Population genetic simulation study of power in association testing across genetic architectures and study designs
Population genetic simulation study of power in association testing across genetic architectures and study designs
复制标题
DOI：
10.1002/gepi.22264
复制
发表时间：
2020-01-01
影响因子：
2.1
通讯作者：
Hernandez, Ryan D.
中科院分区：
文献类型：
--
作者：
Tong, Dominic M. H.;Hernandez, Ryan D.
虽然遗传学可以成为复杂性状群体变异的主要因素，但罕见和常见变异对表型变异的相对贡献仍然是一个相当有争议的问题。在这里，我们模拟了不同病例/控制面板采样策略、测序方法和基于进化力的遗传结构模型的遗传和表型数据，以确定广泛使用的罕见变异关联测试（RVATs）的统计性能。我们发现，rvat的最高统计能力是通过从潜在数量特征分布的极端情况中抽样案例/对照个体来实现的。我们还证明，使用基因分型阵列，结合全基因组测序（WGS）参考面板的插入，可以恢复使用当前工具对病例/对照面板进行测序所能获得的绝大部分（90%）功率。最后，我们表明，对于二分类特征，RVATs的统计性能随着罕见变异在特征结构中变得越来越重要而下降。我们的研究结果扩展了以前的工作，表明RVATs的能力不足以得出关于罕见变异在二分类复杂性状中的作用的一般性结论。
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.