Prerequisites

1. Snakemake
2. Singularity

Run

1. Modify the config.yaml file
2. Variant QC
3. Single-cohort association
4. Meta-analysis
5. Scan signal regions
6. Plot signals
7. Select independent signals

1. Modify the config.yaml file

You'll need to provide 5 information.

input: Path to the input phenotype files including the cohort, group and phenotype wildcards.
The format of the phenotype file needs to be as shown below.

ID	res	zres
SampleA	0.5532	0.312
SampleB	0.323	0.150

The third column will be used as the phenotype value.

cohorts: For each cohort, the plink binary prefix (bfile) and the path to the genetic-relatedness matrix (grm) created using GCTA is required.

group: An identifier for the phenotype group. This currently has no functional importance other than being used as a prefix for output file names.

phenotypes: Either a list of phenotype names or a path to a file containing a list of phenotype names which will be used to fetch the phenotype files based on the input configuration value.

2. Variant QC

This step filters the genotype data according to the thresholds specified in the config.yaml file.

snakemake --cores 1 --use-singularity all1

3. Single-cohort association

Run single-cohort single-point association analysis using GCTA-MLMA

snakemake --cores 1 --resources rate_limit=30 --use-singularity all2

4. Meta-analysis

Run single-point association meta-analysis using METAL

snakemake --cores 1 --use-singularity create_all_metal

5. Scan signal regions

Scan the meta-analysis regions to extract significant signal regions.

snakemake --cores 1 --use-singularity detect_all_peaks

6. Plot signals

Run PeakPlotter on all signal regions to create regional plots and annotated data.

snakemake --cores 1 --resources rate_limit=30 --use-singularity collect_all_peak_csvs

7. Select independent signals

Run LD-clumping and GCTA-COJO to identify independent signals within all signal regions.

snakemake --cores 1 --use-singularity run_all_cojo

Questions

Q. Why do the freq and freq_geno column values in the .jma.cojo file differ? A. freq_geno column is the frequency of the refA column allele in the input bfile (you can use plink --freq to check). The freq column value is the exact value extracted from the input cojofile, where the cojofile was created from the corresponding metal file. So the freq column value comes from the Alt_Freq column value in the metal file, and the Alt_Freq column value is the "weighted average of frequency for Alt allele across all studies". The freq_geno and freq column values differ because freq_geno is just the allele frequency of the variant from the genotype file (plink bfile) that was combined from all cohorts, whereas freq column is the weighted average of frequency across cohorts (calculated by metal).

Q. When I try to run a rule, I get an error saying Text file busy. What do I do? A. Delete the script and restore it using git restore workflow/script/problematic_script.sh. Your rules should run normally after doing this

Snakefile order

0. read-config.smk
1. variant-qc.smk
2. single-cohort.smk
3. meta-analysis.smk
4. detect-peaks.smk
5. peakplot.smk
6. cojo.smk
7. query.smk
8. gwas.smk