A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits.
Despite the widespread popularity of genome-wide association studies (GWAS) for genetic mapping of complex traits, most existing GWAS methodologies are still limited to the use of static phenotypes measured at a single time point. In this work, we propose a new method for association mapping that considers dynamic phenotypes measured at a sequence of time points. Our approach relies on the use of Time-Varying Group Sparse Additive Models (TV-GroupSpAM) for high-dimensional, functional regression.



This new model detects a sparse set of genomic loci that are associated with trait dynamics, and demonstrates increased statistical power over existing methods. We evaluate our method via experiments on synthetic data and perform a proof-of-concept analysis for detecting single nucleotide polymorphisms associated with two phenotypes used to assess asthma severity: forced vital capacity, a sensitive measure of airway obstruction and bronchodilator response, which measures lung response to bronchodilator drugs.



Source code for TV-GroupSpAM freely available for download at http://www.cs.cmu.edu/~mmarchet/projects/tv_group_spam, implemented in MATLAB.



epxing@cs.cmu.edu



Supplementary data are available at Bioinformatics online.
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Micol
M
Marchetti-Bowick
Department of Management Information Systems, University of Arizona, Tucson, AZ, USA.
Junming
J
Yin
Division of Pulmonary and Critical Care Medicine, Penn State University, Milton S. Hershey Medical Center, Hershey, PA, USA.
Judie A
JA
Howrylak
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Eric P
EP
Xing
D002874:Chromosome Mapping; D016678:Genome; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide