Publications by Statistical Genetics and Epidemiology

Our research spans areas of statistical genetics, in particular the development of powerful statistical approaches to analyse genetic data, as well as studying infectious diseases.

Speidel, L. et al. (2019) “A method for genome-wide genealogy estimation for thousands of samples.”, Nature genetics [Preprint], (9).

Li, R. et al. (2019) “A high-resolution map of non-crossover events reveals impacts of genetic diversity on mammalian meiotic recombination”, Nature Communications, 10.

Donnelly, C. et al. (2019) “Worldwide reduction in MERS cases and deaths since 2016”, Emerging Infectious Diseases, 25(9), pp. 1758–1760.

Glaire, M. et al. (2019) “Tumour-infiltrating CD8+ lymphocytes and colorectal cancer recurrence by tumour and nodal stage”, British Journal of Cancer, 121, pp. 474–482.

Donnelly, C. et al. (2019) “Important reductions in the global number of MERS cases and deaths since 2016”, Emerging Infectious Diseases, 25(9), pp. 1758–1760.

Moraga, P. et al. (2019) “epiflows: an R package for risk assessment of travel-related spread of disease”, F1000Research, 7, p. 1374.

Gazal, S. et al. (2019) “Author Correction: Linkage disequilibrium–dependent architecture of human complex traits shows action of negative selection”, Nature Genetics, 51(8), pp. 1295–1295.

Forna, A. et al. (2019) “Case fatality ratio estimates for the 2013 – 2016 West African Ebola epidemic: application of boosted regression trees for imputation”, Clinical Infectious Diseases, 70(12), pp. 2476–2483.

Clerx, M. et al. (2019) “Probabilistic inference on noisy time series (PINTS)”, Journal of Open Research Software, 7(1).

Parag, K. and Donnelly, C. (2019) “Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models”, p. 703751.