Last updated: 2020-08-20
Checks: 6 1
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run
wflow_publish to commit the R Markdown file and build the HTML.
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The results in this page were generated with repository version e33cc65. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
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Report tutorials on the simulated data here …
Example: Fasting Insulin adjusted for BMI
Follow the installation instructions described here.
First, you need to load the
Slope-Hunter R package:
API: public: http://gwas-api.mrcieu.ac.uk/
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Package 'SlopeHunter' version 0.0.1
BMI_incidence <- read_incidence(filename = "tmp", gz = TRUE, eaf_col="Freq_Tested_Allele_in_HRS", effect_allele_col="Tested_Allele", other_allele_col="Other_Allele", pval_col = "P", pos_col = "POS")
Insulin_adj_BMI <- read_prognosis("ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/MahajanA_25625282_GCST007857/Mahajan_2014_SV_ExomeChip_lnFI_AdjForBMI.txt", gz = FALSE, sep = "\t", snp_col = "ID", beta_col = "BETA", se_col = "SE", pval_col = "P", effect_allele_col = "EA", other_allele_col = "NEA", chr_col = "Chromosome", pos_col = "Position")
Data_harmonised <- harmonise_effects(incidence_dat = BMI_incidence, prognosis_dat = Insulin_adj_BMI, by.pos = TRUE, pos_cols = c("POS.incidence", "POS.prognosis"), snp_cols=c("SNP", "SNP"), beta_cols = c("BETA.incidence", "BETA.prognosis"), se_cols=c("SE.incidence", "SE.prognosis"), EA_cols=c("EA.incidence", "EA.prognosis"), OA_cols=c("OA.incidence", "OA.prognosis") )
nrow(Data_harmonised) # No. SNPs present in both datasets attributes(Data_harmonised)$info # Get info on the harmonisation process
Data_to_prune <- Data_harmonised[!Data_harmonised$remove, ] nrow(Data_to_prune) # No. SNPs to be pruned
Data_pruned <- LD_prune(Data_to_prune, Random = TRUE, seed = 15151515)