From GWAS to Translational Insights: Comprehensive Genetic Analysis of Nephrotic Syndrome from Multiple Populations

From GWAS to Translational Insights: Comprehensive Genetic Analysis of Nephrotic Syndrome from Multiple Populations | 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 From GWAS to Translational Insights: Comprehensive Genetic Analysis of Nephrotic Syndrome from Multiple Populations Joseph H. Breeyear, Hannah M. Seagle, Alexis T. Akerele, Nikhil K. Khankari, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7482306/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 Nephrotic syndrome is a rare, heterogeneous kidney disorder characterized by proteinuria, hypoalbuminemia, and edema. To elucidate its genetic architecture, we conducted a large-scale, electronic health record (EHR)-linked, multi-ancestry genome-wide association study comprising 5,214 cases and 1,601,060 controls. We identified 37 distinct loci associated with disease risk, including novel associations at JAML and STPG2 , and confirmed prior signals at PLA2R1 , HMCN1 , and APOL1 . Fine-mapping of the major histocompatibility complex (MHC) revealed the strongest association at DRB103:01:01G in individuals of European ancestry (OR = 2.01, P = 1.4×10⁻²⁰), alongside nominal ancestry-specific associations in African ( DOA01:01:05 ) and Latino ( DPB1*14:01:01G ) populations. Transcriptome-wide association analysis (TWAS) identified 484 significant gene-tissue associations, including C4A in kidney cortex. Expression quantitative trait locus (eQTL) mapping revealed numerous cis-eQTLs in glomerular and tubular renal tissues, largely within the MHC region. We observed significant genetic correlation between adult and pediatric nephrotic syndrome (Rg = 0.63, P = 3.5x10 -11 ), suggesting shared genetic etiology. Pathway analyses implicated estrogen receptor signaling and histone modification. Mendelian randomization implicated APOM expression and apomorphine exposure with increased disease risk (OR = 4.93, P = 1.8x10 -19 ). These results expand the understanding of nephrotic syndrome pathogenesis and highlight ancestry-informed targets for therapeutic development. Genetics Nephrotic Syndrome Genetic Correlation Figures Figure 1 Figure 2 INTRODUCTION Nephrotic syndrome is a rare but serious renal disorder marked by heavy proteinuria (> 3 g/day), hypoalbuminemia and edema, and is frequently accompanied by hyperlipidemia. 1 , 2 Its incidence is estimated at 4.3 per 100,000 adults and ranges from 1.2–16.9 per 100,000 in children. 3 Disease prevalence varies substantially across age, sex, and ancestry. Young children exhibit a 2:1 male-to-female ratio that equalizes in adolescence, while incidence is highest among Asian children and lowest in White children. 3 Histologically, nephrotic syndrome presents with distinct patterns, including focal segmental glomerulosclerosis (FSGS), minimal change disease (MCD), and membranous nephropathy. MCD is predominant in children, while FSGS is most common among African ancestry adults. 4 – 8 In adults, more than 50% of cases are secondary to other conditions, particularly diabetes. 9 Regardless of etiology, nephrotic syndrome reflects disruption of the glomerular filtration barrier, which includes endothelial cells, the glomerular basement membrane, and podocytes. 10 Monogenic forms account for a minority of cases; over 50 genes have been implicated, including NPHS1 , NPHS2 , and TRPC6 . These genes encode proteins critical to slit diaphragm integrity, cytoskeletal function, and mitochondrial processes. 11 Genome-wide association studies (GWAS) of nephrotic syndrome have largely focused on pediatric populations, identifying associations in the human leukocyte antigen (HLA) region and at loci such as PARM1 and CALHM6 . 12 – 16 In adults, PLA2R1 , NFKB1 , and IRF4 have been associated with membranous nephropathy, and APOL1 risk alleles G1 and G2 are linked to multiple renal phenotypes across ages and ancestries. 10 , 17 – 32 To address gaps in our understanding of adult and multi-ancestry nephrotic syndrome genetics, we performed a GWAS meta-analysis across global biobanks using harmonized EHR-derived phenotypes. We further applied HLA fine-mapping, gene expression prediction, eQTL analysis, pathway enrichment, and genetically-informed drug repurposing to characterize the biological underpinnings of nephrotic syndrome and identify ancestry-specific risk factors. RESULTS Single Variant Analyses We conducted genome-wide association analyses of 33,917,175 SNPs with a minor allele frequency (MAF) ≥ 0.01 in up to 5,214 individuals with nephrotic syndrome and 1,640,242 controls without documented renal disease from Biobank Japan, BioVU (Vanderbilt University Medical Center), Electronic Medical Records and Genomics (eMERGE) Network, FinnGen, Million Veteran Program, and UK Biobank. This analysis identified 1,210 unique genome-wide significant SNPs across ancestry groups ( Figure 1 , Table S1a–d ). To identify conditionally independent signals, we applied stepwise conditional analysis using GCTA-COJO with the UK Biobank LD reference panel. This yielded 40 independent genome-wide significant SNPs: 16 in the multi-ancestry meta-analysis, 10 in non-Hispanic European (NH-EUR), 13 in non-Hispanic African (NH-AFR), and 1 in East Asian (EAS) populations ( Table 2 , Table S2a–d ). These 40 SNPs mapped to 33 distinct genomic loci (±50 kb), encompassing both novel and previously reported nephrotic syndrome-associated regions. Notably, we identified novel loci near JAML and STPG2 , and replicated known associations at PLA2R1 (membranous nephropathy), HMCN1 (focal segmental glomerulosclerosis, FSGS), and NPHS1 (pediatric nephrotic syndrome). 33-35 In the NH-AFR subgroup, we confirmed the well-established APOL1 risk locus (G1 M allele odds ratio [OR]: 2.56; 95% confidence interval [CI]: 2.03–3.24; Figure 2 ). Additionally, we report the replication of variants identified from GWAS of related phenotypes, including Pediatric Steroid-Sensitive Nephrotic Syndrome, Steroid-Sensitive Nephrotic Syndrome Without Relapse, and Steroid-Dependent/Frequent Relapse Nephrotic Syndrome ( Table S3 ). We further investigated suspected genome-wide significant singletons using regional association plots, including TRIB1 , RNF169 , MSI2 , and EIPR1 ( Figure S1 ). Additionally, we examined loci at LMF1 and FAM83A , which exhibited supported by multiple SNPs ( Figure S2 ). We observed mild genomic inflation in the NH-AFR group (λ GC = 1.077), compared to multi-ancestry (λ GC = 1.026), NH-EUR (λ GC = 1.019), and EAS (λ GC = 1.011) populations. LD Score Regression (LDSC) intercepts indicated minimal confounding due to population stratification in most groups: multi-ancestry (1.006 ± 0.006), NH-EUR (1.005 ± 0.007), NH-AFR (1.072 ± 0.007), and EAS (0.996 ± 0.007). Using LDSC, we estimated the genetic correlation between adult nephrotic syndrome and related traits. A strong and statistically significant genetic correlation was observed with pediatric nephrotic syndrome (genetic correlation R g (se) = 0.63 (0.10); P = 3.52x10 -11 ( Table S4 )). We also observed modest positive, but not statistically significant, correlations with related traits including estimated glomerular filtration rate (eGFR), blood pressure, type 2 diabetes, blood urea nitrogen, acute kidney injury, and chronic glomerulonephritis ( Table S4 ). Genetically Predicted Gene Expression and Colocalization To identify gene expression profiles associated with nephrotic syndrome, we estimated genetically predicted gene expression (GPGE) using common variants (MAF > 0.01) derived from multi-ancestry and ancestry-specific meta-analyses. Gene expression was imputed across 49 tissues using GTEx v8 reference panels. This analysis identified 484 significant gene–tissue associations with nephrotic syndrome at a Bonferroni-corrected threshold ( P ≤ 1.55×10 -6 ), including 161 associations in the multi-ancestry analysis, 321 in non-Hispanic European (NH-EUR), and 2 in non-Hispanic African (NH-AFR) populations ( Table S5a-d, Figure S3a-d ). Colocalization analysis revealed strong evidence for shared causal variants (posterior probability PP.H4 > 0.8) in 108 of these gene-tissue pairs, 45 in the multi-ancestry and 63 in the NH-EUR analyses. These colocalized signals mapped to 34 unique genes, of which 30 (88%) had not previously been reported in the GWAS Catalog for nephrotic syndrome or related renal phenotypes. One notable finding was the gene C4A, which showed significant association and strong colocalization with predicted expression in kidney cortex tissue in both multi-ancestry ( P = 7.12×10 -13 , PP.H4 = 0.96) and NH-EUR analyses ( P = 3.84×10 -13 , PP.H4 = 0.96) ( Figure S4 ). At the time of this analysis, C4A had not been previously implicated in nephrotic syndrome, highlighting its potential as a novel candidate gene for disease susceptibility. HLA Allele Associations Human leukocyte antigen allele analyses across ancestry groups within All of Us identified several significant and suggestive associations with nephrotic syndrome ( Table 3 ). Among individuals of European ancestry, three HLA alleles reached Bonferroni corrected significance. The strongest association was observed for DRB1*03:01:01G (OR: 2.01, 95% CI: [1.55 – 2.61], P = 1.52×10 -7 ), followed by other classical components of the 8.1 Ancestral Haplotype (AH8.1), including C*07:01:01G (1.74 [1.35 – 2.23], P = 1.55×10 -5 ) and DQA1*05:01:01G (1.64 [1.31 – 2.04], P = 1.50×10 -5 ). While no significant HLA associations were identified in the African/African American (AFR) or American Admixed/Latino (AMR) ancestries, several alleles demonstrated nominal evidence of association. These included A*02:01:01G (2.04 [1.22 – 3.41], P = 0.0070) in the AFR analysis and DPB1*14:01:01G (3.22 [1.59 – 6.52], P = 0.0011) from the AMR subgroup. Examination of linkage disequilibrium (LD) of SNPs in the extended MHC region demonstrates that rs2187668 and rs9275576, tagging DQA105:01:01G and DRB103:01:01G , respectively, are part of the historically conserved AH8.1. These variants show high D′ but low r², consistent with shared haplotype structure but differing allele frequencies. In contrast, SNPs associated with C4A expression (e.g., rs3134942, rs1150754, and rs3130297) exhibit both low D′ and low r² with AH8.1-tagging SNPs, indicating they are not in LD with the AH8.1 haplotype and likely represent independent signals ( Figure S5 ). Expression Quantitative Trait Loci Evaluation We interrogated the significant nephrotic syndrome-associated SNPs for cis-expression quantitative trait loci (eQTL) effects using the Human Kidney eQTL Atlas (susztaklab.org). In glomerular tissue, we identified 4,078 significant eQTLs mapping to 533 unique SNPs and 29 distinct genes. In tubular tissue, 4,801 eQTLs were detected, corresponding to 607 unique SNPs and 29 genes. Analysis of meta-analyzed bulk kidney tissue revealed 6,817 eQTLs involving 1,272 unique SNPs and 37 unique genes ( Table S6a-b ). Notably, all detected eQTLs were located within the HLA region on chromosome 6 (chr6:28,477,797–33,448,354). 