An integrated genetic-epigenetic model of risk for coronary heart disease in the Framingham Heart Study
Creators: name: Dogan, Meeshanthini Vijayendran
Publisher: name: University of Iowa
Year: 2017
Subjects: Coronary Heart Disease
DNA Methylation
Epigenetic
Genetic
Random Forests
Medicine
Machine Learning

description: Coronary Heart Disease (CHD) is the leading cause of mortality and morbidity in the United States. Unfortunately, the first sign of CHD for some patients is a fatal myocardial infarction. A sensitive method for detecting current CHD or risk for future cardiac events could have a substantial clinical impact by preventing some of this mortality, but current biomarkers for asymptomatic CHD are both insensitive and non-specific. Over the years, with the expansion in genome-wide genetic and epigenetic profiling technologies, others and we have shown that this array based single nucleotide polymorphism (SNP) and DNA methylation assessments can further advance our understanding of common complex diseases such as CHD. To that end, three aims were conceived for this dissertation.
description: The first goal was to explore, compare and contrast the DNA methylation signatures of CHD and its conventional risk factors (smoking, total cholesterol, HDL cholesterol, systolic blood pressure and diabetes) in the presence and absence of interactions with genetic variation (i.e. SNP) in 1545 individuals in the Framingham Heart Study. This exploratory section of this dissertation provided an idea of the interplay between genetics and environment on epigenome modifications of CHD and hence, the importance in accounting for both genetics and epigenetics when studying such diseases. Furthermore, it demonstrated the degree of influence of each risk factor on CHD, highlighting the strong role of smoking in CHD pathogenesis. This is consistent with the known adverse role smoking has in the development of CHD.
description: The second aim of this dissertation involved probing the possibility of an alternate novel model to predict symptomatic CHD as an improved option to the widely implemented conventional risk factors model. Building on the idea that CHD risk factors are a conglomeration of genetic and environmental factors, machine learning techniques were used to 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 consisting of four DNA methylation sites, two SNPs, age and gender was trained on n=1545 individuals and tested on n=142 individuals. This classifier was capable of predicting symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.41, respectively, but with a specificity of 0.89 in the test set.
description: Finally, the third goal of this study aimed at demonstrating the feasibility in translating the integrated genetic-epigenetic model into a laboratory diagnostic test. This is important because given that only a handful of markers are included in the prediction model, there needs to be an option to quantify these markers independent of the costly, large genome-wide arrays. Also, this is especially beneficial for the profiling of DNA methylation biomarkers as a wide range of single locus assays are not commercially readily available. Therefore, a droplet digital PCR based assay was designed, developed and validated using 88 individuals from the FACHS cohort.