Jonathan Marchini
Professor in Statistical Genomics
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 and the Wellcome Trust Case Control Consortium. 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.
Recent publications
Correction: Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank
Journal article
Coleman JRI. et al, (2021), Molecular Psychiatry, 26, 5465 - 5465
Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity
Journal article
Akbari P. et al, (2021), Science, 373
Pan-ancestry exome-wide association analyses of COVID-19 outcomes in 586,157 individuals.
Journal article
Kosmicki JA. et al, (2021), American journal of human genetics, 108, 1350 - 1355
Computationally efficient whole-genome regression for quantitative and binary traits.
Journal article
Mbatchou J. et al, (2021), Nature genetics, 53, 1097 - 1103
non-linear regression method for estimation of gene-environment heritability.
Journal article
Kerin M. and Marchini J., (2021), Bioinformatics (Oxford, England), 36, 5632 - 5639