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Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction

Nature Genetics (2026)Cite this article

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Abstract

While respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma share many risk factors, most studies investigate them in isolation and in predominantly European-ancestry populations. Here, we conducted the most powerful multi-trait and multi-ancestry genetic analysis of respiratory diseases and auxiliary traits to date, identifying 25 new loci associated with lung function in individuals of East Asian ancestry. Using these results, we developed PRSxtra (cross-trait and cross-ancestry), a multi-trait and multi-ancestry polygenic risk score (PRS) approach that leverages shared components of heritable risk via pleiotropic effects. PRSxtra significantly improved the prediction of asthma, COPD and lung cancer compared to trait- and ancestry-matched PRSs in a multi-ancestry cohort from the All of Us Research Program, especially in diverse populations. Our results present a new framework for multi-trait and multi-ancestry studies of respiratory diseases to improve genetic discovery and polygenic prediction.

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Fig. 1: Study design overview.
Fig. 2: Meta-analysis results of spirometry GWAS in East Asian (EAS) and multi-ancestry cohorts.
Fig. 3: Shared and distinct heritable components inform the molecular basis of trait differences.
Fig. 4: PRSxtra significantly improves prediction for several respiratory diseases compared to PRS and clinical risk factors.

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Data availability

The individual-level genotype and phenotype data of All of Us are available on the Researcher Workbench. Researchers can register for access at: https://www.researchallofus.org/. The GWAS summary statistics for the EAS and multi-ancestry meta-analyses of lung function are available at the GWAS Catalog (https://www.ebi.ac.uk/gwas) under the accession codes GCST90705067, GCST90705068, GCST90705069, GCST90705070, GCST90705071 and GCST90705072. Data sources for ancestry-specific summary statistics for each trait used in this study are available in Supplementary Table 1. The weights of PRSxtra for each trait are available in Supplementary Tables 19–21.

Code availability

Analyses were conducted using publicly available software: MTAG v.2018 (https://github.com/JonJala/mtag), METAL v.2011-03-25 (https://genome.sph.umich.edu/wiki/METAL), PLINK v.2.0 (https://www.cog-genomics.org/plink/2.0/), PRS-CS v.1.1.0 (https://github.com/getian107/PRScs) and PRS-CSx v.1.1.0 (https://github.com/getian107/PRScsx). The scripts for data analyses are available at https://github.com/yixuanh/lung-mutitrait-multiancestry and at https://zenodo.org/records/17452013.

References

  1. Chen, S. et al. The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020–50: a health-augmented macroeconomic modelling study. Lancet Glob. Health 11, e1183–e1193 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Google Scholar 

  3. Vos, T. et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1204–1222 (2020).

    Article  Google Scholar 

  4. Castro-Rodriguez, J. A. The Asthma Predictive Index: early diagnosis of asthma. Curr. Opin. Allergy Clin. Immunol. 11, 157–161 (2011).

    Article  PubMed  Google Scholar 

  5. He, Y. et al. Prediction and stratification of longitudinal risk for chronic obstructive pulmonary disease across smoking behaviors. Nat. Commun. 14, 8297 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Duffy, D. L., Martin, N. G., Battistutta, D., Hopper, J. L. & Mathews, J. D. Genetics of asthma and hay fever in Australian twins. Am. Rev. Respir. Dis. 142, 1351–1358 (1990).

    Article  PubMed  CAS  Google Scholar 

  7. Ingebrigtsen, T. et al. Genetic influences on chronic obstructive pulmonary disease—a twin study. Respir. Med. 104, 1890–1895 (2010).

    Article  PubMed  Google Scholar 

  8. Silverman, E. K. Genetics of COPD. Annu. Rev. Physiol. 82, 413–431 (2020).

    Article  PubMed  CAS  Google Scholar 

  9. Lichtenstein, P. et al. Environmental and heritable factors in the causation of cancer— analyses of cohorts of twins from Sweden, Denmark, and Finland. N. Engl. J. Med. 343, 78–85 (2000).

    Article  PubMed  CAS  Google Scholar 

  10. Karczewski, K. J. et al. Pan-UK Biobank genome-wide association analyses enhance discovery and resolution of ancestry-enriched effects. Nat. Genet. 57, 2408–2417 (2025).

