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Multi-Gene Single Nucleotide Polymorphism Detection in Gastric Cancer Based on Ion Semiconductor Sequencing Platform

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Single nucleotide polymorphism barcoding to evaluate oral cancer risk using odds ratio-based genetic algorithms.

Cheng-Hong Yang¹, Li-Yeh Chuang, Yu-Huei Cheng

¹Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.

The Kaohsiung Journal of Medical Sciences

|June 26, 2012

Summary

Identifying gene-gene interactions in oral cancer is challenging. An odds ratio-based genetic algorithm (OR-GA) effectively analyzes multiple single nucleotide polymorphisms (SNPs) to identify oral cancer risk and SNP-SNP interactions.

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Area of Science:

Genetics
Bioinformatics
Cancer Research

Background:

Gene-gene interactions play a synergistic role in cancer development.
Identifying these interactions, particularly involving multiple single nucleotide polymorphisms (SNPs), is computationally challenging.
Oral cancer risk is influenced by complex genetic factors requiring advanced analytical methods.

Purpose of the Study:

To develop and validate an odds ratio-based genetic algorithm (OR-GA) for analyzing multiple SNPs in oral cancer.
To identify specific SNP combinations (SNP barcodes) associated with oral cancer risk.
To quantify the synergistic effects of multiple SNPs on oral cancer using odds ratios.

Main Methods:

The study employed an odds ratio-based genetic algorithm (OR-GA) to analyze interactions among four specific SNPs (rs1799782, rs2040639, rs861539, rs2075685) in oral cancer.
The GA algorithm decomposed SNP sets into SNP barcodes, optimizing for fitness values that differentiate cases from controls.
Odds ratios (ORs) were calculated to quantify the risk associated with identified SNP barcodes.

Main Results:

The OR-GA successfully identified specific SNP barcodes with optimized fitness values, indicating significant differences between oral cancer cases and controls.
Analysis of SNP barcodes comprising two to four SNPs revealed odds ratios greater than 1 (approx. 1.72-2.23) for oral cancer risk.
The study demonstrated statistically significant associations (p < 0.03-0.07) between certain SNP barcodes and oral cancer, with confidence intervals ranging from 0.94-5.30.

Conclusions:

The proposed OR-GA method is effective in generating SNP barcodes for evaluating oral cancer risk.
The OR-GA approach successfully identifies potential SNP-SNP interactions contributing to oral cancer.
This method offers a novel computational strategy for dissecting complex genetic architectures in cancer etiology.