VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning

Introduction: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing high-throughput screening methods to identify biomarkers and variants, which help early diagnosis and treatments. Objective: The primary purpose of this study is to develop a classification model using machine teaming for predicting the deleterious effect of variants with respect to Al). Methods: We have constructed a set of 20,401 deleterious and 37,452 control variants from Genome-Wide Association Study (GWAS) and Genotype-Tissue Expression (GTEx) portals, respectively. Recursive feature elimination using cross-validation (RFECV) followed by a forward feature selection method was utilized to select the important features and a random forest classifier was used for distinguishing between deleterious and neutral variants. Results: Our method showed an accuracy of 81.21% on 10-fold cross-validation and 70.63% on a test set of 5785 variants. The same test set was used to compare the performance of CADD and FATHMM and their accuracies are in the range. of 54%-62%. Conclusion: Our model is freely available as the Variant Effect Predictor for Alzheimer's Disease (VEPAD) at http://web.iitm.ac.in/bioinfo2/vepad/ . VEPAD can be used to predict the effect of new variants associated with AD.