Genome-wide survival study identifies a novel non-HLA donor-recipient genetic mismatch associated with kidney allograft survival

Genome-wide survival study identifies a novel non-HLA donor-recipient genetic mismatch associated with kidney allograft survival | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Genome-wide survival study identifies a novel non-HLA donor-recipient genetic mismatch associated with kidney allograft survival Vincent Mauduit , Axelle Durand , Martin Morin , Kane E. Collins , Matej Roder , Peter J. van der Most , Killian Maudet , Nayane SB Silva , Olivia Rousseau , Ashwini Shanmugam , View ORCID Profile Edmund Gilbert , Graham Lord , Gianpiero L Cavelleri , Harold Snieder , Stephan J.L. Bakker , Pierre-Antoine Gourraud , Mathieu Ribatet , Géraldine Jean , Clarisse Kerleau , Magali Giral , Nicolas Vince , View ORCID Profile Martin H. de Borst , Ondrej Viklicky , Peter J Conlon , View ORCID Profile Sophie Limou doi: https://doi.org/10.1101/2025.11.07.25339560 Vincent Mauduit 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Axelle Durand 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Morin 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kane E. Collins 2 School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland , Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matej Roder 3 Department of Immunogenetics, Institute for Clinical and Experimental Medicine , Prague, Czech Republic Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter J. van der Most 4 Department of Epidemiology, University of Groningen, University Medical Center Groningen , the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Killian Maudet 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nayane SB Silva 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France 5 São Paulo State University, Molecular Genetics and Bioinformatics Laboratory, School of Medicine , Botucatu, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Olivia Rousseau 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ashwini Shanmugam 2 School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland , Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Edmund Gilbert 2 School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland , Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Edmund Gilbert Graham Lord 2 School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland , Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gianpiero L Cavelleri 2 School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland , Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Harold Snieder 4 Department of Epidemiology, University of Groningen, University Medical Center Groningen , the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephan J.L. Bakker 4 Department of Epidemiology, University of Groningen, University Medical Center Groningen , the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pierre-Antoine Gourraud 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mathieu Ribatet 6 Nantes Université, Centrale Nantes, CNRS, Laboratoire de Mathématiques Jean Leray LMJL , UMR 6629, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Géraldine Jean 7 Nantes Université, Centrale Nantes, CNRS, Laboratoire des Sciences du Numérique de Nantes LS2N , UMR 6004, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clarisse Kerleau 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Magali Giral 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicolas Vince 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin H. de Borst 4 Department of Epidemiology, University of Groningen, University Medical Center Groningen , the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin H. de Borst Ondrej Viklicky 8 Transplant Laboratory, Institute for Clinical and Experimental Medicine , Prague, Czech Republic 9 Department of Nephrology, Transplant Center, Institute for Clinical and Experimental Medicine , Prague, Czech Republic Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter J Conlon 10 Department of Nephrology and Transplantation, Beaumont Hospital , Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sophie Limou 1 Nantes Université, Centrale Nantes, CHU Nantes, INSERM, Center for Research in Transplantation and Translational Immunology CR2TI , UMR 1064, ITUN, Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sophie Limou For correspondence: sophie.limou{at}univ-nantes.fr Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract BACKGROUND Despite a sharp rise in kidney graft short-term survival rates and better donor-recipient HLA matching, mid- and long-term survival have not sufficiently improved over the past decades. Several studies suggest that non- HLA factors could be involved in kidney allograft injury, but no validated marker has yet been identified. Here, we aimed at finding genetic variations and mismatches associated with graft function and survival. METHODS Using genome-wide strategies, we tested the recipients’ and donors’ common variations (SNPs and CNVs), and the donor-recipient genetic mismatches for association with 1-year kidney graft function and with time-to-death-censored kidney graft failure in a monocentric European cohort of 1,482 complete donor-recipient pairs. We validated our findings through a meta-analysis in two independent European cohorts gathering a total of 1,842 additional complete pairs. RESULTS We did not identify any significant association with 1-year graft function. However, we discovered four non- HLA mismatches (3 SNPs and 1 CNV) associated with time-to-kidney graft failure. One signal in a regulatory region upstream the TOM1L1 gene (p=6.3×10 -9 , HR=4.1) was successfully replicated in the validation cohorts (p meta -analysis =6.7×10 - 9 , HR=2.9) and ranked among the top 50 rejection-specific genes in a pan-organ transcriptomics study. This locus was also associated with time-to-cellular and humoral rejection (p=0.02) in the discovery cohort in patients achieving primary graft function. CONCLUSIONS By running one of the largest ever performed kidney transplantation genomic analyses, we identified and confirmed a novel donor-recipient genetic mismatch in a biologically relevant non- HLA locus associated with kidney allograft failure. Lay Summary Despite significant advances in immunosuppression, kidney transplant recipients remain at risk of graft rejection and, in more severe cases, graft failure, which can lead to retransplantation, return to dialysis, or even patient death. Donor-recipient HLA compatibility is integrated into kidney transplant clinical care, as this genetic region is key for immunity and graft tolerance. However, several studies suggest that compatibility outside of the HLA region could also influence graft survival. We aimed at investigating this hypothesis in a cohort of 1,482 donor-recipient pairs and found a novel genetic region involved in kidney graft dysfunction that was validated after meta-analysis of two independent cohorts. These findings contribute to a better understanding of the impact of donor-recipient genetic compatibility on kidney transplant outcomes. Introduction Kidney transplantation remains the best treatment for end-stage kidney disease, both in terms of survival and quality of life 1 . Despite major improvements in short-term graft survival (above 90% at 1-year in France 2 ), mid- and long-term survival remains challenging (only 76% at 5 years for kidney transplants performed in France in 2017-2018 2 ). Several donor and recipient factors are known to impact kidney transplant outcomes, such as age, medical history, comorbidities, and genetic factors with the importance of HLA (human leukocyte antigen) matching between donor and recipient. HLA genetic mismatches were previously repeatedly demonstrated to trigger immune responses targeting the graft, thus decreasing its survival 3 , 4 . However, those HLA mismatches fail to explain the whole phenotypic variability observed in kidney graft dysfunction 5 , and negative long-term outcomes can still occur even in the context of a perfect HLA matching 3 . Over the past two decades, an increasing number of studies have thus investigated