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studies.tsv
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study_id authors title year doi nber_phenotypes comment description categories status flybase_ref
1 Arya et al. Natural Variation, Functional Pleiotropy and Transcriptional Contexts of <i>Odorant Binding Protein</i> Genes in <i>Drosophila melanogaster</i> 2010 10.1534/genetics.110.123166 5 A theoretical response score >2.5 indicates repulsion, whereas scores <2.5 indicate attraction to the odorant.<br/><br/> Phenotyping data comes from Table S1 in <a href="https://academic.oup.com/genetics/article/186/4/1475/6063705?login=true#supplementary-data">supplementary data</a> section.<br/><br/> Of note, the provided data are summary data (means, sd, cv, ...) across multiple flies from the same DGRP line. The raw data per fly is not available. This study evaluated the behavior response of DGRPs to four different odorants (benzaldehyde, acetophenone, hexanol, and hexanal). They also looked at the longevity of these lines. Ageing,Olfactory,Sensory,Life history traits,Behaviour integrated FBrf0212597
2 Arya et al. The Genetic Basis for Variation in Olfactory Behavior in <i>Drosophila melanogaster</i> 2015 10.1093/chemse/bjv001 14 The provided data are summary data (means, sd, cv, ...) across multiple flies from the same DGRP line. The raw data per fly is not available.<br/><br/> Phenotyping data is available from Table S1 in <a href="https://academic.oup.com/chemse/article/40/4/233/274077?login=true#supplementary-data">supplementary data</a> section This is a follow-up study of <a href="https://dgrpool.epfl.ch/studies/2">Arya et al., 2010</a>. Similarly, the authors studied the behavior response of flies to 14 different odorants. Olfactory,Sensory,Behaviour integrated FBrf0228128
3 Battlay et al. Genomic and Transcriptomic Associations Identify a New Insecticide Resistance Phenotype for the Selective Sweep at the <i>Cyp6g1</i> Locus of <i>Drosophila melanogaster</i> 2016 10.1534/g3.116.031054 5 The data does not seem to be available anymore from the journal's page. This study is about resistance to the organophosphate insecticide azinphos-methyl. It identified Cyp6g1, a gene involved in resistance to DDT and neonicotinoid insecticides, as the top candidate for azinphos-methyl resistance, along with four other candidate genes. Resistance,Toxicity,Metabolism integrated FBrf0233124
4 Bou Sleiman et al. Genetic, molecular and physiological basis of variation in <i>Drosophila</i> gut immunocompetence 2015 10.1038/ncomms8829 2 The phenotyping data (<i>PE</i> resistance) was extracted from the Supplementary Table 1 from the <a href="https://www.nature.com/articles/ncomms8829#Sec22">Supplementary Information</a> file.<br/><br/> The <i>Ecc15</i> resistance data were obtained directly from the authors. To investigate the determinants underlying inter-individual variation in gut immunocompetence, this study performed enteric infection of 140 Drosophila lines with the entomopathogenic bacterium <i>Pseudomonas entomophila (PE)</i> and observed extensive variation in survival. To determine if this variability in survival is specific to enteric infection, the authors assessed the susceptibility of DGRP lines to systemic infection with <i>Erwinia carotovora carotovora 15 (Ecc15)</i>. Resistance,Microbiota,Immunity integrated FBrf0229120
5 Brown et al. Genome-wide association mapping of natural variation in odour-guided behaviour in <i>Drosophila</i> 2013 10.1111/gbb.12048 5 Phenotyping data were extracted from the Supplementary Table S3 in the <a href="https://onlinelibrary.wiley.com/doi/10.1111/gbb.12048#support-information-section">Supporting Information</a> section This study measured the behavior of flies in the presence of olfactory stimuli, here the odorant 2,3-butanedione, a volatile compound present in fermenting fruit.<br/> To measure this behavior sensitively, the authors specifically designed a high-throughput behavioral assay system for temporal and spatial dynamics of odor-guided behavior. Olfactory,Behaviour,Sensory integrated FBrf0222011
6 Carbone et al. Genetic architecture of natural variation in visual senescence in <i>Drosophila</i> 2016 10.1073/pnas.1613833113 10 Each individual fly is assigned a score from 1 (did not move toward the light) to 8 (moved toward the light seven times).<br/><br/> Phenotyping data comes from Dataset S1 in file <a href="https://www.pnas.org/doi/suppl/10.1073/pnas.1613833113/suppl_file/pnas.1613833113.sd01.xlsx">pnas.1613833113.sd01.xlsx</a> and Dataset S4 in file <a href="https://www.pnas.org/doi/suppl/10.1073/pnas.1613833113/suppl_file/pnas.1613833113.sd04.xlsx">pnas.1613833113.sd04.xlsx</a> (Dark/Light phenotype) of the Supporting Information section.<br/><br/> Of note, authors are also using the longevity/lifespan data from <a href="https://dgrpool.epfl.ch/studies/18">Ivanov et al., 2015</a> in Fig. S4 Study of the age-dependent decline in phototaxis (innate tendency to move toward the light) at 1, 2, and 4 weeks, as a proxy for visual senescence Sensory,Life history traits,Ageing integrated FBrf0233864
7 Chaston et al. Host Genetic Control of the Microbiota Mediates the <i>Drosophila</i> Nutritional Phenotype 2016 10.1128/AEM.03301-15 89 Assignments to taxonomic ranks were performed in QIIME using the <a href="https://journals.asm.org/doi/10.1128/AEM.03006-05">Greengenes</a> database (class, order, family, genus, species). Operational taxonomic units (OTUs) were binned based on QIIME taxonomic assignments during correlation analysis and numbered from OTU001 to OTU176. It resulted in 76 OTUs (thus 76 phenotypes) after filtering.<br/><br/> Phenotyping data were extracted from Table S1B in <a href="https://journals.asm.org/doi/suppl/10.1128/AEM.03301-15/suppl_file/zam999116867sd2.xlsx">zam999116867sd2.xlsx</a> file in Supplemental material section. I’ve only included Table S1B, due to stricter selection/cutoffs than S1A. S1B was excluded due to the absence of all OTUs. Of note, this data only has 79 lines, while there are 126 lines described in Supp. Table S2.<br/><br/> The ratio data per 5 family types (Aceto., Lacto., Xantho., Coma., Other) were computed by the curator, using Table S1B in order to reproduce data used in Figure 1A.<br/><br/> The phenotyping data for nutritional traits (Glucose, ...), were not available, so we asked the authors, who provided it. They were added and contain an additional 30 lines (i.e. for a total of 109 lines, still not reaching the 126 lines described in Supp. Table S2). This study examined the impact of different microbiota on host nutritional indices (glucose, glycogen, triglyceride, and protein contents). Authors created gnotobiotic flies (containing defined microbiota) with different bacteria and found that multiple host genes were statistically associated with the abundance of one bacterium, Acetobacter tropicalis. Microbiota,Metabolism,Nutrition integrated FBrf0230551
8 Chow et al. Large Neurological Component to Genetic Differences Underlying Biased Sperm Use in <i>Drosophila</i> 2013 10.1534/genetics.112.146357 3 Phenotyping data were extracted from Table S1 in <b>genetics.112.146357-1</b> pdf file from the <a href="https://academic.oup.com/genetics/article/193/1/177/5935230#supplementary-data">Supplementary data</a> section<br/><br/> Gene expression data was used from <a href="http://flyatlas.org/atlas.cgi">FlyAtlas</a> <a href="https://www.nature.com/articles/ng2049">(Chintapalli et al. 2007)</a><br/><br/> The provided data are summary data (means, sd, cv, ...) across multiple flies from the same DGRP line. The raw data per fly is not available, so we cannot reproduce Fig.1 or Fig. S1b for e.g. This study examines the female factors (and genes) that mediate sperm competition outcomes. Females were tested in double-mating trials, and the amount of progeny was accounted for, especially distinguishing progeny coming from the first vs the second mating (by eye color). One of the lines never engaged in a second mating. Further knockdown studies of three candidate genes (para, Rab2, and Rim) in sensory neurons indicate that sperm competition may be affected by the neural input innervating the female reproductive tract. Life history traits,Mating,Fecundity integrated FBrf0220325
9 Chow et al. Using natural variation in <i>Drosophila</i> to discover previously unknown endoplasmic reticulum stress genes 2013 10.1073/pnas.1307125110 2 Phenotyping data are available in <a href="https://www.pnas.org/doi/suppl/10.1073/pnas.1307125110/suppl_file/sd01.xlsx">sd01.xlsx</a><br/><br/> Some gene expressions are summarized in <a href="https://www.pnas.org/doi/suppl/10.1073/pnas.1307125110/suppl_file/sd02.xlsx">sd02.xlsx</a> (not included in the phenotypes) This study measured the survival time in response to tunicamycin-induced endoplasmic reticulum (ER) stress in DGRP lines. Survival time was significantly associated with polymorphisms in candidate genes with known (i.e., Xbp1) and unknown roles in ER stress. Life history traits,Resistance integrated FBrf0221713
