Ritchie Scott C, Taylor Henry J, Liang Yujian, Manikpurage Hasanga D, Pennells Lisa, Foguet Carles, Abraham Gad, Gibson Joel T, Jiang Xilin, Liu Yang, Xu Yu, Kim Lois G, Mahajan Anubha, McCarthy Mark I, Kaptoge Stephen, Lambert Samuel A, Wood Angela, Sim Xueling, Collins Francis S, Denny Joshua C, Danesh John, Butterworth Adam S, Di Angelantonio Emanuele, Inouye Michael
Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
medRxiv. 2024 Sep 23:2024.08.22.24312440. doi: 10.1101/2024.08.22.24312440.
Combining information from multiple GWASs for a disease and its risk factors has proven a powerful approach for development of polygenic risk scores (PRSs). This may be particularly useful for type 2 diabetes (T2D), a highly polygenic and heterogeneous disease where the additional predictive value of a PRS is unclear. Here, we use a meta-scoring approach to develop a metaPRS for T2D that incorporated genome-wide associations from both European and non-European genetic ancestries and T2D risk factors. We evaluated the performance of this metaPRS and benchmarked it against existing genome-wide PRS in 620,059 participants and 50,572 T2D cases amongst six diverse genetic ancestries from UK Biobank, INTERVAL, the All of Us Research Program, and the Singapore Multi-Ethnic Cohort. We show that our metaPRS was the most powerful PRS for predicting T2D in European population-based cohorts and had comparable performance to the top ancestry-specific PRS, highlighting its transferability. In UK Biobank, we show the metaPRS had stronger predictive power for 10-year risk than all individual risk factors apart from BMI and biomarkers of dysglycemia. The metaPRS modestly improved T2D risk stratification of QDiabetes risk scores for 10-year risk prediction, particularly when prioritising individuals for blood tests of dysglycemia. Overall, we present a highly predictive and transferrable PRS for T2D and demonstrate that the potential for PRS to incrementally improve T2D risk prediction when incorporated into UK guideline-recommended screening and risk prediction with a clinical risk score.
将多种全基因组关联研究（GWAS）中关于一种疾病及其风险因素的信息结合起来，已被证明是开发多基因风险评分（PRS）的有效方法。这对于2型糖尿病（T2D）可能特别有用，因为T2D是一种高度多基因且异质性的疾病，PRS的额外预测价值尚不清楚。在这里，我们使用一种元评分方法来开发一种针对T2D的元PRS，该元PRS纳入了来自欧洲和非欧洲遗传血统以及T2D风险因素的全基因组关联。我们在来自英国生物银行、INTERVAL、我们所有人研究计划和新加坡多民族队列的六个不同遗传血统的620,059名参与者和50,572例T2D病例中评估了这种元PRS的性能，并将其与现有的全基因组PRS进行了基准测试。我们表明，我们的元PRS是欧洲人群队列中预测T2D最有效的PRS，并且与顶级血统特异性PRS具有相当的性能，突出了其可转移性。在英国生物银行中，我们表明，除了BMI和血糖异常生物标志物外，元PRS对10年风险的预测能力比所有个体风险因素都更强。元PRS适度改善了QDiabetes风险评分对10年风险预测的T2D风险分层，特别是在对血糖异常血液检测的个体进行优先排序时。总体而言，我们提出了一种针对T2D的高度预测性和可转移的PRS，并证明了将PRS纳入英国指南推荐的筛查和临床风险评分进行风险预测时，其逐步改善T2D风险预测的潜力。
