PLOS Computational BiologyGenomicsPeer-reviewed journal article
Exome Sequencing and Prediction of Long-Term Kidney Allograft Function
Laurent Mesnard, Thangamani MuthukumariD, Maren Burbach, Carol LiiD, Huimin Shang, Darshana Dadhania, John R Lee, Vijay K Sharma, Jenny XiangiD, Caroline Suberbielle, Maryvonnick Carmagnat, Nacera Ouali, Eric Rondeau, John Friedewald, Michael Abecassis, Manikkam SuthanthiraniD, Fabien Campagne
Abstract
AbstractCurrent strategies to improve graft outcome following kidney transplantation consider information at the HLA loci. Here, we used exome sequencing of DNA from ABO compatible kidney graft recipients and their living donors to determine recipient and donor mismatches at the amino acid level over entire exomes. We estimated the number of amino acid mismatches in transmembrane proteins, more likely to be seen as foreign by the recipients immune system, and designated this tally as the allogenomics mismatch score (AMS). The AMS can be measured prior to transplantation with DNA for potential donor and recipient pairs. We examined the degree of relationship between the AMS and post-transplantation kidney allograft function by linear regression. In a discovery cohort, we found a significant inverse correlation between the AMS and kidney graft function at 36 months post-transplantation (n=10 recipient/donor pairs; 20 exomes) (r2>=0.57, P=0.39, P<0.005). In an additional cohort of living and mostly intra-familial recipient/donor pairs (n=19, 38 exomes), we validated the association after controlling for donor age at time of transplantation. Finally, a model that controls for donor age, HLA mismatches and time post-transplantation yields a consistent AMS effect across these three independent cohorts (P<0.05). Taken together, these results show that the AMS is a strong predictor of long-term graft function in kidney transplant recipients.\n\nOne Sentence SummaryPrediction of long-term kidney graft function with exome sequencing
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- approach: statistical model
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