    Article  PubMed  CAS  Google Scholar 

  11. Zhou, J. J. et al. Heritability of chronic obstructive pulmonary disease and related phenotypes in smokers. Am. J. Respir. Crit. Care Med. 188, 941–947 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Gorman, B. R. et al. Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk. Nat. Commun. 15, 8629 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Moll, M. et al. Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts. Lancet Respir. Med. 8, 696–708 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Tsuo, K. et al. Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity. Cell Genom. 2, 100212 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Tadros, R. et al. Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect. Nat. Genet. 53, 128–134 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Patel, A. P. et al. A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease. Nat. Med. 29, 1793–1803 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults. J. Am. Coll. Cardiol. 72, 1883–1893 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Moll, M. et al. Polygenic risk scores identify heterogeneity in asthma and chronic obstructive pulmonary disease. J. Allergy Clin. Immunol. 152, 1423–1432 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. The 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  24. Bergström, A. et al. Insights into human genetic variation and population history from 929 diverse genomes. Science 367, eaay5012 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Feng, Y.-C. A. et al. Taiwan Biobank: a rich biomedical research database of the Taiwanese population. Cell Genom. 2, 100197 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Jee, Y. H. et al. Genome-wide association studies in a large Korean cohort identify quantitative trait loci for 36 traits and illuminate their genetic architectures. Nat. Commun. 16, 4935 (2025).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Jee, Y. H. et al. Cohort profile: the Korean Cancer Prevention Study-II (KCPS-II) Biobank. Int. J. Epidemiol. 47, 385–386f (2018).

    Article  PubMed  Google Scholar 

  28. Shrine, N. et al. Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk. Nat. Genet. 55, 410–422 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Pirinen, M. linemodels: clustering effects based on linear relationships. Bioinformatics 39, btad115 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Ge, T. et al. Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations. Genome Med. 14, 70 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ruan, Y. et al. Improving polygenic prediction in ancestrally diverse populations. Nat. Genet. 54, 573–580 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Truong, B. et al. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. Cell Genom. 4, 100523 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Hammad, H. & Lambrecht, B. N. The basic immunology of asthma. Cell 184, 1469–1485 (2021).

    Article  PubMed  CAS  Google Scholar 

  35. Ramalho, S. H. R. & Shah, A. M. Lung function and cardiovascular disease: a link. Trends Cardiovasc. Med. 31, 93–98 (2021).

    Article  PubMed  Google Scholar 

  36. El-Azeem, I. A. A., Hamdy, G., Amin, M. & Rashad, A. Pulmonary function changes in diabetic lung. Egypt. J. Chest Dis. Tuberc. 62, 513–517 (2013).

    Article  Google Scholar 

  37. Çolak, Y., Nordestgaard, B. G. & Afzal, S. Morbidity and mortality in carriers of the cystic fibrosis mutation CFTR Phe508del in the general population. Eur. Respir. J. 56, 2000558 (2020).

    Article  PubMed  Google Scholar 

  38. Pereira, S. V.-N., Ribeiro, J. D., Ribeiro, A. F., Bertuzzo, C. S. & Marson, F. A. L. Novel, rare and common pathogenic variants in the CFTR gene screened by high-throughput sequencing technology and predicted by in silico tools. Sci. Rep. 9, 6234 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wang, Y. et al. Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer. Nat. Genet. 46, 736–741 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Cortez Cardoso Penha, R. et al. Common genetic variations in telomere length genes and lung cancer: a Mendelian randomisation study and its novel application in lung tumour transcriptome. eLife 12, e83118 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Kachuri, L. et al. Mendelian randomization and mediation analysis of leukocyte telomere length and risk of lung and head and neck cancers. Int. J. Epidemiol. 48, 751–766 (2019).

    Article  PubMed  Google Scholar 

  42. Shi, J. et al. Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population. Nat. Commun. 14, 3043 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Duckworth, A. et al. Telomere length and risk of idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease: a mendelian randomisation study. Lancet Respir. Med. 9, 285–294 (2021).

    Article  PubMed  CAS  Google Scholar 

  44. Xu, K. et al. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nat. Commun. 11, 5302 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Wang, S. et al. Significant associations of CHRNA2 and CHRNA6 with nicotine dependence in European American and African American populations. Hum. Genet. 133, 575–586 (2014).

