Increased risk of kidney failure in patients with genetic kidney disorders.

BACKGROUNDIt is unknown whether the risk of kidney disease progression and failure differs between patients with and without genetic kidney disorders.METHODSThree cohorts were evaluated: the prospective Cure Glomerulonephropathy Network (CureGN) and 2 retrospective cohorts from Columbia University, including 5,727 adults and children with kidney disease from any etiology who underwent whole-genome or exome sequencing. The effects of monogenic kidney disorders and APOL1 kidney-risk genotypes on the risk of kidney failure, estimated glomerular filtration rate (eGFR) decline, and disease remission rates were evaluated along with diagnostic yields and the impact of American College of Medical Genetics secondary findings (ACMG SFs).RESULTSMonogenic kidney disorders were identified in 371 patients (6.5%), high-risk APOL1 genotypes in 318 (5.5%), and ACMG SFs in 100 (5.2%). Family history of kidney disease was the strongest predictor of monogenic disorders. After adjustment for traditional risk factors, monogenic kidney disorders were associated with an increased risk of kidney failure (hazard ratio [HR] = 1.72), higher rate of eGFR decline (-3.06 vs. 0.25 mL/min/1.73 m2/year), and lower risk of complete remission (odds ratioNot achieving CR = 5.25). High-risk APOL1 genotypes were associated with an increased risk of kidney failure (HR = 1.67) and faster eGFR decline (-2.28 vs. 0.25 mL/min/1.73 m2), replicating prior findings. ACMG SFs were not associated with personal or family history of associated diseases, but were predicted to impact care in 70% of cases.CONCLUSIONSMonogenic kidney disorders were associated with an increased risk of kidney failure, faster eGFR decline, and lower rates of complete remission, suggesting opportunities for early identification and intervention based on molecular diagnosis.TRIAL REGISTRATIONNA.FUNDINGNational Institute of Diabetes and Digestive and Kidney Diseases grants U24DK100845 (formerly UM1DK100845), U01DK100846 (formerly UM1DK100846), U01DK100876 (formerly UM1DK100876), U01DK100866 (formerly UM1DK100866), U01DK100867 (formerly UM1DK100867), U24DK100845, DK081943, RC2DK116690, 2U01DK100876, 1R01DK136765, 5R01DK082753, and RC2-DK122397; NephCure Kidney International; Department of Defense Research Awards PR201425, W81XWH-16-1-0451, and W81XWH-22-1-0966; National Center for Advancing Translational Sciences grant UL1TR001873; National Library of Medicine grant R01LM013061; National Human Genome Research Institute grant 2U01HG008680.

PheW2P2V: a phenome-wide prediction framework with weighted patient representations using electronic health records.

Objective: Electronic health records (EHRs) provide opportunities for the development of computable predictive tools. Conventional machine learning methods and deep learning methods have been widely used for this task, with the approach of usually designing one tool for one clinical outcome. Here we developed PheW2P2V, a Phenome-Wide prediction framework using Weighted Patient Vectors. PheW2P2V conducts tailored predictions for phenome-wide phenotypes using numeric representations of patients' past medical records weighted based on their similarities with individual phenotypes. Materials and Methods: PheW2P2V defines clinical disease phenotypes using Phecode mapping based on International Classification of Disease codes, which reduces redundancy and case-control misclassification in real-life EHR datasets. Through upweighting medical records of patients that are more relevant to a phenotype of interest in calculating patient vectors, PheW2P2V achieves tailored incidence risk prediction of a phenotype. The calculation of weighted patient vectors is computationally efficient, and the weighting mechanism ensures tailored predictions across the phenome. We evaluated prediction performance of PheW2P2V and baseline methods with simulation studies and clinical applications using the MIMIC-III database. Results: Across 942 phenome-wide predictions using the MIMIC-III database, PheW2P2V has median area under the receiver operating characteristic curve (AUC-ROC) 0.74 (baseline methods have values ≤0.72), median max F1-score 0.20 (baseline methods have values ≤0.19), and median area under the precision-recall curve (AUC-PR) 0.10 (baseline methods have values ≤0.10). Discussion: PheW2P2V can predict phenotypes efficiently by using medical concept embeddings and upweighting relevant past medical histories. By leveraging both labeled and unlabeled data, PheW2P2V reduces overfitting and improves predictions for rare phenotypes, making it a useful screening tool for early diagnosis of high-risk conditions, though further research is needed to assess the transferability of embeddings across different databases. Conclusions: PheW2P2V is fast, flexible, and has superior prediction performance for many clinical disease phenotypes across the phenome of the MIMIC-III database compared to that of several popular baseline methods.

