Tools for doing x way meta-analysis
Variants are matched using chr pos ref and alt. For this reason all 37 build results need to first be lifted over to 38 to match FinnGen. lift.wdl can be used to liftover results first if needed.
IMPORTANT: Studies need to be ordered by chr (1-22, x,y,mt) and position. Chromosome can be indicated with numbers 1-25 or 1-22, X, Y, MT, with or without 'chr' prefix and they will be internally coded to numerical values.
meta_analysis.py is the main script for running meta-analysis for a single trait. Meta-analyses to be performed are specified with json configuration file. Example configuration with three studies in (data/conf.json). Script will try to align using both strands as well as by flipping ref vs. alt.
usage: meta_analysis.py [-h] [--not_quiet] [--leave_one_out] [--is_het_test]
[--pairwise_with_first] [--dont_allow_space]
[--chrom CHROM]
config_file path_to_res methods
Run x-way meta-analysis
positional arguments:
config_file Configuration file
path_to_res Result file
methods List of meta-analysis methods to compute separated by commas. Allowed values [n,inv_var,variance]
optional arguments:
-h, --help show this help message and exit
--not_quiet Print matching variants to stdout
--leave_one_out Do leave-one-out meta-analysis
--is_het_test Do heterogeneity tests based on Cochrans Q and output
het_p
--pairwise_with_first
Do pairwise meta-analysis with the first given study
--dont_allow_space Do not allow space as field delimiter
--chrom CHROM Restrict to given chromosome
--flip_indels Try variant aligning by flipping indels also. By default indels are not flippedThe configuration file should be a json file with two or more studies with these elements in each:
"name":"FINNGEN",
"file":"/Users/mitja/projects/finngen/META_ANALYSIS/I9_AF.gz",
"n_cases": 6570 , # number of cases. Used only if sample size weighted meta-analysis is used
"n_controls": 48378, # number of controls. Used only if sample size weighted meta-analysis is used
"chr":"CHR", # chromosome column name in the file
"pos":"POS", # position column name in the file
"ref":"Allele1", # reference allele column name in the file
"alt":"Allele2", # alternate allele column name in the file
"effect":"BETA", # effect size column name in the file
"effect_type":"beta", # effect type. Allowed values: beta, OR. In case of OR the value will be log transformed to beta.
"pval":"p.value" # effect size column name in the file
"se":"SE" # effect standard error. This column is optional if not using inv_var method. Otherwise additional p-value will be added using this as a weight for z-score.meta_analysis.py supports 3 different meta-analysis methods
N: purely weight by sample size and use z-score from p-value,variance: weight z-score from p-value by variance,inv_var: regular inverse variance weighted betas meta-analysis.
inv_var is recommended if betas and variances are comparable. In case of combining data from different models (e.g.) linear vs. logistic you should use sample size weighted meta.
The meta-analysis workflow produces QC statistics and plots with qc.R.
Usage: qc.R [options]
Options:
-f CHARACTER, --file=CHARACTER
dataset file name
-o CHARACTER, --out=CHARACTER
output file name [default=NULL]
-m CHARACTER, --method=CHARACTER
meta-analysis method [default=inv_var]
-l, --loo
use leave-one-out results
--conf=CHARACTER
meta-analysis config json
--pval_thresh=NUMERIC
comma separated list of p-value thresholds used to filter the data for qc
--region=NUMERIC
region size in megabases used when counting unique loci hits
-c CHARACTER, --chr_col=CHARACTER
chromosome column [default=#CHR]
-b CHARACTER, --bp_col=CHARACTER
bp column [default=POS]
-r CHARACTER, --ref_col=CHARACTER
ref column [default=REF]
-a CHARACTER, --alt_col=CHARACTER
alt column [default=ALT]
--af_alt_col_suffix=CHARACTER
af alt column suffix [default=_af_alt]
--pheno=CHARACTER
phenotype name [default=pheno]
-h, --help
Show this help message and exit
qc.R first estimates genome-wide significant hits from input with specified region size (default: 1MB) and then calculates linear regression for those hits against using meta-analysis summary statistics.
