Department of Biostatistics, University of North Carolina, McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC 27599-7420, USA.
Genet Epidemiol. 2010 Jan;34(1):60-6. doi: 10.1002/gepi.20435.
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.
为了鉴定对复杂人类疾病有适度影响的遗传变异，越来越多的网络或联盟被创建，用于共享同一疾病或相关疾病的多个全基因组关联研究的数据。在这一事业中，一个核心问题是从相关研究中获取汇总结果还是个体参与者数据。我们从理论和数值上表明，汇总结果的荟萃分析在统计学上与个体参与者数据的联合分析一样有效（前提是在相同的建模假设下，两种分析都正确执行）。我们用来自芬兰-美国 2 型糖尿病遗传学研究（FUSION）的病例对照数据来说明这种等效性。仅整理汇总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，减少资源利用，并加速发现。
