论文标题
从边际关联和参考样本中推断联合关联
Inferring on joint associations from marginal associations and a reference sample
论文作者
论文摘要
我们提出了一种使用参考面板从边际回归获得的关节回归系数推断的方法。这种类型的方案在遗传映射中很常见，其中在全基因组关联研究（GWAS）中报告了估计的边际关联，并且使用参考面板用于推断关节回归模型中的关联。我们表明，忽略由于使用参考面板而不是原始设计矩阵而导致的不确定性，可能会导致严重的错误发现和缺乏可复制的发现。我们在联合回归模型中得出了估计系数的渐近分布，并显示如何用于产生有效的推断。我们解决了两个设置：预先选择的区域内的推论以及基于相同数据选择的区域。通过真实的数据示例和仿真，我们证明了我们建议的方法的有用性。
We present a method to infer on joint regression coefficients obtained from marginal regressions using a reference panel. This type of scenario is common in genetic fine-mapping, where the estimated marginal associations are reported in genomewide association studies (GWAS), and a reference panel is used for inference on the association in a joint regression model. We show that ignoring the uncertainty due to the use of a reference panel instead of the original design matrix, can lead to a severe inflation of false discoveries and a lack of replicable findings. We derive the asymptotic distribution of the estimated coefficients in the joint regression model, and show how it can be used to produce valid inference. We address two settings: inference within regions that are pre-selected, as well as within regions that are selected based on the same data. By means of real data examples and simulations we demonstrate the usefulness of our suggested methodology.
