines, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists, a bulked phenotyping/genotyping extreme quantitative trait loci design has considerable advantages.

Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.

The molecular architecture of Drosophila melanogaster defense against Beauveria bassiana explored through evolve and resequence and quantitative trait locus mapping

Little is known about the genetic architecture of antifungal immunity in natural populations. Using two population genetic approaches, quantitative trait locus (QTL) mapping and evolve and resequence (E&R), we explored D. melanogaster immune defense against infection with the fungus Beauveria bassiana. The immune defense was highly variable both in the recombinant inbred lines from the Drosophila Synthetic Population Resource used for our QTL mapping and in the synthetic outbred populations used in our E&R study. Survivorship of infection improved dramatically over just 10 generations in the E&R study, and continued to increase for an additional nine generations, revealing a trade-off with uninfected longevity. Populations selected for increased defense against B. bassiana evolved cross resistance to a second, distinct B. bassiana strain but not to bacterial pathogens. The QTL mapping study revealed that sexual dimorphism in defense depends on host genotype, and the E&R study indicated that sexual dimorphism also depends on the specific pathogen to which the host is exposed. Both the QTL mapping and E&R experiments generated lists of potentially causal candidate genes, although these lists were nonoverlapping.

Genomic Responses to Climate Change: Making the Most of the Drosophila Model

It is pressing to understand how animal populations evolve in response to climate change. We argue that new sequencing technologies and the use of historical samples are opening unprecedented opportunities to investigate genome-wide responses to changing environments. However, there are important challenges in interpreting the emerging findings. First, it is essential to differentiate genetic adaptation from phenotypic plasticity. Second, it is extremely difficult to map genotype, phenotype, and fitness. Third, neutral demographic processes and natural selection affect genetic variation in similar ways. We argue that Drosophila melanogaster, a classical model organism with decades of climate adaptation research, is uniquely suited to overcome most of these challenges. In the near future, long-term time series genome-wide datasets of D. melanogaster natural populations will provide exciting opportunities to study adaptation to recent climate change and will lay the groundwork for related research in non-model systems.

The mode of expression divergence in Drosophila fat body is infection-specific

Transcription is controlled by interactions of cis-acting DNA elements with diffusible trans-acting factors. Changes in cis or trans factors can drive expression divergence within and between species, and their relative prevalence can reveal the evolutionary history and pressures that drive expression variation. Previous work delineating the mode of expression divergence in animals has largely used whole-body expression measurements in one condition. Because cis-acting elements often drive expression in a subset of cell types or conditions, these measurements may not capture the complete contribution of cis-acting changes. Here, we quantify the mode of expression divergence in the Drosophila fat body, the primary immune organ, in several conditions, using two geographically distinct lines of D. melanogaster and their F1 hybrids. We measured expression in the absence of infection and in infections with Gram-negative S. marcescens or Gram-positive E. faecalis bacteria, which trigger the two primary signaling pathways in the Drosophila innate immune response. The mode of expression divergence strongly depends on the condition, with trans-acting effects dominating in response to Gram-negative infection and cis-acting effects dominating in Gram-positive and preinfection conditions. Expression divergence in several receptor proteins may underlie the infection-specific trans effects. Before infection, when the fat body has a metabolic role, there are many compensatory effects, changes in cis and trans that counteract each other to maintain expression levels. This work shows that within a single tissue, the mode of expression divergence varies between conditions and suggests that these differences reflect the diverse evolutionary histories of host-pathogen interactions.

Understanding the Early Evolutionary Stages of a Tandem Drosophilamelanogaster-Specific Gene Family: A Structural and Functional Population Study

Gene families underlie genetic innovation and phenotypic diversification. However, our understanding of the early genomic and functional evolution of tandemly arranged gene families remains incomplete as paralog sequence similarity hinders their accurate characterization. The Drosophila melanogaster-specific gene family Sdic is tandemly repeated and impacts sperm competition. We scrutinized Sdic in 20 geographically diverse populations using reference-quality genome assemblies, read-depth methodologies, and qPCR, finding that ∼90% of the individuals harbor 3-7 copies as well as evidence of population differentiation. In strains with reliable gene annotations, copy number variation (CNV) and differential transposable element insertions distinguish one structurally distinct version of the Sdic region per strain. All 31 annotated copies featured protein-coding potential and, based on the protein variant encoded, were categorized into 13 paratypes differing in their 3' ends, with 3-5 paratypes coexisting in any strain examined. Despite widespread gene conversion, the only copy present in all strains has functionally diverged at both coding and regulatory levels under positive selection. Contrary to artificial tandem duplications of the Sdic region that resulted in increased male expression, CNV in cosmopolitan strains did not correlate with expression levels, likely as a result of differential genome modifier composition. Duplicating the region did not enhance sperm competitiveness, suggesting a fitness cost at high expression levels or a plateau effect. Beyond facilitating a minimally optimal expression level, Sdic CNV acts as a catalyst of protein and regulatory diversity, showcasing a possible evolutionary path recently formed tandem multigene families can follow toward long-term consolidation in eukaryotic genomes.

