An Integrated Genetic-Epigenetic Tool for Predicting Atherothrombotic Brain Infarction

ann petrys the street essay science homework help ks2 essays on the natural by bernard malamud pros and cons of homework articles thesis analysis song narrative essay prompts for 4th grade de botton essays in love explain the analogy of the cave in plato's republic essay china running out viagra click creative writing zug gosia herbal viagra disseration over the counter viagra canada substitute easybcd tutorial strong female viagra hsg clomid top dissertation hypothesis editing website us cialis na predaj our so called high school life essay watch 200 word scholarship essays essay education sector in pakistan impaza viagra vs cialis harvard college essay prompts An Integrated Genetic-Epigenetic Tool for Predicting Atherothrombotic Brain Infarction

Author(s): Meesha Dogan and Rob Philibert, Cardio Diagnostics, Coralville IA.

Presented: American Heart Association Scientific Sessions 2018

Atherothrombotic Brain Infarction (ABI) is a common, but potentially preventable cause of death. Unfortunately, secondary to the complexity of the genetic and environmental architecture underlying vulnerability to ABI, sensitive and specific tools to assess risk for and guide treatment this form of stroke have not yet been developed. However, in recent work, we have shown that integrated genetic-epigenetic tools can assess and predict risk for CHD which suggests that a similar approach may work for predicting ABI. Using a related machine learning approach involving gradient boosting and recursive feature elimination with cross-validation, we analyzed the data from 1248 subjects, including 26 individuals with ABI, from Wave 8 of the Framingham Heart Study. Results were compared to those obtained from prediction incorporating only conventional risk factors for stroke such as cholesterol. We found that post-tuning, a panel of 32 markers that included 3 SNPs and 29 DNA methylation markers predicted ABI with average ROC AUC, sensitivity and specificity of ABI status prediction were 0.89+/-0.09, 0.67+/-0.18 and 0.98+/-0.01, respectively. In comparison, after tuning, the average ROC AUC, sensitivity and specificity of ABI status prediction using the risk factors was only 0.67+/-0.22, 0.12+/-0.10 and 0.94+/-0.01, respectively. Overall, our integrated tool has a >50% improvement in our sensitivity, >20% improvement in ROC AUC and ~4% improvement in specificity. These results suggest the possibility that Precision Medicine tools that use machine based learning algorithms with both genetic and epigenetic data that can also be translated into next-generation digital PCR assays may be able to accurately predict the occurrence of ABI. However, in order to effectively construct and implement these tools, larger data sets from longitudinally informative cohorts for ABI as well as other forms of stroke will be necessary.

No Comments

Sorry, the comment form is closed at this time.