Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's, Cambridge, UK.
Eur J Hum Genet. 2010 Jul;18(7):746-50. doi: 10.1038/ejhg.2010.17. Epub 2010 Feb 24.
The past few years have seen tremendous progress in our understanding of the genetics underlying complex disease, with associated variants being identified in dozens of traits. Despite the fact that this growing body of empirical evidence unequivocally shows the necessity for extreme levels of significance and large samples sizes, the reasoning behind these requirements is not always appreciated. As genome-wide association studies reach the limits of their resolution in the search for rarer and weaker effects, the need for appropriate design and interpretation will become ever more important. If the genetic analysis of complex disease is to avoid accumulating false positive claims, as it has in the past, then researchers will need to allow for less tangible variables such as power and prior odds rather than relying exclusively on significance when assessing the results of these studies. In this review, the basic foundations of association testing are explained from a Bayesian perspective and the potential benefits of Bayes factors as a means of measuring the weight of evidence in support of an association are described.
在过去的几年中，我们对复杂疾病遗传基础的理解取得了巨大的进展，相关的变异在数十种特征中被确定。尽管越来越多的经验证据明确表明需要极高的显著性水平和大样本量，但这些要求的背后原因并不总是被理解。随着全基因组关联研究在寻找更罕见和更弱的效应时达到其分辨率的极限，适当的设计和解释将变得更加重要。如果要避免遗传分析复杂疾病像过去那样积累虚假阳性结果，那么研究人员将需要考虑到力量和先验概率等不太明显的变量，而不仅仅是在评估这些研究结果时仅依赖于显著性。在这篇综述中，从贝叶斯的角度解释了关联测试的基本基础，并描述了贝叶斯因子作为衡量支持关联证据权重的一种方法的潜在好处。