10 Dembeck et al. Genetic Architecture of Abdominal Pigmentation in <i>Drosophila melanogaster</i> 2015 10.1371/journal.pgen.1005163 2 Phenotyping data comes from <a href="https://doi.org/10.1371/journal.pgen.1005163.s007">S1 Table</a> in the <a href="https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005163#sec016">Supporting Information</a> section. <br/><br/> We've loaded the raw dataset per sample so that any summary statistics can be recalculated (especially the mean, and standard error that are also available in the supplementary material). This study quantifies the amount of female abdominal pigmentation, as the proportion of melanization on the two most posterior abdominal segments, tergites 5 and 6 (T5, T6). They identified known and novel candidate genes implicated in abdominal pigmentation. Several of these genes are involved in developmental and regulatory pathways, chitin production, cuticle structure, and vesicle formation and transport. Appearance integrated FBrf0228267
11 Dembeck et al. Genetic architecture of natural variation in cuticular hydrocarbon composition in <i>Drosophila melanogaster</i> 2015 10.7554/eLife.09861 66 Linear alkanes are referred to by the abbreviation <i>n</i>-Cx, where ‘‘x’’ is the total carbon number. For example, tricosane is designated <i>n</i>-C23.<br /> Methyl-branched alkanes are referred to with a y-Me-Cx prefix, where 'y' is the carbon onto which the methyl group is bound. For example, 9-Me-C23 is a 23 carbon chain with a methyl on the 9th carbon.<br /> For monoenes (z-Cx:1) and dienes (z,z-Cx:2) the number of double bonds is indicated after the colon and the double bond position/s are 'z' or 'z,z"). For example, 7-C23:1 has one double bond between the 7<sup>th</sup> and 8<sup>th</sup> carbons. <br/><br/> All CHCs are depicted in more details in <a href="https://elifesciences.org/articles/09861#tbl1">Table 1</a><br/><br/> Of note, the data corresponds only to the <b>Proportion of Total CHCs</b> which is depicted in Figure 2B and 2C. The raw signal strength (in pA = picoAmperes) detection for each CHC is NOT available. So Figures like Figure 1 and Figure 2A are NOT reproducible.<br/><br/> Phenotyping data is available from Supplementary file 1: <a href="https://elifesciences.org/articles/09861/figures#SD1-data">elife-09861-supp1-v3.xlsx</a> Cuticular HydroCarbons (or CHCs) are the most common fat molecules in the epicuticle. It's a group of long-chain hydrocarbons that are produced and secreted by the epidermis (outer layer of skin) and play an important role in protecting the insect’s body from desiccation in dry habitats. They also serve as chemical signals that mediate social interactions and behavior. This study looks into CHC composition as a species-specific blend of fatty acid-derived apolar lipids on the epicuticle and uncovered 24 genes that may be involved in CHC manufacture. Anatomy,Appearance integrated FBrf0230707
12 Durham et al. Genome-wide analysis in <i>Drosophila</i> reveals age-specific effects of SNPs on fitness traits 2014 10.1038/ncomms5338 12 Phenotypes loaded here cannot be found on the journal's website. This study observed the extensive natural variation in lifespan, lifetime fecundity (number of eggs) and age-specific fecundity in mated female fruit flies at different ages (1wk, 3wk, 5wk, and 7wk). The authors conducted a genome-wide analysis to examine the effect of single nucleotide polymorphisms (SNPs) on these phenotypes and found that the impact of these SNPs was age-specific. These findings highlight the importance of considering the age at which phenotypes are measured in evolutionary studies. Life history traits,Ageing,Fecundity integrated FBrf0225589
13 Garlapow et al. Quantitative Genetics of Food Intake in <i>Drosophila melanogaster</i> 2015 10.1371/journal.pone.0138129 1 Phenotyping data come from the <a href="https://doi.org/10.1371/journal.pone.0138129.s002">S1 Table</a> in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138129#sec023">Supporting Information</a> section.<br/><br/> We've loaded the raw dataset per sample so that any summary statistics can be recalculated (especially the mean, cv, and variance that are also available in the supplementary material). This study utilized natural variation in food consumption in the DGRP lines to identify and functionally validate novel genes and a SNP affecting food intake. They also studied the extraordinary sexual dimorphism between male and female lines. Most of the uncovered genes have mammalian orthologs that are implicated in the development of many metabolically related diseases, such as type 2 diabetes, while not generally affecting food intake volume directly. Behaviour,Metabolism,Nutrition integrated FBrf0229641
14 Grubbs et al. New Components of Drosophila Leg Development Identified through Genome Wide Association Studies 2013 10.1371/journal.pone.0060261 14 Phenotyping data come from <a href="https://doi.org/10.1371/journal.pone.0060261.s003">Table S1</a> in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0060261#s5">Supporting Information</a> section. I've manually separated the table (which has data per fly, with 24 replicates per sex for each line) into T1 (first) and T2 (second thoracic leg) phenotypes.<br/><br/> Measurements of the legs were recorded using Adobe Photoshop and ImageJ. Measurements of the femur, tibia, and tarsal segments were recorded separately. Of note, I could not find in the paper what was the unit for the measurements? This study focused on leg and antenna development from imaginal discs. They identified additional genes and genetic pathways involved in leg imaginal disc development. In particular, five genes that, when their function is reduced by RNAi, cause an antenna-to-leg transformation. Development,Limb Development,Anatomy,Appearance integrated FBrf0221227
15 Harbison et al. Genome-wide association study of sleep in <i>Drosophila melanogaster</i> 2013 10.1186/1471-2164-14-281 14 Phenotyping data was downloaded from <b>Additional file 3</b> in <a href="https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-14-281#Sec15">Electronic supplementary material</a> section.<br/><br/> Of note, we only have the average values for each DGRP line, not the results per fly.<br/><br/> <b>Definition:</b> Sleep bouts = sleep naps. This study studies the sleep duration, the number of sleep bouts or ‘naps’, and the average sleep bout length during the day and night of male and female flies. Genome-wide association (GWA) study of sleep identified candidate single nucleotide polymorphisms (SNPs) associated with differences in the mean as well as the environmental sensitivity of sleep traits. These SNPs typically had sex-specific or sex-biased effects, and were generally located in non-coding regions. Several of the candidate genes have human homologs that were identified in studies of human sleep, suggesting that genes affecting variation in sleep are conserved across species. Behaviour,Sleep integrated FBrf0221487
16 Howick et al. The genetic architecture of defence as resistance to and tolerance of bacterial infection in <i>Drosophila melanogaster</i> 2017 10.1111/mec.14017 5 Phenotyping data were extracted from the <b>Table S1</b> in the <a href="https://onlinelibrary.wiley.com/doi/10.1111/mec.14017#support-information-section">Supporting Information</a> section.<br/><br/> The definition of <b>"Tolerance"</b> and how it is calculated in this study is explained in the first Result section of the paper. Defense against pathogenic infection can take two forms: resistance and tolerance. Resistance is the ability of the host to limit a pathogen burden, whereas tolerance is the ability to limit the negative consequences of infection at a given level of infection intensity. This study performed a genomewide association study to map the genetic basis of variation in resistance to and tolerance of infection of five- to nine-day-old mated female flies, which were infected with the Gram-negative bacterium <i>Providencia rettgeri</i>. The results of the study indicate that tolerance is not an independent strategy from resistance. Immunity,Resistance integrated FBrf0235063