36 We also identified ancestry-specific eQTLs in the East Asian (EAS) population across all tissue types, including 16 in glomerulus, 16 in tubule, and 26 in bulk kidney tissue. Gene-Set and Pathway Analysis We conducted gene-set enrichment analyses using MAGMA across all ancestry groups. In the multi-ancestry meta-analysis, the most significantly enriched gene set was “GOBP negative regulation of intracellular estrogen receptor signaling pathway”( P = 1.4×10 -05 , 13 genes), followed by “GOBP regulation of histone modification” ( P = 6.8×10 -05 , 122 genes). Notably, the East Asian (EAS) ancestry group demonstrated unique enrichment for the “GOBP sensory perception of umami taste” gene set ( P = 2.9×10 -04 , 6 genes). Among canonical pathways, the top enriched terms in the multi-ancestry analysis included “KEGG antigen processing and presentation,” “ KEGG type 1 diabetes mellitus,” and “KEGG allograft rejection” ( Figure S6 ) reflecting potential immune-related mechanisms underlying nephrotic syndrome susceptibility. Upon filtering out all genes located within the extended HLA region and rerunning the analysis, no gene sets or pathways reached statistical significance in any ancestry group, indicating that the observed pathway enrichment was predominantly driven by signals within the HLA locus. Drug-Repurposing Analysis We applied a previously validated, genetically informed drug-repurposing and safety assessment pipeline to identify potential therapeutic candidates for nephrotic syndrome. This analysis highlighted apomorphine, a dopamine receptor agonist, as a candidate for repurposing based on its known target, the APOM gene. 37 Using drug-target Mendelian randomization (MR), we found that genetically proxied APOM expression was significantly associated with increased risk of nephrotic syndrome (WALD MR OR = 4.932, P = 1.8×10 -19 ), as shown in Table 4 . However, interpretation of this result is limited by weak instrumental variables, as only a single tissue (testis) demonstrated statistically significant genetically predicted gene expression in the underlying genetically predicted gene expression (GPGE) analysis. These findings underscore the importance of cautious interpretation and the need for further experimental validation. DISCUSSION This large-scale, multi-ancestry GWAS of nephrotic syndrome provides new insight into the genetic architecture of the disease by identifying over 30 previously unreported susceptibility loci, revealing shared genetic correlations with related traits, and integrating these findings with gene expression and drug repurposing data. Our results demonstrate that electronic health record (EHR)-based phenotyping using ICD codes can effectively replicate established associations, including PLA2R1 and APOL1 , and power the discovery of novel loci through well-powered meta-analysis. Despite the strengths of using ICD codes for large-scale phenotyping, this approach also introduces limitations. ICD codes may incompletely capture disease heterogeneity, especially in complex conditions such as nephrotic syndrome. In adults, where biopsy data and clinical onset are frequently unavailable, ICD-based definitions may be less specific than in pediatric cohorts. Nevertheless, our findings suggest that ICD-defined nephrotic syndrome in adults may still enrich for individuals with a history of childhood-onset disease, particularly those with steroid-sensitive nephrotic syndrome, which has been linked to long-term renal consequences. 38 Importantly, we found a strong genetic correlation between adult and pediatric nephrotic syndrome (R g = 0.63) suggesting that shared genetic mechanisms span disease onset across the life course. This may explain adult relapses among individuals with childhood-onset disease and supports the use of shared genetic insights in therapeutic development. Several of the identified genes point toward plausible biological mechanisms. For instance, HMCN1 , which encodes an extracellular matrix protein expressed in podocytes, is critical to glomerular barrier integrity, disruption of which is a hallmark of nephrotic syndrome. 33 USP38 , a previously unreported locus in this context, may modulate gene expression via interaction with LSD1 , suggesting an epigenetic mechanism in disease pathogenesis. 39 JAML , another novel gene identified, contributes to kidney injury via macrophage-mediated inflammatory pathways, underscoring the role of immune mechanisms in disease progression. 40 Together, these discoveries connect GWAS findings to biologically relevant pathways in renal function and inflammation. Transcriptome-wide association analyses further revealed 484 significant gene-tissue associations, 108 of which had strong evidence of colocalization. Notably, C4A was significantly associated with nephrotic syndrome in both multi-ancestry and non-Hispanic European analyses and had not been previously linked to the disease. These findings offer promising leads for mechanistic studies and potential therapeutic targeting. Tissue-specific expression patterns of upregulated genes varied by ancestry group, suggesting underlying biological differences in disease manifestation. In the multi-ancestry analysis, whole blood and spleen, both central to immune regulation, emerged as the most significant tissues. Notably, expression patterns in East Asian and non-Hispanic African groups included breast and skin tissues, respectively, potentially reflecting ancestry-specific regulatory mechanisms or comorbidities. Gene-set and pathway analyses using MAGMA revealed several biologically plausible processes implicated in nephrotic syndrome. The most significant gene set in the multi-ancestry analysis involved negative regulation of intracellular estrogen receptor signaling, suggesting potential hormonal contributions to disease susceptibility, followed by pathways related to histone modification, highlighting a possible role for epigenetic regulation. 41 Canonical pathway enrichment included immune-related processes such as antigen processing and allograft rejection, consistent with the inflammatory features of nephrotic syndrome. We also identified ancestry-specific associations in the HLA region, reinforcing the complex immunogenetic basis of nephrotic syndrome. In non-Hispanic European individuals, DRB1*03:01:01G was strongly associated with disease risk (OR: 2.01, P = 1.53×10⁻⁷), consistent with its established role in autoimmunity. In contrast, in non-Hispanic African and Admixed American/Latino individuals, different HLA alleles, such as DPB1*14:01:01G and C*12:03:01G , showed nominal associations, albeit with wider confidence intervals. These findings reflect potential population-specific risk profiles and complement our identification of expression quantitative trait loci (eQTLs) in the MHC region, particularly in kidney-relevant tissues. While our genome-wide association analyses revealed significant SNP associations within the extended MHC region, it is important to acknowledge that these findings may be in linkage disequilibrium (LD) with classical HLA alleles. To assess this, we examined LD between our lead GWAS SNPs and known tag SNPs for selected HLA alleles, including components of the 8.1 ancestral haplotype. These analyses revealed low r² values despite high D′, suggesting limited correlation and likely distinct signals. However, high-resolution HLA typing data were only available for the All of Us dataset, limiting our ability to directly evaluate HLA allele associations across other contributing biobanks. As such, SNP-level signals within the MHC region may partially reflect underlying HLA allele effects that could not be fully resolved in this analysis. Future efforts incorporating imputed or directly typed HLA alleles across multiple cohorts will be essential to disentangle these relationships and further clarify the immunogenetic architecture of nephrotic syndrome. Our genetically informed drug-repurposing analysis identified apomorphine, a dopamine agonist targeting APOM , as a candidate compound. However, Mendelian randomization results suggested that increased APOM expression may elevate nephrotic syndrome risk (OR = 4.932, P = 1.76×10 − 19 ), raising concerns about safety. These findings, though limited by weak instruments in GPGE analysis, suggest that patients receiving apomorphine may warrant clinical monitoring for early renal symptoms. Additional studies are needed to further assess the utility and risk of APOM -modulating compounds in nephrotic syndrome. In summary, this study identifies more than 30 novel loci, numerous gene expression patterns, and ancestry-specific genetic factors associated with nephrotic syndrome. These findings extend our understanding of the disease's molecular underpinnings and provide a foundation for future mechanistic research and precision therapeutic strategies. Declarations ACKNOWLEDGEMENTS Additional support provided by T32-GM145734-01 (H.M.S.), F31-EY033663 (J.H.B.), K12-AR084232 (J.N.H.). The eMERGE Network is supported by NHGRI grants. BioVU is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the CTSA grant UL1-TR000445. This research is based on data from the Million Veteran Program, Office of Research and Development, and Veterans Health Administration. This publication does not represent the views of the Department of Veterans Affairs or the United States Government. This research was funded in part by the Intramural Research Program of the National Institute of Environmental Health Sciences. AUTHOR CONTRIBUTIONS Conceptualization : T.L.E. and J.N.H. Methodology : J.H.B., H.M.S., N.K.K., K.A., A.B.B., T.L.E., and J.N.H. Investigation : J.H.B. and H.M.S. Writing (Original Draft) : J.H.B. and H.M.S. Writing (Review and Editing) : J.H.B., H.M.S., A.T.A., N.K.K., K.A., A.B.B., J.S.H., Y.Z., G.P.J., O.D., M.T.M.L., I.J.K., D.C.F., R.R., K.K., A.A.M.R., K.S., T.L.E., and J.N.H. Visualization : J.H.B. and H.M.S. Resources : A.A.M.R., K.S., T.L.E., and J.N.H. Data Curation : T.L.E. and J.N.H. Supervision : T.L.E. and J.N.H. Funding Acquisition : T.L.E. and J.N.H. 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Method Details We conducted a cross-ancestry genome-wide association study (GWAS) of nephrotic syndrome by integrating imputed genotype data from Vanderbilt University’s BioVU and the Electronic Medical Records and Genetics ( eMERGE ) network with publicly available summary statistics from Biobank Japan ( BBJ ), the Million Veteran Program ( MVP ), FinnGen, the UK Biobank ( UKB ), and the National Institutes of Health’s All of Us ( AoU ) cohort. Our combined sample included 1,606,274 individuals (5,519 cases and 1,640,242 controls; Table 1 ). The AoU, BioVU, eMERGE, MVP, and UKB cases were identified by the nephrotic syndrome PheCode (580.2) or PheCodeX (GU_580.1, GU_580.2), while controls were those without renal disease PheCodes. The BBJ and FinnGen cases were identified by diagnostic codes for NS, while controls were without renal disease diagnosis codes. Individual Level Data BioVU The BioVU DNA Repository is a deidentified database of electronic health records (EHR) that are linked to patient DNA samples at Vanderbilt University Medical Center. A detailed description of the database and how it is maintained has been published elsewhere. 42 BioVU participant DNA samples were genotyped on a custom Illumina Multi-Ethnic Genotyping Array (MEGA-ex). Samples with missingness >2%, withdrawn consent, duplications, or sex discordance were excluded. Imputation was performed on the Michigan Imputation Server (MIS) v1.2.4 using Minimac4 and the Haplotype Reference Consortium (HRC) panel v1.1. The Electronic Medical Records and Genetics Network The eMERGE Network is a consortium of several EHR-linked biorepositories formed with the goal of developing approaches for the use of the EHR in genomic research. Consortium membership has evolved over eMERGE’s 11-year history, with many sites contributing data including Group Health/University of Washington, Marshfield Clinic, Mayo Clinic, Northwestern University, Vanderbilt University, Children’s Hospital of Philadelphia (CHOP), Boston Children’s Hospital (BCH), Cincinnati Children’s Hospital Medical Center (CCHMC), Geisinger Health System, Mount Sinai School of Medicine, Harvard University, and Columbia University. The eMERGE study was approved by the Institutional Review Board at each site and all methods were performed in accordance with the relevant guidelines and regulations. Participants in the eMERGE network were genotyped separately, then imputed and merged. A detailed description of the genotyping, imputation, and quality control of the eMERGE phase III array dataset has been previously reported. 43 All of Us The All of Us research program is a nationwide initiative designed to collect comprehensive health-related information from a diverse participant pool across the United States. 44 The program integrates survey responses, physical measurements, electronic health records (EHRs), and genomic data, providing an unparalleled resource for health research. Data used for this study included electronic health record data and short-read whole genome sequencing (srWGS) derived human leukocyte antigen (HLA) alleles. Nephrotic syndrome cases were classified using PheCodeX codes, requiring 2 or more codes for GU_580, GU_580.1, or GU_580.2. Controls were individuals without any genitourinary PheCodes (GU_*). Quality control for srWGS data has been previously described. 45 HLA alleles were inferred using Kourami v0.9.6 on srWGS data at loci defined in IPD-IMGT/HLA v3.47.0, including both Class I and Class II loci (e.g., HLA-A, -B, -DRB1). 