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Abstract 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.

Abstract 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.

Summary: 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.

Abstract Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.

Abstract African Americans have a significantly higher risk of developing chronic kidney disease, especially focal segmental glomerulosclerosis -, than European Americans. Two coding variants (G1 and G2) in the APOL1 gene play a major role in this disparity. While 13% of African Americans carry the high-risk recessive genotypes, only a fraction of these individuals develops FSGS or kidney failure, indicating the involvement of additional disease modifiers. Here, we show that the presence of the APOL1 p.N264K missense variant, when co-inherited with the G2 APOL1 risk allele, substantially reduces the penetrance of the G1G2 and G2G2 high-risk genotypes by rendering these genotypes low-risk. These results align with prior functional evidence showing that the p.N264K variant reduces the toxicity of the APOL1 high-risk alleles. These findings have important implications for our understanding of the mechanisms of APOL1-associated nephropathy, as well as for the clinical management of individuals with high-risk genotypes that include the G2 allele.

Abstract Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

Abstract Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.

Abstract Pediatric steroid-sensitive nephrotic syndrome (pSSNS) is the most common childhood glomerular disease. Previous genome-wide association studies (GWAS) identified a risk locus in the HLA Class II region and three additional independent risk loci. But the genetic architecture of pSSNS, and its genetically driven pathobiology, is largely unknown. Here, we conduct a multi-population GWAS meta-analysis in 38,463 participants (2440 cases). We then conduct conditional analyses and population specific GWAS. We discover twelve significant associations—eight from the multi-population meta-analysis (four novel), two from the multi-population conditional analysis (one novel), and two additional novel loci from the European meta-analysis. Fine-mapping implicates specific amino acid haplotypes in HLA-DQA1 and HLA-DQB1 driving the HLA Class II risk locus. Non-HLA loci colocalize with eQTLs of monocytes and numerous T-cell subsets in independent datasets. Colocalization with kidney eQTLs is lacking but overlap with kidney cell open chromatin suggests an uncharacterized disease mechanism in kidney cells. A polygenic risk score (PRS) associates with earlier disease onset. Altogether, these discoveries expand our knowledge of pSSNS genetic architecture across populations and provide cell-specific insights into its molecular drivers. Evaluating these associations in additional cohorts will refine our understanding of population specificity, heterogeneity, and clinical and molecular associations.

Abstract We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at $$\sim 80\%$$ ∼ 80 % of the associated loci.

Abstract The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.

Immunoglobulin A protects against infectious disease and contributes to autoimmune and inflammatory disorders. Here, the authors perform a genome-wide association study for serum IgA levels, identifying 20 genome-wide significant loci, providing new insights into the genetic regulation of IgA levels.

Abstract Background Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. Methods We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. Results The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5–4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance. Conclusions By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

Summary: COVID-19 has highly variable clinical courses. The search for prognostic host factors for COVID-19 outcome is a priority. We performed logistic regression for ICU admission against a polygenic score (PGS) for Cystatin C (CyC) production in patients with COVID-19. We analyzed the predictive value of longitudinal plasma CyC levels in an independent cohort of patients hospitalized with COVID-19. In four cohorts spanning European and African ancestry populations, we identified a significant association between CyC-production PGS and odds of critical illness (n cases=2,319), with the strongest association captured in the UKB cohort (OR 2.13, 95% CI 1.58-2.87, p=7.12e-7). Plasma proteomics from an independent cohort of hospitalized COVID-19 patients (n cases = 131) demonstrated that CyC production was associated with COVID-specific mortality (p=0.0007). Our findings suggest that CyC may be useful for stratification of patients and it has functional role in the host response to COVID-19.

Abstract Labeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification.

Abstract Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.

Introduction: Prior cross-sectional studies suggest that health-related quality of life (HRQOL) worsens with more severe glomerular disease. This longitudinal analysis was conducted to assess changes in HRQOL with changing disease status. Methods: Cure Glomerulonephropathy (CureGN) is a cohort of patients with minimal change disease, focal segmental glomerulosclerosis, membranous nephropathy, IgA vasculitis, or IgA nephropathy. HRQOL was assessed at enrollment and follow-up visits 1 to 3 times annually for up to 5 years with the Patient-Reported Outcomes Measurement Information System (PROMIS). Global health, anxiety, and fatigue domains were measured in all; mobility was measured in children; and sleep-related impairment was measured in adults. Linear mixed effects models were used to evaluate HRQOL responsiveness to changes in disease status. Results: A total of 469 children and 1146 adults with PROMIS scores were included in the analysis. HRQOL improved over time in nearly all domains, though group-level changes were modest. Edema was most consistently associated with worse HRQOL across domains among children and adults. A greater number of symptoms also predicted worse HRQOL in all domains. Sex, age, obesity, and serum albumin were associated with some HRQOL domains. The estimated glomerular filtration rate (eGFR) was only associated with fatigue and adult physical health; proteinuria was not associated with any HRQOL domain in adjusted models. Conclusion: HRQOL measures were responsive to changes in disease activity, as indicated by edema. HRQOL over time was not predicted by laboratory-based markers of disease. Patient-reported edema and number of symptoms were the strongest predictors of HRQOL, highlighting the importance of the patient experience in glomerular disease. HRQOL outcomes inform understanding of the patient experience for children and adults with glomerular diseases.

