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

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.

Graphical abstract

Figure 1

18 biobanks in the GBMI contributing discovery GWASs of asthma Distribution of prevalence of asthma (left) and number of cases of asthma (right) across biobanks in the GBMI. Biobanks span different sampling approaches (indicated by color on left) and ancestries (indicated by color on right). AFR, African; AMR, admixed American; EAS, East Asian; MID, Middle Eastern; EUR, European; CSA, Central and South Asian. See also Figure S1 and Table S1.

Figure 2

Top loci associated with asthma (A) Index variants of 49 asthma-associated loci that are potentially novel. Missense variants and cis-eQTLs fine-mapped with PIP > 0.9 that overlapped with an index or tagging variant (r² > 0.8) are annotated here. Frequency and meta-analysis effect size estimate of risk-increasing allele , with association p value, are shown on the right. (B) Frequency and effect size of risk alleles of all 179 index variants. Previously reported genes with large effect sizes are highlighted. See also Figures S1 and S3; Tables S2 and S4.

Figure 3

Consistency of loci across biobanks and asthma age-of-onset subtypes (A) Regression slopes computed using the Deming regression method, which compared effects of index variants in each biobank GWAS against their effects in the corresponding leave-that-biobank-out meta-analysis. The x axis shows the effective sample size of each biobank, computed as 4/(1/cases + 1/controls). Error bars represent 95% confidence intervals of the regression slope estimates. (B) Effect sizes of the index variants discovered in the all-asthma meta-analysis as estimated in the COA versus AOA meta-analyses compared using the Deming regression method. The intercept was set to be 0; the slope estimated from the regression analysis is reported. Error bars represent 95% confidence intervals of the effect size estimates from the corresponding meta-analysis. See also Figures S4 and S5; Tables S5 and S11.

Figure 4

Loci showing heterogeneity in ancestry-specific effect sizes (A) Index variants with the most significant p_{Cochran’s Q}. Effect sizes of these variants in each ancestry-specific meta-analysis are shown. Error bars represent 95% confidence intervals of effect size estimates. (B) LocusZoom plots showing the association with asthma of chr16:27344041:G:A (purple symbol) and variants within 150 kb upstream and downstream. Color coding of other SNPs indicates LD with this SNP. EUR, EAS, and AFR indicate the population from which LD information was estimated. See also Table S7.

Figure 5

PRS performance across ancestries Each panel represents a target cohort in which PRS constructed using PRS-CSx and PRS-CS were evaluated. The reference dataset was the TAGC meta-analysis. Sample sizes for the target cohorts are: cases = 849 and controls = 5,190 for AFR; cases = 500 and controls = 500 for EAS; cases = 1,164 and controls = 7,577 for EUR; cases = 1,232 and controls = 6,744 for CSA. Error bars represent standard deviation of R² on the liability scale across 100 replicates. See also Figure S10; Tables S9 and S10.