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Genetics and Biochemistry Profiles

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Fabio Morgante

Genetics and Biochemistry

Assistant Professor

Self Regional Hall (Greenwood Genetics Lab) 151 [Office]


Educational Background

Ph.D., Genetics, North Carolina State University, 2018
MR., Statistics, North Carolina State University, 2017
M.Sc., Animal Breeding and Genetics, University of Edinburgh (UK), 2013
B.S., Agricultural Sciences, University of Florence (IT), 2009

Profile/About Me

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), where he worked on complex trait prediction problems 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 statistical genetics in the labs of Drs. Matthew Stephens and Yang Li at the University of Chicago (US). Morgante joined the Department of Genetics and Biochemistry at Clemson University (US) as an Assistant Professor in Fall 2020. He is a member of the Center for Human Genetics and the Biomedical Data Science and Informatics program.

Research Interests

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 and diseases 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 works at the intersection of genetics, data science, and statistics, often developing new analytical strategies and statistical methods.

Selected Publications

Morgante F, Huang W, Sørensen P, Maltecca C, Mackay TFC. (2020). Leveraging Multiple Layers of Data to Predict Drosophila Complex Traits. G3: Genes, Genomes, Genetics 10: 4599-4613.

Zhou S, Morgante F, Geisz MS, Ma J, Anholt RRH, Mackay TFC. (2020). Systems Genetics of the Drosophila Metabolome. Genome Research 30: 392-405.

Everett L, [7 authors], Morgante F, [2 authors], Mackay TFC. (2020). Gene Expression Networks in the Drosophila Genetic Reference Panel. Genome Research 30: 485-496.

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 TF, Nascimento ACC, Nascimento M, Serão NV. (2017). Genomic Prediction Accuracies Using Regularized Quantile Regression (RQR) Methodology. Journal of Animal Science 95.supplement2:14-15.

Sørensen P, de los Campos G, Morgante F, Mackay TFC, Sorensen D. (2015). Genetic Control of Environmental Variation of Two Quantitative Traits of Drosophila melanogaster Revealed 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.

Selected Talks

Morgante F, Wang G, Carbonetto P, Sarkar A, Li YI, Stephens M. (2020). Using information across tissues and genes to predict gene expression in Transcriptome-wide Association Studies. The 6th International Conference of Quantitative Genetics. November 3-13, Virtual, Planned by Australian committee.

Morgante F, Huang W, Maltecca C, Mackay TFC. (2016). Effect of Genetic Architecture and Sample Size on the Accuracy of Genomic Prediction of Complex Traits. The Allied Genetics Conference. July 13-17, Orlando, FL, USA.