Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations

Abstract

Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10^-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

Keywords: interleukin-7, genetic architecture, fine-mapping, selective sweeps, polygenic trait score, phenome-wide association study.

Figure 1.

Trans-ethnic and ancestry-specific meta-analyses of blood-cell traits. (a) List of blood-cell phenotypes and analyses that were carried out in this project. Note that RDW and MPV were not available in EAS. (b) Study design of the project. We used a fixed-effect meta-analysis strategy to analyze genetic associations within each of the five populations available, and a mega-regression approach that considers allele frequency heterogeneity for the trans-ethnic association tests. N_max, maximum sample size in each meta-analysis; N_assoc, number of trait-variant associations. A locus is defined as novel when the 500-kb region surrounding its sentinel variant does not physically overlap with previously identified blood-cell trait-associated variants (for any trait) in the corresponding population. (c) Most blood-cell trait-associated loci physically overlap between populations. For this analysis, a locus associated with several blood-cell traits was counted only once. Despite different sample sizes between populations, we note that few loci are found in a single population, suggesting shared genetic architecture. EUR, European-ancestry; EAS, East Asian; AFR, African-ancestry; HA, Hispanic American; SAS, South Asian. See also Figure S1 and Tables S1A–D, S2 and S3A–F.

Figure 2.

Fine-mapping of genome-wide significant loci associated with hematological traits. (a) We restricted fine-mapping to loci with evidence for a single association signal in European-ancestry (EUR) populations. There are no such loci in Hispanic Americans. The 95% credible sets in the trans-ethnic meta-analyses are smaller than in the EUR or East-Asian-ancestry (EAS) meta-analyses. (b) Trans-ethnic fine-mapping of a platelet locus. In EUR individuals, the 95% credible set include seven variants with posterior inclusion probability (PIP) >0.04 and strong pairwise linkage disequilibrium (LD) with the sentinel variant rs10758481 (r²>0.93 in British in England and Scotland (GBR) individuals from 1000 Genomes Project, middle panel). LD is similarly strong in African-, Hispanic/South American-, and South-Asian-ancestry populations from the 1000 Genomes Project. However, LD is weaker in East Asians (r²=0.68 in Japanese individuals (JPT) from the 1000 Genomes Project, bottom panel). In the trans-ethnic meta-analysis, rs10758481 has a PIP>0.99 (top panel). In EUR and EAS, LD is color-coded based on pairwise r² with rs10758481. The dotted line indicates the genome-wide significance threshold (P<5×10⁻⁹). ( c) Proportion of 95% credible sets in each population with a defined number of variants. For instance, in the EUR and trans meta-analysis results, we identified 403 and 433 95% credible sets that contain a single variant, respectively. (d) Prioritization of causal variants using fine-mapping PIP. In each population, we provide the proportion of variants with a PIP within a specified range. For instance, in EUR and trans, we found 314 and 327 variants with a PIP ≥99%, respectively. See also Figure S2.

Figure 3.

Functional annotation of possible causal variants associated with blood-cell traits. (a) Annotation of variants in trans, EUR and EAS shows a similar pattern, with a larger proportion of likely functional variants (e.g. missense, intergenic and intronic variants within ATAC-seq peaks) among variants with higher posterior inclusion probability (PIP). (b) g-chromVAR results for trans variants within 95% credible sets for 15 traits. The Bonferroni-adjusted significance level (corrected for 15 traits and 18 cell types) is indicated by the dotted line. Mono, monocyte; HSC, hematopoietic stem cell; Ery, erythroid; Mega, megakaryocyte; CD4, CD4+ T lymphocyte; CD8, CD8+ T lymphocyte; B, B lymphocyte; NK, natural killer cell; mDC, Myeloid dendritic cell; pDC, Plasmacytoid dendritic cell; MPP, multipotent progenitor; LMPP, lymphoid-primed multipotent progenitor; CMP, common myeloid progenitor; CLP, common lymphoid progenitor; GMP, granulocyte–macrophage progenitor; MEP, megakaryocyte–erythroid progenitor. (c) rs115906455 is a novel variant associated with mean corpuscular volume in the trans-ethnic meta-analysis (P=4.2×10⁻¹², PIP=0.57). It maps to an intron of ELL2 and overlaps with ATAC-seq peaks found in CMP, MEP, erythroblasts but not megakaryocytes. (d) rs941616 is a novel variant associated with eosinophil counts in the trans-ethnic meta-analysis (P=2.4×10⁻⁹, PIP=0.2). It is a strong eQTL for PTGDR located 112-kb downstream and overlaps with ATAC-seq peaks found in CMP, CD8+ lymphocytes and NK cells. See also Figures S3–4 and Table S3H–I.

