Heterogeneity in meta-analyses of genome-wide association investigations.
Heterogeneity in meta-analyses of genome-wide association investigations.
复制标题
DOI：
10.1371/journal.pone.0000841
复制
发表时间：
2007-09-05
期刊：
影响因子：
3.7
通讯作者：
Evangelou E
中科院分区：
文献类型：
--
作者：
Ioannidis JP;Patsopoulos NA;Evangelou E
荟萃分析是对效应量进行系统和定量的综合，并探索不同研究之间的差异。元分析越来越多地用于综合全基因组关联（GWA）研究和其他团队的数据，这些团队试图复制这些研究中出现的遗传变异。研究间异质性对于记录很重要，并且可能指向有趣的线索。为了解决这些问题，我们使用了三项关于2型糖尿病的GWA研究及其重复研究的数据，其中使用固定效应方法（不包括研究间异质性）对所有数据进行的荟萃分析已经发表。我们考虑了11个多态性，至少有一个研究小组认为这是2型糖尿病的易感基因座。11种遗传变异中有6种的I2不一致性度量（测量非偶然性异质性的量）不同于0（未检测到异质性）;其中5种的不一致性为中度至非常大（I2 = 32-77%）。 对于这5种多态性，与固定效应计算相比，纳入研究间异质性的随机效应计算显示汇总效应的p值更保守。详细研究了这5种关联，以突出研究间异质性的潜在解释。这些包括鉴定相关表型的标志物（例如，FTO rs 8050136通过其对肥胖的影响与2型糖尿病相关）;鉴定的遗传标志物与相应的罪魁祸首多态性的研究之间的差异连锁不平衡（例如，可能是CDKAL 1多态性或rs 9300039和连锁不平衡标记的情况，如其他研究所示）;和潜在偏倚。当我们将每个GWA调查的发现和复制数据作为单独的研究时，结果基本相似。研究间异质性有助于记录GWA研究的综合数据，并为进一步阐明基因-疾病关联提供有价值的见解。
Meta-analysis is the systematic and quantitative synthesis of effect sizes and the exploration of their diversity across different studies. Meta-analyses are increasingly applied to synthesize data from genome-wide association (GWA) studies and from other teams that try to replicate the genetic variants that emerge from such investigations. Between-study heterogeneity is important to document and may point to interesting leads. To exemplify these issues, we used data from three GWA studies on type 2 diabetes and their replication efforts where meta-analyses of all data using fixed effects methods (not incorporating between-study heterogeneity) have already been published. We considered 11 polymorphisms that at least one of the three teams has suggested as susceptibility loci for type 2 diabetes. The I2 inconsistency metric (measuring the amount of heterogeneity not due to chance) was different from 0 (no detectable heterogeneity) for 6 of the 11 genetic variants; inconsistency was moderate to very large (I2 = 32–77%) for 5 of them. For these 5 polymorphisms, random effects calculations incorporating between-study heterogeneity revealed more conservative p-values for the summary effects compared with the fixed effects calculations. These 5 associations were perused in detail to highlight potential explanations for between-study heterogeneity. These include identification of a marker for a correlated phenotype (e.g. FTO rs8050136 being associated with type 2 diabetes through its effect on obesity); differential linkage disequilibrium across studies of the identified genetic markers with the respective culprit polymorphisms (e.g., possibly the case for CDKAL1 polymorphisms or for rs9300039 and markers in linkage disequilibrium, as shown by additional studies); and potential bias. Results were largely similar, when we treated the discovery and replication data from each GWA investigation as separate studies. Between-study heterogeneity is useful to document in the synthesis of data from GWA investigations and can offer valuable insights for further clarification of gene-disease associations.
登录
查看更多内容
影响因子：
7
作者：
Huedo-Medina, Tania B.;Sanchez-Meca, Julio;Botella, Juan
通讯作者：
Botella, Juan
影响因子：
56.9
作者：
Frayling, Timothy M.;Timpson, Nicholas J.;McCarthy, Mark I.
通讯作者：
McCarthy, Mark I.
影响因子：
3.7
作者：
Evangelou, Evangelos;Maraganore, Demetrius M.;Ioannidis, John P. A.
通讯作者：
Ioannidis, John P. A.
影响因子：
2
作者：
Higgins, JPT;Thompson, SG
通讯作者：
Thompson, SG
影响因子：
105.7
作者：
Higgins, JPT;Thompson, SG;Altman, DG
通讯作者：
Altman, DG