    Article  PubMed  CAS  Google Scholar 

  46. Thorgeirsson, T. E. et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature 452, 638–642 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Saccone, N. L. et al. Multiple distinct risk loci for nicotine dependence identified by dense coverage of the complete family of nicotinic receptor subunit (CHRN) genes. Am. J. Med. Genet. B Neuropsychiatr. Genet. 150B, 453–466 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Ni, Z. et al. CLPTM1L is overexpressed in lung cancer and associated with apoptosis. PLoS ONE 7, e52598 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Chen, X. F. et al. Multiple variants of TERT and CLPTM1L constitute risk factors for lung adenocarcinoma. Genet. Mol. Res. 11, 370–378 (2012).

    Article  PubMed  Google Scholar 

  50. James, M. A., Vikis, H. G., Tate, E., Rymaszewski, A. L. & You, M. CRR9/CLPTM1L regulates cell survival signaling and is required for Ras transformation and lung tumorigenesis. Cancer Res. 74, 1116–1127 (2014).

    Article  PubMed  CAS  Google Scholar 

  51. Ortega, V. E. et al. The effects of rare SERPINA1 variants on lung function and emphysema in SPIROMICS. Am. J. Respir. Crit. Care Med. 201, 540–554 (2020).

    Article  PubMed  CAS  Google Scholar 

  52. Stoller, J. K. & Aboussouan, L. S. α1-antitrypsin deficiency. Lancet 365, 2225–2236 (2005).

    Article  PubMed  CAS  Google Scholar 

  53. Brenner, D. R., McLaughlin, J. R. & Hung, R. J. Previous lung diseases and lung cancer risk: a systematic review and meta-analysis. PLoS ONE 6, e17479 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Denholm, R. et al. Is previous respiratory disease a risk factor for lung cancer? Am. J. Respir. Crit. Care Med. 190, 549–559 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Kachuri, L. et al. Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility. Nat. Commun. 11, 27 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Zhang, D. et al. Exploring the relationship between Treg-mediated risk in COPD and lung cancer through Mendelian randomization analysis and scRNA-seq data integration. BMC Cancer 24, 453 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Barr, R. G. et al. Pulmonary disease and age at immigration among Hispanics. Results from the Hispanic Community Health Study/Study of Latinos. Am. J. Respir. Crit. Care Med. 193, 386–395 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Pino-Yanes, M. et al. Genetic ancestry influences asthma susceptibility and lung function among Latinos. J. Allergy Clin. Immunol. 135, 228–235 (2015).

    Article  PubMed  Google Scholar 

  59. Kachuri, L. et al. Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture. Nat. Genet. 55, 952–963 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Cooke, C. R. et al. The validity of using ICD-9 codes and pharmacy records to identify patients with chronic obstructive pulmonary disease. BMC Health Serv. Res. 11, 37 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Buhr, R. G. et al. Reversible airflow obstruction predicts future chronic obstructive pulmonary disease development in the SPIROMICS cohort: an observational cohort study. Am. J. Respir. Crit. Care Med. 206, 554–562 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Hobbs, B. D. et al. Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis. Nat. Genet. 49, 426–432 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. The All of Us Research Program Investigators The “All of Us” Research Program. N. Engl. J. Med. 381, 668–676 (2019).

    Article  PubMed Central  Google Scholar 

  64. He, Y. & Martin, A. R. We need more-diverse biobanks to improve behavioural genetics. Nat. Hum. Behav. 8, 197–200 (2023).

    Article  Google Scholar 

  65. He, Y. et al. Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction. Zenodo https://zenodo.org/records/17452013 (2024).

  66. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Li, X. et al. Genome-wide association study of lung function and clinical implication in heavy smokers. BMC Med. Genet. 19, 134 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was supported by the National Human Genome Research Institute (T32HG010464 to Y.H., K99HG013969 to Y.W. and U01HG011719 to A.R.M.), the National Institute of Environmental Health Sciences (R01ES032470 and R01DK137993 to C.J.P.), the National Cancer Institute (U19CA203654 and R01CA243483 to C.I.A. and J.B.), the National Heart, Lung, and Blood Institute (R01HL179112 to A.R.M. and W.L. and R01HL168199, R01HL162813, R01HL153248 and R01HL135142 to M.H.C.) and the National Institute of Mental Health (K99/R00MH117229 to A.R.M.). We are grateful to the All of Us participants for their contributions. We also thank the National Institutes of Health’s All of Us Research Program for making available the participant data examined in this study.