The current strategy to detect acute injury of kidney tubular cells relies on changes in serum levels of creatinine. Yet serum creatinine (sCr) is a marker of both functional and pathological processes and does not adequately assay tubular injury. In addition, sCr may require days to reach diagnostic thresholds, yet tubular cells respond with programs of damage and repair within minutes or hours. To detect acute responses to clinically relevant stimuli, we created mice expressing Rosa26-floxed-stop uracil phosphoribosyltransferase (Uprt) and inoculated 4-thiouracil (4-TU) to tag nascent RNA at selected time points. Cre-driven 4-TU–tagged RNA was isolated from intact kidneys and demonstrated that volume depletion and ischemia induced different genetic programs in collecting ducts and intercalated cells. Even lineage-related cell types expressed different genes in response to the 2 stressors. TU tagging also demonstrated the transient nature of the responses. Because we placed Uprt in the ubiquitously active Rosa26 locus, nascent RNAs from many cell types can be tagged in vivo and their roles interrogated under various conditions. In short, 4-TU labeling identifies stimulus-specific, cell-specific, and time-dependent acute responses that are otherwise difficult to detect with other technologies and are entirely obscured when sCr is the sole metric of kidney damage.

Objectives: IgA nephropathy (IgAN) is thought to involve an autoimmune process wherein galactose-deficient IgA1 (Gd-IgA1), recognized as autoantigen by autoantibodies, forms pathogenic immune complexes. Mounting evidence has implicated abnormal activation of some protein-tyrosine kinases (PTKs) in IgAN. Furthermore, genome-wide association studies (GWAS) of IgAN provided insight into disease pathobiology and genetics. A GWAS locus on chromosome 22q12 contains genes encoding leukemia inhibitory factor (LIF) and oncostatin M, interleukin (IL)-6-related cytokines implicated in mucosal immunity and inflammation. We have previously shown that IL-6 mediates overproduction of Gd-IgA1 through aberrant STAT3 activation. Here, we show that LIF enhanced production of Gd-IgA1 in IgA1-secreting cells of patients with IgAN and provide initial analyses of LIF signaling. Methods: We characterized LIF signaling that is involved in the overproduction of Gd-IgA1, using IgA1-secreting cell lines derived from peripheral blood of patients with IgAN and healthy controls (HC). We used global PTK activity profiling, immunoblotting, lectin ELISA, and siRNA knock-down. Results: LIF stimulation did not significantly affect production of total IgA1 in IgA1-secreting cells from patients with IgAN or HC. However, LIF increased production of Gd-IgA1, but only in the cells from patients with IgAN. LIF stimulation enhanced phosphorylation of STAT1 in IgA1-secreting cells from patients with IgAN to a higher degree than in the cells from HC. siRNA knock-down of STAT1 blocked LIF-mediated overproduction of Gd-IgA1. Unexpectedly, this abnormal phosphorylation of STAT1 in IgA1-secreting cells from patients with IgAN was not mediated by JAK, but rather involved activation of Src-family PTKs (SFKs). Conclusion: Abnormal LIF/STAT1 signaling represents another pathway potentially leading to overproduction of Gd-IgA1 in IgAN, providing possible explanation for the phenotype associated with chromosome 22q12 GWAS locus. Abnormal LIF/STAT1 signaling and the associated SFKs may represent potential diagnostic and/or therapeutic targets in IgAN.

Membranous nephropathy (MN) is a rare autoimmune disease of podocyte-directed antibodies, such as anti-phospholipase A2 receptor. Here, the authors report a genome-wide association study for MN and identify two previously unreported loci encompassing the NFKB1 and IRF4 genes and additional ancestry-specific effects.

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)-rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.

Estimated glomerular filtration rate (eGFR) is a measure of kidney function used to define chronic kidney disease. Here, Morris et al. perform trans-ethnic genome-wide meta-analyses for eGFR in 312,468 individuals and identify novel loci and downstream putative causal genes.

