Document Type

Conference Proceeding

Publication Date

10-18-2016

Department

Mathematics, Statistics, and Computer Science

Keywords

machine learning, multivariate analysis, hypertension, gene expression

Abstract

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Comments

From Genetic Analysis Workshop 19, Vienna, Austria, August 24-26, 2014.

The electronic version of this article can be found online at

https://bmcproc.biomedcentral.com/articles/10.1186/s12919-016-0020-2

Source Publication Title

BMC Proceedings

Publisher

BioMed Central

Volume

10

Issue

Supplement 7

First Page

34

DOI

10.1186/s12919-016-0020-2

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