Author information

Authors and Affiliations

  1. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Yixuan He, Wenhan Lu, Yon Ho Jee, Ying Wang, Kristin Tsuo, Hailiang Huang & Alicia R. Martin

  2. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Yixuan He, Ying Wang, James A. Diao, Hailiang Huang & Alicia R. Martin

  3. Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA

    Yixuan He

  4. Department of Epidemiology, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA

    Yixuan He

  5. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Wenhan Lu, Yon Ho Jee, Ying Wang, Kristin Tsuo, Hailiang Huang & Alicia R. Martin

  6. Department of Public Health & Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan

    Mu-Yi Shih & Yen-Chen Anne Feng

  7. Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA

    Kristin Tsuo

  8. Department of Thoracic Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    David C. Qian

  9. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    James A. Diao & Chirag J. Patel

  10. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    James A. Diao

  11. Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA

    Jinyoung Byun & Christopher I. Amos

  12. Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA

    Jinyoung Byun & Christopher I. Amos

  13. University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA

    Jinyoung Byun & Christopher I. Amos

  14. Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA

    Bogdan Pasaniuc

  15. Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Bogdan Pasaniuc

  16. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    Elizabeth G. Atkinson

  17. Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA

    Elizabeth G. Atkinson

  18. Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Matthew Moll & Michael H. Cho

  19. Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Matthew Moll & Michael H. Cho

  20. Section on Pulmonary, Critical Care, Sleep and Allergy, Department of Veteran Affairs, Boston Healthcare System, West Roxbury, MA, USA

    Matthew Moll

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  1. Yixuan He
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  2. Wenhan Lu
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  18. Alicia R. Martin
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Contributions

Y.H., M.M., M.H.C. and A.R.M. designed the study. Y.H., W.L., Y.H.J. and M.-Y.S. processed, analyzed and conducted statistical analysis of the data. Y.W. and K.T. provided methodological and statistical advice. Y.H.J., M.-Y.S., Y.-C.A.F., H.H., J.B. and C.I.A. contributed data. Y.H., W.L., D.C.Q., J.A.D., Y.-C.A.F., M.M., M.H.C. and A.R.M. interpreted the data. A.R.M. and Y.H. obtained funding. All authors (Y.H., W.L., Y.H.J., M.-Y.S., Y.W., K.T., D.C.Q., J.A.D., H.H., C.J.P., J.B., B.P., E.G.A., C.I.A., Y.-C.A.F., M.M., M.H.C. and A.R.M.) provided critical feedback and revisions for the manuscript.

Corresponding authors

Correspondence to Yixuan He or Alicia R. Martin.

Ethics declarations

Competing interests

M.H.C. has received grant support from GSK, consulting fees from Apogee and BMS, and speaking fees from Illumina. M.M. has received consulting fees from Thea Health, 2nd.MD, Axon Advisors, Verona Pharma and Sanofi. A.R.M. has received speaker fees from Novartis. All other authors declare no competing interests.

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Peer review information

Nature Genetics thanks Haoyu Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Frequency and effect size of risk alleles of the 13 index variants associated with FEV1.

These variants reached genome-wide significance in meta-analyzed GWAS of East Asian ancestry population (P < 5 × 10−8, derived from METAL). Dark blue shaded boxes represent variants that were present and significant (P < 5 × 10−8) in the GWAS. Light blue shaded boxes represent variants that were present but not significant. Unshaded white boxes represent variants that were not present in the GWAS.

Extended Data Fig. 2 Frequency and effect size of risk alleles of the 37 index variants associated with FVC.

These variants reached genome-wide significance in meta-analyzed GWAS of East Asian ancestry population (P < 5 × 10−8, derived from METAL). Dark blue shaded boxes represent variants that were present and significant (P < 5 × 10−8) in the GWAS. Light blue shaded boxes represent variants that were present but not significant. Unshaded white boxes represent variants that were not present in the GWAS.

