Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

Kristin Tsuo; Wei Zhou; Ying Wang; Masahiro Kanai; Shinichi Namba; Rahul Gupta; Lerato Majara; Lethukuthula L Nkambule; Takayuki Morisaki; Yukinori Okada; Benjamin M Neale; Global Biobank Meta-analysis Initiative; Mark J Daly; Alicia R Martin

doi:10.1016/j.xgen.2022.100212

Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

Cell Genom. 2022 Nov 8;2(12):100212. doi: 10.1016/j.xgen.2022.100212. eCollection 2022 Dec 14.

Authors

Kristin Tsuo^{1

2

3}, Wei Zhou^{1

2

3}, Ying Wang^{1

2

3}, Masahiro Kanai^{1

2

3

4

5}, Shinichi Namba⁵, Rahul Gupta^{1

2

3

6}, Lerato Majara⁷, Lethukuthula L Nkambule^{1

2

3}, Takayuki Morisaki⁸, Yukinori Okada^{5

9

10

11

12}, Benjamin M Neale^{1

2

3}; Global Biobank Meta-analysis Initiative; Mark J Daly^{1

2

3

13}, Alicia R Martin^{1

2

3}

Affiliations

¹ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
² Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁴ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
⁵ Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
⁶ Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.
⁷ Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
⁸ Division of Molecular Pathology, The Institute of Medical Science, The University of Tokyo, Minatu-ku, Tokyo, Japan.
⁹ Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
¹⁰ Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan.
¹¹ Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan.
¹² Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita 565-0871, Japan.
¹³ Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.

Abstract

Asthma is a complex disease that varies widely in prevalence across populations. The extent to which genetic variation contributes to these disparities is unclear, as the genetics underlying asthma have been investigated primarily in populations of European descent. As part of the Global Biobank Meta-analysis Initiative, we conducted a large-scale genome-wide association study of asthma (153,763 cases and 1,647,022 controls) via meta-analysis across 22 biobanks spanning multiple ancestries. We discovered 179 asthma-associated loci, 49 of which were not previously reported. Despite the wide range in asthma prevalence among biobanks, we found largely consistent genetic effects across biobanks and ancestries. The meta-analysis also improved polygenic risk prediction in non-European populations compared with previous studies. Additionally, we found considerable genetic overlap between age-of-onset subtypes and between asthma and comorbid diseases. Our work underscores the multi-factorial nature of asthma development and offers insight into its shared genetic architecture.

Keywords: GWAS; asthma; cross-trait; heterogeneity; meta-analysis; multi-ancestry; polygenic risk prediction; subtypes.

Abstract

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