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HARE Genetic Feature Enrichment Analysis Pipeline

Author: Olivia Smith, osmith@utexas.edu, GitHub @ossmith
Publication Corresponding Author: Vagheesh Narasimhan, vagheesh@utexas.edu, GitHub @vagheesh

This script identifies the genes and genomic features associated with autosomal genome-wide significant positions based on GWAS summary statistics for a given phenotype. It then tests for elevated intersections between the phenotype-associated features and the elements of interest (e.g. HARs) based on a simulated background distribution.

Repository structure

HARE
|
│-- README.md
|-- dataDictionary.md
|-- environment.yml
|-- pyproject.toml
|-- MANIFEST.in
|-- hare.reference.assets.tar.gz
|
|---src
│   │-- __init__.py
│   │-- hare.py
│   │-- hareclasses.py
│   │-- intersect.py
│   │-- sigtest.py
│   │-- prerank.py
|
|---example
│   │-- exampleREADME.md
│   │-- example_run.sh
|   |-- example_{filename.extension}
│
|---tests
│   │-- __init__.py
│   │-- intersect_test.py
│   │-- sigtest_test.py
│   │-- prerank_test.py
│   │-- species_test.py
│   |-- input
|       |-- ...

Installation

Dependencies

The following are required dependencies for HARE. Hyperlinks will direct you to the installation documentation for these dependencies. You may either install these independently or through creation of a conda environment using the provided environment.yml file (see below).

*Note: HARE has been tested and is functional in Unix/MacOS environments where dependencies can be installed, but due to some MacOS incompatibilities with Ensembl VEP, it may not be possible to install the necessary dependencies using the environment.yml file. Users can see detailed instructions for installation from Ensembl here and follow links above for the other dependencies.

Install with conda and pip

Start by cloning this repo:

git clone https://github.com/ossmith/HARE.git

You can install the dependencies as a conda environment using the provided environment.yml file. By default it will create an environment named 'hare-env'.

cd HARE
conda env create -f environment.yml
conda activate hare-env

You may then need to update the perl Compress::Raw::Zlib module:

cpan Compress::Raw::Zlib

And then build HARE from the included setup files with:

pip3 install .

You will then need to download a VEP genome cache if you have not already done so. Find the right cache and install using the instructions here.

Example (H. sapiens GRCh37 VEP cache v105):

cd $HOME/.vep
wget https://ftp.ensembl.org/pub/release-105/variation/indexed_vep_cache/homo_sapiens_vep_105_GRCh37.tar.gz
tar xzf homo_sapiens_vep_105_GRCh37.tar.gz

Ensembl recommends that you use the same cache version as your VEP install version (hare-env includes v105). Note that the first time you run the tool after downloading the cache, it will need to do some configuration that will slow down the pipeline. This will only take place the first time you use a given cache.

Included reference assets can be extracted with:

tar xvf hare.reference.assets.tar.gz

Testing installation

You can run the unit tests to confirm functionality (requires path to installed cache and version if different from defaults, defaults are '$HOME/.vep/' and '105') using:

cd tests/
python intersect_test.py --cache_dir {VEP_CACHE_PATH} --cache_ver {VEP_VERSION}
python sigtest_test.py
python prerank_test.py

Please note that while a test suite for functionality on non-human species is available (species_test.py), it requires all 5 non-human example species caches. We recommend instead that users interested in this function test only the species of interest with the datasets found under tests/input/{ref/gwas/eoi}_{species}.txt if necessary.

Running an example

There is an example fileset in the HARE/example directory, including (i) example inputs, (ii) a bash script (example_run.sh) to run each command on the example files, and (iii) example output files. Note that this script still requires users to provide the path to their genome reference (the corresponding GRCh37 reference for this example is located in hare.reference.assets.tar.gz) and VEP cache. Refer to the included exampleREADME.md for additional file details.

Commands

Running annotation, simulation, and intersection

The primary analysis in this repository is performed with the Python intersect function. It performs annotation, simulation, and intersection for the provided GWAS and elements of interest (for intersecting against). The most basic command is:

hare intersect --gwas [GWAS] --eoi [EOI] --ref [REF] --out [OUT_STEM] ... [OPTIONS]

Various options are explained below.

Running significance testing

The significance testing is done using sigtest. This function is separated from the main analysis because it allows for multiple HARE output files from different GWASs to be analyzed together to create a condensed TSV. The command is:

hare sigtest --i [INPUT] --o [OUT_STEM] ... [OPTIONS]

where --input is the [FILE].intersections file (or comma-separated list of these files) created using intersect.

