My name is Arianna and I come from Parma, a town in northern Italy famous for its food, architecture and art. I started my academic path at University of Parma, in my hometown. There I graduated in Food Science, with a thesis evaluating the genetic biodiversity of different cherry and fig tree cultivars, peculiar of the Parma province geographic area. I then decided to further my studies at the University of Bologna, where I obtained a master’s degree in Biodiversity and Evolution, working on a project assessing the possible adaptive evolution of Asian populations in response to rice-based diets. Overall, I am interested in human populations’ biodemography and genetics and in effects of natural selection on human evolution, especially when it is diet triggered.
I joined the IMforFUTURE network in September 2018 as ESR11, working at University of Edinburgh under the supervision of Prof. James Wilson and Dr. Lucija Klarić. My research project “Genetic variants in protein glycosylation” mainly consists in investigating the contribution of low frequency and rare genetic variants to glycomic and glycoproteomic variation.
To date, a large portion of genetic variation influencing complex traits and diseases still needs to be identified. Rare and low-frequency variants, whose contribution to genetics of complex traits has been shown to be not negligible, are very under-studied compared to more common variants. Omics data are extensively employed in genetic association studies as ‘proxies’ for traits of interest. They can be in fact considered as intermediate phenotypes: measurable manifestations of more complex phenotypes, usually more strongly associated to genetic variants than the complex trait or disease itself. Glycomics, studying the whole collection of glycans in biological systems, is an emerging field among omics data. Despite being involved in the aging process and in a wide range of diseases, genetic regulation of glycosylation still remains only partly understood. The aim of my project is to investigate the contribution of low frequency and rare genetic variants to glycomic traits. A combination of highly informative data coming from isolated populations and appropriate techniques, as for example genome-wide association and variant aggregation tests, will be employed to address the knowledge gap in the relation between rarer genetic variants and complex traits.