Exome Sequencing and Prediction of Long-Term Kidney Allograft Function

Laurent Mesnard, Thangamani Muthukumar, Maren Burbach, Carol Li, Huimin Shang, Darshana Dadhania, John R. Lee, Vijay K. Sharma, Jenny Xiang, Caroline Suberbielle, Maryvonnick Carmagnat, Nacera Ouali, Eric Rondeau, John J. Friedewald, Michael M. Abecassis, Manikkam Suthanthiran, Fabien Campagne

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Current strategies to improve graft outcome following kidney transplantation consider information at the human leukocyte antigen (HLA) loci. Cell surface antigens, in addition to HLA, may serve as the stimuli as well as the targets for the anti-allograft immune response and influence long-term graft outcomes. We therefore performed exome sequencing of DNA from kidney graft recipients and their living donors and estimated all possible cell surface antigens mismatches for a given donor/recipient pair by computing the number of amino acid mismatches in trans-membrane proteins. We designated this tally as the allogenomics mismatch score (AMS). We examined the association between the AMS and post-transplant estimated glomerular filtration rate (eGFR) using mixed models, considering transplants from three independent cohorts (a total of 53 donor-recipient pairs, 106 exomes, and 239 eGFR measurements). We found that the AMS has a significant effect on eGFR (mixed model, effect size across the entire range of the score: -19.4 [-37.7, -1.1], P = 0.0042, χ2 = 8.1919, d.f. = 1) that is independent of the HLA-A, B, DR matching, donor age, and time post-transplantation. The AMS effect is consistent across the three independent cohorts studied and similar to the strong effect size of donor age. Taken together, these results show that the AMS, a novel tool to quantify amino acid mismatches in trans-membrane proteins in individual donor/recipient pair, is a strong, robust predictor of long-term graft function in kidney transplant recipients.

Original languageEnglish (US)
Article numbere1005088
JournalPLoS computational biology
Issue number9
StatePublished - Sep 2016
Externally publishedYes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics


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