Optimization of weights and features in use of AHP for SNP prioritization

Yılmaz, Arif
Single Nucleotide Polymorphisms (SNP) holds a promise in identification of genomic footprints of complex diseases such as cancer and diabetes. However identification of SNPs associated to complex diseases is a challenging problem due to the high number and variety of SNPs present in individual genomes. Analysis of genome wide studies of SNP datasets mainly focus on statistical evidence. As there are close to hundred million SNPs in human genome, incorporating biological and functional knowledge about statistically significant SNPs provides valuable features for further selection of SNPs. Analytical Hierarchy Process (AHP) based SNP prioritization approach is a method developed for this purpose. However, AHP requires expert knowledge, which results in subjective decisions. In this work, we propose a novel approach for AHP design and optimization by utilizing Random Forest based AHP (RF-AHP) assessment on categories. We utilized the results of previously developed genomic model on Prostate Cancer. Proposed RF-AHP approach was compared with Delphi-AHP based method on Schizophrenia, Prostate Cancer, Type 2 Diabetes and Alzheimer’s disease genomic datasets and same performance was achieved. Additionally, RegulomeDB database was integrated to RF-AHP. While similar performance was obtained in most of the datasets better prioritization scoring is achieved for Schizophrenia disease.


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The analysis of Single Nucleotide Polymorphisms (SNPs) through Genome Wide Association Studies (GWAS) presents great potential for describing disease loci and gaining insight into the underlying etiology of diseases. Recently described combined p-value approach allows identification of associations at gene and pathway level. The integrated programs like METU-SNP produce simple lists of either SNP id/gene id/pathway title and their p-values and significance status or SNP id/disease id/pathway information. In...
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In many Genome Wide Association Studies (GWAS), the relation between SNPs and complex diseases has being tried to reveal. Moreover it is known that, in GWAS there exist a high amount of data which include relations between SNPs, phenotypes and diseases, etc. Many algorithms have been used to be able to reach the desired information from this huge data. Therefore, in this study, an algorithm one of whose important steps is based on linkage disequilibrium(LD), was constructed to eliminate the redundant inform...
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Citation Formats
A. Yılmaz, “Optimization of weights and features in use of AHP for SNP prioritization,” Ph.D. - Doctoral Program, Middle East Technical University, 2018.