Polygenic scores and their applications in kidney disease.

Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.

Bridging the Gap in Genomic Implementation: Identifying User Needs for Precision Nephrology.

Introduction: Genomic medicine holds transformative potential for personalized nephrology care; however, its clinical integration poses challenges. Automated clinical decision support (CDS) systems in the electronic health record (EHR) offer a promising solution but have shown limited impact. This study aims to glean practical insights into nephrologists' challenges using genomic resources, informing precision nephrology decision support tools. Methods: We conducted an anonymous electronic survey among US nephrologists from January 19, 2021 to May 19, 2021, guided by the Consolidated Framework for Implementation Research. It assessed practice characteristics, genomic resource utilization, attitudes, perceived knowledge, self-efficacy, and factors influencing genetic testing decisions. Survey links were primarily shared with National Kidney Foundation members. Results: We analyzed 319 surveys, with most respondents specializing in adult nephrology. Although respondents generally acknowledged the clinical use of genomic resources, varying levels of perceived knowledge and self-efficacy were evident regarding precision nephrology workflows. Barriers to genetic testing included cost/insurance coverage and limited genomics experience. Conclusion: The study illuminates specific hurdles nephrologists face using genomic resources. The findings are a valuable contribution to genomic implementation research, highlighting the significance of developing tailored interventions to support clinicians in using genomic resources effectively. These findings can guide the future development of CDS systems in the EHR. Addressing unmet informational and workflow support needs can enhance the integration of genomics into clinical practice, advancing personalized nephrology care and improving kidney disease outcomes. Further research should focus on interventions promoting seamless precision nephrology care integration.

Cross-Phenotype GWAS Supports Shared Genetic Susceptibility to Systemic Sclerosis and Primary Biliary Cholangitis.

Objective: An increased risk of primary biliary cholangitis (PBC) has been reported in patients with systemic sclerosis (SSc). Our study aims to investigate the shared genetic susceptibility between the two disorders and to define candidate causal genes using cross-phenotype GWAS meta-analysis. Methods: We performed cross-phenotype GWAS meta-analysis and colocalization analysis for SSc and PBC. We performed both genome-wide and locus-based analysis, including tissue and pathway enrichment analyses, fine-mapping, colocalization analyses with expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) datasets, and phenome-wide association studies (PheWAS). Finally, we used an integrative approach to prioritize candidate causal genes from the novel loci. Results: We detected a strong genetic correlation between SSc and PBC (rg = 0.84, p = 1.7 × 10-6). In the cross-phenotype GWAS meta-analysis, we identified 44 non-HLA loci that reached genome-wide significance (p < 5 × 10-8). Evidence of shared causal variants between SSc and PBC was found for nine loci, five of which were novel. Integrating multiple sources of evidence, we prioritized CD40, ERAP1, PLD4, SPPL3, and CCDC113 as novel candidate causal genes. The CD40 risk locus colocalized with trans-pQTLs of multiple plasma proteins involved in B cell function. Conclusion: Our study supports a strong shared genetic susceptibility between SSc and PBC. Through cross-phenotype analyses, we have prioritized several novel candidate causal genes and pathways for these disorders.

Genome-wide discovery for biomarkers using quantile regression at biobank scale.

Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. Conventional GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. We draw attention here to an alternative, lesser known approach, namely quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest. Quantile regression can be applied efficiently at biobank scale, while having some unique advantages such as (1) identifying variants with heterogeneous effects across quantiles of the phenotype distribution; (2) accommodating a wide range of phenotype distributions including non-normal distributions, with invariance of results to trait transformations; and (3) providing more detailed information about genotype-phenotype associations even for those associations identified by conventional GWAS. We show in simulations that quantile regression is powerful across both homogeneous and various heterogeneous models. Applications to 39 quantitative traits in the UK Biobank demonstrate that quantile regression can be a helpful complement to linear regression in GWAS and can identify variants with larger effects on high-risk subgroups of individuals but with lower or no contribution overall.

