Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.
Department of Statistics, University of Oxford, Oxford, United Kingdom.
PLoS Genet. 2021 Aug 26;17(8):e1009723. doi: 10.1371/journal.pgen.1009723. eCollection 2021 Aug.
Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.
遗传变异会导致许多复杂疾病的个体发病风险增加,并且越来越多地用于预测患者的分层。以前的研究表明,遗传因素对于不同年龄和其他环境下的人类特征的相关性并不相同,尽管其原因尚不清楚。在这里,我们引入了推断疾病遗传相对风险与年龄之间纵向关系形式的方法,并检验了所有遗传风险因素是否表现出相似的行为。我们在基于区间的删失方法内使用比例风险模型来估计 24 种常见疾病在英国生物库英国血统子集中的个体变异对遗传相对风险的年龄变化贡献,应用贝叶斯聚类方法按其年龄相关的相对风险特征对变异进行分组,并通过排列检验进行年龄依赖性和多种特征的检验。我们在 9 种疾病中发现了年龄相关的相对风险特征的证据,包括高血压、皮肤癌、动脉粥样硬化性心脏病、甲状腺功能减退症和胆囊结石,其中一些疾病尽管证据较弱,但具有多种不同的遗传相对风险特征。主要模式表明,遗传风险因素对早期疾病的风险具有最大的相对影响,随着时间的推移呈单调下降,至少对于大多数变异而言是如此,尽管疾病之间下降的幅度和形式有所不同。因此,对于遗传相对风险随年龄下降的疾病,与年龄较大的人群相比,遗传风险因素在较年轻的人群中具有更强的解释力。我们表明,这些模式不能用涉及未观察到的协变量(如环境因素)的简单模型来解释。我们讨论了可以解释我们观察结果的可能模型以及对遗传风险预测的影响。