@article{everett_huang_zhou_carbone_lyman_arya_geisz_ma_morgante_st armour_et al._2020, title={Gene expression networks in the Drosophila Genetic Reference Panel}, volume={30}, ISSN={["1549-5469"]}, DOI={10.1101/gr.257592.119}, abstractNote={A major challenge in modern biology is to understand how naturally occurring variation in DNA sequences affects complex organismal traits through networks of intermediate molecular phenotypes. This question is best addressed in a genetic mapping population in which all molecular polymorphisms are known and for which molecular endophenotypes and complex traits are assessed on the same genotypes. Here, we performed deep RNA sequencing of 200 Drosophila Genetic Reference Panel inbred lines with complete genome sequences and for which phenotypes of many quantitative traits have been evaluated. We mapped expression quantitative trait loci for annotated genes, novel transcribed regions, transposable elements, and microbial species. We identified host variants that affect expression of transposable elements, independent of their copy number, as well as microbiome composition. We constructed sex-specific expression quantitative trait locus regulatory networks. These networks are enriched for novel transcribed regions and target genes in heterochromatin and euchromatic regions of reduced recombination, as well as genes regulating transposable element expression. This study provides new insights regarding the role of natural genetic variation in regulating gene expression and generates testable hypotheses for future functional analyses.}, number={3}, journal={GENOME RESEARCH}, author={Everett, Logan J. and Huang, Wen and Zhou, Shanshan and Carbone, Mary Anna and Lyman, Richard F. and Arya, Gunjan H. and Geisz, Matthew S. and Ma, Junwu and Morgante, Fabio and St Armour, Genevieve and et al.}, year={2020}, month={Mar}, pages={485–496} }
 @article{morgante_huang_sorensen_maltecca_mackay_2020, title={Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits}, volume={10}, ISSN={["2160-1836"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85097210372&partnerID=MN8TOARS}, DOI={10.1534/g3.120.401847}, abstractNote={Abstract
               The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.}, number={12}, journal={G3-GENES GENOMES GENETICS}, author={Morgante, Fabio and Huang, Wen and Sorensen, Peter and Maltecca, Christian and Mackay, Trudy F. C.}, year={2020}, month={Dec}, pages={4599–4613} }
 @article{zhou_morgante_geisz_ma_anholt_mackay_2020, title={Systems genetics of the Drosophila metabolome}, volume={30}, ISSN={["1549-5469"]}, DOI={10.1101/gr.243030.118}, abstractNote={How effects of DNA sequence variants are transmitted through intermediate endophenotypes to modulate organismal traits remains a central question in quantitative genetics. This problem can be addressed through a systems approach in a population in which genetic polymorphisms, gene expression traits, metabolites, and complex phenotypes can be evaluated on the same genotypes. Here, we focused on the metabolome, which represents the most proximal link between genetic variation and organismal phenotype, and quantified metabolite levels in 40 lines of the Drosophila melanogaster Genetic Reference Panel. We identified sex-specific modules of genetically correlated metabolites and constructed networks that integrate DNA sequence variation and variation in gene expression with variation in metabolites and organismal traits, including starvation stress resistance and male aggression. Finally, we asked to what extent SNPs and metabolites can predict trait phenotypes and generated trait- and sex-specific prediction models that provide novel insights about the metabolomic underpinnings of complex phenotypes.}, number={3}, journal={GENOME RESEARCH}, author={Zhou, Shanshan and Morgante, Fabio and Geisz, Matthew S. and Ma, Junwu and Anholt, Robert R. H. and Mackay, Trudy F. C.}, year={2020}, month={Mar}, pages={392–405} }
 @article{morgante_sørensen_sorensen_maltecca_mackay_2015, title={Genetic Architecture of Micro-Environmental Plasticity in Drosophila melanogaster}, volume={5}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/srep09785}, DOI={10.1038/srep09785}, abstractNote={AbstractIndividuals of the same genotype do not have the same phenotype for quantitative traits when reared under common macro-environmental conditions, a phenomenon called micro-environmental plasticity. Genetic variation in micro-environmental plasticity is assumed in models of the evolution of phenotypic variance and is important in applied breeding and personalized medicine. Here, we quantified genetic variation for micro-environmental plasticity for three quantitative traits in the inbred, sequenced lines of the Drosophila melanogaster Genetic Reference Panel. We found substantial genetic variation for micro-environmental plasticity for all traits, with broad sense heritabilities of the same magnitude or greater than those of trait means. Micro-environmental plasticity is not correlated with residual segregating variation, is trait-specific and has genetic correlations with trait means ranging from zero to near unity. We identified several candidate genes associated with micro-environmental plasticity of startle response, including Drosophila Hsp90, setting the stage for future genetic dissection of this phenomenon.}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Morgante, Fabio and Sørensen, Peter and Sorensen, Daniel A. and Maltecca, Christian and Mackay, Trudy F. C.}, year={2015}, month={May} }
 @article{sorensen_campos_morgante_mackay_sorensen, title={Genetic control of environmental variation of two quantitative traits of Drosophila melanogaster revealed by whole-genome sequencing}, volume={201}, number={2}, journal={Genetics}, author={Sorensen, P. and Campos, G. and Morgante, F. and Mackay, T. F. C. and Sorensen, D.}, pages={487-} }