non-HLA antigens, or so-called minor histocompatibility antigens (mHA), involved in graft failure 6 , 7 . In a recent retrospective multicentric French cohort, MICA (MHC class I chain-related gene A) allelic mismatches and anti-MICA donor-specific antibodies (DSA) were associated with decreased graft survival 8 . Antibodies targeting mHA expressed by endothelial cells such as AT1R (angiotensin type 1 receptor) 9 or ETAR (endothelin-1 type A receptor) 10 have also been associated with increased allograft injury in several cohorts. The diversity of potential mHA targets on endothelial cells was recently demonstrated through a crossmatch assay in the context of early acute microvascular rejection 11 . Overall, these results underline that non-HLA genetic factors also play a role in kidney graft failure. Genome-wide association study (GWAS) is a method testing for association between millions of genetic variants and a complex trait or disease. This approach has been implemented in kidney transplantation cohorts 12 to identify new mHA targets by focusing only on the recipient’s genome 13 , 14 or by jointly investigating the donor and recipient’s genomes 15 , 16 . Several post-transplantation outcomes, including kidney graft function, survival and acute rejection events 13 – 16 , have been assessed in the past few years. Overall, few non-HLA signals 13 , 14 have been identified and the replication has proven challenging, including with the largest GWAS to date (n=2,094 pairs in a multi-centric design) 15 . These mixed results could be explained by the limited sample size from each center (from dozens up to 610 recipients), the relatively low number of total events (from 275 to 495), and the heterogeneity in time period (from 1981 to 2016), standard of care and phenotype definition. The integration of biological knowledge in the genetic study design enabled several teams to emphasize the importance of non- HLA regions in donor-recipient compatibility. First, two groups focused on SNPs tagging for homozygous deletions in recipients who received an organ from a non-homozygous donor and identified a significant association with rejection for LIMS1 17 and CFHR 18 loci, respectively. Second, as SNPs identified by GWAS individually exhibit small to moderate effect sizes, a common strategy is to combine their effects into a polygenic risk score capturing the overall genetic burden. Based on the assumption that cell surface antigens play a role in anti-allograft immune responses, Mesnard et al. proposed to sum the number of amino-acid mismatches in transmembrane proteins from whole-exome sequencing data (n=53 pairs). They demonstrated a significant association of this score with the 3-year post-transplant kidney graft function, independently from HLA matching 19 . This non-HLA mismatch score was later adapted to GWAS data (n=477 pairs) by integrating non-synonymous SNPs within genes encoding transmembrane and secreted proteins, and significantly associated with graft failure 20 . Here, we aimed at revisiting these findings by attempting to replicate the conclusions of the past decade in one of the largest homogeneous cohorts focusing on kidney transplantation genetics (n=1,969 pairs) 21 . We also aimed at investigating genome-wide donor-recipient incompatibilities for the first time by running SNP mismatch association studies. In our KiT-GENIE cohort, all transplants were performed in the same center in Nantes, France between 1999 and 2020, and backed by a rich and well curated database (DIVAT, Données Informatisées and VAlidées en Transplantation ) containing precise clinical information, detailed follow-up and well-defined transplant outcomes. We thus conducted GWASs investigating the effects of SNPs and CNVs from European donors and recipients, and tested polygenic scores for association with two main kidney transplant outcomes: 1-year post-transplant kidney allograft function, and time-to-kidney allograft failure. Methods Study participants and phenotypes All samples originally analyzed were part of the KiT-GENIE biobank 21 , which consists of donors’ and recipients’ genetic data and 200+ demographic and clinical variables from the DIVAT database 22 . All included kidney transplants were performed in the same center in Nantes, France between 1999 and 2020 (follow-up data available up to 2021). Patients and donors were eligible for enrollment in our study if consent for genetic testing and a DNA sample from both the recipient and the donor were available (see Supplementary Methods for detailed inclusion criteria). Two primary outcomes were studied: 1-year post-transplant kidney allograft function, which was calculated with the 2021 race-free CKD-EPI eGFR equation 23 using creatinine measured during protocol follow-up visits and excluding patients with insufficient follow-up time; and time-to-death-censored kidney graft failure, which was defined as return to dialysis, preemptive retransplantation or death with a non-functioning graft. In addition, some signals of interest were also explored for rejection events that were defined as the first biopsy-confirmed antibody mediated or T-cell mediated rejection using the latest Banff classification on the day of biopsy review. Surveillance biopsies were performed at the 3-month and 1-year visits. We did not perform genome-wide analyses for 5-year eGFR and time-to-rejection due to a lack of statistical power for these outcomes in the KiT-GENIE cohort. Genotyping, quality control and imputation Samples were genotyped using the Axiom PMRA chip (900K SNPs, Affymetrix). HLA typing was performed using standard protocols by local HLA labs and we had access to low-resolution (one-field) typing results. We performed the quality control steps, SNP, CNV and high-resolution HLA imputation according to the gold standard guidelines 24 (see Supplementary Methods). We focused our analysis on individuals of European ancestry with residual genetic variance captured through principal component analysis (PCA) or a genetic relationship matrix (GRM) computation when using SAIGE. PCA plots of all included cohorts can be found in Figures S1-3. After imputation, a total of 8.2 million SNPs, 7,583 CNVs and 1,687 unique recipients, 1,642 unique donors and 1,482 complete donor-recipient pairs were available for subsequent analyses in the discovery KiT-GENIE cohort. Mismatch definition Donor-recipient HLA epitopic mismatches were computed through HLA-EPI 25 available on the Easy-HLA website, and were defined as the sum of HLA-A , HLA-B , HLA-C , HLA-DRB1 and HLA-DQB1 eplet mismatches. In addition, we defined non- HLA SNP allelic mismatches for complete donor-recipient pairs under a dominant model (0 or 1 code), i.e. when the donor exhibited one or two copies of an allele not found in the recipient’s genome (Figure S4) to assess the hypothesis of potential increased immunogenicity induced by the donor’s ‘new’ genetic material. This choice was also motivated by the fact that the latest combined non-HLA mismatch score from GWAS genotyping data used the very same dominant mismatch model 20 . For insertion/duplication CNVs, a mismatch was defined as the absence of the insertion/duplication in the recipient’s genome while present in the donor’s genome. Symmetrically for deletion CNVs, a mismatch was defined when the recipient was homozygous for the deletion while the donor was not (known as a “genomic collision model”). Here, we investigated both coding and non-coding mismatches in order to capture the possible immunogenic antigens, as well as the possible regulatory variants impacting the expression level of proteins important for alloimmune responses or graft injury. Mismatches occurring in less than 1% of the pairs were discarded for the GWASs (268 variants with MAF>1% and a mismatch frequency <1%). Statistical analyses Demographic tables were generated through the R gtsummary package (v.2.0.4). Chi-square and Fisher’s exact tests were applied for categorical variables and Wilcoxon rank sum tests for continuous variables. All SNP and CNV genome-wide analyses were performed at 3 different levels: recipients’ genetic data, donors’ genetic data, and combination