Description and first insights on a large genomic biobank of lung transplantation

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Individual factors can impact CLAD, but no large genetic investigation has been conducted to date. We established the multicentric Genetic COhort in Lung Transplantation (GenCOLT) biobank upon the rich and homogeneous COLT cohort. GenCOLT collected DNA, high-quality GWAS (genome-wide association study) genotyping and robust HLA data for donors and recipients to supplement COLT clinical data. GenCOLT closely mirrors the global COLT cohort without significant variations in variables like demographics, initial disease and survival rates (P > 0.05). The GenCOLT donors were 45 years-old on average, 44% women, and primarily died of stroke (54%). The recipients were 48 years-old at transplantation on average, 45% women, and the main underlying disease was chronic obstructive pulmonary disease (45%). The mean follow-up time was 67 months and survival at 5 years was 57.3% for the CLAD subgroup and 97.4% for the stable subgroup. After stringent quality controls, GenCOLT gathered more than 7.3 million SNP and HLA genotypes for 387 LT pairs, including 91% pairs composed of donor and recipient of European ancestry. Overall, GenCOLT is an accurate snapshot of LT clinical practice in France and Belgium between 2009 and 2018. It currently represents one of the largest genetic biobanks dedicated to LT with data available simultaneously for donors and recipients. This unique cohort will empower to run comprehensive GWAS investigations of CLAD and other LT outcomes. Lung Transplantation Donor-recipient pairs Cohort DNA biocollection GWAS Genomics HLA CLAD chronic lung allograft dysfunction Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction According to the international registry of the International Society of Heart and Lung Transplantation (ISHLT), the number of lung transplants (LT) performed each year worldwide has gradually increased over the past decades, from about 250 in 1990 to more than 4,000 today( 1 ). In France, 334 lung transplantations were performed in 2022 alone( 2 , 3 ). Despite improvements in surgical techniques( 4 – 7 ) and immunosuppressive treatment management( 8 ), the long-term outcome remains limited with a mean survival of 63% at 5 years post-transplantation in Europe( 9 ). The main limitation to long-term survival is the development of chronic lung allograft dysfunction (CLAD)( 10 , 11 ), which is defined by a persistent decline (≥ 20%) of the FEV1 (forced expiratory volume in 1 second) value from baseline. Several risk factors for CLAD have been suggested including alloimmune factors (cellular or antibody-mediated rejections) and non-alloimmune factors (primary graft dysfunction, pulmonary infection, airway pollution or gastro-esophageal reflux disease)( 11 ). Indubitably, individual factors, either recipient or donor related, play a role in the biological responses of lung injury and in the end, in the occurrence of CLAD( 12 ). To date, no extensive genetic research has been conducted to explore the potential influence of genetic factors on chronic lung allograft dysfunction. In contrast, several genomic initiatives have been conducted in kidney transplantation( 13 ) and confirmed the importance of HLA mismatches, as well as an emerging role for non-HLA factors and mismatches. In LT, the investigation of HLA mismatches has yielded conflicting results( 14 ), but more recent data suggests a link between HLA mismatches and an increased risk for bronchiolitis obliterans syndrome (BOS)( 15 ) and CLAD( 16 ). Given this knowledge gap, we believe it will be highly valuable to develop large-scale genetic biobanks and genomic analyses to identify associations between genotypes and LT-related outcomes. Here, we describe the clinical and genetic characteristics of GenCOLT which gathers DNA samples, GWAS (genome-wide association study) data and clinical records for 392 donor-recipient pairs (n = 784 individuals) from 11 French and Belgian clinical centers. Material and Methods Description of the COLT clinical database GenCOLT was built onto the Cohort in Lung Transplantation (COLT – NCT00980967), whose main focus was the discovery of CLAD risk factors( 16 , 17 ). The ethics committee ( Comité de Protection des Personnes , 2009-A00036-51) approved the study and all participants provided a written informed consent (CNIL French data protection authority, #911142). COLT was a prospective study, which included patients from twelve clinical centers between September 2009 to December 2018: CHU Nantes, Hospices Civils de Lyon, Assistance Publique-Hôpitaux de Marseille, Hôpital Foch (Paris), Hôpital Européen Georges Pompidou (Paris), CHU Grenoble, CHRU Strasbourg, CHU Bordeaux, Hôpital Bichat (Paris), Hôpital Marie Lannelongue (Paris area) and CHU Toulouse from France, as well as the Erasme Hospital (Brussels, Belgium). COLT comprises a total of 1,413 transplanted patients from whom clinical data and biological samples were collected. The clinical records and follow-up data from each participating center are centralized in a secured online database coordinated by the Nantes University Hospital. According to protocol, follow-up visits were conducted at 1-month and 6-month after transplantation, and then every 6 months for a period of up to 5 years for biological sample (blood) collection, and up to 10 years for clinical data. Clinical follow-up data include functional data such as allograft dysfunction, infection, and pulmonary function test. In addition, every relevant clinical event occurring and all pulmonary function tests performed between two protocol visits ( e.g. infection, acute cellular rejection, hospital visit) were recorded in the database and considered by the adjudication committee which includes respiratory physicians from at least five different centers. Clinical phenotypes All COLT patients underwent individual phenotyping by an adjudication committee that gathered at least 5 investigator physicians from the different participating centres. Pulmonary function tests, relevant chest computed tomography and medical history, especially potential confounding factors, were reviewed for a collegial decision on phenotype initially based on the 2014 proposed classification and then on the 2019 ISHLT consensus report on CLAD( 17 ). Recipients were classified as follows: BOS, restrictive allograft syndrome (RAS), azithromycin responsive allograft dysfunction (ARAD), stable and other (death within 3 months after transplantation, death without CLAD, insufficient data to conclude or confounding factors). Description of the GenCOLT genetic data DNA collection and GWAS genotyping GenCOLT is a DNA biobank that was established as an extension of COLT. At this stage, GenCOLT has gathered DNA samples from 392 pairs of LT donors and recipients (n = 784 individuals) across 12 centers. We included all adult patients (age ≥ 18 years-old) with a survival over 3 months post-transplantation, for whom consent for genetic investigations and a DNA sample were available for both the donor and the recipient. The collection of samples from deceased donors for scientific research has been authorized by the Agence de Biomédecine (PFS09-003). A protocol has been put in place, which includes researching whether or not the donor is opposed to the use of his organs or body parts for research and information for relatives. The