Genome-wide pleiotropy analysis reveals shared architecture between renal traits and gastrointestinal tract diseases

Genome-wide pleiotropy analysis reveals shared architecture between renal traits and gastrointestinal tract diseases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide pleiotropy analysis reveals shared architecture between renal traits and gastrointestinal tract diseases Si Li, Shuang Wu, Minghui Jiang, Zhonghe Shao, Yifang Kong, Yunlong Guan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5883069/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Comorbidities between gastrointestinal tract (GIT) and renal diseases have been widely reported, but the shared genetic architecture of gut and renal traits remains unclear. Objective: To investigate the shared genetic etiology and causal relationships between traits or diseases involved in the gut-renal axis. Methods : We explored the global and local genetic correlations, pleiotropic effects at variants and gene levels, causal associations between pair-wise renal traits and GIT diseases, as well as potential target drugs by using the latest large-scale genome-wide association study (GWAS) summary data of five renal traits (BUN, eGFR, CKD, IgAN, KSD) and four GIT diseases (PUD, GORD, IBD, IBS). Results : Renal traits and GIT diseases were widely genetically correlated globally and locally across eight of 20 trait pairs (BUN-GORD, BUN-IBD, BUN-IBS, CKD-IBD, IgAN-IBD, KSD-PUD, KSD-GORD, KSD-IBS). Pleiotropic analysis identified 222 pleiotropic loci and prioritized 169 pleiotropic genes for 20 trait pairs, including 21 novel loci that were not significant in the original GWASs, 21 colocalized loci, as well as 29 drug-targeting genes. Among the novel loci, rs3129861 in HLA-DRA gene was potentially causal for BUN-GORD (PP4 = 0.814). KIF5B is a causal gene for eGFR-IBD and CKD-IBD trait pairs, colocalized by rs12572072 (PP4 = 0.929) and rs61844306 (PP4 = 0.898), both of which are significant eQTLs of KIF5B expressed in cultured fibroblasts cells. CKD and IBD were also colocalized in PVALEF with PP4 = 0.800 for rs138610699. In addition, rs6873866 was identified as a shared casual variant in ERAP2 by IgAN and IBD with PP4=0.800, and rs6873866-C allele was negatively associated with ERAP2 expression in multiple tissues. Furthermore, tissue and cell-type specific enrichment analysis found that pleiotropic loci were over-expressed in the kidney cortex, immune-related tissues and cell types. Mendelian randomization analysis revealed IgAN was negatively associated with IBD, and nominal significant effects were observed for IgAN on IBS, PUD and GORD on eGFR. Conclusion : These findings suggested the shared genetic architecture between renal traits and GIT diseases, and highlighted the potential of pleiotropic analyses in drug repurposing for comorbidities of diseases in the gut-renal axis. gut-renal axis GWAS pleiotropic loci drug repurposing mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Renal and gastrointestinal diseases are significant challenges in global public health due to their widespread prevalence and complex pathogenesis 1 – 11 . Evidence of comorbidities and associations between renal and gastrointestinal diseases have been accumulating and are likely modulated by multi-organ interactions in the gut-renal crosstalk, also known as the gut-renal axis 12 – 15 . The gut-renal axis refers to bidirectional inter-organ communication between the renal traits and gastrointestinal tract, and it involves a variety of biological mechanisms, including inflammatory immune response, gut microbiota, and metabolic dependence 16 – 18 . Underlying the conceptual framework of the gut-kidney axis, the shared genetic etiology might be involved in the associations between renal and gastrointestinal diseases. To date, genome-wide association studies (GWAS) have clarified that multiple genetic variants are associated with gastrointestinal and renal diseases 6 , 19 – 25 . Previous studies have primarily investigated genetic associations and causal relationships between the gut-renal trait pairs, which focused on certain renal diseases and had limited sample sizes 26 – 28 . Increasing sample size enhances the ability to detect genetic associations but is costly and resource-intensive. Instead of focusing on individual diseases separately, a promising strategy called genome-wide cross-trait analysis jointly analyzes pairwise traits or multiple diseases together can uncover shared genetic loci and provide novel insights into the common genetic risks of these diseases 29 – 31 . Furthermore, identifying pleiotropic loci can provide targets for interventions aimed at potentially preventing or treating these diseases 25 , 32 – 34 . Despite these advancements, there has been limited utilization of multi-omics data to fully uncover shared genetic regulatory mechanisms in the gut-renal axis. Thus, further exploration of the genetic mechanisms and drug targets of the gut-renal axis is warranted. Here, we systematically investigated the shared genetic basis of five renal traits (BUN, eGFR, CKD, IgAN, KSD) and four gastrointestinal disorders (PUD, GORD, IBD, IBS) using large-scale GWAS summary data, and sequentially investigated them at the genome-wide, single-nucleotide polymorphisms (SNPs), and gene levels, as well as biological pathways, leveraging advanced statistical genetic algorithm for pleiotropic associations to unravel potential common genetic etiologies. First, we performed genome-wide and local genetic correlation analyses. Subsequently, we utilized pleiotropic analysis under the composite original hypothesis (PLACO) to identify pleiotropic genetic variants or loci associated with paired traits of the intestinal-kidney axis at the SNP level. Leveraging the findings from the pleiotropic analysis, we further conducted pairwise colocalization analyses to identify colocalized loci and gene-level analyses to identify candidate pleiotropic genes. Drug repurposing analysis was performed on the pleiotropic loci to identify potential drug targets in the gut-renal axis. Notably, multiple gene-level enrichment analyses were utilized by integrating transcriptomic and single-cell data to characterize the tissue-specific and cell-type specificity of the pleiotropic genes. Finally, bidirectional mendelian randomization (MR) analysis was performed to assess pairwise causal associations. Methods GWAS summary statistics We acquired recent GWAS summary statistics on renal traits (BUN, eGFR, CKD) from CKDGen Consortium, IgA nephropathy, and KSD meta-analyses. We analyzed 816,412 Europeans (UKB, FinnGen beta 10) for PUD variants. GORD GWAS involved 456,327 UKB Europeans, while IBD data included 462,014 individuals. IBS meta-analysis covered 53,400 cases. All data were aligned to GRCh37 assembly. Our study employed diverse methods to explore shared pathways between renal and GIT diseases (Figure 1). We acquired summary statistics from the largest and recent GWAS summary data of renal function traits (BUN and eGFR, N = 567,460) and CKD (41,395 cases and 439,303 controls) from CKD Genetics (CKDGen) Consortium 19 . GWAS summary statistics of IgA nephropathy (5,556 cases and 21,178 controls) and KSD (17,969 cases and 702,230 controls) were also obtained from previous GWAS meta-analyses 6 , 35 . A total of 816,412 Europeans from the UK biobank (UKB) and the FinnGen (beta 10) 36 were analyzed to identify the variants correlated with PUD 37 . For GORD, GWAS summary statistics of 456,327 Europeans from the UKB were downloaded 21 . GWAS for IBD were obtained from a larger meta-analysis with 462,014 individuals. While for IBS, meta-analysis results of 53,400 cases and 433,201 controls were based on the UKB and the Bellygenes initiative 23 . All summary statistics were aligned to human assembly GRCh37. Details for each GWAS summary statistics can be found in Table S1 . These data were utilized together with a comprehensive set of methods to elucidate etiological pathways underlying the comorbidity between renal and GIT diseases ( Figure 1 ). Genome-wide and local genetic correlation To delineate genetically correlated GIT diseases and renal trait pairs, we employed cross-trait LD scores regression (LDSC) to assess genome-wide genetic correlation 32 . The intercept from LDSC may also suggest potential sample overlap between two GWASs. Given the fact that the genome-wide genetic covariance may be compromised by balanced local genetic covariance, where positive genetic covariance partially counteracts negative genetic covariance 38 , we proceeded to measure the local genetic correlation by ρ-HESS 38 . To estimate local genetic correlation, the whole genome was divided into around 1,703 independent linkage disequilibrium blocks 39 , thus local genetic correlations were identified with Bonferroni-corrected significant threshold set at P -value was below 2.94×10 -6 (0.05/1,703), and such 1,703 local genetic correlations were then integrated into the genome-wide genetic correlation analysis. Pleiotropic analysis For each pairwise trait, we utilized PLACO (pleiotropic analysis under composite null hypothesis) 29 , 30 to identify the pleiotropic effect of each variant, where the joint null hypothesis including three composite null sub-hypotheses 30 , 40 : , and , equivalent to testing whether . Consequently, the PLACO test statistic is computed as . As per PLACO guidelines 29 , 30 , variants with MAF < 0.1 and square of Z scores ≥ 80 were excluded and a potential pleiotropic variant was identified if P PLACO lower than 5×10 -8 . Characterization of genomic loci We employed the PLINK 41 clumping function to identify independent genomic loci, leveraging the LD structure in individuals of European ancestry from the 1000 Genomes Project phase 3 42 . The variants with LD r 2 higher than 0.1 and physical positions within 500 kb of the lead variant were clumped into a locus, represented by the lead variant. Adjacent loci with a distance between LD blocks < 250 kb, were further merged into a single genomic locus using BEDTools 43 . Subsequently, ANNOVAR 44 was used to annotate the consequence and nearest gene of each lead variant. Colocalization analysis To investigate the potential sharing of causal variants between renal traits and GIT, we selected a genomic region spanning a 100 kb window around each lead variant using coloc 45 . This analysis tested five hypotheses regarding pairwise traits within one locus: H 0 , indicating no association with either trait; H 1 or H 2 , suggesting association solely with either trait 1 or trait 2, respectively; H 3 , indicating distinct associations with both traits; and H 4 , suggesting a shared association with both traits. Then the Bayesian posterior probabilities, integrating all feasible configurations 45 , were computed. Pairwise traits were considered colocalized if the posterior probability of H 4 (PP4) exceeded 0.7 46 . Drug repurposing analysis In our endeavor to repurpose drugs, we investigated gene-drug interactions in the DrugBank 47 database to identify genes potentially targeted by drugs. DrugBank, as a comprehensive repository, offers extensive information on drugs, their corresponding gene targets, mechanisms of action, and interaction ns 47 . The latest version 5.1.10 used in our analyses contains data on over 15,000 drugs, with approximately 4,000 of them having received approval. Function annotation and enrichment analyses Tissue specificity was assessed by examining the differentially expressed genes (DEG) identified in the GTEx v8 54 tissue types 48 in FUMA 49 . Two-sided t -tests for expression values were conducted in tissue each against all others. Genes with a P -value ≤ 0.05 after Bonferroni correction and an absolute log-fold change ≥ 0.58 were categorized as DEG sets, for these gene expressions in the given tissue had the largest discrepancy with expression in all other tissues. Additionally, genes that are up-regulated and down-regulated in a specific tissue compared to other tissues were identified by considering the sign of t -statistics. Using gene sets derived from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, hypergeometric tests were conducted to determine whether DEGs were overrepresented in specific biological functions compared to background genes (19,283 protein-coding genes) 49 . Adjusted P -values (≤ 0.05) were used to assess statistical significance, with consideration for Benjamini–Hochberg multiple test corrections per data source. To further explore the gene expression patterns at a finer resolution, gene expression data at the single-cell level were obtained from the GTEx project 48 , potentially revealing cell type-specific expression profiles. Mendelian randomization To explore causal relationships for 20 pairwise traits between renal traits and GIT diseases, bidirectional Mendelian randomization analyses were conducted using the R packages TwoSampleMR 34 , mr.raps 50 and BWMR 51 . Inverse-variance weighted (IVW) method 52 was selected as the primary analysis, with alternative methods such as MR-Egger 53 , weighted median 54 , weighted mode 55 , Bayesian-weighted MR (BWMR) 51 , and robust adjusted profile score (RAPS) 50 used for additional analyses for further validation. The IVW random effects model was chosen to account for potential heterogeneity across instruments. For each pairwise analysis, instrumental variables (IVs) using independent significant IVs ( P < 5×10 -8 with the exposure and LD r 2 < 0.01 within 10 Mb based on the 1000 genome project Europeans 42 ) were kept from each GWAS dataset, while pleiotropic IVs (associated with more than one traits), IVs with incorrect direction (Steiger test one-sided P < 0.05) 56 and MR-PRESSO 57 outliers were excluded. The strength of each IV was assessed using the F statistic with the equation (instruments with F values < 10 are typically considered weak IVs) 49 . Nominal significance was defined as P <0.05, and the significance of multiple test correction was defined as P < 0.0025 (0.05/20). Results Genetic correlations between renal traits and gastrointestinal tract diseases The sample size for each GWAS dataset ranged from 26,734 to 816,412 ( Table S1 ). We estimated nominal significant genome-wide genetic correlations among five trait pairs (BUN-IBS, IgAN-IBD, KSD-PUD, KSD-GORD, KSD-IBS; Table S2 and Figure S2 ) identified by LDSC 32 and ρ-HESS 58 . In addition, another two trait pairs, namely BUN-IBD and CKD-IBD, were identified by LDSC as positive correlations. Besides, BUN was negatively correlated with GORD identified by ρ-HESS. In addition, the directions from the LDSC were highly consistent with those from ρ-HESS. Collectively, eight trait pairs with either nominal significant genome-wide or local genetic correlation were identified, including five pairs passed the multiple testing correction ( P < 0.0025). Of these eight nominal significant trait pairs, five trait pairs were positively correlated (BUN-IBD, CKD-IBD, KSD-PUD, KSD-GORD, KSD-IBS) with genetic correlation from 0.158 to 0.394, and three trait pairs were negatively correlated (BUN-IBS, BUN-GORD, IgAN-IBD) with genetic correlation from −0.838 to −0.053. Notably, the strongest positive genetic correlation was observed between KSD and PUD ( r g = 0.394 by ρ-HESS), whereas the strongest negative genetic correlation was identified in the pairwise IgAN-IBD ( r g = −0.838 by ρ-HESS). Identification of pleiotropic loci and genes A total of 18,442 SNPs (10,783 unique) were identified as significant pleiotropic variants by PLACO in 20 trait pairs of the gut-renal axis, ranging from 51 pleiotropic variants for CKD-IBS to 3,571 pleiotropic variants for KSD-GORD. These pleiotropic variants were further merged into 222 independent genomic loci for pairwise traits, involving 212 unique lead variants ( Table S3 ). ANNOVAR 44 category annotation illustrated 169 unique genes nearest to the lead variants via position mapping. Specially, 103 of 212 lead variants (51.0%) were annotated as intronic variants and 76 of 212 (37.6%) were intergenic variants. Only eight of 212 (3.78%) were exonic variants, including five messenger RNA (mRNA) exonic variants and three noncoding RNA exonic variants ( Table S3 ). We found 21 novel loci in the pleiotropy analyses that were not among risk loci in the original renal traits or GIT disease GWASs ( Table 1 ). The P values of lead variants at these novel loci ranged from 6.40×10 -11 to 4.69×10 -8 in our pleiotropic analyses, while ranging from 8.96×10 -8 to 8.68×10 -6 in the original GWASs. In the BUN-GORD pleiotropy analysis, SLC39A8 at 4q24, HLA-DRA at 6p21.32, TARID at 6q23.2 and PSCA at 8q24.3 were identified as novel loci with P PLACO from to 6.40×10 -11 to 3.95×10 -8 , not associated with either trait in the original GWAS ( P GWAS ranging from 3.02×10 -7 to 2.20×10 -6 ) ( Figure 1A ). In addition, we identified one novel locus, namely KIF5B at 10p11.22, which was associated with eGFR and IBD (rs12572072, P PLACO = 1.91×10 -9 , Figure 1B ), and with CKD and IBD (rs61844306, P PLACO = 4.98×10 -9 , Figure 1C ). Furthermore, two more loci showed significant association with CKD and IBD, including ZFP36L2 at 2p21 with lead SNP rs149290349 and PVALEF at 17q25.3 with lead SNP rs138610699 ( Figure 1C ). In addition, we identified one novel locus specific to IgAN-IBD pleiotropy analysis: ERAP2 at 5q15 (rs6873866, P PLACO = 2.14×10 -9 ) ( Figure 1D ). Table 1. 