Understanding the clinical risk factors for COVID-19 disease severity and outcomes requires a combination of data from electronic health records and patient reports. To facilitate the collection of patient-reported data, as well as accelerate and standardize the collection of data about host factors, we have constructed a COVID-19 survey. This survey is freely available to the scientific community to send electronically for patients to complete online. This patient survey is designed to be comprehensive, yet not overly burdensome, to gather data useful for a range of clinical investigations, and to accommodate a wide variety of implementation settings including at a COVID-19 testing site, at home during infection or after recovery, and/or for individuals while they are hospitalized. A widely adopted standardized survey that can be implemented online with minimal resources can serve as a critical tool for combining and comparing data across studies to improve our understanding of COVID-19 disease.

The prevalence of end-stage renal disease (ESRD) and the number of kidney transplants performed continues to rise every year, straining the procurement of deceased and living kidney allografts and health systems. Genome-wide genotyping and sequencing of diseased populations have uncovered genetic contributors in substantial proportions of ESRD patients. A number of these discoveries are beginning to be utilized in risk stratification and clinical management of patients. Specifically, genetics can provide insight into the primary cause of chronic kidney disease (CKD), the risk of progression to ESRD, and post-transplant outcomes, including various forms of allograft rejection. The International Genetics & Translational Research in Transplantation Network (iGeneTRAiN), is a multi-site consortium that encompasses >45 genetic studies with genome-wide genotyping from over 51,000 transplant samples, including genome-wide data from >30 kidney transplant cohorts (n = 28,015). iGeneTRAiN is statistically powered to capture both rare and common genetic contributions to ESRD and post-transplant outcomes. The primary cause of ESRD is often difficult to ascertain, especially where formal biopsy diagnosis is not performed, and is unavailable in ∼2% to >20% of kidney transplant recipients in iGeneTRAiN studies. We overview our current copy number variant (CNV) screening approaches from genome-wide genotyping datasets in iGeneTRAiN, in attempts to discover and validate genetic contributors to CKD and ESRD. Greater aggregation and analyses of well phenotyped patients with genome-wide datasets will undoubtedly yield insights into the underlying pathophysiological mechanisms of CKD, leading the way to improved diagnostic precision in nephrology.

IgA nephropathy is a chronic renal disease characterized by mesangial immunodeposits that contain autoantigen, which is aberrantly glycosylated IgA1 with some hinge-region O-glycans deficient in galactose. Macroscopic hematuria during an upper respiratory tract infection is common among patients with IgA nephropathy, which suggests a connection between inflammation and disease activity. Interleukin-6 (IL-6) is an inflammatory cytokine involved in IgA immune response. We previously showed that IL-6 selectively increases production of galactose-deficient IgA1 in IgA1-secreting cells from patients with IgA nephropathy. Methods: We characterized IL-6 signaling pathways involved in the overproduction of galactose-deficient IgA1. To understand molecular mechanisms, IL-6 signaling was analyzed by kinomic activity profiling and Western blotting, followed by confirmation assays using siRNA knock-down and small-molecule inhibitors. Results: STAT3 was differentially activated by IL-6 in IgA1-secreting cells from patients with IgA nephropathy compared with those from healthy control subjects. Specifically, IL-6 induced enhanced and prolonged phosphorylation of STAT3 in the cells from patients with IgA nephropathy, which resulted in overproduction of galactose-deficient IgA1. This IL-6−mediated overproduction of galactose-deficient IgA1 could be blocked by small molecule inhibitors of JAK/STAT signaling. Discussion: Our results revealed that IL-6−induced aberrant activation of STAT3-mediated overproduction of galactose-deficient IgA1. STAT3 signaling pathway may thus represent a new target for disease-specific therapy of IgA nephropathy.

Membranous nephropathy is a glomerulopathy that causes nephrotic syndrome and, in at least a third of cases, lasting end-stage kidney disease (ESKD). It is also a rare case of revolutionary changes in our understanding of the disease, that translates from scientific findings to real diagnosis and treatment recommendations in less than ten years. In this review we present: (1) a short history and traditional approach to patients with membranous nephropathy, (2) current recommendations and treatment options that have emerged in recent years, (3) findings of new studies, with a particular focus on serological/immunological methods, genomic and proteomic studies, still requiring validation. With further development in this field, membranous nephropathy may become one of the first nephrological conditions that apply a truly personalized approach with the omission of invasive measures such as kidney biopsy.

