The trans-ancestral genomic architecture of glycemic traits

Abstract

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10^-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Extended Data Fig. 1. Flow diagram of this study

The figure shows the data, key methods and main analyses included in this effort.

Extended Data Fig. 2. Locus diagram

Trans-ancestry locus A contains a trans-ancestry lead variant for one glycemic trait represented by the blue diamond, and another single-ancestry index variant for another glycemic trait represented by the orange triangle. Single-ancestry locus B contains a single-ancestry lead variant represented by the purple square. The orange, blue and purple bars represent a +/- 500Kb window around the orange, blue, and purple variants, respectively. The black bars indicate the full locus window where trans-ancestry locus A contains trans-ancestry lead and single-ancestry index variants for two traits and single-ancestry locus B has a single-ancestry lead variant for a single trait.

Extended Data Fig. 3. Venn diagram

Overlap of TA loci between traits.

Extended Data Fig. 4. Allele frequency versus effect size

Allele frequency versus effect size for all signals detected through the trans-ancestry meta-analyses, for each of the four traits. Frequency and effect size are from the European meta-analyses. The power curves were computed based on the European sample size for each trait, and the mean (m) and standard deviation (sd) computed on the FENLAND study: FG, m=4.83 mmol/l, sd=0.68; FI, m=3.69 mmol/l, sd=0.60; 2hGlu, m=5.30 mmol/l, sd=1.74; HbA1c, m=5.55%, sd=0.48.

Extended Data Fig. 5. EAF correlation and heterogeneity test

Pearson correlation of EAF on the lower tri-angle and p-value of one-side heterogeneity test without multiple testing corrections on the upper tri-angle of the trans-ancestry lead variants associated with each trait between ancestries. Correlations > 0.7 are in bold.

Extended Data Fig. 6. Forest plot of T2D GRS from HbA1c variants

The p-value on the right side is from the two-side test without multiple testing corrections. Vertical points of each diamond represent the point estimate of the odds ratio. The horizontal points of each diamond represent the 95% confidence interval of the odds ratio. Figure shows the association results between HbA1c-associated variants built into a GRS for T2D by taking each HbA1c-associated variant and using a weight that corresponds to its T2D effect size (logOR) based on analysis by the DIAGRAM consortium. The overall GRS is subsequently partitioned according to the HbA1c signal classification. The overall and partitioned GRS were tested for association with T2D based on data from UK biobank.

Extended Data Fig. 7. Enrichment of glycemic trait associated GWAS variants to overlap genomic annotations using GREGOR

Figure shows enrichment for 59 total static and stretch enhancer annotations considered. One-side test significance (red) is determined after Bonferroni correction to account for 59 total annotations tested for each trait; nominal significance (P<0.05) is indicated in yellow.

Extended Data Fig. 8. Enrichment of glycemic trait associated GWAS variants to overlap genomic annotations using fGWAS

Figure shows log2(Fold Enrichment) of GWAS variants to overlap 59 static and stretch enhancer annotations calculated. Significant enrichment (red) is considered if the 95% confidence intervals (shown by the error bars) do not overlap 0.

Extended Data Fig. 9. Enrichment of glycemic trait associated GWAS variants to overlap genomic annotations using GARFIELD

Figure shows the beta or effect size (log odds ratio) for GWAS variants to overlap 59 static and stretch enhancer annotations. GWAS variants were included at two significance thresholds, 1e-05 (A) and 1e-08 (B). One-side test significance (red) is determined after Bonferroni correction to account for effective annotations tested for each trait reported by GARFIELD (see supplementary note); nominal significance (P<0.05) is indicated in yellow. The 95% confidence intervals are shown by the error bars.

Figure 1. Summary of all 242 loci identified in this study.

235 trans-ancestry loci are shown in orange (novel) or black (established) along with seven single-ancestry loci (blue) represented by nearest gene. Each locus is mapped to corresponding chromosome (outer segment). Each set of rows shows the results from the trans-ancestry analysis (orange) and each of the ancestries: European (purple), African American (tan), East Asian (grey), South Asian (green), Hispanic (yellow), sub-Saharan African (Ugandan-pink). Loci with a corresponding type 2 diabetes signal are represented by red circles in the middle of the plot.

Figure 2. Trait variance explained by associated loci.

