目的:我们使用多基因风险评分(PRS)来确定基于人群的队列中原发性开角型青光眼(POAG)的高危人群。
方法:对四项前瞻性基于人群的研究进行二次分析。
方法:我们纳入了四个欧洲血统队列:美国(US)护士健康研究(NHS),NHS2和卫生专业人员后续研究,以及荷兰的鹿特丹研究(RS)。美国队列包括55岁以上的女护士和男性健康专业人员。RS包括居住在鹿特丹的45岁或以上的居民,荷兰。
方法:通过对UK-Biobank的估算基因型和表型数据应用Lassosum方法估算PRS权重。这导致了144,020个变体,单核苷酸多态性(SNP)和indel,我们用来计算目标人群中的PRS的非零贝塔。使用多变量Cox比例风险模型,我们估计了标准化PRS与POAG相对风险之间的关系。此外,通过计算这些模型的一致性(Harrell的C统计量)来检验POAG预测。最后,我们评估了PRS三元性与青光眼相关性状之间的相关性.
方法:POAG和Harrell的C统计量的相对风险(相当于纵向模型的曲线下面积)。
结果:在1,046例和38,809例对照中,在美国队列中,PRS最高五分位数参与者的POAG相对风险(95%置信区间)为3.99(3.08,5.18),鹿特丹研究中的4.89(2.93,8.17),与遗传风险中位数(第3位)的参与者相比。在受限三次样条分析中,在美国和鹿特丹队列中,连续PRS和POAG风险之间的关系呈指数增长(Pspline<0.05)。结合年龄,性别,眼压>25mmHg,和家族史导致meta分析的一致性为0.75(0.73,0.75).将PRS添加到该模型中将一致性提高到0.82(0.80,0.84)。在对所有队列的荟萃分析中,最高三元组的病例在诊断时杯盘比较大,由0.11(0.07,0.15),需要青光眼手术的风险增加2.07倍(1.19,3.60)。
结论:将PRS纳入POAG预测模型可将识别一致性从0.75提高到0.82,支持其指导更具成本效益的筛查策略的潜力。
OBJECTIVE: We used a polygenic risk score (PRS) to identify high-risk groups for primary open-angle glaucoma (POAG) within population-based cohorts.
METHODS: Secondary analysis of four prospective population-based studies.
METHODS: We included four European-ancestry cohorts: the United States (US) based Nurses\' Health Study (NHS), NHS2, and the Health Professionals Follow-up Study, and the Rotterdam Study (RS) in the Netherlands. The US cohorts included female nurses and male health professionals aged 55+ years. The RS included residents aged 45 years or older living in Rotterdam, the Netherlands.
METHODS: PRS weights were estimated by applying the Lassosum method on imputed genotype and phenotype data from the UK-Biobank. This resulted in 144,020 variants, single nucleotide polymorphism (SNPs) and indels, with non-zero betas that we used to calculate a PRS in the target populations. Using multivariable Cox proportional hazard models, we estimated the relationship between the standardized PRS and relative risk for POAG. Additionally, POAG prediction was tested by calculating these models\' concordance (Harrell\'s C-statistic). Finally, we assessed the association between PRS tertiles and glaucoma-related traits.
METHODS: The relative risk for POAG and Harrell\'s C-statistic (the equivalent of an area-under-the-curve for longitudinal models).
RESULTS: Among 1,046 cases and 38,809 controls, the relative risk (95% confidence interval) for POAG for participants in the highest PRS quintile was 3.99 (3.08, 5.18) in the US cohorts, and 4.89 (2.93, 8.17) in the Rotterdam Study, compared with participants with median genetic risk (3rd quintile). In restricted cubic spline analyses, the relation between continuous PRS and POAG risk increased exponentially in the US and Rotterdam cohorts (Pspline<0.05). Combining age, sex, intraocular pressure >25 mmHg, and family history resulted in a meta-analyzed concordance of 0.75 (0.73, 0.75). Adding the PRS to this model improved the concordance to 0.82 (0.80, 0.84). In a meta-analysis of all cohorts, cases in the highest tertile had a larger cup-disc ratio at diagnosis, by 0.11 (0.07, 0.15), and a 2.07-fold increased risk of requiring glaucoma surgery (1.19, 3.60).
CONCLUSIONS: Incorporating a PRS into a POAG predictive model improves identification concordance from 0.75 up to 0.82, supporting its potential for guiding more cost-effective screening strategies.