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[Preprint]. 2024 Oct 11:2024.10.10.24315041.
doi: 10.1101/2024.10.10.24315041.

Genome-wide studies define new genetic mechanisms of IgA vasculitis

Lili Liu  1 Li Zhu  2 Sara Monteiro-Martins  3 Aaron Griffin  4 Lukas J Vlahos  4 Masashi Fujita  5 Cecilia Berrouet  1 Francesca Zanoni  1 Maddalena Marasa  1 Jun Y Zhang  1 Xu-Jie Zhou  2 Yasar Caliskan  6 Oleh Akchurin  7 Samhar Al-Akash  8 Augustina Jankauskiene  9 Monica Bodria  10 Aftab Chishti  11 Ciro Esposito  12 Vittoria Esposito  12 Donna Claes  13 Vladimir Tesar  14 Thomas K Davis  15 Dmitry Samsonov  16 Dorota Kaminska  17 Tomasz Hryszko  18 Gianluigi Zaza  19 Joseph T Flynn  20 Franca Iorember  21 Francesca Lugani  22 Dana Rizk  23 Bruce A Julian  23 Guillermo Hidalgo  24 Mahmoud Kallash  25 Luigi Biancone  26 Antonio Amoroso  26 Luisa Bono  27 Laila-Yasmin Mani  28 Bruno Vogt  28 Fangming Lin  29 Raji Sreedharan  30 Patricia Weng  31 Daniel Ranch  32 Nianzhou Xiao  33 Alejandro Quiroga  34 Raed Bou Matar  35 Michelle N Rheault  36 Scott Wenderfer  37 Dave Selewski  38 Sigrid Lundberg  39 Cynthia Silva  40 Sherene Mason  40 John D Mahan  41 Tetyana L Vasylyeva  42 Krzysztof Mucha  43 Bartosz Foroncewicz  43 Leszek Pączek  44 Michał Florczak  43 Małgorzata Olszewska  45 Agnieszka Gradzińska  46 Maria Szczepańska  47 Edyta Machura  47 Andrzej Badeński  47 Helena Krakowczyk  47 Przemysław Sikora  48 Norbert Kwella  49 Monika Miklaszewska  50 Dorota Drożdż  51 Marcin Zaniew  52 Krzysztof Pawlaczyk  53 Katarzyna SiniewiczLuzeńczyk  54 Andrew S Bomback  55 Gerald B Appel  55 Claudia Izzi  56 Francesco Scolari  56 Anna Materna-Kiryluk  57 Malgorzata Mizerska-Wasiak  58 Laureline Berthelot  59 Evangeline Pillebout  60 Renato C Monteiro  60 Jan Novak  23 Todd Jason Green  23 William E Smoyer  41 M Colleen Hastings  61 Robert J Wyatt  62   63 Raoul Nelson  64 Javier Martin  65 Miguel A González-Gay  66   67 Philip L De Jager  5 Anna Köttgen  3   68 Andrea Califano  4   69   70   71   72   73 Ali G Gharavi  1 Hong Zhang  2 Krzysztof Kiryluk  1
Affiliations

Genome-wide studies define new genetic mechanisms of IgA vasculitis

Lili Liu et al. medRxiv. .

