@article{kocher_ayroles_stone_grozinger_2010, title={Individual variation in pheromone response correlates with reproductive traits and brain gene expression in worker honey bees}, volume={5}, number={2}, journal={PLoS One}, author={Kocher, S. D. and Ayroles, J. F. and Stone, E. A. and Grozinger, C. M.}, year={2010} }
 @article{morozova_ayroles_jordan_duncan_carbone_lyman_stone_govindaraju_ellison_mackay_et al._2009, title={Alcohol Sensitivity in Drosophila: Translational Potential of Systems Genetics}, volume={183}, ISSN={["1943-2631"]}, DOI={10.1534/genetics.109.107490}, abstractNote={Abstract}, number={2}, journal={GENETICS}, author={Morozova, Tatiana V. and Ayroles, Julien F. and Jordan, Katherine W. and Duncan, Laura H. and Carbone, Mary Anna and Lyman, Richard E. and Stone, Eric A. and Govindaraju, Diddahally R. and Ellison, R. Curtis and Mackay, Trudy F. C. and et al.}, year={2009}, month={Oct}, pages={733–745} }
 @article{harbison_carbone_ayroles_stone_lyman_mackay_2009, title={Co-regulated transcriptional networks contribute to natural genetic variation in Drosophila sleep}, volume={41}, ISSN={["1546-1718"]}, DOI={10.1038/ng.330}, abstractNote={Sleep disorders are common in humans, and sleep loss increases the risk of obesity and diabetes. Studies in Drosophila have revealed molecular pathways and neural tissues regulating sleep; however, genes that maintain genetic variation for sleep in natural populations are unknown. Here, we characterized sleep in 40 wild-derived Drosophila lines and observed abundant genetic variation in sleep architecture. We associated sleep with genome-wide variation in gene expression to identify candidate genes. We independently confirmed that molecular polymorphisms in Catsup (Catecholamines up) are associated with variation in sleep and that P-element mutations in four candidate genes affect sleep and gene expression. Transcripts associated with sleep grouped into biologically plausible genetically correlated transcriptional modules. We confirmed co-regulated gene expression using P-element mutants. Quantitative genetic analysis of natural phenotypic variation is an efficient method for revealing candidate genes and pathways.}, number={3}, journal={NATURE GENETICS}, author={Harbison, Susan T. and Carbone, Mary Anna and Ayroles, Julien F. and Stone, Eric A. and Lyman, Richard F. and Mackay, Trudy F. C.}, year={2009}, month={Mar}, pages={371–375} }
 @article{stone_ayroles_2009, title={Modulated Modularity Clustering as an Exploratory Tool for Functional Genomic Inference}, volume={5}, ISSN={["1553-7404"]}, DOI={10.1371/journal.pgen.1000479}, abstractNote={In recent years, the advent of high-throughput assays, coupled with their diminishing cost, has facilitated a systems approach to biology. As a consequence, massive amounts of data are currently being generated, requiring efficient methodology aimed at the reduction of scale. Whole-genome transcriptional profiling is a standard component of systems-level analyses, and to reduce scale and improve inference clustering genes is common. Since clustering is often the first step toward generating hypotheses, cluster quality is critical. Conversely, because the validation of cluster-driven hypotheses is indirect, it is critical that quality clusters not be obtained by subjective means. In this paper, we present a new objective-based clustering method and demonstrate that it yields high-quality results. Our method, modulated modularity clustering (MMC), seeks community structure in graphical data. MMC modulates the connection strengths of edges in a weighted graph to maximize an objective function (called modularity) that quantifies community structure. The result of this maximization is a clustering through which tightly-connected groups of vertices emerge. Our application is to systems genetics, and we quantitatively compare MMC both to the hierarchical clustering method most commonly employed and to three popular spectral clustering approaches. We further validate MMC through analyses of human and Drosophila melanogaster expression data, demonstrating that the clusters we obtain are biologically meaningful. We show MMC to be effective and suitable to applications of large scale. In light of these features, we advocate MMC as a standard tool for exploration and hypothesis generation.}, number={5}, journal={PLOS GENETICS}, author={Stone, Eric A. and Ayroles, Julien F.}, year={2009}, month={May} }
