Last updated: 2020-08-20

Checks: 6 1

Knit directory: SlopeHunter/

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.

R Markdown file: uncommitted changes

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Environment: empty

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Seed: set.seed(20200723)

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Repository version: 61b51b2

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The results in this page were generated with repository version 61b51b2. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
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Unstaged changes:
    Modified:   analysis/tutorial.Rmd

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/tutorial.Rmd) and HTML (docs/tutorial.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 3d9bce3 Osama 2020-08-20 Update tutorial on website - still ongoing
html 3d9bce3 Osama 2020-08-20 Update tutorial on website - still ongoing

Simulated Data

Report tutorials on the simulated data here …

Applied Data Analysis

Example: Fasting Insulin adjusted for BMI

Install Slope-Hunter

Follow the installation instructions described here.

Format Data for Slope-Hunter Analysis

First, you need to load the Slope-Hunter R package:

library(SlopeHunter)
API: public: http://gwas-api.mrcieu.ac.uk/

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/_____/\____/\____/_/  /___/ /_/  /_/\____/_/  \_/ /_/ /____/_/ \_\  version
Package 'SlopeHunter' version 0.0.1

Read in the incidence (the condition trait) data

BMI_incidence <- read_incidence(filename = "Your-incidence-data-file.gz",
  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")

Read in the conditional outcome data (BMI-adjusted Insulin)

Insulin_adj_BMI <- read_prognosis("Your-conditional-outcome-data-file.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")

Harmonisation

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

Extract valid data after harmonisation (those that are eligible for pruning)

Data_to_prune <- Data_harmonised[!Data_harmonised$remove, ]
nrow(Data_to_prune)                 # No. SNPs to be pruned

Pruning data

Data_pruned <- LD_prune(Data_to_prune, Random = TRUE, seed = 15151515)

Apply the Slope-Hunter approach

TBA …


sessionInfo()