nf-core pipeline · burden-test, cocorv, public-data-as-controlset
nf-core/rarevariantburden
Performs summary-count rare variant burden tests comparing sequenced cases against public gnomAD control cohorts.
What nf-core/rarevariantburden is
nf-core/rarevariantburden (CoCoRV-nf) is a bioinformatics pipeline developed by Saima Sultana Tithi at St. Jude Children's Research Hospital and Wenan Chen at Mayo Clinic. It executes rare variant burden testing when researchers possess sequenced case or patient cohorts but lack matched individual-level control data.
To overcome the absence of internal controls, the workflow evaluates case samples against pre-processed and annotated public summary count collections, including gnomAD data. This approach enables investigators to identify disease-predisposition genes while enforcing identical variant filtering criteria across both case and control data.
What the pipeline does
The pipeline ingests a joint-called case VCF with Variant Quality Score Recalibration (VQSR) applied and a list of sample identifiers. Using BCFtools, it splits the VCF by chromosome and performs normalisation and quality control. Variants are annotated with ANNOVAR (the default tool) or Ensembl VEP, and transformed into Genomic Data Structure (GDS) files using the R SeqArray package. Case ancestry is predicted using a gnomAD random forest classifier.
Association testing is executed chromosome by chromosome using the CoCoRV (Consistent summary Count based Rare Variant burden test) R package against pre-processed gnomAD control counts. The workflow merges these test results, computes false discovery rates (FDR), derives genomic inflation factors (lambda), and generates quantile-quantile (QQ) plots adapted for discrete count data. For top candidate genes, it produces detailed lists of identified samples and annotated variants.
Key facts
- Pipeline engine
- Nextflow
- Core methodology
- CoCoRV (Consistent summary Count based Rare Variant burden test)
- Institutions
- St. Jude Children's Research Hospital and Mayo Clinic
- Reference assemblies
- GRCh37, GRCh38
- Supported control sets
- gnomAD v2 exome (GRCh37), gnomAD v4.1 exome (GRCh38), gnomAD v4.1 genome (GRCh38)
- Annotation engines
- ANNOVAR (default), Ensembl VEP
- Input formats
- Joint-called VQSR VCF, plain-text sample ID list
What you can use it for
- Disease-predisposition gene discovery
- Detecting candidate disease genes in sequenced patient cohorts without requiring independently sequenced, matched controls.
- Ancestry-stratified association testing
- Predicting patient genetic ancestry using a random forest classifier to compare case subgroups against matched public population counts.
- Recessive model variant testing
- Testing recessive disease models while excluding double heterozygotes that occur due to high linkage disequilibrium.
- Sex-stratified burden analysis
- Carrying out burden testing separated by sex when patient gender information is supplied alongside input data.
Run it here
The research agent runs nf-core/rarevariantburden with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Enables robust burden testing when only sequenced case data is available by leveraging public summary-level controls.
- Enforces consistent variant filtering across case and control sets to minimise technical bias.
- Accommodates discrete count distributions with dedicated false discovery rate calculations and inflation factor estimates.
- Provides automated ancestry classification using a random forest classifier trained on gnomAD populations.
Watch out for
- Requires case VCF files to be joint-called and processed with Variant Quality Score Recalibration (VQSR) prior to execution.
- Restricted to reference cohorts pre-processed for gnomAD v2 exomes, gnomAD v4.1 exomes, and gnomAD v4.1 genomes.
- Requires substantial external reference and annotation resource downloads from Amazon S3 before running.
Questions
Can I run nf-core/rarevariantburden without my own cluster?
The research agent runs nf-core/rarevariantburden with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/rarevariantburden under?
MIT, like every nf-core pipeline.
What inputs does the pipeline require?
It requires a joint-called, VQSR-applied case VCF file and a text file listing sample identifiers with one identifier per line.
Which reference genomes and gnomAD versions are supported?
The pipeline supports gnomAD v2 exome data on GRCh37, as well as gnomAD v4.1 exome and gnomAD v4.1 genome data on GRCh38.
How are population differences between cases and controls addressed?
A gnomAD-based random forest model predicts the ethnicity of case samples, allowing stratified analyses against group-matched control counts.
Can the workflow evaluate recessive genetic architectures?
Yes, it supports recessive models and can filter out double heterozygotes that arise from high linkage disequilibrium.
How to cite it
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Written from nf-core/rarevariantburden's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.