In a genome-wide association study (GWAS), the probability that a single nucleotide polymorphism (SNP) is not associated with a disease is its local false discovery rate (LFDR). The LFDR for each SNP is relative to a reference class of SNPs. For example, the LFDR of an exonic SNP can vary widely depending on whether it is considered relative to the separate reference class of other exonic SNPs or relative to the combined reference class of all SNPs in the data set. As a result, the analysis of the data based on the combined reference class might indicate that a specific exonic SNP is associated with the disease, while using the separate reference class indicates that it is not associated, or vice versa. To address that, we introduce empirical Bayes methods that simultaneously consider a combined reference class and a separate reference class. Our simulation studies indicate that the proposed methods lead to improved performance. The new maximum entropy method achieves that by depending on the separate class when it has enough SNPs for reliable LFDR estimation and depending solely on the combined class otherwise. We used the new methods to analyze data from a GWAS of 2000 cases and 3000 controls. R functions implementing the proposed methods are available on CRAN https://cran.r-project.org/web/packages/LFDREmpiricalBayes and Shiny https://empiricalbayes.shinyapps.io/lfdrempiricalbayesapp.

中文翻译：

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将有关遗传变异的先验知识整合到遗传关联数据分析中：经验贝叶斯方法。

在全基因组关联研究（GWAS）中，单核苷酸多态性（SNP）与疾病无关的概率是其局部错误发现率（LFDR）。每个SNP的LFDR与SNP的参考类别有关。例如，外显子SNP的LFDR可以变化很大，这取决于是相对于其他外显子SNP的单独参考类别还是相对于数据集中所有SNP的组合参考类别来考虑的。结果，基于组合参考类别的数据分析可能表明特定的外显子SNP与疾病有关，而使用单独的参考类别表明其与疾病无关，反之亦然。为了解决这个问题，我们引入了经验贝叶斯方法，该方法同时考虑了组合参考类和单独的参考类。我们的仿真研究表明，所提出的方法可以提高性能。新的最大熵方法通过在具有足够的SNP进行可靠的LFDR估计时依赖于单独的类别来实现此目的，否则仅依赖于合并的类别。我们使用新方法来分析来自2000个病例和3000个对照的GWAS的数据。可在CRAN https://cran.r-project.org/web/packages/LFDREmpiricalBayes和Shiny https://empiricalbayes.shinyapps.io/lfdrempiricalbayesapp上获得实现所建议方法的R函数。我们使用新方法来分析来自2000个病例和3000个对照的GWAS的数据。可在CRAN https://cran.r-project.org/web/packages/LFDREmpiricalBayes和Shiny https://empiricalbayes.shinyapps.io/lfdrempiricalbayesapp上获得实现建议方法的R函数。我们使用新方法来分析来自2000个病例和3000个对照的GWAS的数据。可在CRAN https://cran.r-project.org/web/packages/LFDREmpiricalBayes和Shiny https://empiricalbayes.shinyapps.io/lfdrempiricalbayesapp上获得实现所建议方法的R函数。