Identification of High-Order Single-Nucleotide Polymorphism Barcodes in Breast Cancer Using a Hybrid Taguchi-Genetic Algorithm: Case-Control Study.

Li-Yeh Chuang,Cheng-San Yang,Huai-Shuo Yang,Cheng-Hong Yang

doi:10.2196/16886

Abstract

BackgroundBreast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci.ObjectiveThis study aimed to investigate whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients.MethodsA hybrid Taguchi-genetic algorithm (HTGA) was proposed to identify the high-order 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. The Taguchi method was integrated into the GA after the crossover operations in order to optimize the generated offspring systematically for enhancing the GA search ability.ResultsThe proposed HTGA effectively converged to a promising region within the problem space and provided excellent SNP barcode identification. Regression analysis was used to validate the association between breast cancer and the identified high-order SNP barcodes. The maximum OR was less than 1 (range 0.870-0.755) for two- to seven-order SNP barcodes.ConclusionsWe systematically evaluated the interaction effects of 26 SNPs within growth factor–related genes for breast carcinogenesis pathways. The HTGA could successfully identify relevant high-order SNP barcodes by evaluating the differences between cases and controls. The validation results showed that the HTGA can provide better fitness values as compared with other methods for the identification of high-order SNP barcodes using breast cancer case-control data sets.

Highlights

Breast cancer has a major disease burden in the female population, with a growing incidence recently [1,2]
The hybrid Taguchi-genetic algorithm (HTGA) could successfully identify relevant high-order single-nucleotide polymorphisms (SNPs) barcodes by evaluating the differences between cases and controls
An HTGA was proposed to effectively identify relevant SNP barcodes among genes related to breast cancer

Summary

Introduction

Breast cancer has a major disease burden in the female population, with a growing incidence recently [1,2]. Several interpretations of associations between breast cancer and tumor characteristics [3,4,5], single-nucleotide polymorphisms (SNPs) [6,7,8], clinicopathological factors [9], and biomarkers [10] revealed relevant association effects between these factors and the risk of cancer. SNPs are crucial genetic variants in genomic association analyses involving leukemia [15], cancers [16], and other diseases [17,18,19]. The detection of SNP barcodes is vital for association analyses of diseases and cancers [20,21,22,23]. Breast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. In genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci

Results

Discussion

Conclusion