Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

KiT-GENIE, the French genetic biobank of kidney transplantation

European Journal of Human Genetics volume 31, pages 1291–1299 (2023)Cite this article

Subjects

Abstract

KiT-GENIE is a monocentric DNA biobank set up to consolidate the very rich and homogeneous DIVAT French cohort of kidney donors and recipients (D/R) in order to explore the molecular factors involved in kidney transplantation outcomes. We collected DNA samples for kidney transplantations performed in Nantes, and we leveraged GWAS genotyping data for securing high-quality genetic data with deep SNP and HLA annotations through imputations and for inferring D/R genetic ancestry. Overall, the biobank included 4217 individuals (n = 1945 D + 2,272 R, including 1969 D/R pairs), 7.4 M SNPs and over 200 clinical variables. KiT-GENIE represents an accurate snapshot of kidney transplantation clinical practice in Nantes between 2002 and 2018, with an enrichment in living kidney donors (17%) and recipients with focal segmental glomerulosclerosis (4%). Recipients were predominantly male (63%), of European ancestry (93%), with a mean age of 51yo and 86% experienced their first graft over the study period. D/R pairs were 93% from European ancestry, and 95% pairs exhibited at least one HLA allelic mismatch. The mean follow-up time was 6.7 years with a hindsight up to 25 years. Recipients experienced biopsy-proven rejection and graft loss for 16.6% and 21.3%, respectively. KiT-GENIE constitutes one of the largest kidney transplantation genetic cohorts worldwide to date. It includes homogeneous high-quality clinical and genetic data for donors and recipients, hence offering a unique opportunity to investigate immunogenetic and genetic factors, as well as donor-recipient interactions and mismatches involved in rejection, graft survival, primary disease recurrence and other comorbidities.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Building the KiT-GENIE DNA biocollection and GWAS SNP data.
Fig. 2: Distribution of KiT-GENIE recipients according to the year of graft.
Fig. 3: PCA projection of KiT-GENIE individuals with the 1000 Genomes Project reference individuals.
Fig. 4: Genetic ancestry matching between donors and recipients.

Similar content being viewed by others

Data availability

The KiT-GENIE data are available from our CR2TI team. Data and summary statistics are available upon reasonable request following approval from the KiT-GENIE steering committee and ethics committee to ensure data protection and privacy in compliance with French and European laws. Collaborations are encouraged through specific research projects using the KiT-GENIE data or through enriching the existing cohort with new patients. Potential collaborators are invited to contact the primary investigator Sophie Limou: sophie.limou@univ-nantes.fr.

References

  1. Hill NR, Fatoba ST, Oke JL, Hirst JA, O’Callaghan CA, Lasserson DS, et al. Global prevalence of chronic kidney disease - a systematic review and meta-analysis. PLoS ONE. 2016;11:e0158765.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dalrymple LS, Katz R, Kestenbaum B, Shlipak MG, Sarnak MJ, Stehman-Breen C, et al. Chronic kidney disease and the risk of end-stage renal disease versus death. J Gen Intern Med. 2011;26:379–85.

    Article  PubMed  Google Scholar 

  3. Garcia GG, Harden P, Chapman J. The global role of kidney transplantation. Kidney Blood Press Res. 2012;35:299–304.

    Article  PubMed  Google Scholar 

  4. Tonelli M, Wiebe N, Knoll G, Bello A, Browne S, Jadhav D, et al. Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes. Am J Transpl. 2011;11:2093–109.

    Article  CAS  Google Scholar 

  5. Wong G, Howard K, Chapman JR, Chadban S, Cross N, Tong A, et al. Comparative survival and economic benefits of deceased donor kidney transplantation and dialysis in people with varying ages and co-morbidities. PLoS ONE. 2012;7:e29591.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Agence de la Biomédecine. Annual report 2020—Kidney transplantation. 2021. https://rams-archives2020.agence-biomedecine.fr/sites/default/files/pdf/2021-08/ABM_PG_Organes_Rein2020.pdf.

  7. Gondos A, Döhler B, Brenner H, Opelz G. Kidney graft survival in Europe and the United States. Transpl J 2013;95:267–74.

