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Bioinformatics. 2016 Oct 1;32(19):2903-10. doi: 10.1093/bioinformatics/btw347. Epub 2016 Jun 13.

A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits.

Author information

1
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
2
Department of Management Information Systems, University of Arizona, Tucson, AZ, USA.
3
Division of Pulmonary and Critical Care Medicine, Penn State University, Milton S. Hershey Medical Center, Hershey, PA, USA.

Abstract

MOTIVATION:

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.

RESULTS:

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.

AVAILABILITY AND IMPLEMENTATION:

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

CONTACT:

epxing@cs.cmu.edu

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
27296983
PMCID:
PMC5942717
DOI:
10.1093/bioinformatics/btw347
[Indexed for MEDLINE]
Free PMC Article

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