17 Huang et al. Natural variation in genome architecture among 205 <i>Drosophila melanogaster</i> Genetic Reference Panel lines 2014 10.1101/gr.171546.113 34 This paper is one of the founding papers for the DGRP lines, along with the main study: <a href="https://dgrpool.epfl.ch/studies/21">(MacKay et al, 2012)</a>. <br/><br/> Many of the phenotypes described there are used as covariates for GWAS analyses (Wolbachia status, Large Chromosomal inversion). This is the case on this website or for e.g. in the <a href="http://dgrp2.gnets.ncsu.edu/">DGRP2 website</a>. The following covariates are the ones used as covariates for any GWAS analysis: <ul> <li><a href="https://dgrpool.epfl.ch/phenotypes/2347">Wolbachia Status</a></li> <li><a href="https://dgrpool.epfl.ch/phenotypes/1520">Inversion (2L)t</a></li> <li><a href="https://dgrpool.epfl.ch/phenotypes/1521">Inversion (2R)NS</a></li> <li><a href="https://dgrpool.epfl.ch/phenotypes/1532">Inversion (3R)P</a></li> <li><a href="https://dgrpool.epfl.ch/phenotypes/1533">Inversion (3R)K</a></li> <li><a href="https://dgrpool.epfl.ch/phenotypes/1534">Inversion (3R)Mo</a></li> </ul> Phenotypes were extracted from the <a href="https://genome.cshlp.org/content/suppl/2014/04/24/gr.171546.113.DC1/Supplemental_Data_Files.xlsx">Supplemental_Data_Files.xlsx</a> file in the <a href="https://genome.cshlp.org/content/24/7/1193/suppl/DC1">Supplemental Material</a> section. In particular: <ul> <li>Number and % segregating variants in each DGRP line, by chromosome arm, was found in <b>Supp. data file S5</b></li> <li>Cytological analysis of polymorphic inversions was found in <b>Supp. data file S6</b> (ST = homozygous for the standard arrangement, INV/ST = heterozygous, INV = homozygous for the inversion)</li> <li><i>Wolbachia</i> infection status was found in <b>Supp. data file S7</b> (y = infected, n = not infected). It extends the original <a href="https://dgrpool.epfl.ch/phenotypes/2758"><i>Wolbachia</i> status</a> that was carried out in the original DGRP study.</li> <li>Genome size estimates from flow cytometry (done in females only) were found in <b>Supp. data file S8</b></li> </ul> The Wolbachia infection status is a binary value, compatible with PLINK, i.e. values can be 1 (not infected) or 2 (infected). Similarly, inversion phenotypes were recoded as 0 (ST/ST), 1 (INV/ST) and 2 (INV/INV). This study produced the genotyping data for the main 205 genotyped DGRP lines. Afterward, the authors mainly focused on genome architecture and population genetics traits such as inversion landscape, or relatedness of the DGRP lines (which mainly comes from the inversions), or variations in genome sizes. The authors also measured <i>Wolbachia pipientis</i> infection status, which extends the measurements done in the DGRP original study. It's worth noting that many of these phenotypes were recognized as covariates and thus are now standardly used as such for all GWAS analyses of the DGRP lines. Genome architecture,Microbiota integrated FBrf0225536
18 Ivanov et al. Longevity GWAS Using the <i>Drosophila</i> Genetic Reference Panel 2015 10.1093/gerona/glv047 4 Data were extracted from the <b>Supplementary Table 1</b> in the <a href=https://academic.oup.com/biomedgerontology/article/70/12/1470/2605217?login=false#supplementary-data>Supplementary data</a> section.<br/><br/> Of note, part of the lifespan data is coming from another study: <a href="https://dgrpool.epfl.ch/studies/1">(Arya et al, 2010)</a><br/><br/> The Wolbachia infection status is a binary value, compatible with PLINK, i.e. values can be 1 (not infected) or 2 (infected). This phenotype was actually taken from another study: <a href="https://dgrpool.epfl.ch/studies/17">(Huang et al, 2014)</a> This study measured the lifespan of virgin females. Genes in the target of the rapamycin pathway or carbohydrate metabolism seem to be associated with lifespan; including the InterPro term DUF227, which has been previously associated with lifespan determination. Ageing integrated FBrf0230043
19 Jordan et al. Genome-Wide Association for Sensitivity to Chronic Oxidative Stress in <i>Drosophila melanogaster</i> 2012 10.1371/journal.pone.0038722 4 Phenotyping data were extracted from <a href="https://doi.org/10.1371/journal.pone.0038722.s006">Table S1</a> in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0038722#s5">Supporting information</a> section.<br/><br/> For the geotaxis assay, each fly received a score for the highest point reached during the assay period according to 26 divisions of 5mm each so that scores ranged from 0 to 26.<br/><br/> Startle responses were quantified by subjecting each fly to a mechanical disturbance by tapping the vial twice against a surface and recording the amount of time the fly is active in the 45-second period immediately following the disturbance. The authors used a block-design and corrected accordingly the raw data for block effects. This study investigates the age-related locomotor impairment caused by reactive oxygen species (ROS), a common byproduct of mitochondrial energy metabolism, which can also be induced by exogenous sources, including UV light, radiation, and environmental toxins. Here, the authors assessed the locomotion of aged flies on standard medium and following chronic exposure to medium supplemented with 3 mM menadione sodium bisulfite (MSB), as a proxy for ROS-induced locomotor decline. Life history traits,Resistance,Locomotion,Ageing integrated FBrf0218606
20 Katzenberger et al. Death following traumatic brain injury in <i>Drosophila</i> is associated with intestinal barrier dysfunction 2015 10.7554/eLife.04790 2 The Mortality Index at 24hr (MI<sub>24</sub>) represents the average percent death for flies with injuries minus the average percent death for flies without injuries.<br/><br/> Phenotyping data comes from the <a href="https://doi.org/10.7554/eLife.04790.020">Supplementary file 1</a> in the <a href="https://elifesciences.org/articles/04790/figures#files">Additional files</a> section. This study evaluated the probability of death following traumatic brain injury (TBI). TBI causes intestinal and blood–brain barrier dysfunction and intestinal barrier dysfunction is highly correlated with the probability of death. Furthermore, the authors found that ingestion of glucose after a primary injury increases the probability of death through a secondary injury mechanism that exacerbates intestinal barrier dysfunction. Life history traits,Resistance integrated FBrf0227948
21 MacKay et al. The <i>Drosophila melanogaster</i> Genetic Reference Panel 2012 10.1038/nature10811 19 This paper is the founding paper for the DGRP lines, along with the extended genotyping study: <a href="https://dgrpool.epfl.ch/studies/17">(Huang et al, 2014)</a>.<br/><br/> Phenotyping data were extracted from the <b>Supplementary Tables</b> pdf file, found in the <a href="https://www.nature.com/articles/nature10811#Sec12">Supplementary information</a> section. It contained the following phenotypes: <ul> <li><b>Supplementary table 4</b>: Number and ratio of segregating sites, by chromosome arm.</li> <li><b>Supplementary table 6</b>: Numbers of transposable elements (TEs) in the DGRP lines. <b>Of note:</b> Unique TEs are a subset of Novel TEs which exist in only one line.</li> <li><b>Supplementary table 9</b>: <i>Wolbachia</i> infection status (Yes or No), transformed into 1:No and 2:Yes to match PLINK's format</li> <li><b>Supplementary table 20</b>: Mean phenotypic values for starvation stress resistance (SRR), chill coma recovery (CC) and startle response (ST)</li> <li><b>Supplementary table 28</b>: <i>Eco</i> R1-RFLP genotypes were <b>NOT included</b> here, because this table is not cited in the main text</li> </ul> <b>Note:</b> Most of the overlapping phenotypes between this study and <a href="https://dgrpool.epfl.ch/studies/17">(Huang et al, 2014)</a> show a good correlation. Except for the <a href="https://dgrpool.epfl.ch/phenotypes/2763">Number of segregating variants on chromosome X</a> which does not correlate at all between the two studies. This is the founding paper of the <i>Drosophila melanogaster</i> Genetic Reference Panel (DGRP) lines. It contains the original 192 inbred DGRP lines derived from a single outbred population, that were later extended. It also contains the original genotyping that was performed on 168 lines, and later extended to 205 lines <a href="https://dgrpool.epfl.ch/studies/17">(Huang et al, 2014)</a>. They also measured the presence of transposable elements (TEs) in each genome, the presence of <i>Wolbachia pipientis</i> in the lines, as well as other phenotypes such as starvation stress resistance (SR), chill coma recovery time (CC), and startle response (ST). Behaviour,Resistance,Locomotion,Microbiota integrated FBrf0217434
22 Mitchell et al. α-amanitin resistance in <i>Drosophila melanogaster</i>: A genome-wide association approach 2017 10.1371/journal.pone.0173162 3 Phenotyping data were found in the <a href="https://doi.org/10.1371/journal.pone.0173162.s001">S1 Table</a> and the <a href="https://doi.org/10.1371/journal.pone.0173162.s002">S2 Table</a>, in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173162#sec017">Supporting information</a> section.<br/><br/> The phenotypes were measured as the fraction of larvae emerging as adults on media supplemented with different α-amanitin concentrations (low, high). Of note, the lethal concentration has only been measured for 37 lines.<br/><br/> Of note, we noticed some discrepancies in the LC<sub>50</sub> average vs raw results in the S2 Table. Thus, we've loaded the raw data only.<br/><br/> Of note, this study also uses RNA-seq data for 40 DGRP lines, coming from <a href="https://dgrpool.epfl.ch/studies/130">(Ayroles et al, 2009)</a> This study tested the mushroom toxin (α-amanitin) resistance of <i>D. melanogaster</i>. While these flies tend to avoid mushrooms in nature, some DGRP lines are surprisingly resistant to α-amanitin. This resistance may represent a pre-adaptation, which might enable this species to invade the mushroom niche in the future. Genes linked to this resistance seem linked to the Target of Rapamycin (TOR) pathway. The authors suggest that endocytosis and autophagy of α-amanitin, followed by lysosomal degradation of the toxin, is one of the mechanisms that confer α-amanitin resistance in the DGRP lines. Toxicity,Resistance integrated FBrf0234898
23 Montgomery et al. Genome-Wide Association Analysis of Tolerance to Methylmercury Toxicity in <i>Drosophila</i> Implicates Myogenic and Neuromuscular Developmental Pathways 2014 10.1371/journal.pone.0110375 11 The development of flies exposed to MeHg during larval and pupal stages was measured by scoring eclosion (adult hatching) as a phenotypic endpoint.<br/> The eclosion index was calculated by normalizing the mean eclosion rate for each MeHg concentration to the eclosion on 0 µM MeHg for each strain.<br/> The cumulative eclosion index was generated by summing the normalized percent eclosion values obtained on the 5, 10, and 15 µM MeHg treatments for each strain.<br/><br/> Phenotyping data come from <a href="https://doi.org/10.1371/journal.pone.0110375.s005">Table S3</a> in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0110375#s6">Supporting information</a> section. Methylmercury (MeHg) is a persistent environmental toxin present in seafood that can compromise the developing nervous system in humans. This study examined how genetic variation impacts MeHg tolerance, by assessing developmental tolerance (measured by eclosion rate) to different MeHg concentrations. In addition, the development of MeHg food supplemented with caffeine, a previously identified dietary modifier of MeHg toxicity in fly development was examined. Toxicity,Resistance,Fecundity integrated FBrf0226648
24 Morgante et al. Genetic Architecture of Micro-Environmental Plasticity in <i>Drosophila melanogaster</i> 2015 10.1038/srep09785 9 Phenotyping data were extracted from <b>Supplementary Data file S1</b> found in the <a href="https://www.nature.com/articles/srep09785#Sec15">Electronic supplementary material</a> section.<br/><br/> The average phenotype values are identical to the ones in <a href="https://dgrpool.epfl.ch/studies/21">(MacKay et al., 2012)</a> for all three phenotypes (resistance to starvation stress, chill coma recovery time and startle response).<br/><br/> <b>Note</b>: While the mean values correspond well to our own recalculation of the means from the raw data, we cannot reproduce the calculation of ln(σE) using the raw data. It is almost ok for the Chill Coma Recovery, but the values seem very different for the Startle response and the Starvation resistance. Is the data corrected for some effects? The paper does not mention it though. This study proposes a new measure for micro-environmental plasticity, as the natural logarithm of the within-line standard deviation ln(σE). They test their values in the context of three traits: resistance to starvation stress, chill coma recovery time, and startle response which were measured in <a href="https://dgrpool.epfl.ch/studies/21">(MacKay et al., 2012)</a>. Behaviour,Resistance,Locomotion integrated FBrf0228381
25 Morozova et al. Polymorphisms in early neurodevelopmental genes affect natural variation in alcohol sensitivity in adult drosophila 2015 10.1186/s12864-015-2064-5 6 Phenotyping data were downloaded from the <b>Additional file 2</b> (Data file S1) in the <a href="https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-015-2064-5#Sec20">Additional files</a> section.<br/><br/> Alcohol knockdown time (Mean Elution Time, MET) was measured after a single (acute) alcohol exposure (E1), and after a second exposure (E2) following a 2 h recovery period, separately for males and females. This study explores the genetic basis of natural variation in alcohol sensitivity and tolerance. Individual variation in alcohol sensitivity in Drosophila is highly polygenic and in part determined by variation in evolutionarily conserved signaling pathways that are associated with catecholamine neurotransmitter biosynthesis and early development of the nervous system. Toxicity,Resistance,Metabolism integrated FBrf0229987
26 Najarro et al. Identifying Loci Contributing to Natural Variation in Xenobiotic Resistance in <i>Drosophila</i> 2015 10.1371/journal.pgen.1005663 1 Phenotyping data was taken from the <a href="https://doi.org/10.1371/journal.pgen.1005663.s015">S5 Table</a> in the <a href="https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005663#sec019">Supporting information</a> section.<br/><br/> <b>Of note</b>, this study uses both flies from the DGRP and flies from another set of Recombinant Inbred Lines (RILs) called DSPR. Of course, we only extracted data corresponding to the DGRP lines, except the line with Bloomington stock number 34061, because we could not find the corresponding DGRP id. Living organisms are subjected to a barrage of toxic compounds, or xenobiotics, in their environment and their diet. Natural populations exhibit a great deal of interindividual genetic variation in the response to toxins, exemplified by the variable clinical efficacy of pharmaceutical drugs in humans, and the evolution of pesticide-resistant insects. This study measured xenobiotic resistance, using caffeine as a model toxic compound. Toxicity,Resistance integrated FBrf0230275
27 Najarro et al. Loci Contributing to Boric Acid Toxicity in Two Reference Populations of <i>Drosophila melanogaster</i> 2017 10.1534/g3.117.041418 1 Phenotyping data are no longer available on the journal's website. Data came from <b>Data S1</b> file.<br/><br/> The measure of resistance to boric acid is taken as the lifespan (in hours) of mated females exposed to media supplemented with 1.5% boric acid.<br/><br/> <b>Of note</b>, this study uses both flies from the DGRP and flies from another set of Recombinant Inbred Lines (RILs) called DSPR. Of course, we only extracted data corresponding to the DGRP lines.<br/><br/> This study measured the resistance to boric acid, a commonly-used household insecticide. Toxicity,Resistance integrated FBrf0235744
28 Richardson et al. Population Genomics of the <i>Wolbachia</i> Endosymbiont in <i>Drosophila melanogaster</i> 2012 10.1371/journal.pgen.1003129 23 Phenotyping data comes from <a href="https://doi.org/10.1371/journal.pgen.1003129.s001">Dataset S1</a> in the <a href="https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003129#s5">Supporting information</a> section.<br/><br/> <b>Of note</b>, this study uses both flies from the DGRP (174 lines) and flies from the Drosophila Population Genomics Project (DPGP). Of course, we only extracted data corresponding to the 174 DGRP lines.<br/> The <i>Wolbachia</i> infection status is a binary value, compatible with PLINK, i.e. values can be 1 (not infected) or 2 (infected)..<br/> I did not know if I should remove some "phenotypes", like the number of reads mapping to the mitochondrial genome, as this looks more like QC than phenotypes. But it could also be seen as a quantitative trait to some extent, so I've kept them for now.<br/><br/> Interestingly, predicted results are strictly equivalent to the ones in (<a href="https://dgrpool.epfl.ch/studies/17">Huang et al, 2014</a>), but somewhat slightly discordant with PCR results. This study provides a complete genomic analysis of the evolutionary mode and temporal dynamics of the <i>Drosophila melanogaster–Wolbachia</i> symbiosis. The authors used whole-genome resequencing data from 290 lines to predict <i>Wolbachia</i> infection status, estimate relative cytoplasmic genome copy number, and reconstruct <i>Wolbachia</i> and mitochondrial genome sequences. Overall, 63% of <i>Drosophila</i> strains were predicted to be infected with <i>Wolbachia</i> by the authors' <i>in silico</i> analysis pipeline, which shows 99% concordance with infection status determined by diagnostic PCR. Genome architecture,Immunity,Microbiota integrated FBrf0220355
29 Rohde et al. Genomic Analysis of Genotype-by-Social Environment Interaction for <i>Drosophila melanogaster</i> Aggressive Behavior 2017 10.1534/genetics.117.200642 7 Phenotyping data were extracted from <b>Table S1</b> in the <a href="https://academic.oup.com/genetics/article/206/4/1969/6072640#supplementary-data">Supplementary data</a> section.<br/><br/> A behavioral arena, the <b>Flydiator arena</b> [<i>sic</i>], was developed to facilitate high-throughput acquisition of aggression data on pairs of individuals; one DGRP and one control fly (CSB). This study investigated the genetic basis of variation in male aggressive behavior for flies reared in a socialized and socially isolated environment. A large proportion of associated genes have previously been associated with aggressive behavior in Drosophila and mice, and have human orthologs that have been associated with neurological disorders, indicating partially shared genetic mechanisms underlying aggression in animal models and human psychiatric disorders. Behaviour integrated FBrf0236263
