Abstract
The success of genome-wide association studies (GWAS) to identify risk loci of complex diseases is now well-established. One persistent major hurdle is the cost of those studies, which make them beyond the reach of most research groups. Performing GWAS on pools of DNA samples may be an effective strategy to reduce the costs of these studies. In this study, we performed pooling-based GWAS with more than 550,000 SNPs in two case-control cohorts consisting of patients with Type II diabetes (T2DM) and with chronic rhinosinusitis (CRS). In the T2DM study, the results of the pooling experiment were compared to individual genotypes obtained from a previously published GWAS. TCF7L2 and HHEX SNPs associated with T2DM by the traditional GWAS were among the top ranked SNPs in the pooling experiment. This dataset was also used to refine the best strategy to correctly identify SNPs that will remain significant based on individual genotyping. In the CRS study, the top hits from the pooling-based GWAS located within ten kilobases of known genes were validated by individual genotyping of 1,536 SNPs. Forty-one percent (598 out of the 1,457 SNPs that passed quality control) were associated with CRS at a nominal P value of 0.05, confirming the potential of pooling-based GWAS to identify SNPs that differ in allele frequencies between two groups of subjects. Overall, our results demonstrate that a pooling experiment on high-density genotyping arrays can accurately determine the minor allelic frequency as compared to individual genotyping and produce a list of top ranked SNPs that captures genuine allelic differences between a group of cases and controls. The low cost associated with a pooling-based GWAS clearly justifies its use in screening for genetic determinants of complex diseases.
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Acknowledgments
Y. Bossé is a research scholar of the Heart and Stroke Foundation of Canada. R. Sladek and J.C. Engert are research scholars of the ‘Fonds de la Recherche en Santé du Québec’. Funding for the CRS studies was provided by the Fondation Antoine Turmel, a non-profit philanthropic organisation. Funding for T2DM studies was provided by Génome Québec and Genome Canada. We would like to acknowledge the bioinformatics research staff at the Translational Genomics Research Institute (TGEN) for their help with the GenePool software. We also want to recognize the bioinformatics staff at the McGill University and Genome Quebec Innovation Center for automating the pooling-based GWAS analyses.
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Bossé, Y., Bacot, F., Montpetit, A. et al. Identification of susceptibility genes for complex diseases using pooling-based genome-wide association scans. Hum Genet 125, 305–318 (2009). https://doi.org/10.1007/s00439-009-0626-9
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DOI: https://doi.org/10.1007/s00439-009-0626-9