Equity in genome sequencing for rare disease diagnosis: a cross-sectional analysis of data from the UK 100,000 Genomes Project

BACKGROUND: Genome sequencing has improved rare disease diagnosis and is now part of routine clinical care in the National Health Service in England. Automated prioritisation pipelines narrow millions of variants per patient to a small subset for clinical review, a process that relies on allele frequency resources that do not fully represent human genetic diversity. We assessed ancestry-related differences in variant prioritisation and diagnostic outcomes in patients from the UK 100,000 Genomes Project. METHODS: We analysed 29,405 rare disease probands with genome sequencing and linked clinical outcomes data. We used multivariable regression to assess ancestry-related differences in the number of variants prioritised for clinical review, the proportion of prioritised variants that were recorded as diagnostic, and diagnostic yield. We also evaluated the use of ancestry-stratified allele frequency filters derived from an independent, diverse UK cohort (n = 33,724). FINDINGS: Compared with the European ancestry group, the East African group had nearly three times more variants prioritised for clinical review (IRR 2.77, 95% CI 2.33-3.29). Other non-European groups also had significantly higher counts. Diagnostic yield was similar across ancestry groups after adjustment (LRT p = 0.1650). Prioritised variants were less likely to be recorded as diagnostic in East African (OR 0.32, 95% CI 0.22-0.46), West African (0.47, 0.39-0.57), South Asian (0.65, 0.58-0.73), and Middle Eastern (0.68, 0.54-0.86) groups. Applying ancestry-stratified allele-frequency filters removed 3.1% of prioritised variants overall-24.3% in the East African group-without loss of diagnostic sensitivity, including 29.5% of recorded VUS in this group. INTERPRETATION: Differences in the likelihood of prioritised variants being recorded as diagnostic partly reflect limitations of current allele frequency resources, which use broad population groupings that mask within-group diversity. Increased representation of diverse ancestries in reference databases and better estimation of ancestry-appropriate allele frequencies will help reduce inefficiencies and improve equity in variant prioritisation for rare disease diagnosis. FUNDING: The UK Department of Health and Social Care and the EU's Horizon 2020 Research and Innovation Programme.

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Journal
EBioMedicine
Published
2026-08-26
DOI
https://doi.org/10.1016/j.ebiom.2026.106452
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
0.00

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article

Equity in genome sequencing for rare disease diagnosis: a cross-sectional analysis of data from the UK 100,000 Genomes Project

Yoonsu Cho, Matthew A. Brown, Loukas Moutsianas, Matt J. Silver et al.
EBioMedicine
Genomics and Rare Diseases
article

Equity in genome sequencing for rare disease diagnosis: a cross-sectional analysis of data from the UK 100,000 Genomes Project

Yoonsu Cho, Matthew A. Brown, Loukas Moutsianas, Matt J. Silver, Jamie M. Ellingford, Sam Tallman, Karoline Kuchenbaecker, Maxine Mackintosh, Thuy Nguyen, Dalia Kasperaviciute
article en

Abstract

BACKGROUND: Genome sequencing has improved rare disease diagnosis and is now part of routine clinical care in the National Health Service in England. Automated prioritisation pipelines narrow millions of variants per patient to a small subset for clinical review, a process that relies on allele frequency resources that do not fully represent human genetic diversity. We assessed ancestry-related differences in variant prioritisation and diagnostic outcomes in patients from the UK 100,000 Genomes Project. METHODS: We analysed 29,405 rare disease probands with genome sequencing and linked clinical outcomes data. We used multivariable regression to assess ancestry-related differences in the number of variants prioritised for clinical review, the proportion of prioritised variants that were recorded as diagnostic, and diagnostic yield. We also evaluated the use of ancestry-stratified allele frequency filters derived from an independent, diverse UK cohort (n = 33,724). FINDINGS: Compared with the European ancestry group, the East African group had nearly three times more variants prioritised for clinical review (IRR 2.77, 95% CI 2.33-3.29). Other non-European groups also had significantly higher counts. Diagnostic yield was similar across ancestry groups after adjustment (LRT p = 0.1650). Prioritised variants were less likely to be recorded as diagnostic in East African (OR 0.32, 95% CI 0.22-0.46), West African (0.47, 0.39-0.57), South Asian (0.65, 0.58-0.73), and Middle Eastern (0.68, 0.54-0.86) groups. Applying ancestry-stratified allele-frequency filters removed 3.1% of prioritised variants overall-24.3% in the East African group-without loss of diagnostic sensitivity, including 29.5% of recorded VUS in this group. INTERPRETATION: Differences in the likelihood of prioritised variants being recorded as diagnostic partly reflect limitations of current allele frequency resources, which use broad population groupings that mask within-group diversity. Increased representation of diverse ancestries in reference databases and better estimation of ancestry-appropriate allele frequencies will help reduce inefficiencies and improve equity in variant prioritisation for rare disease diagnosis. FUNDING: The UK Department of Health and Social Care and the EU's Horizon 2020 Research and Innovation Programme.

EBioMedicineVol. 131
Turing Institute (GB), King's College London (GB), Bristol City Council (GB), University of Manchester (GB), University of Bristol (GB), St Mary's Hospital (GB), London School of Hygiene & Tropical Medicine (GB), Manchester University NHS Foundation Trust (GB), St Mary's Hospital (GB), MRC Unit the Gambia (GM), The Alan Turing Institute (GB), Genomics England (GB), University College London (GB), Medical Research Council (GB)
Department of Health and Social Care, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 11%
Genomics and Rare Diseases
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