Biomedical subjects

Loukas Moutsianas

Publications and source records attributed to Loukas Moutsianas.

2 recordsLinked to original sources

PubMed2026

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.

Humans↗

PubMed2026

The Biobank Rare Variant consortium powers the discovery of rare genetic associations through global collaboration.

Rare coding variants can have large effects on disease risk and provide direct routes from human genetics to disease mechanisms and therapeutic targets, but their discovery is constrained by sample size, particularly for low-prevalence diseases. Here we establish the Biobank Rare Variant Analysis (BRaVa) consortium, a global rare variant association resource that integrates sequencing and linked health-record data from ten biobanks and cohorts comprising over 1.2 million individuals across diverse ancestries. We performed gene-based meta-analyses of rare coding variation across 33 clinical endpoints and 11 quantitative traits. Aggregating evidence across biobanks and ancestries identified 514 gene-trait associations, including 31 not previously reported in prior studies or curated association resources following systematic literature review. Notably, 36.1% of gene-level associations were undetectable in any individual biobank, and 91 emerged only through cross-ancestry meta-analysis, demonstrating that federated integration enables discovery beyond the reach of single cohorts. Similar gains were observed at the variant level, where 25.0% of phenotype-locus associations were detectable only through meta-analysis. Effect size estimates were correlated across ancestries with concordant directions of effect, supporting the generalizability of rare variant associations. The identified signals implicate pathways involved in transcriptional and epigenetic regulation, metabolism, vascular and epithelial biology, and immune function, highlighting rare coding variation as an engine for biological discovery across medical record phenotypes. For example, damaging variation in ANKRD12 implicates inflammatory transcriptional dysregulation in asthma and chronic obstructive pulmonary disease, and ultra-rare predicted loss-of-function variants in NAA15 link protein acetylation processes to type 2 diabetes risk. BRaVa establishes a scalable framework and freely available community resource for rare variant meta-analysis across global biobanks. Public release of gene- and variant-level association summary statistics provides a reference map of rare coding variant associations to support disease gene discovery, biological interpretation, and therapeutic target prioritization as sequencing-linked health-record resources continue to expand.

Journal Article↗