Assisting the analysis of insertions and deletions using regional allele frequencies.

Accurate estimation of population allele frequency (AF) is crucial for gene discovery and genetic diagnostics. However, determining AF for frameshift-inducing small insertions and deletions (indels) faces challenges due to discrepancies in mapping and variant calling methods. Here, we propose an innovative approach to assess indel AF. We developed CRAFTS-indels (Calculating Regional Allele Frequency Targeting Small indels), an algorithm that combines AF of distinct indels within a given region and provides "regional AF" (rAF). We tested and validated CRAFTS-indels using three independent datasets: gnomAD v2 (n=125,748 samples), an internal dataset (IGM; n=39,367), and the UK BioBank (UKBB; n=469,835). By comparing rAF against standard AF, we identified rare indels with rAF exceeding standard AF (sAF≤10-4 and rAF>10-4) as "rAF-hi" indels. Notably, a high percentage of rare indels were "rAF-hi", with a higher proportion in gnomAD v2 (11-20%) and IGM (11-22%) compared to the UKBB (5-9% depending on the CRAFTS-indels' parameters). Analysis of the overlap of regions based on their rAF with low complexity regions and with ClinVar classification supported the pertinence of rAF. Using the internal dataset, we illustrated the utility of CRAFTS-indel in the analysis of de novo variants and the potential negative impact of rAF-hi indels in gene discovery. In summary, annotation of indels with cohort specific rAF can be used to handle some of the limitations of current annotation pipelines and facilitate detection of novel gene disease associations. CRAFTS-indels offers a user-friendly approach to providing rAF annotation. It can be integrated into public databases such as gnomAD, UKBB and used by ClinVar to revise indel classifications.

Risk factors affecting polygenic score performance across diverse cohorts.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N=491,111) and African (N=21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best and worst performing quintiles for certain covariates. 28 covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account non-linear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge GWAS effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Developing a genetic testing panel for evaluation of morbidities in kidney transplant recipients.

Cardiovascular disease, infection, malignancy, and thromboembolism are major causes of morbidity and mortality in kidney transplant recipients (KTR). Prospectively identifying monogenic conditions associated with post-transplant complications may enable personalized management. Therefore, we developed a transplant morbidity panel (355 genes) associated with major post-transplant complications including cardiometabolic disorders, immunodeficiency, malignancy, and thrombophilia. This gene panel was then evaluated using exome sequencing data from 1590 KTR. Additionally, genes associated with monogenic kidney and genitourinary disorders along with American College of Medical Genetics (ACMG) secondary findings v3.2 were annotated. Altogether, diagnostic variants in 37 genes associated with Mendelian kidney and genitourinary disorders were detected in 9.9% (158/1590) of KTR; 25.9% (41/158) had not been clinically diagnosed. Moreover, the transplant morbidity gene panel detected diagnostic variants for 56 monogenic disorders in 9.1% KTRs (144/1590). Cardiovascular disease, malignancy, immunodeficiency, and thrombophilia variants were detected in 5.1% (81), 2.1% (34), 1.8% (29) and 0.2% (3) among 1590 KTRs, respectively. Concordant phenotypes were present in half of these cases. Reviewing implications for transplant care, these genetic findings would have allowed physicians to set specific risk factor targets in 6.3% (9/144), arrange intensive surveillance in 97.2% (140/144), utilize preventive measures in 13.2% (19/144), guide disease-specific therapy in 63.9% (92/144), initiate specialty referral in 90.3% (130/144) and alter immunosuppression in 56.9% (82/144). Thus, beyond diagnostic testing for kidney disorders, sequence annotation identified monogenic disorders associated with common post-transplant complications in 9.1% of KTR, with important clinical implications. Incorporating genetic diagnostics for transplant morbidities would enable personalized management in pre- and post-transplant care.

Cell subtype-specific effects of genetic variation in the Alzheimer's disease brain.

The relationship between genetic variation and gene expression in brain cell types and subtypes remains understudied. Here, we generated single-nucleus RNA sequencing data from the neocortex of 424 individuals of advanced age; we assessed the effect of genetic variants on RNA expression in cis (cis-expression quantitative trait loci) for seven cell types and 64 cell subtypes using 1.5 million transcriptomes. This effort identified 10,004 eGenes at the cell type level and 8,099 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer's disease pathology, after adjusting for APOEε4, providing mechanistic insights into both pathologies. Furthermore, only a TMEM106B variant affects the proportion of cell subtypes. Integration of these results with genome-wide association studies highlighted the targeted cell type and probable causal gene within Alzheimer's disease, schizophrenia, educational attainment and Parkinson's disease loci.

