INTEGRATION OF MACHINE LEARNING AND ENTROPY METHODS FOR POST-GENOME-WIDE ASSOCIATION STUDIES ANALYSIS

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2022-8-31

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Yaldız, Burcu

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Non-linear relationships between genotypes play an essential role in understanding the genetic interactions of complex disease traits. Genome-Wide Association Studies (GWAS) have revealed a statistical association between the SNPs in many complex diseases. As GWAS results could not thoroughly explain the genetic background of these disorders, Genome-Wide Interaction Studies started to gain importance. In recent years, various statistical approaches such as entropy-based methods have been suggested for revealing these non-additive interactions between variants. This study integrates an entropy-based 3-way interaction information method and machine learning (PLINK-Random Forest-Random Forest) workflow to capture the hidden patterns resulting from non-linear relationships between genotypes in Late-Onset Alzheimer’s Disease (LOAD) to discover early and differential diagnosis markers. We have optimized an entropy-based approach that detects the third-order interactions in PLINK-RF-RF models from three different LOAD datasets. A reduced SNP set was selected for all three datasets by 3WII analysis of PLINK-RF-RF prioritized SNPs, promising a model minimization approach. Selected triplets of SNPs that show significant differences between case and control groups in terms of 3WII are proposed as candidate biomarkers for a genotyping-based LOAD diagnosis. Among SNPs prioritized by 3WII, four out of 19 SNPs from GenADA, one out of 27 from ADNI, and four out of 106 NCRAD are mapped to genes directly associated with Alzheimer’s Disease. For the first time, we have integrated the RF-RF model with the entropy-based model for determining the three-way epistatic interactions for LOAD and discovered the common biological pathways for ADNI, GenADA, and NCRAD datasets.

Subject Keywords

Biomarker, three-way interaction, entropy, GWAS, Alzheimer's Disease

URI

https://hdl.handle.net/11511/98646

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Graduate School of Informatics, Thesis

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Citation Formats

B. Yaldız, “INTEGRATION OF MACHINE LEARNING AND ENTROPY METHODS FOR POST-GENOME-WIDE ASSOCIATION STUDIES ANALYSIS,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.