30 Shorter et al. Genetic architecture of natural variation in <i>Drosophila melanogaster</i> aggressive behavior 2015 10.1073/pnas.1510104112 1 Phenotyping data are coming from <b>Dataset S1</b> in the <a href="https://www.pnas.org/doi/full/10.1073/pnas.1510104112#supplementary-materials">Supporting information</a> section.<br/><br/> The authors quantified aggressive behavior for groups of eight flies of the same genotype (“eight fly” assay) or of a single focal fly and seven flies of a different genotype (“focal fly” assay), as previously described (<a href="https://doi.org/10.1371/journal.pgen.0020154">Edwards et al, 2006</a>). The aggression score corresponds to the number of aggressive encounters (wing threats, charges, head butts, chases, kicks, and boxing) that was scored for 2 min in the same vial. In the eight fly assays, all aggressive encounters from all flies in the group were summed to give a single aggression score per replicate vial. In the focal fly assays, only aggressive encounters in which the focal male participated were scored.<br/><br/> An adjusted aggression score was calculated by adjusting the raw data for weekly environmental fluctuations using the deviations from contemporaneous CSB (w<sup>1118</sup> Canton S B) control line means. The overall CSB mean over all weeks was added to all adjusted scores.<br/><br/> <b>Of note</b>, raw data and non-adjusted data are not available, so some plots may not be reproducible. With the exception of genetic variants associated with bioamine signaling, which have been implicated in aggression in many species, the genetic basis of natural variation in aggression is largely unknown. This study identified genes that have been previously involved in aggressive behavior as well as many novel loci, including gustatory receptor 63a (Gr63a), which encodes a subunit of the receptor for CO2, and genes associated with the development and function of the nervous system. Behaviour integrated FBrf0228967
31 Srivastav et al. Paternal Induction of Hybrid Dysgenesis in <i>Drosophila melanogaster</i> Is Weakly Correlated with Both <i>P</i>-Element and <i>hobo</i> Element Dosage 2017 10.1534/g3.117.040634 36 The phenotyping data is no longer available from the journal's website.<br/><br/> The phenotyping data are a bit redundant/correlated since the authors tested 6 different pipelines to estimate the structural variation of Full Length (<i>FL</i>) and <i>KP</i> structural variations in <i>P</i>-Elements, and two pipelines to estimate the structural variation of Full Length (<i>FL</i>) and <i>CN</i> structural variations in <i>Hobo</i> Elements. All the pipelines are described in <b>Table 1</b>.<br/> In addition, for <i>Hobo</i> Elements, two different annotation datasets (TIDAL and TEMP) were used. <br/><br/> <b>Corrigendum:</b> A <a href="https://doi.org/10.1534/g3.117.300500">correction</a> has been published correcting the mislabelling of Figure 1. Transposable elements (TEs) produce specific deleterious mutations by insertional inactivation, inducing DNA damage and participating in ectopic recombination. These effects are often assumed to be dosage-dependent, with stronger effects occurring in the presence of higher TE copy numbers. The authors tested this assumption by considering the relationship between the copy number of two active DNA transposons, <i>P</i>-element and <i>hobo</i> element, and the incidence of hybrid dysgenesis, a sterility syndrome associated with transposon activity in the germline. Genome architecture integrated FBrf0235441
32 Unckless et al. A Genome-Wide Association Study for Nutritional Indices in <i>Drosophila</i> 2015 10.1534/g3.114.016477 28 Phenotyping data comes from <b>Table S2</b> in the <a href="https://academic.oup.com/g3journal/article/5/3/417/6058726?login=true#supplementary-data">Supplementary data</a> section. The quality of dietary nutrition and the assimilation of dietary nutrients have a significant influence on many traits, including lifespan, development, reproduction, and immunity. This study searched for genetic associations with several nutritional indices (total soluble protein, glucose, glycogen, free glycerol, triglycerides, and wet weight) measured after rearing on either a low-glucose diet or a high-glucose diet. Life history traits,Metabolism,Nutrition integrated FBrf0227710
33 Vaisnav et al. Genome-Wide Association Analysis of Radiation Resistance in <i>Drosophila melanogaster</i> 2014 10.1371/journal.pone.0104858 1 Phenotyping data comes from <a href="https://doi.org/10.1371/journal.pone.0104858.s002">Supp. Table S2</a> in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0104858#s5">Supporting information</a> section.<br/><br/> The radiation source was a commercial Cs<sup>137</sup> irradiator emitting 662 keV gamma photons, and the irradiator was calibrated using a PTW N31010 ionization chamber. Survival was defined as the ability of males to fly 24 hours post-irradiation, and survival time was expressed in percentage as the mean of two trials.<br/><br/> <b>Important note:</b> We've loaded the raw data, but the data were weirdly reported in Table S2. We realized that we had to double the survival ratio values to make it work (i.e. being able to reproduce not only the mean values, but also the standard deviation and coefficient of variation provided by the authors)...<br/><br/> <b>Note:</b> The authors also report <i>Wolbachia</i> status, but it comes from another study, so we don't report it here <a href="https://dgrpool.epfl.ch/studies/21">(MacKay et al., 2012)</a>. They also report temporal phenotypic stability (after 5 months) of 12 highly resistant lines (Supp. Table S5), but given the sparsity of the data, we also don't report it here. Ionizing radiation is genotoxic to cells. Healthy tissue toxicity in patients and radiation resistance in tumors present common clinical challenges in delivering effective radiation therapies. Radiation response is a complex, polygenic trait with unknown genetic determinants. This study investigated the genetics of natural variation for sensitivity to radiation, among which 92 radiosensitive lines and 62 radioresistant lines were identified. However. variants in known DNA damage and repair genes associated with radiation response were not present among the significant hits, and no variant met the genome-wide significance threshold (p = 1.49e<sup>10−7</sup>), indicating a necessity for a larger sample size. Resistance integrated FBrf0225939
34 Vonesch et al. Genome-Wide Analysis Reveals Novel Regulators of Growth in <i>Drosophila melanogaster</i> 2016 10.1371/journal.pgen.1005616 27 Raw phenotypic data comes from <a href="https://doi.org/10.1371/journal.pgen.1005616.s013">Table S2</a> in the <a href="https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005616#sec028">Supporting information</a> section.<br/><br/> Centroid size was measured as the square root of the summed squared distances of 14 landmarks from the center of the wing. The interocular distance was measured from eye edge to eye edge along the anterior edge of the posterior ocelli and parallel to the base of the head.<br/><br/> <b>Of note:</b> Most of the phenotypes are not well described in the paper, and there are no units. They were asked to the authors or inferred by the curator. This study performed GWA of organismal size traits and found that the top associated variants differ between traits and sexes, and can only be linked to canonical growth pathway genes by epistasis analysis. The results of this study identified a cluster of associations close to the <i>kek1</i> locus, a well-characterized growth regulator. Organ Size,Anatomy,Appearance integrated FBrf0230608
35 Wang et al. The genetic basis for variation in resistance to infection in the <i>Drosophila melanogaster</i> genetic reference panel 2017 10.1371/journal.ppat.1006260 2 Phenotyping data were extracted from <a href="https://doi.org/10.1371/journal.ppat.1006260.s001">Table S1</a> found in the <a href="https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1006260#sec022">Supporting information</a> section.<br/><br/> Some phenotypes, such as average survival time (see Figure 6-7) are not available, so these figures are not reproducible.<br/><br/> <b>Of note</b>, this study provides <i>Wolbachia</i> Status from (<a href="https://dgrpool.epfl.ch/studies/17">Huang et al., 2014</a>), so we don't put it back here. The authors studied the resistance and tolerance to the fungal pathogen <i>Metarhizium anisopliae</i> Ma549. In addition, they found that host defense to Ma549 was correlated with defense to the bacterium <i>Pseudomonas aeruginosa</i> Pa14, and several previously published DGRP phenotypes including oxidative stress sensitivity, starvation stress resistance, hemolymph glucose levels, and sleep indices. Immunity,Resistance integrated FBrf0235068
36 Weber et al. Genome-Wide Association Analysis of Oxidative Stress Resistance in <i>Drosophila melanogaster</i> 2012 10.1371/journal.pone.0034745 2 Phenotyping data were extracted from <a href="https://doi.org/10.1371/journal.pone.0034745.s001">Table S1</a> found in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0034745#s5">Supporting information</a> section. Oxidative stress, or the overabundance of reactive oxygen species (ROS), is an unavoidable consequence of aerobic respiration. It has been implicated in aging, neurodegenerative and cardiovascular disease and the disruption of cell signaling processes that control cell growth and death. This paper studies acute oxidative stress resistance to two oxidizing agents, paraquat and menadione sodium bisulfite. Variants associated with variation in oxidative stress resistance were often sex-specific and agent-dependent, with a small subset common for both sexes or treatments. Resistance integrated FBrf0218073
