Direct genetic effects and their estimation from matched case-control data

TL;DR

This paper explores how to identify and estimate direct genetic effects on phenotypes from matched case-control data, extending existing methods to better understand genetic influences independent of mediating factors.

Contribution

It introduces an extension of G-estimation for matched case-control studies to assess direct genetic effects, providing a new approach for genetic association analysis.

Findings

Direct effects are sometimes estimable with standard regression.

G-estimation can be extended to matched case-control data.

Application to FTO gene and myocardial infarction.

Abstract

In genetic association studies, a single marker is often associated with multiple, correlated phenotypes (e.g., obesity and cardiovascular disease, or nicotine dependence and lung cancer). A pervasive question is then whether that marker has independent effects on all phenotypes. In this article, we address this question by assessing whether there is a direct genetic effect on one phenotype that is not mediated through the other phenotypes. In particular, we investigate how to identify and estimate such direct genetic effects on the basis of (matched) case-control data. We discuss conditions under which such effects are identifiable from the available (matched) case-control data. We find that direct genetic effects are sometimes estimable via standard regression methods, and sometimes via a more general G-estimation method, which has previously been proposed for random samples and unmatched case-control studies (Vansteelandt, 2009) and is here extended to matched case-control studies. The results are used to assess whether the FTO gene is associated with myocardial infarction other than via an effect on obesity.