@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. Here, we performed deep RNA sequencing of 200 Drosophila Genetic Reference Panel inbred lines with complete genome sequences, and mapped expression quantitative trait loci for annotated genes, novel transcribed regions (most of which are long noncoding RNAs), 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, and 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={An important challenge in genetics is to be able to predict complex traits accurately. Despite recent advances, prediction accuracy for most complex traits remains low. Here, we used the Drosophila Genetic Reference Panel (DGRP), a collection of 200 lines with whole-genome sequences and deep RNA sequencing data, to evaluate the usefulness of using high-quality gene expression levels compared to relying on genotypes for predicting three complex traits. We found that expression levels provided higher accuracy than genotypes for starvation resistance, similar accuracy for chill coma recovery, and lower accuracy for startle response. Models including both genotype and expressions levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included another layer of information, i.e., gene ontology (GO). We found that a limited number of GO terms, some of which had a clear biological interpretation, were strongly predictive of the traits. In summary, this study shows that integrating different sources of information can improve prediction accuracy, especially when large samples are not available.}, 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={Abstract}, 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-} }