46 Assemblies were executed on the All of Us Researcher Workbench using GATK 4.3.0.0. In rare cases where HLA-DOA assembly stalled, the pipeline was rerun excluding this locus. Alleles were reported at G-group specificity and filtered to remove ambiguous calls (i.e., multiple equally likely alleles), alleles with <95% sequence identity to the reference, alleles supported by a MaxFlow parameter <10 (i.e., fewer than 10 reads supporting one or more assembly graph edges). We included only alleles with an allele frequency of at least 1% within each HLA gene for downstream analysis. Significance thresholds for HLA allele associations were adjusted for multiple testing within each ancestry group, EUR: 3.38×10 -04 (0.05 / 148 tested alleles), AFR: 3.05×10 -04 (0.05 / 164), AMR: 2.96×10 -04 (0.05 / 169). Summary Statistics Biobank Japan Biobank Japan is a multi-institutional hospital-based registry comprised of DNA and medical records from individuals of Japanese ancestry. We utilized published summary statistics for acute renal failure identified with diagnostics codes, specifically (ICD-10 N17). 47,48 FinnGen FinnGen is a large public-private partnership comprised of DNA and health data from up to 500,000 Finnish biobank participants. We utilized published summary statistics for acute renal failure as assessed by diagnostic codes from FinnGen, identified with the FinnGen endpoint N14-ACUTERENFAIL (presence of ICD-10 N17 and/or ICD-9 584). 49 [https://r8.risteys.finngen.fi/phenocode/N14_NEPHROTICSYND] Million Veteran Program The MVP is a national research program that incorporated genomic data and health record data, collected from Veterans, to investigate how genes, lifestyle, military experiences, and exposures affect health and wellness. We utilized summary statistics for NS, identified with the PheCode 580.2. 50 UK Biobank The UKB is a large-scale biomedical database that aims to improve public health by enabling scientific discoveries. We utilized summary statistics for NS, identified with the PheCode 580.2. [Pan-UKB team. https://pan.ukbb.broadinstitute.org . 2020.] Quantification and Statistical Analysis Genetic associations with nephrotic syndrome status were modeled as a function of additive genotype, sex, and the top 10 principal components of ancestry, followed by inverse-variance weighted fixed-effects meta-analysis both within and across ancestral groups utilizing METAL. 51 Significant SNPs from the meta-analyses were evaluated as cis eQTLs using the Human Kidney eQTL Atlas (susztaklab.org). We utilized LD Score Regression (LDSC) to calculate the genomic inflation factor (λ GC ) and intercept for all ancestry groups as well as calculate heritability and genetic correlation with related phenotypes. 52,53 HLA allele associations with nephrotic syndrome were modeled as a function of additive allele, age, sex, and the top 10 principal components of ancestry across genetic ancestry groups. FUMA, a functional annotation tool for GWAS results, was used to analyze results for each ancestry group. 54 The GENE2FUNC process was used to annotate significant genes in a biological context 55 . Specifically, MAGMA was used for gene-set analyses where SNP associations are summarized at the gene level, followed by association of gene sets to biological pathways using multiple linear regression. Genetically Predicted Gene Expression Genetically predicted gene expression was evaluated using S-PrediXcan, a gene-level method that estimates the genetically determined component of gene expression in specific tissues and tests its association with an outcome using SNP-level summary statistics. 56 We analyzed common variants (MAF > 0.01) from both within-ancestry and cross-ancestry meta-analyses alongside expression models from GTEx V8. 57 Covariance matrices developed for European (1000 Genomes) and African populations were incorporated: the European matrix was applied to NH-EUR, EAS, and multi-ancestry samples, while the African matrix was used for NH-AFR samples. To correct for multiple testing, a Bonferroni threshold P < 1.55×10 -6 was applied, accounting for the total number of gene models tested across tissues. To assess whether the same causal variant underlies both GWAS and expression quantitative trait loci (eQTL) signals at a locus, we performed colocalization analysis using coloc, a Bayesian method that evaluates summary statistics from GWAS and eQTL data. 58 Input included common variants from combined and ancestry-specific meta-analyses, restricted to variants present in the S-PrediXcan gene expression models, along with corresponding eQTL summary statistics. Coloc outputs posterior probabilities for five hypotheses, with PP.H4 representing the probability that the GWAS and eQTL associations colocalize (i.e., share a causal variant). For each locus, the SNP with the highest PP.H4 and its posterior probability were annotated. We considered a statistically significant S-PrediXcan association together with a PP.H4 > 80% as strong evidence of colocalization. Conditional Analyses We performed conditional analyses of common variants using GCTA-COJO, part of the Genome-wide Complex Trait Analysis (GCTA) software suite, which conducts iterative conditional and joint analyses with stepwise model selection. 59,60 For the NH-EUR, EAS, and multi-ancestry analyses, linkage disequilibrium (LD) was estimated using unrelated non-Hispanic White individuals from the UK Biobank (UKB). For NH-AFR analyses, LD was estimated using unrelated non-Hispanic Black individuals from BioVU. Input summary statistics were drawn from both within- and cross-ancestry meta-analyses. Reference genotype data included a subset of hard-called imputed genotypes: 5,000 UKB Europeans and 2,217 BioVU NH-Black individuals, both in PLINK format. Within each reference panel, LD was computed between all pairwise SNPs. The selection threshold for GCTA was set at P < 5x10 -8 . To address multicollinearity, the default collinearity threshold of 0.9 was applied, excluding SNPs with pairwise r 2 ≥ 0.9. in joint regression. For sets of SNPs in LD (r 2 ≥ 0.1), the most significant SNP (based on minimum P-value across all nephrotic syndrome traits) was retained from the GCTA joint model. Drug Repurposing Analysis To identify potential therapies for nephrotic syndrome, we applied a previously established genetically informed drug repurposing pipeline. 37,61 We began with the 34 unique genes identified via genetically predicted gene expression (GPGE) analysis and mapped them to known drug targets using Open Targets and the Drug-Gene Interaction Database (DGIdb). 62,63 Drug–gene pairs were retained as repurposing candidates if the direction of the drug effect was consistent with the direction of the GPGE association with NS (i.e., drug and GPGE exerted opposing effects). Additionally, we investigated drugs with concordant directions as possible disease-exacerbating agents. To further assess therapeutic potential, Mendelian randomization (MR) analyses were conducted using S-PrediXcan summary statistics as instrumental variables, as previously described. 37,61 Briefly, we evaluated the effect of GPGE on NS risk using fixed-effects inverse-variance weighted MR, implemented in the R package TwoSampleMR. Exposure instruments were gene-tissue pairs identified from S-PrediXcan analyses for three unrelated disease indications—chronic myelogenous leukemia, epithelial ovarian cancer, and Parkinson’s disease. 49,64-66 Among these, only Parkinson’s disease yielded a significant GPGE association ( P < 0.05). The same gene-tissue pair was then used as the outcome instrument with the NS S-PrediXcan results to complete the MR analysis. Tables Table 1 . Nephrotic Syndrome Cases and Controls by Data Source. AoU, BioVU, eMERGE, MVP, and UKB cases were identified by nephrotic syndrome PheCode, while controls were without renal disease PheCodes. BBJ and FinnGen cases were identified by nephrotic syndrome diagnostic codes, while controls were without renal disease codes. All of Us data were not included in the GWAS Meta-analysis. Data Source Genetic Ancestry Cases Controls Total Cases (%) Total Controls (%) Vanderbilt University's BioVU European 157 42,564 2.8 2.6 African 82 8,444 1.5 0.5 eMERGE European 433 41,523 7.8 2.5 African 129 6,618 2.3 0.4 Biobank Japan East Asian 1,314 177,412 23.8 10.8 FinnGen European 763 337,446 13.8 20.6 Million Veteran Program European 1,086 456,170 19.7 27.8 African 579 120,602 10.5 7.4 UK Biobank European 621 401,927 11.3 24.5 East Asian 50 8,354 0.9 0.5 All of Us European 174 25,175 3.2 1.5 African 73 7,322 1.3 0.4 American Admixed/Latino 58 6,685 1.1 0.4 Total Multi-ancestry 5,519 1,640,242 Table 2. Unique Significant LD Pruned SNPs (r² ≤ 0.1) Across Populations. Ancestry rsID CHR BP Mapped Gene EA EAF Beta SE P Multi rs191872995 1 185737251 HMCN1 C 0.99 -4.25 0.74 9.82x10 -09 rs1265889 6 32065839 TNXB A 0.13 0.41 0.04 1.43x10 -20 rs9269032 6 32469977 HLA-DRB9 A 0.10 0.49 0.06 1.09x10 -16 rs9273438 6 32659746 HLA-DQB1 A 0.48 -0.25 0.04 1.67x10 -12 rs149556312 8 125666025 TRIB1 T 0.03 3.52 0.61 6.20x10 -09 rs139132726 17 57566488 MSI2 T 0.01 5.15 0.84 9.46x10 -10 rs186728968 19 34972186 ZNF792 A 0.01 0.95 0.17 2.01x10 -08 rs372598043 19 35945880 LRFN3 T 0.02 1.17 0.13 7.43x10 -20 rs150272170 19 35972253 LRFN3 A 0.98 -1.08 0.12 1.46x10 -19 rs576820096 19 38625740 EIF3K T 0.02 0.81 0.13 3.02x10 -10 rs56092144 19 56232671 ZSCAN5A T 0.95 -0.82 0.13 1.27x10 -10 rs58168942 22 36286688 MYH9 A 0.19 0.98 0.13 1.51x10 -14 EUR rs143802595 19 38991531 FBXO27 T 0.98 -0.77 0.12 2.79x10 -10 rs116501595 2 159990831 PLA2R1 T 0.01 0.81 0.14 1.72x10 -08 rs1270942 6 31951083 CFB A 0.89 -0.49 0.05 5.16x10 -19 rs6932167 6 32645249 HLA-DQA1 A 0.11 0.5 0.06 9.04x10 -18 rs139128886 11 48555288 OR4A47 T 0.98 -1.61 0.27 1.50x10 -09 rs148082328 19 35417556 FFAR2 T 0.02 0.67 0.11 9.77x10 -10 rs62109662 19 35833563 NPHS1 A 0.02 0.73 0.10 2.01x10 -12 rs4806237 19 35948396 LRFN3 A 0.265 0.21 0.04 5.68x10 -09 rs150584091 19 38003682 SIPA1L3 A 0.037 0.52 0.08 6.59x10 -10 rs8102166 19 53583782 ZNF331 A 0.826 -0.71 0.12 1.10x10 -08 ASN rs9269202 6 32481746 HLA-DRB5 T 0.192 0.35 0.06 5.09x10 -10 AFR rs1299376 1 186384083 ORD4 T 0.91 -1.05 0.19 3.25x10 -08 rs76733679 2 173306142 MAP3K20 T 0.03 3.42 0.6 1.20x10 -08 rs10930613 2 173676966 A 0.964 -2.9 0.52 2.07x10 -08 rs7669591 4 97911328 STPG2 A 0.967 -1.85 0.33 2.50x10 -08 rs200195313 6 17006793 STMND1 CAG 0.955 -2.2 0.39 2.29x10 -08 rs9352469 6 63054509 A 0.033 3.87 0.7 3.82x10 -08 rs575869806 7 82901968 PCLO A 0.954 -1.69 0.28 2.90x10 -09 rs151022480 11 118190749 JAML A 0.967 -1.71 0.31 2.88x10 -08 rs78762921 12 63118812 AVPR1A T 0.96 -1.4 0.25 3.26x10 -08 rs28419307 15 46299504 A 0.04 2.24 0.41 4.75x10 -08 rs115553053 19 1082845 ARHGAP45 T 0.036 1.65 0.29 7.29x10 -09 rs200786642 19 43406071 TEX101 CCT 0.926 -1.96 0.31 1.99x10 -10 rs60910145 22 36265988 APOL1 T 0.785 -0.94 0.12 4.53x01 -15 Table 3. HLA Alleles Associated with Nephrotic Syndrome Across Genetic Ancestry Groups in All of Us . Significant associations are bolded while nominal associations are not. Ancestry HLA Alleles Frequency Odds Ratio (95% CI) P Cases Controls EUR A*02:01:01G 0.14 0.61 (0.42 – 0.90) 0.013 147 22107 B*08:01:01G 0.12 1.71 (1.29 – 2.27) 1.88x10 -04 165 23786 B*57:01:01G 0.04 0.23 (0.07 – 0.71) 0.011 165 23673 C*06:02:01G 0.10 0.57 (0.36 – 0.90) 0.017 164 24270 C*07:01:01G 0.16 1.74 (1.35 – 2.23) 1.55x10 -05 167 24376 DOB*01:01:01G 0.78 0.73 (0.58 – 0.92) 0.0085 174 25116 DOB*01:01:03G 0.22 1.42 (1.13 – 1.79) 0.0031 174 25117 DPA1*02:01:02G 0.04 1.80 (1.20 – 2.72) 0.0047 174 25149 DPB1*01:01:01G 0.05 1.81 (1.25 – 2.65) 0.0019 174 25164 DQA1*02:01:01G 0.13 0.54 (0.36 – 0.80) 0.0021 174 25160 DQA1*05:01:01G 0.26 1.64 (1.31 – 2.04) 1.50x10 -05 174 25161 DQB1*02:01:01G 0.22 1.41 (1.11 – 1.79) 0.0048 174 25167 DQB1*03:03:02G 0.05 0.41 (0.20 – 0.87) 0.020 174 25166 DQB1*06:02:01G 0.13 0.66 (0.45 – 0.96) 0.028 174 25166 DQB1*06:04:01G 0.03 1.67 (1.05 – 2.67) 0.031 174 25166 DRB1*03:01:01G 0.12 2.01 (1.55 – 2.61) 1.52x10 -07 174 24980 DRB1*07:01:01G 0.13 0.56 (0.38 – 0.83) 0.0037 174 25078 DRB1*15:01:01G 0.13 0.63 (0.43 – 0.92) 0.016 174 24981 G*01:01:03G 0.06 0.46 (0.25 – 0.87) 0.016 174 25084 H*02:04:01 0.16 1.30 (1.02 – 1.66) 0.032 168 23579 H*02:07:01:02 0.07 0.50 (0.28 – 0.90) 0.020 169 23564 AFR A*02:01:01G 0.06 2.04 (1.22 – 3.41) 0.0070 70 6794 A*68:02:01G 0.07 1.80 (1.05 – 3.07) 0.032 70 6814 C*12:03:01G 0.01 2.60 (1.14 – 5.94) 0.024 72 7116 DOA*01:01:05 0.21 1.59 (1.08 – 2.33) 0.018 68 6855 DQB1*06:04:01G 0.02 2.34 (1.07 – 5.08) 0.032 73 7319 DRB1*14:01:01G 0.02 2.29 (1.00 – 5.26) 0.049 73 7297 AMR B*38:01:01G 0.01 3.60 (1.40 – 9.23) 0.0077 54 6269 C*15:02:01G 0.03 2.83 (1.39 – 5.76) 0.0041 57 6399 DPB1*14:01:01G 0.02 3.22 (1.59 – 6.52) 0.0011 58 6649 DRB1*13:03:01G 0.01 2.79 (1.02 – 7.66) 0.046 58 6624 H*01:02:01:04 0.06 1.77 (1.02 – 3.09) 0.043 53 5998 Table 4. Nephrotic syndrome MR (Wald ratio) for the effect of one standard deviation (SD) increase in APOM gene expression, as a proxy for apomorphine's therapeutic action on risk of Parkinson's disease. Primary Indication Gene Target Proxied Drug Drug Action No. of GPGE Tissues WALD MR OR SE P Parkinson’s Disease APOM Apomorphine Agonist 1 4.932 0.670 1.73x10 -15 Additional Declarations No competing interests reported. Supplementary Files SupplementalTables.xlsx SUPPLEMENTALINFORMATION.