Aberrant O-glycosylation of serum immunoglobulin A1 (IgA1) represents a heritable pathogenic defect in IgA nephropathy, the most common form of glomerulonephritis worldwide, but specific genetic factors involved in its determination are not known. We performed a quantitative GWAS for serum levels of galactose-deficient IgA1 (Gd-IgA1) in 2,633 subjects of European and East Asian ancestry and discovered two genome-wide significant loci, in C1GALT1 (rs13226913, P = 3.2 x 10-11) and C1GALT1C1 (rs5910940, P = 2.7 x 10-8). These genes encode molecular partners essential for enzymatic O-glycosylation of IgA1. We demonstrated that these two loci explain approximately 7% of variability in circulating Gd-IgA1 in Europeans, but only 2% in East Asians. Notably, the Gd-IgA1-increasing allele of rs13226913 is common in Europeans, but rare in East Asians. Moreover, rs13226913 represents a strong cis-eQTL for C1GALT1 that encodes the key enzyme responsible for the transfer of galactose to O-linked glycans on IgA1. By in vitro siRNA knock-down studies, we confirmed that mRNA levels of both C1GALT1 and C1GALT1C1 determine the rate of secretion of Gd-IgA1 in IgA1-producing cells. Our findings provide novel insights into the genetic regulation of O-glycosylation and are relevant not only to IgA nephropathy, but also to other complex traits associated with O-glycosylation defects, including inflammatory bowel disease, hematologic disease, and cancer.

Genomic medicine is moving from research to the clinic. There is a lack of evidence about the impact of genomic medicine interventions on health outcomes. This is due in part to a lack of standardized outcome measures that can be used across different programs to evaluate the impact of interventions targeted to specific genetic conditions. The eMERGE Outcomes working group (OWG) developed measures to collect information on outcomes following the return of genomic results to participants for several genetic disorders. These outcomes were compared to outcome intervention pairs for genetic disorders developed independently by the ClinGen Actionability working group (AWG). In general, there was concordance between the defined outcomes between the two groups. The ClinGen outcomes tended to be from a higher level and the AWG scored outcomes represented a subset of outcomes referenced in the accompanying AWG evidence review. eMERGE OWG outcomes were more detailed and discrete, facilitating a collection of relevant information from the health records. This paper demonstrates that common outcomes for genomic medicine interventions can be identified. Further work is needed to standardize outcomes across genomic medicine implementation projects and to make these publicly available to enhance dissemination and assist in making precision public health a reality.

There is increasing evidence that galactose-deficient IgA1 (Gd-IgA1) and Gd-IgA1-containing immune complexes are important for the pathogenesis of IgA nephropathy (IgAN). In the present study, we assessed a novel noninvasive multi-biomarker approach in the diagnostic test for IgAN.We compared serum levels of IgA, IgG, Gd-IgA1, Gd-IgA1-specific IgG and Gd-IgA1-specific IgA in 135 IgAN patients, 79 patients with non-IgAN chronic kidney disease (CKD) controls and 106 healthy controls. Serum was collected at the time of kidney biopsy from all IgAN and CKD patients.Each serum marker was significantly elevated in IgAN patients compared to CKD (P

IgA nephropathy is the most common form of primary glomerulonephritis worldwide. Mucosal infections and food antigens, including wheat gluten, have been proposed as potential contributing environmental factors. Increased immune reactivity to gluten and/or association with celiac disease, an autoimmune disorder triggered by ingestion of gluten, have been reported in IgA nephropathy. However, studies are inconsistent about this association. We aimed to evaluate the proposed link between IgA nephropathy and celiac disease or immune reactivity to gluten by conducting a comprehensive analysis of associated serologic markers in cohorts of well-characterized patients and controls. Study participants included patients with biopsy-proven IgA nephropathy (n = 99), unaffected controls of similar age, gender, and race (n = 96), and patients with biopsy-proven celiac disease (n = 30). All serum specimens were tested for IgG and IgA antibodies to native gliadin and deamidated gliadin, as well as IgA antibody to transglutaminase 2 (TG2). Anti-TG2 antibody-positive nephropathy patients and unaffected controls were subsequently tested for IgA anti-endomysial antibody and genotyped for celiac disease-associated HLA-DQ2 and -DQ8 alleles. In comparison to unaffected controls, there was not a statistically significant increase in IgA or IgG antibody reactivity to gliadin in individuals with IgA nephropathy. In addition, the levels of celiac disease-specific serologic markers, i.e., antibodies to deamidated gliadin and TG2, did not differ between IgA nephropathy patients and unaffected controls. Results of the additional anti-endomysial antibody testing and HLA genotyping were corroborative. The data from this case-control study do not reveal any evidence to suggest a significant role for celiac disease or immune reactivity to gluten in IgA nephropathy.

