This research focuses firstly on polygenic [risk] scores (PGS), a recently developed method to predict individuals’ genetic predisposition for different heritable traits and diseases. These predictions show promise for preventative medicine and improving health by targeting preventative treatments or earlier screening to those at the highest risk, focussing on applications in cardiovascular disease and multimorbidity. Collaboration with the European Bioinformatics Institute aims to develop tools and resources to enable the reproducible use of polygenic scores in research and clinical applications.
Professor Lambert has led the development of the Polygenic Score (PGS) Catalog which aims to increase the transparency and reproducibility of PGS by distributing the information necessary for both research and translational uses of PGS for a wide range of diseases and traits.
We have also developed the The OmicsPred Atlas, a resource for predicting multi-omics data (proteomics, metabolomics, transcriptomics etc.) directly from genotypes: and the Green Algorithms project, promoting more environmentally sustainable computational science. It regroups calculators that researchers can use to estimate the carbon footprint of their projects, tips on how to be more environmentally friendly, training material, past talks etc.