Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data.

Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data.
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DOI:
10.1002/gepi.20435
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发表时间:
2010-01
影响因子:
2.1
通讯作者:
Zeng, D.
Zeng, D.
中科院分区:
医学4区
文献类型:
--
作者:
Lin, D. Y.;Zeng, D.

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为了识别对复杂的人类疾病具有适度影响的遗传变异,越来越多的网络或联盟被创建,用于共享来自同一疾病或相关疾病的多个全基因组关联研究的数据。这项工作的一个核心问题是,是否从相关研究中获得总结结果或个体参与者数据。我们从理论上和数值上表明,汇总结果的荟萃分析在统计上与单个参与者数据的联合分析一样有效(前提是这两种分析在相同的建模假设下正确执行)。我们用芬兰-美国非胰岛素依赖型糖尿病遗传学调查(FUSION)研究的病例对照数据说明了这一等价性。仅整理摘要结果将增加可用研究的数量和代表性,简化数据收集和分析,降低资源利用率,并加速发现。
To identify genetic variants with modest effects on complex human diseases, a growing number of networks or consortia are created for sharing data from multiple genome-wide association studies on the same disease or related disorders. A central question in this enterprise is whether to obtain summary results or individual participant data from relevant studies. We show theoretically and numerically that meta-analysis of summary results is statistically as efficient as joint analysis of individual participant data (provided that both analyses are performed properly under the same modeling assumptions). We illustrate this equivalence with case-control data from the Finland-United States Investigation of NIDDM Genetics (FUSION) study. Collating only summary results will increase the number and representativeness of available studies, simplify data collection and analysis, reduce resource utilization, and accelerate discovery.
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