IgA nephropathy (IgAN) is a common cause of end-stage renal disease (ESRD) in Asia. In this study, based on a large cohort of Chinese patients with IgAN, we aim to identify independent predictive factors associated with disease progression to ESRD. We collected retrospective clinical data and renal outcomes on 619 biopsy-diagnosed IgAN patients with a mean follow-up time of 41.3 months. In total, 67 individuals reached the study endpoint defined by occurrence of ESRD necessitating renal replacement therapy. In the fully adjusted Cox proportional hazards model, there were four baseline variables with a significant independent effect on the risk of ESRD. These included: eGFR [HR = 0.96(0.95-0.97)], serum albumin [HR = 0.47(0.32-0.68)], hemoglobin [HR = 0.79(0.72-0.88)], and SBP [HR = 1.02(1.00-1.03)]. Based on these observations, we developed a 4-variable equation of a clinical risk score for disease progression. Our risk score explained nearly 22% of the total variance in the primary outcome. Survival ROC curves revealed that the risk score provided improved prediction of ESRD at 24th, 60th and 120th month of follow-up compared to the three previously proposed risk scores. In summary, our data indicate that IgAN patients with higher systolic blood pressure, lower eGFR, hemoglobin, and albumin levels at baseline are at a greatest risk of progression to ESRD. The new progression risk score calculated based on these four baseline variables offers a simple clinical tool for risk stratification.

IgA nephropathy (IgAN), major cause of kidney failure worldwide, is common in Asians, moderately prevalent in Europeans, and rare in Africans. It is not known if these differences represent variation in genes, environment, or ascertainment. In a recent GWAS, we localized five IgAN susceptibility loci on Chr.6p21 (HLA-DQB1/DRB1, PSMB9/TAP1, and DPA1/DPB2 loci), Chr.1q32 (CFHR3/R1 locus), and Chr.22q12 (HORMAD2 locus). These IgAN loci are associated with risk of other immune-mediated disorders such as type I diabetes, multiple sclerosis, or inflammatory bowel disease. We tested association of these loci in eight new independent cohorts of Asian, European, and African-American ancestry (N = 4,789), followed by meta-analysis with risk-score modeling in 12 cohorts (N = 10,755) and geospatial analysis in 85 world populations. Four susceptibility loci robustly replicated and all five loci were genome-wide significant in the combined cohort (P = 5×10⁻³²-3×10⁻¹⁰), with heterogeneity detected only at the PSMB9/TAP1 locus (I² = 0.60). Conditional analyses identified two new independent risk alleles within the HLA-DQB1/DRB1 locus, defining multiple risk and protective haplotypes within this interval. We also detected a significant genetic interaction, whereby the odds ratio for the HORMAD2 protective allele was reversed in homozygotes for a CFHR3/R1 deletion (P = 2.5×10⁻⁴). A seven-SNP genetic risk score, which explained 4.7% of overall IgAN risk, increased sharply with Eastward and Northward distance from Africa (r = 0.30, P = 3×10⁻¹²⁸). This model paralleled the known East-West gradient in disease risk. Moreover, the prediction of a South-North axis was confirmed by registry data showing that the prevalence of IgAN-attributable kidney failure is increased in Northern Europe, similar to multiple sclerosis and type I diabetes. Variation at IgAN susceptibility loci correlates with differences in disease prevalence among world populations. These findings inform genetic, biological, and epidemiological investigations of IgAN and permit cross-comparison with other complex traits that share genetic risk loci and geographic patterns with IgAN.

Macular Telangiectasia type 2 (MacTel) is a relatively rare macular disease of adult onset presenting with distortions in the visual field and leading to progressive loss of visual acuity. For the purpose of a gene mapping study, several pedigrees were ascertained with multiple affected family members. Seventeen families with a total of 71 individuals (including 45 affected or possibly affected) were recruited at clinical centers in 7 countries under the auspices of the MacTel Project. The disease inheritance was consistent with autosomal dominant segregation with reduced penetrance. Genome-wide linkage analysis was performed, followed by analysis of recombination breakpoints. Linkage analysis identified a single peak with multi-point LOD score of 3.45 on chromosome 1 at 1q41-42 under a dominant model. Recombination mapping defined a minimal candidate region of 15.6 Mb, from 214.32 (rs1579634; 219.96 cM) to 229.92 Mb (rs7542797; 235.07 cM), encompassing the 1q41-42 linkage peak. Sanger sequencing of the top 14 positional candidates genes under the linkage peak revealed no causal variants in these pedigrees.