21 significant pleiotropic loci not associated with original GWASs. Trait pair Lead SNP Consequence Nearest gene Cytoband P trait1 P trait2 P PLACO PP4 Best causal maxSNP.PP4 BUN-PUD rs55987018 intergenic FAM110C 2p25.3 6.09×10 -7 2.06×10 -4 1.95×10 -8 0.569 rs55987018 0.106 BUN-PUD rs13171906 intergenic PIK3R1 5q13.1 1.88×10 -6 2.03×10 -4 2.12×10 -8 0.504 rs13171906 0.216 BUN-PUD rs3129861 intergenic HLA-DRA 6p21.32 3.02×10 -7 2.39×10 -4 6.22×10 -9 0.696 rs3129861 0.266 BUN-GORD rs13135092 intronic SLC39A8 4q24 2.78×10 -4 7.60×10 -7 3.95×10 -8 0.282 rs13135092 0.906 BUN-GORD rs3129861 intergenic HLA-DRA 6p21.32 3.02×10 -7 2.20×10 -6 6.40×10 -11 0.814 rs3129861 0.204 BUN-GORD rs2327426 ncRNA intronic TARID 6q23.2 5.64×10 -6 5.20×10 -5 1.85×10 -8 0.543 rs2327426 0.173 BUN-GORD rs2976395 UTR3 PSCA 8q24.3 1.05×10 -6 1.90×10 -4 1.74×10 -8 0.657 rs2976395 0.067 BUN-IBD rs2662260 intronic RGMB 5q15 9.29×10 -5 2.66×10 -6 1.87×10 -8 0.275 rs2662260 0.561 BUN-IBD rs148345586 intronic SERGEF 11p15.1 3.01×10 -5 2.62×10 -5 2.83×10 -8 0.646 rs148345586 0.910 BUN-IBS rs2854275 ncRNA exonic HLA-DQB1-AS1 6p21.32 1.19×10 -5 1.64×10 -5 9.76×10 -9 0.320 rs2854275 0.864 eGFR-PUD rs10911211 intronic LAMC1 1q25.3 6.03×10 -6 6.31×10 -6 1.97×10 -8 0.648 rs10911211 0.035 eGFR-IBD rs12572072 intronic KIF5B 10p11.22 8.96×10 -8 1.65×10 -5 1.91×10 -9 0.929 rs12572072 0.109 eGFR-IBS rs12602010 intronic NFE2L1 17q21.32 1.43×10 -6 5.29×10 -5 4.69×10 -8 0.548 rs12602010 0.155 CKD-PUD rs2860422 ncRNA intronic FAM13A-AS1 4q22.1 3.15×10 -5 2.05×10 -5 2.55×10 -8 0.679 rs2860422 0.061 CKD-GORD rs4560110 intergenic FAM49A 2p24.2 1.15×10 -6 3.40×10 -4 4.24×10 -8 0.228 rs4632345 0.120 CKD-IBD rs149290349 exonic ZFP36L2 2p21 7.28×10 -4 6.13×10 -8 1.35×10 -8 0.666 rs149290349 0.774 CKD-IBD rs61844306 intergenic KIF5B 10p11.22 6.81×10 -6 1.14×10 -5 4.98×10 -9 0.898 rs61844306 0.093 CKD-IBD rs138610699 intronic PVALEF 17q25.3 2.33×10 -4 3.18×10 -6 4.16×10 -8 0.800 rs138610699 0.646 IgAN-IBD rs6873866 intronic ERAP2 5q15 6.86×10 -6 6.32×10 -6 2.14×10 -9 0.800 rs6873866 0.183 KSD-PUD rs11888760 intergenic SF3B1 2q33.1 1.10×10 -6 7.21×10 -5 2.03×10 -9 0.630 rs11888760 0.231 KSD-IBS rs12723578 intergenic UTP25 1q32.2 2.52×10 -6 3.56×10 -4 3.00×10 -8 0.447 rs12723578 0.478 P trait1 , P trait2 , and P PLACO represent the P values of trait 1 GWAS, trait 2 GWAS and pleiotropic analysis, respectively. PP4 is the posterior probability of H4, and PP4>0.7 is marked as bold. Abbreviations: BUN, Blood urea nitrogen; eGFR, Estimated glomerular filtration rate; CKD, Chronic kidney disease; IgAN, IgA nephropathy; KSD, Kidney stone disease; PUD, Peptic ulcer disease; GORD, Gastro-oesophageal Reflux Disease; IBD, Inflammatory bowel disease; IBS, irritable bowel syndrome, PP3, posterior probability of H3; PP4, posterior probability of H4; maxSNP.PP4, posterior probability of the best causal variant. Colocalization analysis for the causal variant Out of the total loci examined, there were 21 (9.5%) shared causal variants to prove the associations of 12 pairwise traits using colocalization analysis based on the average value of PP4 across all regions, and were mapped to 19 unique genes (PP4 > 0.7; Table S4 ). A total of 19 lead variants were identified as candidate causal SNPs with the largest SNP PP4 in 16 unique pleiotropic loci for 11 pairwise traits. The highest posterior probability of colocalization (PP4 = 0.976) was identified at the locus 9q34.3 between IgAN and IBD, involving candidate SNP rs4077515 in CARD9 . Interestingly, the locus 6p21.32, which was identified as a novel pleiotropic locus for three pairwise traits, was only colocalized between BUN-GORD (PP4 = 0.814) and eGFR-PUD (PP4 = 0.721) rather than BUN-PUD (PP4 = 0.696), with the same potential shared causal variant rs3129861 identified in BUN-GORD and BUN-PUD (nearest gene: HLA-DRA ). Another three loci were also colocalized by more than one pairwise trait, including locus 6p22.1 colocalized between eGFR-GORD (PP4 = 0.965, candidate SNP rs7752448) and CKD-GORD (PP4 = 0.970, candidate SNP rs34788973), locus 6p21.33 colocalized between IgAN-GORD (PP4 = 0.973, candidate SNP rs3099844) and KSD-IBS (PP4 = 0.768, candidate SNP rs9266244), and locus 10p11.22 colocalized between eGFR-IBD (PP4 = 0.929, candidate SNP rs12572072) and CKD-IBD (PP4= 0.898, candidate SNP rs61844306). Notably, we identified four novel pleiotropic loci that were colocalized for three pairwise traits. Rs3129861 (Chr6: 32401532, T>A), an intergenic variant in the HLA-DRA gene, was colocalized between BUN and GORD (PP4 = 0.814), despite not being associated with either trait in the original GWAS ( P PLACO = 6.40×10 -11 , P BUN = 3.02×10 -7 , P GORD = 2.20×10 -6 ) ( Table 1, Figure 1A and Figure 2A ). Rs12572072 (Chr10: 32307971, T> C), an intronic variant in the KIF5B gene, was colocalized between eGFR and IBD as mentioned above (PP4 = 0.929, P PLACO = 1.91×10 -9 , P eGFR = 8.96×10 -8 , P IBD = 1.65×10 -5 ) ( Table 1, Figure 1B and Figure 2B ). Rs61844306 (Chr10: 32352388, T> C), an intergenic variant in the KIF5B gene, was colocalized between CKD and IBD (PP4 = 0.898, P PLACO = 4.98×10 -9 , P CKD = 6.81×10 -6 , P IBD = 1.14×10 -5 ) ( Table 1, Figure 1C and Figure 2C ). Rs138610699 (Chr17: 79145123, T> G), an intronic variant in the PVALEF gene, was colocalized between CKD and IBD (PP4 = 0.800, P PLACO = 4.16×10 -8 , P CKD = 2.33×10 -4 , P IBD = 3.18×10 -6 ) ( Table 1, Figure 1C and Figure 2D ). Rs6873866 (Chr5 : 96247810, T> C), an intronic variant in the ERAP2 gene, was colocalized between IgAN and IBD (PP4 = 0.800, P PLACO = 2.14×10 -9 , P IgAN = 6.86×10 -6 , P IBD = 6.32×10 -6 ) ( Table 1, Figure 1D and Figure 2E ). Drug repurposing analysis The DrugBank database was applied to identify the available drugs targeting the annotated genes mapping to potential pleiotropic loci 47 . A total of 41 pleiotropic loci were identified across 15 pairwise traits, encompassing 38 unique lead variants mapped to 29 distinct genes, which have approved or investigational drugs available ( Table S5 ). Notably, eight drugs, namely Zinc for CPN1 , Aminocaproic Acid for LPA , Tenapanor for SLC9A3 , Fostamatinib for MAP3K11 , and Calcium Citrate for CHP1 , CTA018 for CYP24A1 , Abacavir for HLA-B and Lansoprazole for MAPT have been utilized or investigated in trials for the treatment of renal or GIT diseases 47 . Tissue-specificity and pathway enrichment for the pleiotropic loci To detect tissue-specific expression patterns, we generated the interactive heatmap of gene expression ( Figure S3 ) and DEG Sets analyses based on GTEx reference panels 48 ( Figure S4A ). Among 54 primary human tissues, the tissue-specifically expressed genes of 26 tissues were enriched for the pleiotropic genes, including the kidney, circulatory system, brain tissue, pancreas, liver, esophagus, lung, etc. The expression of pleiotropic genes was found to be up-regulated in kidney cortex, liver and artery tibial tissues, and down-regulated in the kidney, circulatory system, brain tissue, pancreas and esophagus. The effects of pleiotropic genes on the gut-renal axis related phenotype may play a role in different tissues. Furthermore, a subset of prioritized pleiotropic genes is involved in immune system and cytokine signaling pathways. Gene set enrichment analysis identified 15 significantly enriched pathways (normalized enrichment score > 2 and adjusted P < 0.05), comprising 14 GO terms and one pathways ( Figure S4B ). These enriched pathways are primarily involved in cell death, cell migration and immune system functions. For instance, negative regulation of tubulin deacetylation, critical for cell migration, was identified in 50% of overlapping genes. Among the four novel colocalized loci, the expressions of annotated genes were investigated using the expression quantitative trait loci (eQTL) database from GTEx Consortium 48 . The C allele of lead variant rs6873866 at 5q15 locus ( P PLACO = 2.14×10 -9 for IgAN-IBD; Table 1, Figure 2E and 3A ) was negatively associated with ERAP2 expression in several tissues, including EBV-transformed lymphocytes cells, kidney cortex, and small intestine terminal ileum. The lead variant rs12572072 ( P PLACO =1.91×10 -9 for eGFR-IBD) and rs681343 ( P PLACO =4.98×10 -9 for CKD-IBD) at 10p11.22 had similar identical eQTL regulation information ( Table 1, Figure 2 and 3 ), regulating the expression of KIF5B . The C allele of rs12572072 ( P =1.35×10 -14 ) and T allele of rs681343 ( P =4.81×10 -16 ) were negatively correlated with the expression of KIF5B gene in cultured fibroblast cells. The C allele of lead variant rs6873866 at 5q15 locus ( P PLACO = 2.14×10 -9 for IgAN-IBD; Table 1, Figure 2E and 3A ) was negatively associated with ERAP2 expression in several tissues, including EBV-transformed lymphocytes cells, kidney cortex, and small intestine terminal ileum. The lead variant rs12572072 ( P PLACO =1.91×10 -9 for eGFR-IBD) and rs681343 ( P PLACO =4.98×10 -9 for CKD-IBD) at 10p11.22 had similar identical eQTL regulation information ( Table 1, Figure 2 and 3 ), regulating the expression of KIF5B . The C allele of rs12572072 ( P =1.35×10 -14 ) and T allele of rs681343 ( P =4.81×10 -16 ) were negatively correlated with the expression of KIF5B gene in EBV-transformed lymphocytes cells. These results suggest that these genetic variants may influence gut-renal related diseases by regulating gene expression 48 . In addition, these colocalized genes HLA-DRA , KIF5B and ERAP2 were most highly expressed in B cell, dendritic cell (DC) and DC/macrophages subsets in breast, esophagus mucosa, and esophagus muscularis from GTEx Consortium ( Figure 3B ). Causal inference between renal traits and gastrointestinal tract diseases Bidirectional Mendelian randomization analyses were conducted between five renal traits and four GIT diseases. F values of all selected IVs were greater than 10, indicating stronger instruments. When IBS was the exposure and KSD was the outcome, only one valid instrumental variable remained after MR-PRESSO outlier exclusion, rendering the main MR analysis inapplicable. The primary IVW data analysis revealed that IgAN was associated with a decreased risk of IBD (OR = 0.91, P = 1.11×10 -4 ). For the other pairwise traits, no significant causal effects were found after the Bonferroni correction (all P values > 0.0025). Nonetheless, nominal significant causal effects were noted that PUD showed a tiny protective effect on eGFR (β = 0.006, P = 0.009), as well as PUD (β = 0.006, P = 0.009) and GORD (β = 0.007, P = 0.037) showed a tiny protective effect on eGFR ( Figure S5, Tables S6 ). These findings were also found in several sensitivity analyses ( Table S6 ). Summary of overall results As shown in Figure 4 , all the results from genetic correlation, MR, and colocalization analyses based on effects and levels of statistical significance/direction. The results from genetic correlation and MR analyses were both statistically significant and direct, while colocalization analysis was only statistically significant. The MR results comprehensively considered the primary analysis of the IVW method and excluded the potential bias of pleiotropy. To include more possible outcomes, we applied less strict thresholds and presented possible explanations: (ⅰ) Significant results among genetic correlation, colocalization, and MR analyses support strong genetic association and causal inference for IgAN-IBD pairwise trait; (ⅱ) Non-significant genetic correlation with significant colocalization and MR supports potential causality in genome-wide and regional scale for eGFR-PUD and eGFR-GORD despite weak genetic correlations; (ⅲ) Significant genetic correlation and colocalization with non-significant MR may indicate incomplete causal understanding or uncontrollably biases whereas shared genetic biological mechanisms for BUN-GORD, CKD-IBD, KSD-PUD, and KSD-IBD; (ⅳ) Significant genetic correlation with non-significant colocalization analysis and non-significant MR analysis may imply genetic correlations, while present, are not a direct reflection of causality and further research may be needed to validate in BUN-IBS, BUN-IBD, and KSD- GORD; (ⅴ) Non-significant genetic correlation with colocalization analysis and significant MR analysis may indicate that potential causality for IgAN-IBS despite the absence of strong genetic signals in correlation or colocalization; (ⅵ) Non-significant genetic correlation with significant colocalization analysis but non-significant MR analysis may indicate no enough causality but with shared causal genetic variants for eGFR-IBD, eGFR-IBS, CKD-GORD, IgAN-GORD and KSD-IBD. Discussion In this study, we explored the shared genetic effects in the gut-renal axis traits and diseases using the latest and largest GWAS datasets. We found that renal function was genetically correlated with some GIT diseases, while only IgAN and IBD were identified as strong genetic evidence for the causal association and genetic correlation. By pleiotropic analyses, we identified the shared genes, the tissue- and cell type-specify and biological pathways, supporting the role of the gut-renal axis in the shared genetic etiology. Besides, some potential drugs for repurposing were also suggested for the treatment of renal traits and GIT diseases. The genome-wide genetic associations estimated by 𝝆-HESS and LDSC were in the same direction, and the jointly identified significant associations remained significant after correction by multiple tests (BUN-IBS, IgAN-IBD, KSD-PUD, KSD-GORD, KSD-IBS), indicating the robustness of the estimations. We observed the strongest genetic negative association in IgAN-IBD, reflecting the low genetic risk of IBD associated with IgAN. In addition, KSD and gastrointestinal disorders (PUD, GORD, IBS) were detected relatively higher positive genetic associations, reflecting the genetic risk of GIT diseases strongly associated with KSD. However, the average width of the local region is about 1.6 Mb 58 , 59 , and the variable effect of the variation scale also exists. For instance, in the BUN-IBS pleiotropy analysis, we found mixed signals at the genomic risk loci, with five of the eight lead variants showing the same direction of effect between BUN and IBS GWASs, while the other three lead variants showed opposite effects. Therefore, we propose to assess shared genetic architecture at the finer resolution by using PLACO in trait pairs to further determine the pleiotropic variants aiding in understanding the polygenic overlap despite the insignificant genetic correlation, followed by colocalization analysis to identify potential shared causal variants in each pleiotropic locus. Findings of pleiotropic analyses combined with drug repurposing provide promising insights into potential therapeutic avenues and enhance the understanding of mechanisms related to LPA and HLA-B in renal and gastrointestinal diseases. In the pleiotropic analyses, we identified the variant rs11751605 in LPA that was causally associated with eGFR and PUD ( P PLACO =7.86×10 − 12 ), with the PP4 of 0.724. LPA encoded Apolipoprotein(a), a serine protease that inhibits tissue plasminogen activator I activity and is involved in multiple biological pathways of plasma lipoprotein (lipoprotein A) assembly, remodeling, clearance, and cholesterol metabolism 60 . Given that LPA inhibitors like Aminocaproic Acid are already used in blood disorder treatments, our study strengthens the rationale for exploring LPA inhibitors in clinical trials for treating CKD 47 , leveraging their known mechanisms in lipid metabolism and potential impacts on renal function. KSD and IBS are colocalized in HLA-B (PP4 = 0.768). HLA-B encodes antigen-presenting major histocompatibility complex class I (MHCI) molecule, which is involved in the recognition and presentation of endogenous antigens that regulate CTL killing 60 . A large number of studies have shown that the increase of somatic mutation rate in HLA is significantly related to HLA dysfunction, as a potential mechanism of immune escape 61 – 63 . An inhibitor of HLA-B, Abacavir, is in clinical trials for the treatment of renal insufficiency 47 . These findings highlight the potential for drug repurposing of immune-related genes and and development of new treatments that target these pathways in renal and gastrointestinal diseases. The advanced approach of cross-trait pleiotropy analysis enabled us to identify 21 novel loci that were not significant in the original GWASs. Four of the five lead variants showed evidence of colocalization. KIF5B is a causal gene for eGFR-IBD and CKD-IBD trait pairs, which has not been reported in previous GWASs about GIT diseases or renal traits (Fig. 1 , Fig. 2 ). There was no previous GWAS report that KIF5B was associated with gastrointestinal diseases or renal traits from the NHGRI-EBI GWAS catalog 64 . KIF5B encodes the kinesin-1 heavy chain with multiple roles in immune responses, such as being involved in NK cell-mediated cytotoxicity and driving immune synaptic polarization between effector NK lymphocytes and target cells 60 . Considering that persistent inflammation is one of the signs of the development, progression and complications of CKD 65 , and IBD is an immune-mediated disease, we inferred that rs13107325 and rs61844306 may interfere with immune response by regulating the expression of KIF5B in immune tissues and gut-renal related tissues, thereby affecting renal function and gastrointestinal diseases. In the future, more experiments and research are necessary to validate mechanisms into the genetic basis of immune dysregulation in CKD and IBD. Understanding the genetic mechanisms might lead to novel therapeutic targets aimed at modulating immune responses to alleviate inflammation and improve outcomes in these chronic diseases. To the best of our knowledge, this study is the first one to investigate the systematical and comprehensive genetic association between renal traits and GIT diseases, balancing genetic evidence from genetic correlation, MR, colocalization and gene enrichment analyses. Genetic correlations and colocalization signals can stem from vertical pleiotropy, suggesting potential causal associations between diseases 66 . Applying multiple methods with different model assumptions could provide complementary evidence and allow deep investigation of the underlying pleiotropic associations in gut-renal related traits from different perspectives. First of all, we observed negative genetic correlations between IgAN and IBD across genome-wide and local genetic scales. Recent cohort studies have reported an increased risk of IBD in patients with IgAN 67 – 69 . Given the possible confounding factors and potential biases inherent in observational studies, further research is needed to validate the shared pathophysiology of these conditions. MR approach capitalizes on the random allocation of genetic