of both through donor-recipient mismatches (see Figure S4). For 1-year post-transplant eGFR outcomes, GWASs were performed with multivariable linear mixed models implemented in SAIGE 26 . For time-to-graft failure, genome-wide survival studies (GWSSs) were performed using Cox proportional hazards models and the R Survival package (v3.2-11) 27 . We also tested the genomic collision model for CNV-tagging SNPs association with time-to-rejection as in the original paper 17 . Statistical significance was assessed with the Bonferroni threshold. For more details on model corrections and power analyses, see Supplementary Methods and Figures S5-S7. Polygenic scores computation We computed the published chronic kidney disease (CKD) genome-wide polygenic score (GPS) constructed from kidney function GWASs 28 . We then fitted a logistic regression model using the R base stats package to test the association between the GPS and the CKD vs. non-CKD status as originally described. Additionally, we tested the linear association between the recipients’ and donors’ GPS with 1- and 5-year post-transplant eGFR, as well as their association with kidney graft survival using Cox proportional hazards models. We also investigated the previously published genome-wide non- HLA mismatch score that only focused on non-synonymous SNVs located in genes encoding secreted and transmembrane proteins 20 . We fitted a Cox proportional hazards model to test its association with time-to-graft failure as originally described. Details regarding the scores computation and models adjustments are available in the Supplementary Methods. Top signals validation We had access to two independent European validation cohorts for assessing only the SNPs and CNV significantly associated with graft failure in the KiT-GENIE discovery cohort: (1) the United Kingdom and Ireland Renal Transplant Consortium (UKIRTC, n=1,477 pairs including 364 graft failure events) and (2) the Prague cohort from Czech Republic (n=365 pairs including 54 graft failure events. Similar quality controls, mismatch and phenotype definitions, and statistical models were used in each cohort as in KiT-GENIE (for more details, see Supplementary Methods and Table S1). Each cohort’s summary statistics were pooled through a fixed effect meta-analysis using the R metafor package (v4.4) 29 . Results Description of the KiT-GENIE cohort Table 1 gathers the main demographics and clinical characteristics for KiT-GENIE’s European recipients (N=1,674), donors (N=1,631) and full donor-recipient pairs (N=1,482) with available genetic data. The median post-transplantation follow-up time was 6.1 years in our cohort. Kidney allograft function data at 1-year were available for 92% of recipients (134 patients excluded due to insufficient follow-up time) with a median of 51 mL/min/1.73m 2 . For full pairs, there were 303 reported graft failure events (20%) with a median time of 5.1 years, and 171 (12%) transplants experienced at least one rejection event. Additional information on the KiT-GENE demographics can be found in the Supplementary Results and Table S2. View this table: View inline View popup Table 1 Demographic data for the KiT-GENIE cohort gathering recipients’, donors’ and graft characteristics. SNP association analysis The time-to-failure GWSS was underpowered for finding low frequency variants (MAF<1%) with moderate effect size (Figure S6), and the 1-year eGFR GWAS was underpowered for identifying variants with a MAF<10% (Figure S7). No systemic inflation nor deflation was observed for the 1-year eGFR outcome on the QQplots (λ≤1.03), and only a minor inflation was detected for graft failure in the donor-recipient pairs analysis (λ=1.06, Figure S8). For kidney allograft function at 1-year post-transplantation, no genome-wide significant association was detected for the analyses focusing on recipients’ genome, donors’ genome or pairs’ genetic mismatches ( Figures 1A-C ). For the analyses on time-to-allograft failure, no significant association was detected with the recipient-restricted and the donor-restricted analyses ( Figures 1D-E ). Fourteen donor-recipient mismatches from three independent loci were significantly associated with time-to-graft failure ( Figure 1F ), in chromosomes 10 (rs75606962, p=2.0×10 - 8 , HR=3.5 [2.3-5.4], mismatch frequency=3.2%), 17 (top rs16955257, p=6.3×10 - 9 , HR=4.1[2.6-6.5], mismatch frequency=2.4%) and 21 (rs117176234, p=3.6.0×10 - 8 , HR=5.1 [2.8-9.0], mismatch frequency=1.7%). The kidney graft survival was consistently significantly worse for the mismatched pairs than the matched pairs for these three loci in KiT-GENIE ( Figures 2A and S9) according to the log-rank test (p<0.0001). Locus Zoom regional plots for chromosomes 10 and 17 signals are displayed in Figures S10-S11 to emphasize the linkage disequilibrium (LD) pattern around the top hits, whereas chromosome 21 signal was a singleton. Download figure Open in new tab Figure 1 Summary of SNP association tests Results of the association tests between recipients’ SNPs (panels A, D), donors’ (panels B, E) SNPs, and donor-recipient mismatches (panels C, F) with 1-year eGFR (panels A-C) and time-to-graft failure (panels D-F) in the KiT-GENIE cohort. The x-axis represents the variants’ position in the genome and the y-axis corresponds to the log-transformed p-values of the association tests. The Bonferroni genome-wide significance threshold is shown as a solid red line. Regression models were corrected for graft year, graft rank, recipient’s and donor’s age and sex, donor’s type (living or deceased), HLA epitopic mismatches and PCA-computed genetic ancestry. Three significant associations were identified for mismatches and time-to-graft failure (panel F) in chromosomes 10 (p=2.0×10 -8 , HR=3.5 [2.3–5.4]), 17 (p=6.3×10 -9 , HR=4.1 [2.5–6.5]) and 21 (p=3.6×10 -8 , HR=5.1 [2.9–9.0]). The chromosome 17 signal is composed of several significant SNPs in LD (r 2 >0.4): rs76528364, rs76466707, rs76132171, rs78872800, rs16955257 , rs78430798 , rs80106929 , rs79467893 , rs78383882 , rs117887990 , rs77785449 , and rs7211161 (top SNPs in bold, these are in full LD). Download figure Open in new tab Figure 2 A replicated signal in chromosome 17 The top association between donor-recipient mismatches and time-to-kidney graft failure in the KiT-GENIE discovery cohort was located in chromosome 17. (A) Kaplan Meier plot comparing graft survival for the top signal (chr17, rs16955257) in KiT-GENIE. Mismatched donor-recipient pairs displayed a significantly worse graft survival than the matched pairs. (B) Forest plot showing hazard ratios (with 95% confidence interval, log-scale) of the mismatch in different cohorts. The association was tested in two independent validation cohorts. The signal did not reach nominal significance in individual validation cohorts (red dots). However, a combination of the two validation cohorts (yellow dot) through a meta-analysis yielded significance (p=0.02, HR=1.9 [1.4-3.2]). (C) Focus on the known regulatory elements within the top chromosome 17 signal region. Vertical red lines correspond to the top significant SNPs (7 SNPs in full LD) and vertical orange lines to the other significant SNPs, in LD with the top SNPs (r 2 >0.4). The signal overlaps with an open and active chromatin site (peaks in the layered H3K27Ac and DNAse tracks), and with several enhancers (marked in yellow on the ENCODE cCREs track) upstream the TOM1L1 gene, which encodes a trafficking protein highly expressed in kidney and is part of a pan-organ rejection signature. Independent replication of the SNP mismatch findings We tested these three SNP mismatch signals for replication in two independent validation cohorts. The variants’ frequencies were comparable between the three European cohorts (Tables S3-S5). Only the rs16955257 mismatch on chromosome 17 reached nominal significance in the validation meta-analysis (n=1,842 pairs) with an effect size in the same direction as in the KiT-GENIE cohort (p=0.02, HR=1.9 [1.4-3.2], Figure 2B and Table S6). The global meta-analysis of the discovery and validation cohorts exhibited strong statistical significance (n=3,324 pairs, p=6.7×10 -9 , HR=2.9 [2.0-4.1]). The leading rs16955257 variant is located 35kb upstream the TOM1L1 gene ( Figures 2C and S11). Lead chromosome 17 signal robustness and exploration To identify potential genetic or non-genetic biases driving the lead variant association, we tested several iterations of the original Cox model and systematically found a hazard ratio of the same magnitude as in the original model, underlining the signal robustness (Table S7 and Supplementary Results). Notably, the association was driven by pairs where the donor brings the A alternative allele to the recipient, indicating that the donor’s brought genetic material may increase immunogenicity (p=1.3×10 -4 , HR=2.2 [1.5-3.4] when disregarding directionality and testing the donor-recipient genotype absolute difference, Table S7). As graft failure is a heterogeneous outcome, we compared the causes of graft failure between the matched and mismatched groups and did not find a significant difference (p=0.05, Table S8). Chronic graft dysfunction was the first cause of graft loss in both groups. Graft failure events caused by vascular origin (21% vs 3.9%), disease recurrence (11% vs 6.3%) and rejection under immunosuppression treatment (11% vs 4.9%) were more prevalent in the mismatched group. To further assess the potential influence of non-genetic effects, we compared the matched and mismatched pairs in the discovery cohort for over 70 demographic and clinical factors (Table S9). No statistically significant difference was observed between the matched and mismatched group, although the mismatched group received nominally less tacrolimus at induction (72% vs 90%, p=1.9×10 -3 ) and more MMF (86% vs 68%, p=1.9×10 -2 ). However, the tacrolimus circulating levels were similar between the matched and mismatched groups over the follow-up course (Figure S12), indicating that the strength and adherence to the immunosuppression was similar in both groups. The mismatch association remained robust after adjusting for the induction treatment (tacrolimus and MMF, HR=4.0 [2.5-6.4], Table S7). Although we did not find an association between the TOM1L1 replicated mismatch and time-to-rejection (Table S7, p=0.62), we observed a nominally significant association for time-to-ABMR (58 events, p=0.02, HR=4.1 [1.3-13.8]) and time-to-TCMR (63 events, p=0.02, HR=4.2 [1.3-13.8]) in patients achieving primary graft function, suggesting this mismatch could be relevant for allograft outcomes beyond graft failure. Finally, we ran in silico explorations on this replicated chromosome 17 signal to gain more insight on its biological relevance (Supplementary Methods). The frequency for rs16955257 and the SNPs in LD lies below the inclusion threshold for eQTL computations in most public datasets (MAF≥5%) 30 , so we currently lack data to directly characterize their functional impact on TOM1L1 expression. However, we identified several kidney tissue-specific TOM1L1 eQTLs close to the lead signal (Figure S11), suggesting that our chr17 variants could impact TOM1L1 expression. In addition, several of the lead SNPs lay within an open and active chromatin site (peaks in the layered H3K27Ac signal and DNAse tracks, Figure 2C ) with several enhancers (ENCODE cCREs track), which strongly indicates that our mismatch would capture a regulatory role. Lastly, the high conservation of the rs16955257-C allele across evolution (Figure S13) and the predicted pathogenicity of the A allele (CADD - Combined Annotation Dependent Depletion - score of 18.47, i.e. in the top 1.4% of all the human genome variants’ CADD score distribution) further suggests that this signal could have a functional pathogenic role. Additionally, querying 25 public transcriptomic datasets 31 revealed that the TOM1L1 gene expression was systematically decreased in kidney graft during rejection. CNV association analysis No systemic inflation nor deflation was detected (Figure S14). We reported a chromosome 2 duplication mismatch (HGSV_38325, tagged by the rs2392713 SNP, r 2 =0.87 in KiT-GENIE) significantly associated with time-to-kidney graft failure (Figures S15-16, p=1.1.x10 -6 , HR=4.5 [2.5-8.4], mismatch frequency=1.4%, which was not validated in the independent cohorts (p=0.70, Table S6, Supplementary Results). In parallel, we assessed 39 deletion-tagging mismatches for association with time-to-rejection as originally tested 17 , 18 . Only the LIMS1 rs893403 mismatch reached the multiple testing significance threshold in our cohort (p=0.001, HR=1.8 [1.3-2.6], Figure S17, Table S11, Supplementary Results). Genome-wide polygenic scores First, the CKD GPS was not associated with CKD status (where the recipients represented CKD cases, p=0.7, Table S12) and the score distribution was similar between recipients and unrelated donors in our cohort (Figure S18). However, we found a nominally significant association between the recipient’s CKD GPS and 1-year post-transplant eGFR (p=0.04, OR=2.7 [1.1-6.7], Table S13, Supplementary Results). Second, we computed the genome-wide non- HLA mismatch score as previously described 20 (Figure S19) but did not replicate the association between the non- HLA mismatch score and time-to-kidney graft failure (p=0.14, HR=0.93 [0.84-1.03], Figure 3A and Table S13) despite having 3-times more pairs (n=1,482 vs. 477 pairs (39,420 SNVs vs. 59,268 in the original paper) and 100% statistical power (Table S14). We further implemented various iterations of the model, but the observed effect’s direction was almost systematically opposite to what was expected (HR<1, Table S15, Supplementary Results). Download figure Open in new tab Figure 3 Survival curves for non-HLA and class II HLA mismatch scores Kidney graft survival curves stratified by Reindl et al. non-HLA mismatch score quartiles (panel A) and by HLA II epitopic mismatches quartiles (panel B) in the KiT-GENIE cohort. While the highest HLA II quartile presents the worse survival (p=0.01), we did not identify a similar effect for the non-HLA mismatch score (p=0.14). Finally, there was a nominal association between time-to-kidney graft failure and class II HLA epitopic mismatches (p=0.01, HR=1.16 [1.03-1.30], Figure 3B and Table S13), as well as for HLA ABDR mismatches (p=0.01, HR=1.10 [1.03,1.19]), but not with class I HLA epitopic mismatches (p=0.23, HR=1.08 [0.96-1.21]). Discussion In this report, we aimed at investigating the recent findings of genetic studies revolving around kidney graft dysfunction and at conducting the first genome-wide investigation of non-HLA mismatches in a single large and homogeneous monocentric cohort (KiT-GENIE) with two independent validation cohorts (n total =3,324 European donor-recipient pairs), hence representing one of the largest studies ever performed in kidney transplantation genomics. Firstly, our GWAS efforts did not replicate previous candidates or identify any individual SNPs or CNVs from the recipients or from the donors that were strongly associated with kidney graft function or time-to-graft failure, which supports the conclusions from a meta-analysis effort in 2,094 pairs 15 . Secondly, we systematically investigated genome-wide donor-recipient mismatches at the SNP and CNV levels to assess the hypothesis of potential increased immunogenicity induced by ‘new’ genetic material brought by the donor. We investigated both coding and non-coding mismatches to capture the possible immunogenic antigens, as well as the possible regulatory variants impacting the expression level of proteins important for alloimmune responses or graft injury. CNV-tagging SNPs had previously been studied and two deletion-tagging mismatches were highlighted by two different groups in the LIMS1 17 and CFHR 18 loci. With a larger number of European pairs (n=1,482 vs. 705 and 1,025 pairs, respectively), we validated the LIMS1 association with rejection, but not the CFHR one. Notably, another LIMS1 haplotype mismatch was recently associated with kidney graft failure, further highlighting the potential importance of this genomic region as a mHA 32 . We also identified four novel donor-recipient mismatches (1 CNV mismatch and 3 SNP mismatch loci) significantly associated with time-to-kidney