    Article  Google Scholar 

  8. Sasaki N, Idica A The HLA-matching effect in different cohorts of kidney transplant recipients: 10 years later. Clin Transpl. 2010;261‑82.

  9. Tam V, Patel N. Benefits and limitations of genome-wide association studies. Nat Rev Genet. 2019;20:467–84.

    Article  CAS  PubMed  Google Scholar 

  10. Levy SE, Myers RM. Advancements in next-generation sequencing. Annu Rev Genomics Hum Genet. 2016;17:95–115.

    Article  CAS  PubMed  Google Scholar 

  11. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018;562:203–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hanif Z, Sufiyan N, Patel M, Akhtar MZ. Role of biobanks in transplantation. Ann Med Surg. 2018;28:30–3.

    Article  CAS  Google Scholar 

  13. Cambon-Thomsen A, Ducournau P, Gourraud PA, Pontille D. Biobanks for Genomics and Genomics for Biobanks. Comp Funct Genomics. 2003;4:628–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Alaa AM, Bolton T, Di Angelantonio E, Rudd JHF, van der Schaar M. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants. PLOS ONE. 2019;14:e0213653. Aalto-Setala K, Aalto-Setala K, éditeurs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Xu X, Eales JM, Jiang X, Sanderson E, Drzal M, Saluja S, et al. Contributions of obesity to kidney health and disease: insights from Mendelian randomization and the human kidney transcriptomics. Cardiovascular Res. 2022;118:3151–61.

    Article  CAS  Google Scholar 

  16. Yu Z, Jin J, Tin A, Köttgen A, Yu B, Chen J, et al. Polygenic risk scores for kidney function and their associations with circulating proteome, and incident kidney diseases. J Am Soc Nephrol. 2021;32:3161–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ghisdal L, Baron C, Lebranchu Y, Viklický O, Konarikova A, Naesens M, et al. Genome-wide association study of acute renal graft rejection. Am J Transpl. 2017;17:201–9.

    Article  CAS  Google Scholar 

  18. Hernandez-Fuentes MP, Franklin C, Rebollo-Mesa I, Mollon J, Delaney F, Perucha E, et al. Long- and short-term outcomes in renal allografts with deceased donors: a large recipient and donor genome-wide association study. Am J Transpl. 2018;18:1370–9.

    Article  CAS  Google Scholar 

  19. Massart A, Ghisdal L, Viklicky O, Naesens M, Abramowicz D, Abramowicz M. Reply to Hernandez et al.—GWAS of acute renal graft rejection. Am J Transplant. 2018;18:2098–9.

    Article  PubMed  Google Scholar 

  20. Pihlstrøm HK, Mjøen G, Mucha S, Haraldsen G, Franke A, Jardine A, et al. Single nucleotide polymorphisms and long-term clinical outcome in renal transplant patients: a validation study. Am J Transpl. 2017;17:528–33.

    Article  Google Scholar 

  21. Reindl-Schwaighofer R, Heinzel A, Signorini L, Thaunat O, Oberbauer R. Mechanisms underlying human genetic diversity: consequence for antigraft antibody responses. Transpl Int. 2018;31:239–50.

    Article  CAS  PubMed  Google Scholar 

  22. Steers NJ, Li Y, Drace Z, D’Addario JA, Fischman C, Liu L, et al. Genomic mismatch at LIMS1 locus and kidney allograft rejection. N Engl J Med. 2019;380:1918–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Reindl-Schwaighofer R, Heinzel A, Kainz A, van Setten J, Jelencsics K, Hu K, et al. Contribution of non-HLA incompatibility between donor and recipient to kidney allograft survival: genome-wide analysis in a prospective cohort. Lancet. 2019;393:910–7.

    Article  PubMed  Google Scholar 

  24. Ba R, Geffard E, Douillard V, Simon F, Mesnard L, Vince N, et al. Surfing the big data wave: omics data challenges in transplantation. Transplantation. 2022;106:e114–25.

    Article  PubMed  Google Scholar 

  25. Zanoni F, Kiryluk K. Genetic background and transplantation outcomes: insights from genome-wide association studies. Curr Opin Organ Transpl. 2020;25:35–41.