variation, reducing the likelihood of confounding 52 , 70 . Bidirectional MR analysis demonstrated a protective causal effect of IgAN on IBD and excluded the reverse causal association. This effect may be attributed to genetically elevated secretory IgA, known for its homeostatic anti-inflammatory and immunosuppressive effects at the intestinal mucosal level. Moreover, pleiotropic loci identified were significantly enriched in immune, blood, and intestinal mucosal tissues, aligning with previous drug trials that targeted intestinal inflammation as an effective therapy for IgAN 71 . In addition, we identified that IBD and IgAN share genetic susceptibility loci in CARD9 (rs4077515) and ERAP2 (rs6873866), reinforcing the genetic link between these diseases. Importantly, we initially identified a causal variant rs6873866 (PP4 = 0.800) shared by IgAN and IBD in ERAP2 . The C allele of rs6873866 was negatively associated with ERAP2 expression in multiple tissues, such as whole blood, EBV-transformed lymphocytes, and kidney cortex and small intestine terminal ileum. The ERAP2 -encoded aminopeptidase plays a central role in peptide pruning that generates most HLA class I-binding peptides 72 . Our newly identified immune-related genes add new insights into the intricate pathogenesis of IgAN and IBD and may provide new biological evidence for the pathogenesis of comorbid traits associated with the gut-renal axis. This discovery enables a deeper analysis of disease associations and serves as a crucial foundation for preventing clinical complications. Future pathological experiments and clinical trials are suggested to be put forward to investigate more common risk factors or confounding factors underlying the association between IgAN and IBD risk. Several limitations are acknowledged in our study. Firstly, the GWAS included in our analysis were restricted to European populations, which limits the generalizability of our findings to other ancestral groups. Future investigations should incorporate multi-ancestry GWAS to confirm the robustness of these associations across diverse populations. Secondly, while we observed global vertical pleiotropy in specific trait pairs, the precise association patterns of these pleiotropic loci remain undefined. Thirdly, in terms of the somewhat small sample size of cases in IgAN and GIT diseases, the statistical power might be insufficient. Nevertheless, the universal genetic correlations between renal diseases and GIT diseases pairwise traits were successfully recognized, followed by the identification of pleiotropic loci for renal diseases and GIT diseases, which displayed enrichment in the gut and kidney transcriptome. Concerning unavoidable overlapped samples among international largest and latest GWAS summary data, which may bias the causal estimates, the intercepts from LDSC were estimated, only to indicate little potential sample overlap between two pairwise traits of GWAS. Additionally, the lack of external validation hinders the broader applicability of our results, particularly due to limited sample sizes in available GWAS data for certain phenotypes like IgAN. Lastly, our cell-type specific analyses were constrained by the absence of kidneys and complete digestive tract correlation datasets, potentially impacting the comprehensiveness and reliability of our tissue-specific findings. Conclusions Underlying shared genetic architecture between renal traits and GIT diseases, we identified shared SNPs, loci, and risk genes. Additionally, we identified common biological mechanisms relating to enriched tissues like the kidney cortex and small intestine, pathways of cell death coagulation and immune responses, as well as distinct immune cell types. This study uncovers genetic pleiotropy for 20 pairwise traits in the gut-renal axis and offers evidence for personalized treatment options. Furthermore, we showed that the drug targets of pleiotropic immune-related genes will provide novel therapeutic insights for renal traits and GIT diseases. Abbreviations BWMR: Bayesian-weighted MR CKD: Chronic Kidney Disease DEG: Differentially Expressed Genes eGFR: Estimated Glomerular Filtration Rate GIT: Gastrointestinal Tract GWAS: Genome-Wide Association Studies IgAN: Immunoglobulin A Nephropathy IV: Instrumental Variable IVW: Inverse-Variance Weighted KSD: Kidney Stone Disease PUD: Peptic Ulcer Disease GORD: Gastroesophageal Reflux Disease IBD: Inflammatory Bowel Disease IBS: Irritable Bowel Syndrome PLACO: Pleiotropic Analysis under the Composite Null Hypothesis RAPS: Robust Adjusted Profile Score SNP: Single-Nucleotide Polymorphism Declarations Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Availability of data and material: The original contributions of this study are included in the article and supplementary information. Further inquiries can be directed to the corresponding author X.H. Competing interests: Not applicable. Funding: XH reported receipt of grants from the National Natural Science Foundation of China (Award number: 32470658). The funders had no role in designing the study, the analysis, or the decision to submit the paper. Authors' contributions: XH conceptualized the study and acquired funding. XH, SL and SW had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. SL, SW, MJ, YK, ZS, XH, YG, and XC conducted the analysis and interpreted the data. YK, ZS, XH, YG, and XC accessed and verified the underlying statistical calculation. SL and SW produced figures, and wrote the manuscript. XH, SL and MJ designed and supervised the study. All authors read and approved the final version of the manuscript. Acknowledgements: We thank participants and investigators who contributed to the GWAS summary statistics and omics data included in our analyses. References Kovesdy CP. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl (2011) . Apr 2022;12(1):7-11. doi:10.1016/j.kisu.2021.11.003 Xie Y, Bowe B, Mokdad AH, et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int . Sep 2018;94(3):567-581. doi:10.1016/j.kint.2018.04.011 Ying M, Shao X, Qin H, et al. Disease burden and epidemiological trends of chronic kidney disease at the global, regional, national levels from 1990 to 2019. Nephron . Sep 15 2023;doi:10.1159/000534071 Romagnani P, Remuzzi G, Glassock R, et al. Chronic kidney disease. Nat Rev Dis Primers . Nov 23 2017;3:17088. doi:10.1038/nrdp.2017.88 Gansevoort RT, Matsushita K, van der Velde M, et al. Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney Int . Jul 2011;80(1):93-104. doi:10.1038/ki.2010.531 Kiryluk K, Sanchez-Rodriguez E, Zhou XJ, et al. Genome-wide association analyses define pathogenic signaling pathways and prioritize drug targets for IgA nephropathy. Nature genetics . Jul 2023;55(7):1091-1105. doi:10.1038/s41588-023-01422-x Nihei Y, Haniuda K, Higashiyama M, et al. Identification of IgA autoantibodies targeting mesangial cells redefines the pathogenesis of IgA nephropathy. Sci Adv . Mar 22 2023;9(12):eadd6734. doi:10.1126/sciadv.add6734 Kiryluk K, Novak J. The genetics and immunobiology of IgA nephropathy. J Clin Invest . Jun 2014;124(6):2325-32. doi:10.1172/JCI74475 Lai KN, Tang SC, Schena FP, et al. IgA nephropathy. Nat Rev Dis Primers . Feb 11 2016;2:16001. doi:10.1038/nrdp.2016.1 Dhayat NA, Bonny O, Roth B, et al. Hydrochlorothiazide and Prevention of Kidney-Stone Recurrence. The New England journal of medicine . Mar 2 2023;388(9):781-791. doi:10.1056/NEJMoa2209275 Peery AF, Crockett SD, Murphy CC, et al. Burden and Cost of Gastrointestinal, Liver, and Pancreatic Diseases in the United States: Update 2021. Gastroenterology . Feb 2022;162(2):621-644. doi:10.1053/j.gastro.2021.10.017 Lehto M, Groop PH. The Gut-Kidney Axis: Putative Interconnections Between Gastrointestinal and Renal Disorders. Front Endocrinol (Lausanne) . 2018;9:553. doi:10.3389/fendo.2018.00553 Khoury T, Tzukert K, Abel R, Abu Rmeileh A, Levi R, Ilan Y. The gut-kidney axis in chronic renal failure: A new potential target for therapy. Hemodial Int . Jul 2017;21(3):323-334. doi:10.1111/hdi.12486 Liao Y, Fan L, Bin P, et al. GABA signaling enforces intestinal germinal center B cell differentiation. Proc Natl Acad Sci U S A . Nov 2022;119(44):e2215921119. doi:10.1073/pnas.2215921119 Bartochowski P, Gayrard N, Bornes S, et al. Gut-Kidney Axis Investigations in Animal Models of Chronic Kidney Disease. Toxins (Basel) . Sep 7 2022;14(9)doi:10.3390/toxins14090626 Ramos CI, Armani RG, Canziani MEF, et al. Effect of prebiotic (fructooligosaccharide) on uremic toxins of chronic kidney disease patients: a randomized controlled trial. Nephrol Dial Transplant . Nov 1 2019;34(11):1876-1884. doi:10.1093/ndt/gfy171 Ranganathan N, Friedman EA, Tam P, Rao V, Ranganathan P, Dheer R. Probiotic dietary supplementation in patients with stage 3 and 4 chronic kidney disease: a 6-month pilot scale trial in Canada. Curr Med Res Opin . Aug 2009;25(8):1919-30. doi:10.1185/03007990903069249 Vaziri ND, Wong J, Pahl M, et al. Chronic kidney disease alters intestinal microbial flora. Kidney Int . Feb 2013;83(2):308-15. doi:10.1038/ki.2012.345 Wuttke M, Li Y, Li M, et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nature genetics . Jun 2019;51(6):957-972. doi:10.1038/s41588-019-0407-x Singh P, Harris PC, Sas DJ, Lieske JC. The genetics of kidney stone disease and nephrocalcinosis. Nat Rev Nephrol . Apr 2022;18(4):224-240. doi:10.1038/s41581-021-00513-4 Wu Y, Murray GK, Byrne EM, Sidorenko J, Visscher PM, Wray NR. GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression. Nature communications . Feb 19 2021;12(1):1146. doi:10.1038/s41467-021-21280-7 Duerr RH, Taylor KD, Brant SR, et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science (New York, NY) . Dec 1 2006;314(5804):1461-3. doi:10.1126/science.1135245 Eijsbouts C, Zheng T, Kennedy NA, et al. Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders. Nature genetics . Nov 2021;53(11):1543-1552. doi:10.1038/s41588-021-00950-8 Banaszczyk K. Risankizumab in the treatment of psoriasis - literature review. Reumatologia . 2019;57(3):158-162. doi:10.5114/reum.2019.86426 Reay WR, Cairns MJ. Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet . Oct 2021;22(10):658-671. doi:10.1038/s41576-021-00387-z Shi D, Zhong Z, Wang M, et al. Identification of susceptibility locus shared by IgA nephropathy and inflammatory bowel disease in a Chinese Han population. J Hum Genet . Mar 2020;65(3):241-249. doi:10.1038/s10038-019-0699-9 Ren F, Jin Q, Jin Q, et al. Genetic evidence supporting the causal role of gut microbiota in chronic kidney disease and chronic systemic inflammation in CKD: a bilateral two-sample Mendelian randomization study. Frontiers in immunology . 2023;14:1287698. doi:10.3389/fimmu.2023.1287698 Zhang H, Huang Y, Zhang J, Su H, Ge C. Causal effects of inflammatory bowel diseases on the risk of kidney stone disease: a two-sample bidirectional mendelian randomization. BMC Urol . Oct 12 2023;23(1):162. doi:10.1186/s12894-023-01332-4 Ray D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer. PLoS genetics . Dec 2020;16(12):e1009218. doi:10.1371/journal.pgen.1009218 Ray D, Venkataraghavan S, Zhang W, et al. Pleiotropy method reveals genetic overlap between orofacial clefts at multiple novel loci from GWAS of multi-ethnic trios. PLoS genetics . Jul 2021;17(7):e1009584. doi:10.1371/journal.pgen.1009584 Gong W, Guo P, Li Y, et al. Role of the Gut-Brain Axis in the Shared Genetic Etiology Between Gastrointestinal Tract Diseases and Psychiatric Disorders: A Genome-Wide Pleiotropic Analysis. JAMA Psychiatry . Apr 1 2023;80(4):360-370. doi:10.1001/jamapsychiatry.2022.4974 Bulik-Sullivan B, Finucane HK, Anttila V, et al. An atlas of genetic correlations across human diseases and traits. Nature genetics . Nov 2015;47(11):1236-41. doi:10.1038/ng.3406 Finucane HK, Bulik-Sullivan B, Gusev A, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nature genetics . Nov 2015;47(11):1228-35. doi:10.1038/ng.3404 Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife . May 30 2018;7doi:10.7554/eLife.34408 Hao X, Shao Z, Zhang N, et al. Integrative genome-wide analyses identify novel loci associated with kidney stones and provide insights into its genetic architecture. Nature communications . Nov 18 2023;14(1):7498. doi:10.1038/s41467-023-43400-1 Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature . Jan 2023;613(7944):508-518. doi:10.1038/s41586-022-05473-8 He Y, Koido M, Sutoh Y, et al. East Asian-specific and cross-ancestry genome-wide meta-analyses provide mechanistic insights into peptic ulcer disease. Nature genetics . Dec 2023;55(12):2129-2138. doi:10.1038/s41588-023-01569-7 Shi HWB, Mancuso N, Spendlove S, Pasaniuc B. Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits. American journal of human genetics . Nov 2 2017;101(5):737-751. doi:10.1016/j.ajhg.2017.09.022 Berisa T, Pickrell JK. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics (Oxford, England) . Jan 15 2016;32(2):283-5. doi:10.1093/bioinformatics/btv546 Shao Z, Wang T, Zhang M, Jiang Z, Huang S, Zeng P. IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA. PLoS computational biology . Aug 2021;17(8):e1009250. doi:10.1371/journal.pcbi.1009250 Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. American journal of human genetics . Sep 2007;81(3):559-75. doi:10.1086/519795 Genomes Project C, Auton A, Brooks LD, et al. A global reference for human genetic variation. Nature . Oct 1 2015;526(7571):68-74. doi:10.1038/nature15393 Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics (Oxford, England) . Mar 15 2010;26(6):841-2. doi:10.1093/bioinformatics/btq033 Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res . Sep 2010;38(16):e164. doi:10.1093/nar/gkq603 Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS genetics . May 2014;10(5):e1004383. doi:10.1371/journal.pgen.1004383 Smith-Byrne K, Hedman Å, Dimitriou M, et al. Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers. Nature communications . 2024/04/29 2024;15(1):3621. doi:10.1038/s41467-024-46834-3 Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res . Jan 4 2018;46(D1):D1074-D1082. doi:10.1093/nar/gkx1037 Consortium GT. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science (New York, NY) . Sep 11 2020;369(6509):1318-1330. doi:10.1126/science.aaz1776 Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nature communications . Nov 28 2017;8(1):1826. doi:10.1038/s41467-017-01261-5 Zhao Q, Wang J, Hemani G, Bowden J, Small DS. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. Ann Stat . 2020;48(3):1742-1769, 28. Zhao J, Ming J, Hu X, Chen G, Liu J, Yang C. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics . Mar 1 2020;36(5):1501-1508. doi:10.1093/bioinformatics/btz749 Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genetic epidemiology . Nov 2013;37(7):658-65. doi:10.1002/gepi.21758 Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol . Apr 2015;44(2):512-25. doi:10.1093/ije/dyv080 Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol . May 2016;40(4):304-14. doi:10.1002/gepi.21965 Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol . Dec 1 2017;46(6):1985-1998. doi:10.1093/ije/dyx102 Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet . Nov 2017;13(11):e1007081. doi:10.1371/journal.pgen.1007081 Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet . May 2018;50(5):693-698. doi:10.1038/s41588-018-0099-7 Shi H, Mancuso N, Spendlove S, Pasaniuc B. Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits. Am J Hum Genet . Nov 2 2017;101(5):737-751. doi:10.1016/j.ajhg.2017.09.022 Zhang Y, Lu Q, Ye Y, et al. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biol . Sep 7 2021;22(1):262. doi:10.1186/s13059-021-02478-w Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics . Jun 20 2016;54:1 30 1-1 30 33. doi:10.1002/cpbi.5 Assarsson E, Sidney J, Oseroff C, et al. A quantitative analysis of the variables affecting the repertoire of T cell specificities recognized after vaccinia virus infection. Journal of immunology (Baltimore, Md : 1950) . Jun 15 2007;178(12):7890-901. doi:10.4049/jimmunol.178.12.7890 McGranahan N, Rosenthal R, Hiley CT, et al. Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution. Cell . Nov 30 2017;171(6):1259-1271 e11. doi:10.1016/j.cell.2017.10.001 Chowell D, Morris LGT, Grigg CM, et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science (New York, NY) . Feb 2 2018;359(6375):582-587. doi:10.1126/science.aao4572 MacArthur J, Bowler E, Cerezo M, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res . Jan 4 2017;45(D1):D896-D901. doi:10.1093/nar/gkw1133 Petreski T, Piko N, Ekart R, Hojs R, Bevc S. Review on Inflammation Markers in Chronic Kidney Disease. Biomedicines . Feb 11 2021;9(2)doi:10.3390/biomedicines9020182 van Rheenen W, Peyrot WJ, Schork AJ, Lee SH, Wray NR. Genetic correlations of polygenic disease traits: from theory to practice. Nat Rev Genet . Oct 2019;20(10):567-581. doi:10.1038/s41576-019-0137-z Nurmi R, Pohjonen J, Metso M, et al. Prevalence of Inflammatory Bowel Disease and Celiac Disease in Patients with IgA Nephropathy over Time. Nephron . 