Clinical Applications of Genetic Discoveries in Kidney Transplantation: a Review

A Primer in Precision Nephrology: Optimizing Outcomes in Kidney Health and Disease through Data-Driven Medicine

This year marks the 63rd anniversary of the International Society of Nephrology, which signaled nephrology's emergence as a modern medical discipline. In this article, we briefly trace the course of nephrology's history to show a clear arc in its evolution-of increasing resolution in nephrological data-an arc that is converging with computational capabilities to enable precision nephrology. In general, precision medicine refers to tailoring treatment to the individual characteristics of patients. For an operational definition, this tailoring takes the form of an optimization, in which treatments are selected to maximize a patient's expected health with respect to all available data. Because modern health data are large and high resolution, this optimization process requires computational intervention, and it must be tuned to the contours of specific medical disciplines. An advantage of this operational definition for precision medicine is that it allows us to better understand what precision medicine means in the context of a specific medical discipline. The goal of this article was to demonstrate how to instantiate this definition of precision medicine for the field of nephrology. Correspondingly, the goal of precision nephrology was to answer two related questions: ( 1 ) How do we optimize kidney health with respect to all available data? and ( 2 ) How do we optimize general health with respect to kidney data?

A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation

Emerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or sum these SNP matching scores across multiple gene-coding regions, which makes it challenging to interpret the association findings. We propose a multi-marker Joint Score Test (JST) to jointly test for association between recipient genotype SNP effects and a gene-based matching score with transplant outcomes. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. In addition, JST allows for the matching effect and the recipient genotype effect to follow different biological mechanisms, which is not the case for other multi-marker methods. Extensive simulation studies show that JST is competitive when compared with existing methods, such as the sequence kernel association test (SKAT), especially under scenarios where associated SNPs are in low linkage disequilibrium with non-associated SNPs or in gene regions containing a large number of SNPs. Applying the method to paired donor/recipient genetic data from kidney transplant studies yields various gene regions that are potentially associated with incidence of acute rejection after transplant.

Mutational landscape of TRPC6, WT1, LMX1B, APOL1, PTPRO, PMM2, LAMB2 and WT1 genes associated with Steroid resistant nephrotic syndrome

BACKGROUND

Nephrotic syndrome appears as a group of symptoms like proteinuria, edema and hyperlipidemia. Identification of monogenic forms revealed the physiology and pathogenesis of the SRNS.

METHODS AND RESULTS

We performed Illumina panel sequencing of seven genes in 90 Indian patients to determine the role of these genetic mutations in nephrotic syndrome prognosis. Samtool was used for variants calling, and SnpEff and Snpsift did variants annotation. Clinical significance and variant classification were performed by the ClinVar database. In SSNS and SRNS patients, we found 0.78% pathogenic and 3.41% likely pathogenic mutations. Pathogenic mutations were found in LAMB2, LMX1B and WT1 genes, while likely pathogenic mutations were found in (6/13) LAMB2, (2/13) LMX1B, (2/13) TRPC6, (2/13) PTPRO and (1/13) PMM2 genes. Approximately 46% likely pathogenic mutations were contributed to the LAMB2 gene in SSNS and SRNS patients. We also detect 30 VUS (variants of uncertain significance), which were found (17/30) pathogenic and (13/30) likely pathogenic by different prediction tools.

CONCLUSIONS

Multigene panels were used for genetic screening of heterogeneous disorders like nephrotic syndrome in the Indian population. We found pathogenic, likely pathogenic and certain VUS, which were responsible for the pathogenesis of the disease. Therefore, mutational analysis of SSNS and SRNS is necessary to avoid adverse effects of corticosteroids, modify the intensity of immunosuppressing agents, and prevent the disease's progression.