Extended Data Fig. 3 Frequency and effect size of risk alleles of the 24 index variants associated with FEV1/FVC.

These variants reached genome-wide significance in meta-analyzed GWAS of East Asian ancestry population (P < 5 × 10−8, derived from METAL). Dark blue shaded boxes represent variants that were present and significant (P < 5 × 10−8) in the GWAS. Light blue shaded boxes represent variants that were present but not significant. Unshaded white boxes represent variants that were not present in the GWAS.

Extended Data Fig. 4 Multi-ancestry GWAS of FEV1.

The largest published GWAS of FEV1 to date is depicted in the Manhattan plot in red (bottom, with its signals in orange dots). Integrating EAS results in a multi-ancestry meta-analysis identified new signals, depicted in the Manhattan plot in blue (top, with potentially novel loci in triangles). Unadjusted two-sided P values derived from METAL are on a −log10 scale. Novel loci with P < 10−10 are annotated with the nearest gene.

Extended Data Fig. 5 Multi-ancestry GWAS of FVC.

The largest published GWAS of FVC to date is depicted in the Manhattan plot in red (bottom, with its signals in orange dots). Integrating EAS results in a multi-ancestry meta-analysis identified new signals, depicted in the Manhattan plot in blue (top, with potentially novel loci in triangles). Unadjusted two-sided P values derived from METAL are on a −log10 scale. Novel loci with P < 10−10 are annotated with the nearest gene.

Extended Data Fig. 6 Shared and distinct heritable components between asthma and other traits.

a–n, Comparison of effect sizes of variants from GWAS for asthma vs. five other traits (columns) across all available ancestry groups (rows) in models fitted with two lines. Effect sizes of variants on asthma are on the x-axis, and effect sizes of variants on the other traits are on the y-axis. Each point represents a variant significantly associated (P < 5 × 10−8) with at least one of the corresponding pair of traits. In a shared variants analysis, variants predominantly (with posterior probability >99%) associated with asthma are colored blue, and variants predominantly associated with the other trait are colored red. Gray variants were not confidently assigned to either trait (posterior probability < 99%). The colored shaded ellipse range indicates the 95% probability regions of the fitted bivariate effect size distributions with each class. Empty space means either the two traits do not have enough overlapped variants or GWAS results are not applicable for the corresponding ancestry group.

Extended Data Fig. 7 Shared and distinct heritable components between COPD and other traits.

a–k, Comparison of effect sizes of variants from GWAS for COPD vs. four other traits (columns) across all available ancestry groups (rows) in models fitted with two lines. Effect sizes of variants on COPD are on the x-axis, and effect sizes of variants on the other traits are on the y-axis. Each point represents a variant significantly associated (P < 5 × 10−8) with at least one of the corresponding pair of traits. In a shared variants analysis, variants predominantly (with posterior probability >99%) associated with COPD are colored blue, and variants predominantly associated with the other trait are colored red. Gray variants were not confidently assigned to either trait (posterior probability < 99%). The colored shaded ellipse range indicates the 95% probability regions of the fitted bivariate effect size distributions with each class. Empty space means either the two traits do not have enough overlapped variants or GWAS results are not applicable for the corresponding ancestry group.

Extended Data Fig. 8 Shared and distinct heritable components between lung cancer and other traits.

a–f, Comparison of effect sizes of variants from GWAS for lung cancer vs. three other traits (columns) across all available ancestry groups (rows) in models fitted with two lines. Effect sizes of variants on lung cancer are on the x-axis, and effect sizes of variants on the other traits are on the y-axis. Each point represents a variant significantly associated (P < 5 × 10−8) with at least one of the corresponding pair of traits. In a shared variants analysis, variants predominantly (with posterior probability >99%) associated with lung cancer are colored blue, and variants predominantly associated with the other trait are colored red. Gray variants were not confidently assigned to either trait (posterior probability < 99%). The colored shaded ellipse range indicates the 95% probability regions of the fitted bivariate effect size distributions with each class. Empty space means either the two traits do not have enough overlapped variants or GWAS results are not applicable for the corresponding ancestry group.

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He, Y., Lu, W., Jee, Y.H. et al. Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction. Nat Genet (2026). https://doi.org/10.1038/s41588-025-02470-1

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