2021;145(1):78-84. doi:10.1159/000511555 Rehnberg J, Symreng A, Ludvigsson JF, Emilsson L. Inflammatory Bowel Disease Is More Common in Patients with IgA Nephropathy and Predicts Progression of ESKD: A Swedish Population-Based Cohort Study. J Am Soc Nephrol . Feb 2021;32(2):411-423. doi:10.1681/ASN.2020060848 Joher N, Gosset C, Guerrot D, et al. Immunoglobulin A nephropathy in association with inflammatory bowel diseases: results from a national study and systematic literature review. Nephrol Dial Transplant . Feb 25 2022;37(3):531-539. doi:10.1093/ndt/gfaa378 Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ (Clinical research ed) . Jul 12 2018;362:k601. doi:10.1136/bmj.k601 Hansen IS, Baeten DLP, den Dunnen J. The inflammatory function of human IgA. Cell Mol Life Sci . Mar 2019;76(6):1041-1055. doi:10.1007/s00018-018-2976-8 Tanioka T, Hattori A, Masuda S, et al. Human leukocyte-derived arginine aminopeptidase. The third member of the oxytocinase subfamily of aminopeptidases. J Biol Chem . Aug 22 2003;278(34):32275-83. doi:10.1074/jbc.M305076200 Additional Declarations No competing interests reported. Supplementary Files Supplementtablesandfigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5883069","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":408124211,"identity":"dfae4b0c-64f5-4472-b40a-1612eb3543ea","order_by":0,"name":"Si Li","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Si","middleName":"","lastName":"Li","suffix":""},{"id":408124212,"identity":"3c3beb68-91e8-4b46-baac-2f0297f5f90c","order_by":1,"name":"Shuang Wu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Wu","suffix":""},{"id":408124213,"identity":"86312f26-ccee-498d-bf1b-4320fc401090","order_by":2,"name":"Minghui Jiang","email":"","orcid":"","institution":"Beijing Tian Tan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Minghui","middleName":"","lastName":"Jiang","suffix":""},{"id":408124214,"identity":"c5dd1b04-0fdf-4b6a-9de0-15167e649797","order_by":3,"name":"Zhonghe Shao","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhonghe","middleName":"","lastName":"Shao","suffix":""},{"id":408124215,"identity":"1e93b998-bfc8-49e5-86dc-bbd4f81f761c","order_by":4,"name":"Yifang Kong","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yifang","middleName":"","lastName":"Kong","suffix":""},{"id":408124216,"identity":"8cc502ac-2107-4482-8c57-a42259b9c8eb","order_by":5,"name":"Yunlong Guan","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Guan","suffix":""},{"id":408124217,"identity":"a742d068-fd48-4cff-bd2a-f75bc295a86a","order_by":6,"name":"Xi Cao","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Cao","suffix":""},{"id":408124218,"identity":"3fc59ab7-88d1-45b4-b789-e41a72d10fe7","order_by":7,"name":"Xingjie Hao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACZiCWMGCQM0BwidRiDNTC2ECcFihI3EC0FoPjvIdfWBTYpG+XSH/+gKHCOrGB/ewBvFokm/nSLCQM0nJ3zsgxbGA4k57YwJOXgFcLPzOPmYGEweHcDTdyGBsY2w4nNkjwGODVwgbR8j/d4Eb6wwbGf0RoAdpi/EDC4ECCwY0EwwbGBiK0SDbzmAEDOdlww5k3hjMSjqUbt/Hk4NdicP6M8WeJP3byBsfTH3z4UGMt289+Br8WkHekJWDMBBCXkHogYP74gQhVo2AUjIJRMIIBANYrQI+8RjtyAAAAAElFTkSuQmCC","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Xingjie","middleName":"","lastName":"Hao","suffix":""}],"badges":[],"createdAt":"2025-01-22 18:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5883069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5883069/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75060294,"identity":"dd9ec556-949c-46b1-b30a-f57a6f26476b","added_by":"auto","created_at":"2025-01-30 04:02:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1011277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan plots of (A) BUN-GORD, (B) eGFR-IBD, (C) CKD-IBD, and (D) IgAN-IBD pleiotropic analyses.\u003c/strong\u003e The red dashed lines indicate the genome-wide significance level at \u003cem\u003eP\u003c/em\u003e=5×10\u003csup\u003e-8\u003c/sup\u003e, and the black dashed lines indicate the suggestive significance level at \u003cem\u003eP\u003c/em\u003e = 1×10\u003csup\u003e-6\u003c/sup\u003e. The blue point indicates the locus was associated with renal traits (\u003cem\u003eP \u003c/em\u003evalues of the variants within the lead variant 500 kb were lower than 510\u003csup\u003e−8\u003c/sup\u003e); orange indicates the locus was associated with GIT disease; purple indicates the locus was associated with both traits; red indicates the locus were associated with neither trait.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BUN, Blood urea nitrogen; eGFR, Estimated glomerular filtration rate; CKD, Chronic kidney disease; IgAN, IgA nephropathy; GORD, Gastro-oesophageal Reflux Disease; IBD, Inflammatory bowel disease; GIT, gastrointestinal tract.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5883069/v1/4c51ecb3958adda3ff4b758f.png"},{"id":75060292,"identity":"6aa85e03-6264-46b4-9a37-30af5fbb4e58","added_by":"auto","created_at":"2025-01-30 04:02:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3366429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional association plots of Renal GWAS, GIT GWAS, PLACO GWAS and Renal-GIT comparations.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) The Four rows are of BUN, GORD GWASs, pleiotropic analysis, and the regional comparations of BUN-GORD respectively; (\u003cstrong\u003eB\u003c/strong\u003e) The Four rows are of eGFR, IBD GWASs, pleiotropic analysis, and the regional comparations of eGFR-IBD respectively; (\u003cstrong\u003eC\u003c/strong\u003e) The Four rows are of CKD, IBD GWASs, pleiotropic analysis, and the regional comparations of CKD-IBD respectively. (\u003cstrong\u003eD\u003c/strong\u003e) The Four rows are of CKD, IBD GWASs, pleiotropic analysis, and the regional comparations of CKD-IBD respectively. (\u003cstrong\u003eE\u003c/strong\u003e) The Four rows are of IgAN, IBD GWASs, pleiotropic analysis, and the regional comparations of IgAN-IBD respectively. Each dot represents a genetic variant, with color shifting from blue to red indicating higher LD 𝑟² values relative to the lead variants highlighted in purple.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BUN, Blood urea nitrogen; eGFR, Estimated glomerular filtration rate; CKD, Chronic kidney disease; IgAN, IgA nephropathy; GORD, Gastro-oesophageal Reflux Disease; IBD, Inflammatory bowel disease.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5883069/v1/31ac4b9df7709c3847d7e01f.png"},{"id":75060293,"identity":"7da12df9-6d6f-4526-af39-7023f6a50702","added_by":"auto","created_at":"2025-01-30 04:02:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":693415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eViolin plots of SNPs associated with gene expression (A) and gene expression in single-cell eQTL mapping (B). (A) \u003c/strong\u003eHorizontal coordinates indicate different genotypes and the number of samples; vertical coordinates are normalized gene expression, and horizontal lines and boxes in the graph indicate their median and interquartile spacing; \u003cem\u003eP\u003c/em\u003e indicates the \u003cem\u003eP\u003c/em\u003e-value of the T-test between genotypes and gene expression. \u003cstrong\u003e(B) \u003c/strong\u003eHorizontal coordinates indicate a subset of tissues from 16 donors were also characterized using single-cell RNA sequencing.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5883069/v1/e43dd9311ec3c90955e43438.png"},{"id":75060305,"identity":"d230da0a-64f9-4b5e-b790-d61058143c21","added_by":"auto","created_at":"2025-01-30 04:02:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":230303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of results in gut-renal trait pairs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e values less than 0.05 in the genetic correlation and MR analyses were considered suggestive of evidence for a potential association, and a PP4 level greater than 0.7 was considered suggestive of evidence for a causal genetic variant for both traits. Trait pairs that passed the multiple tests (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.0025) were marked in bold.\u003c/p\u003e\n\u003cp\u003e*: Coloc, colocalization; \u003cem\u003erg\u003c/em\u003e, genetic corralation; MR, Mendelian randomization.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BUN, Blood urea nitrogen; eGFR, Estimated glomerular filtration rate; CKD, Chronic kidney disease; IgAN, IgA nephropathy; KSD, Kidney stone disease; PUD, Peptic ulcer disease; GORD, Gastro-oesophageal Reflux Disease; IBD, Inflammatory bowel disease; IBS, irritable bowel syndrome.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5883069/v1/dd208f61472c570c0a097523.png"},{"id":75084893,"identity":"8854814d-3a60-4b38-b285-d67b9c978b41","added_by":"auto","created_at":"2025-01-30 09:53:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5978364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5883069/v1/16a2dfb7-74b0-4c64-a379-15feaec1c5d9.pdf"},{"id":75060295,"identity":"ce77aa19-1af4-43f0-a48c-875cb63005f1","added_by":"auto","created_at":"2025-01-30 04:02:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2447420,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementtablesandfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5883069/v1/289c9d5aae25f645a3da8d9c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide pleiotropy analysis reveals shared architecture between renal traits and gastrointestinal tract diseases","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal and gastrointestinal diseases are significant challenges in global public health due to their widespread prevalence and complex pathogenesis\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Evidence of comorbidities and associations between renal and gastrointestinal diseases have been accumulating and are likely modulated by multi-organ interactions in the gut-renal crosstalk, also known as the gut-renal axis\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The gut-renal axis refers to bidirectional inter-organ communication between the renal traits and gastrointestinal tract, and it involves a variety of biological mechanisms, including inflammatory immune response, gut microbiota, and metabolic dependence\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Underlying the conceptual framework of the gut-kidney axis, the shared genetic etiology might be involved in the associations between renal and gastrointestinal diseases.\u003c/p\u003e \u003cp\u003eTo date, genome-wide association studies (GWAS) have clarified that multiple genetic variants are associated with gastrointestinal and renal diseases\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Previous studies have primarily investigated genetic associations and causal relationships between the gut-renal trait pairs, which focused on certain renal diseases and had limited sample sizes\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Increasing sample size enhances the ability to detect genetic associations but is costly and resource-intensive. Instead of focusing on individual diseases separately, a promising strategy called genome-wide cross-trait analysis jointly analyzes pairwise traits or multiple diseases together can uncover shared genetic loci and provide novel insights into the common genetic risks of these diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Furthermore, identifying pleiotropic loci can provide targets for interventions aimed at potentially preventing or treating these diseases\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Despite these advancements, there has been limited utilization of multi-omics data to fully uncover shared genetic regulatory mechanisms in the gut-renal axis. Thus, further exploration of the genetic mechanisms and drug targets of the gut-renal axis is warranted.\u003c/p\u003e \u003cp\u003eHere, we systematically investigated the shared genetic basis of five renal traits (BUN, eGFR, CKD, IgAN, KSD) and four gastrointestinal disorders (PUD, GORD, IBD, IBS) using large-scale GWAS summary data, and sequentially investigated them at the genome-wide, single-nucleotide polymorphisms (SNPs), and gene levels, as well as biological pathways, leveraging advanced statistical genetic algorithm for pleiotropic associations to unravel potential common genetic etiologies. First, we performed genome-wide and local genetic correlation analyses. Subsequently, we utilized pleiotropic analysis under the composite original hypothesis (PLACO) to identify pleiotropic genetic variants or loci associated with paired traits of the intestinal-kidney axis at the SNP level. Leveraging the findings from the pleiotropic analysis, we further conducted pairwise colocalization analyses to identify colocalized loci and gene-level analyses to identify candidate pleiotropic genes. Drug repurposing analysis was performed on the pleiotropic loci to identify potential drug targets in the gut-renal axis. Notably, multiple gene-level enrichment analyses were utilized by integrating transcriptomic and single-cell data to characterize the tissue-specific and cell-type specificity of the pleiotropic genes. Finally, bidirectional mendelian randomization (MR) analysis was performed to assess pairwise causal associations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eGWAS summary statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acquired recent GWAS summary statistics on renal traits (BUN, eGFR, CKD) from CKDGen Consortium, IgA nephropathy, and KSD meta-analyses. We analyzed 816,412 Europeans (UKB, FinnGen beta 10) for PUD variants. GORD GWAS involved 456,327 UKB Europeans, while IBD data included 462,014 individuals. IBS meta-analysis covered 53,400 cases. All data were aligned to GRCh37 assembly. Our study employed diverse methods to explore shared pathways between renal and GIT diseases (Figure 1).\u003c/p\u003e\n\u003cp\u003eWe acquired summary statistics from the largest and recent GWAS summary data of renal function traits (BUN and eGFR, N = 567,460) and CKD (41,395 cases and 439,303 controls) from CKD Genetics (CKDGen) Consortium\u003csup\u003e19\u003c/sup\u003e. GWAS summary statistics of IgA nephropathy (5,556 cases and 21,178 controls) and KSD (17,969 cases and 702,230 controls) were also obtained from previous GWAS meta-analyses\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e. A total of 816,412 Europeans from the UK biobank (UKB) and the FinnGen (beta 10)\u003csup\u003e36\u003c/sup\u003e were analyzed to identify the variants correlated with PUD\u003csup\u003e37\u003c/sup\u003e. For GORD, GWAS summary statistics of 456,327 Europeans from the UKB were downloaded \u003csup\u003e21\u003c/sup\u003e. GWAS for IBD were obtained from a larger meta-analysis with 462,014 individuals. While for IBS, meta-analysis results of 53,400 cases and 433,201 controls were based on the UKB and the Bellygenes initiative\u003csup\u003e23\u003c/sup\u003e. All summary statistics were aligned to human assembly GRCh37. Details for each GWAS summary statistics can be found in \u003cstrong\u003eTable S1\u003c/strong\u003e. These data were utilized together with a comprehensive set of methods to elucidate etiological pathways underlying the comorbidity between renal and GIT diseases (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide and local genetic correlation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo delineate genetically correlated GIT diseases and renal trait pairs, we employed cross-trait LD scores regression (LDSC) to assess genome-wide genetic correlation\u003csup\u003e32\u003c/sup\u003e. The intercept from LDSC may also suggest potential sample overlap between two GWASs. Given the fact that the genome-wide genetic covariance may be compromised by balanced local genetic covariance, where positive genetic covariance partially counteracts negative genetic covariance\u003csup\u003e38\u003c/sup\u003e, we proceeded to measure the local genetic correlation by \u0026rho;-HESS\u003csup\u003e38\u003c/sup\u003e. To estimate local genetic correlation, the whole genome was divided into around 1,703 independent linkage disequilibrium blocks\u003csup\u003e39\u003c/sup\u003e, thus local genetic correlations were identified with Bonferroni-corrected significant threshold set at \u003cem\u003eP\u003c/em\u003e-value was below 2.94\u0026times;10\u003csup\u003e-6\u003c/sup\u003e (0.05/1,703), and such 1,703 local genetic correlations were then integrated into the genome-wide genetic correlation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePleiotropic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each pairwise trait, we utilized PLACO (pleiotropic analysis under composite null hypothesis)\u003csup\u003e29\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e to identify the pleiotropic effect of each variant, where the joint null hypothesis including three composite null sub-hypotheses\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e40\u003c/sup\u003e: , and , equivalent to testing whether . Consequently, the PLACO test statistic is computed as . As per PLACO guidelines\u003csup\u003e29\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e, variants with MAF \u0026lt; 0.1 and square of Z scores \u0026ge; 80 were excluded and a potential pleiotropic variant was identified if \u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e lower than 5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of genomic loci\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed the PLINK\u003csup\u003e41\u003c/sup\u003e clumping function to identify independent genomic loci, leveraging the LD structure in individuals of European ancestry from the 1000 Genomes Project phase 3 \u003csup\u003e42\u003c/sup\u003e. The variants with LD \u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e higher than 0.1 and physical positions within 500 kb of the lead variant were clumped into a locus, represented by the lead variant. Adjacent loci with a distance between LD blocks \u0026lt; 250 kb, were further merged into a single genomic locus using BEDTools\u003csup\u003e43\u003c/sup\u003e. Subsequently, ANNOVAR\u003csup\u003e44\u003c/sup\u003e was used to annotate the consequence and nearest gene of each lead variant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eColocalization analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the potential sharing of causal variants between renal traits and GIT, we selected a genomic region spanning a 100 kb window around each lead variant using \u003cem\u003ecoloc\u003csup\u003e45\u003c/sup\u003e\u003c/em\u003e. This analysis tested five hypotheses regarding pairwise traits within one locus: H\u003csub\u003e0\u003c/sub\u003e, indicating no association with either trait; H\u003csub\u003e1\u003c/sub\u003e or H\u003csub\u003e2\u003c/sub\u003e, suggesting association solely with either trait 1 or trait 2, respectively; H\u003csub\u003e3\u003c/sub\u003e, indicating distinct associations with both traits; and H\u003csub\u003e4\u003c/sub\u003e, suggesting a shared association with both traits. Then the Bayesian posterior probabilities, integrating all feasible configurations\u003csup\u003e45\u003c/sup\u003e, were computed. Pairwise traits were considered colocalized if the posterior probability of H\u003csub\u003e4\u003c/sub\u003e (PP4) exceeded 