Authors
Noemi N Piga, Michael A Portelli, Nick Shrine, Jing Chen, Richard Packer, Kayesha Coley, Alexander T Williams, Chiara Batini, Catherine John, Sharon M Lutz, Kirsten R Voorhies, Albert M Levin, Nazanin Zounemat-Kermani, James E Gern, Diane R Gold, Tina V Hartert, Daniel J Jackson, Christine C Johnson, Gurjit K Khurana Hershey, Rachel L Miller, Christine M Seroogy, Edward M Zoratti, Don D Sin, Maarten van den Berge, Yohan Bossé, Robert J Hall, Dominick Shaw, Zara E K Pogson, Andrew Fogarty, Liam G Heaney, Adel H Mansur, Rekha Chaudhuri, Neil C Thomson, John W Holloway, Kian Fan Chung, John D Blakey, Louise V Wain, Carole Ober, Ann C Wu, Ian M Adcock, Ian P Hall, Christopher E Brightling, Glenda Lassi, Ian Sayers, Katherine A Fawcett
Published in
ERJ open research. Volume 12. Issue 5. Epub Sep 01, 2026.
Abstract
In ∼10% of asthma patients, symptoms remain uncontrolled despite maximal treatment, representing an unmet clinical need. The causal variants, genes and pathways underlying genetic risk factors have not been fully elucidated, and it is unclear whether there are unique genetic risk factors for this asthma subtype.
We used electronic healthcare records linked to UK Biobank to identify asthma patients with high treatment burden and/or worse outcomes. We performed a genome-wide association study (GWAS) with this case population and healthy controls. We sought replication for associated (p≤5×10-6) signals in four independent studies (12 152 cases and 32 316 controls). Replicated signals were fine-mapped and linked to genes and pathways.
In total, 7681 participants met our case definition and showed enrichment for adult-onset asthma, female gender and higher body mass index compared to asthma individuals not meeting case criteria. GWAS with 7681 cases and 38 405 controls revealed 21 reproducible association signals that had previously been associated with asthma, but had a larger effect size in our study. Variant-to-gene mapping highlighted 85 candidate genes, five of which were considered high confidence (BACH2, D2HGDH, IL1RL1, RPS26, SMAD3).
We present the first use of electronic healthcare records in UK Biobank to identify a subtype of asthma enriched for patients with high treatment burden and/or worse outcomes. Our findings support the role of known asthma genes, highlighting genetic risk variants with stronger effect in these groups of patients. The prioritised genes provide potential therapeutic opportunities for this difficult-to-treat patient population.
PMID:
42682922
Bibliographic data and abstract were imported from PubMed on 02 Sep 2026.
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