In genome-wide association studies, hundreds of thousands of genetic features (genes, proteins, etc.) in a given case-control population are tested to verify existence of an association between each genetic marker and a specific disease. A popular approach in this regard is to estimate local false discovery rate (LFDR), the posterior probability that the null hypothesis is true, given an observed test statistic. However, the existing LFDR estimation methods in the literature are usually complicated. Assuming a chi-square model with one degree of freedom, which covers many situations in genome-wide association studies, we use the method of moments and introduce a simple, fast and efficient approach for LFDR estimation. We perform two different simulation strategies and compare the performance of the proposed approach with three popular LFDR estimation methods. We also examine the practical utility of the proposed method by analyzing a comprehensive 1000 genomes-based genome-wide association data containing approximately 9.4 million single nucleotide polymorphisms, and a microarray data set consisting of genetic expression levels for 6033 genes for prostate cancer patients. The R package implementing the proposed method is available on CRAN https://cran.r-project.org/web/packages/LFDR.MME.

在全基因组关联研究中，测试了给定病例对照人群中成千上万的遗传特征（基因，蛋白质等），以验证每种遗传标记物与特定疾病之间是否存在关联。在这方面，一种流行的方法是估计局部错误发现率（LFDR），这是假定观察到的测试统计数据为零的假设成立的后验概率。但是，文献中现有的LFDR估计方法通常很复杂。假设具有一个自由度的卡方模型涵盖了全基因组关联研究中的许多情况，我们使用矩量法，并介绍了一种简单，快速且有效的方法来进行LFDR估计。我们执行两种不同的仿真策略，并将所提出的方法与三种流行的LFDR估计方法的性能进行比较。我们还通过分析全面的1000个基于基因组的全基因组关联数据（包含大约940万个单核苷酸多态性）和一个由6033个前列腺癌患者的基因表达水平组成的微阵列数据集，来研究提出的方法的实用性。可在CRAN https://cran.r-project.org/web/packages/LFDR.MME上获得实现该建议方法的R包。以及由6033个前列腺癌患者基因表达水平组成的微阵列数据集。可在CRAN https://cran.r-project.org/web/packages/LFDR.MME上获得实现该建议方法的R包。以及由6033个前列腺癌患者基因表达水平组成的微阵列数据集。可在CRAN https://cran.r-project.org/web/packages/LFDR.MME上获得实现该建议方法的R包。