The boxplots show the maximum, first quartile, median, third quartile and minimum of trait variance explained when using a genetic score with single-ancestry lead and index variants (EUR, AA, EAS, HISP and SAS) or a combination of individual trait trans-ancestry lead variants and single-ancestry lead and index variants (TA+EUR, TA+AA, TA+EAS, TA+HISP and TA+SAS). Variance explained for each trait (FG, FI and HbA1c) in each ancestry is shown on different panels and in different colors. Data points represent the variance explained in individual cohorts used in this analysis. R² was estimated in 1 to 11 cohorts with sample sizes ranging from 489 to 9,758 (Supplementary Tables 8-11).

Figure 3. Transferability of PGS across ancestries.

For each trait, the barplots represent trait variance explained when using a European ancestry-derived PGS in European, East Asian and African American test datasets. Variance explained (the height of each bar) for each trait (FG, FI and HbA1c) in each ancestry is shown on different panels and in different colors.

Figure 4. Trans-ancestry fine-mapping.

A) Number of plausible causal variants at each locus-trait association derived from FINEMAP. B) Number of variants within each 99% credible set. Twenty-one locus-trait associations at 19 loci were mapped to a single variant in the 99% credible set. C) Fine-mapping resolution. For each of the 98 locus-trait associations with a predicted single causal variant in both trans-ancestry and single-ancestry analyses, the number of variants included in the 99% credible set in the single-ancestry fine-mapping (x axis; logarithmic scale) is plotted against those in the trans-ancestry fine-mapping (y axis; logarithmic scale). Trans-ancestry and single-ancestry fine-mapping were based on the same set of variants. After removing eight locus-trait associations with one variant in the 99% credible sets in both trans-ancestry and single-ancestry analyses, there were 18 locus-trait associations (in grey) where trans-ancestry fine-mapping did not improve the resolution of fine-mapping results (i.e. number of variants in the 99% credible set did not decrease). Of the 72 locus-trait associations with improved trans-ancestry fine-mapping resolution (blue and red) further analyses in European fine-mapping emulating the total sample size in trans-ancestry fine-mapping demonstrated that 34 locus-trait associations (in red) were improved because of both total sample size and differences across ancestries, while 38 locus-trait associations (in blue) were only improved due to increased sample size in the original trans-ancestry fine-mapping analysis.

Figure 5. Epigenomic landscape of trait-associated variants.

A: Enrichment of GWAS variants to overlap genomic regions including ‘Static Annotations’ which are common or ‘static’ across cell types and ‘Stretch Enhancers’ which are identified in each tissue/cell type. The numbers of signals for each trait are indicated in parentheses. Enrichment was calculated using GREGOR . One-sided test for significance (red) is determined after Bonferroni correction to account for 59 total annotations tested for each trait; nominal significance (P<0.05) is indicated in yellow. B: Enrichment for HbA1c GWAS signals partitioned into “hard” Glycemic and Red Blood Cell cluster (signals from “hard” mature Red Blood Cell and reticulocyte clusters together) to overlap annotations including StrEs in Islets and the blood-derived leukemia cell line K562, respectively (additional partitioned results in Supplementary Table 17). C: Individual FI GWAS signals that drive enrichment in Adipose and Skeletal Muscle StrEs. D, E: Genome browser shots of FI GWAS signals – intronic region of the COL4A2 gene (D) and an inter-genic region ~25kb from LINC01214 gene (E) showing GWAS SNPs (lead and LD r²>0.8 proxies), ATAC-seq signal tracks and chromatin state annotations in different tissues/cell types.

Figure 6. Tissues and cell types significantly enriched for genes within glycemic-associated loci.

Top panel FG-associated loci, middle panel FI-associated loci, bottom panel Hba1c-associated loci. FDR thresholds are shown in red (q<0.05), orange (q<0.2), black (q≥0.2).

Figure 7. Gene-set enrichment analyses.

Results from affinity-propagation clustering of significantly enriched gene-sets (FDR<0.05) identified by DEPICT for A) FG, B) FI, and C) HbA1c. Each node is a meta gene-set which is represented by an exemplar gene-set within the meta gene-set. For example, in B. “chronic myeloid leukemia “ is an exemplar gene-set representing a much broader meta gene-set relating to cancer and represented in the zoomed in section on the right. Similarities between the meta gene-sets are represented by Pearson correlation coefficients (r>0.3). The nodes are colored according to the minimum gene-set enrichment p-value for gene-sets in that meta gene-set.. PPI=protein-protein interaction network.