graft failure in chromosomes 2, 10, 17 and 21. Importantly, the three SNP mismatches lie within potential regulatory regions ( i.e. introns or upstream genes) and in genetic loci that were previously among the most associated genes with CKD or a CKD-related trait: eGFR for chromosome 10 signal (p=2.4×10 -7 ), gout for chromosome 17 (p=9.0×10 -21 ) and CKD for chromosome 21 (p=4.7×10 -9 ) 33 . This result underlines the importance of non-coding mismatches for kidney graft outcomes and the biological relevance of the identified loci. Biological and clinical relevance of the non-HLA mismatch findings The chromosome 2 locus corresponds to a CNV duplication of 92 kb located between a block of genes and EPHA4 . This genetic region was previously associated with immune responses and urate levels 34 – 37 . We did not have access to CNV data in the validation cohorts, so further work might be needed to replicate this signal. The chromosome 10 rs75606962 SNP mismatch is located in an intron of the PALD1 gene encoding a tyrosine phosphatase. The rs75606962 SNP genotypes were previously correlated to gene expression levels of TYSND1 and SLC29A3 . TYSND1 was reported with a decreased gene expression in CKD kidneys compared to normal kidneys 38 , and SLC29A3 mutated mice exhibit an altered hematopoiesis and abnormal kidneys morphology, which emphasize the relevance of this whole genetic locus 39 . The chromosome 21 rs117176234 mismatch lies within an intronic region of CHODL , which encodes a chondrolectin previously associated with T-cell maturation and hypertension 40 , 41 . However, chromosomes 10 and 21 signal were not replicated in the validation cohorts (p=0.4 and p=0.7 respectively). Finally, we successfully replicated the association with graft failure for the chromosome 17 mismatch (lead variant rs16955257) in two independent cohorts (n=1,842 pairs) through meta-analysis. This mismatch is located upstream TOM1L1 , encoding a membrane trafficking protein with a clathrin-binding activity that is highly expressed in kidney cortex (median 10.6 transcripts per million) and medulla (16.1/million - notably the highest expression across all GTEx tissues) 42 . TOM1L1 protein expression is also reported in tubules in the Human Protein Atlas data 43 . Importantly, TOM1L1 was reported to have a two-times lower expression in acute rejection kidney graft biopsies compared to stable graft biopsies 44 and was identified as one of the 50 rejection-specific genes in a recent pan-organ transcriptomics study 31 , with consistently lower TOM1L1 expression during organ rejection and fibrosis. In KiT-GENIE patients achieving primary graft function, we found a nominal association between the TOM1L1 mismatch and both time-to-ABMR (p=0.02) and time-to-TCMR (p=0.02), suggesting a potential link with kidney transplant rejection in addition from graft failure. These observations will have to be confirmed in a larger cohort, considering the limited rejection rate in our cohort (12.2%, 58 and 63 events for ABMR and TCMR, respectively) and the fact that these outcomes were not available in the validation cohorts. Interestingly, our in silico explorations revealed that our top chromosome 17 signal is located close to TOM1L1 kidney tissue-specific eQTLs 30 and encompasses an open and active chromatin site comprising several enhancers upstream TOM1L1 , which strongly indicates that our signal would capture a regulatory role. Additionally, the high conservation of the rs16955257-C allele across evolution and the predicted pathogenicity of the A allele further suggest this mismatch could drive a deleterious functional impact. If it remains challenging to interpret how a non-coding genetic mismatch can drive immunogenicity and affect graft survival, one could hypothesize that the A allele (present in the donor but not the recipient) decreases the enhancer activity, leading to a lower TOM1L1 expression in the graft, which is associated with organ injury and rejection 31 . This differential expression could also induce immune responses, in the likes of autoantigens reactivity 45 , 46 . Overall, there is promising supporting biological evidence for our TOM1L1 mismatch association, but functional experimental assays will be necessary to validate this hypothesis and to further disentangle the causal locus and pathophysiological mechanism at stake. This replicated chromosome 17 TOM1L1 signal should therefore be a priority candidate for further functional investigations. Polygenic burden associated with kidney graft injury Third, we investigated polygenic risk scores to capture the overall genetic burden associated with kidney allograft function and failure. Indeed, individual SNPs from the recipients or donors might only exhibit small to moderate effect sizes that are challenging to identify through a GWAS without very large sample size cohorts. When computing the CKD genome-wide polygenic score built from the general population in our cohort of kidney transplanted patients, we could not discriminate recipients who are CKD cases from unrelated donors who represent controls from the general population. However, higher recipients’ CKD scores were associated with lower 1-year post-transplant eGFR, without a sustained effect at 5-years. While polygenic risk scores are often underpowered for binary classification, these findings highlight the CKD score’s potential utility in the post-transplant context. Then, we implemented a non- HLA mismatch score to capture all the non-synonymous mismatches from secreted and transmembrane protein-coding genes that could represent potential immunogenic antigens as previously proposed 19 , 20 . The underlying hypothesis is that each donor-recipient pair might carry different mismatch combinations and that the risk of kidney graft dysfunction and failure increases with a higher mismatch load. Using three times more pairs than the original paper (n=1,482 vs. 477) and benefiting from 100% statistical power for discovery, we unfortunately did not replicate the association between the non- HLA mismatch score and time-to-kidney graft failure, but we confirmed the well-described role of class II HLA mismatches. We applied the same model and the exact same SNP selection strategy to build the score as presented in the original manuscript, but we did not have access to the original list of variants. It is worth noting that Markkinen et al. 18 did not replicate the association with time-to-graft failure either, while they found an association between the score quartiles and time-to-acute rejection in an adjusted model (we did not, Table S15). Several reasons could explain this lack of replication. Firstly, graft failure is a heterogeneous outcome that is often caused by non-immune mediated factors 47 , which increases the potential effect size variability between cohorts. Secondly, although both European cohorts had a similar median follow-up (6.1 years for KiT-GENIE vs 5.3 for the original paper), other factors, including differences in adjustments for HLA in the allocation algorithm, immunosuppression regimens or period of inclusion (wider in KiT-GENIE during 1999-2020 vs 2005-2015 in the original paper), could have all contributed to the absence of replication. Thirdly, there was a substantial higher number of SNVs included in the original paper (39,420 vs. 59,268 SNVs). Even though the mismatch count was comparable between the two studies (1752 [IQR 1712-1798] vs 1892 [IQR 1850–1936] in the original paper), it is possible that some key variants were not present in our data. Lastly, Mesnard and colleagues emphasized the crucial role of rare variants in the original allogenomics mismatch score computed from WES data 19 . Despite great improvements in rare variants’ imputation, genotyping data may simply not be optimal for capturing refined donor-recipient non-HLA compatibility. In this line, further assessing the kidney transplantation genetic cohorts with next-generation sequencing might be critical. More studies are therefore necessary to build a robust and comprehensive non- HLA genetic score that would capture the genetic factors and mismatches impacting the risk of kidney