Association study of human leukocyte antigen variants and idiopathic pulmonary fibrosis

Guillén-Guío, Beatriz; Paynton, Megan L; Allen, Richard J W; Chin, Daniel P.W.; Donoghue, Lauren J.; Stockwell, Amy; Leavy, Olivia C.; Hernández-Beeftink, Tamara; Reynolds, Carl R.; Cullinan, Paul; Martinez, Fernando Limon; Booth, Helen L.; Fahy, William A.; Hall, Ian P; Hart, Simon P.; Hill, Mike R; Hirani, Nik; Hubbard, Richard B.; McAnulty, Robin J; Millar, Ann B; Navaratnam, Vidya; Oballa, Eunice; Parfrey, Helen; Saini, Gauri; Sayers, Ian; Tobin, Martin D.; Whyte, Moira K.B; Adegunsoye, Ayodeji; Kaminski, Naftali; Ma, Shwu-Fan; Strek, Mary E.; Zhang, Yingze; Fingerlin, Tasha E.; Molina-Molina, María; Neighbors, Margaret; Sheng, X Rebecca; Oldham, Justin M.; Maher, Toby M.; Molyneaux, Philip L.; Flores, Carlos; Noth, Imre; Schwartz, David A.; Yaspan, Brian L; Jenkins, R. Gisli; Wain, Louise V; Hollox, Edward J.

doi:10.1183/23120541.00553-2023

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Association study of human leukocyte antigen variants and idiopathic pulmonary fibrosis

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Author(s): Beatriz Guillen-Guio ¹ ^, ² ^, ³⁷ , Megan L. Paynton ¹ ^, ³⁷ , Richard J. Allen ¹ ^, ² , Daniel P.W. Chin ¹ ^, ² , Lauren J. Donoghue ³ , Amy Stockwell ³ , Olivia C. Leavy ¹ ^, ² , Tamara Hernandez-Beeftink ¹ ^, ² , Carl Reynolds ⁴ , Paul Cullinan ⁴ , Fernando Martinez ⁵ , Helen L. Booth ⁶ , William A. Fahy ⁷ , Ian P. Hall ⁸ ^, ⁹ , Simon P. Hart ¹⁰ , Mike R. Hill ¹¹ , Nik Hirani ¹² , Richard B. Hubbard ⁸ ^, ⁹ , Robin J. McAnulty ¹³ , Ann B. Millar ¹⁴ , Vidya Navaratnam ¹⁵ ^, ¹⁶ ^, ¹⁷ , Eunice Oballa ⁷ , Helen Parfrey ¹⁸ , Gauri Saini ⁸ , Ian Sayers ¹⁹ , Martin D. Tobin ¹ ^, ² , Moira K.B. Whyte ¹² , Ayodeji Adegunsoye ²⁰ , Naftali Kaminski ²¹ , Shwu-Fan Ma ²² , Mary E. Strek ²⁰ , Yingze Zhang ²³ , Tasha E. Fingerlin ²⁴ , Maria Molina-Molina ²⁵ ^, ²⁶ ^, ²⁷ , Margaret Neighbors ³ , X. Rebecca Sheng ³ , Justin M. Oldham ²⁸ , Toby M. Maher ²⁹ ^, ³⁰ , Philip L. Molyneaux ²⁹ ^, ³¹ , Carlos Flores ²⁷ ^, ³² ^, ³³ ^, ³⁴ , Imre Noth ²² , David A. Schwartz ³⁵ , Brian L. Yaspan ³ , R. Gisli Jenkins ²⁹ , Louise V. Wain ¹ ^, ² ^, ³⁸ , Edward J. Hollox ³⁶ ^, ³⁸ ^,

Publication date (Electronic): 19 February 2024

Journal: ERJ Open Research

Publisher: European Respiratory Society

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Abstract

Introduction

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial pneumonia marked by progressive lung fibrosis and a poor prognosis. Recent studies have highlighted the potential role of infection in the pathogenesis of IPF, and a prior association of the HLA-DQB1 gene with idiopathic fibrotic interstitial pneumonia (including IPF) has been reported. Owing to the important role that the human leukocyte antigen (HLA) region plays in the immune response, here we evaluated if HLA genetic variation was associated specifically with IPF risk.

Methods

We performed a meta-analysis of associations of the HLA region with IPF risk in individuals of European ancestry from seven independent case–control studies of IPF (comprising 5159 cases and 27 459 controls, including a prior study of fibrotic interstitial pneumonia). Single nucleotide polymorphisms, classical HLA alleles and amino acids were analysed and signals meeting a region-wide association threshold of p<4.5×10 ⁻⁴ and a posterior probability of replication >90% were considered significant. We sought to replicate the previously reported HLA-DQB1 association in the subset of studies independent of the original report.

Results

The meta-analysis of all seven studies identified four significant independent single nucleotide polymorphisms associated with IPF risk. However, none met the posterior probability for replication criterion. The HLA-DQB1 association was not replicated in the independent IPF studies.

Conclusion

Variation in the HLA region was not consistently associated with risk in studies of IPF. However, this does not preclude the possibility that other genomic regions linked to the immune response may be involved in the aetiology of IPF.

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Variation in the HLA region is not consistently associated with risk in studies of IPF. However, this does not preclude the possibility that other genomic regions linked to the immune response may be involved in the aetiology of IPF. https://bit.ly/477T8XN

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Christopher Chang, Carson Chow, Laurent Tellier … (2015)

PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

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Fast model-based estimation of ancestry in unrelated individuals.

