选择调整的推断：应用于cis-eQTL效应量的置信区间
Selection-adjusted inference: an application to confidence intervals for cis-eQTL effect sizes
摘要：
eQTL研究的目的是鉴定影响生物体中基因表达水平的遗传变异。高通量技术使这样的研究成为可能：在给定的组织样本中，它使我们能够量化大约20，000个基因的表达水平，并记录数百万个遗传多态性中存在的等位基因。虽然一旦标本在手，获得这些数据相对便宜，但获得人体组织仍然是一项昂贵的努力。因此，eQTL研究仍然基于相对较小的样本量，对于最直接的医学相关性的组织，这种限制特别严重。鉴于这些数据集的高维性质和大量的假设测试，科学界已经采用了早期的多重性调整程序，主要控制错误发现率，以识别影响表达水平的遗传变异。相比之下，一个问题，没有得到太多的关注，日期是提供估计的影响大小与这些变量，在某种程度上，占相当数量的选择。我们说明了如何最近开发的条件推理方法可以部署到获得置信区间的eQTL效应大小可靠的覆盖。我们提出的程序是基于一个随机化的分层策略，既反映了通常采用的最先进的调查，并介绍了使用随机性，而不是数据分裂，以最大限度地利用现有数据的步骤。对GTEx Liver数据集（v6）的分析表明，单纯获得的置信区间可能无法覆盖效应量的真实值，并且可能低估了影响基因表达水平的局部遗传多态性的数量。
英文摘要：
The goal of eQTL studies is to identify the genetic variants that influence the expression levels of the genes in an organism. High throughput technology has made such studies possible: in a given tissue sample, it enables us to quantify the expression levels of approximately 20,000 genes and to record the alleles present at millions of genetic polymorphisms. While obtaining this data is relatively cheap once a specimen is at hand, obtaining human tissue remains a costly endeavor. Thus, eQTL studies continue to be based on relatively small sample sizes, with this limitation particularly serious for tissues of most immediate medical relevance. Given the high dimensional nature of this datasets and the large number of hypotheses tested, the scientific community has adopted early on multiplicity adjustment procedures, which primarily control the false discoveries rate for the identification of genetic variants with influence on the expression levels. In contrast, a problem that has not received much attention to date is that of providing estimates of the effect sizes associated to these variants, in a way that accounts for the considerable amount of selection. We illustrate how the recently developed conditional inference approach can be deployed to obtain confidence intervals for the eQTL effect sizes with reliable coverage. The procedure we propose is based on a randomized hierarchical strategy that both reflects the steps typically adopted in state of the art investigations and introduces the use of randomness instead of data splitting to maximize the use of available data. Analysis of the GTEx Liver dataset (v6) suggests that naively obtained confidence intervals would likely not cover the true values of effect sizes and that the number of local genetic polymorphisms influencing the expression level of genes might be underestimated.
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