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Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens

Abstract

We performed a genome-wide association study (GWAS) of IgA nephropathy (IgAN), the most common form of glomerulonephritis, with discovery and follow-up in 20,612 individuals of European and East Asian ancestry. We identified six new genome-wide significant associations, four in ITGAM-ITGAX, VAV3 and CARD9 and two new independent signals at HLA-DQB1 and DEFA. We replicated the nine previously reported signals, including known SNPs in the HLA-DQB1 and DEFA loci. The cumulative burden of risk alleles is strongly associated with age at disease onset. Most loci are either directly associated with risk of inflammatory bowel disease (IBD) or maintenance of the intestinal epithelial barrier and response to mucosal pathogens. The geospatial distribution of risk alleles is highly suggestive of multi-locus adaptation, and genetic risk correlates strongly with variation in local pathogens, particularly helminth diversity, suggesting a possible role for host–intestinal pathogen interactions in shaping the genetic landscape of IgAN.

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Figure 1: Results of the combined meta-analysis across all 20,612 individuals.
Figure 2: Pleiotropic effects of IgAN GWAS loci and their cumulative effect on age at disease onset.
Figure 3: Loci for autoimmunity and inflammatory disorders and risk of IgAN.
Figure 4: IgAN genetic risk is correlated with worldwide pathogen diversity.

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Acknowledgements

We are grateful to all the study participants for their contribution to this work. This study was supported by US National Institutes of Health (NIH) grants R01DK082753 (A.G.G. and J. Novak) and R01DK095510 (A.G.G. and R.P.L.) from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and by the Center for Glomerular Diseases at Columbia University. R.P.L. is an investigator of the Howard Hughes Medical Institute. K.K. is supported by NIH/NIDDK grants K23DK090207 and R03DK099564 and by a Carl W. Gottschalk Research Scholar Grant from the American Society of Nephrology (ASN). S.S.-C. is supported by NIH/NIDDK grant R21DK098531 and by American Heart Association (AHA) grant 13GRNT14680075. G.M.G. is supported by the Joint Italian Ministry of Health and NIH Ricerca Finalizzata and by the Fondazione Malattie Renali nel Bambino. D.C. and the HYPERGENES Consortium are supported by InterOmics (PB05 MIUR-CNR Italian Flagship Project). Additionally, we would like to acknowledge individuals from the following organizations: the IgA Nephropathy Foundation of America for facilitating recruitment of individuals in the United States; the Columbia University Glomerular Center (New York), including J. Radhakrishnan, D. Cohen, C. Kunis, A. Bomback and P. Canetta, for referrals of IgAN cases; the Polish Registry of Congenital Malformations (PRCM; Poznan, Poland), including A. Materna-Kiryluk and A. Latos-Bieleńska (supported by the Polish Ministry of Health), for facilitating the recruitment of the Polish IgAN cohort; J. Nagy at the University of Pécs, Hungary (supported by SROP-4.2.2/B-10/1/2010-0029); the GN-PROGRESS study, including F. Martinez, F. Vrtovsnik and D. Droz, for adjudicating all IgAN cases as well as individual center investigators, including X. Belenfant (Hôpital A Grégoire, Montreuil, France); B. Charpentier and A. Durrbach (Assistance Publique–Hôpitaux de Paris (AP-HP) Hôpital Bicêtre, Kremlin-Bicêtre, France); G. Rostoker (Hôpital C Galien, Quincy/Senart, France); J. Rossert and C. Jacquot (AP-HP, Hôpital Européen G Pompidou, Paris); P. Lang and P. Remy (AP-HP Hôpital H. Mondor, Créteil, France); O. Kourilsky (Hôpital L. Michel, Evry, France); J.-P. Grünfeld and D. Chauveau (AP-HP Hôpital Necker, Paris); G. Deray and H. Izzedine (AP-HP Hôpital Pitié Salpétrière, Paris); C. Legendre and F. Martinez (AP-HP Hôpital Saint-Louis, Paris); and P. Ronco and E. Rondeau (AP-HP Hôpital Tenon, Paris). We would also like to thank L. Sturg from the Dalla Lana School of Public Health at the University of Toronto (Toronto) for contributing the R code for HD-GWAS analysis.