Figure 4.

Phenotypic variance and hematological disease prediction using polygenic trait scores (PTS) in independent participants from the BioMe Biobank. (a) For each blood-cell trait, PTS_trans were calculated using genome-wide significant variants identified in the trans-ethnic meta-analyses. Trait-increasing alleles were weighted using effect sizes derived from fixed-effect trans-ethnic meta-analyses. (b) Receiver operating characteristic (ROC) curve and area under the curve (AUC and 95% confidence interval) for neutropenia (defined as <1500 NEU/mL) in BioMe participants of African-ancestry without (black) or with (red) the PTS_trans for neutrophil count in the predictive model. Age, sex, and the first 10 principal components were used in the basic prediction model. (c) As for b, but for thrombocytopenia (defined as <150×10⁹ PLT/L) and the PTS_trans for platelet count in Hispanic participants from BioMe. See also Figure S5 and Table S4B–C.

Figure 5.

A South-Asian-ancestry IL7 missense variant associates with increased lymphocyte count in humans and IL7 secretion in vitro. ( a) Lymphocyte count association results at the IL7 locus in South Asians (SAS), European-ancestry participants (EUR) and East Asians (EAS). In SAS, there are seven genome-wide significant variants near IL7, but only rs201412253 is coding. Linkage disequilibrium (LD) r² is from 1000 Genomes Project SAS populations. In EUR, the sentinel variant is located downstream of IL7; rs201412253 is rare (minor allele frequency=4×10⁻⁴) and not significant (P=0.073). In EAS, the locus is not associated with lymphocyte count. rs201412253 is monomorphic in 1000 Genomes Project EUR and EAS so we could not calculate pairwise LD. (b) Association between genotypes at rs201412253 and normalized IL7 expression levels in lymphoid cell lines from 75 Gujarati Indians from HapMap3. The T-allele frequency is 2.7% and the association is not significant (P=0.62). (c) The 18Ile allele at IL7-rs201412253 increases IL7 secretion in a heterologous cellular system. Our ELISA assay did not detect secreted IL7 in clones generated with an empty vector. We tested eight independent clones for each IL7 alleles. Each experiment was done in duplicate, and we performed the experiments three times. The black dots and vertical lines indicate means and standard deviations. We assess statistical significance by linear regression correcting for experimental batch effects. See also Table S5A–C.

Figure 6.

Comparisons of effect sizes for variants with posterior inclusion probabilities (PIP) >0.5. We retained only variants with an analyzed sample size ≥70,000 in East Asians (EAS) and ≥100,000 in European-ancestry participants (EUR). (a) We retrieved minor allele frequencies (MAF), effect sizes (Beta), P-values (P) and PIP for all variants with PIP >0.5 in EUR. By definition, all these variants are significant in EUR (P<5×10⁻⁹). For these variants, we then retrieved the corresponding results in EAS. Effect sizes (standard errors (SE)) in EUR and EAS are plotted on the x- and y-axis, respectively. (b) as in a, but for variants with PIP >0.5 in EAS. In a and b, when we provide detailed information on a specific variant, the first number always corresponds to EUR and the second to EAS (e.g. for rs77046277, Beta_EUR=0.712 and Beta_EAS=0.348). See also Figure S4 and Table S6C.

Figure 7.

Selective sweep and association with platelet count at the IL6 locus in East Asians. The grey rectangle highlights a genomic region upstream of IL6 that is strongly associated with platelet (PLT) count. This association signal is driven by results from East Asians (EAS), and is absent from other populations, including European- (EUR) and African-ancestry (AFR) individuals (green). The region overlaps several selective sweeps detected in EAS from the 1000 Genomes Project (Chinese Dai in Xishuangbanna (CDX), Southern Han Chinese (CHS), Japanese in Tokyo (JPT)). In orange, we provide standardized population branch site (stdPBS) metrics in EUR and EAS, indicative of allele frequency differentiation at this locus between these two populations. Coordinates are chr7:22–23.5Mb (hg19). See also Figure S7 and Table S7A–C.