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. 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09:42:58","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":235022,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7482306/v1/c324c06d465f9a32e42c94b0.html"},{"id":93026904,"identity":"f8354a37-cf4c-42f0-be9b-19a05d694c45","added_by":"auto","created_at":"2025-10-08 09:34:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":199503,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots illustrating GWAS results for Nephrotic Syndrome in each ancestry group, A) Multi-ancestry (MAF \u0026gt; 0.01), B) NH-EUR ancestry (MAF \u0026gt; 0.01), C) NH-AFR ancestry (MAF ≥ 0.03), D) EAS ancestry (MAF ≥ 0.02). The x-axis displays the genomic position across chromosomes. The y-axis displays the -log10(P) for each SNP. The red horizontal line represents the genome-wide significance threshold of \u003cem\u003eP\u003c/em\u003e = 5.0x10\u003csup\u003e-8\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7482306/v1/91a5181c43815d926eb31da4.png"},{"id":93026902,"identity":"24015669-2432-4cee-9e7c-522b4ec87dd2","added_by":"auto","created_at":"2025-10-08 09:34:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50409,"visible":true,"origin":"","legend":"\u003cp\u003eLocusZoom plot of APOL1 G1M variant (OR: 2.56 [2.03 – 3.24]) in the NH-AFR group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7482306/v1/0b5b4857a4bac1607aaadd25.png"},{"id":105916592,"identity":"774b44d7-2885-44ba-bb18-75c4db63b534","added_by":"auto","created_at":"2026-04-01 11:27:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1751040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7482306/v1/dd851d4f-6f59-47c0-b201-935c8f0d1964.pdf"},{"id":93026928,"identity":"1f40cca6-7190-4b0a-bd87-fe3c9b6ff7e5","added_by":"auto","created_at":"2025-10-08 09:35:02","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":75879735,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7482306/v1/7549de184a697c14807bf54e.xlsx"},{"id":93026905,"identity":"d0bb780e-bb96-4a17-a24e-7260d9b74021","added_by":"auto","created_at":"2025-10-08 09:34:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1720929,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTALINFORMATION.docx","url":"https://assets-eu.researchsquare.com/files/rs-7482306/v1/24ca98bb162890efb18ab908.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"From GWAS to Translational Insights: Comprehensive Genetic Analysis of Nephrotic Syndrome from Multiple Populations","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNephrotic syndrome is a rare but serious renal disorder marked by heavy proteinuria (\u0026gt;\u0026thinsp;3 g/day), hypoalbuminemia and edema, and is frequently accompanied by hyperlipidemia.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Its incidence is estimated at 4.3 per 100,000 adults and ranges from 1.2\u0026ndash;16.9 per 100,000 in children.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Disease prevalence varies substantially across age, sex, and ancestry. Young children exhibit a 2:1 male-to-female ratio that equalizes in adolescence, while incidence is highest among Asian children and lowest in White children.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eHistologically, nephrotic syndrome presents with distinct patterns, including focal segmental glomerulosclerosis (FSGS), minimal change disease (MCD), and membranous nephropathy. MCD is predominant in children, while FSGS is most common among African ancestry adults.\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e In adults, more than 50% of cases are secondary to other conditions, particularly diabetes.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Regardless of etiology, nephrotic syndrome reflects disruption of the glomerular filtration barrier, which includes endothelial cells, the glomerular basement membrane, and podocytes.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eMonogenic forms account for a minority of cases; over 50 genes have been implicated, including \u003cem\u003eNPHS1\u003c/em\u003e, \u003cem\u003eNPHS2\u003c/em\u003e, and \u003cem\u003eTRPC6\u003c/em\u003e. These genes encode proteins critical to slit diaphragm integrity, cytoskeletal function, and mitochondrial processes.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Genome-wide association studies (GWAS) of nephrotic syndrome have largely focused on pediatric populations, identifying associations in the human leukocyte antigen (HLA) region and at loci such as \u003cem\u003ePARM1\u003c/em\u003e and \u003cem\u003eCALHM6\u003c/em\u003e.\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e In adults, \u003cem\u003ePLA2R1\u003c/em\u003e, \u003cem\u003eNFKB1\u003c/em\u003e, and \u003cem\u003eIRF4\u003c/em\u003e have been associated with membranous nephropathy, and \u003cem\u003eAPOL1\u003c/em\u003e risk alleles G1 and G2 are linked to multiple renal phenotypes across ages and ancestries.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo address gaps in our understanding of adult and multi-ancestry nephrotic syndrome genetics, we performed a GWAS meta-analysis across global biobanks using harmonized EHR-derived phenotypes. We further applied HLA fine-mapping, gene expression prediction, eQTL analysis, pathway enrichment, and genetically-informed drug repurposing to characterize the biological underpinnings of nephrotic syndrome and identify ancestry-specific risk factors.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cu\u003eSingle Variant Analyses\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted genome-wide association analyses of 33,917,175 SNPs with a minor allele frequency (MAF) ≥ 0.01 in up to 5,214 individuals with nephrotic syndrome and 1,640,242 controls without documented renal disease from Biobank Japan, BioVU (Vanderbilt University Medical Center), Electronic Medical Records and Genomics (eMERGE) Network, FinnGen, Million Veteran Program, and UK Biobank. This analysis identified 1,210 unique genome-wide significant SNPs across ancestry groups (\u003cstrong\u003eFigure 1\u003c/strong\u003e, \u003cstrong\u003eTable S1a–d\u003c/strong\u003e). To identify conditionally independent signals, we applied stepwise conditional analysis using GCTA-COJO with the UK Biobank LD reference panel. This yielded 40 independent genome-wide significant SNPs: 16 in the multi-ancestry meta-analysis, 10 in non-Hispanic European (NH-EUR), 13 in non-Hispanic African (NH-AFR), and 1 in East Asian (EAS) populations (\u003cstrong\u003eTable 2\u003c/strong\u003e, \u003cstrong\u003eTable S2a–d\u003c/strong\u003e). These 40 SNPs mapped to 33 distinct genomic loci (±50 kb), encompassing both novel and previously reported nephrotic syndrome-associated regions. Notably, we identified novel loci near \u003cem\u003eJAML\u003c/em\u003e and \u003cem\u003eSTPG2\u003c/em\u003e, and replicated known associations at \u003cem\u003ePLA2R1\u003c/em\u003e (membranous nephropathy), \u003cem\u003eHMCN1\u003c/em\u003e (focal segmental glomerulosclerosis, FSGS), and \u003cem\u003eNPHS1\u003c/em\u003e (pediatric nephrotic syndrome).\u003csup\u003e33-35\u003c/sup\u003e In the NH-AFR subgroup, we confirmed the well-established \u003cem\u003eAPOL1\u003c/em\u003e risk locus (G1\u003csup\u003eM\u003c/sup\u003e allele odds ratio [OR]: 2.56; 95% confidence interval [CI]: 2.03–3.24; \u003cstrong\u003eFigure 2\u003c/strong\u003e). Additionally, we report the replication of variants identified from GWAS of related phenotypes, including Pediatric Steroid-Sensitive Nephrotic Syndrome, Steroid-Sensitive Nephrotic Syndrome Without Relapse, and Steroid-Dependent/Frequent Relapse Nephrotic Syndrome (\u003cstrong\u003eTable S3\u003c/strong\u003e). We further investigated suspected genome-wide significant singletons using regional association plots, including \u003cem\u003eTRIB1\u003c/em\u003e, \u003cem\u003eRNF169\u003c/em\u003e, \u003cem\u003eMSI2\u003c/em\u003e, and \u003cem\u003eEIPR1\u003c/em\u003e (\u003cstrong\u003eFigure S1\u003c/strong\u003e). Additionally, we examined loci at \u003cem\u003eLMF1\u003c/em\u003e and \u003cem\u003eFAM83A\u003c/em\u003e, which exhibited supported by multiple SNPs (\u003cstrong\u003eFigure S2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eWe observed mild genomic inflation in the NH-AFR group (λ\u003csub\u003eGC\u003c/sub\u003e = 1.077), compared to multi-ancestry (λ\u003csub\u003eGC\u003c/sub\u003e = 1.026), NH-EUR (λ\u003csub\u003eGC\u003c/sub\u003e = 1.019), and EAS (λ\u003csub\u003eGC\u003c/sub\u003e = 1.011) populations. LD Score Regression (LDSC) intercepts indicated minimal confounding due to population stratification in most groups: multi-ancestry (1.006 ± 0.006), NH-EUR (1.005 ± 0.007), NH-AFR (1.072 ± 0.007), and EAS (0.996 ± 0.007). Using LDSC, we estimated the genetic correlation between adult nephrotic syndrome and related traits. A strong and statistically significant genetic correlation was observed with pediatric nephrotic syndrome (genetic correlation R\u003csub\u003eg\u0026nbsp;\u003c/sub\u003e(se) = 0.63 (0.10); \u003cem\u003eP\u003c/em\u003e = 3.52x10\u003csup\u003e-11\u003c/sup\u003e (\u003cstrong\u003eTable S4\u003c/strong\u003e)). We also observed modest positive, but not statistically significant, correlations with related traits including estimated glomerular filtration rate (eGFR), blood pressure, type 2 diabetes, blood urea nitrogen, acute kidney injury, and chronic glomerulonephritis (\u003cstrong\u003eTable S4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eGenetically Predicted Gene Expression and Colocalization\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo identify gene expression profiles associated with nephrotic syndrome, we estimated genetically predicted gene expression (GPGE) using common variants (MAF \u0026gt; 0.01) derived from multi-ancestry and ancestry-specific meta-analyses. Gene expression was imputed across 49 tissues using GTEx v8 reference panels. This analysis identified 484 significant gene–tissue associations with nephrotic syndrome at a Bonferroni-corrected threshold (\u003cem\u003eP\u003c/em\u003e ≤ 1.55×10\u003csup\u003e-6\u003c/sup\u003e), including 161 associations in the multi-ancestry analysis, 321 in non-Hispanic European (NH-EUR), and 2 in non-Hispanic African (NH-AFR) populations (\u003cstrong\u003eTable S5a-d, Figure S3a-d\u003c/strong\u003e). Colocalization analysis revealed strong evidence for shared causal variants (posterior probability PP.H4 \u0026gt; 0.8) in 108 of these gene-tissue pairs, 45 in the multi-ancestry and 63 in the NH-EUR analyses. These colocalized signals mapped to 34 unique genes, of which 30 (88%) had not previously been reported in the GWAS Catalog for nephrotic syndrome or related renal phenotypes. One notable finding was the gene C4A, which showed significant association and strong colocalization with predicted expression in kidney cortex tissue in both multi-ancestry (\u003cem\u003eP\u003c/em\u003e = 7.12×10\u003csup\u003e-13\u003c/sup\u003e, PP.H4 = 0.96) and NH-EUR analyses (\u003cem\u003eP\u003c/em\u003e = 3.84×10\u003csup\u003e-13\u003c/sup\u003e, PP.H4 = 0.96) (\u003cstrong\u003eFigure S4\u003c/strong\u003e). At the time of this analysis, \u003cem\u003eC4A\u003c/em\u003e had not been previously implicated in nephrotic syndrome, highlighting its potential as a novel candidate gene for disease susceptibility.