Mapping beyond diseases: Controlled variable selection for secondary phenotypes using tilted knockoffs
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超越疾病研究：用倾斜 knockoffs 控制次级表型变量选择
- 研究问题
- 病例对照等非随机抽样数据用于次级表型研究时，如何在大量候选暴露中选择相关变量，并避免选择偏差破坏错误控制？
- 方法与思路
- 作者用选择概率对总体分布进行倾斜，并依照倾斜分布构建 model-X knockoff 变量，以进行条件独立性筛选。
- 材料陈述的贡献
- 摘要称朴素 knockoff 在偏倚抽样下无法控制错误发现率，而倾斜构造可实现控制；模拟及遗传研究展示其表现。
- 阅读关注点
- 核对筛选目标对应的总体、选择概率及协变量分布的获取方式，以及倾斜构造满足错误发现率控制的具体条件。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明选择概率未知时的处理，筛选关联不等于因果效应。
原文摘要
Researchers in biomedical studies often work with samples that are not selected uniformly at random from the population of interest, a major example being a case-control study. While these designs are motivated by specific scientific questions, it is often of interest to use the data collected to pursue secondary lines of investigations. In these cases, ignoring the fact that observations are not sampled uniformly at random can lead to spurious results. For example, in a case-control study, one might identify a spurious association between an exposure and a secondary phenotype when both affect the case-control status. This phenomenon is known as collider bias in the causal inference literature. While tests of independence under biased sampling are available, these methods typically do not apply when the number of variables is large. Here, we are interested in using the biased sample to select important exposures among a multitude of possible variables with replicability guarantees. While the model-X knockoff framework has been developed to test conditional independence hypotheses with False Discovery Rate (FDR) control, we show that its naive application fails to control FDR in the presence of biased sampling. We show how tilting the population distribution with the selection probability and constructing knockoff variables according to this tilted distribution instead leads to selection with FDR control. We study the FDR and power of the tilted knockoff method using simulated examples, and apply it to identify genetic underpinning of endophenotypes in a case-control study.