    Article  Google Scholar 

  26. InternationalGenetics & Translational Research in Transplantation Network (iGeneTRAiN).Design and implementation of the international genetics and translational research in transplantation network.Transplantation.2015;99:2401–12.

    Article  Google Scholar 

  27. Masset C, Kerleau C, Garandeau C, Ville S, Cantarovich D, Hourmant M, et al. A third injection of the BNT162b2 mRNA COVID-19 vaccine in kidney transplant recipients improves the humoral immune response. Kidney Int. 2021;100:1132–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Foucher Y, Lorent M, Albano L, Roux S, Pernin V, Le Quintrec M, et al. Renal transplantation outcomes in obese patients: a French cohort-based study. BMC Nephrol. 2021;22:79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Dujardin A, Chesneau M, Dubois F, Danger R, Bui L, Kerleau C, et al. Clinical and immunological follow-up of very long-term kidney transplant recipients treated with calcineurin inhibitors indicates dual phenotypes. Kidney Int. 2021;99:1418–29.

    Article  CAS  PubMed  Google Scholar 

  30. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Browning BL, Tian X, Zhou Y, Browning SR. Fast two-stage phasing of large-scale sequence data. Am J Hum Genet. 2021;108:1880–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zheng X, Shen J, Cox C, Wakefield JC, Ehm MG, Nelson MR, et al. HIBAG—HLA genotype imputation with attribute bagging. Pharmacogenomics J. 2014;14:192–200.

    Article  CAS  PubMed  Google Scholar 

  33. Byrska-Bishop M, Evani US, Zhao X, Basile AO, Abel HJ, Regier AA, et al. High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios. Cell. 2022;185:3426–40.e19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Douillard V, Castelli EC, Mack SJ, Hollenbach JA, Gourraud PA, Vince N, et al. Approaching genetics through the MHC Lens: tools and methods for HLA research. Front Genet. 2021;12:774916.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Shiina T, Hosomichi K, Inoko H, Kulski JK. The HLA genomic loci map: expression, interaction, diversity and disease. J Hum Genet. 2009;54:15–39.

    Article  CAS  PubMed  Google Scholar 

  36. Olson E, Geng J, Raghavan M. Polymorphisms of HLA-B: influences on assembly and immunity. Curr Opin Immunol. 2020;64:137–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mesnard L, Muthukumar T, Burbach M, Li C, Shang H, Dadhania D, et al. Exome sequencing and prediction of long-term kidney allograft function. PLoS Comput Biol. 2016;12:e1005088.

    Article  PubMed  PubMed Central  Google Scholar 

  38. O’Brien RP, Phelan PJ, Conroy J, O’Kelly P, Green A, Keogan M, et al. A genome-wide association study of recipient genotype and medium-term kidney allograft function. Clin Transpl. 2013;27:379–87.

    Article  Google Scholar 

  39. Divers J, Ma L, Brown WM, Palmer ND, Choi Y, Israni AK, et al. Genome‐wide association study for time to failure of kidney transplants from African American deceased donors. Clin Transpl. 2020;34:e13827.

    Article  CAS  Google Scholar 

  40. Lam NN, Lloyd A, Lentine KL, Quinn RR, Ravani P, Hemmelgarn BR, et al. Changes in kidney function follow living donor nephrectomy. Kidney Int. 2020;98:176–86.

    Article  PubMed  Google Scholar 

  41. Mjøen G, Hallan S, Hartmann A, Foss A, Midtvedt K, Øyen O, et al. Long-term risks for kidney donors. Kidney Int 2014;86:162–7.

    Article  PubMed  Google Scholar 

  42. Muzaale AD, Massie AB, Wang MC, Montgomery RA, McBride MA, Wainright JL, et al. Risk of end-stage renal disease following live kidney donation. JAMA. 2014;311:579–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Rudnicki M. FSGS recurrence in adults after renal transplantation. BioMed Res Int. 2016;2016:1–7.

