背景:表达数量性状基因座(eQTL)分析旨在检测影响一种或多种基因表达的遗传变异。基因水平的eQTL测试形成了具有明确生物学重要性的自然分组假设测试策略。早期已经提出了控制分组测试的家庭错误率或错误发现率的方法,但可能不强大或不容易应用于eQTL数据,对于某些结构化的替代方案可能是有道理的,并可能使研究人员避免过于保守的方法。
结果:在经验贝叶斯背景下,我们提出了一种新的方法来控制分组假设的错误发现率(FDR)。这里,每个基因组成一个群体,SNP注释到对应于个体假设的基因。通过引入随机效应成分来考虑不同组中效应大小的异质性。我们的方法,题为“集团级FDR控制(REG-FDR)的随机效应模型和测试程序”,假设eQTL数据的替代假设模型,并通过自适应阈值控制FDR。作为一种方便的替代方法,我们还提出了Z-REG-FDR,REG-FDR的近似版本,仅使用每个基因-SNP对的基因型和表达之间的关联的Z统计。使用模拟数据和真实数据评估Z-REG-FDR的性能。仿真表明,Z-REG-FDR的性能与REG-FDR相似,但是计算速度大大提高了。
结论:我们的结果表明,与其他方法相比,Z-REG-FDR方法在统计功效和FDR控制方面表现良好。由于其快速计算和仅使用汇总数据拟合的能力,因此对于用于eQTL分析或统计基因组学中的类似问题的分组假设检验具有重要的实际用途。
BACKGROUND: Expression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches.
RESULTS: In an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed.
CONCLUSIONS: Our results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data.