Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104-6021, USA.
Genet Epidemiol. 2011 Apr;35(3):133-8. doi: 10.1002/gepi.20551. Epub 2011 Jan 31.
Quantitative traits (QT) are an important focus of human genetic studies both because of interest in the traits themselves and because of their role as risk factors for many human diseases. For large-scale QT association studies including genome-wide association studies, investigators usually focus on genetic loci showing significant evidence for SNP-QT association, and genetic effect size tends to be overestimated as a consequence of the winner's curse. In this paper, we study the impact of the winner's curse on QT association studies in which the genetic effect size is parameterized as the slope in a linear regression model. We demonstrate by analytical calculation that the overestimation in the regression slope estimate decreases as power increases. To reduce the ascertainment bias, we propose a three-parameter maximum likelihood method and then simplify this to a one-parameter method by excluding nuisance parameters. We show that both methods reduce the bias when power to detect association is low or moderate, and that the one-parameter model generally results in smaller variance in the estimate.
数量性状（QT）是人类遗传研究的一个重要焦点，这既是因为人们对这些性状本身感兴趣，也是因为它们是许多人类疾病的风险因素。对于包括全基因组关联研究在内的大规模 QT 关联研究，研究人员通常关注显示 SNP-QT 关联有显著证据的遗传位点，由于赢家诅咒的影响，遗传效应大小往往被高估。在本文中，我们研究了赢家诅咒对 QT 关联研究的影响，其中遗传效应大小被参数化为线性回归模型中的斜率。我们通过分析计算证明，随着功效的增加，回归斜率估计的高估程度会降低。为了减少鉴定偏差，我们提出了一个三参数最大似然方法，然后通过排除不必要的参数将其简化为一个参数方法。我们表明，这两种方法都可以降低关联检测功效较低或中等时的偏差，并且通常情况下，单参数模型的估计方差较小。