 @article{ayroles_carbone_stone_jordan_lyman_magwire_rollmann_duncan_lawrence_anholt_et al._2009, title={Systems genetics of complex traits in Drosophila melanogaster}, volume={41}, ISSN={["1546-1718"]}, DOI={10.1038/ng.332}, abstractNote={Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically intercorrelated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression and transcription factor binding sites. The high degree of transcriptional connectivity allows us to infer genetic networks and the function of predicted genes from annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provide insight into the molecular basis of pleiotropy between complex traits.}, number={3}, journal={NATURE GENETICS}, author={Ayroles, Julien F. and Carbone, Mary Anna and Stone, Eric A. and Jordan, Katherine W. and Lyman, Richard F. and Magwire, Michael M. and Rollmann, Stephanie M. and Duncan, Laura H. and Lawrence, Faye and Anholt, Robert R. H. and et al.}, year={2009}, month={Mar}, pages={299–307} }
 @misc{ayroles_gibson_2006, title={Analysis of variance of microarray data}, volume={411}, journal={Dna microarrays, part b: databases and statistics}, publisher={San Diego: Elsevier Academic Press Inc}, author={Ayroles, J. F. and Gibson, G.}, year={2006}, pages={214-} }
 @article{edwards_ayroles_stone_carbone_lyman_mackay, title={A transcriptional network associated with natural variation in Drosophila aggressive behavior}, volume={10}, number={7}, journal={Genome Biology}, author={Edwards, A. C. and Ayroles, J. F. and Stone, E. A. and Carbone, M. A. and Lyman, R. F. and Mackay, T. F. C.} }
 @article{huang_richards_carbone_zhu_anholt_ayroles_duncan_jordan_lawrence_magwire_et al., title={Epistasis dominates the genetic architecture of Drosophila quantitative traits}, volume={109}, number={39}, journal={Proceedings of the National Academy of Sciences of the United States of America}, author={Huang, W. and Richards, S. and Carbone, M. A. and Zhu, D. H. and Anholt, R. R. H. and Ayroles, J. F. and Duncan, L. and Jordan, K. W. and Lawrence, F. and Magwire, M. M. and et al.}, pages={15553–15559} }
 @article{ayroles_laflamme_stone_wolfner_mackay, title={Functional genome annotation of Drosophila seminal fluid proteins using transcriptional genetic networks}, volume={93}, number={6}, journal={Genetical Research}, author={Ayroles, J. F. and Laflamme, B. A. and Stone, E. A. and Wolfner, M. F. and Mackay, T. F. C.}, pages={387–395} }
 @article{carbone_ayroles_yamamoto_morozova_west_magwire_mackay_anholt, title={Overexpression of myocilin in the Drosophila eye activates the unfolded protein response: Implications for glaucoma}, volume={4}, number={1}, journal={PLoS One}, author={Carbone, M. A. and Ayroles, J. F. and Yamamoto, A. and Morozova, T. V. and West, S. A. and Magwire, M. M. and Mackay, T. F. C. and Anholt, R. R. H.} }
 @article{jumbo-lucioni_ayroles_chambers_jordan_leips_mackay_de_luca, title={Systems genetics analysis of body weight and energy metabolism traits in Drosophila melanogaster}, volume={11}, journal={BMC Genomics}, author={Jumbo-Lucioni, P. and Ayroles, J. F. and Chambers, M. M. and Jordan, K. W. and Leips, J. and Mackay, T. F. C. and De and Luca, M.} }
 @article{mackay_richards_stone_barbadilla_ayroles_zhu_casillas_han_magwire_cridland_et al., title={The Drosophila melanogaster genetic reference panel}, volume={482}, number={7384}, journal={Nature}, author={Mackay, T. F. C. and Richards, S. and Stone, E. A. and Barbadilla, A. and Ayroles, J. F. and Zhu, D. H. and Casillas, S. and Han, Y. and Magwire, M. M. and Cridland, J. M. and et al.}, pages={173–178} }
 @article{zwarts_vanden broeck_cappuyns_ayroles_magwire_vulsteke_clements_mackay_callaerts, title={The genetic basis of natural variation in mushroom body size in Drosophila melanogaster}, volume={6}, journal={Nature Communications}, author={Zwarts, L. and Vanden Broeck, L. and Cappuyns, E. and Ayroles, J. F. and Magwire, M. M. and Vulsteke, V. and Clements, J. and Mackay, T. F. C. and Callaerts, P.} }
 @misc{mackay_stone_ayroles, title={The genetics of quantitative traits: Challenges and prospects}, volume={10}, number={8}, journal={Nature Reviews. Genetics}, author={Mackay, T. F. C. and Stone, E. A. and Ayroles, J. F.}, pages={565–577} }
 @article{ober_ayroles_stone_richards_zhu_gibbs_stricker_gianola_schlather_mackay_et al., title={Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster}, volume={8}, number={5}, journal={PLoS Genetics}, author={Ober, U. and Ayroles, J. F. and Stone, E. A. and Richards, S. and Zhu, D. H. and Gibbs, R. A. and Stricker, C. and Gianola, D. and Schlather, M. and Mackay, T. F. C. and et al.} }