Characterizing the genetic basis of copper toxicity in Drosophila reveals a complex pattern of allelic, regulatory, and behavioral variation

A range of heavy metals are required for normal cell function and homeostasis. However, the anthropogenic release of metal compounds into soil and water sources presents a pervasive health threat. Copper is one of many heavy metals that negatively impacts diverse organisms at a global scale. Using a combination of quantitative trait locus (QTL) mapping and RNA sequencing in the Drosophila Synthetic Population Resource, we demonstrate that resistance to the toxic effects of ingested copper in D. melanogaster is genetically complex and influenced by allelic and expression variation at multiple loci. QTL mapping identified several QTL that account for a substantial fraction of heritability. Additionally, we find that copper resistance is impacted by variation in behavioral avoidance of copper and may be subject to life-stage specific regulation. Gene expression analysis further demonstrated that resistant and sensitive strains are characterized by unique expression patterns. Several of the candidate genes identified via QTL mapping and RNAseq have known copper-specific functions (e.g., Ccs, Sod3, CG11825), and others are involved in the regulation of other heavy metals (e.g., Catsup, whd). We validated several of these candidate genes with RNAi suggesting they contribute to variation in adult copper resistance. Our study illuminates the interconnected roles that allelic and expression variation, organism life stage, and behavior play in copper resistance, allowing a deeper understanding of the diverse mechanisms through which metal pollution can negatively impact organisms.

Gene-level quantitative trait mapping in Caenorhabditis elegans

The Caenorhabditis elegans multiparental experimental evolution (CeMEE) panel is a collection of genome-sequenced, cryopreserved recombinant inbred lines useful for mapping the evolution and genetic basis of quantitative traits. We have expanded the resource with new lines and new populations, and here report the genotype and haplotype composition of CeMEE version 2, including a large set of putative de novo mutations, and updated additive and epistatic mapping simulations. Additive quantitative trait loci explaining 4% of trait variance are detected with >80% power, and the median detection interval approaches single-gene resolution on the highly recombinant chromosome arms. Although CeMEE populations are derived from a long-term evolution experiment, genetic structure is dominated by variation present in the ancestral population.

Predicting evolutionary change at the DNA level in a natural Mimulus population

Evolution by natural selection occurs when the frequencies of genetic variants change because individuals differ in Darwinian fitness components such as survival or reproductive success. Differential fitness has been demonstrated in field studies of many organisms, but it remains unclear how well we can quantitatively predict allele frequency changes from fitness measurements. Here, we characterize natural selection on millions of Single Nucleotide Polymorphisms (SNPs) across the genome of the annual plant Mimulus guttatus. We use fitness estimates to calibrate population genetic models that effectively predict allele frequency changes into the next generation. Hundreds of SNPs experienced "male selection" in 2013 with one allele at each SNP elevated in frequency among successful male gametes relative to the entire population of adults. In the following generation, allele frequencies at these SNPs consistently shifted in the predicted direction. A second year of study revealed that SNPs had effects on both viability and reproductive success with pervasive trade-offs between fitness components. SNPs favored by male selection were, on average, detrimental to survival. These trade-offs (antagonistic pleiotropy and temporal fluctuations in fitness) may be essential to the long-term maintenance of alleles. Despite the challenges of measuring selection in the wild, the strong correlation between predicted and observed allele frequency changes suggests that population genetic models have a much greater role to play in forward-time prediction of evolutionary change.

