The compact genetic algorithm based method for disease associated study

The central goal of genetic studies is to identify the mapping relationship between DNA variation and diseases. Single nucleotide polymorphisms (SNPs) are the most common form of DNA variation. The availability of high-throughput genotyping technologies enable researchers to investigate SNPs that contribute to disease risks. However, contrasting to the number of SNPs involved, the available datasets generally have a fairly small sample size, which is a challenge to traditional data analysis methods. In this paper, the compact genetic algorithm (cGA) is used to identify the disease-associated SNPs. The algorithm used in this paper was tested on three datasets: Crohn's disease, Lung cancer and Tick-borne encephalitis. The experimental results showed that the method used in this paper has better performance than the existing algorithms and can greatly improve the prediction accuracy. It is potentially interesting as an alternative tool in disease association studies.