Mapping beyond diseases: Controlled variable selection for secondary phenotypes using tilted knockoffs

TL;DR

This paper introduces a method using tilted knockoffs to control false discovery rate when selecting important variables in biased samples, such as case-control studies, ensuring valid secondary phenotype analysis.

Contribution

It develops a novel tilted knockoff approach that accounts for biased sampling, enabling reliable variable selection with FDR control in complex biomedical studies.

Findings

Tilted knockoffs effectively control FDR in biased sampling scenarios.

The method demonstrates good power in simulated examples.

Application to genetic data reveals meaningful secondary phenotypes.

Abstract

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.