0.7\u003csup\u003e46\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug repurposing analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our endeavor to repurpose drugs, we investigated gene-drug interactions in the DrugBank\u003csup\u003e47\u003c/sup\u003e database to identify genes potentially targeted by drugs. DrugBank, as a comprehensive repository, offers extensive information on drugs, their corresponding gene targets, mechanisms of action, and interaction\u003c/p\u003e\n\u003cp\u003ens\u003csup\u003e47\u003c/sup\u003e. The latest version 5.1.10 used in our analyses contains data on over 15,000 drugs, with approximately 4,000 of them having received approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunction annotation and enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTissue specificity was assessed by examining the differentially expressed genes (DEG) identified in the GTEx v8 54 tissue types\u003csup\u003e48\u003c/sup\u003e in FUMA\u003csup\u003e49\u003c/sup\u003e. Two-sided \u003cem\u003et\u003c/em\u003e-tests for expression values were conducted in tissue each against all others. Genes with a \u003cem\u003eP\u003c/em\u003e-value \u0026le; 0.05 after Bonferroni correction and an absolute log-fold change \u0026ge; 0.58 were categorized as DEG sets, for these gene expressions in the given tissue had the largest discrepancy with expression in all other tissues. Additionally, genes that are up-regulated and down-regulated in a specific tissue compared to other tissues were identified by considering the sign of \u003cem\u003et\u003c/em\u003e-statistics. Using gene sets derived from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, hypergeometric tests were conducted to determine whether DEGs were overrepresented in specific biological functions compared to background genes (19,283 protein-coding genes)\u003csup\u003e49\u003c/sup\u003e. Adjusted \u003cem\u003eP\u003c/em\u003e-values (\u0026le; 0.05) were used to assess statistical significance, with consideration for Benjamini\u0026ndash;Hochberg multiple test corrections per data source. To further explore the gene expression patterns at a finer resolution, gene expression data at the single-cell level were obtained from the GTEx project\u003csup\u003e48\u003c/sup\u003e, potentially revealing cell type-specific expression profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMendelian randomization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore causal relationships for 20 pairwise traits between renal traits and GIT diseases, bidirectional Mendelian randomization analyses were conducted using the R packages TwoSampleMR\u003csup\u003e34\u003c/sup\u003e, mr.raps\u003csup\u003e50\u003c/sup\u003e and BWMR\u003csup\u003e51\u003c/sup\u003e. Inverse-variance weighted (IVW) method\u003csup\u003e52\u003c/sup\u003e was selected as the primary analysis, with alternative methods such as MR-Egger\u003csup\u003e53\u003c/sup\u003e, weighted median\u003csup\u003e54\u003c/sup\u003e, weighted mode\u003csup\u003e55\u003c/sup\u003e, Bayesian-weighted MR (BWMR)\u003csup\u003e51\u003c/sup\u003e, and robust adjusted profile score (RAPS)\u003csup\u003e50\u003c/sup\u003e used for additional analyses for further validation. The IVW random effects model was chosen to account for potential heterogeneity across instruments. For each pairwise analysis, instrumental variables (IVs) using independent significant IVs (\u003cem\u003eP\u003c/em\u003e \u0026lt; 5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e with the exposure and LD \u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e \u0026lt; 0.01 within 10 Mb based on the 1000 genome project Europeans\u003csup\u003e42\u003c/sup\u003e) were kept from each GWAS dataset, while pleiotropic IVs (associated with more than one traits), IVs with incorrect direction (Steiger test one-sided \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05)\u003csup\u003e56\u003c/sup\u003e and MR-PRESSO\u003csup\u003e57\u003c/sup\u003e outliers were excluded. The strength of each IV was assessed using the F statistic with the equation (instruments with \u003cem\u003eF\u003c/em\u003e values \u0026lt; 10 are typically considered weak IVs)\u003csup\u003e49\u003c/sup\u003e. Nominal significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, and the significance of multiple test correction was defined as \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0025 (0.05/20).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGenetic correlations between renal traits and gastrointestinal tract diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size for each GWAS dataset ranged from 26,734 to 816,412 (\u003cstrong\u003eTable S1\u003c/strong\u003e). We estimated nominal significant genome-wide genetic correlations among five trait pairs (BUN-IBS, IgAN-IBD, KSD-PUD, KSD-GORD, KSD-IBS; \u003cstrong\u003eTable S2 and Figure S2\u003c/strong\u003e) identified by LDSC\u003csup\u003e32\u003c/sup\u003e and \u0026rho;-HESS\u003csup\u003e58\u003c/sup\u003e. In addition, another two trait pairs, namely BUN-IBD and CKD-IBD, were identified by LDSC as positive correlations. Besides, BUN was negatively correlated with GORD identified by \u0026rho;-HESS. In addition, the directions from the LDSC were highly consistent with those from \u0026rho;-HESS. Collectively, eight trait pairs with either nominal significant genome-wide or local genetic correlation were identified, including five pairs passed the multiple testing correction (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0025).\u003c/p\u003e\n\u003cp\u003eOf these eight nominal significant trait pairs, five trait pairs were positively correlated (BUN-IBD, CKD-IBD, KSD-PUD, KSD-GORD, KSD-IBS) with genetic correlation from 0.158 to 0.394, and three trait pairs were negatively correlated (BUN-IBS, BUN-GORD, IgAN-IBD) with genetic correlation from \u0026minus;0.838 to \u0026minus;0.053. Notably, the strongest positive genetic correlation was observed between KSD and PUD (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e = 0.394 by \u0026rho;-HESS), whereas the strongest negative genetic correlation was identified in the pairwise IgAN-IBD (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e = \u0026minus;0.838 by \u0026rho;-HESS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of pleiotropic loci and genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 18,442 SNPs (10,783 unique) were identified as significant pleiotropic variants by PLACO in 20 trait pairs of the gut-renal axis, ranging from 51 pleiotropic variants for CKD-IBS to 3,571 pleiotropic variants for KSD-GORD. These pleiotropic variants were further merged into 222 independent genomic loci for pairwise traits, involving 212 unique lead variants (\u003cstrong\u003eTable S3\u003c/strong\u003e). ANNOVAR\u003csup\u003e44\u003c/sup\u003e category annotation illustrated 169 unique genes nearest to the lead variants via position mapping. Specially, 103 of 212 lead variants (51.0%) were annotated as intronic variants and 76 of 212 (37.6%) were intergenic variants. Only eight of 212 (3.78%) were exonic variants, including five messenger RNA (mRNA) exonic variants and three noncoding RNA exonic variants (\u003cstrong\u003eTable S3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eWe found 21 novel loci in the pleiotropy analyses that were not among risk loci in the original renal traits or GIT disease GWASs (\u003cstrong\u003eTable 1\u003c/strong\u003e). The \u003cem\u003eP\u003c/em\u003e values of lead variants at these novel loci ranged from 6.40\u0026times;10\u003csup\u003e-11\u003c/sup\u003e to 4.69\u0026times;10\u003csup\u003e-8\u003c/sup\u003e in our pleiotropic analyses, while ranging from 8.96\u0026times;10\u003csup\u003e-8\u003c/sup\u003e to 8.68\u0026times;10\u003csup\u003e-6\u003c/sup\u003e in the original GWASs. In the BUN-GORD pleiotropy analysis, \u003cem\u003eSLC39A8\u003c/em\u003e at 4q24, \u003cem\u003eHLA-DRA\u003c/em\u003e at 6p21.32, \u003cem\u003eTARID\u003c/em\u003e at 6q23.2 and \u003cem\u003ePSCA\u003c/em\u003e at 8q24.3 were identified as novel loci with \u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e from to 6.40\u0026times;10\u003csup\u003e-11\u003c/sup\u003e to 3.95\u0026times;10\u003csup\u003e-8\u003c/sup\u003e, not associated with either trait in the original GWAS (\u003cem\u003eP\u003csub\u003eGWAS\u003c/sub\u003e\u003c/em\u003e ranging from 3.02\u0026times;10\u003csup\u003e-7\u003c/sup\u003e to 2.20\u0026times;10\u003csup\u003e-6\u003c/sup\u003e) (\u003cstrong\u003eFigure 1A\u003c/strong\u003e). In addition, we identified one novel locus, namely \u003cem\u003eKIF5B\u003c/em\u003e at 10p11.22, which was associated with eGFR and IBD (rs12572072, \u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e = 1.91\u0026times;10\u003csup\u003e-9\u003c/sup\u003e, \u003cstrong\u003eFigure 1B\u003c/strong\u003e), and with CKD and IBD (rs61844306, \u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e = 4.98\u0026times;10\u003csup\u003e-9\u003c/sup\u003e, \u003cstrong\u003eFigure 1C\u003c/strong\u003e). Furthermore, two more loci showed significant association with CKD and IBD, including \u003cem\u003eZFP36L2\u003c/em\u003e at 2p21 with lead SNP rs149290349 and \u003cem\u003ePVALEF\u003c/em\u003e at 17q25.3 with lead SNP rs138610699 (\u003cstrong\u003eFigure 1C\u003c/strong\u003e). In addition, we identified one novel locus specific to IgAN-IBD pleiotropy analysis: \u003cem\u003eERAP2\u003c/em\u003e at 5q15 (rs6873866, \u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e = 2.14\u0026times;10\u003csup\u003e-9\u003c/sup\u003e) (\u003cstrong\u003eFigure 1D\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. 21 significant pleiotropic loci not associated with original GWASs.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait pair\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLead SNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNearest gene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCytoband\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003etrait1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003etrait2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003ePLACO\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePP4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBest causal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e\u003cstrong\u003emaxSNP.PP4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eBUN-PUD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers55987018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eFAM110C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2p25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e6.09\u0026times;10\u003csup\u003e-7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.06\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.95\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers55987018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eBUN-PUD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers13171906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003ePIK3R1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e5q13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e1.88\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.03\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.12\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers13171906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eBUN-PUD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers3129861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eHLA-DRA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e6p21.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e3.02\u0026times;10\u003csup\u003e-7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.39\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e6.22\u0026times;10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers3129861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eBUN-GORD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers13135092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC39A8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e4q24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e2.78\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e7.60\u0026times;10\u003csup\u003e-7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e3.95\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers13135092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eBUN-GORD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers3129861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eHLA-DRA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e6p21.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e3.02\u0026times;10\u003csup\u003e-7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.20\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e6.40\u0026times;10\u003csup\u003e-11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.814\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers3129861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eBUN-GORD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers2327426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003encRNA intronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eTARID\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e6q23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e5.64\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e5.20\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.85\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers2327426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eBUN-GORD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers2976395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eUTR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003ePSCA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e8q24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e1.05\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.90\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.74\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers2976395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eBUN-IBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers2662260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eRGMB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e5q15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e9.29\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.66\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.87\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers2662260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eBUN-IBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers148345586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eSERGEF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e11p15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e3.01\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.62\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.83\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers148345586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eBUN-IBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers2854275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003encRNA exonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eHLA-DQB1-AS1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e6p21.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e1.19\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.64\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e9.76\u0026times;10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers2854275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eeGFR-PUD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers10911211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eLAMC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1q25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e6.03\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e6.31\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.97\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers10911211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eeGFR-IBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers12572072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eKIF5B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e10p11.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e8.96\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.65\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.91\u0026times;10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.929\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers12572072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eeGFR-IBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers12602010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eNFE2L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e17q21.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e1.43\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e5.29\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e4.69\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers12602010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eCKD-PUD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers2860422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003encRNA intronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eFAM13A-AS1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e4q22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e3.15\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.05\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.55\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers2860422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eCKD-GORD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers4560110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eFAM49A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2p24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e1.15\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e3.40\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e4.24\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers4632345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eCKD-IBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers149290349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eZFP36L2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2p21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e7.28\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e6.13\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.35\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers149290349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eCKD-IBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers61844306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eKIF5B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e10p11.