While it is well established that genetics can be a major contributor to population variation of complex traits, the relative contributions of rare and common variants to phenotypic variation remains a matter of considerable debate. Here, we simulate genetic and phenotypic data across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of rare variant association tests (RVATs) widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole-genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that for dichotomous traits, the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our results extend previous work to show that RVATs are insufficiently powered to make generalizable conclusions about the role of rare variants in dichotomous complex traits.

中文翻译：

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跨基因体系结构和研究设计的关联测试中的人口遗传能力模拟研究。

众所周知，遗传学可以成为复杂性状种群变异的主要贡献者，但稀有和常见变异对表型变异的相对贡献仍然是一个有争议的问题。在这里，我们基于进化力模拟跨案例/控制面板采样策略，测序方法和遗传架构模型的遗传和表型数据，以确定广泛使用的稀有变异关联测试（RVAT）的统计性能。我们发现，RVATs的最高统计能力是通过从基本定量特征分布的极端情况下对病例/对照个体进行采样来实现的。我们还证明了基因分型阵列的使用以及全基因组测序（WGS）参考小组的推算，可以恢复使用当前工具对机箱/控制面板进行排序所能获得的绝大部分功率（90％）。最后，我们表明对于二分性状，RVAT的统计性能会下降，因为稀有变体在性状结构中变得越来越重要。我们的结果扩展了以前的工作，表明RVAT没有足够的能力来得出关于稀有变异在二分法复杂性状中的作用的一般性结论。