David H. Alexander, John Novembre, Kenneth Lange (2009)

Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.

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METAL: fast and efficient meta-analysis of genomewide association scans

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Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu

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Author and article information

Journal

Journal ID (nlm-ta): ERJ Open Res

Journal ID (iso-abbrev): ERJ Open Res

Journal ID (publisher-id): ERJOR

Journal ID (hwp): erjor

Title: ERJ Open Research

Publisher: European Respiratory Society

ISSN (Electronic): 2312-0541

Publication date Collection: January 2024

Publication date (Electronic): 19 February 2024

Volume: 10

Issue: 1

Electronic Location Identifier: 00553-2023

Affiliations

[1 ]Department of Population Health Sciences, University of Leicester, Leicester, UK

[2 ]NIHR Leicester Biomedical Research Centre, Leicester, UK

[3 ]Genentech, San Francisco, CA, USA

[4 ]National Heart & Lung Institute, Imperial College London, London, UK

[5 ]Department of Medicine, Weill Cornell Medicine, New York, NY, USA

[6 ]University College Hospital, University College London, London, UK

[7 ]GlaxoSmithKline, London, UK

[8 ]School of Medicine, University of Nottingham, Nottingham, UK

[9 ]National Institute for Health Research, Nottingham Biomedical Research Centre, Nottingham, UK

[10 ]Hull York Medical School, University of Hull, Hull, UK

[11 ]MRC Population Health Unit, University of Oxford, Oxford, UK

[12 ]Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK

[13 ]Division of Medicine, University College London, London, UK

[14 ]Bristol Medical School, University of Bristol, Bristol, UK

[15 ]Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK

[16 ]National Institute for Health Research, Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust, Nottingham, UK

[17 ]Queensland Lung Transplant Service, The Prince Charles Hospital, Brisbane, QLD, Australia

[18 ]Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK

[19 ]Centre for Respiratory Research, NIHR Nottingham Biomedical Research Centre, School of Medicine, Biodiscovery Institute, University of Nottingham, Nottingham, UK

[20 ]Department of Medicine, University of Chicago, Chicago, IL, USA

[21 ]Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA

[22 ]Department of Medicine, University of Virginia, Charlottesville, VA, USA

[23 ]Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA

[24 ]Department of Immunology and Genomic Medicine, National Jewish Health, Denver, CO, USA

[25 ]Servei de Pneumologia, Laboratori de Pneumologia Experimental, Instituto de Investigación Biomédica de Bellvitge (IDIBELL), Barcelona, Spain

[26 ]Campus de Bellvitge, Universitat de Barcelona, Barcelona, Spain

[27 ]Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain

[28 ]Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, USA

[29 ]National Heart and Lung Institute, Imperial College London, London, UK

[30 ]Division of Pulmonary and Critical Care Medicine, University of Southern California, Los Angeles, USA

[31 ]Royal Brompton and Harefield Hospitals, Guy's and St Thomas’ NHS Foundation Trust, London, UK

[32 ]Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain

[33 ]Genomics Division, Instituto Tecnologico y de Energias Renovables, Santa Cruz de Tenerife, Spain

[34 ]Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain

[35 ]Department of Medicine, University of Colorado, Anscuhtz, CO, USA

[36 ]Department of Genetics and Genome Biology, University of Leicester, Leicester, UK

[37 ]Joint first authors

[38 ]Joint senior authors

Author notes

Corresponding author: Edward J. Hollox ( ejh33@ 123456leicester.ac.uk )

Author information

Daniel P.W. Chin https://orcid.org/0000-0002-7349-895X

Carl Reynolds https://orcid.org/0000-0002-7415-258X

Simon P. Hart https://orcid.org/0000-0003-4606-9559

Helen Parfrey https://orcid.org/0000-0002-4515-5634

Naftali Kaminski https://orcid.org/0000-0001-5917-4601

Yingze Zhang https://orcid.org/0000-0001-6947-2901

Philip L. Molyneaux https://orcid.org/0000-0003-1301-8800

Brian L. Yaspan https://orcid.org/0000-0002-3787-2510

Edward J. Hollox https://orcid.org/0000-0002-0115-2298

Article

Publisher ID: 00553-2023

DOI: 10.1183/23120541.00553-2023

PMC ID: 10875457

PubMed ID: 38375425

SO-VID: 4c442d5a-8bba-43bc-9b9b-2ff0674786c8

License:

This version is distributed under the terms of the Creative Commons Attribution Licence 4.0.

History

Date received : 01 August 2023

Date accepted : 05 November 2023

Funding

Funded by: Wellcome Trust, doi 10.13039/100010269;

Award ID: 221680/Z/20/Z

Funded by: GSK/Asthma+Lung UK

Award ID: C17-1

Funded by: Spanish Ministry of Science and Innovation

Award ID: RTC-2017-6471-1

Funded by: National Heart, Lung, and Blood Institute, doi 10.13039/100000050;

Award ID: R56HL158935 and K23HL138190

Funded by: Instituto de Salud Carlos III

Award ID: PI20/00876

Funded by: Medical Research Council, doi 10.13039/501100000265;

Award ID: MR/V00235X/1

Association study of human leukocyte antigen variants and idiopathic pulmonary fibrosis

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Abstract

Introduction

Methods

Results

Conclusion

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Second-generation PLINK: rising to the challenge of larger and richer datasets

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METAL: fast and efficient meta-analysis of genomewide association scans

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