Genome wide association studies based on integrated dataset — Hirschsprung disease as a model

Finding associated Single nucleotide polymorphism (SNP) for complex diseases has been the goal of many genetic studies. By screening candidate SNPs and by performing genome wide association studies (GWAS), we can get risk loci of specific diseases. Case-control study is a kind of important method in this field. However, most of case-control studies are suffering from the puzzle that sample is too small and control data is not enough. To change this situation, we integrate three SNP databases together by quality control and data matching. To deal with this database, we propose a new pipeline to find the disease related SNPs from different race and dataset. We take the real data set about Hirschsprung (HSCR) disease as an example, According to the experiment in real dataset and compare the prior studies about this disease, we prove that our pipeline to do case-control study based on integrated dataset is useful.