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

罕见变异（在不到1%的人口中发生的遗传变异）是研究人类健康的有力工具。与普通变异相比，它们对性状的影响更大，而且与附近变异的相关性更小。到目前为止，大规模的努力主要集中在常见的变体上。罕见变异研究的主要挑战是：(i)生物库规模数据的分析方法不发达；（ii）由于存在精细尺度的种群结构，跨祖先的研究结果具有普遍性。简洁树序列是一种变革性的新数据结构，它根据序列数据的进化关系对序列数据进行编码。它为数百万个全基因组的分析提供了动力，并消除了罕见变异识别的障碍。该项目涉及(i)发展基于树序列的方法来识别自然选择下的基因组区域（作为负选择，一种已知会产生过量有害稀有变异的自然选择）。（ii）将该方法应用于生物库规模的测序数据，以发现影响人体小分子水平的罕见变异；（iii）利用这些发现改进风险预测研究。BBSRC战略主题：生物科学促进对健康的综合理解；转型技术：bbsrc优先领域：数据驱动生物学；终身健康和幸福

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