Exploring the Major Sources and Extent of Heterogeneity in a Genome-Wide Association Meta-Analysis.
Exploring the Major Sources and Extent of Heterogeneity in a Genome-Wide Association Meta-Analysis.
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在全基因组关联荟萃分析中探索异质性的主要来源和程度
DOI：
10.1111/ahg.12143
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发表时间：
2016-03
影响因子：
1.9
通讯作者：
Deng HW
中科院分区：
文献类型：
--
作者：
Pei YF;Tian Q;Zhang L;Deng HW
全基因组关联（GWA）荟萃分析已经成为发现导致复杂疾病的遗传变异的一种流行方法。研究间异质性效应是一个严重的问题，可能使结果的解释复杂化。为了提高对meta分析结果的解释，我们对异质性效应的程度和来源进行了实证探讨。我们分析了之前报道的一项关于肥胖的GWA荟萃分析，其中对来自7个个体样本的21,000多名受试者进行了荟萃分析。我们首先评估了整个基因组异质性的程度和分布。然后，我们用随机效应元回归模型研究了几个潜在的混杂因素，包括年龄、种族、性别组成、研究类型和基因型归因对异质性的影响。在总共4,325,550个被测试的snp中，25.4%的snp异质性中等到非常大。snp的异质性更严重，关联信号更强。种族、平均年龄和基因型输入准确性对异质性有显著影响。探索种族的影响可以为已知影响肥胖的两个基因座MC4R和MTCH2的潜在种族特异性影响提供线索。我们的分析有助于澄清对肥胖机制的理解，并可能为未来GWA meta分析的有效设计提供指导。
Genome-wide association (GWA) meta-analysis has become a popular approach for discovering genetic variants responsible for complex diseases. The between-study heterogeneity effect is a severe issue that may complicate the interpretation of results. Aiming to improve the interpretation of meta-analysis results, we empirically explored the extent and source of heterogeneity effect. We analyzed a previously reported GWA meta-analysis of obesity, in which over 21,000 subjects from seven individual samples were meta-analyzed. We first evaluated the extent and distribution of heterogeneity across the entire genome. We then studied the effects of several potentially confounding factors, including age, ethnicity, gender composition, study type and genotype imputation on heterogeneity with a random-effects meta-regression model. Of the total 4,325,550 SNPs being tested, heterogeneity was moderate to very large for 25.4% of the total SNPs. Heterogeneity was more severe in SNPs with stronger association signals. Ethnicity, average age and genotype imputation accuracy had significant effects on the heterogeneity. Exploring the effects of ethnicity can provide clues to the potential ethnic-specific effects for two loci known to affect obesity, MC4R and MTCH2. Our analysis can help to clarify understanding of the obesity mechanism and may provide guidance for an effective design of future GWA meta-analysis.