Dr Jonathan Marchini
University Lecturer in Statistical Genomics
Fellow of Mansfield College
marchini at stats.ox.ac.uk
+44 (0)1865 272860 (Department)
+44 (0)1865 271125 (Direct)
+44 (0)1865 272595 (Fax)
Jonathan Marchini
Research interests
Statistical Genetics, Genome-wide Association Studies, Computationally Intensive Statistics, Bayesian Statistics, Image Analysis
J. Marchini, D. Cutler, N. Patterson, M. Stephens, E. Eskin, E. Halperin, S. Lin, Z.S. Qin, H.M. Munro, G.R. Abecasis, P. Donnelly, and International HapMap Consortium (2006) A Comparison of Phasing Algorithms for Trios and Unrelated Individuals. Amercian Journal of Human Genetics, 78 437-450
J. Marchini, P. Donnelly and L. R Cardon (2005) Genome-wide strategies for detecting multiple loci influencing complex diseases. Nature Genetics, 37 413-417
J. Marchini, L. Cardon, M. Phillips, and P. Donnelly (2004) The effects of human population structure on large genetic association studies. Nature Genetics 36 512 – 517
Marchini, J. L. and Smith, S. M., (2003) On bias in the estimation of autocorrelations for fMRI voxel time series analysis. NeuroImage 18: 83-90
Marchini, J. L., and Ripley, B. D., (2000) A New Statistical Approach to Detecting Significant Activation in Functional MRI. NeuroImage 12: 366-380
Biographical Sketch
I studied Mathematics and Statistics at Exeter University from 1991-1994. I then trained as a secondary school Mathematics teacher for 1 year before working as a VSO volunteer in Tanzania for three years teaching A-level Mathematics. I came to Oxford in 1998 to do a Dphil on the statistical analysis of fMRI brain images supervised by Professor Brian Ripley. In 2002 i started to work in the area of statistical genetics as a postdoc supervised by Professor Peter Donnelly and Professor Lon Cardon. I became a University Lecturer in Statistical Genomics in September 2005 and am a Senior Research Fellow at Mansfield College, Oxford.
The main focus of my research is the development of statistical methods for the localization and detection of disease genes in genome-wide association studies. These studies consist of measurements on thousands of individuals at up to 1 million locations throughout the genome. We aim to develop powerful methods that can extract the signal of association but at the same time account for the many confounding factors that affect these studies. Recently this has involved working on genotype calling algorithms, detection of copy number variants, genotype imputation and haplotype phase inference, fine mapping, non-parametric association tests, detection of gene-gene interactions and algorithms for the detection and characterization of population structure. Much of this work has been stimulated by my involvement as an analysis group member of the International HapMap Project (www.hapmap.org) and the Wellcome Trust Case Control Consortium (www.wtccc.org.uk). I also have a continuing interest in spatio-temporal statistics applied to the area of functional MRI of the brain in collaboration with the Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (www.fmrib.ox.ac.uk).
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