    Article  Google Scholar 

  44. Hanks SC, Forer L, Schönherr S, LeFaive J, Martins T, Welch R, et al. Extent to which array genotyping and imputation with large reference panels approximate deep whole-genome sequencing. Am J Hum Genet. 2022;109:1653–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Fishman CE, Mohebnasab M, van Setten J, Zanoni F, Wang C, Deaglio S, et al. Genome-wide study updates in the international genetics and translational research in transplantation network (iGeneTRAiN). Front Genet. 2019;10:1084.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank everyone who helped in the design, collection, genotyping experiments, data cleaning and analyses. We are especially indebted to all donors and recipients who participated in this study, to the clinical staff and physicians who care and manage the patients on a daily basis and helped recruiting the patients, and to research associates who participated in the data collection. Clinical data were collected from the French DIVAT multicentric prospective cohort of kidney and/or pancreatic transplant recipients by focusing on the Nantes centre (www.divat.fr, n° CNIL 914184, ClinicalTrials.gov identifier: NCT02900040). The analysis and interpretation of these data are the responsibility of the authors. We would like to acknowledge the local blood bank and HLA typing lab (Etablissement Français du Sang [EFS] Nantes) for facilitating the access to DNA samples. Finally, we thank the GenoBiRD and Curie genomic platforms for technical support and GWAS genotyping.

Funding

The KiT-GENIE cohort has been funded by several entities: (1) The Etoiles Montantes funding by the Pays de la Loire region (n°2018-09998), (2) The IRCT Dialyse research project by the Société Francophone de Néphrologie, Dialyse et Transplantation (SFNDT), (3) The Greffe research project by French Agence de la Biomédecine (ABM, n°18GREFFE014). In addition, this work has benefited from government support through the National Research Agency (ANR) under the future investment program (n°ANR-17-RHUS-0010) and from the European Union’s Horizon 2020 Research and Innovation Programme (n°754995). Finally, we thank the Roche Pharma, Novartis, Astellas, Chiesi, Sandoz and Sanofi laboratories for supporting the DIVAT cohort as the CENTAURE Foundation (http://www.fondation-centaure.org).

Author information

Authors and Affiliations

  1. Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France

    Rokhaya Ba, Axelle Durand, Vincent Mauduit, Christine Chauveau, Stéphanie Le Bas-Bernardet, Sonia Salle, Martin Morin, Clémence Petit, Venceslas Douillard, Olivia Rousseau, Gilles Blancho, Nicolas Vince, Magali Giral, Pierre-Antoine Gourraud & Sophie Limou

  2. CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France

    Pierrick Guérif, Clémence Petit, Gilles Blancho, Clarisse Kerleau & Magali Giral

Authors
  1. Rokhaya Ba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Axelle Durand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vincent Mauduit
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christine Chauveau
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stéphanie Le Bas-Bernardet
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sonia Salle
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pierrick Guérif
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Martin Morin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Clémence Petit
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Venceslas Douillard
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Olivia Rousseau
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Gilles Blancho
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Clarisse Kerleau
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Nicolas Vince
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Magali Giral
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Pierre-Antoine Gourraud
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Sophie Limou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RB, AD and SL were involved in the design, conception, implementation and data collection of KiT-GENIE. RB, AD, CC, SLBB, SS, PG, and CP were involved in DNA collection and normalisation. RB, AD, VM, MM, VD, OR and SL were involved in quality controls, statistical description, imputation of missing data, PCA analysis and ancestry analysis. CK realized clinical data extraction from the DIVAT database. MG and GB provided clinical guidance and ensured access to the DIVAT clinical data. RB and SL wrote the article. SL, PAG and NV supervised the building of KiT-GENIE, revised and formalized the scientific content of the article and mentored RB. All authors have read and approved the final version of the manuscript.

Corresponding author

Correspondence to Sophie Limou.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

Written consent was obtained from each participant for access to clinical data and participation to DNA analyses (DIVAT n° CNIL 914184). Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ba, R., Durand, A., Mauduit, V. et al. KiT-GENIE, the French genetic biobank of kidney transplantation. Eur J Hum Genet 31, 1291–1299 (2023). https://doi.org/10.1038/s41431-023-01294-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41431-023-01294-z

This article is cited by

Search

Advanced search

Quick links