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IgA nephropathy (IgAN) represents the leading cause of kidney failure among East Asian populations and the most frequent form of primary glomerulonephritis among Europeans. Patients with IgAN develop characteristic IgA1-containing immune complexes that deposit in the glomerular mesangium, producing progressive kidney injury. Recent studies define IgAN as an autoimmune trait of complex architecture with a strong genetic determination. This Review summarizes new insights into the role of the O-glycosylation pathway, anti-glycan immune response, mucosal immunity, antigen processing and presentation, and the alternative complement pathway in the pathogenesis of IgAN.

Elliott et al thank Dr. Wang for the recognition of the important clinical implications of their work. In regard to the issue of CureGN participants with diabetes, the exclusion of individuals with diabetes occurred at the time of enrollment into the study, not at the time of biopsy. We used the presence of diabetes mellitus at the time of kidney biopsy as a covariate in our analyses, as diabetes is well described to influence kidney disease progression and outcomes. Only a small proportion of the CureGN cohort (18 individuals, 1%) had diabetes mellitus at the time of biopsy that had resolved at enrollment and were thus included in the analysis cohort.

Recent years have brought notable progress in the field of IgA nephropathy. Here, we highlight important new directions and latest developments, including successful discovery of several genetic susceptibility loci, formulation of the multihit pathogenesis model, introduction of the Oxford pathology scoring system, and formalization of the Kidney Disease Improving Global Outcomes (KDIGO) consensus treatment guidelines. We focus on the latest genetic findings that confirm a strong contribution of inherited factors and explain some of the geoethnic disparities in disease susceptibility. Most IgA nephropathy susceptibility loci discovered to date encode genes involved in the maintenance of the intestinal epithelial barrier and response to mucosal pathogens. The concerted pattern of interpopulation allelic differentiation across all genetic loci parallels the disease prevalence and correlates with variation in local pathogens, suggesting that multilocus adaptation might have shaped the present-day landscape of IgA nephropathy. Importantly, the ‘Intestinal Immune Network for IgA Production’ emerged as one of the new targets for potential therapeutic intervention. We place these findings in the context of the multihit pathogenesis model and existing knowledge of IgA immunobiology. Lastly, we provide our perspective on the existing treatment options, discuss areas of clinical uncertainty, and outline ongoing clinical trials and translational studies.

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. Here, the authors propose using quantile regression for genome-wide association studies with quantitative traits in UK Biobank, showing its advantages over linear regression in handling nonnormal distributions and identifying heterogeneous genetic effects.

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. Polygenic factors may partially explain the observed variability in the penetrance of monogenic diseases. Here, the authors show that a polygenic risk score for chronic kidney disease is significantly associated with a higher risk of renal dysfunction in the two most common monogenic forms of kidney disease, suggesting that accounting for polygenic factors improves risk stratification in monogenic kidney disease.

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

The OAS1/2/3 cluster has been identified as a risk locus for severe COVID-19 among individuals of European ancestry, with a protective haplotype of approximately 75 kilobases (kb) derived from Neanderthals in the chromosomal region 12q24.13. This haplotype contains a splice variant of OAS1 , which occurs in people of African ancestry independently of gene flow from Neanderthals. Using trans-ancestry fine-mapping approaches in 20,779 hospitalized cases, we demonstrate that this splice variant is likely to be the SNP responsible for the association at this locus, thus strongly implicating OAS1 as an effector gene influencing COVID-19 severity. Multi-ancestry fine-mapping of the OAS1/2/3 region shows that a splice site variant in OAS1 is likely responsible for the association of this locus with the risk of severe COVID-19.

Genetic research in nephrology is rapidly advancing. Key studies published in 2020 demonstrate that genetic findings can provide new tools for patient diagnosis and risk stratification as well as important insights into kidney physiology and disease mechanisms that could potentially lead to novel therapies.Key advancesA genome-wide association study (GWAS) of membranous nephropathy identifies novel loci and validates a genetic risk score that could improve diagnosis of this rare disease1.GWAS that integrate genomic and metabolomic data from patients with chronic kidney disease identify novel candidate mechanisms of metabolite detoxification and excretion in the kidney3.Analysis of the contributions of monogenic variants and polygenic risk scores to probability of disease in carriers of risk variants for tier 1 genomic conditions demonstrates that polygenic background modifies the risk of monogenic diseases4.High-resolution imaging of uromodulin, which is encoded by an important kidney disease gene, identifies a potential mechanism by which this protein might defend against urinary tract infections8.

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. Identifying spatially variable genes (SVGs) is essential for linking molecular cell functions with tissue phenotypes. Here, authors introduce a non-parametric model that detects SVGs from two or three-dimensional spatial transcriptomics data by comparing gene expression patterns at granularities.

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 ∼ 80 % of the associated loci.