Multi-population genome-wide association study implicates immune and non-immune factors in pediatric steroid-sensitive nephrotic syndrome

Abstract

Pediatric steroid-sensitive nephrotic syndrome (pSSNS) is the most common childhood glomerular disease. Previous genome-wide association studies (GWAS) identified a risk locus in the HLA Class II region and three additional independent risk loci. But the genetic architecture of pSSNS, and its genetically driven pathobiology, is largely unknown. Here, we conduct a multi-population GWAS meta-analysis in 38,463 participants (2440 cases). We then conduct conditional analyses and population specific GWAS. We discover twelve significant associations-eight from the multi-population meta-analysis (four novel), two from the multi-population conditional analysis (one novel), and two additional novel loci from the European meta-analysis. Fine-mapping implicates specific amino acid haplotypes in HLA-DQA1 and HLA-DQB1 driving the HLA Class II risk locus. Non-HLA loci colocalize with eQTLs of monocytes and numerous T-cell subsets in independent datasets. Colocalization with kidney eQTLs is lacking but overlap with kidney cell open chromatin suggests an uncharacterized disease mechanism in kidney cells. A polygenic risk score (PRS) associates with earlier disease onset. Altogether, these discoveries expand our knowledge of pSSNS genetic architecture across populations and provide cell-specific insights into its molecular drivers. Evaluating these associations in additional cohorts will refine our understanding of population specificity, heterogeneity, and clinical and molecular associations.

Fig. 1. Flowchart of study design.

12 datasets across six populations were used for population-specific and multi-population GWAS meta-analyses. The population assignment and number of cases for each dataset are indicated (yellow=European (EUR), green=African (AFR), blue=East Asian (EAS), orange=South Asian (SAS), purple=Maghrebian (MAG), red=Admixed American (AMR)). Post-GWAS analyses include colocalization with both kidney and immune eQTL datasets and overlap of SNPs within credible sets with single-cell kidney and immune open chromatin (ATAC-seq). HLA imputation with HLA-TAPAS was used to identify classical alleles and specific amino acids associated with pSSNS, followed by modeling of the HLA protein and stability predictions. Dataset summary statistics were used to generate polygenic risk scores using PRS-CSx and associations with clinical covariates were tested. pSSNS= pediatric steroid-sensitive nephrotic syndrome, eQTL = expression quantitative trait loci.

Fig. 2. GWAS results.

All loci are labeled by nearest gene with novel associations in red. A Multi-population meta-analysis of 2440 cases vs. 36,023 controls. The P value from test of deviance of full meta-regression model compared to the null model using MR-MEGA. B Multi-population conditional meta-analysis. The P value from multiple linear regression with COJO. C European meta-analysis of 674 cases vs. 6817 controls. Discoveries that included the summary statistics from suggestive SNPs available from Dufek et al. are indicated with + and only novel associations are labeled. The P values are from meta-analysis with METAL. D Multi-population and single-population odds ratios with 95% confidence interval for novel multi-population significant SNPs. The P value for MICA is from the conditional analysis with COJO, Maghrebian, and Admixed American P values are from logistic regression, and the rest are from inverse-variance fixed-effects meta-analysis with METAL. All P values in A–D are unadjusted for multiple testing and all tests are two-sided. Number of cases in each analysis: Admixed American n = 98, African n = 109, East Asian n = 1311, European n = 674, Maghrebian n = 55, South Asian n = 193, Meta-Analysis n = 2440.

Fig. 3. Colocalization of SSNS GWAS and eQTL datasets.

Each eQTL dataset is labeled with colocalized loci (left) and enrichment estimates (right). The source of each eQTL dataset is labeled on vertical gray bars. BP BLUEPRINT, NEP NEPTUNE. Genes with regional colocalization probability (RCP) > 0.2 in at least one tissue/cell are included. pSSNS GWAS loci that colocalized with tissue/cell-type eQTLs are indicated by black dots, with larger dots indicating higher RCP. GTEx tissues without associations are excluded from this figure (see Supp. Fig. 7). Enrichment estimates from fastENLOC are based on genome-wide summary statistics from GWAS and include a shrinkage parameter that results in 0 enrichment for multiple tissues/cell types. Estimates are presented as the logarithm of the odds ratio ± standard error. logOR = 2 ~ OR = 7.5, logOR=3 ~OR = 20.1, logOR = 4 ~ OR = 54.6. eQTL sample sizes: NEPTUNE glomerulus n = 240, tubulointerstitial n = 311, BLUEPRINT n = 200 DICE n = 91.

Fig. 4. HLA-DQA1 amino-acid associations and stability prediction.

A Increased risk and predicted stability change of the two-amino-acid residue haplotypes at HLA-DQA1 positions 47 and 52. Odds ratios and P values (two-sided) are from a joint logistic regression with arginine₄₇-serine₅₂, the most common, set as reference, adjusting for population-specific principal components and continental populations. The reference haplotype confers the strongest protection (i.e., odds ratios indicate increase in risk compared to arginine₄₇-serine₅₂). Decreasing values of the predicted stability change indicate decreasing stability. B Protein structure for the reference haplotype arginine₄₇-serine₅₂ (left, blue) and lysine₄₇-histidine₅₂ (right, red). The residues in green display a potential interacting amino acid with mutated amino acids. The color scheme for interactions (dashed lines) is as follows: cyan for Van der Waals [VDW], red for hydrogen bonds, green for hydrophobic bonds, sky blue for carbonyl bonds, and orange for polar bonds. Amino acids displayed with no visible bonds indicate a prediction of weak VDW bonds.