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e6.81\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1.14\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e4.98\u0026times;10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.898\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers61844306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eCKD-IBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers138610699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003ePVALEF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e17q25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e2.33\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e3.18\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e4.16\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.800\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers138610699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eIgAN-IBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers6873866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eERAP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e5q15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e6.86\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e6.32\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.14\u0026times;10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.800\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers6873866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eKSD-PUD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers11888760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eSF3B1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2q33.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e1.10\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e7.21\u0026times;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e2.03\u0026times;10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers11888760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003eKSD-IBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers12723578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9565%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0435%;\"\u003e\n \u003cp\u003e\u003cem\u003eUTP25\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e1q32.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.69565%;\"\u003e\n \u003cp\u003e2.52\u0026times;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e3.56\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.6087%;\"\u003e\n \u003cp\u003e3.00\u0026times;10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.34783%;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003ers12723578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.78261%;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eP\u003csub\u003etrait1\u003c/sub\u003e\u003c/em\u003e, \u003cem\u003eP\u003csub\u003etrait2\u003c/sub\u003e\u003c/em\u003e, and \u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e represent the \u003cem\u003eP\u003c/em\u003e values of trait 1 GWAS, trait 2 GWAS and pleiotropic analysis, respectively. PP4 is the posterior probability of H4, and PP4\u0026gt;0.7 is marked as bold.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BUN, Blood urea nitrogen; eGFR, Estimated glomerular filtration rate; CKD, Chronic kidney disease; IgAN, IgA nephropathy; KSD, Kidney stone disease; PUD, Peptic ulcer disease; GORD, Gastro-oesophageal Reflux Disease; IBD, Inflammatory bowel disease; IBS, irritable bowel syndrome, PP3, posterior probability of H3; PP4, posterior probability of H4; maxSNP.PP4, posterior probability of the best causal variant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eColocalization analysis for the causal variant\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of the total loci examined, there were 21 (9.5%) shared causal variants to prove the associations of 12 pairwise traits using colocalization analysis based on the average value of PP4 across all regions, and were mapped to 19 unique genes (PP4 \u0026gt; 0.7; \u003cstrong\u003eTable S4\u003c/strong\u003e). A total of 19 lead variants were identified as candidate causal SNPs with the largest SNP PP4 in 16 unique pleiotropic loci for 11 pairwise traits. The highest posterior probability of colocalization (PP4 = 0.976) was identified at the locus 9q34.3 between IgAN and IBD, involving candidate SNP rs4077515 in \u003cem\u003eCARD9\u003c/em\u003e. Interestingly, the locus 6p21.32, which was identified as a novel pleiotropic locus for three pairwise traits, was only colocalized between BUN-GORD (PP4 = 0.814) and eGFR-PUD (PP4 = 0.721) rather than BUN-PUD (PP4 = 0.696), with the same potential shared causal variant rs3129861 identified in BUN-GORD and BUN-PUD (nearest gene: \u003cem\u003eHLA-DRA\u003c/em\u003e). Another three loci were also colocalized by more than one pairwise trait, including locus 6p22.1 colocalized between eGFR-GORD (PP4 = 0.965, candidate SNP rs7752448) and CKD-GORD (PP4 = 0.970, candidate SNP rs34788973), locus 6p21.33 colocalized between IgAN-GORD (PP4 = 0.973, candidate SNP rs3099844) and KSD-IBS (PP4 = 0.768, candidate SNP rs9266244), and locus 10p11.22 colocalized between eGFR-IBD (PP4 = 0.929, candidate SNP rs12572072) and CKD-IBD (PP4= 0.898, candidate SNP rs61844306).\u003c/p\u003e\n\u003cp\u003eNotably, we identified four novel pleiotropic loci that were colocalized for three pairwise traits. Rs3129861 (Chr6: 32401532, T\u0026gt;A), an intergenic variant in the \u003cem\u003eHLA-DRA\u0026nbsp;\u003c/em\u003egene, was colocalized between BUN and GORD (PP4 = 0.814), despite not being associated with either trait in the original GWAS (\u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e = 6.40\u0026times;10\u003csup\u003e-11\u003c/sup\u003e, \u003cem\u003eP\u003csub\u003eBUN\u003c/sub\u003e\u003c/em\u003e = 3.02\u0026times;10\u003csup\u003e-7\u003c/sup\u003e, \u003cem\u003eP\u003csub\u003eGORD\u003c/sub\u003e\u003c/em\u003e = 2.20\u0026times;10\u003csup\u003e-6\u003c/sup\u003e) (\u003cstrong\u003eTable 1,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 1A\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Rs12572072 (Chr10: 32307971, T\u0026gt; C), an intronic variant in the \u003cem\u003eKIF5B\u0026nbsp;\u003c/em\u003egene, was colocalized between eGFR and IBD as mentioned above (PP4 = 0.929, \u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e = 1.91\u0026times;10\u003csup\u003e-9\u003c/sup\u003e, \u003cem\u003eP\u003csub\u003eeGFR\u003c/sub\u003e\u003c/em\u003e = 8.96\u0026times;10\u003csup\u003e-8\u003c/sup\u003e, \u003cem\u003eP\u003csub\u003eIBD\u003c/sub\u003e\u003c/em\u003e = 1.65\u0026times;10\u003csup\u003e-5\u003c/sup\u003e) (\u003cstrong\u003eTable 1,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 1B\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 2B\u003c/strong\u003e). Rs61844306 (Chr10: 32352388, T\u0026gt; C), an intergenic variant in the \u003cem\u003eKIF5B\u0026nbsp;\u003c/em\u003egene, was colocalized between CKD and IBD (PP4 = 0.898, \u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e = 4.98\u0026times;10\u003csup\u003e-9\u003c/sup\u003e, \u003cem\u003eP\u003csub\u003eCKD\u003c/sub\u003e\u003c/em\u003e = 6.81\u0026times;10\u003csup\u003e-6\u003c/sup\u003e, \u003cem\u003eP\u003csub\u003eIBD\u003c/sub\u003e\u003c/em\u003e = 1.14\u0026times;10\u003csup\u003e-5\u003c/sup\u003e) (\u003cstrong\u003eTable 1,\u003c/strong\u003e \u003cstrong\u003eFigure 1C\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 2C\u003c/strong\u003e). Rs138610699 (Chr17: 79145123, T\u0026gt; G), an intronic variant in the \u003cem\u003ePVALEF\u0026nbsp;\u003c/em\u003egene, was colocalized between CKD and IBD (PP4 = 0.800, \u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e = 4.16\u0026times;10\u003csup\u003e-8\u003c/sup\u003e, \u003cem\u003eP\u003csub\u003eCKD\u003c/sub\u003e\u003c/em\u003e = 2.33\u0026times;10\u003csup\u003e-4\u003c/sup\u003e, \u003cem\u003eP\u003csub\u003eIBD\u003c/sub\u003e\u003c/em\u003e = 3.18\u0026times;10\u003csup\u003e-6\u003c/sup\u003e) (\u003cstrong\u003eTable 1,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 1C\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 2D\u003c/strong\u003e). Rs6873866 (Chr5 : 96247810, T\u0026gt; C), an intronic variant in the \u003cem\u003eERAP2\u003c/em\u003e gene, was colocalized between IgAN and IBD (PP4 = 0.800, \u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e = 2.14\u0026times;10\u003csup\u003e-9\u003c/sup\u003e, \u003cem\u003eP\u003csub\u003eIgAN\u003c/sub\u003e\u003c/em\u003e = 6.86\u0026times;10\u003csup\u003e-6\u003c/sup\u003e, \u003cem\u003eP\u003csub\u003eIBD\u003c/sub\u003e\u003c/em\u003e = 6.32\u0026times;10\u003csup\u003e-6\u003c/sup\u003e) (\u003cstrong\u003eTable 1,\u003c/strong\u003e \u003cstrong\u003eFigure 1D\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 2E\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug repurposing analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DrugBank database was applied to identify the available drugs targeting the annotated genes mapping to potential pleiotropic loci\u003csup\u003e47\u003c/sup\u003e. A total of 41 pleiotropic loci were identified across 15 pairwise traits, encompassing 38 unique lead variants mapped to 29 distinct genes, which have approved or investigational drugs available (\u003cstrong\u003eTable S5\u003c/strong\u003e). Notably, eight drugs, namely Zinc for \u003cem\u003eCPN1\u003c/em\u003e, Aminocaproic Acid for \u003cem\u003eLPA\u003c/em\u003e, Tenapanor for \u003cem\u003eSLC9A3\u003c/em\u003e, Fostamatinib for \u003cem\u003eMAP3K11\u003c/em\u003e, and Calcium Citrate for \u003cem\u003eCHP1\u003c/em\u003e, CTA018 for \u003cem\u003eCYP24A1\u003c/em\u003e, Abacavir for \u003cem\u003eHLA-B\u003c/em\u003e and Lansoprazole for \u003cem\u003eMAPT\u003c/em\u003e have been utilized or investigated in trials for the treatment of renal or GIT diseases\u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue-specificity and pathway enrichment for the pleiotropic loci\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo detect tissue-specific expression patterns, we generated the interactive heatmap of gene expression (\u003cstrong\u003eFigure S3\u003c/strong\u003e) and DEG Sets analyses based on GTEx reference panels\u003csup\u003e48\u003c/sup\u003e (\u003cstrong\u003eFigure S4A\u003c/strong\u003e). Among 54 primary human tissues, the tissue-specifically expressed genes of 26 tissues were enriched for the pleiotropic genes, including the kidney, circulatory system, brain tissue, pancreas, liver, esophagus, lung, etc. The expression of pleiotropic genes was found to be up-regulated in kidney cortex, liver and artery tibial tissues, and down-regulated in the kidney, circulatory system, brain tissue, pancreas and esophagus. The effects of pleiotropic genes on the gut-renal axis related phenotype may play a role in different tissues.\u003c/p\u003e\n\u003cp\u003eFurthermore, a subset of prioritized pleiotropic genes is involved in immune system and cytokine signaling pathways. Gene set enrichment analysis identified 15 significantly enriched pathways (normalized enrichment score \u0026gt; 2 and adjusted \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), comprising 14 GO terms and one pathways (\u003cstrong\u003eFigure S4B\u003c/strong\u003e). These enriched pathways are primarily involved in cell death, cell migration and immune system functions. For instance, negative regulation of tubulin deacetylation, critical for cell migration, was identified in 50% of overlapping genes.\u003c/p\u003e\n\u003cp\u003eAmong the four novel colocalized loci, the expressions of annotated genes were investigated using the expression quantitative trait loci (eQTL) database from GTEx Consortium\u003csup\u003e48\u003c/sup\u003e. The C allele of lead variant rs6873866 at 5q15 locus (\u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e = 2.14\u0026times;10\u003csup\u003e-9\u003c/sup\u003e for IgAN-IBD;\u003cstrong\u003e\u0026nbsp;Table 1,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 2E and 3A\u003c/strong\u003e) was negatively associated with \u003cem\u003eERAP2\u003c/em\u003e expression in several tissues, including EBV-transformed lymphocytes cells, kidney cortex, and small intestine terminal ileum. The lead variant rs12572072 (\u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e=1.91\u0026times;10\u003csup\u003e-9\u0026nbsp;\u003c/sup\u003efor eGFR-IBD) and rs681343 (\u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e=4.98\u0026times;10\u003csup\u003e-9\u003c/sup\u003e for CKD-IBD) at 10p11.22 had similar identical eQTL regulation information (\u003cstrong\u003eTable 1,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 2 and 3\u003c/strong\u003e), regulating the expression of \u003cem\u003eKIF5B\u003c/em\u003e. The C allele of rs12572072 (\u003cem\u003eP\u003c/em\u003e=1.35\u0026times;10\u003csup\u003e-14\u003c/sup\u003e) and T allele of rs681343 (\u003cem\u003eP\u003c/em\u003e=4.81\u0026times;10\u003csup\u003e-16\u003c/sup\u003e) were negatively correlated with the expression of \u003cem\u003eKIF5B\u003c/em\u003e gene in cultured fibroblast cells. The C allele of lead variant rs6873866 at 5q15 locus (\u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e = 2.14\u0026times;10\u003csup\u003e-9\u003c/sup\u003e for IgAN-IBD;\u003cstrong\u003e\u0026nbsp;Table 1,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 2E and 3A\u003c/strong\u003e) was negatively associated with \u003cem\u003eERAP2\u003c/em\u003e expression in several tissues, including EBV-transformed lymphocytes cells, kidney cortex, and small intestine terminal ileum. The lead variant rs12572072 (\u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e=1.91\u0026times;10\u003csup\u003e-9\u0026nbsp;\u003c/sup\u003efor eGFR-IBD) and rs681343 (\u003cem\u003eP\u003csub\u003ePLACO\u003c/sub\u003e\u003c/em\u003e=4.98\u0026times;10\u003csup\u003e-9\u003c/sup\u003e for CKD-IBD) at 10p11.22 had similar identical eQTL regulation information (\u003cstrong\u003eTable 1,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 2 and 3\u003c/strong\u003e), regulating the expression of \u003cem\u003eKIF5B\u003c/em\u003e. The C allele of rs12572072 (\u003cem\u003eP\u003c/em\u003e=1.35\u0026times;10\u003csup\u003e-14\u003c/sup\u003e) and T allele of rs681343 (\u003cem\u003eP\u003c/em\u003e=4.81\u0026times;10\u003csup\u003e-16\u003c/sup\u003e) were negatively correlated with the expression of \u003cem\u003eKIF5B\u003c/em\u003e gene in EBV-transformed lymphocytes cells. These results suggest that these genetic variants may influence gut-renal related diseases by regulating gene expression\u003csup\u003e48\u003c/sup\u003e. In addition, these colocalized genes \u003cem\u003eHLA-DRA\u003c/em\u003e, \u003cem\u003eKIF5B\u003c/em\u003e and \u003cem\u003eERAP2\u003c/em\u003e were most highly expressed in B cell, dendritic cell (DC) and DC/macrophages subsets in breast, esophagus mucosa, and esophagus muscularis from GTEx Consortium (\u003cstrong\u003eFigure 3B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCausal inference between renal traits and gastrointestinal tract diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBidirectional Mendelian randomization analyses were conducted between five renal traits and four GIT diseases. F values of all selected IVs were greater than 10, indicating stronger instruments. When IBS was the exposure and KSD was the outcome, only one valid instrumental variable remained after MR-PRESSO outlier exclusion, rendering the main MR analysis inapplicable. The primary IVW data analysis revealed that IgAN was associated with a decreased risk of IBD (OR = 0.91, \u003cem\u003eP\u003c/em\u003e = 1.11\u0026times;10\u003csup\u003e-4\u003c/sup\u003e). For the other pairwise traits, no significant causal effects were found after the Bonferroni correction (all \u003cem\u003eP\u003c/em\u003e values \u0026gt; 0.0025). Nonetheless, nominal significant causal effects were noted that PUD showed a tiny protective effect on eGFR (\u0026beta; = 0.006, \u003cem\u003eP\u003c/em\u003e = 0.009), as well as PUD (\u0026beta; = 0.006, \u003cem\u003eP\u003c/em\u003e = 0.009) and GORD (\u0026beta; = 0.007, \u003cem\u003eP\u003c/em\u003e = 0.037) showed a tiny protective effect on eGFR (\u003cstrong\u003eFigure S5, Tables S6\u003c/strong\u003e). These findings were also found in several sensitivity analyses (\u003cstrong\u003eTable S6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSummary of overall results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFigure 4\u003c/strong\u003e, all the results from genetic correlation, MR, and colocalization analyses based on effects and levels of statistical significance/direction. The results from genetic correlation and MR analyses were both statistically significant and direct, while colocalization analysis was only statistically significant. The MR results comprehensively considered the primary analysis of the IVW method and excluded the potential bias of pleiotropy.