37 Zhou et al. The Genetic Basis for Variation in Sensitivity to Lead Toxicity in <i>Drosophila melanogaster</i> 2016 10.1289/ehp.1510513 8 Phenotyping data was downloaded from <b>Excel File S5</b> in the Zip file found in the <a href="https://ehp.niehs.nih.gov/doi/suppl/10.1289/ehp.1510513">Supplemental material</a> section.<br/><br/> <b>Of note</b>, all phenotyping data are qualitative, not quantitative. Quantitative data are not available on the journal's website. But we asked and received the quantitative data from the authors of the study.<br/><br/> To measure lead toxicity 50 first-instar larvae were placed either on a control medium or a medium supplemented with 0.5 mM lead acetate. Heavy metal toxicity is a worldwide health problem, and lead exposure, especially, is of concern due to the adverse effects of low concentrations on cognitive development in children. The aim of this study was to identify evolutionarily conserved candidate genes associated with individual variation in susceptibility to lead exposure. They measured the effects of lead exposure on development time, viability, and adult activity. Toxicity,Life history traits integrated FBrf0233045
38 Zwarts et al. The genetic basis of natural variation in mushroom body size in <i>Drosophila melanogaster</i> 2015 10.1038/ncomms10115 11 Phenotyping data can be downloaded from the <b>Supplementary Data 1</b> file found in the <a href="https://www.nature.com/articles/ncomms10115#Sec16">Supplementary information</a> section.<br/><br/> The authors also compared their phenotypes, with phenotypes from other studies: aggressive behaviour, copulation latency, fitness, startle response, ethanol resistance, starvation resistance, lifespan, chill coma recovery and sleep traits (<a href="https://dgrpool.epfl.ch/studies/15">Harbison et al., 2013</a>) (<a href="https://dgrpool.epfl.ch/studies/130">Ayroles et al., 2009</a>) (<a href="https://dgrpool.epfl.ch/studies/131">Morozova et al., 2009</a>)<br/><br/> <b>Of note:</b> We added a "Normal" phenotype to complete the ratio of abnormal traits, for each DGRP. The mammalian cerebral cortex and the insect mushroom bodies (MBs) are key higher-order brain centers for the integration and processing of sensory information. The size of the cerebral cortex and the MBs has been regarded as a proxy for cognitive ability and behavioral plasticity. Here the authors mapped variants affecting natural variation in mushroom body morphology and identified 139 genes and 39 transcription factors with effects on development and adult plasticity. They also showed correlations between morphology and aggression, sleep, and lifespan. Organ Size,Anatomy,Behaviour integrated FBrf0230412
39 Harbison et al. Genome-Wide Association Study of Circadian Behavior in <i>Drosophila melanogaster</i> 2018 10.1007/s10519-018-9932-0 3 Phenotyping data comes from <b>Table S2</b> found in the <b>Supplementary material 3</b> of the <a href="https://link.springer.com/article/10.1007/s10519-018-9932-0#Sec25">Electronic Supplementary Material</a> section.<br/><br/> <b>Table S12</b> contains some gene expression data for one DGRP line, but the data seem not deposited in SRA or a gene expression repository. Thus, this data is not included here.<br/><br/> NA values in the periodic data stands for arrhythmic flies. The authors observed that ~ 12% of the flies were arrhythmic. Circadian rhythms influence physiological processes from sleep–wake cycles to body temperature and are controlled by highly conserved cycling molecules. The authors measured the circadian period (Ʈ) and rhythmicity index in the DGRP. Seven DGRP lines had sexually dimorphic arrhythmicity and one line had an exceptionally long circadian period Ʈ. Behaviour,Life history traits,Sleep integrated FBrf0241121
40 Huang et al. Context-dependent genetic architecture of Drosophila life span 2020 10.1371/journal.pbio.3000645 6 Phenotyping data are available from the <a href="https://doi.org/10.1371/journal.pbio.3000645.s001">S1 table</a> found in the <a href="https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000645#sec019">Supporting information</a> section. <b>Of note</b>, raw data is also available from <a href="https://github.com/qgg-lab/dgrp-lifespan/">GitHub</a>, so we primarily uploaded this data, instead of the summarized data.<br/><br/> The authors also created an Advanced Intercross Population (AIP) from a subset of extreme long-living DGRP lines that were maintained for over 100 generations with a large effective population size before performing “extreme quantitative trait locus (xQTL)” mapping for life span. In this study, the authors quantified variation in life span in males and females reared in 3 thermal environments. Quantitative genetic analyses of life span and the micro-environmental variance of life span in the DGRP revealed significant genetic variance for both traits within each sex and environment, as well as significant genotype-by-sex interaction (GSI) and genotype-by-environment interaction (GEI). We only kept the micro-environmental variance (ln<i>σ</i><sub>ε</sub>) summary calculation from the authors, but it's a simple logarithmic transformation of the standard deviation. Evolution,Life history traits,Ageing integrated FBrf0245198
41 Watanabe et al. Exercise-induced changes in climbing performance 2021 10.1098/rsos.211275 2 Phenotyping data were extracted from <b>Table S1</b>, available on <a href="https://doi.org/10.6084/m9.figshare.c.5696157">Figshare</a>.<br/><br/> The exercise-treated flies were subjected to 2h/day exercise session (exercising in response to rotational stimulation on a Treadwheel), for 5 days. Physical fitness in the exercise-treated and control animals was assayed using a rapid iterative negative geotaxis (RING) assay to determine a climbing index for each fly line. With this measure, a larger climbing index indicates that the flies were able to climb the vial walls faster after having been knocked to the bottom of the enclosure (range of possible climbing indexes: 1–4). The authors investigated how the climbing ability changes in response to a regular exercise schedule. They found extensive variation in baseline climbing ability and exercise-induced changes. Climbing ability, and its exercise-induced change, are sex- and genotype-dependent. Life history traits,Locomotion integrated FBrf0251956
42 Hope et al. The Drosophila Gene <i>Sulfateless</i> Modulates Autism-Like Behaviors 2019 10.3389/fgene.2019.00574 6 Mean phenotyping data were downloaded from <b>Supplemental Data 1</b> found in the <a href="https://www.frontiersin.org/articles/10.3389/fgene.2019.00574/full#supplementary-material">Supplementary material</a> section. Of note, we rather used the raw phenotyping data available in <b>Supplemental Data 4</b>. This study investigates autism spectrum disorder (ASD) risk, through three phenotypes with analogous behavior domains: impaired social communication, social reciprocity and repetitive behaviors or restricted interests. They studied ASD gene orthologs (<i>neurexin 4</i> and <i>neuroligin 2</i>), an intellectual disability (ID) gene homolog (<i>kirre</i>), and a gene encoding a heparan sulfate (HS) modifying enzyme called <i>sulfateless</i> (<i>sfl</i>). SNPs in <i>sfl</i> were associated with all three ASD-like behaviors. Behaviour,Life history traits accepted FBrf0242954
43 Rohde et al. Genetic Signatures of Drug Response Variability in <i>Drosophila melanogaster</i> 2019 10.1534/genetics.119.302381 4 The control treatment (SUC) consisted of a 5% sucrose solution (with 5% green food dye) and the MPH treatment was a 5% sucrose solution (with 5% green food dye) containing 1.5 mg ml−1 MPH (Sigma [Sigma Chemical], St. Louis, MO).<br/> Phenotyping data were downloaded from the <b>data_file_S1.csv</b> file accessible in the supplemental material available at <a href="https://doi.org/10.25386/genetics.9368636">Figshare</a>. This study explores the response to methylphenidate (MPH), a drug used in the treatment of attention-deficit/hyperactivity disorder. The authors exposed DGRPs to MPH and control treatment and observed an increase in locomotor activity in MPH-exposed individuals. Based on the DGRP, they showed that the behavioral response to MPH was strongly genotype-dependent. Behaviour,Locomotion integrated FBrf0243724
44 Zhao et al. The metabolome as a biomarker of aging in <i>Drosophila melanogaster</i> 2022 10.1111/acel.13548 15 This study followed adult cohorts of 20 DGRP strains chosen to represent the breadth of lifespan variation. They phenotyped lifespan, baseline mortality, and rate of aging and associated these parameters with age-specific functional traits including fecundity, climbing activity, and age-specific targeted metabolomic profiles. They show that activity levels and metabolome-wide profiles are strongly associated with age, that numerous individual metabolites show a strong association with lifespan, and that the metabolome provides a biological clock that predicts not only sample age but also future mortality rates and lifespan. Behaviour,Life history traits,Ageing accepted FBrf0252669