GenCOLT cohort is located and managed by the Centre des Ressources Biologiques at the Nantes University Hospital. Each DNA sample was assessed for volume, concentration, purity (260/230 and 260/280 absorbance ratios) and degradation by analyzing migration on an agarose gel. Following the manufacturer recommendations, the DNA samples were normalized to a volume of 20µL and a concentration of 10ng/µL. They were randomized based on sex and donor/recipient status in 96-well plates to minimize batch effects. Subsequently, the DNA samples underwent genotyping using the Axiom PMRA (Precision Medicine Research Array) chips (ThermoFisher, Waltham MA, USA), which cover 902,560 genetic variants (or SNPs, single nucleotide polymorphisms) including those found in the HLA and KIR polymorphic genomic regions, as well as other relevant genes for research in cancer and immunology. We followed the Axiom 2.0 Thermofisher standardized protocols and guidelines during the genotyping process. Data processing and imputations To ensure the reliability and quality of the genomic data (Fig. 1 A), we implemented several essential quality control (QC) steps. First, we performed the technological QC using AxAs (Axiom Analysis Suite) only to retain high-quality individuals, plates, and genetic variants. For individuals, a DishQC > 0.82 ensured the proper separation of AT and GC fluorescence signals from noise in nonpolymorphic test probes, and the sample proportion with an assigned genotype (or call rate) was set to > 97%. Similarly, we applied a call rate of > 98.5% and > 95% for plates and genetic variants, respectively. In addition, we evaluated for each SNP the genotype cluster quality assignment (Fisher’s linear discrimination > 3.6) and the clear distinction between homozygous and heterozygous clusters (heterozygosity > 95%). A total of 852,344 SNPs and 387 pairs (n = 775 individuals) passed this technological QC process. We performed additional QC with PLINK( 18 ) to prevent genotyping errors. We checked individuals with missingness > 2% and evaluated the relatedness between samples; however, no individuals were excluded at this stage. For SNPs, we excluded those with missingness > 2% and deviations from the Hardy-Weinberg equilibrium (p < 10 − 6 , Fig. 1 B). To address missing data, we employed imputation methods using the TOPMED( 19 ) tool and TOPMED reference panel for SNP imputation. For HLA imputation, we employed HIBAG( 20 ) along with a global reference panel (multiethnic samples from 1,000 Genomes Project)( 21 , 22 ). In both cases, we only retained SNPs and HLA alleles with high imputation quality (r 2 > 0.8). Finally, we carefully compared the imputed sex and HLA one-field alleles with sex and HLA allele information from clinical records to prevent potential errors of sample management during genotyping. As a result, GenCOLT collected robust high-quality imputed GWAS data for 7,337,433 SNPs and including 387 pairs (n = 775 individuals). Genetic ancestry To further describe GenCOLT, we assessed donors and recipients genetic ancestry using ADMIXTURE( 23 ) and principal component analyses (PCA) from GWAS SNPs with a minor allele frequency (MAF) ≥ 1%. By comparing GenCOLT with the diverse 1,000 Genomes Project( 24 ) populations (n = 2,504 individuals from African (AFR), American (AMR), East Asian (EAS), European (EUR), and South Asian (SAS) reference populations), we aimed at detecting potential population stratification and at capturing ancestry-related variability in GenCOLT. ADMIXTURE defined genetic ancestry percentages per individual with a detailed breakdown within the five major ancestral groups. Each donor and recipient were attributed to an ancestry group when the ancestry percentage was ≥ 80%. When their ancestry did not meet the criterion of at least 80% contribution from any of the five major ancestral populations, the individuals were classified as admixed. Statistical analysis Descriptive statistics were expressed as mean ± standard deviation and the min-max range for continuous variables, and as percentage for categorical variables. Difference among groups was tested using one-way ANOVA and chi-square tests for categorical variables. Kaplan-Meier analysis was performed to estimate the 5-year and 10-year survival after LT, and the differences in survival rate were compared using a log-rank test. All the analyses were performed in R 4.2.1. Two-sided p-values were considered significant for p < 0.001 to account for multiple testing, and nominally significant for p < 0.05. Results GenCOLT demographics and clinical characteristics GenCOLT incorporates clinical data related to the donor ( Table 1 ) and the recipient ( Table 2 ). Women represented 43.9% donors and 44.9% recipients. The mean age was 48 years-old and the BMI was 21.8 (kg/m²) for recipients. The primary cause of death for donors was stroke with 53.6%. The main underlying disease leading to LT was chronic obstructive pulmonary disease (COPD) (44.6%), followed by cystic fibrosis (21.4%), interstitial lung disease (16.3%) and pulmonary hypertension (4.2%). We compared all clinical variables between the GenCOLT and COLT groups ( Tables S1-S3 ). Importantly, only the patients’ distribution between clinical centers and the preservation fluid usage (Perfadex vs. Celsior) were statistically different between both groups (p < 0.001), directly reflecting the accessibility to DNA samples within centers and difference of preservation fluid used by center. We will consider these features as covariates in future GWAS analyses. Additional variables, such as immunosuppressive or induction treatment, corticosteroids therapy and lung reduction, were nominally significant (p < 0.05) but did not significantly differ between COLT and GenCOLT when accounting for multiple testing. Overall, the GenCOLT recipients do not differ from the rest of the COLT cohort in terms of clinical data, pre, per and post transplantation. Additionally, we examined the four major underlying causes of LT, as different etiologies could differentially impact FEV1 spirometry values, and no significant difference was observed ( Tables S4-S7 ). Survival Analysis We described the post-transplantation survival in GenCOLT by stratifying according to clinical phenotypes: stable, CLAD, and other (Fig. 2 ). The median survival for CLAD and the other group was 62 and 26 months, respectively. For stable recipients, the survival rate was 97.4% at 5 years, and it declined to 60.2% at 10 years. For CLAD recipients, the survival rate was 57.3% at 5 years and it fell to 16.2% at 10 years. Finally, for the other group the survival rate was 34.4% at 5 years and it dropped to 22.8% at 10 years. In a subsequent analysis, we compared the survival rates between COLT and GenCOLT patients (Fig. 3 ). The median survival for CLAD transplant patients from COLT and GenCOLT was 66 and 62 months, respectively. For stable COLT patients, the survival rate was 97.5% at 5 years and it declined to 70.5% at 10 years. For COLT patients with CLAD, the survival rate was 57.4% at 5 years and it dropped to 16.5% at 10 years. In both cohorts, the survival was significantly shorter for CLAD patients than for stable patients (log-rank p < 0.0001). There was no difference in lung allograft survival between COLT and GenCOLT for stable (p = 0.3) and CLAD (p = 0.6) patients. Overall, our analysis demonstrated similar survival rates for transplanted patients from COLT and GenCOLT. Data cleaning for GWAS Robustness and quality of GenCOLT genetic data We assessed the genotyping robustness and quality of our genetic data throughout the whole GenCOLT building process (DNA extraction, sample handling, normalization and genotyping, Fig. 1 ). Importantly, we compared genetically inferred sex and HLA alleles with information from medical records and we did not identify any discrepancy, confirming the proper sample management. After careful standard quality controls for SNPs, we imputed 7,337,433 SNPs with accurate genotypes (r²>0.8) from 400K genotyped SNPs (Figs. 1 B and S1). Before imputation, we observed an enrichment in rare variants in accordance with the PMRA design. After imputation, the number of SNPs with MAF > 2% has significantly increased across the whole MAF spectrum. All these high-quality genetic variants are available for subsequent association testing. We compared HLA allele information available before and after imputation ( Figure S2 ). We observed a considerable gain of allele calling for the HLA-C gene (from 1.1% before imputation to 100% after imputation). Importantly, HLA imputation preserved the distribution of allelic diversity while increasing the available HLA information. As expected, the HLA-B locus exhibits a higher level of polymorphism( 20 , 25 , 26 ) compared to other HLA genes such as HLA - DQB1 for example. Genetic ancestry We conducted a PCA to examine genetic ancestry and population stratification within our genetic dataset in comparison with the reference populations from the 1,000 Genomes Project( 27 ). This analysis revealed a distinct separation of individuals from the 1,000 Genomes Project into five clusters (Fig. 4 ) representing the five major reference ancestral populations. The GenCOLT samples were spread across all the reference populations with an important concentration among individuals of European ancestry. To go further, we defined genetic ancestry percentages within the five major ancestral groups per individual. When focusing on donor-recipient pairs, our analysis revealed that the majority of pairs (90.8%) shared European ancestry ( Figure S3 ). In addition, 7.6% pairs were composed of a European donor and an admixed recipient, and four pairs (0.5%) were composed of a European recipient and an East Asian donor. GenCOLT also gathered three pairs with at least one individual (donor or recipient) with admixed ancestry and five pairs with Asian ancestry. The remaining 2% pairs included donors and recipients of non-European ancestry. Discussion The GenCOLT biobank represents a unique and valuable resource in Europe for investigating genetic and immunogenetic factors in LT. It was established upon COLT, which has been a comprehensive source of clinical data on LT since 2009. Over a span of 10 years, we have created a robust and consistent multicentric cohort that accurately reflects the clinical practices and medical records of LT in France and Belgium. Of the hundreds of available clinical variables, only two are significantly different between GenCOLT and COLT (center and preserving liquid), which is why we consider GenCOLT to be a smaller but accurate representation of COLT, and therefore a snapshot of LT clinical practice between 2009 and 2018. To ensure data homogeneity and quality, the GenCOLT genomic data were generated using a standardized experimental pipeline, including the same genotyping chips, technological protocols and analytical pipelines. Rigorous quality controls were implemented to guarantee the highest data quality. Additional checks were conducted to verify sex and HLA matching between genetically inferred information and medical records, minimizing the risk of sample mishandling. This was particularly important considering the retrieval of samples that were stored for more than 10 years, which increased the potential for human errors. Furthermore, SNP to HLA allele imputation was also performed to enhance the immunogenetic genotypes within the GenCOLT dataset. This imputation process addressed the issue of incomplete HLA data in the COLT clinical records, particularly for HLA-C , where 98.9% data was missing in both recipients and donors. By retrospectively filling these missing gaps, SNP to HLA imputation provided consistent HLA annotations for future immunogenetic association testing. Additionally, by projecting SNP genetic data from GenCOLT and individuals of the 1,000 Genomes Project, we were able to assess the genetic ancestry of donors and recipients without directly collecting ancestry background information, as it is prohibited in France and Belgium. This approach helped prevent population stratification and provided insights into the genetic ancestry within the GenCOLT dataset. Overall, the establishment of GenCOLT involved meticulous processes to ensure data conformity, sample traceability, and comprehensive genetic and immunogenetic information. It represents a valuable resource for studying LT outcomes and its associated molecular factors in a standardized and controlled manner. However, the GenCOLT cohort has some limits. First of all, the sample size is limited compared with studies on other solid organ transplantation such as kidney transplantation( 28 ), which might limit our statistical power for discovery in our future GWAS analyses. Similarly, GenCOLT is essentially composed of European individuals, and as such, we will only be able to draw conclusions on the genetic risk factors for lung graft rejection at the European level. To address this limitation, we are planning future collaborations with partners from the International Genetics and Translational Research in Transplantation Network (iGeneTRAiN)( 29 , 30 ), an international collaborative effort and the largest genetic initiative focusing on better understanding transplant rejections and complications to date. Meta-analyses and additional patient recruitment will increase the statistical power for discoveries, while potentially increasing the genetic diversity of the study population. In conclusion, GenCOLT stands as one of the most extensive LT DNA cohorts worldwide, encompassing 387 donor-recipient pairs (n = 775 individuals). This comprehensive cohort not only comprises clinical data but also genetic information for both donors and recipients, making it an invaluable resource for investigating the underlying mechanisms of CLAD, as well as other LT outcomes and complications. The genetic profiles of the recipients are likely to significantly impact a wide range of transplantation outcomes, from their susceptibility to experiencing rejection, response to medications (pharmacogenetics), to the development of various medical conditions linked to the prolonged use of immunosuppressive treatments( 31 ). Furthermore, the genetic makeup of the organ donors may also serve as a predictor for the function of the transplanted organ. Finally, the genetic compatibilities between the donor and recipient could play a crucial role in influencing both the likelihood of rejection and the long-term survival of transplanted organs. The identification of HLA and non- HLA genetic associations within GenCOLT would significantly enhance our understanding of CLAD and other LT outcomes, and may uncover potential therapeutic targets. Moreover, the identification of predictive biomarkers in GenCOLT would help stratifying individual risk at an early stage, before the onset of symptoms, ultimately