About
Fabio Morgante is an Assistant Professor at the Clemson University Center for Human Genetics (US). He holds a BS and an MS in Agricultural Sciences from the University of Florence (IT), an MSc in Animal Breeding and Genetics from The University of Edinburgh (UK), an MR in Statistics and a PhD in Genetics from North Carolina State University (US), working under the mentorship of Dr. Trudy Mackay. He did postdoctoral research in statistical genetics at The University of Chicago (US) with Drs. Matthew Stephens and Yang Li. His research interest has been elucidating the genetic architecture of complex traits as well as improving their prediction accuracy. To this end, during his PhD, Morgante used data from the Drosophila Genetic Reference Panel (DGRP) — a collection of 205 fully sequenced, inbred lines — to show that (1) not only phenotypic mean but also phenotypic variance is under genetic control; (2) accounting for genetic architecture is the key to improve polygenic prediction accuracy; and (3) including multiple layers of information (i.e., genomic, transcriptomic, metabolomic and functional annotation data) has the potential to improve prediction accuracy of complex traits. Starting with his postdoc, Morgante got interested in human genetics and statistical method development. He has developed a method that leverages the sharing of information between related traits to improve prediction accuracy. Through collaborations with other health-related researchers at Clemson University and elsewhere, Morgante is helping produce more accurate polygenic prediction models and understand the genetics of complex traits such as Alzheimer’s Disease.
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How their research is transforming health care
Understanding the genetic architecture of complex traits is critical for personalized medicine, as knowledge of underlying risk loci is required for treatment and prediction of disease state. Genome wide association studies (GWAS) have identified hundreds of thousands of loci affecting a plethora of complex traits and diseases. This information – incorporated in Polygenic Scores (PGS) – has provided levels of prediction accuracy for some complex diseases and health-related traits comparable to those obtained for monogenic diseases. However, prediction accuracy is still too low for most complex traits, which hinders the use of PGS in clinical practice. Research in my group aims at developing statistical methods and analytical strategies to improve prediction accuracy of complex traits and diseases by exploiting complexities in genetic effect sharing and multiple layers of information.
Health research keywords
polygenic prediction, polygenic scores, complex trait genetics, genome wide association study, precision medicine
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