Odds Ratio-Based Genetic Algorithms for Generating SNP Barcodes of Genotypes to Predict Disease Susceptibility

Abstract

Genome-wide association analysis involving many single nucleotide polymorphisms (SNPs) data is challenging mathematically and computationally. It is time consuming to classify the combination of multilocus genotypes into high- and low-risk groups without false positive and negative errors. Hence, we propose the odds ratio-based genetic algorithms (OR-GA) method that uses the odds ratio as a new quantitative measure of disease risk among many SNP combinations. Genetic algorithms (GA) are applied to generate SNP "barcodes" of genotypes, which propose the maximal difference of occurrence between the case and control groups, to predict disease susceptibility (e.g., osteoporosis). When individuals are grouped into a low and high bone mass density (BMD) range, different SNP barcode patterns may occur several times in each of these two groups. Our results showed that a GA can effectively identify a specific SNP barcode with an optimized fitness value. SNP barcodes with a low fitness value will naturally be discarded from the population. A representative SNP barcode with a variable number of SNPs is processed by odds ratio analysis to determine the maximum difference between the low and high BMD groups in a statistical manner. Therefore, this paper introduces a powerful procedure for analysis of disease-associated SNP barcode in genome-wide genes.