improving personalized and predictive medicine in the field of lung transplantation. The wealth of information within GenCOLT therefore opens up new avenues for research and has the potential to advance our knowledge and management of lung transplant patients. Abbreviations ARAD: azithromycin responsive allograft dysfunction BMI: body mass index BOS: bronchiolitis obliterans syndrome CLAD: chronic lung allograft dysfunction COLT: cohort in lung transplantation COPD: chronic obstructive pulmonary disease DNA: deoxyribonucleic acid GWAS: genome-wide association study HLA: human leukocyte antigen LT: lung transplantation MAF: minor allele frequency PCA: principal component analysis PMRA: precision medicine research array QC: quality control RAS: restrictive allograft syndrome SNP: single nucleotide polymorphism Declarations Data availability statement The GenCOLT data are available from our CR2TI team. Data and summary statistics are available upon request following approval from the GenCOLT 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 GenCOLT data or through enriching the existing cohort with new patients. Potential collaborators are invited to contact the primary investigator Sophie Limou: [email protected] . Acknowledgments We thank the participating patients and their families, whose trust, support, and cooperation were essential for the collection of the data used in this study. We thank the COLT Consortium for their organizational achievement and the LUNG innovation (LUNG O2) cluster. The authors thank everyone who helped in the design, collection, genotyping experiments, data cleaning and analyses. We are grateful to the Genomics Core Facility GenoA, member of Biogenouest and France Genomique (ANR-11-INBS-0013), for the use of its resources and technical support. Finally, we thank the biological resource center for biobanking (CHU Nantes, Hôtel Dieu, Centre des ressources biologique s (CRB, BRIF: BB-0033-00040)). Author Contribution Data curation and statistical analysis, S.B.; Methodology and software, S.L., M.M., O.R., V.M., N.D., A.D. and N.V.; Writing original draft, S.B.; Writing review and editing, S.B., S.L., A.T. and M.S.; Providing samples and monitoring patients, B.R., B.C., X.D., L.F., J.L., A.R., T.V., C.K., JF.M., M.S., V.B., A.M.; Provided genomics expertise, P.A.G. All authors have read and agreed to the published version of the manuscript. Funding The Cohort in Lung Transplantation was funded by Vaincre La Mucoviscidose, l’Association Grégory Lemarchal, the French Research Ministry (Agence Nationale de la Recherche grant), the French Government (Programme Hospitalier de Recherche Clinique – DGOS 20-11), the European Union (FP7 collaborative project HEALTH.2012.2.1.2-1 – grant agreement number: 305457) and Nantes Metropole. The study sponsor(s) or funder(s) had no role in the study design, in the collection, analysis, and interpretation of data, in the report writing and, in the decision, to submit the article for publication. Researchers were independent from funders and all authors, external and internal, had full access to all data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. S. Brocard benefits from the support of IMT Atlantique, Centrale Nantes and Institut de Recherche en Santé Respiratoire des Pays de la Loire for his PhD research. Ethical approval This study protocol was approved by ethics committee ( Comité de Protection des Personnes , 2009-A00036-51) the study and all participants provided a written informed consent (CNIL French data protection authority, #911142). Competing interest PA Gourraud is the founder of Methodomics (2008) and a co-founder of Big data Santé (2018). 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Additional Declarations There is no duality of interest Supplementary Files SupplementaryMaterial.docx Table1demographictablefordonorsGenCOLT.xlsx Table2demographictableforrecipientsGenCOLT.xlsx Cite Share Download PDF Status: Published Journal Publication published 20 Aug, 2024 Read the published version in European Journal of Human Genetics → Version 1 posted Editorial decision: revise 09 Jul, 2024 Review # 3 received at journal 01 Jul, 2024 Reviewer # 3 agreed at journal 12 Jun, 2024 Review # 2 received at journal 12 Apr, 2024 Reviewer # 2 agreed at journal 01 Apr, 2024 Reviewer # 1 agreed at journal 22 Mar, 2024 Reviewers invited by journal 20 Mar, 2024 Submission checks completed at journal 29 Feb, 2024 Editor assigned by journal 29 Feb, 2024 First submitted to journal 29 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3999519","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":281834504,"identity":"46d5fcbb-75df-42c8-9552-db05adc6557e","order_by":0,"name":"Sophie Limou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYHACNoYEBgYZNhDzAwMDD0KC+QBeLTwgLYwz4FoSQBIJuLUwQFUyI6zAo0W+vffYgwcVDDx8YoePSdu23ZExOMB78HPhDxt7BjbeB9i0GJw5l26QcAboMOm0NOnctmc8Bgf4kqVnJKQlNrCxG2DVIpFjJpHYBtKSYwbUcphHcv4bA2mehMMJDPJt2B02A6TlH1SLJUhLA4/xb56E/0CHsWH3/A2QlgaoFkagFn4GHjOgLQcYG3BoAfolTSLhmATIL8mWPecgWqx50pIT23BoAYWY5I8aGzn52ckHb/woO2zPxsBjfJvHxs6eH5fDIDEigUUClwaUFDIKRsEoGAWjACsAAMNMSad4p/4bAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7702-8234","institution":"Nantes University","correspondingAuthor":true,"prefix":"","firstName":"Sophie","middleName":"","lastName":"Limou","suffix":""},{"id":281834505,"identity":"d3697f7e-1cd8-4022-b40b-6f540a677f66","order_by":1,"name":"Simon Brocard","email":"","orcid":"","institution":"Nantes University","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Brocard","suffix":""},{"id":281834506,"identity":"fcdd40da-4b40-471c-84d9-172bbe8989cc","order_by":2,"name":"Martin Morin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Morin","suffix":""},{"id":281834507,"identity":"3d028e2d-1d72-4423-b358-b11113e56ecd","order_by":3,"name":"Nayane Dos Santos Brito Silva","email":"","orcid":"","institution":"Nantes University","correspondingAuthor":false,"prefix":"","firstName":"Nayane","middleName":"Dos Santos Brito","lastName":"Silva","suffix":""},{"id":281834508,"identity":"6649d6af-f4f2-4fef-8c58-39849f0ded9d","order_by":4,"name":"Benjamin Renaud-Picard","email":"","orcid":"","institution":"Department of Respiratory Medicine and Strasbourg Lung Transplant Program","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Renaud-Picard","suffix":""},{"id":281834509,"identity":"b3542c13-57f7-4eaa-a502-9be149b853ae","order_by":5,"name":"Benjamin Coiffard","email":"","orcid":"","institution":"Aix Marseille Univ, Department of Respiratory Medicine and Lung Transplantation","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Coiffard","suffix":""},{"id":281834510,"identity":"51c3c56c-2e1a-4ee7-accc-b4ff9d1ef273","order_by":6,"name":"Xavier Demant","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Bordeaux","correspondingAuthor":false,"prefix":"","firstName":"Xavier","middleName":"","lastName":"Demant","suffix":""},{"id":281834511,"identity":"4884e612-87c4-46dc-9a44-fc53704665e1","order_by":7,"name":"Loïc Falque","email":"","orcid":"","institution":"Service Hospitalier Universitaire de Pneumologie et Physiologie, CHU Grenoble Alpes","correspondingAuthor":false,"prefix":"","firstName":"Loïc","middleName":"","lastName":"Falque","suffix":""},{"id":281834512,"identity":"07c79ee0-3aa4-4a5f-aed5-dec4d07ee50e","order_by":8,"name":"Jérome Le Pavec","email":"","orcid":"","institution":"Service de Pneumologie et Transplantation Pulmonaire, Groupe hospitalier Marie-Lannelongue -Saint Joseph, Le Plessis-Robinson","correspondingAuthor":false,"prefix":"","firstName":"Jérome","middleName":"Le","lastName":"Pavec","suffix":""},{"id":281834513,"identity":"6f7ee25d-1801-439f-8b20-30afb56e8d03","order_by":9,"name":"Antoine Roux","email":"","orcid":"","institution":"Pneumology, Adult Cystic Fibrosis Center and Lung Transplantation Department Hôpital Foch, Suresnes","correspondingAuthor":false,"prefix":"","firstName":"Antoine","middleName":"","lastName":"Roux","suffix":""},{"id":281834514,"identity":"6185fbfc-f660-44d4-b522-5cab72469b23","order_by":10,"name":"Thomas Villeneuve","email":"","orcid":"","institution":"CHU Toulouse, Service de Pneumologie","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Villeneuve","suffix":""},{"id":281834515,"identity":"69842716-89db-4257-9597-6fc088052eb1","order_by":11,"name":"Christiane Knoop","email":"","orcid":"","institution":"Service de Pneumologie, CHU Erasme, Bruxelles","correspondingAuthor":false,"prefix":"","firstName":"Christiane","middleName":"","lastName":"Knoop","suffix":""},{"id":281834516,"identity":"88e97ec9-73d1-497b-bfa4-25231f3d05ce","order_by":12,"name":"Jean-François Mornex","email":"","orcid":"","institution":"Service de pneumologie, Orphalung, RESPIFIL Lyon","correspondingAuthor":false,"prefix":"","firstName":"Jean-François","middleName":"","lastName":"Mornex","suffix":""},{"id":281834517,"identity":"2be52f79-b317-4e13-8ba7-0134e8137507","order_by":13,"name":"Mathilde Salpin","email":"","orcid":"","institution":"APHP Nord-Université Paris Cité, Hôpital Bichat, Service de Pneumologie B et Transplantation Pulmonaire, Paris","correspondingAuthor":false,"prefix":"","firstName":"Mathilde","middleName":"","lastName":"Salpin","suffix":""},{"id":281834518,"identity":"fc85fc6f-a2f6-4485-a0e7-46657a4070d8","order_by":14,"name":"Véronique Boussaud","email":"","orcid":"","institution":"Service de Pneumologie, Hôpital Cochin, Paris","correspondingAuthor":false,"prefix":"","firstName":"Véronique","middleName":"","lastName":"Boussaud","suffix":""},{"id":281834519,"identity":"f14e3e16-4b6d-4abb-a26d-64e545b59951","order_by":15,"name":"Olivia Rousseau","email":"","orcid":"https://orcid.org/0000-0002-5726-9620","institution":"Nantes Université,","correspondingAuthor":false,"prefix":"","firstName":"Olivia","middleName":"","lastName":"Rousseau","suffix":""},{"id":281834520,"identity":"de1c518f-a5cc-43ab-839b-c2b0007585a4","order_by":16,"name":"Vincent Mauduit","email":"","orcid":"https://orcid.org/0000-0001-7352-8162","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Mauduit","suffix":""},{"id":281834521,"identity":"fdd0d010-8054-4114-95be-bb32b84c0f41","order_by":17,"name":"Axelle Durand","email":"","orcid":"https://orcid.org/0000-0002-4291-2281","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Axelle","middleName":"","lastName":"Durand","suffix":""},{"id":281834522,"identity":"01b5a365-212a-4452-a4df-e46a7300d48c","order_by":18,"name":"Antoine Magnan","email":"","orcid":"","institution":"Hôpital Foch, Université de Versailles Saint Quentin Paris-Saclay","correspondingAuthor":false,"prefix":"","firstName":"Antoine","middleName":"","lastName":"Magnan","suffix":""},{"id":281834523,"identity":"faca6225-44ed-4bec-b65d-f8d4b35893cb","order_by":19,"name":"Pierre-Antoine Gourraud","email":"","orcid":"","institution":"Université de Nantes","correspondingAuthor":false,"prefix":"","firstName":"Pierre-Antoine","middleName":"","lastName":"Gourraud","suffix":""},{"id":281834524,"identity":"a47128f9-a5db-423c-8288-10c26b8b5a0f","order_by":20,"name":"Nicolas Vince","email":"","orcid":"https://orcid.org/0000-0002-3767-6210","institution":"Nantes Université","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Vince","suffix":""},{"id":281834525,"identity":"814dd49f-af6d-40e9-a984-b3d8b9ec7f8c","order_by":21,"name":"Mario Südholt","email":"","orcid":"","institution":"Nantes University","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Südholt","suffix":""},{"id":281834526,"identity":"a16d0697-8331-4e3a-8a58-d08e3bc5dc29","order_by":22,"name":"Adrien Tissot","email":"","orcid":"","institution":"Nantes University","correspondingAuthor":false,"prefix":"","firstName":"Adrien","middleName":"","lastName":"Tissot","suffix":""}],"badges":[],"createdAt":"2024-02-29 11:35:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3999519/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3999519/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41431-024-01683-y","type":"published","date":"2024-08-20T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53266687,"identity":"509d7b03-dadc-4e53-82f0-79f2433c124b","added_by":"auto","created_at":"2024-03-22 15:44:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165175,"visible":true,"origin":"","legend":"\u003cp\u003eBuilding the GenCOLT biobank and robust GWAS SNP data\u003c/p\u003e\n\u003cp\u003eA: Establishment of the GenCOLT DNA cohort.\u003c/p\u003e\n\u003cp\u003eInitially, a total of 784 individuals were selected from the COLT biobank for DNA extraction and GWAS genotyping. After the initial screening, five individuals were excluded due to missing or deteriorated DNA sample. Subsequently, during the Axiom quality control procedure, four individuals were excluded due to failed experiments. Finally, a total of 387 pairs (n=775 individuals), with DNA sample, accurate genomic and clinical data were included in the GenCOLT biobank.\u003c/p\u003e\n\u003cp\u003eB: Steps for GWAS genotyping data cleaning.\u003c/p\u003e\n\u003cp\u003eThe Axiom PMRA chip used for GWAS genotyping covers 902,560 SNPs. According to the manufacturer guidelines, 852,344 SNPs passed the primary technological quality controls. Upstream SNP imputation, we excluded SNPs with high level of missingness (\u0026gt;2%), with low frequency (\u0026lt;1%) and not respecting the HWE (p\u0026lt;10\u003csup\u003e-6\u003c/sup\u003e). Overall, GenCOLT contains 7.3 million high-quality SNP genotypes (DR or r\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.8) for 387 LT pairs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN.B. R, recipient; D, donor; GWAS, genome-wide association study; QC, quality control; SNP, single nucleotide polymorphism; MAF, minor allele frequency; HWE, Hardy-Weinberg equilibrium.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3999519/v1/ae0a654824d3099d59bbc91f.png"},{"id":53266686,"identity":"76bad4f3-e7db-45c6-bc57-4b06804dae30","added_by":"auto","created_at":"2024-03-22 15:44:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42723,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier lung graft survival curves according to the rejection phenotypes vs. the stable grafts in GenCOLT\u003c/p\u003e\n\u003cp\u003eThe ‘CLAD’ subgroup includes the 4 ISHLT consensus phenotypes: BOS, RAS, mixed, and undefined. The ‘other’ subgroup refers to other types of primary rejection, including infectious-induced rejection and azithromycin-responsive allograft dysfunction (ARAD) in the first month’s post-surgery.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN.B. BOS, bronchiolitis obliterans syndrome; RAS, restrictive allograft syndrome; ARAD, Azithromycin reversible allograft dysfunction; ISHLT, International Society for Heart and Lung Transplantation.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3999519/v1/671af8e0a515d2ec7a970482.png"},{"id":53266691,"identity":"d4018b56-f70b-456a-a89a-c4038c87b68c","added_by":"auto","created_at":"2024-03-22 15:44:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58310,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier lung graft curves according to the CLAD vs. stable phenotypes for COLT and GenCOLT\u003c/p\u003e\n\u003cp\u003eThe ‘CLAD’ subgroup includes the 4 ISHLT consensus phenotypes: BOS, RAS, mixed, and undefined.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN.B. BOS, bronchiolitis obliterans syndrome; RAS, restrictive allograft syndrome.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3999519/v1/669a94a5485ff74bca932e9d.png"},{"id":53267480,"identity":"1eeab027-a495-47f6-b3ba-1177f0d26dad","added_by":"auto","created_at":"2024-03-22 15:52:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":433432,"visible":true,"origin":"","legend":"\u003cp\u003ePCA projection of GenCOLT recipients (A) and donors (B) with the 1,000 Genomes Project reference individuals\u003c/p\u003e\n\u003cp\u003eUsing the GWAS SNP data, we projected GenCOLT individuals (represented by triangle) with the 1,000 Genomes Project individuals (represented by circles) from 5 large reference populations. The triangle color refers to the percent of European ancestry for GenCOLT individuals as defined with ADMIXTURE (with darker shades indicating higher European ancestry).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN.B. PCA, principal component analysis; AFR, African; AMR, American; EAS, East Asian; EUR, European; SAS, South Asian.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3999519/v1/f79023abd32846294359df78.png"},{"id":63997993,"identity":"6bf2c86d-4542-4513-b028-616a2969f93f","added_by":"auto","created_at":"2024-09-04 18:03:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1110010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3999519/v1/4dbdfcb3-8e4b-4ce1-9285-a5ff4d620333.pdf"},{"id":53266689,"identity":"1472cf9b-cb31-4062-9f9a-d889671ce053","added_by":"auto","created_at":"2024-03-22 15:44:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":300835,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3999519/v1/a09763ea43612e55fafb860d.docx"},{"id":53266690,"identity":"37d282fa-019b-48da-b508-60804711a85a","added_by":"auto","created_at":"2024-03-22 15:44:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10644,"visible":true,"origin":"","legend":"","description":"","filename":"Table1demographictablefordonorsGenCOLT.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3999519/v1/41a3530ab4452c71c2149a5a.xlsx"},{"id":53266692,"identity":"b6c831d9-0ce0-4423-ae71-a938197ecf3b","added_by":"auto","created_at":"2024-03-22 15:44:07","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11270,"visible":true,"origin":"","legend":"","description":"","filename":"Table2demographictableforrecipientsGenCOLT.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3999519/v1/a2ac49212acc982e5c983a23.xlsx"}],"financialInterests":"There is no duality of interest","formattedTitle":"Description and first insights on a large genomic biobank of lung transplantation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the international registry of the International Society of Heart and Lung Transplantation (ISHLT), the number of lung transplants (LT) performed each year worldwide has gradually increased over the past decades, from about 250 in 1990 to more than 4,000 today(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In France, 334 lung transplantations were performed in 2022 alone(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Despite improvements in surgical techniques(\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and immunosuppressive treatment management(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), the long-term outcome remains limited with a mean survival of 63% at 5 years post-transplantation in Europe(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The main limitation to long-term survival is the development of chronic lung allograft dysfunction (CLAD)(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), which is defined by a persistent decline (\u0026ge;\u0026thinsp;20%) of the FEV1 (forced expiratory volume in 1 second) value from baseline.\u003c/p\u003e \u003cp\u003eSeveral risk factors for CLAD have been suggested including alloimmune factors (cellular or antibody-mediated rejections) and non-alloimmune factors (primary graft dysfunction, pulmonary infection, airway pollution or gastro-esophageal reflux disease)(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Indubitably, individual factors, either recipient or donor related, play a role in the biological responses of lung injury and in the end, in the occurrence of CLAD(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). To date, no extensive genetic research has been conducted to explore the potential influence of genetic factors on chronic lung allograft dysfunction. In contrast, several genomic initiatives have been conducted in kidney transplantation(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and confirmed the importance of \u003cem\u003eHLA\u003c/em\u003e mismatches, as well as an emerging role for non-HLA factors and mismatches. In LT, the investigation of \u003cem\u003eHLA\u003c/em\u003e mismatches has yielded conflicting results(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), but more recent data suggests a link between \u003cem\u003eHLA\u003c/em\u003e mismatches and an increased risk for bronchiolitis obliterans syndrome (BOS)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and CLAD(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Given this knowledge gap, we believe it will be highly valuable to develop large-scale genetic biobanks and genomic analyses to identify associations between genotypes and LT-related outcomes.\u003c/p\u003e \u003cp\u003eHere, we describe the clinical and genetic characteristics of GenCOLT which gathers DNA samples, GWAS (genome-wide association study) data and clinical records for 392 donor-recipient pairs (n\u0026thinsp;=\u0026thinsp;784 individuals) from 11 French and Belgian clinical centers.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eDescription of the COLT clinical database\u003c/p\u003e \u003cp\u003eGenCOLT was built onto the Cohort in Lung Transplantation (COLT \u0026ndash; NCT00980967), whose main focus was the discovery of CLAD risk factors(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The ethics committee (\u003cem\u003eComit\u0026eacute; de Protection des Personnes\u003c/em\u003e, 2009-A00036-51) approved the study and all participants provided a written informed consent (CNIL French data protection authority, #911142). COLT was a prospective study, which included patients from twelve clinical centers between September 2009 to December 2018: CHU Nantes, Hospices Civils de Lyon, Assistance Publique-H\u0026ocirc;pitaux de Marseille, H\u0026ocirc;pital Foch (Paris), H\u0026ocirc;pital Europ\u0026eacute;en Georges Pompidou (Paris), CHU Grenoble, CHRU Strasbourg, CHU Bordeaux, H\u0026ocirc;pital Bichat (Paris), H\u0026ocirc;pital Marie Lannelongue (Paris area) and CHU Toulouse from France, as well as the Erasme Hospital (Brussels, Belgium). COLT comprises a total of 1,413 transplanted patients from whom clinical data and biological samples were collected. The clinical records and follow-up data from each participating center are centralized in a secured online database coordinated by the Nantes University Hospital. According to protocol, follow-up visits were conducted at 1-month and 6-month after transplantation, and then every 6 months for a period of up to 5 years for biological sample (blood) collection, and up to 10 years for clinical data. Clinical follow-up data include functional data such as allograft dysfunction, infection, and pulmonary function test. In addition, every relevant clinical event occurring and all pulmonary function tests performed between two protocol visits (\u003cem\u003ee.g.\u003c/em\u003e infection, acute cellular rejection, hospital visit) were recorded in the database and considered by the adjudication committee which includes respiratory physicians from at least five different centers.\u003c/p\u003e \u003cp\u003eClinical phenotypes\u003c/p\u003e \u003cp\u003eAll COLT patients underwent individual phenotyping by an adjudication committee that gathered at least 5 investigator physicians from the different participating centres. Pulmonary function tests, relevant chest computed tomography and medical history, especially potential confounding factors, were reviewed for a collegial decision on phenotype initially based on the 2014 proposed classification and then on the 2019 ISHLT consensus report on CLAD(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Recipients were classified as follows: BOS, restrictive allograft syndrome (RAS), azithromycin responsive allograft dysfunction (ARAD), stable and other (death within 3 months after transplantation, death without CLAD, insufficient data to conclude or confounding factors).\u003c/p\u003e \u003cp\u003eDescription of the GenCOLT genetic data\u003c/p\u003e \u003cp\u003eDNA collection and GWAS genotyping\u003c/p\u003e \u003cp\u003eGenCOLT is a DNA biobank that was established as an extension of COLT. At this stage, GenCOLT has gathered DNA samples from 392 pairs of LT donors and recipients (n\u0026thinsp;=\u0026thinsp;784 individuals) across 12 centers. We included all adult patients (age\u0026thinsp;\u0026ge;\u0026thinsp;18 years-old) with a survival over 3 months post-transplantation, for whom consent for genetic investigations and a DNA sample were available for both the donor and the recipient. The collection of samples from deceased donors for scientific research has been authorized by the \u003cem\u003eAgence de Biom\u0026eacute;decine\u003c/em\u003e (PFS09-003). A protocol has been put in place, which includes researching whether or not the donor is opposed to the use of his organs or body parts for research and information for relatives. The GenCOLT cohort is located and managed by the \u003cem\u003eCentre des Ressources Biologiques\u003c/em\u003e at the Nantes University Hospital.\u003c/p\u003e \u003cp\u003eEach DNA sample was assessed for volume, concentration, purity (260/230 and 260/280 absorbance ratios) and degradation by analyzing migration on an agarose gel. Following the manufacturer recommendations, the DNA samples were normalized to a volume of 20\u0026micro;L and a concentration of 10ng/\u0026micro;L. They were randomized based on sex and donor/recipient status in 96-well plates to minimize batch effects. Subsequently, the DNA samples underwent genotyping using the Axiom PMRA (Precision Medicine Research Array) chips (ThermoFisher, Waltham MA, USA), which cover 902,560 genetic variants (or SNPs, single nucleotide polymorphisms) including those found in the \u003cem\u003eHLA\u003c/em\u003e and \u003cem\u003eKIR\u003c/em\u003e polymorphic genomic regions, as well as other relevant genes for research in cancer and immunology. We followed the Axiom 2.0 Thermofisher standardized protocols and guidelines during the genotyping process.\u003c/p\u003e \u003cp\u003eData processing and imputations\u003c/p\u003e \u003cp\u003eTo ensure the reliability and quality of the genomic data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), we implemented several essential quality control (QC) steps. First, we performed the technological QC using AxAs (Axiom Analysis Suite) only to retain high-quality individuals, plates, and genetic variants. For individuals, a DishQC\u0026thinsp;\u0026gt;\u0026thinsp;0.82 ensured the proper separation of AT and GC fluorescence signals from noise in nonpolymorphic test probes, and the sample proportion with an assigned genotype (or call rate) was set to \u0026gt;\u0026thinsp;97%. Similarly, we applied a call rate of \u0026gt;\u0026thinsp;98.5% and \u0026gt;\u0026thinsp;95% for plates and genetic variants, respectively. In addition, we evaluated for each SNP the genotype cluster quality assignment (Fisher\u0026rsquo;s linear discrimination\u0026thinsp;\u0026gt;\u0026thinsp;3.6) and the clear distinction between homozygous and heterozygous clusters (heterozygosity\u0026thinsp;\u0026gt;\u0026thinsp;95%). A total of 852,344 SNPs and 387 pairs (n\u0026thinsp;=\u0026thinsp;775 individuals) passed this technological QC process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe performed additional QC with PLINK(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) to prevent genotyping errors. We checked individuals with missingness\u0026thinsp;\u0026gt;\u0026thinsp;2% and evaluated the relatedness between samples; however, no individuals were excluded at this stage. For SNPs, we excluded those with missingness\u0026thinsp;\u0026gt;\u0026thinsp;2% and deviations from the Hardy-Weinberg equilibrium (p\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). To address missing data, we employed imputation methods using the TOPMED(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) tool and TOPMED reference panel for SNP imputation. For \u003cem\u003eHLA\u003c/em\u003e imputation, we employed HIBAG(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) along with a global reference panel (multiethnic samples from 1,000 Genomes Project)(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In both cases, we only retained SNPs and HLA alleles with high imputation quality (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.8).\u003c/p\u003e \u003cp\u003eFinally, we carefully compared the imputed sex and \u003cem\u003eHLA\u003c/em\u003e one-field alleles with sex and \u003cem\u003eHLA\u003c/em\u003e allele information from clinical records to prevent potential errors of sample management during genotyping. As a result, GenCOLT collected robust high-quality imputed GWAS data for 7,337,433 SNPs and including 387 pairs (n\u0026thinsp;=\u0026thinsp;775 individuals).\u003c/p\u003e \u003cp\u003eGenetic ancestry\u003c/p\u003e \u003cp\u003eTo further describe GenCOLT, we assessed donors and recipients genetic ancestry using ADMIXTURE(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and principal component analyses (PCA) from GWAS SNPs with a minor allele frequency (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;1%. By comparing GenCOLT with the diverse 1,000 Genomes Project(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) populations (n\u0026thinsp;=\u0026thinsp;2,504 individuals from African (AFR), American (AMR), East Asian (EAS), European (EUR), and South Asian (SAS) reference populations), we aimed at detecting potential population stratification and at capturing ancestry-related variability in GenCOLT. ADMIXTURE defined genetic ancestry percentages per individual with a detailed breakdown within the five major ancestral groups. Each donor and recipient were attributed to an ancestry group when the ancestry percentage was \u0026ge;\u0026thinsp;80%. When their ancestry did not meet the criterion of at least 80% contribution from any of the five major ancestral populations, the individuals were classified as admixed.