Dr Leonardo Bottolo is a Reader in Statistics for Biomedicine at the University of Cambridge and a fellow at The Alan Turing Institute. He received his PhD in Methodological Statistics from the University of Trento, Italy, in 2001. Before joining the University of Cambridge, he was appointed Senior Lecturer in Statistics in the Department of Mathematics, Imperial College. He worked as a postdoc in the Mathematical Genetics group, University of Oxford and at the Institute of Mathematical Sciences, Imperial College.
Dr Leonardo Bottolo’s goal is to develop scalable Bayesian models and design their efficient software implementations to tackle the problem of statistical and machine learning in large, diverse and complex data sets that are standard in modern molecular biology, for example, single-cell RNA-sequencing (scRNA-seq) and single-cell Assay for Transposase-Accessible Chromatin with high-throughput sequencing (scATAC-seq) and biomedicine, for instance, data collected in neurodegenerative diseases, multilocus imprinting disturbances and mitochondrial and cardiovascular diseases.
- Ochoa, E., Lee, S., Lan-Leung, B., Renuka, D., Ong, K.K., Radley, J.A., Pérez de Nanclares, , Martinez, R., Clark, G., Martin, E., Castanos, L. Bottolo, L., and Maher, E. (2021). ImprintSeq, a novel tool to interrogate DNA methylation at human imprinted regions. Accepted Genetics in Medicine.
- Ruffieux, H., Fairfax, B., Nassiri, I., Vigorito, E., Wallace, C., Richardson, S., and Bottolo, L. (2021). EPISPOT: An epigenome-driven approach for detecting hotspots in molecular QTL studies. American Journal of Human Genetics, 108(6):983-1000
- Bottolo, L., Banterle, M., Richardson, S., Ala-Korpela, M., Järvelin, M-R., and Lewin, A. (2021). A computationally efficient Bayesian Seemingly Unrelated Regressions model for high-dimensional Quantitative Trait Loci discovery. Journal of the Royal Statistical Society: Series C. 70(4):886-908
- Alexopoulos, A., and Bottolo, L. (2020). Bayesian Variable Selection for Gaussian copula regression models. Journal of Computational and Graphical Statistics, 30(3):578-593
- Zhang, X., …, Bottolo, L., and Ware, J. (2020). Disease-specific variant pathogenicity prediction significantly improves clinical variant interpretation in inherited cardiac conditions. Genetics in Medicine, 23: 69-79.
- 2019 – (Co-I) MRC Research Grant (£ 1,175,347) with Johnson (PI), E. Petretto (Co-I), P. K. Srivastava (Co-I), ICL, S. Sisodiya (Co-I), M. Thom (Co-I), UCL: “An integrated systems-level framework for deciphering multidrug-resistant epilepsy”.