Abstract

IgA vasculitis (IgAV) is a pediatric disease with skin and systemic manifestations. Here, we conducted genome, transcriptome, and proteome-wide association studies in 2,170 IgAV cases and 5,928 controls, generated IgAV-specific maps of gene expression and splicing from blood of 255 pediatric cases, and reconstructed myeloid-specific regulatory networks to define disease master regulators modulated by the newly identified disease driver genes. We observed significant association at the HLA-DRB1 (OR=1.55, P=1.1×10-25) and fine-mapped specific amino-acid risk substitutions in DRβ1. We discovered two novel non-HLA loci: FCAR (OR=1.51, P=1.0×10-20) encoding a myeloid IgA receptor FcαR, and INPP5D (OR=1.34, P=2.2×10-9) encoding a known inhibitor of FcαR signaling. The FCAR risk locus co-localized with a cis-eQTL increasing FCAR expression; the risk alleles disrupted a PRDM1 binding motif within a myeloid enhancer of FCAR. Another risk locus was associated with a higher genetically predicted levels of plasma IL6R. The IL6R risk haplotype carried a missense variant contributing to accelerated cleavage of IL6R into a soluble form. Using systems biology approaches, we prioritized IgAV master regulators co-modulated by FCAR, INPP5D and IL6R in myeloid cells. We additionally identified 21 shared loci in a cross-phenotype analysis of IgAV with IgA nephropathy, including novel loci PAID4, WLS, and ANKRD55.

