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Jonathan Marchini

Jonathan_Marchini

Dr Jonathan Marchini
Associate Professor in Statistical Genomics
Fellow of Somerville College

marchini at stats.ox.ac.uk
+44 (0)1865 272860 (Department)
+44 (0)1865 271125 (Direct)
+44 (0)1865 272595 (Fax)

Research interests
Statistical Genetics,  Genome-wide Association Studies, Computationally Intensive Statistics, Bayesian Statistics, Image Analysis

Selected publications

O. Delaneau, J-F. Zagury and J. Marchini (2013) Improved whole chromosome phasing for disease and population genetic studies. Nature Methods 10, 5-6.

The 1000 Genomes Project Consortium (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56-65.

B. Howie, C. Fuchsberger, M. Stephens, J. Marchini, G. Abecasis (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing.  Nature Genetics. DOI: 10.1038/ng.2354

The Wellcome Trust Case Control Consortium (2007) Genomewide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447;661-78.


J. Marchini, B. Howie, S. Myers, G. McVean and P. Donnelly (2007) A new multipoint method for genome-wide association studies via imputation of genotypes. Nature Genetics 39 : 906-913

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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