Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study.

ABSTRACT: An improved method for detecting coronary heart disease (CHD) could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated cla

SUBMITTER: Dogan MV 

PROVIDER: S-EPMC5749823 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature