Imperial College London


Faculty of MedicineNational Heart & Lung Institute

Visiting Researcher







542Huxley BuildingSouth Kensington Campus





# Google Scholar #

I am mainly interested in the application of Bayesian computational methods  to exploit causes of genetic variation in final and intermediate phenotypes  and their interaction, i.e. complex traits and transcriptional abundance  respectively. My goal is to build a class of general purpose statistical tools that, together with portable, efficient and publicly available software solutions, can be used in the future to better understand the molecular mechanisms of disease pathogenesis.

My collaborators are: Petros Dellaportas (Dept of Statistics, Athens University of Economics and Business), Adrian Dobra (Dept of Statistics, University of Washington), Christos-Savvas Bouganis (EEE, Imperial College London) and Sylvia Richardson (MRC Biostatistics Unit, Cambridge).

I have also strong links with biologists and epidemiologists: Mario Falchi (Dept Genomics and Common Disease, Imperial College London), Enrico Petretto (MRC Clinical Sciences Centre London) and Paolo Vineis (EPH, Imperiall College London).



Zuber V, Cameron A, Myserlis E, et al., 2021, Leveraging genetic data to elucidate the relationship between Covid-19 and ischemic stroke, Journal of the American Heart Association, Vol:10, ISSN:2047-9980, Pages:1-24

Alexopoulos A, Bottolo L, 2021, Bayesian Variable Selection for Gaussian Copula Regression Models, Journal of Computational and Graphical Statistics, Vol:30, ISSN:1061-8600, Pages:578-593

Ruffieux H, Fairfax BP, Nassiri I, et al., 2021, EPISPOT: An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies, American Journal of Human Genetics, Vol:108, ISSN:0002-9297, Pages:983-1000

Ruffieux H, Fairfax BP, Nassiri I, et al., 2020, EPISPOT: an epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies

Lawler K, Huang-Doran I, Sonoyama T, et al., 2020, Leptin-Mediated Changes in the Human Metabolome, Journal of Clinical Endocrinology & Metabolism, Vol:105, ISSN:0021-972X, Pages:2541-2552

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