Frontiers in Genetics (Nov 2019)

Genome-Wide Study Updates in the International Genetics and Translational Research in Transplantation Network (iGeneTRAiN)

  • Claire E. Fishman,
  • Maede Mohebnasab,
  • Jessica van Setten,
  • Francesca Zanoni,
  • Chen Wang,
  • Silvia Deaglio,
  • Silvia Deaglio,
  • Antonio Amoroso,
  • Antonio Amoroso,
  • Lauren Callans,
  • Teun van Gelder,
  • Sangho Lee,
  • Krzysztof Kiryluk,
  • Matthew B. Lanktree,
  • Brendan J. Keating

DOI
https://doi.org/10.3389/fgene.2019.01084
Journal volume & issue
Vol. 10

Abstract

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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.

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