\u003c/p\u003e\n\u003cp\u003eTo include more possible outcomes, we applied less strict thresholds and presented possible explanations: (ⅰ) Significant results among genetic correlation, colocalization, and MR analyses support strong genetic association and causal inference for IgAN-IBD pairwise trait; (ⅱ) Non-significant genetic correlation with significant colocalization and MR supports potential causality in genome-wide and regional scale for eGFR-PUD and eGFR-GORD despite weak genetic correlations; (ⅲ) Significant genetic correlation and colocalization with non-significant MR may indicate incomplete causal understanding or uncontrollably biases whereas shared genetic biological mechanisms for BUN-GORD, CKD-IBD, KSD-PUD, and KSD-IBD; (ⅳ) Significant genetic correlation with non-significant colocalization analysis and non-significant MR analysis may imply genetic correlations, while present, are not a direct reflection of causality and further research may be needed to validate in BUN-IBS, BUN-IBD, and KSD- GORD; (ⅴ) Non-significant genetic correlation with colocalization analysis and significant MR analysis may indicate that potential causality for IgAN-IBS despite the absence of strong genetic signals in correlation or colocalization; (ⅵ) Non-significant genetic correlation with significant colocalization analysis but non-significant MR analysis may indicate no enough causality but with shared causal genetic variants for eGFR-IBD, eGFR-IBS, CKD-GORD, IgAN-GORD and KSD-IBD.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we explored the shared genetic effects in the gut-renal axis traits and diseases using the latest and largest GWAS datasets. We found that renal function was genetically correlated with some GIT diseases, while only IgAN and IBD were identified as strong genetic evidence for the causal association and genetic correlation. By pleiotropic analyses, we identified the shared genes, the tissue- and cell type-specify and biological pathways, supporting the role of the gut-renal axis in the shared genetic etiology. Besides, some potential drugs for repurposing were also suggested for the treatment of renal traits and GIT diseases.\u003c/p\u003e \u003cp\u003eThe genome-wide genetic associations estimated by \u0026#120646;-HESS and LDSC were in the same direction, and the jointly identified significant associations remained significant after correction by multiple tests (BUN-IBS, IgAN-IBD, KSD-PUD, KSD-GORD, KSD-IBS), indicating the robustness of the estimations. We observed the strongest genetic negative association in IgAN-IBD, reflecting the low genetic risk of IBD associated with IgAN. In addition, KSD and gastrointestinal disorders (PUD, GORD, IBS) were detected relatively higher positive genetic associations, reflecting the genetic risk of GIT diseases strongly associated with KSD. However, the average width of the local region is about 1.6 Mb\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, and the variable effect of the variation scale also exists. For instance, in the BUN-IBS pleiotropy analysis, we found mixed signals at the genomic risk loci, with five of the eight lead variants showing the same direction of effect between BUN and IBS GWASs, while the other three lead variants showed opposite effects. Therefore, we propose to assess shared genetic architecture at the finer resolution by using PLACO in trait pairs to further determine the pleiotropic variants aiding in understanding the polygenic overlap despite the insignificant genetic correlation, followed by colocalization analysis to identify potential shared causal variants in each pleiotropic locus.\u003c/p\u003e \u003cp\u003eFindings of pleiotropic analyses combined with drug repurposing provide promising insights into potential therapeutic avenues and enhance the understanding of mechanisms related to \u003cem\u003eLPA\u003c/em\u003e and \u003cem\u003eHLA-B\u003c/em\u003e in renal and gastrointestinal diseases. In the pleiotropic analyses, we identified the variant rs11751605 in \u003cem\u003eLPA\u003c/em\u003e that was causally associated with eGFR and PUD (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ePLACO\u003c/em\u003e\u003c/sub\u003e=7.86\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e), with the PP4 of 0.724. \u003cem\u003eLPA\u003c/em\u003e encoded Apolipoprotein(a), a serine protease that inhibits tissue plasminogen activator I activity and is involved in multiple biological pathways of plasma lipoprotein (lipoprotein A) assembly, remodeling, clearance, and cholesterol metabolism\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Given that LPA inhibitors like Aminocaproic Acid are already used in blood disorder treatments, our study strengthens the rationale for exploring LPA inhibitors in clinical trials for treating CKD\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, leveraging their known mechanisms in lipid metabolism and potential impacts on renal function. KSD and IBS are colocalized in \u003cem\u003eHLA-B\u003c/em\u003e (PP4\u0026thinsp;=\u0026thinsp;0.768). \u003cem\u003eHLA-B\u003c/em\u003e encodes antigen-presenting major histocompatibility complex class I (MHCI) molecule, which is involved in the recognition and presentation of endogenous antigens that regulate CTL killing\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. A large number of studies have shown that the increase of somatic mutation rate in \u003cem\u003eHLA\u003c/em\u003e is significantly related to HLA dysfunction, as a potential mechanism of immune escape\u003csup\u003e\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. An inhibitor of HLA-B, Abacavir, is in clinical trials for the treatment of renal insufficiency\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. These findings highlight the potential for drug repurposing of immune-related genes and and development of new treatments that target these pathways in renal and gastrointestinal diseases.\u003c/p\u003e \u003cp\u003eThe advanced approach of cross-trait pleiotropy analysis enabled us to identify 21 novel loci that were not significant in the original GWASs. Four of the five lead variants showed evidence of colocalization. \u003cem\u003eKIF5B\u003c/em\u003e is a causal gene for eGFR-IBD and CKD-IBD trait pairs, which has not been reported in previous GWASs about GIT diseases or renal traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There was no previous GWAS report that KIF5B was associated with gastrointestinal diseases or renal traits from the NHGRI-EBI GWAS catalog\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eKIF5B\u003c/em\u003e encodes the kinesin-1 heavy chain with multiple roles in immune responses, such as being involved in NK cell-mediated cytotoxicity and driving immune synaptic polarization between effector NK lymphocytes and target cells\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Considering that persistent inflammation is one of the signs of the development, progression and complications of CKD\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, and IBD is an immune-mediated disease, we inferred that rs13107325 and rs61844306 may interfere with immune response by regulating the expression of \u003cem\u003eKIF5B\u003c/em\u003e in immune tissues and gut-renal related tissues, thereby affecting renal function and gastrointestinal diseases. In the future, more experiments and research are necessary to validate mechanisms into the genetic basis of immune dysregulation in CKD and IBD. Understanding the genetic mechanisms might lead to novel therapeutic targets aimed at modulating immune responses to alleviate inflammation and improve outcomes in these chronic diseases.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this study is the first one to investigate the systematical and comprehensive genetic association between renal traits and GIT diseases, balancing genetic evidence from genetic correlation, MR, colocalization and gene enrichment analyses. Genetic correlations and colocalization signals can stem from vertical pleiotropy, suggesting potential causal associations between diseases\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Applying multiple methods with different model assumptions could provide complementary evidence and allow deep investigation of the underlying pleiotropic associations in gut-renal related traits from different perspectives. First of all, we observed negative genetic correlations between IgAN and IBD across genome-wide and local genetic scales. Recent cohort studies have reported an increased risk of IBD in patients with IgAN\u003csup\u003e\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Given the possible confounding factors and potential biases inherent in observational studies, further research is needed to validate the shared pathophysiology of these conditions. MR approach capitalizes on the random allocation of genetic variation, reducing the likelihood of confounding\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Bidirectional MR analysis demonstrated a protective causal effect of IgAN on IBD and excluded the reverse causal association. This effect may be attributed to genetically elevated secretory IgA, known for its homeostatic anti-inflammatory and immunosuppressive effects at the intestinal mucosal level. Moreover, pleiotropic loci identified were significantly enriched in immune, blood, and intestinal mucosal tissues, aligning with previous drug trials that targeted intestinal inflammation as an effective therapy for IgAN\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. In addition, we identified that IBD and IgAN share genetic susceptibility loci in \u003cem\u003eCARD9\u003c/em\u003e (rs4077515) and \u003cem\u003eERAP2\u003c/em\u003e (rs6873866), reinforcing the genetic link between these diseases. Importantly, we initially identified a causal variant rs6873866 (PP4\u0026thinsp;=\u0026thinsp;0.800) shared by IgAN and IBD in \u003cem\u003eERAP2\u003c/em\u003e. The C allele of rs6873866 was negatively associated with \u003cem\u003eERAP2\u003c/em\u003e expression in multiple tissues, such as whole blood, EBV-transformed lymphocytes, and kidney cortex and small intestine terminal ileum. The \u003cem\u003eERAP2\u003c/em\u003e-encoded aminopeptidase plays a central role in peptide pruning that generates most HLA class I-binding peptides\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Our newly identified immune-related genes add new insights into the intricate pathogenesis of IgAN and IBD and may provide new biological evidence for the pathogenesis of comorbid traits associated with the gut-renal axis. This discovery enables a deeper analysis of disease associations and serves as a crucial foundation for preventing clinical complications. Future pathological experiments and clinical trials are suggested to be put forward to investigate more common risk factors or confounding factors underlying the association between IgAN and IBD risk.\u003c/p\u003e \u003cp\u003eSeveral limitations are acknowledged in our study. Firstly, the GWAS included in our analysis were restricted to European populations, which limits the generalizability of our findings to other ancestral groups. Future investigations should incorporate multi-ancestry GWAS to confirm the robustness of these associations across diverse populations. Secondly, while we observed global vertical pleiotropy in specific trait pairs, the precise association patterns of these pleiotropic loci remain undefined. Thirdly, in terms of the somewhat small sample size of cases in IgAN and GIT diseases, the statistical power might be insufficient. Nevertheless, the universal genetic correlations between renal diseases and GIT diseases pairwise traits were successfully recognized, followed by the identification of pleiotropic loci for renal diseases and GIT diseases, which displayed enrichment in the gut and kidney transcriptome. Concerning unavoidable overlapped samples among international largest and latest GWAS summary data, which may bias the causal estimates, the intercepts from LDSC were estimated, only to indicate little potential sample overlap between two pairwise traits of GWAS. Additionally, the lack of external validation hinders the broader applicability of our results, particularly due to limited sample sizes in available GWAS data for certain phenotypes like IgAN. Lastly, our cell-type specific analyses were constrained by the absence of kidneys and complete digestive tract correlation datasets, potentially impacting the comprehensiveness and reliability of our tissue-specific findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUnderlying shared genetic architecture between renal traits and GIT diseases, we identified shared SNPs, loci, and risk genes. Additionally, we identified common biological mechanisms relating to enriched tissues like the kidney cortex and small intestine, pathways of cell death coagulation and immune responses, as well as distinct immune cell types. This study uncovers genetic pleiotropy for 20 pairwise traits in the gut-renal axis and offers evidence for personalized treatment options. Furthermore, we showed that the drug targets of pleiotropic immune-related genes will provide novel therapeutic insights for renal traits and GIT diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBWMR:\u003c/em\u003e\u003c/strong\u003e Bayesian-weighted MR\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCKD:\u003c/em\u003e\u003c/strong\u003e Chronic Kidney Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDEG:\u003c/em\u003e\u003c/strong\u003e Differentially Expressed Genes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eeGFR:\u003c/em\u003e\u003c/strong\u003e Estimated Glomerular Filtration Rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGIT:\u003c/em\u003e\u003c/strong\u003e Gastrointestinal Tract\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGWAS:\u003c/em\u003e\u003c/strong\u003e Genome-Wide Association Studies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIgAN:\u003c/em\u003e\u003c/strong\u003e Immunoglobulin A Nephropathy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIV:\u003c/em\u003e\u003c/strong\u003e Instrumental Variable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIVW:\u003c/em\u003e\u003c/strong\u003e Inverse-Variance Weighted\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eKSD:\u003c/em\u003e\u003c/strong\u003e Kidney Stone Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePUD:\u003c/em\u003e\u003c/strong\u003e Peptic Ulcer Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGORD:\u003c/em\u003e\u003c/strong\u003e Gastroesophageal Reflux Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIBD:\u003c/em\u003e\u003c/strong\u003e Inflammatory Bowel Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIBS:\u003c/em\u003e\u003c/strong\u003e Irritable Bowel Syndrome\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePLACO:\u003c/em\u003e\u003c/strong\u003e Pleiotropic Analysis under the Composite Null Hypothesis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRAPS:\u003c/em\u003e\u003c/strong\u003e Robust Adjusted Profile Score\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSNP:\u003c/em\u003e\u003c/strong\u003e Single-Nucleotide Polymorphism\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eThe original contributions of this study are included in the article and supplementary information. Further inquiries can be directed to the corresponding author X.H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e XH reported receipt of grants from the National Natural Science Foundation of China (Award number: 32470658). The funders had no role in designing the study, the analysis, or the decision to submit the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e XH conceptualized the study and acquired funding. XH, SL and SW had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. SL, SW, MJ, YK, ZS, XH, YG, and XC conducted the analysis and interpreted the data. YK, ZS, XH, YG, and XC accessed and verified the underlying statistical calculation. SL and SW produced figures, and wrote the manuscript. XH, SL and MJ designed and supervised the study. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe thank participants and investigators who contributed to the GWAS summary statistics and omics data included in our analyses.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKovesdy CP. Epidemiology of chronic kidney disease: an update 2022. \u003cem\u003eKidney Int Suppl (2011)\u003c/em\u003e. Apr 2022;12(1):7-11. doi:10.1016/j.kisu.2021.11.003\u003c/li\u003e\n\u003cli\u003eXie Y, Bowe B, Mokdad AH, et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. \u003cem\u003eKidney Int\u003c/em\u003e. Sep 2018;94(3):567-581. doi:10.1016/j.kint.2018.04.011\u003c/li\u003e\n\u003cli\u003eYing M, Shao X, Qin H, et al. Disease burden and epidemiological trends of chronic kidney disease at the global, regional, national levels from 1990 to 2019. \u003cem\u003eNephron\u003c/em\u003e. Sep 15 2023;doi:10.1159/000534071\u003c/li\u003e\n\u003cli\u003eRomagnani P, Remuzzi G, Glassock R, et al. Chronic kidney disease. \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e. Nov 23 2017;3:17088. doi:10.1038/nrdp.2017.88\u003c/li\u003e\n\u003cli\u003eGansevoort RT, Matsushita K, van der Velde M, et al. Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. \u003cem\u003eKidney Int\u003c/em\u003e. Jul 2011;80(1):93-104. doi:10.1038/ki.2010.531\u003c/li\u003e\n\u003cli\u003eKiryluk K, Sanchez-Rodriguez E, Zhou XJ, et al. Genome-wide association analyses define pathogenic signaling pathways and prioritize drug targets for IgA nephropathy. \u003cem\u003eNature genetics\u003c/em\u003e. Jul 2023;55(7):1091-1105. doi:10.1038/s41588-023-01422-x\u003c/li\u003e\n\u003cli\u003eNihei Y, Haniuda K, Higashiyama M, et al. Identification of IgA autoantibodies targeting mesangial cells redefines the pathogenesis of IgA nephropathy. \u003cem\u003eSci Adv\u003c/em\u003e. Mar 22 2023;9(12):eadd6734. doi:10.1126/sciadv.add6734\u003c/li\u003e\n\u003cli\u003eKiryluk K, Novak J. The genetics and immunobiology of IgA nephropathy. \u003cem\u003eJ Clin Invest\u003c/em\u003e. Jun 2014;124(6):2325-32. doi:10.1172/JCI74475\u003c/li\u003e\n\u003cli\u003eLai KN, Tang SC, Schena FP, et al. IgA nephropathy. \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e. Feb 11 2016;2:16001. doi:10.1038/nrdp.2016.1\u003c/li\u003e\n\u003cli\u003eDhayat NA, Bonny O, Roth B, et al. Hydrochlorothiazide and Prevention of Kidney-Stone Recurrence. