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Graphical Abstract:

Steroid-resistant nephrotic syndrome is a frequent cause of chronic kidney disease almost inevitably progressing to end-stage renal disease. More than 58 monogenic causes of SRNS have been discovered and majority of known steroid-resistant nephrotic syndrome causing genes are predominantly expressed in glomerular podocytes, placing them at the center of disease pathogenesis. Herein, we describe two unrelated families with steroid-resistant nephrotic syndrome with homozygous mutations in the KIRREL1gene. One mutation showed high frequency in the European population (minor allele frequency 0.0011) and this patient achieved complete remission following treatment, but later progressed to chronic kidney disease. We found that mutant KIRREL1proteins failed to localize to the podocyte cell membrane, indicating defective trafficking and impaired podocytes function. Thus, the KIRREL1gene product has an important role in modulating the integrity of the slit diaphragm and maintaining glomerular filtration function.

Estimating the dependence structure of concurrent extremes is a fundamental issue for accurate assessment of their occurrence probabilities. Identifying the extremal dependence behavior is also crucial for scientific understanding of interactions between the variables of a multidimensional environmental process. This study investigates the suitability of parametric multivariate extreme value models to correctly represent and estimate the dependence structure of concurrent extremes. Probabilistic aspects of multivariate extreme value theory with point process representation are discussed and illustrated with application to the concurrence of rainfall deficits, soil moisture deficits, and high temperatures. Application is concerned with the investigation of extremal behavior and risk assessment in Marathwada, a drought‐prone region of Maharashtra state, India. To characterize the multivariate extremes, marginal distributions are specified first and transformed into unit Fréchet margins. Standardized distributions are represented by a Poisson point process and coordinates of data points are transformed to pseudo‐polar coordinates to make the dependence form more explicit. The extremal dependence structure is described through angular densities on the unit simplex. Strong dependence is observed between soil moisture deficits and high temperatures, whereas rainfall deficits are mildly dependent on these two variables. Overall, a weak dependence is observed between the variables considered. Estimated extremal dependence is further used to compute probabilities of a few critical extreme combinations. Results demonstrate the ability of parametric multivariate models to characterize the complex dependence structure of concurrent extremes. These models can provide a powerful new perspective for appropriate statistical analysis of dependent hydroclimatic extremes in higher dimensions. Utility of parametric multivariate extreme value models is explored for modeling concurrent hydroclimatic extremes in Marathwada, IndiaExtremal dependence structure of rainfall, soil moisture, and temperatures is explicitly described through angular densities on two‐dimensional simplexThe results demonstrate the suitability of parametric models to disentangle the complex interactions of multidimensional concurrent extremes Utility of parametric multivariate extreme value models is explored for modeling concurrent hydroclimatic extremes in Marathwada, India Extremal dependence structure of rainfall, soil moisture, and temperatures is explicitly described through angular densities on two‐dimensional simplex The results demonstrate the suitability of parametric models to disentangle the complex interactions of multidimensional concurrent extremes