META ANALYSIS tools

Tools for doing x way meta-analysis

Variant matching across studies

Variants are matched using chr pos ref and alt. For this reason all 37 build results need to first be lifted over to 38. 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.

Munging

Workflow for data munging (liftover, harmonization with GnomAD, general QC) can be run for sumstats prior to meta-analysis: munge.wdl

Running single trait meta-analysis

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 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 flipped

The configuration file should be a json file with these elements:

            "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", #is the effect column beta or 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" <- this parameter is optional. If given for compared studies 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 recommeneded 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.