A functional data analysis approach for genetic association studies

Matthew Reimherr, Dan Nicolae

Research output: Contribution to journalArticle

19 Citations (Scopus)

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 L2 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.

Original languageEnglish (US)
Pages (from-to)406-429
Number of pages24
JournalAnnals of Applied Statistics
Volume8
Issue number1
DOIs
StatePublished - Mar 2014

Fingerprint

Genetic Association
Functional Data Analysis
Asthma
Association reactions
Genes
Trajectories
Interaction Effects
Longitudinal Data
Principal Components
Lung
Phenotype
Covariates
Genome
Simulation Study
Scalar
Trajectory
Methodology
Term
Childhood
Program management

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

Cite this

@article{7e6cbd6f394448ed8899065048681f4f,
title = "A functional data analysis approach for genetic association studies",
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 L2 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.",
author = "Matthew Reimherr and Dan Nicolae",
year = "2014",
month = "3",
doi = "10.1214/13-AOAS692",
language = "English (US)",
volume = "8",
pages = "406--429",
journal = "Annals of Applied Statistics",
issn = "1932-6157",
publisher = "Institute of Mathematical Statistics",
number = "1",

}

A functional data analysis approach for genetic association studies. / Reimherr, Matthew; Nicolae, Dan.

In: Annals of Applied Statistics, Vol. 8, No. 1, 03.2014, p. 406-429.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A functional data analysis approach for genetic association studies

AU - Reimherr, Matthew

AU - Nicolae, Dan

PY - 2014/3

Y1 - 2014/3

N2 - 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 L2 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.

AB - 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 L2 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.

UR - http://www.scopus.com/inward/record.url?scp=84898042011&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84898042011&partnerID=8YFLogxK

U2 - 10.1214/13-AOAS692

DO - 10.1214/13-AOAS692

M3 - Article

AN - SCOPUS:84898042011

VL - 8

SP - 406

EP - 429

JO - Annals of Applied Statistics

JF - Annals of Applied Statistics

SN - 1932-6157

IS - 1

ER -