Genetic versus self-reported African ancestry of the recipient and neighborhood predictors of kidney transplantation outcomes in 2 multiethnic urban cohorts.

African American (AA) kidney recipients have a higher risk of allograft rejection and failure compared to non-AAs, but to what extent these outcomes are due to genetic versus environmental effects is currently unknown. Herein, we tested the effects of recipient self-reported race versus genetic proportion of African ancestry (pAFR), and neighborhood socioeconomic status (SES) on kidney allograft outcomes in multiethnic kidney transplant recipients from Columbia University (N = 1083) and the University of Pennsylvania (N = 738). All participants were genotyped with SNP arrays to estimate genetic admixture proportions. US census tract variables were used to analyze the effect of neighborhood factors. In both cohorts, self-reported recipient AA race and pAFR were individually associated with increased risk of rejection and failure after adjustment for known clinical risk factors and neighborhood SES factors. Joint analysis confirmed that self-reported recipient AA race and pAFR were both associated with a higher risk of allograft rejection (AA: HR 1.61 (1.31-1.96), P = 4.05E-06; pAFR: HR 1.90 (1.46-2.48), P = 2.40E-06) and allograft failure (AA: HR 1.52 (1.18-1.97), P = .001; pAFR: HR 1.70 (1.22-2.35), P = .002). Further research is needed to disentangle the role of genetics versus environmental, social, and structural factors contributing to poor transplantation outcomes in kidney recipients of African ancestry.

Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations.

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Combined Serologic and Genetic Risk Score and Prognostication of Phospholipase A2 receptor-Associated Membranous Nephropathy.

INTRODUCTION: The aim of this study was to test whether a combined risk score on the basis of genetic risk and serology can improve the prediction of kidney failure in phospholipase A2 receptor (PLA2R)-associated primary membranous nephropathy. METHODS: We performed a retrospective analysis of 519 biopsy-proven PLA2R-associated primary membranous nephropathy patients with baseline eGFR ≥25 ml/min per 1.73 m 2 . The combined risk score was calculated by combining the genetic risk score with PLA2R ELISA antibody titers. The primary end point was kidney disease progression defined as a 50% reduction in eGFR or kidney failure. Cox proportional hazard regression analysis and C-statistics were applied to compare the performance of PLA2R antibody, genetic risk score, and combined risk score, as compared with clinical factors alone, in predicting primary outcomes. RESULTS: The median age was 56 years (range, 15-82 years); the male-to-female ratio was 1:0.6, the median eGFR at biopsy was 99 ml/min per 1.73 m 2 (range: 26-167 ml/min per 1.73 m 2 ), and the median proteinuria was 5.3 g/24 hours (range: 1.5-25.8 g/24 hours). During a median follow-up of 67 (5-200) months, 66 (13%) had kidney disease progression. In Cox proportional hazard regression models, PLA2R antibody titers, genetic risk score, and combined risk score were all individually associated with kidney disease progression with and without adjustments for age, sex, proteinuria, eGFR, and tubulointerstitial lesions. The best-performing clinical model to predict kidney disease progression included age, eGFR, proteinuria, serum albumin, diabetes, and tubulointerstitial lesions (C-statistic 0.76 [0.69-0.82], adjusted R 2 0.51). Although the addition of PLA2R antibody titer improved the performance of this model (C-statistic: 0.78 [0.72-0.84], adjusted R 2 0.61), replacing PLA2R antibody with the combined risk score improved the model further (C-statistic: 0.82 [0.77-0.87], adjusted R 2 0.69, difference of C-statistics with clinical model=0.06 [0.03-0.10], P < 0.001; difference of C-statistics with clinical-serologic model=0.04 [0.01-0.06], P < 0.001). CONCLUSIONS: In patients with PLA2R-associated membranous nephropathy, the combined risk score incorporating inherited risk alleles and PLA2R antibody enhanced the prediction of kidney disease progression compared with PLA2R serology and clinical factors alone.

