Photo: Ristiina, Finland, August 2020
Photo: Cabane des Aiguilles Rouges, Switzerland, July 2020
Photo: Patras, Greece, December 2017
I am a postdoctoral researcher at the Systems and Population Genetics Group, University of Lausanne. My research is mainly focused in the development of statistical methods for genotype imputation and haplotype phasing.
I did my DPhil at the Department of Statistics of the University of Oxford, supervised by Jonathan Marchini and Pier Palamara and graduated in May 2020. During my DPhil I developed IMPUTE5, a software for genotype imputation of SNP array datasets.
In my free time I like to go hiking in the Swiss/Italian Alps or explore the neighbouring villages with my bike. When not in Switzerland, I am likely to be found in a quiet Finnish forest picking berries or in a sauna!
Download my resumé.
Phd in Statistical Genomics, 2020
Department of Statistics, University of Oxford, UK
Msc in Computer Science, 2014
Department of Computer Science, University of Milano-Bicocca, IT
Research Assistant, 2014
Department of Computer Science, University of Oxford, UK
In this work, we address the challenge of genotype imputation and haplotype phasing of low-coverage sequencing datasets using a reference panel of haplotypes. To this aim, we propose a novel method, GLIMPSE (Genotype Likelihoods Imputation and PhaSing mEthod), that is designed for large-scale studies and reference panels, typically comprising thousands of genomes. We show the remarkable performance of GLIMPSE using low-coverage whole genome sequencing data for both European and African American populations, and we demonstrate that low-coverage sequencing can be confidently used in downstream analyses. We provide GLIMPSE as a part of an open source software suite that makes imputation for low-coverage sequencing data as convenient as for traditional SNP array platforms.
Genome-wide association studies (GWAS) typically use microarray technology to measure genotypes at several hundred thousand positions in the genome. However reference panels of genetic variation consist of haplotype data at >100 fold more positions in the genome. Genotype imputation makes genotype predictions at all the reference panel sites using the GWAS data. Reference panels are continuing to grow in size and this improves accuracy of the predictions, however methods need to be able to scale this increased size. We have developed a new version of the popular IMPUTE software than can handle reference panels with millions of haplotypes, and has better performance than other published approaches. A notable property of the new method is that it scales sub-linearly with reference panel size. Keeping the number of imputed markers constant, a 100 fold increase in reference panel size requires less than twice the computation time.