Distinct and shared genetic architectures of gestational diabetes mellitus and type 2 diabetes

Abstract

Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting more than 16 million pregnancies annually worldwide^1,2. GDM is related to an increased lifetime risk of type 2 diabetes (T2D)^1-3, with over a third of women developing T2D within 15 years of their GDM diagnosis. The diseases are hypothesized to share a genetic predisposition^1-7, but few studies have sought to uncover the genetic underpinnings of GDM. Most studies have evaluated the impact of T2D loci only^8-10, and the three prior genome-wide association studies of GDM^11-13 have identified only five loci, limiting the power to assess to what extent variants or biological pathways are specific to GDM. We conducted the largest genome-wide association study of GDM to date in 12,332 cases and 131,109 parous female controls in the FinnGen study and identified 13 GDM-associated loci, including nine new loci. Genetic features distinct from T2D were identified both at the locus and genomic scale. Our results suggest that the genetics of GDM risk falls into the following two distinct categories: one part conventional T2D polygenic risk and one part predominantly influencing mechanisms disrupted in pregnancy. Loci with GDM-predominant effects map to genes related to islet cells, central glucose homeostasis, steroidogenesis and placental expression.

Fig. 1. Genome-wide association results for GDM.

a, Manhattan plot of GWAS of GDM in 12,332 cases and 131,109 parous female controls of Finnish ancestry with REGENIE 2.2.4. The x axis reflects chromosomal positions, and the y axis reflects −log₁₀(P) values for the two-tailed association test for each variant, presented on a log scale. Red dotted line indicates the significance threshold (P = 5 × 10⁻⁸). Colored SNPs represent the credible set members for the 13 genome-wide significant loci, with blue indicating loci previously associated with GDM and orange indicating new associations. Labels indicate the gene nearest to the fine-mapped lead SNP. b, The genetic correlation (SNP-r_g) between GDM and 53 other diseases, traits or biomarkers was computed using LD score regression. We plot the SNP-r_g with confidence intervals for all traits that were significant after Bonferroni correction for two-sided tests of 53 traits (P < 9.4 × 10⁻⁴). Results for all tested traits are reported in Supplementary Tables 19 and 20. Colors indicate phenotype category.

Fig. 2. Classification of the genetic effects of SNPs in GDM and T2D.

Comparison of log odds ratios in GWAS of GDM (x axis) and T2D in males (y axis) for top-associated SNPs from GDM (13 SNPs) and T2D (15 SNPs). The following two distinct classes of SNP effects were identified by a Bayesian classifier in shared variants analysis: class T (blue) containing SNPs with T2D-predominant genetic effects and class G (red) with GDM-predominant effects (Supplementary Table 27). Gray SNPs were not confidently assigned to either class (posterior probability >95%). Dotted ellipses indicate the 95% probability regions of the fitted bivariate effect size distributions with each class.

Fig. 3. Cell-type specificity analysis of GDM and T2D highlights different cell associations.

Cell-type specificity analysis was performed for GDM (a) and for prior meta-analysis of T2D (b) from ref. using high-quality mouse single-cell RNA-seq datasets with FUMA v1.3.4 (Supplementary Tables 32–35). Unadjusted P values are reported for the two-sided association test between relative gene expression in the given cell type and multimarker analysis of genomic annotation (MAGMA) gene-level associations in the GWAS. Results are shown for cell types that both are significantly associated with at least one GWAS after correction for multiple testing of all cell types in all datasets (a) and have putatively independent association conditional on other cell types in the same RNA-seq dataset (b). Colors indicate the RNA-seq dataset source and significance.