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Figures

Figure 1.
Figure 1.. GWAS meta-analysis for IgAV identified 3 genome-wide significant regions.
(A) Manhattan plot with genome-wide significant loci highlighted in red; the dotted horizontal line indicates a genome-wide significance threshold (P=5×10–8). (B) Regional plots for the INPP5D locus (left), the HLA locus (middle), and the FCAR locus (right). The x-axis shows the physical position in kilobases (kb, hg19 coordinates) and includes known gene annotations; the left y-axis presents -log10 p-values for association statistics and the right y-axis shows the recombination rates; the dotted horizontal line indicates a genome-wide significant threshold of 5×10−8.
Figure 2.
Figure 2.. Effects of the IL6R locus.
(A) Miami plot for PWAS and TWAS analyses. The red lines indicate the Bonferroni adjusted significance thresholds (5.6×10−5 for PWAS and 2.5×10−6 for TWAS). (B) Regional association plots for the IL6R locus. SNPs are plotted by position (hg19, 250kb window) versus −log10(P-values) from GWAS of IgAV (top), pQTL of plasma sIL6R levels (middle) and eQTL of IL6R expression in whole blood (bottom). The purple diamond highlights the most significant SNP for each association. SNPs are color-coded to reflect their LD with this SNP using LocusZoom with HapMap CEU reference. The missense variant IL6R p.Asp358Ala (rs2228145) is indicated in each plot. (C) The p.Asp358Ala substitution in relation to the cleavage site of the membrane bound IL6R. (D) Structure of the ADAM17 catalytic domain/IL-6R complex. The structure of ADAM17 (pale cyan ribbon render) was modeled with bound peptide 355-PVQDSSS-361 (yellow sticks in upper panel) or 355-PVQASSS-361 (pale orange sticks in lower panel) corresponding to a segment of the stalk region of IL-6R including the cleavage site (P355/V356). Residues of ADAM17 within 5 angstroms of D358 or A358 are shown as sticks. Polar interactions between residue 358 and ADAM17 are noted by dashed lines. The change D358A eliminates the interaction with N389 of ADAM17, potentially reducing the affinity of IL-6R for ADAM 17. Zinc and Sodium ions are shown as slate and purple spheres, respectively. Images generated with PyMOL (Molecular Graphics System, Version 2.0, Schrödinger, LCC).
Figure 3.
Figure 3.. Fine mapping of the HLA region in East Asian and European ancestry cohorts.
(A) Regional plot for the East Asian cohort with bi-allelic and multi-allelic association statistics for all imputed variants and classical HLA alleles; the strongest association signal was for HLA-DRB1 (upper panel); the results for HLA-DQA1 and HLA-DQB1 are highlighted for reference; after controlling for HLA-DRB1 classical alleles (black arrow), there was no residual association across the entire 3-Mb region (lower panel). (B) Regional plot for the European cohorts with bi-allelic and multi-allelic association statistics for all imputed variants and classical HLA alleles; after controlling for HLA-DRB1 classical alleles, there were no significant associations in the region (lower panel). The dotted horizontal line indicates the genome-wide significance threshold (α=5×10−8). (C) East Asian analysis of polymorphic amino acid positions within DRβ1 (blue), DQα1 (green) and DQβ1 (orange) using conditional-haplotype tests; the horizontal dash line indicates the genome-wide significance level. The most strongly associated polymorphic site was position 11 in DRβ1 (left panel); after controlling for this position, there was no residual association (right panel). (D) European analysis of polymorphic amino acid positions within DRβ1 (blue), DQα1 (green) and DQβ1 (orange); consistent with East Asian analysis, the DRβ1 amino acid position 11 (left panel) provided the strongest signal; no additional independent positions were found upon conditioning on this position (right panel). (E) Structure of the DRα1-DRβ1 complex. The protective variant (P11, left) and risk variants (V11, middle and L11, right) are shown in surface model viewed into the peptide binding groove. Models are colored in a background of light blue shade, with individual peptide amino acid binding pockets shaded according to the provided color key.
Figure 4.
Figure 4.. Functional annotations of the FCAR locus
(a) The heatmap of FUN-LDA functional scores for the lead SNP (rs77149320, red) and its high LD proxies (r2>0.8, only SNPs and cell types with positive scores are depicted). The 7 SNPs in LD (names in blue) intersected an ABC model-predicted intronic enhancer of FCAR. This regulatory region was further confirmed by an ATAC-seq peak in monocytes before and after LPS treatment. The prioritized SNPs were analyzed against transcription factor binding motifs predicted by JASPAR, and two risk alleles (rs4806602-G and rs73065472-T in perfect LD and two base pairs apart) were found to disrupt the PRDM1 binding motif; (b) Co-localization of the FCAR locus between GWAS for IgAV and blood eQTL for FCAR in IgAV (top two panels), and GWAS for IgAN and blood sQTL for FCAR in IgAV (bottom two panels). The eQTL effect shown in the right upper panel (x-axis: genotypes, y-axis: normalized expression of FCAR). The sQTL effect shown in the right bottom panel (x-axis: genotypes, y-axis: normalized exclusion rate of chr19:54885525–54885805 region based on hg38 annotations, the splice model depicted above).
Figure 5.
Figure 5.. Master regulators of IgA vasculitis modulated by the GWAS candidate genes.
(a) the UMAP plot for the reference single cell RNA-seq data from 163 healthy individuals. The blood cells were clustered and annotated into 15 different immune cell types highlighted in different colors; (b) Deconvolution and differential gene expression analysis based on blood bulk RNA-seq data from 255 IgAV cases and 38 health controls. The upper panel shows the distribution of different cell type fractions across all 293 individuals. The volcano plot (lower panel: x-axis denotes the log2 Fold Change and the y-axis indicates the adjusted P values) with differentially expressed genes in red (Padjusted<0.05 and log2 Fold Change>0.2) after adjusting for cell type fractions; (c) The myeloid regulons reconstructed transcriptome-wide based on the myeloid lineage cells (circled in red above) using ARACNe3 software (a subset of only 3 regulons shown to illustrate this step). (d) Differential activity analysis based on the myeloid regulons and cell fraction-adjusted differential gene expression signature in IgAV cases compared to controls. The x-axis indicates the proportional enrichment score (effect size for gene set enrichment that was normalized by set’s size, parameterization, and gene expression signature). The y-axis shows the normalized score compared to the expectation under the null hypothesis. The red and blue colors indicate the regulators with higher and lower activity, respectively, in IgAV cases compared to healthy controls. (e) Regulators co-modulated by the GWAS candidate genes: 1,072 regulators co-modulated by FCAR and INPP5D with mostly opposed effects on their biological activities (upper panel) and 1,009 regulators co-modulated by FCAR and IL6R with direction consistent effects on their biological activities (lower panel). The targets of the top master regulators co-modulated by all 3 genes were enriched in multiple immune related pathways based on FDR< 0.05 (depicted as enrichment map with a node size proportional to the statistical significance of enrichment and edge thickness proportional to gene set overlaps).

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