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Case study · Population genomics
78,000 genomes scored for gene burden inside Genomics England
University of Southampton researchers built a reproducible Nextflow workflow for GenePy and ran it on Lifebit CloudOS inside the Genomics England Research Environment. Their paper names “the Genomics England (GEL) Lifebit platform” in its title. The data never leaves the source.
01 · The challenge
Score every gene for every participant, without moving the data
Rare-disease analysis usually looks at one variant at a time and can miss the combined effect of several variants in the same gene. GenePy gives one burden score per gene per person, but computing it across a national cohort means working through very large genome files inside a governed environment.
Variant-by-variant analysis misses the combined burden in a gene
Per-chromosome genome files of 80 to 200 GB each
All work has to stay inside the Genomics England Research Environment
02 · What we deployed
A containerized Nextflow workflow on Lifebit CloudOS
The team containerized GenePy, wrote CloudOS resource profiles, and automated the multi-step workflow. It runs chunk by chunk for each chromosome on cloud compute under Genomics England’s governance controls. The authors describe it as “integrated within the GEL Research Environment via the Lifebit CloudOS platform,” with “elastic scaling, managed scheduling, standardized metadata capture, and full compliance with GEL data-governance requirements.”
Reproducible by design
Docker images, version-controlled Nextflow code and checksums let anyone repeat the run exactly.
Elastic scale
Chromosome chunks run in parallel, with automatic retries when a step fails.
Data stays in place
The run happens inside the Research Environment; the output is a per-participant matrix of gene scores.
How it works
From connected data to approved results
Inside the Research Environment, variants for about 78,000 participants are cut to coding regions plus 25 bp, shrinking each chromosome file to about 1–6 GB.
A Nextflow DSL2 workflow splits each chromosome into windows and annotates variants with Ensembl VEP v114, gnomAD frequencies, and CADD v1.6.
Quality control resolves multiallelic sites, chunk boundaries, and X-chromosome ploidy, then variants are grouped into one record per gene.
GenePy scores every gene for every participant on Lifebit CloudOS, giving one gene-by-participant matrix; checksums and containers make the run repeatable.
What’s in the stack
03 · The results
A gene-burden matrix for the whole cohort
The workflow scored about 24,000 genes for each of about 78,000 participants in 214 hours of summed runtime, at an estimated $5,243.28 in compute. The group’s 2024 study scored about 2,900 recessive genes in the 100,000 Genomes Project and found 456 potential diagnoses; the new workflow covers every gene in one repeatable run. Researchers can now rank candidate genes for unsolved rare disease without reopening full genome files. The code is open on GitHub and Zenodo, so approved Genomics England researchers can rerun it. Published in NAR Genomics and Bioinformatics in September 2026 (Nazari, Cheng, Ashton, and Ennis).
Timeline
About this use case
Population genomics
Gene-burden scoring adds up the effect of all harmful variants a person carries in each gene, instead of judging one variant at a time. For rare disease, this helps rank candidate genes for patients without a diagnosis. Here it was computed for a national cohort inside a governed environment, and the output is one score per gene per person.
Who uses it here: Researchers in Human Genetics and Genomic Medicine at the University of Southampton (Nazari, Cheng, Ashton, and Ennis); approved Genomics England users can request files to verify the run.
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