Assistant Professor, Department of Genetics and Biochemistry
Email: email@example.comVisit the Morgante Lab
Dr. Morgante started his academic career at the University of Florence (Italy), where he earned a BS and an MS in Agricultural Sciences with a special focus on animal science. He attended the University of Edinburgh (Scotland) and obtained an MSc in Animal Breeding and Genetics. Morgante earned an MR in Statistics and a PhD in Genetics from North Carolina State University (US), advised by Dr. Trudy Mackay. During this period, he also spent a total of 5 months at Aarhus University (Denmark) to work with Drs. Peter Sørensen and Daniel Sorensen. He then performed postdoctoral research in the labs of Drs. Matthew Stephens and Yang Li at the University of Chicago (US). Morgante joined the Center for Human Genetics and the Department of Genetics and Biochemistry at Clemson University (US) as an Assistant Professor in Fall 2020.
Morgante’s research interests lie in quantitative and statistical genetics. In particular, his research focuses not only on understanding the genetic architecture of complex traits in humans and model species, but also on using this information to improve phenotypic prediction accuracy for such traits. To achieve this goal, his group often develops new analytical strategies and statistical methods.
Everett LJ, Huang W, Zhou S, Carbone MA, Lyman RF, Arya GH, Geisz MS, Ma J, Morgante F, St Armour G, Turlapati L, Anholt RRH and Mackay TFC. 2020. Gene expression networks in the Drosophila Genetic Reference Panel. Genome Res 30: 485-496. doi: 10.1101/gr.257592.119.
Morgante F, Huang W, Sørensen P, Maltecca C and Mackay TFC. 2020. Leveraging multiple layers of data to predict Drosophila complex traits. G3 (Bethesda) 10: 4599-4613. doi: 10.1534/g3.120.401847.
Zhou S, Morgante F, Geisz MS, Ma J, Anholt RRH and Mackay TFC. 2020. Systems genetics of the Drosophila metabolome. Genome Res 30: 392-405. doi: 10.1101/gr.243030.118.
Morgante F, Huang W, Sørensen P, Maltecca C and Mackay TFC. 2019. Leveraging multiple layers of data to predict Drosophila complex traits. bioRxiv 824896. doi: 10.1101/824896.
Morgante F, Huang W, Maltecca C, Mackay TFC. (2018). Effect of Genetic Architecture on the Prediction Accuracy of Quantitative Traits in Samples of Unrelated Individuals. Heredity 120: 500-514.
Barroso LM, Morgante F, Mackay TFC, Nascimento ACC, Nascimento M, Serão NV. (2017). Genomic Prediction Accuracies Using Regularized Quantile Regression (RQR) Methodology. Journal of Animal Sciencesupplement2:14-15.
Sørensen P, de los Campos G, Morgante F, Mackay TFC, Sorensen D. (2015). Genetic Control of Environmental Variation of Two Quantitative Traits of Drosophila melanogasterRevealed by Whole-Genome Sequencing. Genetics 201:487-497.
Morgante F, Sørensen P, Sorensen DA, Maltecca C, Mackay TFC. (2015). Genetic Architecture of Micro-environmental Plasticity in Drosophila melanogaster. Scientific Reports 5: 9785.