University Lecturer in Haematological Genomics
Phone: 01223 747221
William studied mathematics at the University of Cambridge and trained for a PhD in Statistical Genetics with David Balding at Imperial College, London. He was subsequently a post-doctoral researcher, working in biostatistics and quantitative genetics, at Imperial College at McGill University and at the University of Cambridge. He joined the CEU in July 2016. He is a visiting worker at the MRC Biostatistics Unit and the Wellcome Trust Sanger Institute.
William is interested in the genetic basis of variation in haematological risk factors for cardiovascular diseases and is involved in the development and application of biostatistical methods to understand the biological mechanisms underlying such risk factors. His methodological and applied work covers genomewide association studies, metabonomics, the analysis of NMR spectra and RNA-Seq datasets and gene expression studies.
A major focus of his current work is the analysis of the blood trait phenotypes measured in the UK Biobank and INTERVAL studies. A collaboration with colleagues from the CEU and with scientists from the Department of Haematology and the Wellcome Trust Sanger Institute, this work has led to a 10-fold increase in the number of genetic loci known to modulate cellular properties of the blood and identified reticulocyte count as possible causal risk factor for coronary heart disease.
Ongoing applied research projects include the genetic analysis of extended full blood count phenotypes measured by Sysmex haematology analysers, the analysis of peripheral blood foetal haemoglobin levels and (in collaboration with Dr Kate Downes and Prof. Willem Ouwehand) the analysis of platelet functional response phenotypes. Methodological projects include the development (with Prof John Aston of the Statistical Laboratory) of methods to derive biologically informative statistics from images of blood smears.
Mayer L., et al. Nbeal2 interacts with Dock7, Sec16a, and Vac14 Blood Vol 131 No. 9 (2018)
Peterson R., et al. Platelet function is modified by common sequence variation in megakaryocyte super enhancers . Nature Communications, Vol 8 (2017)
Astle W., Elding H., Jiang T. et al. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease Cell No. 167 (2016)
Sivapalaratnam S. et al. Rare variants in GP1BB are responsible for autosomal dominant macrothrombocytopenia Blood Vol 129 No. 4 (2016)
Iotchkova V. et al. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps Nature Genetics (2016) Epub, ahead of print
Stritt S., Nurden P., Turro E., et al. A gain-of-function variant in DIAPH1 causes dominant macrothrombocytopenia and hearing loss Blood Vol. 127, No. 23 (2016) 2903-2914
Hao J., Liebeke, M., Astle W.J., De Iorio M., Bundy J.G. and Ebbels T.M.D. Bayesian Deconvolution and Quantitation of Metabolites in Complex 1-D NMR Spectra Using BATMAN Nature Protocols Vol. 9, No. 6 (2014), 1416-1427
Chen L., Kostadima M., et al. Transcriptional diversity during lineage commitment of human blood progenitors Science Vol. 35, No. 6204 (2014), 180-188
Turro, E., Astle W.J. and Tavar´e S. Flexible analysis of RNA-seq data using mixed effects models. Bioinformatics Vol. 30, No. 2 (2014), 180-188
Direk K., Cecelja M., Astle W., Chowienczyk, P., Spector T., Falchi M., and Andrew T. The relationship between DXA-based and anthropometric measures of visceral fat and morbidity in women, BMC Cardiovascular Disorders, Vol. 13, No. 25 (2013)
Astle W.J., De Iorio M., Richardson S., Stephens D. and Ebbels T. A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures. Journal of the American Statistical Association, Vol. 107, No. 500 (2012), 1259-1271.
Hao, J., Astle, W.J., De Iorio, M. and Ebbels, T. BATMAN – an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model Bioinformatics, Vol. 28, No. 15 (2012), 2088-2090.
Astle W.J., Balding D.J. Population Structure and Cryptic Relatedness in Genetic Association Studies, Statistical Science Vol. 24, No. 4 (2009), 451-471.