graft failure. Integrating biological knowledge on genetic variant impact ( e.g. non-synonymous variants, but also regulatory variants as highlighted in our SNP/CNV mismatch analyses), protein localization ( e.g. transmembrane, secreted), or gene/protein tissue expression ( e.g. immune cells vs. kidney from transcriptomics and proteomics assays) and regulation ( e.g. epigenomics) could help refining such a score and should be compared to unsupervised strategies. Limitations Despite our efforts, our study presents some limitations and we can wonder why, overall, no more replicated signals have been identified in kidney transplant genetics studies so far. Firstly, sample size is key when conducting omics studies, and even though KiT-GENIE is large in comparison to the kidney transplantation standards, GWASs tend to require thousands of cases for detecting low or moderate effect sizes. Larger sample sizes are especially essential when the associated genetic variants present with low frequency, as for our 3 significant SNP mismatches. It is important to underline here that our study was underpowered for identifying low frequency variants with moderate effect size and that the exclusion of patients with graft failure from creatinine-based analyses may have introduced a survival bias. Capturing variants with low effect size on kidney graft injury will likely require a large meta-analysis effort from the community. Secondly, graft failure, the primary phenotype examined in this study, is characterized by considerable heterogeneity 47 . There is indeed a wide diversity of possible graft failure causes, both immune and non-immune related, making it a suboptimal outcome for new association discovery. While the replicated signal’s matched and mismatched groups were similar for a wide a range of non-genetic factors and the association remained robust after several additional adjustments (Table S7), some moderate differences were observed in terms of graft failure cause. However, the challenges in determining and interpreting graft failure causes, the fact that more than one cause can contribute to graft loss 47 , and the limited sample size within our top signal mismatched group (n=19 events) prevented us from further stratifying our analysis for graft failure cause. We initially considered using kidney transplant rejection as the primary outcome for this study, as it might seem less heterogeneous than graft failure, but we lacked statistical power for genome-wide association discovery (5% for HR=2 and MAF=2%) due to the low rejection rate observed in our cohort (12%) 48 . Thirdly, there are potential unaccounted for confounding factors that might restrict our study power. Even though we tried to systematically mitigate biases, the monocentric nature of the KiT-GENIE cohort may limit the generalizability of some of our results. Our study time period remains large (from 1999 to 2020), which inevitably induces some heterogeneity in terms of standard of care and patient management ( e.g. immunosuppression standards, crossmatch assay). Lastly, the leading signal’s hazard ratio magnitude varies greatly between the discovery and one of the validation cohorts, notably for UKIRTC that has comparable sample size and genetic ancestry background. While the mismatch frequency (2.4% vs 1.7%), event rate (20% vs 25%) and proportion of living donor transplants (19% vs 27%) were somewhat comparable, UKIRTC’s transplants are significantly older than KiT-GENIE’s (median transplant year is 2011 in KiT-GENIE vs 2001 in UKIRTC), which inevitably induces differences in HLA compatibility adjustments in the allocation system, patient management and immunosuppression regimen. Unfortunately, granular clinical and phenotypic data were not available in the validation cohorts to directly test for other biases. Beyond the inter-cohort heterogeneity, the observed hazard ratio’s discrepancies could also be attributed to a phenomenon known as the winner’s curse, which corresponds to an overestimation of effects ascertained by thresholding 49 . In this line, a direct genome-wide meta-analysis of the kidney transplant cohorts, even though significantly more time-intensive, would provide better effect size estimates and improve the findings’ robustness. Overall, our study confirmed the absence of strong associations for individual common SNPs from the recipients or from the donors with short-term kidney allograft function and with time-to-graft failure. We did not replicate the association for the CFHR CNV mismatch and for the non-HLA mismatch score with kidney transplantation outcomes. However, we confirmed the role of class II HLA mismatches, we replicated the LIMS1 deletion association with time-to-rejection and thanks to the integration of donor-recipient genetic compatibility, we discovered 4 non-HLA mismatches associated with time-to-graft failure, including one chromosome 17 signal that was replicated in independent cohorts. As only the integration of donor-recipient matching data led to significant associations for low frequency non-coding variants, we believe our results advocate for further studies in the kidney transplantation genetics field through sample size increase, donor-recipient pairs analysis, more refined phenotype definitions ( e.g. specific rejection subgroups or histologic lesions), biological knowledge integration and by implementing next-generation sequencing efforts. In this regard, KiT-GENIE is part of the global data aggregation and meta-analysis effort of the transplant genetics community within the iGeneTRAiN consortium 50 , whose main objectives are to run large meta-analyses of kidney transplantation and cross-organ transplantation outcomes (including graft survival and rejection). We expect these on-going perspectives will lead to a better delineation of molecular mechanisms underlying progressive kidney graft dysfunction and failure. Disclosure statements PA Gourraud is the founder of Methodomics (2008) and the co-founder of Big data Santé (2018). He consults for major pharmaceutical companies, and start-ups, all of which are handled through academic pipelines (AstraZeneca, Biogen, Boston Scientific, Cook, Docaposte, Edimark, Ellipses, Elsevier, Janssen, IAGE, Lek, Methodomics, Merck, Mérieux, Octopize, Sanofi-Genzyme, Lifen, Aspire UAE). PA Gourraud is a volunteer board member at AXA not-for-profit mutual insurance company (2021). He has no prescription activity with either drugs or devices. He receives no wages from these activities. DIVAT (Données Informatisées et VAlidées en Transplantation) Consortium Nantes : Gilles Blancho, Julien Branchereau, Diego Cantarovich, Agnès Chapelet, Jacques Dantal, Clément Deltombe, Lucile Figueres, Claire Garandeau, Magali Giral, Caroline Gourraud-Vercel, Eloise Grenon, Clarisse Kerleau, Thibault Letellier, Christophe Masset, Aurélie Meurette, Simon Ville, Christine Kandell, Karine Renaudin, Florent Delbos, Alexandre Walencik, Anne Devis. The authors would like to thank the iGeneTRAiN consortium (PI: B Keating) for introducing us to P Conlon and MH de Borst, and for integrating our cohorts into the on-going global collaborative meta-analysis effort in genomics of organ transplantation. We are pleased to have become active members of this initiative. Author contributions VM and SL led the design and implementation of the primary analysis. VM and AD were involved in phenotypic data organization and preparation. MM was involved in the SNP imputation and the CNV analysis, realized the CNV imputation as well as the KiT-GENIE and Prague genotyping data quality control. GL and PC established the UKIRTC consortium. KC, AS, EG, PC, GC, PVDM, MDB, HS, SB, MRo and OV were in charge of the replication cohorts and analysis. KM computed the non-HLA mismatch score. OR, GJ, MRi and PAG helped on the statistical analysis and figure generation. NSB and NV were involved in the HLA imputation. MG and CK granted access to the DIVAT database. VM and SL wrote this paper. SL supervised the analysis, revised and formalized the scientific content of the article and mentored VM. All authors have read and approved the final version of the manuscript. 