The impact of a fine-scale population stratification on rare variant association test results

Population stratification is a well-known confounding factor in both common and rare variant association analyses. Rare variants tend to be more geographically clustered than common variants, because of their more recent origin. However, it is not yet clear if population stratification at a very fine scale (neighboring administrative regions within a country) would lead to statistical bias in rare variant analyses. As the inclusion of convenience controls from external studies is indeed a common procedure, in order to increase the power to detect genetic associations, this problem is important. We studied through simulation the impact of a fine scale population structure on different rare variant association strategies, assessing type I error and power. We showed that principal component analysis (PCA) based methods of adjustment for population stratification adequately corrected type I error inflation at the largest geographical scales, but not at finest scales. We also showed in our simulations that adding controls obviously increased power, but at a considerably lower level when controls were drawn from another population.

群体分层（Population stratification）是常见变异与罕见变异关联分析中公认的混杂因素。由于起源时间较晚，罕见变异往往比常见变异具有更强的地理聚集性。然而，目前尚不明确，在极精细尺度（即一国之内的邻近行政区划层面）的群体分层是否会对罕见变异关联分析造成统计偏倚。鉴于借助外部研究的便利对照（convenience controls）以提升遗传关联检测效力已是常规研究流程，该问题的探讨具有重要意义。本研究通过模拟实验，探究了精细尺度群体结构对不同罕见变异关联分析策略的影响，并评估了I型错误（Type I error）与检验效能。研究结果显示，基于主成分分析（PCA）的群体分层校正方法，可在较大地理尺度上有效校正I型错误膨胀，但在最精细尺度下无法实现该校正效果。此外，模拟实验还证实，加入对照确实能够提升检验效能，但当对照取自其他群体时，效能的提升幅度会显著降低。