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Evaluation of Breast Cancer Susceptibility Using Improved Genetic Algorithms to Generate Genotype SNP Barcodes by Cheng-Hong Yang; Yu-Da Lin; Li-Yeh Chuang; Hsueh-Wei Chang is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex diseases, modern geneticists face the challenge of detecting interactions between loci. A genetic algorithm (GA) is developed to detect the association of genotype frequencies of cancer cases and noncancer cases based on statistical analysis. An improved genetic algorithm (IGA) is proposed to improve the reliability of the GA method for high-dimensional SNP-SNP interactions. The strategy offers the top five results to the random population process, in which they guide the GA toward a significant search course. The IGA increases the likelihood of quickly detecting the maximum ratio difference between cancer cases and noncancer cases. The study systematically evaluates the joint effect of 23 SNP combinations of six steroid hormone metabolisms, and signaling-related genes involved in breast carcinogenesis pathways were systematically evaluated, with IGA successfully detecting significant ratio differences between breast cancer cases and noncancer cases. The possible breast

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Author

Cheng-Hong Yang; Yu-Da Lin; Li-Yeh Chuang; Hsueh-Wei Chang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2013

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)