45 Chi et al. RNA-binding protein syncrip regulates starvation-induced hyperactivity in adult Drosophila 2021 10.1371/journal.pgen.1009396 5 Behaviour accepted FBrf0248324
46 Frochaux et al. cis-regulatory variation modulates susceptibility to enteric infection in the Drosophila genetic reference panel 2020 10.1186/s13059-019-1912-z 1 Immunity accepted FBrf0244562
47 Ørsted et al. Environmental variation partitioned into separate heritable components 2017 10.1111/evo.13391 17 Of note, the inversion and <i>Wolbachia pipientis</i> phenotypes are coming from <a href="https://dgrpool.epfl.ch/studies/17">Huang et al, 2014</a> Genome architecture,Resistance accepted FBrf0237717
48 Lavoy et al. Genetic Modifiers of Neurodegeneration in a <i>Drosophila<i> Model of Parkinson’s Disease 2018 10.1534/genetics.118.301119 10 Behaviour accepted FBrf0239578
49 Salvador-MartÃnez et al. Mapping Selection within Drosophila melanogaster Embryo’s Anatomy 2017 10.1093/molbev/msx266 0 Development,Anatomy,Appearance accepted FBrf0237601
50 Horváth et al. A novel method for quantifying the rate of embryogenesis uncovers considerable genetic variation for the duration of embryonic development in Drosophila melanogaster 2016 10.1186/s12862-016-0776-z 0 Development accepted FBrf0233634
51 Morozova et al. A <i>Cyclin E</i>Centered Genetic Network Contributes to Alcohol-Induced Variation in Drosophila Development 2018 10.1534/g3.118.200260 0 Sensory,Development,Behaviour accepted FBrf0239702
52 Litovchenko et al. Extensive tissue-specific expression variation and novel regulators underlying circadian behavior 2021 10.1126/sciadv.abc3781 0 Behaviour accepted FBrf0247917
53 He et al. Epistatic partners of neurogenic genes modulate Drosophila olfactory behavior 2016 10.1111/gbb.12279 3 Behaviour accepted FBrf0230854
54 Francis et al. Genome wide analysis in <i>Drosophila<i> reveals diet by gene interactions and uncovers diet-responsive genes 2019 10.1101/718304 0 Resistance,Metabolism accepted
55 Akhund-Zade et al. Genetic basis of offspring number–body weight tradeoff in <i>Drosophila melanogaster</i> 2021 10.1093/g3journal/jkab129 0 Life history traits accepted
56 Campbell et al. Genome-Wide Association Analysis of Anoxia Tolerance in <i>Drosophila melanogaster</i> 2019 10.1534/g3.119.400421 3 Resistance accepted FBrf0243370
57 Spierer et al. Natural variation in the regulation of neurodevelopmental genes modifies flight performance in Drosophila 2021 10.1371/journal.pgen.1008887 1 Behaviour accepted FBrf0248437
58 Rodrigues et al. The genetic basis and adult reproductive consequences of developmental thermal plasticity 2022 10.1111/1365-2656.13664 18 Life history traits accepted FBrf0253732
59 Lovejoy et al. Genetic basis of susceptibility to low‐dose paraquat and variation between the sexes in <i>Drosophila melanogaster</i> 2021 10.1111/mec.15878 65 Sensory,Behaviour accepted FBrf0248852
60 Huang et al. Epistasis dominates the genetic architecture of <i>Drosophila</i> quantitative traits 2012 10.1073/pnas.1213423109 0 Life history traits accepted FBrf0219551
61 Dembeck et al. Genetic basis of natural variation in body pigmentation in <i>Drosophila melanogaster</i> 2015 10.1080/19336934.2015.1102807 5 Appearance accepted FBrf0230671
62 Swarup et al. Analysis of natural variation reveals neurogenetic networks for <i>Drosophila</i> olfactory behavior 2012 10.1073/pnas.1220168110 0 Sensory accepted FBrf0220538
63 Duneau et al. Signatures of insecticide selection in the genome of <i>Drosophila melanogaster</i> 2018 10.1101/287250 15 Resistance accepted
64 Morgante et al. Leveraging multiple layers of data to predict <i>Drosophila</i> complex traits 2019 10.1101/824896 0 accepted
65 Wang et al. Genetic variation for resistance to the specific fly pathogen Entomophthora muscae 2020 10.1038/s41598-020-71262-w 16 Immunity,Resistance accepted FBrf0246635
66 Garud et al. Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data 2021 10.1371/journal.pgen.1009373 0 Genome architecture accepted FBrf0248389
67 Baker et al. Genetic Basis of Variation in Cocaine and Methamphetamine Consumption in Outbred Populations of <i>Drosophila melanogaster</i> 2021 10.1101/2021.03.01.433403 0 Sensory,Behaviour accepted
68 Zhang et al. Testing Implications of the Omnigenic Model for the Genetic Analysis of Loci Identified through Genome-wide Association 2021 10.1016/j.cub.2020.12.023 0 Appearance accepted FBrf0248411
69 Patel et al. Identification of genetic modifiers of lifespan on a high sugar diet in the Drosophila Genetic Reference Panel 2021 10.1016/j.heliyon.2021.e07153 1 Life history traits accepted FBrf0249297
70 Zhang et al. Identification of a genetic network for an ecologically relevant behavioural phenotype in <i>Drosophila melanogaster</i> 2020 10.1111/mec.15341 0 Anatomy accepted FBrf0244858
71 Yanagawa et al. Genetic Basis of Natural Variation in Spontaneous Grooming in <i>Drosophila melanogaster</i> 2020 10.1534/g3.120.401360 0 Behaviour accepted FBrf0246612
72 Knudsen et al. Genetic Variation and Potential for Resistance Development to the tTA Overexpression Lethal System in Insects 2020 10.1534/g3.120.400990 0 Genome architecture,Life history traits accepted FBrf0245357
73 Rohde et al. Functional Validation of Candidate Genes Detected by Genomic Feature Models 2018 10.1534/g3.118.200082 1 Behaviour accepted FBrf0238818
74 Adebambo et al. Toxicological Study and Genetic Basis of BTEX Susceptibility in Drosophila melanogaster 2020 10.3389/fgene.2020.594179 0 Development,Resistance accepted FBrf0247219
75 Bou Sleiman et al. Enteric infection induces Lark-mediated intron retention at the 5′ end of Drosophila genes 2020 10.1186/s13059-019-1918-6 2 Genome architecture,Immunity accepted FBrf0244561
76 Lafuente et al. Genetic basis of thermal plasticity variation in <i>Drosophila melanogaster</i>body size 2018 10.1101/268201 3 Appearance accepted
77 Ahlers et al. Insulin potentiates JAK/STAT signaling to broadly inhibit flavivirus replication in insect vectors 2019 10.1101/701714 0 Immunity,Metabolism accepted
78 Watanabe et al. Genetic networks underlying natural variation in basal and induced activity levels in <i>Drosophila melanogaster</i> 2018 10.1101/444380 3 Behaviour accepted
79 Evangelou et al. Unpredictable effects of the genetic background of transgenic lines in physiological quantitative traits 2018 10.1101/494419 6 Life history traits,Metabolism accepted
80 Dean et al. Masculinization of gene expression is associated with male quality in <i>Drosophila melanogaster</i> 2018 10.1111/evo.13618 5 Behaviour,Life history traits accepted FBrf0240924
81 Gao et al. Incorporating Gene Annotation into Genomic Prediction of Complex Phenotypes 2017 10.1534/genetics.117.300198 0 Sensory,Behaviour,Metabolism accepted FBrf0236841
82 Signor et al. A Large Panel of Drosophila simulans Reveals an Abundance of Common Variants 2017 10.1093/gbe/evx262 0 Evolution accepted FBrf0237786
83 Schmidt et al. Insights into DDT Resistance from the <i>Drosophila melanogaster<i> Genetic Reference Panel 2017 10.1534/genetics.117.300310 0 Genome architecture,Resistance accepted FBrf0237089
84 Riddle Variation in the response to exercise stimulation in Drosophila: marathon runner versus sprinter genotypes 2020 10.1242/jeb.229997 5 Behaviour accepted FBrf0246800
85 Harrison et al. The metabolome as a link in the genotype-phenotype map for peroxide resistance in the fruit fly, Drosophila melanogaster 2020 10.1186/s12864-020-6739-1 59 Resistance,Metabolism accepted FBrf0245603
86 Massey et al. Pleiotropic effects of <i>ebony</i>and <i>tan</i>on pigmentation and cuticular hydrocarbon composition in <i>Drosophila melanogaster</i> 2019 10.1101/538090 0 Appearance accepted
87 Gabrawy et al. Lisinopril Preserves Physical Resilience and Extends Life Span in a Genotype-Specific Manner in Drosophila melanogaster 2019 10.1093/gerona/glz152 0 Sensory,Life history traits accepted FBrf0244035
88 Champer et al. CRISPR Gene Drive Efficiency and Resistance Rate Is Highly Heritable with No Common Genetic Loci of Large Effect 2019 10.1534/genetics.119.302037 0 Genome architecture,Evolution accepted FBrf0242214
89 Bonfini et al. Multiscale analysis reveals that diet-dependent midgut plasticity emerges from alterations in both stem cell niche coupling and enterocyte size 2021 10.7554/elife.64125 13 Organ Size,Development,Metabolism accepted
90 Manoel et al. Beta-blockers in Traumatic Brain Injury 2018 10.5005/jp-journals-10030-1241 0 accepted
91 Kumar et al. Identification of Genes Contributing to a Long Circadian Period in <i>Drosophila Melanogaster</i> 2020 10.1177/0748730420975946 0 Genome architecture accepted FBrf0248902
92 Guzman et al. Natural genetic variation in <i>Drosophila melanogaster<i> reveals genes associated with <i>Coxiella burnetii<i> infection 2021 10.1093/genetics/iyab005 0 Immunity accepted FBrf0248571
