I am Postdoctoral Research Associate working with Prof. Sylvia Richardson and Dr Leonardo Bottolo on Bayesian modelling for sparse regression problems. My work involves designing scalable hierarchical approaches to infer complex biological structures from genome-wide association and molecular quantitative trait locus data, with a special emphasis on the modelling of pleiotropic signals.
I hold a Ph.D. in Mathematics from Ecole Polytechnique Fédérale de Lausanne (EPFL), jointly advised by Prof. Anthony C. Davison and Dr Jörg Hager. Before and during my doctoral studies, I have also worked for the Swiss Institute of Bioinformatics and Nestlé Research alongside geneticists and computational biologists, which reinforced my interest in developing methodologies to improve basic biological research and its translation into medical care.Selected papers / preprints
- H. Ruffieux, A. C. Davison, J. Hager, J. Inshaw, B. Fairfax, S. Richardson, and L. Bottolo. (2020) A global-local approach for detecting hotspots in multiple response regression, The Annals of Applied Statistics, 14:905-928.
- H. Ruffieux, J. Carayol, R. Popescu, M. E. Harper, R. Dent, W. H. M. Saris, A. Astrup, A. C. Davison, J. Hager, and A. Valsesia. (2020) A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma, PLOS Computational Biology, 16:e1007882.
- A. Valsesia, Q.-P. Wang, N. Gheldof, J. Carayol, H. Ruffieux, T. Clark, V. Shenton, L. J. Oyston, G. Lefebvre, S. Metairon, et al. (2019) Genome-wide gene-based analyses of weight loss interventions identify a potential role for NKX6.3 in metabolism, Nature Communications 10: 540
- H. Ruffieux, A. C. Davison, J. Hager, and I. Irincheeva. (2017) Efficient inference for genetic association studies with multiple outcomes, Biostatistics 18: 618–636
- ATLASQTL (fast global-local hotspot QTL detection)
- ECHOSEQ (synthetic-data generator to emulate genotyping and molecular datasets)
- EPISPOT (annotation-driven approach for large-scale joint regression with multiple responses)
- LOCUS (large-scale variational inference for variable selection in sparse multiple-response regression)