QTL mapping of adult plant and seedling resistance to leaf rust (Puccinia triticina Eriks.) in a multiparent advanced generation intercross (MAGIC) wheat population

The Bavarian MAGIC Wheat population, comprising 394 F6:8 recombinant inbred lines was phenotyped for Puccinia triticina resistance in multi-years' field trials at three locations and in a controlled environment seedling test. Simple intervall mapping revealed 19 QTL, corresponding to 11 distinct chromosomal regions. The biotrophic rust fungus Puccinia triticina is one of the most important wheat pathogens with the potential to cause yield losses up to 70%. Growing resistant cultivars is the most cost-effective and environmentally friendly way to encounter this problem. The emergence of leaf rust races being virulent against common resistance genes increases the demand for wheat varieties with novel resistances. In the past decade, the use of complex experimental populations, like multiparent advanced generation intercross (MAGIC) populations, has risen and offers great advantages for mapping resistances. The genetic diversity of multiple parents, which has been recombined over several generations, leads to a broad phenotypic diversity, suitable for high-resolution mapping of quantitative traits. In this study, interval mapping was performed to map quantitative trait loci (QTL) for leaf rust resistance in the Bavarian MAGIC Wheat population, comprising 394 F_6:8 recombinant inbred lines (RILs). Phenotypic evaluation of the RILs for adult plant resistance was carried out in field trials at three locations and two years, as well as in a controlled-environment seedling inoculation test. In total, interval mapping revealed 19 QTL, which corresponded to 11 distinct chromosomal regions controlling leaf rust resistance. Six of these regions may represent putative new QTL. Due to the elite parental material, RILs identified to be resistant to leaf rust can be easily introduced in breeding programs.

Influence of Lab Adapted Natural Diet and Microbiota on Life History and Metabolic Phenotype of Drosophila melanogaster

Symbiotic microbiota can help its host to overcome nutritional challenges, which is consistent with a holobiont theory of evolution. Our project investigated the effects produced by the microbiota community, acquired from the environment and horizontal transfer, on metabolic traits related to obesity. The study applied a novel approach of raising Drosophila melanogaster, from ten wild-derived genetic lines on naturally fermented peaches, preserving genuine microbial conditions. Larvae raised on the natural and standard lab diets were significantly different in every tested phenotype. Frozen peach food provided nutritional conditions similar to the natural ones and preserved key microbial taxa necessary for survival and development. On the peach diet, the presence of parental microbiota increased the weight and development rate. Larvae raised on each tested diet formed microbial communities distinct from each other. The effect that individual microbial taxa produced on the host varied significantly with changing environmental and genetic conditions, occasionally to the degree of opposite correlations.

Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding

Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.

Inferring the Allelic Series at QTL in Multiparental Populations

Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Synthetic Population Resource (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.

Power on environmental emotions and behavior

Purpose

The purpose of this study is to reveal indications of effective climate change communication through presenters holding powerful positions. This study aims to achieve this by examining how people perceive emotional campaigns on climate change and to what extent they ultimately perform actions to achieve adequate responses to environmental hazards and protection.

Design/methodology/approach

This study measured environmental behavior directly through donations to environmental charities in two experimental conditions (i.e. top-down vs same-level communication). Environmental emotions were measured via pride and guilt levels about their own country’s environmental actions.

Findings

Powerful individuals appeal to those people who are usually less driven to behave sustainably, and thus induce guilt regardless of the participant’s environmental identity. Conversely, powerful speakers did not succeed in addressing low identity participants using positive emotions. In fact, high power results in even lower pride levels, indicating a potentially adverse effect of power.

Research limitations/implications

While this paper successfully used an organizational leader as a powerful individual, it would be a fruitful avenue to use the experimental framework and examine different presenters (such as politicians, non-governmental organization leaders or scientific experts) who embody environmental advocacy.

Practical implications

The results on top-down communication are intended to add to the understanding of emotional power in environmental contexts and help policy-makers to foster environmental advocacy using emotion-inducing campaigns.

Originality/value

The study is among the first to examine and elucidate the circumstances under which powerful individuals can encourage pro-environmental behavior. This study provides evidence that power can be a useful tool to appeal to those people who are usually less driven to behave sustainably. However, this paper also found that power does not increase emotions and behavior per se.