Return of polygenic risk scores in research: Stakeholders' views on the eMERGE-IV study.

Research on polygenic risk scores (PRSs) for common, genetically complex chronic diseases aims to improve health-related predictions, tailor risk-reducing interventions, and improve health outcomes. Yet, the study and use of PRSs in clinical settings raise equity, clinical, and regulatory challenges that can be greater for individuals from historically marginalized racial, ethnic, and other minoritized communities. As part of the National Human Genome Research Institute-funded Electronic Medical Records and Genomics IV Network, we conducted online focus groups with patients/community members, clinicians, and members of institutional review boards to explore their views on key issues, including PRS research, return of PRS results, clinical translation, and barriers and facilitators to health behavioral changes in response to PRS results. Across stakeholder groups, our findings indicate support for PRS development and a strong interest in having PRS results returned to research participants. However, we also found multi-level barriers and significant differences in stakeholders' views about what is needed and possible for successful implementation. These include researcher-participant interaction formats, health and genomic literacy, and a range of structural barriers, such as financial instability, insurance coverage, and the absence of health-supporting infrastructure and affordable healthy food options in poorer neighborhoods. Our findings highlight the need to revisit and implement measures in PRS studies (e.g., incentives and resources for follow-up care), as well as system-level policies to promote equity in genomic research and health outcomes.

The chromatin landscape of healthy and injured cell types in the human kidney.

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

Participant-guided development of bilingual genomic educational infographics for Electronic Medical Records and Genomics Phase IV study.

OBJECTIVE: Developing targeted, culturally competent educational materials is critical for participant understanding of engagement in a large genomic study that uses computational pipelines to produce genome-informed risk assessments. MATERIALS AND METHODS: Guided by the Smerecnik framework that theorizes understanding of multifactorial genetic disease through 3 knowledge types, we developed English and Spanish infographics for individuals enrolled in the Electronic Medical Records and Genomics Network. Infographics were developed to explain concepts in lay language and visualizations. We conducted iterative sessions using a modified "think-aloud" process with 10 participants (6 English, 4 Spanish-speaking) to explore comprehension of and attitudes towards the infographics. RESULTS: We found that all but one participant had "awareness knowledge" of genetic disease risk factors upon viewing the infographics. Many participants had difficulty with "how-to" knowledge of applying genetic risk factors to specific monogenic and polygenic risks. Participant attitudes towards the iteratively-refined infographics indicated that design saturation was reached. DISCUSSION: There were several elements that contributed to the participants' comprehension (or misunderstanding) of the infographics. Visualization and iconography techniques best resonated with those who could draw on prior experiences or knowledge and were absent in those without. Limited graphicacy interfered with the understanding of absolute and relative risks when presented in graph format. Notably, narrative and storytelling theory that informed the creation of a vignette infographic was most accessible to all participants. CONCLUSION: Engagement with the intended audience who can identify strengths and points for improvement of the intervention is necessary to the development of effective infographics.

Pathologic-genomic correlation identified a novel variant in FN1 and established the diagnosis of recurrent fibronectin glomerulopathy in the kidney allograft.

Fibronectin glomerulopathy is a rare inherited kidney disease, characterized by abnormal accumulation of fibronectin in the glomeruli. We report an exceptional case of recurrent fibronectin glomerulopathy first diagnosed in the kidney allograft. The presence of IgA staining in the native kidney biopsy and the reported family history of IgA nephropathy had led to initial pretransplant diagnosis of IgA nephropathy. At 4.5 years posttransplant, the patient presented with kidney insufficiency and minimal proteinuria. The allograft biopsy revealed glomerular deposits with very weak staining for immunoglobulins and vague filamentous material. Immunostaining for fibronectin was positive, and genetic studies showed a variant of unknown significance in the fibronectin 1 gene. Proteomic analyses of the glomeruli in the native kidney biopsy demonstrated large amount of fibronectin with abundant accumulation of the peptide synthesized by the detected variant. These findings established the diagnosis of recurrent fibronectin glomerulopathy secondary to a novel variant in the fibronectin 1 gene. This report sheds light on recurrent fibronectin glomerulopathy in the allograft, highlights the diagnostic pitfalls of the disease, and underscores the importance of pathologic-genomic correlation to establish the correct diagnosis.