Model performance based on concordance index.

<p>Model performance based on concordance index.</p

Effect size estimates, fraction of patients with events (F) and number of Samples (N) for most important clinical variables (n = 789).

<p>Effect size estimates, fraction of patients with events (F) and number of Samples (N) for most important clinical variables (n = 789).</p

Sample characteristics of the HFHS heart failure study cohort.

<p>Sample characteristics of the HFHS heart failure study cohort.</p

Comparison of empirical power and Type-1 error rates of gene-based association tests in simulated datasets for moderate linkage disequilibrium.

<p>DSL denotes the number of disease susceptibility markers. Machine learning test is based on ensemble learning variation 1 with the following components: logistic regression, support vector machine with linear kernel and random forests with m_try = 1 and n_tree = 1000.</p><p>Comparison of empirical power and Type-1 error rates of gene-based association tests in simulated datasets for moderate linkage disequilibrium.</p

Comparison of empirical Power and Type-1 error rates of gene-based association tests for a quantitative trait simulated under models with interactions.

<p>TAS denotes the number of trait associated SNPs. Machine learning test is based on ensemble learning variation 1 with the following components: multiple linear regression, support vector machine with linear kernel and random forests with m_try = 1 and n_tree = 1000.</p><p>Comparison of empirical Power and Type-1 error rates of gene-based association tests for a quantitative trait simulated under models with interactions.</p

Comparison of empirical power and Type-1 error rates of gene-based association tests on simulated datasets for strong linkage disequilibrium.

<p>DSL denotes the number of disease susceptibility markers. Machine learning test is based on ensemble learning variation 1 with the following components: logistic regression, support vector machine with linear kernel and random forests with m_try = 1 and n_tree = 1000.</p><p>Comparison of empirical power and Type-1 error rates of gene-based association tests on simulated datasets for strong linkage disequilibrium.</p

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning

<div><p>Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.</p></div

Comparison of empirical power and Type-1 error rates of gene-based association tests for simulated datasets assuming linkage equilibrium.

<p>DSL denotes the number of disease susceptibility markers. Machine learning test is based on ensemble learning variation 1 with the following components: logistic regression, support vector machine with linear kernel and random forests with m_try = 1 and n_tree = 1000.</p><p>Comparison of empirical power and Type-1 error rates of gene-based association tests for simulated datasets assuming linkage equilibrium.</p

Gene-based <i>p</i> values for previously reported asthma-related genes in 3,772 Latino individuals from the GALA study.

<p>Gene-based <i>p</i> values for previously reported asthma-related genes in 3,772 Latino individuals from the GALA study.</p

Relative risks of environmental variables for Type 2 Diabetes.

<p>Relative risks of environmental variables for Type 2 Diabetes.</p