Hypertension, among diabetes, obesity and others, is one of the common human diseases that is genetically expressed as complex traits to which genetic, environmental, and demographic factors contribute interactively. Identifying the underlying genes and examining their interactions, a crucial step in understanding the molecular pathogenesis of complex diseases, is both a statistical and a computational challenge, stressing the need for novel strategies to move this process forward. In this paper we propose a new method to study the association of multiple gene interactions for complex diseases. Our method is carried out by two steps. First, we sequentially select additionally associated SNP loci combinations by minimizing the p-value of a test based on an information measure, measure of information discrepancy. Therefore, this approach is called MID method. Second, the significance of the selected associated loci combinations is assessed by an χ2 independence-test. The MID method is model-free and nonparametric, it is easy to compute and implement. The capability of the MID method is confirmed by applying it to investigate the multiple gene interactions on risk of hypertension in northern Han Chinese, where thirty-three SNP loci with three-genotype in eleven candidate genes are examined. Some results are consistent with those of Gu et al(2006). Additionally, we get some other new findings. This indicates that our idea is indeed feasible and useful in practice.
Hypertension SNP locus multilocus interactions measure of information discrepancy
Junhua Zhang Wentao Huang Zhiyuan Zhao Biao Li Yuelan Wang Dongfeng Gu Guoying Li Runsheng Chen
Academy of Mathematics and Systems Science,CAS,Beijing,China Key Laboratory of Random Complex Struct College of Electronics and Information Engineering,South-Central University for Nationalities,Wuhan, Actuarial Department,China Life Insurance Company Limited,Beijing,China School of Informatics and Computing,Indiana University,Bloomington,Indiana,USA Institute of Biotechnology,Beijing,China Division of Population Genetics and Prevention,Cardiovascular Institute and Fu Wai Hospital,Chinese Academy of Mathematics and Systems Science,CAS,Beijing,China Institute of Biophysics,CAS,Beijing,China