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Contributions

K.K., R.P.L. and A.G.G. conceptualized and designed the study. S.S.-C., F.S., H.J.S., G.A., C.I., B.F.V., N.D., L.D.V., C.B., E.S., F.E.B., A. Amoroso, S. Savoldi, M.R., A. Amore, L. Peruzzi, R.C., M.S., P.R., R. Magistroni, G.M.G., G. Caridi, M.B., F.L., L.A., M.D., M. Maiorana, A.M., G.F., E.B., G.B., C.P., R. Mignani, C.M., D.D.L., D.S., A.P., R.P., S.F., S.C., M. Galliani, M. Gigante, L.G., P.Z., G.G.B., M.G., D.M., V.T., F.E., T.R., J. Floege, T.K., J. Nagy, K.M., L. Pączek, M.Z., M.M.-W., M.R.-B., K.P., D.G., J.B., L.T., F.B., G. Canaud, A.B., M. Metzger, U.P., H.S., S.G., I.N., Y.C., J.X., P.H., N.C., H.Z., R.J.W., J. Novak, B.A.J., J. Feehally, B.S. and D.C. recruited study participants, contributed DNA samples and performed the clinical characterization of subjects. D.G., J.B., J. Feehally, A.B., B.S. and D.C. contributed genotype data. Y.L., S.P., S. Shapiro, C.F., Y.C., J.X. and P.H. prepared DNA samples. Y.L., S.P., S. Shapiro, C.F., Y.C., J.X. and P.H. assisted in genotyping, sequencing and wet-lab experiments. K.K. and Y.L. managed clinical and genetic data. K.K., M.V., D.F., S.L., M.C. and A.G.G. analyzed the data. K.K., R.P.L. and A.G.G. wrote the manuscript.

Corresponding authors

Correspondence to Richard P Lifton or Ali G Gharavi.

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Supplementary Figure 1 Principal-components analysis (PCA) of the discovery cohorts.

(a) The Italian data set of 1,045 cases and 1,340 controls (2 significant PCs). (b) The Chinese data set of 1,194 cases and 902 controls (2 significant PCs). (c) The French data set of 205 cases and 159 controls (3 significant PCs). (d) The US data set of 303 cases and 1,551 controls (3 significant PCs). In all cohorts, cases and controls were distributed evenly along the main axes of significant principal components.

Supplementary Figure 2 Principal-components analysis of the discovery cohorts in relationship to the HapMap reference populations.

The Chinese discovery cohort (n = 2,096) forms a tight cluster with the HapMap East Asian (CHB + JPT) populations. The three European discovery cohorts (n = 4,603) cluster tightly with the HapMap European (CEU) population. The North Africans (YRI) form a distinct cluster from the European and East Asian populations. This analysis was performed using a subset of 4,347 independent markers (pairwise r2 < 0.01) that were directly typed across all seven data sets (call rate > 99.9%); only unrelated founders were included from the HapMap trios.

Supplementary Figure 3 Quantile-quantile plots for the imputed GWAS results in the discovery cohorts.

(a) The Italian discovery cohort. (b) The Chinese discovery cohort. (c) The French discovery cohort. (d) The US discovery cohort. Lambda is the genomic inflation factor.

Supplementary Figure 4 Meta-analysis of the discovery cohorts (stage 1, n = 6,699).

(a) Quantile-quantile plot for the discovery meta-analysis before (dark blue) and after (light blue) exclusion of SNPs within the MHC region. The genomic inflation factor (λ) was estimated at 1.047. The shaded area represents the 95% probability bounds for the expected P values under the null hypothesis. (b) P values from the genome scan and their corresponding positive FDRs (q values). The P-value cut-off of 5 × 10–5 corresponds to a q value of 0.13 (FDR = 13%). (c) Manhattan plot for the discovery meta-analysis. The solid red line represents the follow-up threshold (P = 5 × 10–5), and the dotted red line represents the genome-wide significance level (P = 5 × 10–8). Representative SNPs for suggestive signals that reached the follow-up threshold were genotyped in 10 additional independent cohorts (Stage 2, n = 13,913).

Supplementary Figure 5 Forest plots for the six novel genome-wide significant loci.

The effect estimates are indicated as risk allele odds ratios. The size of the black box represents the weight that each study contributes to the pooled effect. The horizontal lines correspond to the widths of the 95% confidence intervals. The black diamond at the bottom represents the pooled effect estimate. The vertical line represents ‘no effect’ (OR = 1.00). Asian cohorts are highlighted in blue, European cohorts are highlighted in green and pooled effects are highlighted in red. Note that the rs11574637[T] allele is fixed in East Asians; thus, the Asian cohorts do not contribute to the pooled effect estimate.

Supplementary Figure 6 HLA and TNFSF13 loci.

