| About eighty percent of common human disorders belong to complex trait. Complex diseases are often caused by the interaction of many loci and environmental effects, and exhibit a strong familial component and population genetic heterogeneity. A simple relationship between the observable phenotypes and the underlying genetic effects does not exist. Variants have been deemed to confer susceptibility to common diseases and response to drug therapy in modern medicine study. It advances a challenge for gene mapping using polymorphic markers. Consequently, in our study we propose a new idea for locating interactive multiple genes and constructing their interactive network responsible to complex disease utilizing single-nucleotide polymorphisms (SNPs). We demonstrate the properties of this novel approach via an application to the alcoholism data in GAW14.We have made comparisons among current methods for gene mapping. Most methods based on linkage analysis and association analysis considers one marker at a time and does not take into account the correlated structure of multiple linked markers. In this paper, we treat the puzzle for gene mapping as a pattern recognition problem and propose a feature selection algorithm(MPISC) to mine SNP combination remarkably associated with complex trait.. This method offers us a new way for gene mapping from a global view.Complex diseases are controlled by genetic mechanism and environmental effects. Hence, in our method of gene mapping for interactive multiple genes we take into account both of the factors. We transform the profile of SNP into IBD profiling and use the MPISC algorithm proposed to extract SNP combination with differential IBD distributions among the affected groups of sibpairs. We can generate dummy interaction network of SNPs based on the mined SNP combinations, and further reflect the interactions among genes that are mapped by the SNPs and construct interaction network of genes. |
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