Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
PLoS One. 2012;7(4):e33716. doi: 10.1371/journal.pone.0033716. Epub 2012 Apr 9.
The issue of large-scale testing has caught much attention with the advent of high-throughput technologies. In genomic studies, researchers are often confronted with a large number of tests. To make simultaneous inference for the many tests, the false discovery rate (FDR) control provides a practical balance between the number of true positives and the number of false positives. However, when few hypotheses are truly non-null, controlling the FDR may not provide additional advantages over controlling the family-wise error rate (e.g., the Bonferroni correction). To facilitate discoveries from a study, weighting tests according to prior information is a promising strategy. A 'weighted FDR control' (WEI) and a 'prioritized subset analysis' (PSA) have caught much attention. In this work, we compare the two weighting schemes with systematic simulation studies and demonstrate their use with a genome-wide association study (GWAS) on type 1 diabetes provided by the Wellcome Trust Case Control Consortium. The PSA and the WEI both can increase power when the prior is informative. With accurate and precise prioritization, the PSA can especially create substantial power improvements over the commonly-used whole-genome single-step FDR adjustment (i.e., the traditional un-weighted FDR control). When the prior is uninformative (true disease susceptibility regions are not prioritized), the power loss of the PSA and the WEI is almost negligible. However, a caution is that the overall FDR of the PSA can be slightly inflated if the prioritization is not accurate and precise. Our study highlights the merits of using information from mounting genetic studies, and provides insights to choose an appropriate weighting scheme to FDR control on GWAS.
随着高通量技术的出现，大规模测试的问题引起了广泛关注。在基因组学研究中，研究人员经常面临大量的测试。为了对许多测试进行同时推断，错误发现率（FDR）控制在真阳性和假阳性的数量之间提供了实际的平衡。然而，当很少的假设真正是非零假设时，控制 FDR 可能不会比控制总体错误率（例如 Bonferroni 校正）提供额外的优势。为了促进研究中的发现，根据先验信息对测试进行加权是一种很有前途的策略。一种“加权 FDR 控制”（WEI）和一种“优先子集分析”（PSA）引起了广泛关注。在这项工作中，我们通过系统模拟研究比较了这两种加权方案，并使用来自 Wellcome Trust Case Control Consortium 的 1 型糖尿病全基因组关联研究（GWAS）展示了它们的使用。当先验信息丰富时，PSA 和 WEI 都可以提高功效。通过准确和精确的优先级排序，PSA 可以在常用的全基因组单步 FDR 调整（即传统的无加权 FDR 控制）的基础上，特别是可以显著提高功效。当先验信息不丰富时（真正的疾病易感性区域没有被优先排序），PSA 和 WEI 的功效损失几乎可以忽略不计。然而，需要注意的是，如果优先级排序不准确和精确，PSA 的总体 FDR 可能会略微膨胀。我们的研究强调了利用越来越多的遗传研究信息的优点，并提供了选择适当的加权方案来控制 GWAS 中 FDR 的见解。