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eHLA Allele Associations\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eHuman leukocyte antigen allele analyses across ancestry groups within \u003cem\u003eAll of Us\u003c/em\u003e identified several significant and suggestive associations with nephrotic syndrome (\u003cstrong\u003eTable 3\u003c/strong\u003e). Among individuals of European ancestry, three HLA alleles reached Bonferroni corrected significance. The strongest association was observed for \u003cem\u003eDRB1*03:01:01G\u003c/em\u003e (OR: 2.01, 95% CI: [1.55 – 2.61], \u003cem\u003eP\u003c/em\u003e = 1.52×10\u003csup\u003e-7\u003c/sup\u003e), followed by other classical components of the 8.1 Ancestral Haplotype (AH8.1), including \u003cem\u003eC*07:01:01G\u003c/em\u003e (1.74 [1.35 – 2.23], \u003cem\u003eP\u003c/em\u003e = 1.55×10\u003csup\u003e-5\u003c/sup\u003e) and \u003cem\u003eDQA1*05:01:01G\u003c/em\u003e (1.64 [1.31 – 2.04], \u003cem\u003eP\u003c/em\u003e = 1.50×10\u003csup\u003e-5\u003c/sup\u003e). While no significant HLA associations were identified in the African/African American (AFR) or American Admixed/Latino (AMR) ancestries, several alleles demonstrated nominal evidence of association. These included \u003cem\u003eA*02:01:01G\u003c/em\u003e (2.04 [1.22 – 3.41], \u003cem\u003eP\u003c/em\u003e = 0.0070) in the AFR analysis and \u003cem\u003eDPB1*14:01:01G\u003c/em\u003e (3.22 [1.59 – 6.52], \u003cem\u003eP\u003c/em\u003e = 0.0011) from the AMR subgroup.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExamination of linkage disequilibrium (LD) of SNPs in the extended MHC region demonstrates that rs2187668 and rs9275576, tagging \u003cem\u003eDQA105:01:01G\u003c/em\u003e and \u003cem\u003eDRB103:01:01G\u003c/em\u003e, respectively, are part of the historically conserved AH8.1. These variants show high D′ but low r², consistent with shared haplotype structure but differing allele frequencies. In contrast, SNPs associated with \u003cem\u003eC4A\u003c/em\u003e expression (e.g., rs3134942, rs1150754, and rs3130297) exhibit both low D′ and low r² with AH8.1-tagging SNPs, indicating they are not in LD with the AH8.1 haplotype and likely represent independent signals (\u003cstrong\u003eFigure S5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eExpression Quantitative Trait Loci Evaluation\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe interrogated the significant nephrotic syndrome-associated SNPs for cis-expression quantitative trait loci (eQTL) effects using the Human Kidney eQTL Atlas (susztaklab.org). In glomerular tissue, we identified 4,078 significant eQTLs mapping to 533 unique SNPs and 29 distinct genes. In tubular tissue, 4,801 eQTLs were detected, corresponding to 607 unique SNPs and 29 genes. Analysis of meta-analyzed bulk kidney tissue revealed 6,817 eQTLs involving 1,272 unique SNPs and 37 unique genes (\u003cstrong\u003eTable S6a-b\u003c/strong\u003e). Notably, all detected eQTLs were located within the \u0026nbsp;HLA region on chromosome 6 (chr6:28,477,797–33,448,354).\u003csup\u003e36\u003c/sup\u003e We also identified ancestry-specific eQTLs in the East Asian (EAS) population across all tissue types, including 16 in glomerulus, 16 in tubule, and 26 in bulk kidney tissue.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eGene-Set and Pathway Analysis\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted gene-set enrichment analyses using MAGMA across all ancestry groups. In the multi-ancestry meta-analysis, the most significantly enriched gene set was “GOBP negative regulation of intracellular estrogen receptor signaling pathway”(\u003cem\u003eP\u003c/em\u003e = 1.4×10\u003csup\u003e-05\u003c/sup\u003e, 13 genes), followed by “GOBP regulation of histone modification” (\u003cem\u003eP\u003c/em\u003e = 6.8×10\u003csup\u003e-05\u003c/sup\u003e, 122 genes). Notably, the East Asian (EAS) ancestry group demonstrated unique enrichment for the “GOBP sensory perception of umami taste” gene set (\u003cem\u003eP\u003c/em\u003e = 2.9×10\u003csup\u003e-04\u003c/sup\u003e, 6 genes). Among canonical pathways, the top enriched terms in the multi-ancestry analysis included “KEGG antigen processing and presentation,” “ KEGG type 1 diabetes mellitus,” and “KEGG allograft rejection” (\u003cstrong\u003eFigure S6\u003c/strong\u003e) reflecting potential immune-related mechanisms underlying nephrotic syndrome susceptibility. Upon filtering out all genes located within the extended HLA region and rerunning the analysis, no gene sets or pathways reached statistical significance in any ancestry group, indicating that the observed pathway enrichment was predominantly driven by signals within the HLA locus.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDrug-Repurposing Analysis\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe applied a previously validated, genetically informed drug-repurposing and safety assessment pipeline to identify potential therapeutic candidates for nephrotic syndrome. This analysis highlighted apomorphine, a dopamine receptor agonist, as a candidate for repurposing based on its known target, the \u003cem\u003eAPOM\u003c/em\u003e gene.\u003csup\u003e37\u003c/sup\u003e Using drug-target Mendelian randomization (MR), we found that genetically proxied \u003cem\u003eAPOM\u003c/em\u003e expression was significantly associated with increased risk of nephrotic syndrome (WALD MR OR = 4.932, \u003cem\u003eP\u003c/em\u003e = 1.8×10\u003csup\u003e-19\u003c/sup\u003e), as shown in \u003cstrong\u003eTable 4\u003c/strong\u003e. However, interpretation of this result is limited by weak instrumental variables, as only a single tissue (testis) demonstrated statistically significant genetically predicted gene expression in the underlying genetically predicted gene expression (GPGE) analysis. These findings underscore the importance of cautious interpretation and the need for further experimental validation.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis large-scale, multi-ancestry GWAS of nephrotic syndrome provides new insight into the genetic architecture of the disease by identifying over 30 previously unreported susceptibility loci, revealing shared genetic correlations with related traits, and integrating these findings with gene expression and drug repurposing data. Our results demonstrate that electronic health record (EHR)-based phenotyping using ICD codes can effectively replicate established associations, including \u003cem\u003ePLA2R1\u003c/em\u003e and \u003cem\u003eAPOL1\u003c/em\u003e, and power the discovery of novel loci through well-powered meta-analysis.\u003c/p\u003e\u003cp\u003eDespite the strengths of using ICD codes for large-scale phenotyping, this approach also introduces limitations. ICD codes may incompletely capture disease heterogeneity, especially in complex conditions such as nephrotic syndrome. In adults, where biopsy data and clinical onset are frequently unavailable, ICD-based definitions may be less specific than in pediatric cohorts. Nevertheless, our findings suggest that ICD-defined nephrotic syndrome in adults may still enrich for individuals with a history of childhood-onset disease, particularly those with steroid-sensitive nephrotic syndrome, which has been linked to long-term renal consequences.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eImportantly, we found a strong genetic correlation between adult and pediatric nephrotic syndrome (R\u003csub\u003eg\u003c/sub\u003e = 0.63) suggesting that shared genetic mechanisms span disease onset across the life course. This may explain adult relapses among individuals with childhood-onset disease and supports the use of shared genetic insights in therapeutic development.\u003c/p\u003e\u003cp\u003eSeveral of the identified genes point toward plausible biological mechanisms. For instance, \u003cem\u003eHMCN1\u003c/em\u003e, which encodes an extracellular matrix protein expressed in podocytes, is critical to glomerular barrier integrity, disruption of which is a hallmark of nephrotic syndrome.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eUSP38\u003c/em\u003e, a previously unreported locus in this context, may modulate gene expression via interaction with \u003cem\u003eLSD1\u003c/em\u003e, suggesting an epigenetic mechanism in disease pathogenesis.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eJAML\u003c/em\u003e, another novel gene identified, contributes to kidney injury via macrophage-mediated inflammatory pathways, underscoring the role of immune mechanisms in disease progression.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Together, these discoveries connect GWAS findings to biologically relevant pathways in renal function and inflammation.\u003c/p\u003e\u003cp\u003eTranscriptome-wide association analyses further revealed 484 significant gene-tissue associations, 108 of which had strong evidence of colocalization. Notably, \u003cem\u003eC4A\u003c/em\u003e was significantly associated with nephrotic syndrome in both multi-ancestry and non-Hispanic European analyses and had not been previously linked to the disease. These findings offer promising leads for mechanistic studies and potential therapeutic targeting. Tissue-specific expression patterns of upregulated genes varied by ancestry group, suggesting underlying biological differences in disease manifestation. In the multi-ancestry analysis, whole blood and spleen, both central to immune regulation, emerged as the most significant tissues. Notably, expression patterns in East Asian and non-Hispanic African groups included breast and skin tissues, respectively, potentially reflecting ancestry-specific regulatory mechanisms or comorbidities.\u003c/p\u003e\u003cp\u003eGene-set and pathway analyses using MAGMA revealed several biologically plausible processes implicated in nephrotic syndrome. The most significant gene set in the multi-ancestry analysis involved negative regulation of intracellular estrogen receptor signaling, suggesting potential hormonal contributions to disease susceptibility, followed by pathways related to histone modification, highlighting a possible role for epigenetic regulation.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Canonical pathway enrichment included immune-related processes such as antigen processing and allograft rejection, consistent with the inflammatory features of nephrotic syndrome.\u003c/p\u003e\u003cp\u003eWe also identified ancestry-specific associations in the HLA region, reinforcing the complex immunogenetic basis of nephrotic syndrome. In non-Hispanic European individuals, \u003cem\u003eDRB1*03:01:01G\u003c/em\u003e was strongly associated with disease risk (OR: 2.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.53\u0026times;10⁻⁷), consistent with its established role in autoimmunity. In contrast, in non-Hispanic African and Admixed American/Latino individuals, different HLA alleles, such as \u003cem\u003eDPB1*14:01:01G\u003c/em\u003e and \u003cem\u003eC*12:03:01G\u003c/em\u003e, showed nominal associations, albeit with wider confidence intervals. These findings reflect potential population-specific risk profiles and complement our identification of expression quantitative trait loci (eQTLs) in the MHC region, particularly in kidney-relevant tissues.\u003c/p\u003e\u003cp\u003eWhile our genome-wide association analyses revealed significant SNP associations within the extended MHC region, it is important to acknowledge that these findings may be in linkage disequilibrium (LD) with classical HLA alleles. To assess this, we examined LD between our lead GWAS SNPs and known tag SNPs for selected HLA alleles, including components of the 8.1 ancestral haplotype. These analyses revealed low r\u0026sup2; values despite high D\u0026prime;, suggesting limited correlation and likely distinct signals. However, high-resolution HLA typing data were only available for the \u003cem\u003eAll of Us\u003c/em\u003e dataset, limiting our ability to directly evaluate HLA allele associations across other contributing biobanks. As such, SNP-level signals within the MHC region may partially reflect underlying HLA allele effects that could not be fully resolved in this analysis. Future efforts incorporating imputed or directly typed HLA alleles across multiple cohorts will be essential to disentangle these relationships and further clarify the immunogenetic architecture of nephrotic syndrome.