Last updated: 2021-03-04
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Knit directory: fitnessGWAS/
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This script creates a SQLite3 database holding two tables: one with annotations for each variant (provided by the Mackay lab), and one with annotations for each gene (from annotation hub). In step 3, we also add the GWAS results to this database, allowing memory-efficient handling of the results. Using this database also avoids having to load Bioconductor packages that conflict with dplyr::select(), etc.
This script uses Bayesian mixed models implemented in brms to estimate the line means for our four fitness traits.
Using linear mixed models implemented in the R package sommer to estimate the heritability of, and genetic covariance among, our four fitness traits.
The script first performs quality control and imputation on the dataset of SNPs and indels for the DGRP. Second, it runs association tests on our four phenotypes using the software command-line software GEMMA. Third, it uses PLINK to identify groups of loci that are in complete linkage disequilibrium (‘SNP clumps’).
Here, we use the R package mashr to perform multivariate adaptive shrinkage on the results of the GWAS. We ran mashr using both possible modes: canonical, and data-driven. The canonical approach is useful for testing hypotheses about the relative abundance of different types of fitness-affecting SNPs, e.g. sexually antagonistic, sexually concordant, sex-limited, or null. The data-driven approach adjusts the GWAS estimates most accurately, as it estimates the patterns of covariances in the SNPs’ effects on the 4 traits from the data; this is useful for deriving shrinked estimates of all the SNPs’ effect sizes.
This script generates some plots of the SNP effect sizes under the two forms of shrinkage (canonical and data-driven) to check that mashr is performing as expected.
This script uses the transcriptomic data from Huang et al. 2015 PNAS on the DGRP. In this script, we
mashr in canonical and data-driven modes.Text here
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Here, we plot the correlations in the estimated effects of each variant (or group of variants in perfect linkage disequilibrium with one another). For the GWAS results, we run linear models to test whether variants differ in their effect on fitness according to chromosome, site class, and minor allele frequency. For the TWAS results, we run linear models to test whether transcripts differ in their effect on fitness according to chromosome, average expression level, and the sex bias in expression.
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