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Record W2905137810 · doi:10.1016/s2213-2600(18)30389-8

Moderate-to-severe asthma in individuals of European ancestry: a genome-wide association study

2018· article· en· W2905137810 on OpenAlexaff
Nick Shrine, Michael A. Portelli, Catherine John, María Soler Artigas, Neil Bennett, Robert J. Hall, Jon Lewis, Amanda P. Henry, Charlotte K. Billington, Azaz Ahmad, Richard Packer, Dominick Shaw, Zara Pogson, Andrew Fogarty, Tricia M. McKeever, Amisha Singapuri, Liam G. Heaney, Adel Mansur, Rekha Chaudhuri, Neil C. Thomson, John W. Holloway, Gabrielle A. Lockett, Peter Howarth, Ratko Djukanović, Jenny Hankinson, Robert Niven, Angela Simpson, Kian Fan Chung, Peter J. Sterk, John Blakey, Ian M. Adcock, Sile Hu, Yike Guo, Ma’en Obeidat, Don D. Sin, Maarten van den Berge, David C. Nickle, Yohan Bossé, Martin D. Tobin, Ian P. Hall, Christopher E. Brightling, Louise V. Wain, Ian Sayers

Bibliographic record

VenueThe Lancet Respiratory Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité LavalSt. Paul's HospitalUniversity of British Columbia
FundersMedical Research CouncilGlaxoSmithKlineAsthma and Lung UKVirginia Commonwealth UniversityTeva Pharmaceutical IndustriesNational Institute for Health and Care ResearchDepartment of Chemical Engineering and Biotechnology, University of CambridgeRegeneron PharmaceuticalsPfizerBritish Lung FoundationRosetrees TrustSanofiAstraZenecaAmgen
KeywordsAsthmaMedicineBiobankGenome-wide association studyCohortGenetic associationStage (stratigraphy)Internal medicineSingle-nucleotide polymorphismBioinformaticsGeneticsGenotypeGeneBiology

Abstract

fetched live from OpenAlex

Background Few genetic studies that focus on moderate-to-severe asthma exist. We aimed to identity novel genetic variants associated with moderate-to-severe asthma, see whether previously identified genetic variants for all types of asthma contribute to moderate-to-severe asthma, and provide novel mechanistic insights using expression analyses in patients with asthma. Methods In this genome-wide association study, we used a two-stage case-control design. In stage 1, we genotyped patient-level data from two UK cohorts (the Genetics of Asthma Severity and Phenotypes [GASP] initiative and the Unbiased BIOmarkers in PREDiction of respiratory disease outcomes [U-BIOPRED] project) and used data from the UK Biobank to collect patient-level genomic data for cases and controls of European ancestry in a 1:5 ratio. Cases were defined as having moderate-to-severe asthma if they were taking appropriate medication or had been diagnosed by a doctor. Controls were defined as not having asthma, rhinitis, eczema, allergy, emphysema, or chronic bronchitis as diagnosed by a doctor. For stage 2, an independent cohort of cases and controls (1:5) was selected from the UK Biobank only, with no overlap with stage 1 samples. In stage 1 we undertook a genome-wide association study of moderate-to-severe asthma, and in stage 2 we followed up independent variants that reached the significance threshold of p less than 1 × 10 −6 in stage 1. We set genome-wide significance at p less than 5 × 10 −8 . For novel signals, we investigated their effect on all types of asthma (mild, moderate, and severe). For all signals meeting genome-wide significance, we investigated their effect on gene expression in patients with asthma and controls. Findings We included 5135 cases and 25 675 controls for stage 1, and 5414 cases and 21 471 controls for stage 2. We identified 24 genome-wide significant signals of association with moderate-to-severe asthma, including several signals in innate or adaptive immune-response genes. Three novel signals were identified: rs10905284 in GATA3 (coded allele A, odds ratio [OR] 0·90, 95% CI 0·88–0·93; p=1·76 × 10 −10 ), rs11603634 in the MUC5AC region (coded allele G, OR 1·09, 1·06–1·12; p=2·32 × 10 −8 ), and rs560026225 near KIAA1109 (coded allele GATT, OR 1·12, 1·08–1·16; p=3·06 × 10 −9 ). The MUC5AC signal was not associated with asthma when analyses included mild asthma. The rs11603634 G allele was associated with increased expression of MUC5AC mRNA in bronchial epithelial brush samples via proxy SNP rs11602802; (p=2·50 × 10 −5 ) and MUC5AC mRNA was increased in bronchial epithelial samples from patients with severe asthma (in two independent analyses, p=0·039 and p=0·022). Interpretation We found substantial shared genetic architecture between mild and moderate-to-severe asthma. We also report for the first time genetic variants associated with the risk of developing moderate-to-severe asthma that regulate mucin production. Finally, we identify candidate causal genes in these loci and provide increased insight into this difficult to treat population. Funding Asthma UK, AirPROM, U-BIOPRED, UK Medical Research Council, and Rosetrees Trust.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.329
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations257
Published2018
Admission routes1
Has abstractyes

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