Leveraging evolutionary genetics methods to understand the effects of rare variation in metabolism and improve polygenic risk score prediction

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Rare variants (genetic variation occurring in less than 1% of the population) are powerful tools in the study of human health. In comparison to common variants, they can have larger effects on traits and are less confounded by correlations to nearby variants. To date, large-scale efforts have primarily focused on common variants. The main challenges in rare variant studies are (i) underdeveloped analysis methods for biobank-scale data and (ii) the generalisability of findings across ancestries, due to the presence of fine-scale population structure. The succinct tree sequence is a transformative new data structure that encodes sequence data in terms of their evolutionary relationships. It powers the analysis of millions of whole genomes and removes the barriers in rare variant identification. This project involves (i) developing tree sequence-based methodology to identify regions of the genome under natural selection (as negative selection, a type of natural selection is known to generate an excess of deleterious rare variants). (ii) applying this methodology to biobank-scale sequencing data to find rare variants affecting small-molecule levels in the human body and (iii) using these findings to improve risk prediction studies.BBSRC strategic themes:Bioscience for an integrated understanding of health; Transformative technologiesBBSRC priority areas:Data-driven biology; lifelong health and wellbeing