Using linkage genome scans to improve power of association in genome scans
Using linkage genome scans to improve power of association in genome scans
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DOI：
10.1086/500026
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发表时间：
2006-02-01
影响因子：
9.8
通讯作者：
Devlin, B
中科院分区：
文献类型：
--
作者：
Roeder, K;Bacanu, SA;Devlin, B
扫描基因组以寻找标记与复杂疾病之间的关联通常需要测试数十万个遗传多态性。测试如此大量的假设加剧了检测有意义关联的能力与做出错误发现的机会之间的权衡。即使在扫描完整基因组之前，研究人员也经常根据先前调查的结果（例如先前的连锁扫描）来偏爱某些区域。同时研究基因组的其余区域，因为与招募参与者进行基因研究的成本相比，基因分型相对便宜，并且因为先前的证据很少足以排除这些区域含有赋予责任变异的基因（责任基因）。然而，广泛的基因组搜索中固有的多重测试削弱了检测关联的能力，即使对于落在先验有利的基因组区域中的基因也是如此。这种性质的多重测试问题非常适合错误发现率（FDR）原理的应用，这可以提高功效。为了进一步增强功效，提出了一种新的 FDR 方法，该方法涉及根据先前数据对假设进行加权。我们提出了一种使用关联数据对关联 P 值进行加权的方法。我们的调查表明，如果连锁研究提供了丰富的信息，则该程序会大大提高功效。值得注意的是，即使连锁研究没有提供信息，功率损失也很小。对于一类遗传模型，我们计算从连锁研究中获得有用的先验信息所需的样本量。这项调查表明，在遗传信息看似相同的遗传模型中，有些模型比其他模型更有前景用于这种分析模式。
Scanning the genome for association between markers and complex diseases typically requires testing hundreds of thousands of genetic polymorphisms. Testing such a large number of hypotheses exacerbates the trade-off between power to detect meaningful associations and the chance of making false discoveries. Even before the full genome is scanned, investigators often favor certain regions on the basis of the results of prior investigations, such as previous linkage scans. The remaining regions of the genome are investigated simultaneously because genotyping is relatively inexpensive compared with the cost of recruiting participants for a genetic study and because prior evidence is rarely sufficient to rule out these regions as harboring genes with variation of conferring liability ( liability genes). However, the multiple testing inherent in broad genomic searches diminishes power to detect association, even for genes falling in regions of the genome favored a priori. Multiple testing problems of this nature are well suited for application of the false-discovery rate ( FDR) principle, which can improve power. To enhance power further, a new FDR approach is proposed that involves weighting the hypotheses on the basis of prior data. We present a method for using linkage data to weight the association P values. Our investigations reveal that if the linkage study is informative, the procedure improves power considerably. Remarkably, the loss in power is small, even when the linkage study is uninformative. For a class of genetic models, we calculate the sample size required to obtain useful prior information from a linkage study. This inquiry reveals that, among genetic models that are seemingly equal in genetic information, some are much more promising than others for this mode of analysis.