By default, the workflow compares each study and the calculated meta-analysis summary statistics against each other and produces metrics and plots which can be used for checking whether different studies are agreeing with each other.
The QC report contains the following fields (example of a 3-way meta-analysis with studies FINNGEN, UKBB and ESTBB):
| Column | Description |
|---|---|
| pheno | phenotype name |
| FINNGEN_n_cases | FINNGEN number of cases |
| FINNGEN_n_controls | FINNGEN number of controls |
| UKBB_n_cases | UKBB number of cases |
| UKBB_n_controls | UKBB number of controls |
| ESTBB_n_cases | ESTBB number of cases |
| ESTBB_n_controls | ESTBB number of controls |
| FINNGEN_N_hits | Estimated number of genome-wide significant hits in FINNGEN |
| UKBB_N_hits | Estimated number of genome-wide significant hits in UKBB |
| ESTBB_N_hits | Estimated number of genome-wide significant hits in ESTBB |
| all_inv_var_meta_N_hits | Estimated number of genome-wide significant hits in the 3-way meta-analysis |
| leave_FINNGEN_inv_var_meta_N_hits | Estimated number of genome-wide significant hits in 2-way meta-analysis (FINNGEN left out) |
| leave_UKBB_inv_var_meta_N_hits | Estimated number of genome-wide significant hits in 2-way meta-analysis (UKBB left out) |
| leave_ESTBB_inv_var_meta_N_hits | Estimated number of genome-wide significant hits in 2-way meta-analysis (ESTBB left out) |
| FINNGEN_beta_vs_UKBB_beta_slope | Linear regression slope of FINNGEN vs UKBB betas |
| FINNGEN_beta_vs_UKBB_beta_r2 | Linear regression r2 of FINNGEN vs UKBB betas |
| FINNGEN_beta_vs_UKBB_beta_r2adj | Linear regression adjusted r2 of FINNGEN vs UKBB betas |
| FINNGEN_beta_vs_ESTBB_beta_slope | Linear regression slope of FINNGEN vs ESTBB betas |
| FINNGEN_beta_vs_ESTBB_beta_r2 | Linear regression r2 of FINNGEN vs ESTBB betas |
| FINNGEN_beta_vs_ESTBB_beta_r2adj | Linear regression adjusted r2 of FINNGEN vs ESTBB betas |
| FINNGEN_beta_vs_all_inv_var_meta_beta_slope | Linear regression slope of FINNGEN vs 3-way meta-analysis betas |
| FINNGEN_beta_vs_all_inv_var_meta_beta_r2 | Linear regression r2 of FINNGEN vs 3-way meta-analysis betas |
| FINNGEN_beta_vs_all_inv_var_meta_beta_r2adj | Linear regression adjusted r2 of FINNGEN vs 3-way meta-analysis betas |
| pct_pval_stronger_in_all_inv_var_meta_p_vs_FINNGEN | Percentage of p-values that are stronger in 3-way meta-analysis vs FINNGEN |
| pct_pval_stronger_in_leave_FINNGEN_inv_var_meta_p_vs_FINNGEN | Percentage of p-values that are stronger in 2-way meta-analysis (FINNGEN left-out) vs FINNGEN |
| pct_pval_stronger_in_leave_UKBB_inv_var_meta_p_vs_FINNGEN | Percentage of p-values that are stronger in 2-way meta-analysis (UKBB left-out) vs FINNGEN |
| pct_pval_stronger_in_leave_ESTBB_inv_var_meta_p_vs_FINNGEN | Percentage of p-values that are stronger in 2-way meta-analysis (FINNGEN left-out) vs FINNGEN |
| het_p_fdr_signif_in_meta_pct | Percentage of heterogeneity p-values (FDR corrected) that are siginificant with threshold 0.05. |