\u003c/p\u003e\u003cp\u003eOur genetically informed drug-repurposing analysis identified apomorphine, a dopamine agonist targeting \u003cem\u003eAPOM\u003c/em\u003e, as a candidate compound. However, Mendelian randomization results suggested that increased \u003cem\u003eAPOM\u003c/em\u003e expression may elevate nephrotic syndrome risk (OR\u0026thinsp;=\u0026thinsp;4.932, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.76\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e), raising concerns about safety. These findings, though limited by weak instruments in GPGE analysis, suggest that patients receiving apomorphine may warrant clinical monitoring for early renal symptoms. Additional studies are needed to further assess the utility and risk of \u003cem\u003eAPOM\u003c/em\u003e-modulating compounds in nephrotic syndrome.\u003c/p\u003e\u003cp\u003eIn summary, this study identifies more than 30 novel loci, numerous gene expression patterns, and ancestry-specific genetic factors associated with nephrotic syndrome. These findings extend our understanding of the disease's molecular underpinnings and provide a foundation for future mechanistic research and precision therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional support provided by T32-GM145734-01 (H.M.S.), F31-EY033663 (J.H.B.), K12-AR084232 (J.N.H.). The eMERGE Network is supported by NHGRI grants. BioVU is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the CTSA grant UL1-TR000445. This research is based on data from the Million Veteran Program, Office of Research and Development, and Veterans Health Administration. This publication does not represent the views of the Department of Veterans Affairs or the United States Government. This research was funded in part by the Intramural Research Program of the National Institute of Environmental Health Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization\u003c/strong\u003e: T.L.E. and J.N.H. \u003cstrong\u003eMethodology\u003c/strong\u003e: J.H.B., H.M.S., N.K.K., K.A., A.B.B., T.L.E., and J.N.H. \u003cstrong\u003eInvestigation\u003c/strong\u003e: J.H.B. and H.M.S. \u003cstrong\u003eWriting (Original Draft)\u003c/strong\u003e: J.H.B. and H.M.S. \u003cstrong\u003eWriting (Review and Editing)\u003c/strong\u003e: J.H.B., H.M.S., A.T.A., N.K.K., K.A., A.B.B., J.S.H., Y.Z., G.P.J., O.D., M.T.M.L., I.J.K., D.C.F., R.R., K.K., A.A.M.R., K.S., T.L.E., and J.N.H. \u003cstrong\u003eVisualization\u003c/strong\u003e: J.H.B. and H.M.S. \u003cstrong\u003eResources\u003c/strong\u003e: A.A.M.R., K.S., T.L.E., and J.N.H. \u003cstrong\u003eData\u003c/strong\u003e \u003cstrong\u003eCuration\u003c/strong\u003e: T.L.E. and J.N.H. \u003cstrong\u003eSupervision\u003c/strong\u003e: T.L.E. and J.N.H. \u003cstrong\u003eFunding\u003c/strong\u003e \u003cstrong\u003eAcquisition\u003c/strong\u003e: T.L.E. and J.N.H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF INTERESTS\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVestergaard, S. 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K.\u003cem\u003e et al.\u003c/em\u003e Using Mendelian randomisation to identify opportunities for type 2 diabetes prevention by repurposing medications used for lipid management. \u003cem\u003eEBioMedicine\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e, 104038 (2022). https://doi.org/10.1016/j.ebiom.2022.104038\u003c/li\u003e\n\u003cli\u003eMountjoy, E.\u003cem\u003e et al.\u003c/em\u003e An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 1527-1533 (2021). https://doi.org/10.1038/s41588-021-00945-5\u003c/li\u003e\n\u003cli\u003eOchoa, D.\u003cem\u003e et al.\u003c/em\u003e The next-generation Open Targets Platform: reimagined, redesigned, rebuilt. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, D1353-d1359 (2023). https://doi.org/10.1093/nar/gkac1046\u003c/li\u003e\n\u003cli\u003eJiang, L., Zheng, Z., Fang, H. \u0026amp; Yang, J. A generalized linear mixed model association tool for biobank-scale data. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 1616-1621 (2021). https://doi.org/10.1038/s41588-021-00954-4\u003c/li\u003e\n\u003cli\u003ePhelan, C. M.\u003cem\u003e et al.\u003c/em\u003e Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 680-691 (2017). https://doi.org/10.1038/ng.3826\u003c/li\u003e\n\u003cli\u003eChobert, M. N.\u003cem\u003e et al.\u003c/em\u003e High hepatic gamma-glutamyltransferase (gamma-GT) activity with normal serum gamma-GT in children with progressive idiopathic cholestasis. \u003cem\u003eJ Hepatol\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 22-25 (1989). https://doi.org/10.1016/0168-8278(89)90157-8\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"METHODS ","content":"\u003cp\u003e\u003cstrong\u003eResource Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLead Contact\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFurther information and requests for resources should be directed to and will be fulfilled by the lead contact, Jacklyn Hellwege ([email protected]).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod Details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a cross-ancestry genome-wide association study (GWAS) of nephrotic syndrome by integrating imputed genotype data from Vanderbilt University\u0026rsquo;s \u003cstrong\u003eBioVU\u003c/strong\u003e and the Electronic Medical Records and Genetics (\u003cstrong\u003eeMERGE\u003c/strong\u003e) network with publicly available summary statistics from Biobank Japan (\u003cstrong\u003eBBJ\u003c/strong\u003e), the Million Veteran Program (\u003cstrong\u003eMVP\u003c/strong\u003e), FinnGen, the UK Biobank (\u003cstrong\u003eUKB\u003c/strong\u003e), and the National Institutes of Health\u0026rsquo;s \u003cem\u003eAll of Us\u003c/em\u003e (\u003cstrong\u003eAoU\u003c/strong\u003e) cohort. Our combined sample included 1,606,274 individuals (5,519 cases and 1,640,242 controls; \u003cstrong\u003eTable 1\u003c/strong\u003e). The AoU, BioVU, eMERGE, MVP, and UKB cases were identified by the nephrotic syndrome PheCode (580.2) or PheCodeX (GU_580.1, GU_580.2), while controls were those without renal disease PheCodes. The BBJ and FinnGen cases were identified by diagnostic codes for NS, while controls were without renal disease diagnosis codes.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eIndividual Level Data\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBioVU\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe BioVU DNA Repository is a deidentified database of electronic health records (EHR) that are linked to patient DNA samples at Vanderbilt University Medical Center. A detailed description of the database and how it is maintained has been published elsewhere.\u003csup\u003e42\u003c/sup\u003e BioVU participant DNA samples were genotyped on a custom Illumina Multi-Ethnic Genotyping Array (MEGA-ex). Samples with missingness \u0026gt;2%, withdrawn consent, duplications, or sex discordance were excluded. Imputation was performed on the Michigan Imputation Server (MIS) v1.2.4 using Minimac4 and the Haplotype Reference Consortium (HRC) panel v1.1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Electronic Medical Records and Genetics Network\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe eMERGE Network is a consortium of several EHR-linked biorepositories formed with the goal of developing approaches for the use of the EHR in genomic research. Consortium membership has evolved over eMERGE\u0026rsquo;s 11-year history, with many sites contributing data including Group Health/University of Washington, Marshfield Clinic, Mayo Clinic, Northwestern University, Vanderbilt University, Children\u0026rsquo;s Hospital of Philadelphia (CHOP), Boston Children\u0026rsquo;s Hospital (BCH), Cincinnati Children\u0026rsquo;s Hospital Medical Center (CCHMC), Geisinger Health System, Mount Sinai School of Medicine, Harvard University, and Columbia University. The eMERGE study was approved by the Institutional Review Board at each site and all methods were performed in accordance with the relevant guidelines and regulations. Participants in the eMERGE network were genotyped separately, then imputed and merged. A detailed description of the genotyping, imputation, and quality control of the eMERGE phase III array dataset has been previously reported.\u003csup\u003e43\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll of Us\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eAll of Us\u003c/em\u003e research program is a nationwide initiative designed to collect comprehensive health-related information from a diverse participant pool across the United States.\u003csup\u003e44\u003c/sup\u003e The program integrates survey responses, physical measurements, electronic health records (EHRs), and genomic data, providing an unparalleled resource for health research. Data used for this study included electronic health record data and short-read whole genome sequencing (srWGS) derived human leukocyte antigen (HLA) alleles. Nephrotic syndrome cases were classified using PheCodeX codes, requiring 2 or more codes for GU_580, GU_580.1, or GU_580.2. Controls were individuals without any genitourinary PheCodes (GU_*).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQuality control for srWGS data has been previously described.\u003csup\u003e45\u003c/sup\u003e HLA alleles were inferred using Kourami v0.9.6 on srWGS data at loci defined in IPD-IMGT/HLA v3.47.0, including both Class I and Class II loci (e.g., HLA-A, -B, -DRB1).\u003csup\u003e46\u003c/sup\u003e Assemblies were executed on the \u003cem\u003eAll of Us\u003c/em\u003e Researcher Workbench using GATK 4.3.0.0. In rare cases where HLA-DOA assembly stalled, the pipeline was rerun excluding this locus. Alleles were reported at G-group specificity and filtered to remove ambiguous calls (i.e., multiple equally likely alleles), alleles with \u0026lt;95% sequence identity to the reference, alleles supported by a MaxFlow parameter \u0026lt;10 (i.e., fewer than 10 reads supporting one or more assembly graph edges). We included only alleles with an allele frequency of at least 1% within each HLA gene for downstream analysis. Significance thresholds for HLA allele associations were adjusted for multiple testing within each ancestry group, EUR: 3.38\u0026times;10\u003csup\u003e-04\u003c/sup\u003e (0.05 / 148 tested alleles), AFR: 3.05\u0026times;10\u003csup\u003e-04\u003c/sup\u003e (0.05 / 164), AMR: 2.96\u0026times;10\u003csup\u003e-04\u003c/sup\u003e (0.05 / 169).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSummary Statistics\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBiobank Japan\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBiobank Japan is a multi-institutional hospital-based registry comprised of DNA and medical records from individuals of Japanese ancestry. We utilized published summary statistics for acute renal failure identified with diagnostics codes, specifically (ICD-10 N17).\u003csup\u003e47,48\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFinnGen\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFinnGen is a large public-private partnership comprised of DNA and health data from up to 500,000 Finnish biobank participants. We utilized published summary statistics for acute renal failure as assessed by diagnostic codes from FinnGen, identified with the FinnGen endpoint N14-ACUTERENFAIL (presence of ICD-10 N17 and/or ICD-9 584).\u003csup\u003e49\u003c/sup\u003e [https://r8.risteys.finngen.fi/phenocode/N14_NEPHROTICSYND]\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMillion Veteran Program\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe MVP is a national research program that incorporated genomic data and health record data, collected from Veterans, to investigate how genes, lifestyle, military experiences, and exposures affect health and wellness. We utilized summary statistics for NS, identified with the PheCode 580.2.\u003csup\u003e50\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUK Biobank\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe UKB is a large-scale biomedical database that aims to improve public health by enabling scientific discoveries. We utilized summary statistics for NS, identified with the PheCode 580.2. [Pan-UKB team.\u0026nbsp;\u003ca href=\"https://pan.ukbb.broadinstitute.org/\"\u003ehttps://pan.ukbb.broadinstitute.org\u003c/a\u003e. 2020.]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification and Statistical Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetic associations with nephrotic syndrome status were modeled as a function of additive genotype, sex, and the top 10 principal components of ancestry, followed by inverse-variance weighted fixed-effects meta-analysis both within and across ancestral groups utilizing METAL.