Jonathan’s main research focus is the development of statistical methods for genetic studies of human disease and human population genetics.
Recent technological advances have allowed collection of genetic data in many thousands of individuals, and these datasets hold the potential to locate genes that influence risk of developing specific diseases. The human genome is huge so these datasets are almost always very large and present challenging statistical problems. Jonathan’s work is a mix of statistical modelling and development of computational statistical methods to fit these models efficiently to such large datasets.
Over recent years Jonathan has played key roles in the analysis of some international collaborative projects, such as the HapMap Project, the Wellcome Trust Case-Control Consortium Studies and the currently the 1000 Genomes Project. His research group has developed methodology central to the analysis of these projects. His work on genotype imputation (the prediction of unobserved genetic variants) is now widely used in almost all genome-wide association studies.
More generally, Jonathan is interested in Bayesian statistical methods and computational statistical techniques. His doctoral work was in the area of statistical analysis of functional brain imaging datasets, and he continues to maintain an interest in this field.
His research group website is here https://jmarchini.org/
Some recent publications include
 Victoria Hore, Ana Viñuela, Alfonso Buil, Julian Knight, Mark I McCarthy, Kerrin Small, Jonathan Marchini. Tensor decomposition for multi-tissue gene expression experiments. Nature Genetics 10.1038/ng.3624 [Link] [Supplementary Material] [Software]
 Jared O’Connell, Kevin Sharp, Nick Shrine, Louise Wain, Ian Hall, Martin Tobin, Jean-Francois Zagury, Olivier Delaneau, Jonathan Marchini. Haplotype estimation for biobank scale datasets. Nature Genetics 10.1038/ng.3583 [Link]
 Kevin Sharp, Olivier Delaneau, Warren Kretzschmar, Jonathan Marchini. Phasing for medical sequencing using rare variants and large haplotype reference panels. Bioinformatics doi:10.1093/bioinformatics/btw065 [Link]
 Andrew Dahl, Valentina Iotchkova, Amelie Baud, Åsa Johansson, Ulf Gyllensten, Nicole Soranzo, Richard Mott, Andreas Kranis, Jonathan Marchini. A multiple phenotype imputation method for genetic studies. Nature Genetics doi:10.1038/ng.3513 [Link] [Supplementary Material] [R package]
 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
 Wellcome Trust Case-Control Consortium (2010) Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature. 2010;464;713-20.
 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.
 The International HapMap Consortium (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449(7164):851-61