Two Synthetic 18-Way Outcrossed Populations of Diploid Budding Yeast with Utility for Complex Trait Dissection

Advanced-generation multiparent populations (MPPs) are a valuable tool for dissecting complex traits, having more power than genome-wide association studies to detect rare variants and higher resolution than F2 linkage mapping. To extend the advantages of MPPs in budding yeast, we describe the creation and characterization of two outbred MPPs derived from 18 genetically diverse founding strains. We carried out de novo assemblies of the genomes of the 18 founder strains, such that virtually all variation segregating between these strains is known, and represented those assemblies as Santa Cruz Genome Browser tracks. We discovered complex patterns of structural variation segregating among the founders, including a large deletion within the vacuolar ATPase VMA1, several different deletions within the osmosensor MSB2, a series of deletions and insertions at PRM7 and the adjacent BSC1, as well as copy number variation at the dehydrogenase ALD2 Resequenced haploid recombinant clones from the two MPPs have a median unrecombined block size of 66 kb, demonstrating that the population is highly recombined. We pool-sequenced the two MPPs to 3270× and 2226× coverage and demonstrated that we can accurately estimate local haplotype frequencies using pooled data. We further downsampled the pool-sequenced data to ∼20-40× and showed that local haplotype frequency estimates remained accurate, with median error rates 0.8 and 0.6% at 20× and 40×, respectively. Haplotypes frequencies are estimated much more accurately than SNP frequencies obtained directly from the same data. Deep sequencing of the two populations revealed that 10 or more founders are present at a detectable frequency for > 98% of the genome, validating the utility of this resource for the exploration of the role of standing variation in the architecture of complex traits.

The metabolome as a link in the genotype-phenotype map for peroxide resistance in the fruit fly, Drosophila melanogaster

BACKGROUND

Genetic association studies that seek to explain the inheritance of complex traits typically fail to explain a majority of the heritability of the trait under study. Thus, we are left with a gap in the map from genotype to phenotype. Several approaches have been used to fill this gap, including those that attempt to map endophenotype such as the transcriptome, proteome or metabolome, that underlie complex traits. Here we used metabolomics to explore the nature of genetic variation for hydrogen peroxide (H2O2) resistance in the sequenced inbred Drosophila Genetic Reference Panel (DGRP).

RESULTS

We first studied genetic variation for H2O2 resistance in 179 DGRP lines and along with identifying the insulin signaling modulator u-shaped and several regulators of feeding behavior, we estimate that a substantial amount of phenotypic variation can be explained by a polygenic model of genetic variation. We then profiled a portion of the aqueous metabolome in subsets of eight 'high resistance' lines and eight 'low resistance' lines. We used these lines to represent collections of genotypes that were either resistant or sensitive to the stressor, effectively modeling a discrete trait. Across the range of genotypes in both populations, flies exhibited surprising consistency in their metabolomic signature of resistance. Importantly, the resistance phenotype of these flies was more easily distinguished by their metabolome profiles than by their genotypes. Furthermore, we found a metabolic response to H2O2 in sensitive, but not in resistant genotypes. Metabolomic data further implicated at least two pathways, glycogen and folate metabolism, as determinants of sensitivity to H2O2. We also discovered a confounding effect of feeding behavior on assays involving supplemented food.

CONCLUSIONS

This work suggests that the metabolome can be a point of convergence for genetic variation influencing complex traits, and can efficiently elucidate mechanisms underlying trait variation.

Macdonald S. Dissecting the Genetic Basis of Variation in Drosophila Sleep Using a Multiparental QTL Mapping Resource

There is considerable variation in sleep duration, timing and quality in human populations, and sleep dysregulation has been implicated as a risk factor for a range of health problems. Human sleep traits are known to be regulated by genetic factors, but also by an array of environmental and social factors. These uncontrolled, non-genetic effects complicate powerful identification of the loci contributing to sleep directly in humans. The model system, Drosophila melanogaster, exhibits a behavior that shows the hallmarks of mammalian sleep, and here we use a multitiered approach, encompassing high-resolution QTL mapping, expression QTL data, and functional validation with RNAi to investigate the genetic basis of sleep under highly controlled environmental conditions. We measured a battery of sleep phenotypes in >750 genotypes derived from a multiparental mapping panel and identified several, modest-effect QTL contributing to natural variation for sleep. Merging sleep QTL data with a large head transcriptome eQTL mapping dataset from the same population allowed us to refine the list of plausible candidate causative sleep loci. This set includes genes with previously characterized effects on sleep and circadian rhythms, in addition to novel candidates. Finally, we employed adult, nervous system-specific RNAi on the Dopa decarboxylase, dyschronic, and timeless genes, finding significant effects on sleep phenotypes for all three. The genes we resolve are strong candidates to harbor causative, regulatory variation contributing to sleep.

