Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease
Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease
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DOI：
10.1038/ng1071
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发表时间：
2003-02-01
期刊：
影响因子：
30.8
通讯作者：
Hirschhorn, JN
中科院分区：
文献类型：
--
作者：
Lohmueller, KE;Pearce, CL;Hirschhorn, JN
关联研究为识别影响常见疾病易感性的遗传变异提供了一种潜在的强有力的方法（1-4），但却被它们不能始终重复的印象所困扰（5,6）。原则上，这种不一致可能是由于假阳性研究、假阴性研究或不同人群之间相关性的真正变异性（4-8）。关键的问题是，假阳性是否能压倒性地解释这种不一致。我们分析了301项已发表的研究，涵盖了25种不同的关联报道。有大量的研究重复了最初的阳性报告，这与没有真正正相关的假设不一致（P < 10(-14)）。这种过量的重复不能用发表偏倚合理地解释，并且集中在25个协会中的11个。对于这11种关联中的8种，对后续研究的汇总分析得出了与第一份报告有统计学意义的重复，并估计了适度的遗传影响。因此，相当一部分（但不到一半）报告的关联有强有力的复制证据；对于这些，假阴性、低强度的研究可能导致不一致的复制。我们得出的结论是，人类基因组中可能有许多常见的变异，它们对常见疾病的风险有适度但实际的影响，使用大样本的研究将令人信服地确定这些变异。
Association studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease(1-4), but are plagued by the impression that they are not consistently reproducible(5,6). In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations(4-8). The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10(-14)). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.