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e. Mar 2 2023;388(9):781-791. doi:10.1056/NEJMoa2209275\u003c/li\u003e\n\u003cli\u003ePeery AF, Crockett SD, Murphy CC, et al. Burden and Cost of Gastrointestinal, Liver, and Pancreatic Diseases in the United States: Update 2021. \u003cem\u003eGastroenterology\u003c/em\u003e. Feb 2022;162(2):621-644. doi:10.1053/j.gastro.2021.10.017\u003c/li\u003e\n\u003cli\u003eLehto M, Groop PH. The Gut-Kidney Axis: Putative Interconnections Between Gastrointestinal and Renal Disorders. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e. 2018;9:553. doi:10.3389/fendo.2018.00553\u003c/li\u003e\n\u003cli\u003eKhoury T, Tzukert K, Abel R, Abu Rmeileh A, Levi R, Ilan Y. The gut-kidney axis in chronic renal failure: A new potential target for therapy. \u003cem\u003eHemodial Int\u003c/em\u003e. Jul 2017;21(3):323-334. doi:10.1111/hdi.12486\u003c/li\u003e\n\u003cli\u003eLiao Y, Fan L, Bin P, et al. GABA signaling enforces intestinal germinal center B cell differentiation. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e. Nov 2022;119(44):e2215921119. doi:10.1073/pnas.2215921119\u003c/li\u003e\n\u003cli\u003eBartochowski P, Gayrard N, Bornes S, et al. Gut-Kidney Axis Investigations in Animal Models of Chronic Kidney Disease. \u003cem\u003eToxins (Basel)\u003c/em\u003e. Sep 7 2022;14(9)doi:10.3390/toxins14090626\u003c/li\u003e\n\u003cli\u003eRamos CI, Armani RG, Canziani MEF, et al. Effect of prebiotic (fructooligosaccharide) on uremic toxins of chronic kidney disease patients: a randomized controlled trial. \u003cem\u003eNephrol Dial Transplant\u003c/em\u003e. Nov 1 2019;34(11):1876-1884. doi:10.1093/ndt/gfy171\u003c/li\u003e\n\u003cli\u003eRanganathan N, Friedman EA, Tam P, Rao V, Ranganathan P, Dheer R. Probiotic dietary supplementation in patients with stage 3 and 4 chronic kidney disease: a 6-month pilot scale trial in Canada. \u003cem\u003eCurr Med Res Opin\u003c/em\u003e. Aug 2009;25(8):1919-30. doi:10.1185/03007990903069249\u003c/li\u003e\n\u003cli\u003eVaziri ND, Wong J, Pahl M, et al. Chronic kidney disease alters intestinal microbial flora. \u003cem\u003eKidney Int\u003c/em\u003e. Feb 2013;83(2):308-15. doi:10.1038/ki.2012.345\u003c/li\u003e\n\u003cli\u003eWuttke M, Li Y, Li M, et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. \u003cem\u003eNature genetics\u003c/em\u003e. Jun 2019;51(6):957-972. doi:10.1038/s41588-019-0407-x\u003c/li\u003e\n\u003cli\u003eSingh P, Harris PC, Sas DJ, Lieske JC. The genetics of kidney stone disease and nephrocalcinosis. \u003cem\u003eNat Rev Nephrol\u003c/em\u003e. Apr 2022;18(4):224-240. doi:10.1038/s41581-021-00513-4\u003c/li\u003e\n\u003cli\u003eWu Y, Murray GK, Byrne EM, Sidorenko J, Visscher PM, Wray NR. GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression. \u003cem\u003eNature communications\u003c/em\u003e. Feb 19 2021;12(1):1146. doi:10.1038/s41467-021-21280-7\u003c/li\u003e\n\u003cli\u003eDuerr RH, Taylor KD, Brant SR, et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. \u003cem\u003eScience (New York, NY)\u003c/em\u003e. Dec 1 2006;314(5804):1461-3. doi:10.1126/science.1135245\u003c/li\u003e\n\u003cli\u003eEijsbouts C, Zheng T, Kennedy NA, et al. Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders. \u003cem\u003eNature genetics\u003c/em\u003e. Nov 2021;53(11):1543-1552. doi:10.1038/s41588-021-00950-8\u003c/li\u003e\n\u003cli\u003eBanaszczyk K. Risankizumab in the treatment of psoriasis - literature review. \u003cem\u003eReumatologia\u003c/em\u003e. 2019;57(3):158-162. doi:10.5114/reum.2019.86426\u003c/li\u003e\n\u003cli\u003eReay WR, Cairns MJ. Advancing the use of genome-wide association studies for drug repurposing. \u003cem\u003eNat Rev Genet\u003c/em\u003e. Oct 2021;22(10):658-671. doi:10.1038/s41576-021-00387-z\u003c/li\u003e\n\u003cli\u003eShi D, Zhong Z, Wang M, et al. Identification of susceptibility locus shared by IgA nephropathy and inflammatory bowel disease in a Chinese Han population. \u003cem\u003eJ Hum Genet\u003c/em\u003e. Mar 2020;65(3):241-249. doi:10.1038/s10038-019-0699-9\u003c/li\u003e\n\u003cli\u003eRen F, Jin Q, Jin Q, et al. Genetic evidence supporting the causal role of gut microbiota in chronic kidney disease and chronic systemic inflammation in CKD: a bilateral two-sample Mendelian randomization study. \u003cem\u003eFrontiers in immunology\u003c/em\u003e. 2023;14:1287698. doi:10.3389/fimmu.2023.1287698\u003c/li\u003e\n\u003cli\u003eZhang H, Huang Y, Zhang J, Su H, Ge C. Causal effects of inflammatory bowel diseases on the risk of kidney stone disease: a two-sample bidirectional mendelian randomization. \u003cem\u003eBMC Urol\u003c/em\u003e. Oct 12 2023;23(1):162. doi:10.1186/s12894-023-01332-4\u003c/li\u003e\n\u003cli\u003eRay D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer. \u003cem\u003ePLoS genetics\u003c/em\u003e. Dec 2020;16(12):e1009218. doi:10.1371/journal.pgen.1009218\u003c/li\u003e\n\u003cli\u003eRay D, Venkataraghavan S, Zhang W, et al. Pleiotropy method reveals genetic overlap between orofacial clefts at multiple novel loci from GWAS of multi-ethnic trios. \u003cem\u003ePLoS genetics\u003c/em\u003e. Jul 2021;17(7):e1009584. doi:10.1371/journal.pgen.1009584\u003c/li\u003e\n\u003cli\u003eGong W, Guo P, Li Y, et al. Role of the Gut-Brain Axis in the Shared Genetic Etiology Between Gastrointestinal Tract Diseases and Psychiatric Disorders: A Genome-Wide Pleiotropic Analysis. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e. Apr 1 2023;80(4):360-370. doi:10.1001/jamapsychiatry.2022.4974\u003c/li\u003e\n\u003cli\u003eBulik-Sullivan B, Finucane HK, Anttila V, et al. An atlas of genetic correlations across human diseases and traits. \u003cem\u003eNature genetics\u003c/em\u003e. Nov 2015;47(11):1236-41. doi:10.1038/ng.3406\u003c/li\u003e\n\u003cli\u003eFinucane HK, Bulik-Sullivan B, Gusev A, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. \u003cem\u003eNature genetics\u003c/em\u003e. Nov 2015;47(11):1228-35. doi:10.1038/ng.3404\u003c/li\u003e\n\u003cli\u003eHemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. \u003cem\u003eElife\u003c/em\u003e. May 30 2018;7doi:10.7554/eLife.34408\u003c/li\u003e\n\u003cli\u003eHao X, Shao Z, Zhang N, et al. Integrative genome-wide analyses identify novel loci associated with kidney stones and provide insights into its genetic architecture. \u003cem\u003eNature communications\u003c/em\u003e. Nov 18 2023;14(1):7498. doi:10.1038/s41467-023-43400-1\u003c/li\u003e\n\u003cli\u003eKurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. \u003cem\u003eNature\u003c/em\u003e. Jan 2023;613(7944):508-518. doi:10.1038/s41586-022-05473-8\u003c/li\u003e\n\u003cli\u003eHe Y, Koido M, Sutoh Y, et al. East Asian-specific and cross-ancestry genome-wide meta-analyses provide mechanistic insights into peptic ulcer disease. \u003cem\u003eNature genetics\u003c/em\u003e. Dec 2023;55(12):2129-2138. doi:10.1038/s41588-023-01569-7\u003c/li\u003e\n\u003cli\u003eShi HWB, Mancuso N, Spendlove S, Pasaniuc B. Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits. \u003cem\u003eAmerican journal of human genetics\u003c/em\u003e. Nov 2 2017;101(5):737-751. doi:10.1016/j.ajhg.2017.09.022\u003c/li\u003e\n\u003cli\u003eBerisa T, Pickrell JK. Approximately independent linkage disequilibrium blocks in human populations. \u003cem\u003eBioinformatics (Oxford, England)\u003c/em\u003e. Jan 15 2016;32(2):283-5. doi:10.1093/bioinformatics/btv546\u003c/li\u003e\n\u003cli\u003eShao Z, Wang T, Zhang M, Jiang Z, Huang S, Zeng P. IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA. \u003cem\u003ePLoS computational biology\u003c/em\u003e. Aug 2021;17(8):e1009250. doi:10.1371/journal.pcbi.1009250\u003c/li\u003e\n\u003cli\u003ePurcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. \u003cem\u003eAmerican journal of human genetics\u003c/em\u003e. Sep 2007;81(3):559-75. doi:10.1086/519795\u003c/li\u003e\n\u003cli\u003eGenomes Project C, Auton A, Brooks LD, et al. A global reference for human genetic variation. \u003cem\u003eNature\u003c/em\u003e. Oct 1 2015;526(7571):68-74. doi:10.1038/nature15393\u003c/li\u003e\n\u003cli\u003eQuinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. \u003cem\u003eBioinformatics (Oxford, England)\u003c/em\u003e. Mar 15 2010;26(6):841-2. doi:10.1093/bioinformatics/btq033\u003c/li\u003e\n\u003cli\u003eWang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. Sep 2010;38(16):e164. doi:10.1093/nar/gkq603\u003c/li\u003e\n\u003cli\u003eGiambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. \u003cem\u003ePLoS genetics\u003c/em\u003e. May 2014;10(5):e1004383. doi:10.1371/journal.pgen.1004383\u003c/li\u003e\n\u003cli\u003eSmith-Byrne K, Hedman \u0026Aring;, Dimitriou M, et al. Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers. \u003cem\u003eNature communications\u003c/em\u003e. 2024/04/29 2024;15(1):3621. doi:10.1038/s41467-024-46834-3\u003c/li\u003e\n\u003cli\u003eWishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. Jan 4 2018;46(D1):D1074-D1082. doi:10.1093/nar/gkx1037\u003c/li\u003e\n\u003cli\u003eConsortium GT. The GTEx Consortium atlas of genetic regulatory effects across human tissues. \u003cem\u003eScience (New York, NY)\u003c/em\u003e. Sep 11 2020;369(6509):1318-1330. doi:10.1126/science.aaz1776\u003c/li\u003e\n\u003cli\u003eWatanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. \u003cem\u003eNature communications\u003c/em\u003e. Nov 28 2017;8(1):1826. doi:10.1038/s41467-017-01261-5\u003c/li\u003e\n\u003cli\u003eZhao Q, Wang J, Hemani G, Bowden J, Small DS. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. \u003cem\u003eAnn Stat\u003c/em\u003e. 2020;48(3):1742-1769, 28. \u003c/li\u003e\n\u003cli\u003eZhao J, Ming J, Hu X, Chen G, Liu J, Yang C. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. \u003cem\u003eBioinformatics\u003c/em\u003e. Mar 1 2020;36(5):1501-1508. doi:10.1093/bioinformatics/btz749\u003c/li\u003e\n\u003cli\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. \u003cem\u003eGenetic epidemiology\u003c/em\u003e. Nov 2013;37(7):658-65. doi:10.1002/gepi.21758\u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. \u003cem\u003eInt J Epidemiol\u003c/em\u003e. Apr 2015;44(2):512-25. doi:10.1093/ije/dyv080\u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. \u003cem\u003eGenet Epidemiol\u003c/em\u003e. May 2016;40(4):304-14. doi:10.1002/gepi.21965\u003c/li\u003e\n\u003cli\u003eHartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. \u003cem\u003eInt J Epidemiol\u003c/em\u003e. Dec 1 2017;46(6):1985-1998. doi:10.1093/ije/dyx102\u003c/li\u003e\n\u003cli\u003eHemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. \u003cem\u003ePLoS Genet\u003c/em\u003e. Nov 2017;13(11):e1007081. doi:10.1371/journal.pgen.1007081\u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. \u003cem\u003eNat Genet\u003c/em\u003e. May 2018;50(5):693-698. doi:10.1038/s41588-018-0099-7\u003c/li\u003e\n\u003cli\u003eShi H, Mancuso N, Spendlove S, Pasaniuc B. Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits. \u003cem\u003eAm J Hum Genet\u003c/em\u003e. Nov 2 2017;101(5):737-751. doi:10.1016/j.ajhg.2017.09.022\u003c/li\u003e\n\u003cli\u003eZhang Y, Lu Q, Ye Y, et al. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. \u003cem\u003eGenome Biol\u003c/em\u003e. Sep 7 2021;22(1):262. doi:10.1186/s13059-021-02478-w\u003c/li\u003e\n\u003cli\u003eStelzer G, Rosen N, Plaschkes I, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. \u003cem\u003eCurr Protoc Bioinformatics\u003c/em\u003e. Jun 20 2016;54:1 30 1-1 30 33. doi:10.1002/cpbi.5\u003c/li\u003e\n\u003cli\u003eAssarsson E, Sidney J, Oseroff C, et al. A quantitative analysis of the variables affecting the repertoire of T cell specificities recognized after vaccinia virus infection. \u003cem\u003eJournal of immunology (Baltimore, Md : 1950)\u003c/em\u003e. Jun 15 2007;178(12):7890-901. doi:10.4049/jimmunol.178.12.7890\u003c/li\u003e\n\u003cli\u003eMcGranahan N, Rosenthal R, Hiley CT, et al. Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution. \u003cem\u003eCell\u003c/em\u003e. Nov 30 2017;171(6):1259-1271 e11. doi:10.1016/j.cell.2017.10.001\u003c/li\u003e\n\u003cli\u003eChowell D, Morris LGT, Grigg CM, et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. \u003cem\u003eScience (New York, NY)\u003c/em\u003e. Feb 2 2018;359(6375):582-587. doi:10.1126/science.aao4572\u003c/li\u003e\n\u003cli\u003eMacArthur J, Bowler E, Cerezo M, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). \u003cem\u003eNucleic Acids Res\u003c/em\u003e. Jan 4 2017;45(D1):D896-D901. doi:10.1093/nar/gkw1133\u003c/li\u003e\n\u003cli\u003ePetreski T, Piko N, Ekart R, Hojs R, Bevc S. Review on Inflammation Markers in Chronic Kidney Disease. \u003cem\u003eBiomedicines\u003c/em\u003e. Feb 11 2021;9(2)doi:10.3390/biomedicines9020182\u003c/li\u003e\n\u003cli\u003evan Rheenen W, Peyrot WJ, Schork AJ, Lee SH, Wray NR. Genetic correlations of polygenic disease traits: from theory to practice. \u003cem\u003eNat Rev Genet\u003c/em\u003e. Oct 2019;20(10):567-581. doi:10.1038/s41576-019-0137-z\u003c/li\u003e\n\u003cli\u003eNurmi R, Pohjonen J, Metso M, et al. Prevalence of Inflammatory Bowel Disease and Celiac Disease in Patients with IgA Nephropathy over Time. \u003cem\u003eNephron\u003c/em\u003e. 2021;145(1):78-84. doi:10.1159/000511555\u003c/li\u003e\n\u003cli\u003eRehnberg J, Symreng A, Ludvigsson JF, Emilsson L. Inflammatory Bowel Disease Is More Common in Patients with IgA Nephropathy and Predicts Progression of ESKD: A Swedish Population-Based Cohort Study. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e. Feb 2021;32(2):411-423. doi:10.1681/ASN.2020060848\u003c/li\u003e\n\u003cli\u003eJoher N, Gosset C, Guerrot D, et al. Immunoglobulin A nephropathy in association with inflammatory bowel diseases: results from a national study and systematic literature review. \u003cem\u003eNephrol Dial Transplant\u003c/em\u003e. Feb 25 2022;37(3):531-539. doi:10.1093/ndt/gfaa378\u003c/li\u003e\n\u003cli\u003eDavies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. \u003cem\u003eBMJ (Clinical research ed)\u003c/em\u003e. Jul 12 2018;362:k601. doi:10.1136/bmj.k601\u003c/li\u003e\n\u003cli\u003eHansen IS, Baeten DLP, den Dunnen J. The inflammatory function of human IgA. \u003cem\u003eCell Mol Life Sci\u003c/em\u003e. Mar 2019;76(6):1041-1055. doi:10.1007/s00018-018-2976-8\u003c/li\u003e\n\u003cli\u003eTanioka T, Hattori A, Masuda S, et al. Human leukocyte-derived arginine aminopeptidase. The third member of the oxytocinase subfamily of aminopeptidases. \u003cem\u003eJ Biol Chem\u003c/em\u003e. Aug 22 2003;278(34):32275-83. doi:10.1074/jbc.M305076200\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gut-renal axis, GWAS, pleiotropic loci, drug repurposing, mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-5883069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5883069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eComorbidities between gastrointestinal tract (GIT) and renal diseases have been widely reported, but the shared genetic architecture of gut and renal traits remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo investigate the shared genetic etiology and causal relationships between traits or diseases involved in the gut-renal axis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We explored the global and local genetic correlations, pleiotropic effects at variants and gene levels, causal associations between pair-wise renal traits and GIT diseases, as well as potential target drugs by using the latest large-scale genome-wide association study (GWAS) summary data of five renal traits (BUN, eGFR, CKD, IgAN, KSD) and four GIT diseases (PUD, GORD, IBD, IBS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Renal traits and GIT diseases were widely genetically correlated globally and locally across eight of 20 trait pairs (BUN-GORD, BUN-IBD, BUN-IBS, CKD-IBD, IgAN-IBD, KSD-PUD, KSD-GORD, KSD-IBS). Pleiotropic analysis identified 222 pleiotropic loci and prioritized 169 pleiotropic genes for 20 trait pairs, including 21 novel loci that were not significant in the original GWASs, 21 colocalized loci, as well as 29 drug-targeting genes. Among the novel loci, rs3129861 in \u003cem\u003eHLA-DRA\u003c/em\u003e gene was potentially causal for BUN-GORD (PP4 = 0.814). \u003cem\u003eKIF5B\u003c/em\u003e is a causal gene for eGFR-IBD and CKD-IBD trait pairs, colocalized by rs12572072 (PP4 = 0.929) and rs61844306 (PP4 = 0.898), both of which are significant eQTLs of \u003cem\u003eKIF5B\u003c/em\u003e expressed in cultured fibroblasts cells. CKD and IBD were also colocalized in \u003cem\u003ePVALEF \u003c/em\u003ewith PP4 = 0.800 for rs138610699. In addition, rs6873866 was identified as a shared casual variant in \u003cem\u003eERAP2\u003c/em\u003e by IgAN and IBD with PP4=0.800, and rs6873866-C allele was negatively associated with \u003cem\u003eERAP2\u003c/em\u003e expression in multiple tissues. Furthermore, tissue and cell-type specific enrichment analysis found that pleiotropic loci were over-expressed in the kidney cortex, immune-related tissues and cell types. Mendelian randomization analysis revealed IgAN was negatively associated with IBD, and nominal significant effects were observed for IgAN on IBS, PUD and GORD on eGFR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: These findings suggested the shared genetic architecture between renal traits and GIT diseases, and highlighted the potential of pleiotropic analyses in drug repurposing for comorbidities of diseases in the gut-renal axis.\u003c/p\u003e","manuscriptTitle":"Genome-wide pleiotropy analysis reveals shared architecture between renal traits and gastrointestinal tract diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-30 04:02:53","doi":"10.21203/rs.3.rs-5883069/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db61d0c4-5312-4376-bf63-6802aa81fa38","owner":[],"postedDate":"January 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-30T09:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-30 04:02:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5883069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5883069","identity":"rs-5883069","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}