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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.

Filippi, S. and Holmes, C. (2016) “A Bayesian nonparametric approach to testing for dependence between random variables”, Bayesian Analysis, 12(4), pp. 919–938.
Kim, E. et al. (2016) “Effect of Microvascular Anastomosis Technique on End Product Outcome in Simulated Training: A Prospective Blinded Randomized Controlled Trial”, Journal of Reconstructive Microsurgery, 32(07), pp. 556–561.
Holton, D. et al. (2016) “Application of a novel approach to assess the thermal evolution processes associated with the disposal of high-heat-generating waste in a geological disposal facility”, Engineering Geology, 211, pp. 102–119.
Gray-Davies, T., Holmes, C. and Caron, F. (2016) “Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks”, Electronic Journal of Statistics, 10(2), pp. 1807–1828.
Myers, S. et al. (2016) “Rapid genotype imputation from sequence without reference panels”, Nature Genetics, 48, pp. 965–969.
Titsias, M., Holmes, C. and Yau, C. (2016) “Statistical Inference in Hidden Markov Models Using k-Segment Constraints”, Journal of the American Statistical Association, 111(513), pp. 200–215.
Ghanem, A. et al. (2016) “Anastomosis Lapse Index (ALI): A Validated End Product Assessment Tool for Simulation Microsurgery Training”, Journal of Reconstructive Microsurgery, 32(03), pp. 233–241.
Hellenthal, G. et al. (2016) “The Kalash Genetic Isolate? The evidence for recent admixture”, American Journal of Human Genetics, 98(2), pp. 396–397.
Davies, B. et al. (2016) “Re-engineering the zinc fingers of PRDM9 reverses hybrid sterility in mice”, Nature, 530(2016), pp. 171–176.
McGree, J. et al. (2016) “Big data and design of experiments”, in Computational and Statistical Methods for Analysing Big Data with Applications, pp. 111–129.