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March 2014 A functional data analysis approach for genetic association studies
Matthew Reimherr, Dan Nicolae
Ann. Appl. Stat. 8(1): 406-429 (March 2014). DOI: 10.1214/13-AOAS692

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.

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Matthew Reimherr. Dan Nicolae. "A functional data analysis approach for genetic association studies." Ann. Appl. Stat. 8 (1) 406 - 429, March 2014. https://doi.org/10.1214/13-AOAS692

Information

Published: March 2014
First available in Project Euclid: 8 April 2014

zbMATH: 06302241
MathSciNet: MR3191996
Digital Object Identifier: 10.1214/13-AOAS692

Keywords: functional analysis of variance , Functional data analysis , functional linear model , genome wide association study , Hypothesis testing , longitudinal data analysis

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 1 • March 2014
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