Nephritic syndrome is a rare disease of glomerular filtration barrier failure causing massive urinary excretion of protein, that can progress to chronic kidney disease and end-stage renal disease NS is a heterogeneous disease, so we use the histologic descriptions of glomeruli on kidney biopsy to diagnose individuals with “minimal change disease” and “focal segmental glomerulosclerosis.” Additionally, we use an individual’s response to these treatments to give them a post hoc diagnosis of steroid-sensitive NS or steroid-resistant NS. Understanding how human genetic variation contributes to the development and progression of NS has been a fruitful strategy in gaining a more precise understanding of the molecular underpinnings of NS. More than 50 genes have been discovered that harbour rare variants sufficient to cause SRNS (“Mandolin” NS). Through genome-wide association studies and exude-chip studies, common genetic variants have been discovered that contribute to the pathogenesis of FSGS, pediatric SSNS, and membranous nephropathy. Rare variant association studies in FSGS have implicated a small set of genes harbouring an increased burden of rare, deleterious variants. We are challenged to discover additional forms of NS-associated genetic variation to gain biological and clinical insights.Expression quantitative trait loci studies, which use mRNA expression as a proximal (and continuous) cellular end phenotype, have increased power for the discovery of statistically significant genetic effects as compared to GWASs and provides inherent biological meaning in the associations between a regulatory variant and its associated gene The Get project has generated data which is publicly available and has been used extensively to help interpret GWAS signals emerging from other complex traits. Meaningful eat discoveries using the affected tissues in other human diseases suggest their potential for NS genomic discovery as well. This is appealing given that we often obtain kidney tissue via biopsy from affected individuals. With regards to kidney eQTLs, the final release of Get will have only 73 kidney cortex samples. There is also an absence of any other major public kidney eQTL datasets. This represents a significant barrier for genomic discovery in nephrology.