Evidence map›Paper›PMID 41592539›Full record

ArticleAllergy, asthma & immunology research2026

Genome-wide Association Study Identifies

Huashi Li, Girish N Nadkarni, Charles A Powell, Monica Kraft, Xingnan Li

Abstract read
In one paragraph

Article in Allergy, asthma & immunology research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Huashi LiStatistics Consulting Lab, BIO5 Institute, University of Arizona, Tucson, AZ, USA.ORCID https://orcid.org/0009-0005-4231-2082
Girish N NadkarniDivision of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA.
Charles A PowellDivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA.
Monica KraftDivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA.
Xingnan LiDivision of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA.ORCID https://orcid.org/0000-0002-1208-8658

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePatients with asthma-chronic obstructive pulmonary disease (COPD) overlap (ACO) tend to have more severe respiratory symptoms than those with asthma or COPD only. In this study, we sought to identify genetic variants associated with ACO.

methodsACO was defined as patients with physician-diagnosed or self-reported asthma and COPD. Meta-analysis of genome-wide association studies (GWASs) of ACO was performed in non-Hispanic White (NHW: 4,292 ACO vs. 114,816 controls) and non-Hispanic Black or African American (NHB-AA: 2,335 ACO vs. 45,949 controls). Candidate genes for asthma or COPD were further pairwise compared among ACO, asthma, COPD, and control groups.

resultsThe prevalence rates of ACO/asthma/COPD were 2.46/8.89/3.57 (% of 200,369), 4.06/10.00/3.89 (% of 70,329), 1.40/10.21/1.40 (% of 6,093), and 0.43/4.55/0.58 (% of 12,783) for NHW, NHB-AA, Hispanic or Latino White, and non-Hispanic Asian, respectively. Patients with ACO or COPD and asthma were predominantly smokers and non-smokers, respectively. Patients with ACO showed highest blood eosinophil counts. Meta-analysis of GWASs identified rs1420101 (odds ratio [OR], 1.11;

conclusionsPrevalence of chronic respiratory diseases differs in racial/ethnic groups. On the basis of our phenotypic and genetic findings, we speculate that patients who carry asthma and COPD risk alleles, further influenced by smoking, may develop into ACO.

Indexed as

All of Us Research Programasthmaasthma-COPD overlapchronic obstructive pulmonary diseasegenome-wide association studyIL1RL1KIAA1217

Identifiers

PMID41592539
PMCPMC12865170

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.