COMPARISON OF ENTROPY AND ENSEMBLE-BASED FEATURE SELECTION THROUGH NETWORK ANALYSIS OF ALZHEIMERS DISEASE-ASSOCIATED VARIANTS

2022-2-7
Rafatov, Sevda
Alzheimer’s Disease (AD) is a complex, progressive and irreversible brain disorder that slowly destroys memory and thinking skills and eventually loses the ability to do daily tasks. Our group is currently developing in-silico AD models in which genotyping and phenotyping data are integrated for the differential diagnosis Late-On-Set AD (LOAD) cases. Meta-analysis of four different LOAD data sets provided by ADNI and dbGAP, which includes the genotyping data of more than 5000 LOAD patients, is done. In this study, we provided the biological interpretation of the variants selected through two different approaches, namely entropy and ensemble modeling. First, the LOAD-associated variants are annotated for their genomic location, consequence, gene and protein products, and biological pathways. The protein-coding variants prioritized were selected for experimental validation based on their relationship with LOAD-related biological pathways after network, PPI, and enrichment analysis. For 32 variants, pyrosequencing primers were designed, and sequencing primers were optimized. As a part of the study, a case-control group with 43 LOAD diagnosed and 38 healthy participants were formed, and genotyping for the prioritized variants was completed. We have shown that machine learning models capture hidden, new, and informative patterns by considering nonlinear interactions where multiple variants determine the risk. Further analysis of interconnected networks for selected genes and proteins can identify affected biological pathways underlying the molecular etiology of AD susceptibility. Understanding the affected molecular pathways can reveal potential causative variants that lead to novel preventative therapeutics for AD.

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Citation Formats
S. Rafatov, “COMPARISON OF ENTROPY AND ENSEMBLE-BASED FEATURE SELECTION THROUGH NETWORK ANALYSIS OF ALZHEIMERS DISEASE-ASSOCIATED VARIANTS,” M.S. - Master of Science, Middle East Technical University, 2022.