Genome-Wide Association Study in Two Cohorts from a Multi-generational Mouse Advanced Intercross Line Highlights the Difficulty of Replication Due to Study-Specific Heterogeneity

There has been extensive discussion of the "Replication Crisis" in many fields, including genome-wide association studies (GWAS). We explored replication in a mouse model using an advanced intercross line (AIL), which is a multigenerational intercross between two inbred strains. We re-genotyped a previously published cohort of LG/J x SM/J AIL mice (F34; n = 428) using a denser marker set and genotyped a new cohort of AIL mice (F39-43; n = 600) for the first time. We identified 36 novel genome-wide significant loci in the F34 and 25 novel loci in the F39-43 cohort. The subset of traits that were measured in both cohorts (locomotor activity, body weight, and coat color) showed high genetic correlations, although the SNP heritabilities were slightly lower in the F39-43 cohort. For this subset of traits, we attempted to replicate loci identified in either F34 or F39-43 in the other cohort. Coat color was robustly replicated; locomotor activity and body weight were only partially replicated, which was inconsistent with our power simulations. We used a random effects model to show that the partial replications could not be explained by Winner's Curse but could be explained by study-specific heterogeneity. Despite this heterogeneity, we performed a mega-analysis by combining F34 and F39-43 cohorts (n = 1,028), which identified four novel loci associated with locomotor activity and body weight. These results illustrate that even with the high degree of genetic and environmental control possible in our experimental system, replication was hindered by study-specific heterogeneity, which has broad implications for ongoing concerns about reproducibility.

Diverse biological processes coordinate the transcriptional response to nutritional changes in a Drosophila melanogaster multiparent population

Background

Environmental variation in the amount of resources available to populations challenge individuals to optimize the allocation of those resources to key fitness functions. This coordination of resource allocation relative to resource availability is commonly attributed to key nutrient sensing gene pathways in laboratory model organisms, chiefly the insulin/TOR signaling pathway. However, the genetic basis of diet-induced variation in gene expression is less clear.

Results

To describe the natural genetic variation underlying nutrient-dependent differences, we used an outbred panel derived from a multiparental population, the Drosophila Synthetic Population Resource. We analyzed RNA sequence data from multiple female tissue samples dissected from flies reared in three nutritional conditions: high sugar (HS), dietary restriction (DR), and control (C) diets. A large proportion of genes in the experiment (19.6% or 2471 genes) were significantly differentially expressed for the effect of diet, and 7.8% (978 genes) for the effect of the interaction between diet and tissue type (LRT, P_adj. < 0.05). Interestingly, we observed similar patterns of gene expression relative to the C diet, in the DR and HS treated flies, a response likely reflecting diet component ratios. Hierarchical clustering identified 21 robust gene modules showing intra-modularly similar patterns of expression across diets, all of which were highly significant for diet or diet-tissue interaction effects (FDR P_adj. < 0.05). Gene set enrichment analysis for different diet-tissue combinations revealed a diverse set of pathways and gene ontology (GO) terms (two-sample t-test, FDR < 0.05). GO analysis on individual co-expressed modules likewise showed a large number of terms encompassing many cellular and nuclear processes (Fisher exact test, P_adj. < 0.01). Although a handful of genes in the IIS/TOR pathway including Ilp5, Rheb, and Sirt2 showed significant elevation in expression, many key genes such as InR, chico, most insulin peptide genes, and the nutrient-sensing pathways were not observed.

Conclusions

Our results suggest that a more diverse network of pathways and gene networks mediate the diet response in our population. These results have important implications for future studies focusing on diet responses in natural populations.

Learning to collaborate: bringing together behavior and quantitative genomics

The genetic basis of complex trait like learning and memory have been well studied over the decades. Through those groundbreaking findings, we now have a better understanding about some of the genes and pathways that are involved in learning and/or memory. However, few of these findings identified the naturally segregating variants that are influencing learning and/or memory within populations. In this special issue honoring the legacy of Troy Zars, we review some of the traditional approaches that have been used to elucidate the genetic basis of learning and/or memory, specifically in fruit flies. We highlight some of his contributions to the field, and specifically describe his vision to bring together behavior and quantitative genomics with the aim of expanding our knowledge of the genetic basis of both learning and memory. Finally, we present some of our recent work in this area using a multiparental population (MPP) as a case study and describe the potential of this approach to advance our understanding of neurogenetics.

