SLALOM

SLALOM (suspicious loci analysis of meta-analysis summary statistics) is a summary statistics-based QC method that identifies suspicious loci for meta-analysis fine-mapping by detecting association statistics outliers based on local LD structure. SLALOM only takes GWAS summary statistics and ancestry-matched external LD reference (e.g., gnomAD) as input and predicts whether each locus shows a suspicious pattern that called into question fine-mapping accuracy. The outlier detection was built upon the simplified version of the DENTIST method.

Analysis and figure generation code for Kanai, M. et al. (2022) is available here. Fine-mapping pipeline is available here.

Requirements

• Python 3.7 or later
• Hail v0.2
• numpy
• scipy
• pandas

To run our WDL pipeline on Google Cloud, you additionally need:

• Cromwell
• Active Google Cloud project
  • Note: A part of reference files are located in a public requester-pays bucket (gs://finucane-requester-pays)

To run SLALOM locally, you need:

• Cloud Storage Connector

The following command would be the easiest way of installation.

curl -sSL https://broad.io/install-gcs-connector | python3 - --gcs-requester-pays-project YOUR_PROJECT_ID

Usage

(Recommended) WDL pipeline

Please modify wdl/slalom_example.json and submit with wdl/slalom.wdl and wdl/slalom_sub.zip.

Running per-locus

Example files are available at ./example which was created from the GBMI meta-analysis summary statistics for COPD available here.

PYSPARK_SUBMIT_ARGS="--conf spark.driver.memory=1g pyspark-shell" \
python3 slalom.py \
        --snp example/example.snp \
        --out example/example.slalom.txt \
        --out-summary example/example.summary.txt \
        --annotate-consequence \
        --annotate-cups \
        --annotate-gnomad-freq \
        --export-r \
        --lead-variant-choice "prob" \
        --weighted-average-r afr=n_afr amr=n_amr eas=n_eas fin=n_fin nfe=n_nfe \
        --dentist-s \
        --abf \
        --summary \
        --case-control \
        --reference-genome GRCh38

Input format

(Required) per-locus .snp file

Required minimum columns are as follows:

• chromosome: chromosome either in GRCh37 (1, 2, 3...) or in GRCh38 (chr1, chr2, chr3, ...). Users can specify a reference genome by --reference-genome GRCh38.
• position: position
• allele1: reference allele in a specified reference genome. If users are unsure about reference/alternative alleles, set --align-alleles to make it consistent with gnomAD.
• allele2: alternative allele in a specified reference genome. This allele is assumed to be an effect allele regardless of --align-alleles.
• beta: effect size
• se: standard error
• p: P-value

Other input column specifications are as follows:

• If --weighted-average-r is specified, sample size columns supplied by this argument are also required, such as n_afr, n_eas, n_nfe, ...
• If a total sample size n_samples (and n_cases for --case-control) exist in the input, additional output columns min_neff_r2 and max_neff_r2 will be added.
• Any other input columns will remain in an output except for those overwritten by SLALOM.

To make SLALOM-compatible per-locus .snp files from a genome-wide summary statistics, you can also use make_finemap_inputs.py from our fine-mapping pipeline.

(Optional) WDL input (.json)

To use our WDL pipeline, please modify wdl/slalom_example.json. Specifications for the following options are as follows:

• slalom.sumstats_pattern: Path pattern for a summary statistics where {PHENO} will be repalced by a phenotype name. E.g., gs://YOUR_BUCKET/{PHENO}.sumstats.txt.gz.
• slalom.phenolistfile: Path to a plain text file without header. The first column corresponds to a phenotype name ({PHENO} above). The second column corresponds to an argument for ``--weighted-average-r`.

Citation

Kanai, M. et al. Meta-analysis fine-mapping is often miscalibrated at single-variant resolution. Cell Genomics 2, 100210 (2022)

Contact

Masahiro Kanai (mkanai@broadinstitute.org)