背景:全基因组测试,包括种系遗传变异的全基因组关联研究(GWAS),癌症体细胞突变的驱动测试,和RNAseq数据的全转录组关联测试,携带高多重测试负担。这种负担可以通过注册更大的队列来克服,或者通过使用先前的生物学知识来支持某些假设而不是其他假设来减轻。在这里,我们比较这两种方法的能力,以提高假设检验的能力。
结果:我们为队列规模的进展提供了定量估计,并对经口硬先验的功效进行了理论分析:选择假设子集进行测试的先验,并保证所有真实阳性都在测试子集内。该理论表明,对于GWAS,将测试限制在100-1000个基因的强先验比典型的队列规模每年增加20-40%的能力要少。此外,即使从测试集中排除一小部分真阳性的非脉络膜先验的表现也比根本不使用先验的表现差.
结论:我们的结果为简单,GWAS的无偏单变量假设检验:如果一个统计问题可以通过更大的队列大小来回答,应该通过更大的队列规模来回答,而不是通过涉及前科的更复杂的偏倚方法来回答。我们建议先验更适合生物学的非统计方面,如路径结构和因果关系,标准假设检验还不容易捕捉到。
BACKGROUND: Genome-wide tests, including genome-wide association studies (GWAS) of germ-line genetic variants, driver tests of cancer somatic mutations, and transcriptome-wide association tests of RNAseq data, carry a high multiple testing burden. This burden can be overcome by enrolling larger cohorts or alleviated by using prior biological knowledge to favor some hypotheses over others. Here we compare these two methods in terms of their abilities to boost the power of hypothesis testing.
RESULTS: We provide a quantitative estimate for progress in cohort sizes and present a theoretical analysis of the power of oracular hard priors: priors that select a subset of hypotheses for testing, with an oracular guarantee that all true positives are within the tested subset. This theory demonstrates that for GWAS, strong priors that limit testing to 100-1000 genes provide less power than typical annual 20-40% increases in cohort sizes. Furthermore, non-oracular priors that exclude even a small fraction of true positives from the tested set can perform worse than not using a prior at all.
CONCLUSIONS: Our results provide a theoretical explanation for the continued dominance of simple, unbiased univariate hypothesis tests for GWAS: if a statistical question can be answered by larger cohort sizes, it should be answered by larger cohort sizes rather than by more complicated biased methods involving priors. We suggest that priors are better suited for non-statistical aspects of biology, such as pathway structure and causality, that are not yet easily captured by standard hypothesis tests.