Evolution of Reproductive Behavior

Behaviors associated with reproduction are major contributors to the evolutionary success of organisms and are subject to many evolutionary forces, including natural and sexual selection, and sexual conflict. Successful reproduction involves a range of behaviors, from finding an appropriate mate, courting, and copulation, to the successful production and (in oviparous animals) deposition of eggs following mating. As a consequence, behaviors and genes associated with reproduction are often under strong selection and evolve rapidly. Courtship rituals in flies follow a multimodal pattern, mediated through visual, chemical, tactile, and auditory signals. Premating behaviors allow males and females to assess the species identity, reproductive state, and condition of their partners. Conflicts between the "interests" of individual males, and/or between the reproductive strategies of males and females, often drive the evolution of reproductive behaviors. For example, seminal proteins transmitted by males often show evidence of rapid evolution, mediated by positive selection. Postmating behaviors, including the selection of oviposition sites, are highly variable and Drosophila species span the spectrum from generalists to obligate specialists. Chemical recognition features prominently in adaptation to host plants for feeding and oviposition. Selection acting on variation in pre-, peri-, and postmating behaviors can lead to reproductive isolation and incipient speciation. Response to selection at the genetic level can include the expansion of gene families, such as those for detecting pheromonal cues for mating, or changes in the expression of genes leading to visual cues such as wing spots that are assessed during mating. Here, we consider the evolution of reproductive behavior in Drosophila at two distinct, yet complementary, scales. Some studies take a microevolutionary approach, identifying genes and networks involved in reproduction, and then dissecting the genetics underlying complex behaviors in D. melanogaster Other studies take a macroevolutionary approach, comparing reproductive behaviors across the genus Drosophila and how these might correlate with environmental cues. A full synthesis of this field will require unification across these levels.

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

Pupation site choice of Drosophila third-instar larvae is critical for the survival of individuals, as pupae are exposed to various biotic and abiotic dangers while immobilized during the 3-4 days of metamorphosis. This singular behavioural choice is sensitive to both environmental and genetic factors. Here, we developed a high-throughput phenotyping approach to assay the variation in pupation height in Drosophila melanogaster, while controlling for possibly confounding factors. We find substantial variation of mean pupation height among sampled natural stocks and we show that the Drosophila Genetic Reference Panel (DGRP) reflects this variation. Using the DGRP stocks for genome-wide association (GWA) mapping, 16 loci involved in determining pupation height could be resolved. The candidate genes in these loci are enriched for high expression in the larval central nervous system. A genetic network could be constructed from the candidate loci, which places scribble (scrib) at the centre, plus other genes known to be involved in nervous system development, such as Epidermal growth factor receptor (Egfr) and p53. Using gene disruption lines, we could functionally validate several of the initially identified loci, as well as additional loci predicted from network analysis. Our study shows that the combination of high-throughput phenotyping with a genetic analysis of variation captured from the wild can be used to approach the genetic dissection of an environmentally relevant behavioural phenotype.

An assessment of the immune costs associated with meiotic drive elements in Drosophila

Most organisms are constantly adapting to pathogens and parasites that exploit their host for their own benefit. Less studied, but perhaps more ubiquitous, are intragenomic parasites or selfish genetic elements. These include transposable elements, selfish B chromosomes and meiotic drivers that promote their own replication without regard to fitness effects on hosts. Therefore, intragenomic parasites are also a constant evolutionary pressure on hosts. Gamete-killing meiotic drive elements are often associated with large chromosomal inversions that reduce recombination between the drive and wild-type chromosomes. This reduced recombination is thought to reduce the efficacy of selection on the drive chromosome and allow for the accumulation of deleterious mutations. We tested whether gamete-killing meiotic drive chromosomes were associated with reduced immune defence against two bacterial pathogens in three species of Drosophila. We found little evidence of reduced immune defence in lines with meiotic drive. One line carrying the Drosophila melanogaster autosomal Segregation Distorter did show reduced defence, but we were unable to attribute that reduced defence to either genotype or immune gene expression differences. Our results suggest that though gamete-killing meiotic drive chromosomes probably accumulate deleterious mutations, those mutations do not result in reduced capacity for immune defence.

