The efficacy of detecting variants with small effects on the Affymetrix 6.0 platform using pooled DNA.
The efficacy of detecting variants with small effects on the Affymetrix 6.0 platform using pooled DNA.
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DOI：
10.1007/s00439-011-0974-0
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发表时间：
2011-11
期刊：
影响因子：
5.3
通讯作者：
Hirschhorn JN
中科院分区：
文献类型：
--
作者：
Chiang CW;Gajdos ZK;Korn JM;Butler JL;Hackett R;Guiducci C;Nguyen TT;Wilks R;Forrester T;Henderson KD;Le Marchand L;Henderson BE;Haiman CA;Cooper RS;Lyon HN;Zhu X;McKenzie CA;Palmert MR;Hirschhorn JN
长期以来，使用群体而不是单个样本的全基因组基因分型一直被认为是进行全基因组关联（GWA）研究的一种节省成本的替代方法。然而，迄今为止，使用混合基因分型成功的疾病基因定位仅限于检测具有大效应量的常见变异，而这些变异往往不存在于许多复杂的常见疾病或特征中。因此，为了使DNA池化成为开展GWA研究的可行策略，确定常用的全基因组SNP阵列平台（如Affymetrix 6.0阵列）是否能够可靠地使用池化DNA检测小效应大小的常见变异是很重要的。以肥胖和初潮年龄作为人类复杂性状的例子，我们评估了混合DNA全基因组基因分型作为表型关联单阶段设计的可行性。通过对汇集鉴定的顶级关联进行单独基因分型，我们获得了名义上与表型相关的snp的14至16倍富集，但我们可能错过了顶级的真实关联。此外，我们通过比较最具成本效益的3阶段设计（检测基因型相对风险为1.1的常见变异的80%功率），评估了混合DNA基因分型是否可以作为具有大量样本的多阶段设计的第二阶段廉价筛选。考虑到我们采用的特定技术的现状和相关的基因分型成本，我们通过模拟表明，涉及合用的设计将比没有合用的设计贵1.07倍。因此，虽然汇集DNA的数据中存在大量信息，但我们的分析并不支持将汇集DNA的基因分型作为有效识别对感兴趣表型影响较小的常见变异的手段。虽然我们的结论是基于我们采用的特定技术和研究设计，但本文提出的方法将有助于评估其他或未来全基因组基因分型平台在汇总DNA研究中的效用。
Genome-wide genotyping of a cohort using pools rather than individual samples has long been proposed as a cost-saving alternative for performing genome-wide association (GWA) studies. However, successful disease gene mapping using pooled genotyping has thus far been limited to detecting common variants with large effect sizes, which tend not to exist for many complex common diseases or traits. Therefore, for DNA pooling to be a viable strategy for conducting GWA studies, it is important to determine whether commonly used genome-wide SNP array platforms such as the Affymetrix 6.0 array can reliably detect common variants of small effect sizes using pooled DNA. Taking obesity and age at menarche as examples of human complex traits, we assessed the feasibility of genome-wide genotyping of pooled DNA as a single-stage design for phenotype association. By individually genotyping the top associations identified by pooling, we obtained a 14- to 16-fold enrichment of SNPs nominally associated with the phenotype, but we likely missed the top true associations. In addition, we assessed whether genotyping pooled DNA can serve as an inexpensive screen as the second stage of a multi-stage design with a large number of samples by comparing the most cost-effective 3-stage designs with 80% power to detect common variants with genotypic relative risk of 1.1, with and without pooling. Given the current state of the specific technology we employed and the associated genotyping costs, we showed through simulation that a design involving pooling would be 1.07 times more expensive than a design without pooling. Thus, while a significant amount of information exists within the data from pooled DNA, our analysis does not support genotyping pooled DNA as a means to efficiently identify common variants contributing small effects to phenotypes of interest. While our conclusions were based on the specific technology and study design we employed, the approach presented here will be useful for evaluating the utility of other or future genome-wide genotyping platforms in pooled DNA studies.
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