\u003csup\u003e51\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSignificant SNPs from the meta-analyses were evaluated as cis eQTLs using the Human Kidney eQTL Atlas (susztaklab.org).\u0026nbsp;We utilized LD Score Regression (LDSC) to calculate the genomic inflation factor (\u0026lambda;\u003csub\u003eGC\u003c/sub\u003e) and intercept for all ancestry groups as well as calculate heritability and genetic correlation with related phenotypes.\u003csup\u003e52,53\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHLA allele associations with nephrotic syndrome were modeled as a function of additive allele, age, sex, and the top 10 principal components of ancestry across genetic ancestry groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFUMA, a functional annotation tool for GWAS results, was used to analyze results for each ancestry group.\u003csup\u003e54\u003c/sup\u003e The GENE2FUNC process was used to annotate significant genes in a biological context\u003csup\u003e55\u003c/sup\u003e. Specifically, MAGMA was used for gene-set analyses where SNP associations are summarized at the gene level, followed by association of gene sets to biological pathways using multiple linear regression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eGenetically Predicted Gene Expression\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eGenetically predicted gene expression was evaluated using S-PrediXcan, a gene-level method that estimates the genetically determined component of gene expression in specific tissues and tests its association with an outcome using SNP-level summary statistics.\u003csup\u003e56\u003c/sup\u003e We analyzed common variants (MAF \u0026gt; 0.01) from both within-ancestry and cross-ancestry meta-analyses alongside expression models from GTEx V8.\u003csup\u003e57\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCovariance matrices developed for European (1000 Genomes) and African populations were incorporated: the European matrix was applied to NH-EUR, EAS, and multi-ancestry samples, while the African matrix was used for NH-AFR samples. To correct for multiple testing, a Bonferroni threshold \u003cem\u003eP\u003c/em\u003e \u0026lt; 1.55\u0026times;10\u003csup\u003e-6\u003c/sup\u003e was applied, accounting for the total number of gene models tested across tissues.\u003c/p\u003e\n\u003cp\u003eTo assess whether the same causal variant underlies both GWAS and expression quantitative trait loci (eQTL) signals at a locus, we performed colocalization analysis using coloc, a Bayesian method that evaluates summary statistics from GWAS and eQTL data.\u003csup\u003e58\u003c/sup\u003e Input included common variants from combined and ancestry-specific meta-analyses, restricted to variants present in the S-PrediXcan gene expression models, along with corresponding eQTL summary statistics. Coloc outputs posterior probabilities for five hypotheses, with PP.H4 representing the probability that the GWAS and eQTL associations colocalize (i.e., share a causal variant). For each locus, the SNP with the highest PP.H4 and its posterior probability were annotated. We considered a statistically significant S-PrediXcan association together with a PP.H4 \u0026gt; 80% as strong evidence of colocalization.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConditional Analyses\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe performed conditional analyses of common variants using GCTA-COJO, part of the Genome-wide Complex Trait Analysis (GCTA) software suite, which conducts iterative conditional and joint analyses with stepwise model selection.\u003csup\u003e59,60\u003c/sup\u003e For the NH-EUR, EAS, and multi-ancestry analyses, linkage disequilibrium (LD) was estimated using unrelated non-Hispanic White individuals from the UK Biobank (UKB). For NH-AFR analyses, LD was estimated using unrelated non-Hispanic Black individuals from BioVU.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInput summary statistics were drawn from both within- and cross-ancestry meta-analyses. Reference genotype data included a subset of hard-called imputed genotypes: 5,000 UKB Europeans and 2,217 BioVU NH-Black individuals, both in PLINK format. Within each reference panel, LD was computed between all pairwise SNPs. The selection threshold for GCTA was set at \u003cem\u003eP\u003c/em\u003e \u0026lt; 5x10\u003csup\u003e-8\u003c/sup\u003e. To address multicollinearity, the default collinearity threshold of 0.9 was applied, excluding SNPs with pairwise r\u003csup\u003e2\u003c/sup\u003e \u0026ge; 0.9. in joint regression. For sets of SNPs in LD (r\u003csup\u003e2\u003c/sup\u003e \u0026ge; 0.1), the most significant SNP (based on minimum P-value across all nephrotic syndrome traits) was retained from the GCTA joint model.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDrug Repurposing Analysis\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo identify potential therapies for nephrotic syndrome, we applied a previously established genetically informed drug repurposing pipeline.\u003csup\u003e37,61\u003c/sup\u003e We began with the 34 unique genes identified via genetically predicted gene expression (GPGE) analysis and mapped them to known drug targets using Open Targets and the Drug-Gene Interaction Database (DGIdb).\u003csup\u003e62,63\u003c/sup\u003e Drug\u0026ndash;gene pairs were retained as repurposing candidates if the direction of the drug effect was consistent with the direction of the GPGE association with NS (i.e., drug and GPGE exerted opposing effects). Additionally, we investigated drugs with concordant directions as possible disease-exacerbating agents.\u003c/p\u003e\n\u003cp\u003eTo further assess therapeutic potential, Mendelian randomization (MR) analyses were conducted using S-PrediXcan summary statistics as instrumental variables, as previously described.\u003csup\u003e37,61\u003c/sup\u003e Briefly, we evaluated the effect of GPGE on NS risk using fixed-effects inverse-variance weighted MR, implemented in the R package TwoSampleMR. Exposure instruments were gene-tissue pairs identified from S-PrediXcan analyses for three unrelated disease indications\u0026mdash;chronic myelogenous leukemia, epithelial ovarian cancer, and Parkinson\u0026rsquo;s disease.\u003csup\u003e49,64-66\u003c/sup\u003e Among these, only Parkinson\u0026rsquo;s disease yielded a significant GPGE association (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The same gene-tissue pair was then used as the outcome instrument with the NS S-PrediXcan results to complete the MR analysis.\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Nephrotic Syndrome Cases and Controls by Data Source. AoU, BioVU, eMERGE, MVP, and UKB cases were identified by nephrotic syndrome PheCode, while controls were without renal disease PheCodes. BBJ and FinnGen cases were identified by nephrotic syndrome diagnostic codes, while controls were without renal disease codes. \u003cem\u003eAll of Us\u003c/em\u003e data were not included in the GWAS Meta-analysis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"681\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData Source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic Ancestry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCases (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eControls (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 162px;\"\u003e\n \u003cp\u003eVanderbilt University\u0026apos;s BioVU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e42,564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eAfrican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e8,444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 162px;\"\u003e\n \u003cp\u003eeMERGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e41,523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eAfrican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6,618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eBiobank Japan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eEast Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1,314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e177,412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e337,446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 162px;\"\u003e\n \u003cp\u003eMillion Veteran Program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1,086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e456,170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eAfrican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e120,602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 162px;\"\u003e\n \u003cp\u003eUK Biobank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e401,927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eEast Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e8,354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cem\u003eAll of Us\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e25,175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eAfrican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7,322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eAmerican Admixed/Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6,685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMulti-ancestry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5,519\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,640,242\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eUnique Significant LD Pruned SNPs (r\u0026sup2; \u0026le; 0.1) Across Populations.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"706\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAncestry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ersID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMapped Gene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 87px;\"\u003e\n \u003cp\u003eMulti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers191872995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e185737251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eHMCN1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e9.82x10\u003csup\u003e-09\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers1265889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e32065839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eTNXB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.43x10\u003csup\u003e-20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers9269032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e32469977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eHLA-DRB9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n 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style=\"width: 80px;\"\u003e\n \u003cp\u003e1.67x10\u003csup\u003e-12\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers149556312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e125666025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eTRIB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n 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style=\"width: 80px;\"\u003e\n \u003cp\u003e5.09x10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"13\" style=\"width: 87px;\"\u003e\n \u003cp\u003eAFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers1299376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e186384083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eORD4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n 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style=\"width: 80px;\"\u003e\n \u003cp\u003e1.20x10\u003csup\u003e-08\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers10930613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e173676966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2.07x10\u003csup\u003e-08\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers7669591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e97911328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eSTPG2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2.50x10\u003csup\u003e-08\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers200195313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17006793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eSTMND1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2.29x10\u003csup\u003e-08\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers9352469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e63054509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e3.82x10\u003csup\u003e-08\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers575869806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e82901968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003ePCLO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2.90x10\u003csup\u003e-09\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers151022480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e118190749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eJAML\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2.88x10\u003csup\u003e-08\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers78762921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e63118812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eAVPR1A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e3.26x10\u003csup\u003e-08\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers28419307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e46299504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4.75x10\u003csup\u003e-08\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers115553053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1082845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eARHGAP45\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e7.29x10\u003csup\u003e-09\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers200786642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e43406071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eTEX101\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eCCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.99x10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ers60910145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e36265988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eAPOL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4.53x01\u003csup\u003e-15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e HLA Alleles Associated with Nephrotic Syndrome Across Genetic Ancestry Groups in \u003cem\u003eAll of Us\u003c/em\u003e. Significant associations are bolded while nominal associations are not.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"719\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAncestry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHLA Alleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"21\" style=\"width: 81px;\"\u003e\n \u003cp\u003eEUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eA*02:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.61 (0.42 \u0026ndash; 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e22107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB*08:01:01G\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.71 (1.29 \u0026ndash; 2.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.88x10\u003csup\u003e-04\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e165\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23786\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eB*57:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.23 (0.07 \u0026ndash; 0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e23673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eC*06:02:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.57 (0.36 \u0026ndash; 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e24270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eC*07:01:01G\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.74 (1.35 \u0026ndash; 2.23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.55x10\u003csup\u003e-05\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e167\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24376\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDOB*01:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.73 (0.58 \u0026ndash; 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDOB*01:01:03G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1.42 (1.13 \u0026ndash; 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDPA1*02:01:02G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1.80 (1.20 \u0026ndash; 2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDPB1*01:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1.81 (1.25 \u0026ndash; 2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDQA1*02:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.54 (0.36 \u0026ndash; 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDQA1*05:01:01G\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.64 (1.31 \u0026ndash; 2.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.50x10\u003csup\u003e-05\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e174\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25161\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDQB1*02:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1.41 (1.11 \u0026ndash; 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDQB1*03:03:02G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.41 (0.20 \u0026ndash; 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDQB1*06:02:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.66 (0.45 \u0026ndash; 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDQB1*06:04:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1.67 (1.05 \u0026ndash; 2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDRB1*03:01:01G\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.01 (1.55 \u0026ndash; 2.61)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.52x10\u003csup\u003e-07\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e174\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24980\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDRB1*07:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.56 (0.38 \u0026ndash; 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDRB1*15:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.63 (0.43 \u0026ndash; 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e24981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eG*01:01:03G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.46 (0.25 \u0026ndash; 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e25084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eH*02:04:01\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1.30 (1.02 \u0026ndash; 1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e23579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eH*02:07:01:02\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.50 (0.28 \u0026ndash; 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e23564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 81px;\"\u003e\n \u003cp\u003eAFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eA*02:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e2.04 (1.22 \u0026ndash; 3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eA*68:02:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1.80 (1.05 \u0026ndash; 3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eC*12:03:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e2.60 (1.14 \u0026ndash; 5.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDOA*01:01:05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1.59 (1.08 \u0026ndash; 2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDQB1*06:04:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e2.34 (1.07 \u0026ndash; 5.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDRB1*14:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e2.29 (1.00 \u0026ndash; 5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 81px;\"\u003e\n \u003cp\u003eAMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eB*38:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e3.60 (1.40 \u0026ndash; 9.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eC*15:02:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e2.83 (1.39 \u0026ndash; 5.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDPB1*14:01:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e3.22 (1.59 \u0026ndash; 6.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eDRB1*13:03:01G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e2.79 (1.02 \u0026ndash; 7.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eH*01:02:01:04\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1.77 (1.02 \u0026ndash; 3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eNephrotic syndrome MR (Wald ratio) for the effect of one standard deviation (SD) increase in \u003cem\u003eAPOM\u0026nbsp;\u003c/em\u003egene expression, as a proxy for apomorphine\u0026apos;s therapeutic action on risk of Parkinson\u0026apos;s disease.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"720\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary Indication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Target\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProxied Drug\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug Action\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of GPGE Tissues\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWALD MR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eParkinson\u0026rsquo;s Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eAPOM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eApomorphine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eAgonist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.73x10\u003csup\u003e-15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":"Genetics, Nephrotic Syndrome, Genetic Correlation","lastPublishedDoi":"10.21203/rs.3.rs-7482306/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7482306/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNephrotic syndrome is a rare, heterogeneous kidney disorder characterized by proteinuria, hypoalbuminemia, and edema. To elucidate its genetic architecture, we conducted a large-scale, electronic health record (EHR)-linked, multi-ancestry genome-wide association study comprising 5,214 cases and 1,601,060 controls. We identified 37 distinct loci associated with disease risk, including novel associations at \u003cem\u003eJAML\u003c/em\u003e and \u003cem\u003eSTPG2\u003c/em\u003e, and confirmed prior signals at \u003cem\u003ePLA2R1\u003c/em\u003e, \u003cem\u003eHMCN1\u003c/em\u003e, and \u003cem\u003eAPOL1\u003c/em\u003e. Fine-mapping of the major histocompatibility complex (MHC) revealed the strongest association at \u003cem\u003eDRB103:01:01G\u003c/em\u003e in individuals of European ancestry (OR = 2.01, \u003cem\u003eP\u003c/em\u003e = 1.4×10⁻²⁰), alongside nominal ancestry-specific associations in African (\u003cem\u003eDOA01:01:05\u003c/em\u003e) and Latino (\u003cem\u003eDPB1*14:01:01G\u003c/em\u003e) populations. Transcriptome-wide association analysis (TWAS) identified 484 significant gene-tissue associations, including \u003cem\u003eC4A\u003c/em\u003e in kidney cortex. Expression quantitative trait locus (eQTL) mapping revealed numerous cis-eQTLs in glomerular and tubular renal tissues, largely within the MHC region. We observed significant genetic correlation between adult and pediatric nephrotic syndrome (Rg = 0.63, \u003cem\u003eP\u003c/em\u003e = 3.5x10\u003csup\u003e-11\u003c/sup\u003e), suggesting shared genetic etiology. Pathway analyses implicated estrogen receptor signaling and histone modification. Mendelian randomization implicated \u003cem\u003eAPOM\u003c/em\u003e expression and apomorphine exposure with increased disease risk (OR = 4.93, \u003cem\u003eP\u003c/em\u003e = 1.8x10\u003csup\u003e-19\u003c/sup\u003e). These results expand the understanding of nephrotic syndrome pathogenesis and highlight ancestry-informed targets for therapeutic development.\u003c/p\u003e","manuscriptTitle":"From GWAS to Translational Insights: Comprehensive Genetic Analysis of Nephrotic Syndrome from Multiple Populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 09:34:53","doi":"10.21203/rs.3.rs-7482306/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":"9d7a646c-6340-467d-a5e0-1af9c27e8bba","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T11:27:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 09:34:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7482306","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7482306","identity":"rs-7482306","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}