A functional data analysis approach for genetic association studies

TL;DR

This paper introduces a novel Functional Data Analysis method for genetic association studies that leverages the temporal structure of longitudinal data, providing new insights into SNP effects on lung function in asthma patients.

Contribution

The paper presents a new FDA-based approach that assembles longitudinal phenotypes into functional trajectories and uses a variance reduction test, differing from traditional PCA-based methods.

Findings

Identified a new SNP associated with lung function decline.

Detected an interaction effect between a SNP and asthma treatment.

Demonstrated advantages of FDA over traditional multivariate methods in simulations.

Abstract

We present a new method based on Functional Data Analysis (FDA) for detecting associations between one or more scalar covariates and a longitudinal response, while correcting for other variables. Our methods exploit the temporal structure of longitudinal data in ways that are otherwise difficult with a multivariate approach. Our procedure, from an FDA perspective, is a departure from more established methods in two key aspects. First, the raw longitudinal phenotypes are assembled into functional trajectories prior to analysis. Second, we explore an association test that is not directly based on principal components. We instead focus on quantifying the reduction in $L^2$ variability as a means of detecting associations. Our procedure is motivated by longitudinal genome wide association studies and, in particular, the childhood asthma management program (CAMP) which explores the long term effects of daily asthma treatments. We conduct a simulation study to better understand the advantages (and/or disadvantages) of an FDA approach compared to a traditional multivariate one. We then apply our methodology to data coming from CAMP. We find a potentially new association with a SNP negatively affecting lung function. Furthermore, this SNP seems to have an interaction effect with one of the treatments.