Application of two machine learning algorithms to genetic association studies in the presence of covariates.

ABSTRACT:

Background

Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized.

Methods and results

In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individua

SUBMITTER: Nonyane BA 

PROVIDER: S-EPMC2620353 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature