A Simple Yet Efficient Parametric Method of Local False Discovery Rate Estimation Designed for Genome-Wide Association Data Analysis
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In genome-wide association studies (GWAS), hundreds of thousands of genetic features (genes, proteins, etc.) in a given case-control population are tested in favor of the null hypothesis that there is no 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. Assuming a certain structure for the underlying model, covering many situations in genome-wide association studies, we use the method of moments and introduce a simple, fast and efficient method for LFDR estimation. We evaluate the performance of the proposed approach by performing two different simulation strategies. As well, we examine the practical utility of the proposed algorithm 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.
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