JMIR Publications

ABSTRACT

Background:

Breast cancer is a major disease burden among female population, which is a highly genomic associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions between loci.

Objective:

This study investigates whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients.

Methods:

A hybrid Taguchi-genetic algorithm (HTGA) was proposed to identify the high-order single-nucleotide polymorphism (SNP) barcodes in a breast cancer case–control study. A Taguchi method was used to enhance a genetic algorithm (GA) for identifying high-order SNP barcodes. Taguchi method was hybrid into GA after the crossover operations, in order to optimize the generated offspring systematically to enhance the GA search ability.

Results:

The proposed HTGA can effectively converge to a promising region within the problem space and provide excellent SNP barcode identification. The regression analysis was used to validate the association between breast cancer and identified high-order SNP barcode. The maximum odds ratio (OR) was less than 1 (ranged between 0.870 and 0.755) for two-to seven-order SNP barcodes.

Conclusions:

We systematically evaluated the interaction effects of 26 SNPs within growth factor-related genes for breast carcinogenesis pathways. HTGA could successfully identified the significant high-order SNP barcodes by evaluating the differences between cases and controls. The validation results showed that HTGA can provide better fitness values than compared methods in identification of high-order SNP barcodes using breast cancer cases-controls datasets.