Bossé Yohan, Bacot François, Montpetit Alexandre, Rung Johan, Qu Hui-Qi, Engert James C, Polychronakos Constantin, Hudson Thomas J, Froguel Philippe, Sladek Robert, Desrosiers Martin
Laval Hospital Research Center, Laval University, Pavillon Margeritte-d'Youville, chemin Sainte-Foy, Quebec, QC, Canada.
Hum Genet. 2009 Apr;125(3):305-18. doi: 10.1007/s00439-009-0626-9. Epub 2009 Jan 29.
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.
全基因组关联研究(GWAS)在识别复杂疾病风险位点方面的成功现已得到充分证实。一个长期存在的主要障碍是这些研究的成本,这使得大多数研究团队难以企及。对DNA样本池进行GWAS可能是降低这些研究成本的有效策略。在本研究中,我们在两个病例对照队列中对超过55万个单核苷酸多态性(SNP)进行了基于样本池的GWAS,这两个队列分别由II型糖尿病(T2DM)患者和慢性鼻-鼻窦炎(CRS)患者组成。在T2DM研究中,将样本池实验的结果与先前发表的GWAS中获得的个体基因型进行了比较。传统GWAS确定的与T2DM相关的TCF7L2和HHEX SNP在样本池实验中位列排名靠前的SNP之中。该数据集还用于完善最佳策略,以正确识别基于个体基因分型仍将具有显著性的SNP。在CRS研究中,通过对1536个SNP进行个体基因分型验证了基于样本池的GWAS中位于已知基因10千碱基范围内的顶级命中结果。41%(在通过质量控制的1457个SNP中有598个)在名义P值为0.05时与CRS相关,证实了基于样本池的GWAS在识别两组受试者之间等位基因频率不同SNP方面的潜力。总体而言,我们的结果表明,与个体基因分型相比,对高密度基因分型阵列进行样本池实验可以准确确定次要等位基因频率,并生成一份排名靠前SNP的列表,该列表捕捉了一组病例与对照之间真正的等位基因差异。基于样本池GWAS相关的低成本显然证明了其在筛查复杂疾病遗传决定因素方面的应用价值。