Identification of susceptibility genes for complex diseases using pooling-based genome-wide association scans
Identification of susceptibility genes for complex diseases using pooling-based genome-wide association scans
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DOI：
10.1007/s00439-009-0626-9
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发表时间：
2009-04-01
期刊：
影响因子：
5.3
通讯作者：
Desrosiers, Martin
中科院分区：
文献类型：
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作者：
Bosse, Yohan;Bacot, Francois;Desrosiers, Martin
全基因组关联研究(GWAS)在确定复杂疾病的风险基因方面的成功现在已经确立。一个持续存在的主要障碍是这些研究的成本，这使得它们超出了大多数研究小组的承受能力。对DNA样本池进行GWA可能是降低这些研究成本的有效策略。在这项研究中，我们在两个病例对照队列中对超过550,000个SNP进行了基于合并的GWA，其中包括II型糖尿病(T2 DM)和慢性鼻窦炎(CRS)患者。在T2 DM研究中，将合并实验的结果与从先前发表的GWA中获得的单个基因类型进行了比较。TCF7L2和HHEX SNPs是合并实验中与T2 DM相关的两个SNPs。该数据集还被用来改进最佳策略，以正确识别基于个体基因分型仍具有重要意义的SNPs。在CRS研究中，位于已知基因十千碱基以内的基于池的GWA的最高命中率通过对1,536个SNP的单独基因分型进行了验证。41%(在通过质量控制的1,457个SNP中有598个)与CRS相关，名义P值为0.05，证实了基于汇集的GWAS识别两组受试者之间等位基因频率不同的SNPs的潜力。总体而言，我们的结果表明，高密度基因分型阵列上的汇集实验可以准确地确定与单个基因分型相比的次要等位基因频率，并产生一个排名最高的SNPs列表，该列表捕获了一组病例和对照之间的真实等位基因差异。与基于汇集的GWAS相关的低成本显然证明了它在复杂疾病的遗传决定因素筛查中的使用是合理的。
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.