Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge, United Kingdom.
Genet Epidemiol. 2010 Jul;34(5):463-8. doi: 10.1002/gepi.20504.
Neighboring common polymorphisms are often correlated (in linkage disequilibrium (LD)) as a result of shared ancestry. An association between a polymorphism and a disease trait may therefore be the indirect result of a correlated functional variant, and identifying the true causal variant(s) from an initial disease association is a major challenge in genetic association studies. Here, we present a method to estimate the sample size needed to discriminate between a functional variant of a given allele frequency and effect size, and other correlated variants. The sample size required to conduct such fine-scale mapping is typically 1-4 times larger than required to detect the initial association. Association studies in populations with different LD patterns can substantially improve the power to isolate the causal variant. An online tool to perform these calculations is available at http://moya.srl.cam.ac.uk/ocac/FineMappingPowerCalculator.html.
由于共同的祖先，相邻的常见多态性往往是相关的（连锁不平衡（LD））。因此，多态性与疾病特征之间的关联可能是相关功能变体的间接结果，并且从最初的疾病关联中识别真正的因果变体是遗传关联研究中的主要挑战。在这里，我们提出了一种方法来估计区分给定等位基因频率和效应大小的功能变体与其他相关变体所需的样本量。进行这种精细映射所需的样本量通常比检测初始关联所需的样本量大 1-4 倍。在具有不同 LD 模式的人群中进行关联研究可以大大提高分离因果变体的能力。执行这些计算的在线工具可在 http://moya.srl.cam.ac.uk/ocac/FineMappingPowerCalculator.html 上获得。
