Meta-analysis in genome-wide association datasets: strategies and application in Parkinson disease.
Meta-analysis in genome-wide association datasets: strategies and application in Parkinson disease.
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DOI：
10.1371/journal.pone.0000196
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发表时间：
2007-02-07
期刊：
影响因子：
3.7
通讯作者：
Ioannidis, John P. A.
中科院分区：
文献类型：
--
作者：
Evangelou, Evangelos;Maraganore, Demetrius M.;Ioannidis, John P. A.
全基因组关联研究为识别调节复杂疾病易感性的常见遗传变异提供了巨大的希望。然而，对于检测微小的遗传效应，单一研究可能力度不足。通过将全基因组数据集与元分析技术相结合，可以提高功效。单阶段和两阶段全基因组数据都可以组合，并且有几种可能的策略。在两阶段框架中，我们考虑了（1）增强复制数据和（2）增强第一阶段数据的选项，然后，我们还考虑了（3）包括所有第一阶段和第二阶段数据的联合荟萃分析。这些策略使用来自帕金森病的两个全基因组关联研究（三个数据集）的数据进行了经验性检查。在这三种策略中，我们分别推导出12、5和49个单核苷酸多态性，这些多态性在传统的统计学显著性水平上显示出显著的相关性。在对每种策略中进行的分析数量进行保守调整后，这些结果均不显著。然而，有些可能需要进一步考虑：用3种策略中的至少2种鉴定了6种SNP，用所有3种策略鉴定了3种SNP [3号染色体上的rs1000291，4号染色体上的rs2241743和11号染色体上的rs3018626]，并且没有或具有最小的数据集间异质性（I2分别=0，0和15%）。 分析主要受到不同数据集之间测试的多态性的次优重叠的限制（例如，只有31，192个在两个1级数据集之间共享多态性）。Meta分析可用于提高功效并检查全基因组关联研究的数据集间异质性。前瞻性设计可能是最有效的，如果他们试图最大限度地提高基因分型平台的重叠，并预测跨许多全基因组关联研究的数据组合。
Genome-wide association studies hold substantial promise for identifying common genetic variants that regulate susceptibility to complex diseases. However, for the detection of small genetic effects, single studies may be underpowered. Power may be improved by combining genome-wide datasets with meta-analytic techniques. Both single and two-stage genome-wide data may be combined and there are several possible strategies. In the two-stage framework, we considered the options of (1) enhancement of replication data and (2) enhancement of first-stage data, and then, we also considered (3) joint meta-analyses including all first-stage and second-stage data. These strategies were examined empirically using data from two genome-wide association studies (three datasets) on Parkinson disease. In the three strategies, we derived 12, 5, and 49 single nucleotide polymorphisms that show significant associations at conventional levels of statistical significance. None of these remained significant after conservative adjustment for the number of performed analyses in each strategy. However, some may warrant further consideration: 6 SNPs were identified with at least 2 of the 3 strategies and 3 SNPs [rs1000291 on chromosome 3, rs2241743 on chromosome 4 and rs3018626 on chromosome 11] were identified with all 3 strategies and had no or minimal between-dataset heterogeneity (I2 = 0, 0 and 15%, respectively). Analyses were primarily limited by the suboptimal overlap of tested polymorphisms across different datasets (e.g., only 31,192 shared polymorphisms between the two tier 1 datasets). Meta-analysis may be used to improve the power and examine the between-dataset heterogeneity of genome-wide association studies. Prospective designs may be most efficient, if they try to maximize the overlap of genotyping platforms and anticipate the combination of data across many genome-wide association studies.
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