Multiple genetic loci affect place learning and memory performance in Drosophila melanogaster

Learning and memory are critical functions for all animals, giving individuals the ability to respond to changes in their environment. Within populations, individuals vary, however the mechanisms underlying this variation in performance are largely unknown. Thus, it remains to be determined what genetic factors cause an individual to have high learning ability and what factors determine how well an individual will remember what they have learned. To genetically dissect learning and memory performance, we used the Drosophila synthetic population resource (DSPR), a multiparent mapping resource in the model system Drosophila melanogaster, consisting of a large set of recombinant inbred lines (RILs) that naturally vary in these and other traits. Fruit flies can be trained in a "heat box" to learn to remain on one side of a chamber (place learning) and can remember this (place memory) over short timescales. Using this paradigm, we measured place learning and memory for ~49 000 individual flies from over 700 DSPR RILs. We identified 16 different loci across the genome that significantly affect place learning and/or memory performance, with 5 of these loci affecting both traits. To identify transcriptomic differences associated with performance, we performed RNA-Seq on pooled samples of seven high performing and seven low performing RILs for both learning and memory and identified hundreds of genes with differences in expression in the two sets. Integrating our transcriptomic results with the mapping results allowed us to identify nine promising candidate genes, advancing our understanding of the genetic basis underlying natural variation in learning and memory performance.

New opportunities: Drosophila as a model system for exercise research

Because of the growing rates of obesity in much of the world, exercise as a treatment option for obesity and as part of a healthy lifestyle is of great interest to the general public, health policy makers, and scientists alike. Despite the long history of exercise promotion and exercise research, there are still significant gaps in our understanding of how exercise impacts individuals and what role genetics plays in determining an individual’s response to exercise. Model organisms are positioned uniquely to help address these questions because of the challenges associated with carrying out large-scale, well-controlled studies in humans. The fruit fly model system, Drosophila melanogaster, has joined the models used for exercise research only recently but already has made significant contributions to the field. In this review, we highlight the opportunities for exercise research in Drosophila. We review the resources available to researchers interested in using Drosophila for exercise research, focusing on the existing systems to induce exercise in Drosophila, to measure the amount of exercise performed, and to assess physical fitness. We illustrate the potential of the Drosophila system by drawing attention to pioneering studies in Drosophila exercise research and emphasize the unique opportunities this model system represents.

Anesthetics Influence Mortality in a Drosophila Model of Blunt Trauma With Traumatic Brain Injury

BACKGROUND

Exposure to anesthetics is common in the majority of early survivors of life-threatening injuries. Whether and to what degree general anesthetics influence outcomes from major trauma is unknown. Potential confounding effects of general anesthetics on outcome measures are usually disregarded. We hypothesized that exposure to isoflurane or sevoflurane modulates the outcome from blunt trauma with traumatic brain injury (bTBI).

METHODS

We tested the hypothesis in a novel model of bTBI implemented in Drosophila melanogaster. Fruit flies of the standard laboratory strain w were cultured under standard conditions. We titrated the severity of bTBI to a mortality index at 24 hours (MI24) of approximately 20% under control conditions. We administered standard doses of isoflurane and sevoflurane before, before and during, or after bTBI and measured the resulting MI24. We report the MI24 as mean ± standard deviation.

RESULTS

Isoflurane or sevoflurane administered for 2 hours before bTBI reduced the MI24 from 22.3 ± 2.6 to 10.4 ± 1.8 (P < 10, n = 12) and from 19.3 ± 0.9 to 8.9 ± 1.1 (P < .0001, n = 8), respectively. In contrast, administration of isoflurane after bTBI increased the MI24 from 18.5% ± 4.3% to 25.3% ± 9.1% (P = .0026, n = 22), while sevoflurane had no effect (22.4 ± 7.1 and 21.5 ± 5.8, n = 22).

CONCLUSIONS

In a whole animal model of bTBI, general anesthetics were not indifferent with respect to early mortality. Therefore, collateral effects of general anesthetics should be considered in the interpretation of results obtained in vertebrate trauma models. Invertebrate model organisms can serve as a productive platform to interrogate anesthetic targets that mediate collateral effects and to inform trauma research in higher organisms about the potential impact of anesthetics on outcomes.

Determinants of QTL Mapping Power in the Realized Collaborative Cross

The Collaborative Cross (CC) is a mouse genetic reference population whose range of applications includes quantitative trait loci (QTL) mapping. The design of a CC QTL mapping study involves multiple decisions, including which and how many strains to use, and how many replicates per strain to phenotype, all viewed within the context of hypothesized QTL architecture. Until now, these decisions have been informed largely by early power analyses that were based on simulated, hypothetical CC genomes. Now that more than 50 CC strains are available and more than 70 CC genomes have been observed, it is possible to characterize power based on realized CC genomes. We report power analyses from extensive simulations and examine several key considerations: 1) the number of strains and biological replicates, 2) the QTL effect size, 3) the presence of population structure, and 4) the distribution of functionally distinct alleles among the founder strains at the QTL. We also provide general power estimates to aide in the design of future experiments. All analyses were conducted with our R package, SPARCC (Simulated Power Analysis in the Realized Collaborative Cross), developed for performing either large scale power analyses or those tailored to particular CC experiments.