93 Ma et al. Commensal Gut Bacteria Buffer the Impact of Host Genetic Variants on Drosophila Developmental Traits under Nutritional Stress 2019 10.1016/j.isci.2019.07.048 0 Development,Limb Development,Appearance accepted FBrf0243652
94 McCracken et al. The hidden costs of dietary restriction: implications for its evolutionary and mechanistic origins 2019 10.1101/533711 0 Life history traits accepted
95 Rohde et al. Genotype and Trait Specific Responses to Rapamycin Intake in Drosophila melanogaster 2021 10.3390/insects12050474 2 Life history traits,Resistance accepted FBrf0249203
96 Hall et al. Identification of novel genes associated with longevity in Drosophila melanogaster - a computational approach 2019 10.18632/aging.102527 0 Genome architecture,Life history traits accepted FBrf0244333
97 Pitchers et al. A Multivariate Genome-Wide Association Study of Wing Shape in <i>Drosophila melanogaster</i> 2019 10.1534/genetics.118.301342 0 Limb Development accepted FBrf0242020
98 Green et al. <i>Cis</i>and <i>trans</i>-acting variants contribute to survivorship in a naïve <i>Drosophila melanogaster</i>population exposed to ryanoid insecticides 2018 10.1101/502161 7 Resistance,Metabolism accepted
99 Battlay et al. Structural Variants and Selective Sweep Foci Contribute to Insecticide Resistance in the <i>Drosophila</i>Genetic Reference Panel 2018 10.1534/g3.118.200619 0 Genome architecture,Resistance accepted FBrf0240570
100 Jin et al. Genetic and metabolomic architecture of variation in diet restriction-mediated lifespan extension in Drosophila 2020 10.1371/journal.pgen.1008835 0 Life history traits,Metabolism accepted FBrf0246151
101 Jehrke et al. The impact of genome variation and diet on the metabolic phenotype and microbiome composition of Drosophila melanogaster 2018 10.1038/s41598-018-24542-5 0 Microbiota,Metabolism accepted FBrf0238766
102 Rajpurohit et al. Spatiotemporal dynamics and genome-wide association analysis of desiccation tolerance in <i>Drosophila melanogaster</i> 2018 10.1111/mec.14814 19 Resistance,Metabolism accepted FBrf0239872
103 Wilson et al. GWAS for Lifespan and Decline in Climbing Ability in Flies upon Dietary Restriction Reveal decima as a Mediator of Insulin-like Peptide Production 2020 10.1016/j.cub.2020.05.020 0 Behaviour,Life history traits,Metabolism accepted FBrf0246266
104 McCracken et al. The relationship between longevity and diet is genotype dependent and sensitive to desiccation in <i>Drosophila melanogaster</i> 2020 10.1242/jeb.230185 0 Life history traits accepted FBrf0247456
105 Freda et al. Stage-specific genotype-by-environment interactions for cold and heat hardiness in Drosophila melanogaster 2019 10.1038/s41437-019-0236-9 6 Development,Resistance accepted FBrf0243444
106 Okada et al. Sex‐dependent and sex‐independent regulatory systems of size variation in natural populations 2019 10.15252/msb.20199012 3 Appearance accepted FBrf0244128
107 Rolandi et al. Genetic variation for tolerance to high temperatures in a population of <i>Drosophila melanogaster</i> 2018 10.1002/ece3.4409 3 Resistance accepted FBrf0240724
108 Bryant et al. The Intracellular Symbiont <i>Wolbachia pipientis</i> Enhances Recombination in a Dose-Dependent Manner 2020 10.3390/insects11050284 0 Genome architecture accepted FBrf0245608
109 Sharma et al. Musashi expression in intestinal stem cells attenuates radiation-induced decline in intestinal permeability and survival in <i>Drosophila</i> 2020 10.1038/s41598-020-75867-z 0 Resistance accepted FBrf0247145
110 Scharenbrock et al. Interactions among Genetic Background, Anesthetic Agent, and Oxygen Concentration Shape Blunt Traumatic Brain Injury Outcomes in <i>Drosophila melanogaster</i> 2020 10.3390/ijms21186926 0 Behaviour accepted FBrf0246825
111 Newell et al. The <i>Drosophila</i> Post-mating Response: Gene Expression and Behavioral Changes Reveal Perdurance and Variation in Cross-Tissue Interactions 2020 10.1534/g3.119.400963 0 Behaviour accepted FBrf0245057
112 Palu et al. Decoupling of Apoptosis from Activation of the ER Stress Response by the <i>Drosophila</i> Metallopeptidase <i>superdeath</i> 2020 10.1534/genetics.119.303004 0 Appearance accepted FBrf0245428
113 Davis et al. Characterizing dopaminergic neuron vulnerability using genome-wide analysis 2021 10.1093/genetics/iyab081 1 Behaviour accepted FBrf0252031
114 Masson et al. Blind killing of both male and female <i>Drosophila<i> embryos by a natural variant of the endosymbiotic bacterium <i>Spiroplasma poulsonii</i> 2020 10.1111/cmi.13156 0 Development,Life history traits,Immunity accepted FBrf0245270
115 Savola et al. Testing evolutionary explanations for the lifespan benefit of dietary restriction in fruit flies ( <i>Drosophila melanogaster<i> ) 2021 10.1111/evo.14146 0 Life history traits accepted FBrf0248261
116 Siva-Jothy et al. Dissecting genetic and sex-specific sources of host heterogeneity in pathogen shedding and spread 2021 10.1371/journal.ppat.1009196 0 Life history traits,Immunity accepted FBrf0247956
117 Amstutz et al. Cluster-Based Analysis of Retinitis Pigmentosa Modifiers Using Drosophila Eye Size and Gene Expression Data 2022 10.3390/genes13020386 0 Appearance accepted FBrf0252765
118 Schiffman et al. Ageing and genetic background influence anaesthetic effects in a <i>D. melanogaster</i> model of blunt trauma with brain injury† 2020 10.1016/j.bja.2020.03.029 0 Life history traits,Ageing accepted FBrf0245944
119 Chen et al. Female genetic contributions to sperm competition in <i>Drosophila melanogaster</i> 2018 10.1101/500546 0 Life history traits accepted
120 Hoffman et al. Sex, mating and repeatability of <i>Drosophila melanogaster</i> longevity 2021 10.1098/rsos.210273 16 Life history traits,Ageing accepted FBrf0250359
121 Siva-Jothy et al. Viral infection causes sex-specific changes in fruit fly social aggregation behaviour 2019 10.1098/rsbl.2019.0344 0 Behaviour accepted FBrf0243480
122 Brown et al. Behavioral and Transcriptional Response to Selection for Olfactory Behavior in <i>Drosophila</i> 2020 10.1534/g3.120.401117 0 Sensory,Behaviour accepted FBrf0245363
130 Ayroles et al. Systems genetics of complex traits in Drosophila melanogaster 2009 10.1038/ng.332 0 submitted
131 Morozova et al. Alcohol Sensitivity in Drosophila: Translational Potential of Systems Genetics 2009 10.1534/genetics.109.107490 0 submitted
132 Morozova et al. Modulation of the Drosophila transcriptome by developmental exposure to alcohol 2022 10.1186/s12864-022-08559-9 0 submitted
133 Palmer et al. Isolation of a natural DNA virus of Drosophila melanogaster, and characterisation of host resistance and immune responses 2018 10.1371/journal.ppat.1007050 0 submitted
134 Saha et al. Genetic architecture of natural variation of cardiac performance from flies to humans 2022 10.7554/eLife.82459 0 submitted
135 Sidisky et al. Genome-wide analysis reveals novel regulators of synaptic maintenance in <i>Drosophila</i> 2023 10.1093/genetics/iyad025 0 submitted
136 Patlar et al. A predominant role of genotypic variation in both expression of sperm competition genes and paternity success in <i>Drosophila melanogaster</i> 2023 10.1098/rspb.2023.1715 0 submitted
137 Ãzsoy et al. Epistasis for head morphology in <i>Drosophila melanogaster</i> 2021 10.1093/g3journal/jkab285 0 submitted
138 Wong et al. Pleiotropic fitness effects across sexes and ages in the <i>Drosophila<i> genome and transcriptome 2023 10.1093/evolut/qpad163 0 submitted
139 Cannavò et al. Genetic variants regulating expression levels and isoform diversity during embryogenesis 2016 10.1038/nature20802 0 submitted
140 Leiva et al. Intraspecific variation of heat tolerance in a model ectotherm: The role of oxygen, cell size and body size 2023 10.1111/1365-2435.14485 0 submitted
141 Leiva et al. The role of cell size in shaping responses to oxygen and temperature in fruit flies 2023 10.1111/1365-2435.14294 0 submitted
142 Dobson et al. Host genetic determinants of microbiota-dependent nutrition revealed by genome-wide analysis of <i>Drosophila melanogaster</i> 2015 10.1038/ncomms7312 1 Despite being a study about microbiota-dependent nutrition, we could not find any other phenotypes than the Wolbachia Status in this study?<br/> <br/> Phenotyping data is available from Supp Table S6 in <a href="https://www.nature.com/articles/ncomms7312#Sec14">supplementary data</a> section. This study examined the genetic basis for how gut microbiota affect five nutritional traits (weight, protein, lipid, glucose, and glycogen contents) in fruit flies. The authors found significant genotype-dependent variations in these traits when the microbiota were eliminated, identifying specific genetic polymorphisms associated with these variations and validating key genes involved in these microbiota-dependent nutritional effects. Additionally, they observed that the presence of the intracellular bacterium Wolbachia influenced glycogen content, with Wolbachia-positive lines showing higher glycogen levels compared to Wolbachia-free lines. Microbiota,Metabolism,Nutrition integrated