This a post GWAS Summary Statistics Pipeline repository! This repository is intended to perform essential operations on a set of Genome-Wide Association Study (GWAS) summary statistics. With this pipeline, you can gain deeper insights into your genetic data and discover potential biological implications.
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QQ-plots and Histograms. The pipeline generates QQ-plots and histograms to visualize the distribution of test p-values from your GWAS summary statistics. These plots help you assess the presence of significant associations and potential deviations from expected null distributions.
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Manhattan Plots. The pipeline creates Manhattan plots, labeling the top-significant Single Nucleotide Polymorphisms (SNPs) identified in your GWAS. These plots showcase genome-wide associations, highlighting genomic regions with strong associations.
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Genomic Inflation Factor (GIF) Calculation. The pipeline calculates the Genomic Inflation Factor (GIF) on a median basis. GIF is a measure used to assess the presence of systematic inflation in test statistics due to population stratification or other confounding factors. The GIF values are tested against a null-simulated distribution.
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LDSC Analysis. The pipeline employs LDSC (LD Score Regression) to identify potential confounding phenomena in your GWAS data. LDSC is a Python-developed tool that estimates the average heritability in M SNPs (h2/M) and the confounding contribution (a) due to linkage disequilibrium inflation effect on the χ2 statistic. Pairwise genetic correlations (Rg) are performed using LDSC on common SNPs/variants of each comparison. The resulting correlations are visualized using hierarchical clustering with the "Pheatmap" R-package.
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DEPICT Analysis. Using DEPICT (Data-driven Expression-Prioritized Integration for Complex Traits), the pipeline performs gene prioritization, pathway enrichment, and tissue enrichments based on your GWAS summary statistics. DEPICT uncovers potential biological mechanisms and tissue-specific associations associated with your genetic data.