Process summary stats for Open Target Genetics

Set up needed software

sudo apt-get update
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p $HOME/miniconda
echo export PATH="$HOME/miniconda/bin:\$PATH" >> ~/.profile
. ~/.profile

Create env to be used with these sort of repos

Mixing pyspark, scipy ipython, etc packages

# Install dependencies into isolated environment
conda env create -n <conda env name> --file environment.yaml

# Activate environment
source activate <conda env name>

Simple script to check pyspark is able to work properly test_pyspark.py

Workflows for processing summary statistics file for Open Targets Genetics.

1. Run ingest pipeline to get unfiltered (full) data:

• GWAS: gs://genetics-portal-dev-sumstats/unfiltered/gwas
• Molecular trait: gs://genetics-portal-dev-sumstats/unfiltered/molecular_trait

2. Filter to p < 0.005:

• GWAS: gs://genetics-portal-dev-sumstats/filtered/pvalue_0.005/gwas
• Molecular trait: gs://genetics-portal-dev-sumstats/filtered/pvalue_0.005/molecular_trait

3. Filter to keep regions within 2Mb of a "significant" association:

• GWAS: gs://genetics-portal-dev-sumstats/filtered/significant_window_2mb/gwas
• Molecular trait: gs://genetics-portal-dev-sumstats/filtered/significant_window_2mb/molecular_trait

All datasets are in Apache Parquet format. These can be read in python using Spark or Pandas via pyarrow or fastparquet.

Requirements when adding new datasets

• Alleles should be harmonised so that ref and alt alleles are on the forward strand and the orientation matches the Ensembl VCF: https://github.com/opentargets/sumstat_harmoniser
• Alt allele should always be the effect allele
• For case-control studies where OR are not reported, betas should be converted to log_odds. If association test was run using a linear model (e.g. BOLT-LMM, Hail) then the correct formula to calculate log odds is:

  * log_OR    = β / (μ * (1 - μ))
  * log_ORse  = se / (μ * (1 - μ))
  * where μ   = case fraction = (n_cases / (n_cases + n_controls))
  * OR        = exp(log_OR)
  * OR 95% CI = exp(log_OR ± 1.96 * log_ORse)
  * Citation: https://data.broadinstitute.org/alkesgroup/BOLT-LMM/#x1-5200010.2
  

• Chromosome must be one of ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', 'X', 'Y', 'MT']
• Rows should be filtered to only contain variants with sufficiently high number of minor allele counts. As of May 2019, we are using MAC>=10 for GWAS studies and MAC>=5 for molecular traits.
• If pval == 0, set to minimum float64

Important note. The fine-mapping and coloc pipelines currently use Dask to read the parquet files in python. We should continue to do this until pyarrow has implemented row group filtering (predicate pushdown), expected v0.14.0. In the mean time, all parquet files written in Spark should have the following option enabled: pyspark.sql.SparkSession.builder.config("parquet.enable.summary-metadata", "true").getOrCreate()

An update about this here. It seems to be safe stop using summary-metadata as soon as we get versions up to date.

Columns

• type: The study type. GWAS studies must be gwas. Molecular trait studies can take other values, e.g. eqtl, pqtl.
• study_id: A unique identifier for the study, should match the root parquet dataset name.
• phenotype_id: Null for type == gwas. ID for the measured phenotype for molecular traits. E.g. Illumina probe, or Ensembl gene/transcript ID.
• bio_feature: Null for type == gwas. An ID for the tissue measured in the molecular QTL. Ideally, this should be from an ontology such as CLO or UBERON.
• gene_id: Null for type == gwas. Ensembl gene ID otherwise.
• chrom: Chromosome of the variant.
• pos: Position of the variant in GRCh38 build.
• ref: Reference allele.
• alt: Alternative allele. All effects should be with respect to the alt allele.
• beta: The effect size wrt to alt. LogOR for binary traits.
• se: The standard error for beta. LogORse for binary traits.
• pval: Association p-value
• n_total: The total number of samples including cases and controls
• n_cases: Null for quantitative traits. The number of cases.
• eaf: The effect (alt) allele frequency. This could be estimated from a reference panel if not known.
• mac: The minor allele count in all samples.
• mac_cases: The minor allele count in cases.
• num_tests: The number of variants tested for each gene in the molecular QTL analysis.
• info: The imputation quality information.
• is_cc: Whether the study is case-control or not.

Schema

message spark_schema {
  required binary type (UTF8);
  required binary study_id (UTF8);
  optional binary phenotype_id (UTF8);
  optional binary bio_feature (UTF8);
  optional binary gene_id (UTF8);
  optional binary chrom (UTF8);
  optional int32 pos;
  optional binary ref (UTF8);
  optional binary alt (UTF8);
  optional double beta;
  optional double se;
  optional double pval;
  optional int32 n_total;
  optional int32 n_cases;
  optional double eaf;
  optional double mac;
  optional double mac_cases;
  optional int32 num_tests; # molecular qtl only
  optional double info;
  optional boolean is_cc;
}