Running gene preranking

Other enrichment analysis tools such as WebGestalt and GSEA make use of ranked gene lists (HGNC symbol:score pairings). HARE can annotate and rank genes from any dataset with genomic position and p-value data (e.g. selection scan, GWAS). It can also be used for peak calling by identifying genes with multiple positions of genome-wide significance (by p-value).

hare prerank --i [INPUT] --o [OUT_STEM] ... [OPTIONS]

File Inputs and Outputs

intersect

Inputs

  • [GWAS]: Tab-separated GWAS summary statistics file which contains information on the chromosome, position, MAF, and p-value for each SNP
  • [EOI]: BED file with elements of interest to intersect against (EOIs). The Richard, et al., 2020 HARs BED file can be found in hare.reference.assets.tar.gz
  • [REF]: Reference genome file containing chromosomes and associated lengths (see more details of what is required and how to make one here). Reference genome files for gene annotation of GRCh37 and GRCh38 can be found in hare.reference.assets.tar.gz

Outputs

  • [OUT_STEM].snps: List of variants which pass QC conditions (biallelic, MAF threshold, p-value threshold) and will be used for annotation and analysis
  • [OUT_STEM].annotation: Raw VEP annotation results
  • [OUT_STEM].annotation_summary.html: VEP run log produced by command line run
  • [OUT_STEM].biomart: Output from Biomart query containing Ensembl gene ID, gene start and end position, chromosome, gene name (symbol), and strand
  • [OUT_STEM].locations.bed: BED file containing all the locations of the genes selected for inclusion in the element set
  • [OUT_STEM].intersections: Calculated intersections/bp between EOIs (e.g. HARs) and simulated or phenotype-associated element sets

sigtest

Inputs

  • [INPUT].intersections: Filepath to results file from intersect (naming convention [OUT_STEM].intersections) with simulation and element set intersection/bp values. Can also be a comma-separated list of such filenames

Outputs

  • [OUT_STEM].stats: Tab-separated text file with the test statistics and resulting p-value
  • [OUT_STEM].png: Figure showing the distribution of intersections/bp of the simulations against the intersections/bp of the phenotype-associated element set

prerank

Inputs

  • [INPUT]: File containing p-values associated with each position. Chromosome, position, and p-value columns are required.
  • [INCLUDE].bed (optional): BED format file containing a list of positions to filter to for analysis. Only listed positions will be included.
  • [EXCLUDE].bed (optional): BED format file containing a list of positions to remove from analysis.

Outputs

  • [OUTPUT].rnk: File containing HGNC symbol and score for each annotated gene. Use --score_method to choose whether min or mean p-value from associated positions is used to score.
  • [OUTPUT].dmp (if --call_peaks used): File containing peaks for which a sufficient number of positions within a given distance meet genome-wide significance threshold. Example: peaks with at least 3 positions within 1,000 bp of a gene have a p-value below 1e-06.

Arguments and Options

You can also view this using the hare {FUNCTION} -h option after installation.

intersect

Option Data Type Description
--gwas, -g string (Required) Filepath for GWAS summary statistics.
--ref string (Required) Filepath for genome reference annotation (either a BED or fai file works). This file is used to simulate matched element sets. Find more details about what is required here GRCh37 and GRCh38 genome files are available in hare.reference.assets.tar.gz.
--eoi string Filepath for BED-format elements of interest (EOIs). Required except when using --anno_only option (see below). The Richard, et al., 2020 HAR supplement files (original GRCh37 and a GRCh38 liftover) are available in hare.reference.assets.tar.gz.
--out, -o string Output filepath stem. If nothing is provided, will use the stem from the input GWAS file.
--pval, -p float P-value threshold for inclusion of variants in the annotation. Default is 1e-6.
--gwas_p string Column name for p-values in GWAS summary statistics. Default is "P".
--use_z - Use Z score (in "Z" column) to calculate p-values for GWAS summary statistics. By default is OFF.
--maf, -m float Minimum minor allele frequency (MAF). Default is 0.01.
--gwas_maf string Column name for minor allele frequency (MAF) in GWAS summary statistics. Default is "MAF".
--gwas_ref string Column name for reference allele in GWAS summary statistics. Default is "REF".
--gwas_alt string Column name for alternate allele in GWAS summary statistics. Default is "ALT".
--snp_map string If SNPs provided as IDs instead of genomic locations (CHR, POS), provide a BED file which maps IDs to locations.
--source_neale - Use Neale Lab GWAS summary statistics format. Will take priority over --gwas_{COLUMN_NAME} options.
--source_bolt - Use BOLT-LMM GWAS Summary Statistics format. Will take priority over --gwas_{COLUMN_NAME} options. Uses P_BOLT_LMM_INF for p-values.
--anno_only - Only perform annotation, do not simulate element sets and perform intersections. EOI file is not required when this option is used.
--ref_build, -r string Genome reference build (only for human data). Options are either 37 (for GRCh37, hg19) or 38 (for GRCh38, hg38). Default is 37.
--dist, -d int Distance to transcript for which VEP assigns upstream and downstream consequences. Default is 5,000 bp. For more details, see VEP CLI documentation.
--cache_dir string VEP cache directory to use. Default is "$HOME/.vep/". For more details, see VEP CLI documentation
--cache_version string VEP cache version to use. Default is 105 (version used during development).
--species string Default is "homo_sapiens". Latin name of the species for your data e.g. "arabidopsis_thaliana". If not human, pair with a sister flag (below, e.g. --plant, --metazoa). This is required in order to find the correct VEP cache and Ensembl site.
--vertebrate string Indicate data is from non-human, vertebrate species. Requires a corresponding species name using the --species flag.
--plant string Indicate data is from a plant species. Requires a corresponding species name using the --species flag.
--metazoa string Indicate data is from a metazoic species. Requires a corresponding species name using the --species flag.
--fungi string Indicate data is from a fungal species. Requires a corresponding species name using the --species flag.
--biotypes string Allowed biotypes for annotation. Options are "protein_coding", "protein_all", and "all_features". Default is "protein_all." See VEP's biotype documentation for details.
--draws, -n int Number of simulations (draws) to use for background distribution. Default is n=1000.
--keep_tmp - Keep all temporary files created during the run.

sigtest

Option Data Type Description
--input, -i string (Required) Filepath for output of intersect (default output has file extension .intersections).
--out, -o string Output filepath stem. If nothing is provided, will use "hare".
--skip_plot - Do not generate plots. Default is OFF (will plot by default).

prerank

Option Data Type Description
--input, -i string (Required) Filepath of file with p-values.
--output, -o string Output filepath stem. If nothing is provided, will use input stem.
--ref_build, -b string GRCh reference build. Options are either 37 (hg19) or 38 (hg38). Default is 37.
--topN, -n int Number of lowest p-value positions to use. No default (all positions will be scored and ranked).
--pval, -p float P-value threshold for annotated genes. Default is 1 (all positions will be scored and ranked).
--pval_col, -vc string Column with p-values. Default is 'P'.
--chr, -c int Filter to a single given chromosome (multi-chromosome or chromosome:region syntax is not supported). Will analyze all positions if not provided. Recommended for large (whole genome) datasets.
--chr_col, -cc string Column with chromosome. Default is 'CHR'.
--pos_col, -pc string Column with position. Default is 'POS'.
--incl, -f string Filepath of BED file with positions to include in analysis.
--excl, -x string Filepath of BED file with positions to exclude from analysis.
--buffer int Number of bp to look upstream and downstream for annotations. Default is 0 (will only annotate genes if it is within the bounds).
--biotypes, -t str Allowed gene types when annotating. Options are 'all' or 'coding'. Default 'all'. See HGNC documentation for gene type details.
--score_method, -m str Method for how to compute score. Options are 'mean' or 'min'. Mean will use the average p-value of all positions annotated to the gene and min will select the minimum p-value of those positions. Default is minimum.
--call_peaks - Call peaks. See --dmp_{OPTION} options for settings. Default is OFF (does not call peaks).
--dmpN int Number of positions annotating to a given gene required within provided distance for peak calling. Default is 3. Set to 1 to retrieve all.
--dmpP float P-value threshold for peak (differential methylation) calling. Default is 1e-08.
--dmpD int Distance (bp) allowed for which dmpN and dmpP conditions must be met for peak calling. If none provided, will use --buffer distance.

Reporting Bugs and Contribution Guidelines

Please submit issues, bug reports, and feature requests using GitHub Issues.

If you would like to contribute to the code, please fork this repository and then submit a pull request (PR) with your changes. We require that any changes or additions to functionality also include a corresponding automated test.

Citation

If you use this pipeline in your work, please cite these articles:

Smith OS, Kun E, and Narasimhan VM, (2024). HARE: A Python workflow for analyzing genomic feature enrichment in GWAS datasets. Journal of Open Source Software, 9(97), 6359, https://doi.org/10.21105/joss.06359

Kun E, Javan EM, Smith OS, et al., (2023). The genetic architecture and evolution of the human skeletal form. Science 381, eadf8009, https://doi.org/10.1126/science.adf8009

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HARE: Genetic Feature Enrichment Analysis Pipeline

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