(a) Broad view of the association signals in the HLA region: –log P values for individual SNPs (left y axis) versus physical distance in kilobases (x axis). The results for the discovery and combined cohorts are shown as blue and red diamonds, respectively. The light blue line represents the average recombination rates based on the phased HapMap haplotypes (right y axis). The horizontal line corresponds to the genome-wide significance level (P = 5 × 10–8). The names of the individual SNPs analyzed in the replication cohorts are provided. (b) Regional plot of the TNFSF13 locus. When the two SNPs from previous GWAS in Chinese were typed in the replication cohorts, only rs3803800 supported an association with IgAN (P = 9.3 × 10–6), whereas rs4227 did not replicate (P = 0.17).

Supplementary Figure 7 Associations of the HORMAD2 and TNFSF13 loci with serum IgA levels among 1,925 IgAN cases with available measurements.

Mean serum IgA levels (and 95% confidence intervals) by locus genotype: (a) rs2412971 (HORMAD2 locus), (b) rs3803800 (TNFSF13 locus) and (c) both loci combined. The P values refer to the age- and sex-adjusted additive allelic test of association (linear regression). There was no statistically significant interaction between these loci (P = 0.60). The levels of IgA were determined at the time of diagnosis.

Supplementary Figure 8 The geographical genetic risk pattern in IgAN is suggestive of polygenic adaptation.

(a) Median standardized genetic scores for the continental groupings of the HGDP populations on the basis of the 7-SNP (old) and 15-SNP (new) genetic risk models for IgA nephropathy. (b) Null distribution for correlations between the 15-SNP genetic risk score and distance from Africa (10,000 permutations). The vertical red line represents the observed geospatial correlation statistic; shaded in blue is the fraction of the permuted statistics that were more extreme than the observed statistic (corresponding empirical P = 0.026). (c) The null distribution for the geospatial correlations of genetic risk calculated after the exclusion of HLA loci (10,000 permutations). The vertical red line represents the observed statistic (empirical P = 0.013). (d) Correlations between GWAS risk allele effect sizes and frequency differences between East Asian and European controls (Spearman’s correlation coefficient ρ = 0.53, P = 0.041). (e) Correlations between the effect size and risk allele frequency difference between East Asians and HapMap 3 Yorubans (ρ = 0.65, P = 0.0087).

Supplementary Figure 9 Autoimmune and inflammatory SNPs and risk of IgAN.

(a) Quantile-quantile plot for autoimmune SNPs defined by the NHGRI GWAS catalog in the discovery cohorts. The observed association results (red) and the results from the 10,000 phenotype-permuted replicates (gray) are shown. A large fraction of the observed results deviate from the expected null distribution. (b) The statistical significance of the autoimmune hypothesis in IgAN was established via a statistic that summed over the association evidence of all 582 SNPs. The red vertical line represents the observed sum statistic in relationship to the 10,000 permutation-based sum statistics shown as a histogram (gray). There was not a single permutation-based sum statistic more extreme than the observed statistic (empirical P < 0.0001).

Supplementary Figure 10 Sequential network and enrichment analysis of autoimmune and inflammatory loci associated with IgAN at varying significance thresholds.

(a) Sequential protein-protein interaction network analysis of genes defined by genome-wide significant IgAN loci, genome-wide significant and suggestive inflammatory and autoimmune loci at P < 5.9 × 10–3 (FDR < 10%), and genome-wide significant and suggestive inflammatory and autoimmune loci at P < 0.05 (FDR < 25%). The node colors reflect empirical P values for greater connectivity than expected by chance (based on 1,000 within-degree node label permutations). Indirect protein-protein interactions are not depicted. The final PPI network defined by these loci has a greater connectivity compared to chance expectation, with a mean direct connectivity of 2.1 (empirical P = 0.014) and a mean indirect connectivity (number of connections involving a common interacting protein) of 53.8 (empirical P = 0.016). (d) The network summary statistics demonstrate that the inclusion of suggestive loci at varying FDR thresholds improves both direct and indirect network connectivity and enhances enrichment for genes participating in the intestinal immune network for IgA production (the top enriched KEGG pathway for each of the subnetworks). (e) The KEGG pathway overlap matrix for the candidate genes from genome-wide significant and suggestive loci defined by P < 0.05 (FDR < 25%) in the immune subset analysis; rows represent the top-ranked KEGG pathways based on gene-set enrichment analysis and columns represent individual genes intersecting with the tested pathways. The points of intersection are shaded in dark blue.

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Kiryluk, K., Li, Y., Scolari, F. et al. Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens. Nat Genet 46, 1187–1196 (2014). https://doi.org/10.1038/ng.3118

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