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.

Fong, E., Lyddon, S. and Holmes, C. (2019) “Scalable nonparametric sampling from multimodal posteriors with the posterior bootstrap”, in 36th International Conference on Machine Learning, ICML 2019, pp. 3443–3464.

Li, R. et al. (2019) “Genetic diversity and its unexpected impacts on recombination, genome evolution, speciation and sterility in mammals”, in EUROPEAN JOURNAL OF HUMAN GENETICS, pp. 1073–1073.

Doherty, A. et al. (2018) “GWAS identifies 14 loci for device-measured physical activity and sleep duration”, Nature Communications, 9.

Wang, Z. et al. (2018) “Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration”, iScience, 9, pp. 451–460.

Jewson, J., Smith, J. and Holmes, C. (2018) “Principles of Bayesian inference using general divergence criteria”, Entropy, 20(6), p. 442.

Brown, S. et al. (2018) “High-throughput mouse phenomics for characterizing mammalian gene function”, Nature Reviews Genetics, 19(6), pp. 357–370.

Willetts, M. et al. (2018) “Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants”, Scientific Reports, 8.

Glaire, M. et al. (2018) “Tumour-infiltrating CD8 + lymphocytes as a prognostic marker in colorectal cancer: A retrospective, pooled analysis of the QUASAR2 and VICTOR trials.”, Journal of Clinical Oncology. American Society of Clinical Oncology (ASCO).

Behr, M., Holmes, C. and Munk, A. (2018) “Multiscale blind source separation”, The Annals of Statistics, 46(2), pp. 711–744.

Bycroft, C. et al. (2018) “Patterns of genetic differentiation and the footprints of historical migrations in the Iberian Peninsula”, Nature Communications [Preprint].