So Hon-Cheong, Sham Pak C
School of Biomedical Sciences, Chinese University of Hong Kong, Shatin, Hong Kong.
KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Zoology Institute of Zoology and Chinese University of Hong Kong, Hong Kong.
Bioinformatics. 2017 Mar 15;33(6):886-892. doi: 10.1093/bioinformatics/btw745.
It is hoped that advances in our knowledge in disease genomics will contribute to personalized medicine such as individualized preventive strategies or early diagnoses of diseases. With the growth of genome-wide association studies (GWAS) in the past decade, how far have we reached this goal? In this study we explored the predictive ability of polygenic risk scores (PRSs) derived from GWAS for a range of complex disease and traits.
We first proposed a new approach to evaluate predictive performances of PRS at arbitrary P -value thresholds. The method was based on corrected estimates of effect sizes, accounting for possible false positives and selection bias. This approach requires no distributional assumptions and only requires summary statistics as input. The validity of the approach was verified in simulations. We explored the predictive power of PRS for ten complex traits, including type 2 diabetes (DM), coronary artery disease (CAD), triglycerides, high- and low-density lipoprotein, total cholesterol, schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder and anxiety disorders. We found that the predictive ability of PRS for CAD and DM were modest (best AUC = 0.608 and 0.607) while for lipid traits the prediction R-squared ranged from 16.1 to 29.8%. For psychiatric disorders, the predictive power for SCZ was estimated to be the highest (best AUC 0.820), followed by BD. Predictive performance of other psychiatric disorders ranged from 0.543 to 0.585. Psychiatric traits tend to have more gradual rise in AUC when significance thresholds increase and achieve the best predictive power at higher P -values than cardiometabolic traits.
hcso@cuhk.edu.hk ; pcsham@hku.hk.
Supplementary data are available at Bioinformatics online.
人们希望疾病基因组学知识的进步能推动个性化医疗的发展，例如制定个性化的预防策略或实现疾病的早期诊断。在过去十年中，随着全基因组关联研究（GWAS）的不断发展，我们在实现这一目标的道路上取得了多大进展呢？在本研究中，我们探讨了从GWAS得出的多基因风险评分（PRS）对一系列复杂疾病和性状的预测能力。
我们首先提出了一种新方法，用于评估在任意P值阈值下PRS的预测性能。该方法基于效应大小的校正估计，考虑了可能的假阳性和选择偏差。此方法无需分布假设，仅需汇总统计数据作为输入。通过模拟验证了该方法的有效性。我们研究了PRS对十种复杂性状的预测能力，包括2型糖尿病（DM）、冠状动脉疾病（CAD）、甘油三酯、高密度和低密度脂蛋白、总胆固醇、精神分裂症（SCZ）、双相情感障碍（BD）、重度抑郁症和焦虑症。我们发现，PRS对CAD和DM的预测能力一般（最佳AUC = 0.608和0.607），而对血脂性状的预测R平方值在16.1%至29.8%之间。对于精神疾病，对SCZ的预测能力估计最高（最佳AUC为0.820），其次是BD。其他精神疾病的预测性能在0.543至0.585之间。与心血管代谢性状相比，精神性状在显著性阈值增加时AUC往往有更平缓的上升，并在较高的P值时达到最佳预测能力。
hcso@cuhk.edu.hk；pcsham@hku.hk。
补充数据可在《生物信息学》在线获取。
