GWAS of longitudinal trajectories at biobank scale
GWAS of longitudinal trajectories at biobank scale
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DOI：
10.1016/j.ajhg.2022.01.018
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发表时间：
2022-03-03
影响因子：
9.8
通讯作者：
Zhou, Jin J.
中科院分区：
文献类型：
--
作者：
Ko, Seyoon;German, Christopher A.;Zhou, Jin J.
与大量纵向电子健康记录（EHR）数据相关联的生物库使许多新的遗传研究问题变得可行。其中之一是生物标志物轨迹的研究。例如，高血压测量在访问强烈预测中风发作，并持续高空腹血糖和Hb1Ac水平定义糖尿病。最近的研究表明，不仅生物标志物轨迹的平均水平，而且其波动或受试者内（WS）变异性也是许多疾病的风险因素。例如，糖基化变化最近被认为是糖尿病管理中的重要临床指标。识别改变生物标志物轨迹的平均值或改变WS变异性的遗传因素至关重要。与传统的横断面研究相比，轨迹分析利用了更多的数据点，并捕捉了随时间变化的因素，包括用药史和生活方式的影响的全貌。目前，在生物样本库规模上，还没有有效的工具用于生物标志物轨迹的全基因组关联研究（GWAS），即使只是平均效应。我们提出了TrajGWAS，一种基于线性混合效应模型的方法，用于测试改变生物标志物轨迹的平均值或改变WS变异性的遗传效应。它可扩展到具有100，000到1，000，000个个体和许多纵向测量的生物库数据，并且对分布假设具有鲁棒性。仿真研究证实，TrajGWAS控制I类错误率，是强大的。分析11个生物标志物纵向测量和提取自英国生物银行初级保健数据超过150，000名参与者与1，800，000观察揭示基因座，显着改变平均值或WS变异性。
Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.