The evolutionary potential of diet-dependent effects on lifespan and fecundity in a multi-parental population of Drosophila melanogaster

The nutritional conditions experienced by a population have a major role in shaping trait evolution in many taxa. Constraints exerted by nutrient limitation or nutrient imbalance can influence the maximal value that fitness components such as reproduction and lifespan attains, and organisms may shift how resources are allocated to different structures and functions in response to changes in nutrition. Whether the phenotypic changes associated with changes in nutrition represent an adaptive response is largely unknown. Further, it is unclear whether the response of fitness components to diet even has the potential to evolve in most systems. In this study, we use an admixed multi-parental population of Drosophila melanogaster reared in three different diet conditions to estimate quantitative genetic parameters for lifespan and fecundity. We find significant genetic variation for both traits in our population and show that lifespan has moderate to high heritabilities within diets. Genetic correlations for lifespan between diets were significantly less than one, demonstrating a strong genotype by diet interaction. These findings demonstrate substantial standing genetic variation in our population that is comparable to natural populations and highlights the potential for adaptation to changing nutritional environments.

Host-pathogen coevolution increases genetic variation in susceptibility to infection

It is common to find considerable genetic variation in susceptibility to infection in natural populations. We have investigated whether natural selection increases this variation by testing whether host populations show more genetic variation in susceptibility to pathogens that they naturally encounter than novel pathogens. In a large cross-infection experiment involving four species of Drosophila and four host-specific viruses, we always found greater genetic variation in susceptibility to viruses that had coevolved with their host. We went on to examine the genetic architecture of resistance in one host species, finding that there are more major-effect genetic variants in coevolved host-pathogen interactions. We conclude that selection by pathogens has increased genetic variation in host susceptibility, and much of this effect is caused by the occurrence of major-effect resistance polymorphisms within populations.

GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity

Understanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics. We develop a novel statistical approach to comparative genetic mapping to detect large-scale structural mutations from low-level sequencing data. The procedure, called Genome Order Optimization by Genetic Algorithm (GOOGA), couples a Hidden Markov Model with a Genetic Algorithm to analyze data from genetic mapping populations. We demonstrate the method using both simulated data (calibrated from experiments on Drosophila melanogaster) and real data from five distinct crosses within the flowering plant genus Mimulus. Application of GOOGA to the Mimulus data corrects numerous errors (misplaced sequences) in the M. guttatus reference genome and confirms or detects eight large inversions polymorphic within the species complex. Finally, we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus (QTL) mapping.

Dissection of Complex, Fitness-Related Traits in Multiple Drosophila Mapping Populations Offers Insight into the Genetic Control of Stress Resistance

We leverage two complementary Drosophila melanogaster mapping panels to genetically dissect starvation resistance-an important fitness trait. Using >1600 genotypes from the multiparental Drosophila Synthetic Population Resource (DSPR), we map numerous starvation stress QTL that collectively explain a substantial fraction of trait heritability. Mapped QTL effects allowed us to estimate DSPR founder phenotypes, predictions that were correlated with the actual phenotypes of these lines. We observe a modest phenotypic correlation between starvation resistance and triglyceride level, traits that have been linked in previous studies. However, overlap among QTL identified for each trait is low. Since we also show that DSPR strains with extreme starvation phenotypes differ in desiccation resistance and activity level, our data imply multiple physiological mechanisms contribute to starvation variability. We additionally exploited the Drosophila Genetic Reference Panel (DGRP) to identify sequence variants associated with starvation resistance. Consistent with prior work these sites rarely fall within QTL intervals mapped in the DSPR. We were offered a unique opportunity to directly compare association mapping results across laboratories since two other groups previously measured starvation resistance in the DGRP. We found strong phenotypic correlations among studies, but extremely low overlap in the sets of genomewide significant sites. Despite this, our analyses revealed that the most highly associated variants from each study typically showed the same additive effect sign in independent studies, in contrast to otherwise equivalent sets of random variants. This consistency provides evidence for reproducible trait-associated sites in a widely used mapping panel, and highlights the polygenic nature of starvation resistance.