The goal of the research group is to devise computational and statistical approaches to unravel the interplay of genotype, cellular factors and external influences and their implications for phenotype. We combine statistics with mechanistic modelling concepts to tie together genetic variation data, molecular profiling information and organismal phenotypes. Current research directions include statistical method development for genome-wide association studies, methods to dissect the genetics of molecular traits and causal modelling to predict functional targets for molecular intervention. Methodological research aims are embedded in close collaborations with experimental partners, providing ample opportunities to apply innovative methods to address pertinent biological questions.
The candidate will work on developing statistical and machine learning methods for analysing and integrating high-throughput data. The initial goal will be to predict causal molecular networks between genotype and downstream phenotypes, integrating genetic variation data, gene expression levels and organ-level phenotypes. The fellow will have opportunities to interact with colleges in statistics in Cambridge and closely collaborate with experimental groups in human genetics on the Genome Campus.
http://ig14.i-grasp.com//fe/tpl_embl01.asp?newms=jj&id=49988&aid=15470