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). This work was supported by Nantes Métropole, Région des Pays de la Loire and European Union (FEDER) via the Programme d’investissements d’Avenir (NExT, SHLARC Project, Nantes Université). 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 ). This research has been conducted as part of the AIby4 project (AI by/for Human,Health and Industry), funded by Centrale Nantes and the French Ministry of Education and Research and the French National Research Agency (ANR-20-THIA-0011) The UKIRTC was supported by grants awarded from the Wellcome Trust (090355/A/09/Z, 090355/B/09/Z and 088849/Z/09/Z, “WTCCC3”), the Medical Research Council (grants G0600698 and MR/J006742/1 to S.H.S.; G0802068 to G.M.L. and MR/K002996/1 to G.M.L; grants G0801537/ID: 88245), Guy’s and St Thomas’ Charity (grants R080530 and R090782) to M.H.F. and G.M.L., from the European Union FP7 (grant agreement no HEALTHF5–2010–260687 to M.H.F. and project number 305147: BIO-DrIM to M.H.F. and I.R.M.); and by the National Institute for Health Research Biomedical Research Centre at Guy’s and St Thomas’ and King’s College London. The Prague cohort was co-funded by the Ministry of Health of the Czech Republic under grant NU21-06-00021. 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. The UKIRTC was approved (or granted exemption) by the appropriate institutional and/or national research ethics committee which was the Hammersmith and Queen Charlotte’s & Chelsea Research Ethics Committee REC No 08/H0707/1. Data sharing The GWAS aggregated data analyzed during the current study will be available for scientific review from the corresponding author upon reasonable request from the GWAS Catalog ( GCP001044 ). Regarding subjects’ personal data under the responsibility of the sponsor, those data will be available for scientific review under the conditions defined by the EU General Data Protection Regulation. A data transfer agreement shall be signed with the sponsor defining the scope of the data transfer, as required by the GDPR and including an obligation to use the data for the sole purpose of the scientific review and forbidding their disclosure to third parties. 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. KiT-GENIE 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. We thank the GenoBiRD and Curie genomic platforms for technical support and GWAS genotyping. We are also grateful to the Bioinformatics Core Facility BiRD, member of Biogenouest and Institut Français de Bioinformatique (IFB) (ANR-11-INBS-0013) for the use of their resources and their technical support. We would like to thank the Centre de Calcul Intensif des Pays de la Loire . We would like to thank the DNA laboratory of the department of immunogenetics, IKEM for preparing the DNA samples and the Department of Electronic Information Resources and Data Science, IKEM for preparing the clinical data of the Prague cohort. The authors would like to thank the members of the DIVAT consortium for their involvement in the study, the physicians who helped recruit patients, and all patients who participated in this study. We also thank the clinical research associates who participated in the data collection. The analysis and interpretation of these data are the responsibility of the authors. Footnotes ↵ † For the DIVAT consortium References 1. ↵ Wolfe RA , Ashby VB , Milford EL , et al. Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant . N Engl J Med . 1999 ; 341 ( 23 ): 1725 – 1730 . doi: 10.1056/NEJM199912023412303 OpenUrl CrossRef PubMed Web of Science 2. ↵ Biomedecine P . ACTIVITÉ DE PRÉLÈVEMENT ET DE GREFFE D’ORGANES EN 2023 et baromètre d’opinion 2024 . Agence de la biomédecine . February 12, 2024. Accessed May 24, 2024 . https://presse.agence-biomedecine.fr/?p=3744 3. ↵ Sasaki N , Idica A . The HLA-matching effect in different cohorts of kidney transplant recipients: 10 years later . Clin Transpl. Published online 2010 : 261 – 282 . 4. ↵ Lefaucheur C , Loupy A , Hill GS , et al. Preexisting Donor-Specific HLA Antibodies Predict Outcome in Kidney Transplantation . JASN . 2010 ; 21 ( 8 ): 1398 – 1406 . doi: 10.1681/ASN.2009101065 OpenUrl Abstract / FREE Full Text 5. ↵ Callemeyn J , Lamarthée B , Koenig A , Koshy P , Thaunat O , Naesens M . Allorecognition and the spectrum of kidney transplant rejection . Kidney International . 2022 ; 101 ( 4 ): 692 – 710 . doi: 10.1016/j.kint.2021.11.029 OpenUrl CrossRef 6. ↵ Lebraud E , Eloudzeri M , Rabant M , Lamarthée B , Anglicheau D . Microvascular Inflammation of the Renal Allograft: A Reappraisal of the Underlying Mechanisms . Front Immunol . 2022 ; 13 : 864730 . doi: 10.3389/fimmu.2022.864730 OpenUrl CrossRef 7. ↵ Zanoni F , Kiryluk K . Genetic Background and Transplantation Outcomes: Insights from GWAS . Curr Opin Organ Transplant . 2020 ; 25 ( 1 ): 35 – 41 . doi: 10.1097/MOT.0000000000000718 OpenUrl CrossRef PubMed 8. ↵ Carapito R , Aouadi I , Verniquet M , et al. The MHC class I MICA gene is a histocompatibility antigen in kidney transplantation . Nat Med . 2022 ; 28 ( 5 ): 989 – 998 . doi: 10.1038/s41591-022-01725-2 OpenUrl CrossRef PubMed 9. ↵ Dragun D , Müller DN , Bräsen JH , et al. Angiotensin II Type 1–Receptor Activating Antibodies in Renal-Allograft Rejection . New England Journal of Medicine . 2005 ; 352 ( 6 ): 558 – 569 . doi: 10.1056/NEJMoa035717 OpenUrl CrossRef PubMed Web of Science 10. ↵ Liu C , Kang ZY , Yin Z , et al. Levels of angiotensin II type-1 receptor antibodies and endothelin-1 type-A receptor antibodies correlate with antibody-mediated rejection and poor graft function in kidney-transplantation patients . Transplant Immunology . 2022 ; 74 : 101674 . doi: 10.1016/j.trim.2022.101674 OpenUrl CrossRef 11. ↵ Delville M , Lamarthée B , Pagie S , et al. Early Acute Microvascular Kidney Transplant Rejection in the Absence of Anti-HLA Antibodies Is Associated with Preformed IgG Antibodies against Diverse Glomerular Endothelial Cell Antigens . JASN . 2019 ; 30 ( 4 ): 692 – 709 . doi: 10.1681/ASN.2018080868 OpenUrl Abstract / FREE Full Text 12. ↵ Ba R , Geffard E , Douillard V , et al. Surfing the Big Data Wave: Omics Data Challenges in Transplantation . Transplantation . 2022 ; 106 ( 2 ): e114 – e125 . doi: 10.1097/TP.0000000000003992 OpenUrl CrossRef PubMed 13. ↵ O’Brien RP , Phelan PJ , Conroy J , et al. A genome-wide association study of recipient genotype and medium-term kidney allograft function . Clinical Transplantation . 2013 ; 27 ( 3 ): 379 – 387 . doi: 10.1111/ctr.12093 OpenUrl CrossRef PubMed 14. ↵ Ghisdal L , Baron C , Lebranchu Y , et al. Genome-Wide Association Study of Acute Renal Graft Rejection . American Journal of Transplantation . 2017 ; 17 ( 1 ): 201 – 209 . doi: 10.1111/ajt.13912 OpenUrl CrossRef PubMed 15. ↵ Hernandez-Fuentes MP , Franklin C , Rebollo-Mesa I , et al. Long- and short-term outcomes in renal allografts with deceased donors: A large recipient and donor genome-wide association study . American Journal of Transplantation . 2018 ; 18 ( 6 ): 1370 – 1379 . doi: 10.1111/ajt.14594 OpenUrl CrossRef 16. ↵ Stapleton CP , Heinzel A , Guan W , et al. The impact of donor and recipient common clinical and genetic variation on estimated glomerular filtration rate in a European renal transplant population . American Journal of Transplantation . 2019 ; 19 ( 8 ): 2262 – 2273 . doi: 10.1111/ajt.15326 OpenUrl CrossRef PubMed 17. ↵ Steers NJ , Li Y , Drace Z , et al. Genomic Mismatch at LIMS1 Locus and Kidney Allograft Rejection . N Engl J Med . 2019 ; 380 ( 20 ): 1918 – 1928 . doi: 10.1056/NEJMoa1803731 OpenUrl CrossRef PubMed 18. ↵ Markkinen S , Helanterä I , Lauronen J , Lempinen M , Partanen J , Hyvärinen K . Mismatches in Gene Deletions and Kidney-related Proteins as Candidates for Histocompatibility Factors in Kidney Transplantation . Kidney International Reports. Published online September 2022 : S2468024922017491 . doi: 10.1016/j.ekir.2022.08.032 OpenUrl CrossRef 19. ↵ Mesnard L , Muthukumar T , Burbach M , et al. Exome Sequencing and Prediction of Long-Term Kidney Allograft Function. Peters B, ed . PLoS Comput Biol . 2016 ; 12 ( 9 ): e1005088 . doi: 10.1371/journal.pcbi.1005088 OpenUrl CrossRef PubMed 20. ↵ Reindl-Schwaighofer R , Heinzel A , Kainz A , et al. Contribution of non-HLA incompatibility between donor and recipient to kidney allograft survival: genome-wide analysis in a prospective cohort . The Lancet . 2019 ; 393 ( 10174 ): 910 – 917 . doi: 10.1016/S0140-6736(18)32473-5 OpenUrl CrossRef PubMed 21. ↵ Ba R , Durand A , Mauduit V , et al. KiT-GENIE, the French genetic biobank of kidney transplantation . Eur J Hum Genet. Published online February 3 , 2023 : 1 – 9 . doi: 10.1038/s41431-023-01294-z OpenUrl CrossRef 22. ↵ Nantes University Hospital . The French DIVAT: A Clinical Database Associated With a Biological Banking Accessible to Initiate Epidemiological and Translational Collaborative Researches in Kidney Transplantation . clinicaltrials.gov ; 2016 . Accessed January 1, 2024 . https://clinicaltrials.gov/study/NCT02900040 23. ↵ Inker LA , Eneanya ND , Coresh J , et al. New Creatinine- and Cystatin C–Based Equations to Estimate GFR without Race . N Engl J Med . 2021 ; 385 ( 19 ): 1737 – 1749 . doi: 10.1056/NEJMoa2102953 OpenUrl CrossRef PubMed 24. ↵ Douillard V , Castelli EC , Mack SJ , et al. Approaching Genetics Through the MHC Lens: Tools and Methods for HLA Research . Front Genet . 2021 ; 12 . doi: 10.3389/fgene.2021.774916 OpenUrl CrossRef 25. ↵ Geffard E , Boussamet L , Walencik A , et al. HLA-EPI: A new EPIsode in exploring donor/recipient epitopic compatibilities . HLA . 2022 ; 99 ( 2 ): 79 – 92 . doi: 10.1111/tan.14505 OpenUrl CrossRef PubMed 26. ↵ Zhou W , Nielsen JB , Fritsche LG , et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies . Nat Genet . 2018 ; 50 ( 9 ): 1335 – 1341 . doi: 10.1038/s41588-018-0184-y OpenUrl CrossRef PubMed 27. ↵ Terry M Therneau . A Package for Survival Analysis in R . Published online 2024. Accessed September 21, 2024 . https://CRAN.R-project.org/package=survival 28. ↵ Khan A , Turchin MC , Patki A , et al. Genome-wide polygenic score to predict chronic kidney disease across ancestries . Nat Med . 2022 ; 28 ( 7 ): 1412 – 1420 . doi: 10.1038/s41591-022-01869-1 OpenUrl CrossRef PubMed 29. ↵ Viechtbauer W . Conducting Meta-Analyses in R with the metafor Package . Journal of Statistical Software . 2010 ; 36 : 1 – 48 . doi: 10.18637/jss.v036.i03 OpenUrl CrossRef PubMed 30. ↵ Liu H , Doke T , Guo D , et al. Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease . Nat Genet. Published online June 16 , 2022 . doi: 10.1038/s41588-022-01097-w OpenUrl CrossRef PubMed 31. ↵ Robertson H , Kim HJ , Li J , et al. Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas . Nat Med . 2024 ; 30 ( 12 ): 3748 – 3757 . doi: 10.1038/s41591-024-03030-6 OpenUrl CrossRef 32. ↵ Sun Z , Zhang Z , Banu K , et al. Multiscale genetic architecture of donor-recipient differences reveals intronic LIMS1 mismatches associated with kidney transplant survival . J Clin Invest . 2023 ; 133 ( 21 ): e170420 . doi: 10.1172/JCI170420 OpenUrl CrossRef PubMed 33. ↵ Wuttke M , König E , Katsara MA , et al. Imputation-powered whole-exome analysis identifies genes associated with kidney function and disease in the UK Biobank . Nat Commun . 2023 ; 14 ( 1 ): 1287 . doi: 10.1038/s41467-023-36864-8 OpenUrl CrossRef PubMed 34. ↵ Tian C , Hromatka BS , Kiefer AK , et al. Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections . Nat Commun . 2017 ; 8 ( 1 ): 599 . doi: 10.1038/s41467-017-00257-5 OpenUrl CrossRef PubMed 35. Cipriani V , Tierney A , Griffiths JR , et al. Beyond factor H: The impact of genetic-risk variants for age-related macular degeneration on circulating factor-H-like 1 and factor-H-related protein concentrations . Am J Hum Genet . 2021 ; 108 ( 8 ): 1385 – 1400 . doi: 10.1016/j.ajhg.2021.05.015 OpenUrl CrossRef PubMed 36. Mathebula EM , Sengupta D , Govind N , et al. A genome-wide association study for rheumatoid arthritis replicates previous HLA and non-HLA associations in a cohort from South Africa . Hum Mol Genet . 2022 ; 31 ( 24 ): 4286 – 4294 . doi: 10.1093/hmg/ddac178 OpenUrl CrossRef PubMed 37. ↵ Huffman JE , Albrecht E , Teumer A , et al. Modulation of genetic associations with serum urate levels by body-mass-index in humans . PLoS One . 2015 ; 10 ( 3 ): e0119752 . doi: 10.1371/journal.pone.0119752 OpenUrl CrossRef PubMed 38. ↵ Nakagawa S , Nishihara K , Miyata H , et al. Molecular Markers of Tubulointerstitial Fibrosis and Tubular Cell Damage in Patients with Chronic Kidney Disease . PLoS One . 2015 ; 10 ( 8 ): e0136994 . doi: 10.1371/journal.pone.0136994 OpenUrl CrossRef PubMed 39. ↵ Nair S , Strohecker AM , Persaud AK , et al. Adult stem cell deficits drive Slc29a3 disorders in mice . Nat Commun . 2019 ; 10 ( 1 ): 2943 . doi: 10.1038/s41467-019-10925-3 OpenUrl CrossRef PubMed 40. ↵ Weng L , Van Bockstaele DR , Wauters J , et al. A novel alternative spliced chondrolectin isoform lacking the transmembrane domain is expressed during T cell maturation . J Biol Chem . 2003 ; 278 ( 21 ): 19164 – 19170 . doi: 10.1074/jbc.M300653200 OpenUrl Abstract / FREE Full Text 41. ↵ Sun D , Richard M , Musani SK , et al. Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits . HGG Adv . 2021 ; 2 ( 1 ): 100013 . doi: 10.1016/j.xhgg.2020.100013 OpenUrl CrossRef PubMed 42. ↵ The GTEx Consortium . The GTEx Consortium atlas of genetic regulatory effects across human tissues . Science . 2020 ; 369 ( 6509 ): 1318 – 1330 . doi: 10.1126/science.aaz1776 OpenUrl Abstract / FREE Full Text 43. ↵ Karlsson M , Zhang C , Méar L , et al. A single–cell type transcriptomics map of human tissues . Science Advances . 2021 ; 7 ( 31 ):eabh2169. doi: 10.1126/sciadv.abh2169 OpenUrl FREE Full Text 44. ↵ Flechner SM , Kurian SM , Head SR , et al. Kidney Transplant Rejection and Tissue Injury by Gene Profiling of Biopsies and Peripheral Blood Lymphocytes . Am J Transplant . 2004 ; 4 ( 9 ): 1475 – 1489 . doi: 10.1111/j.1600-6143.2004.00526.x OpenUrl CrossRef PubMed Web of Science 45. ↵ Banasik M , Boratyńska M , Kościelska-Kasprzak K , et al. Non-HLA Antibodies: Angiotensin II Type 1 Receptor (Anti-AT1R) and Endothelin-1 Type A Receptor (Anti-ETAR) Are Associated With Renal Allograft Injury and Graft Loss . Transplantation Proceedings . 2014 ; 46 ( 8 ): 2618 – 2621 . doi: 10.1016/j.transproceed.2014.09.029 OpenUrl CrossRef PubMed 46. ↵ Cardinal H , Dieudé M , Brassard N , et al. Antiperlecan Antibodies Are Novel Accelerators of Immune-Mediated Vascular Injury . American Journal of Transplantation . 2013 ; 13 ( 4 ): 861 – 874 . doi: 10.1111/ajt.12168 OpenUrl CrossRef PubMed 47. ↵ Mayrdorfer M , Liefeldt L , Wu K , et al. Exploring the Complexity of Death-Censored Kidney Allograft Failure . JASN . 2021 ; 32 ( 6 ): 1513 – 1526 . doi: 10.1681/ASN.2020081215 OpenUrl Abstract / FREE Full Text 48. ↵ Colas L , Bui L , Kerleau C , et al. Time-dependent blood eosinophilia count increases the risk of kidney allograft rejection . eBioMedicine . 2021 ; 73 . doi: 10.1016/j.ebiom.2021.103645 OpenUrl CrossRef 49. ↵ Palmer C , Pe’er I . Statistical correction of the Winner’s Curse explains replication variability in quantitative trait genome-wide association studies . PLOS Genetics . 2017 ; 13 ( 7 ): e1006916 . doi: 10.1371/journal.pgen.1006916 OpenUrl CrossRef PubMed 50. ↵ Fishman CE , Mohebnasab M , van Setten J , et al. Genome-Wide Study Updates in the International Genetics and Translational Research in Transplantation Network (iGeneTRAiN) . Front Genet . 2019 ; 10 : 1084 . doi: 10.3389/fgene.2019.01084 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted November 09, 2025. 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Bakker , Pierre-Antoine Gourraud , Mathieu Ribatet , Géraldine Jean , Clarisse Kerleau , Magali Giral , Nicolas Vince , Martin H. de Borst , Ondrej Viklicky , Peter J Conlon , Sophie Limou medRxiv 2025.11.07.25339560; doi: https://doi.org/10.1101/2025.11.07.25339560 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Transplantation Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15228) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6598) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9231) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a0056b059d7506eb',t:'MTc3OTU1MzU0Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();