PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Association mapping of traits over time using Gaussian processes
Oliver Stegle and Karsten Borgwardt
In: Machine Learning in Computational Biology 2009, Whistler(2009).

Abstract

The goal of genetic association studies is to identify the causal genetic regulators that explain the variation of the phenome across individuals or samples. While for univariate, multivariate and groups of phenotypic traits, various computational approaches have been proposed, suitable methods for mapping phenotypes that are recorded over time yet remain to be explored. In this work we propose a Gaussian-process based association model that exploits the temporal structure of time series of phenotypes. Our model is able to identify predictive regulators for time series recordings by jointly clustering the observed time series and mapping the uncovered cluster structure to genetic loci. On synthetic datasets, our GP model detects true genetic regulators with higher accuracy than two state-of-the-art comparison methods. In experiments on clinical data, our GP association model allows us to accurately predict antidepressant treatment response over time based on the genetic state of a patient.

EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6500
Deposited By:Oliver Stegle
Deposited On:08 March 2010