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and the min-max range for continuous variables, and as percentage for categorical variables. Difference among groups was tested using one-way ANOVA and chi-square tests for categorical variables. Kaplan-Meier analysis was performed to estimate the 5-year and 10-year survival after LT, and the differences in survival rate were compared using a log-rank test. All the analyses were performed in R 4.2.1. Two-sided p-values were considered significant for p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 to account for multiple testing, and nominally significant for p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eGenCOLT demographics and clinical characteristics\u003c/p\u003e \u003cp\u003eGenCOLT incorporates clinical data related to the donor (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e) and the recipient (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). Women represented 43.9% donors and 44.9% recipients. The mean age was 48 years-old and the BMI was 21.8 (kg/m\u0026sup2;) for recipients. The primary cause of death for donors was stroke with 53.6%. The main underlying disease leading to LT was chronic obstructive pulmonary disease (COPD) (44.6%), followed by cystic fibrosis (21.4%), interstitial lung disease (16.3%) and pulmonary hypertension (4.2%).\u003c/p\u003e \u003cp\u003eWe compared all clinical variables between the GenCOLT and COLT groups (\u003cb\u003eTables S1-S3\u003c/b\u003e). Importantly, only the patients\u0026rsquo; distribution between clinical centers and the preservation fluid usage (Perfadex \u003cem\u003evs.\u003c/em\u003e Celsior) were statistically different between both groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), directly reflecting the accessibility to DNA samples within centers and difference of preservation fluid used by center. We will consider these features as covariates in future GWAS analyses. Additional variables, such as immunosuppressive or induction treatment, corticosteroids therapy and lung reduction, were nominally significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but did not significantly differ between COLT and GenCOLT when accounting for multiple testing. Overall, the GenCOLT recipients do not differ from the rest of the COLT cohort in terms of clinical data, pre, per and post transplantation. Additionally, we examined the four major underlying causes of LT, as different etiologies could differentially impact FEV1 spirometry values, and no significant difference was observed (\u003cb\u003eTables S4-S7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eSurvival Analysis\u003c/p\u003e \u003cp\u003eWe described the post-transplantation survival in GenCOLT by stratifying according to clinical phenotypes: stable, CLAD, and other (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The median survival for CLAD and the other group was 62 and 26 months, respectively. For stable recipients, the survival rate was 97.4% at 5 years, and it declined to 60.2% at 10 years. For CLAD recipients, the survival rate was 57.3% at 5 years and it fell to 16.2% at 10 years. Finally, for the other group the survival rate was 34.4% at 5 years and it dropped to 22.8% at 10 years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn a subsequent analysis, we compared the survival rates between COLT and GenCOLT patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The median survival for CLAD transplant patients from COLT and GenCOLT was 66 and 62 months, respectively. For stable COLT patients, the survival rate was 97.5% at 5 years and it declined to 70.5% at 10 years. For COLT patients with CLAD, the survival rate was 57.4% at 5 years and it dropped to 16.5% at 10 years. In both cohorts, the survival was significantly shorter for CLAD patients than for stable patients (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). There was no difference in lung allograft survival between COLT and GenCOLT for stable (p\u0026thinsp;=\u0026thinsp;0.3) and CLAD (p\u0026thinsp;=\u0026thinsp;0.6) patients. Overall, our analysis demonstrated similar survival rates for transplanted patients from COLT and GenCOLT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData cleaning for GWAS\u003c/p\u003e \u003cp\u003eRobustness and quality of GenCOLT genetic data\u003c/p\u003e \u003cp\u003eWe assessed the genotyping robustness and quality of our genetic data throughout the whole GenCOLT building process (DNA extraction, sample handling, normalization and genotyping, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Importantly, we compared genetically inferred sex and \u003cem\u003eHLA\u003c/em\u003e alleles with information from medical records and we did not identify any discrepancy, confirming the proper sample management.\u003c/p\u003e \u003cp\u003eAfter careful standard quality controls for SNPs, we imputed 7,337,433 SNPs with accurate genotypes (r\u0026sup2;\u0026gt;0.8) from 400K genotyped SNPs (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and S1). Before imputation, we observed an enrichment in rare variants in accordance with the PMRA design. After imputation, the number of SNPs with MAF\u0026thinsp;\u0026gt;\u0026thinsp;2% has significantly increased across the whole MAF spectrum. All these high-quality genetic variants are available for subsequent association testing.\u003c/p\u003e \u003cp\u003eWe compared \u003cem\u003eHLA\u003c/em\u003e allele information available before and after imputation (\u003cb\u003eFigure S2\u003c/b\u003e). We observed a considerable gain of allele calling for the \u003cem\u003eHLA-C\u003c/em\u003e gene (from 1.1% before imputation to 100% after imputation). Importantly, \u003cem\u003eHLA\u003c/em\u003e imputation preserved the distribution of allelic diversity while increasing the available \u003cem\u003eHLA\u003c/em\u003e information. As expected, the \u003cem\u003eHLA-B\u003c/em\u003e locus exhibits a higher level of polymorphism(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) compared to other \u003cem\u003eHLA\u003c/em\u003e genes such as \u003cem\u003eHLA\u003c/em\u003e-\u003cem\u003eDQB1\u003c/em\u003e for example.\u003c/p\u003e \u003cp\u003eGenetic ancestry\u003c/p\u003e \u003cp\u003eWe conducted a PCA to examine genetic ancestry and population stratification within our genetic dataset in comparison with the reference populations from the 1,000 Genomes Project(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This analysis revealed a distinct separation of individuals from the 1,000 Genomes Project into five clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) representing the five major reference ancestral populations. The GenCOLT samples were spread across all the reference populations with an important concentration among individuals of European ancestry. To go further, we defined genetic ancestry percentages within the five major ancestral groups per individual. When focusing on donor-recipient pairs, our analysis revealed that the majority of pairs (90.8%) shared European ancestry (\u003cb\u003eFigure S3\u003c/b\u003e). In addition, 7.6% pairs were composed of a European donor and an admixed recipient, and four pairs (0.5%) were composed of a European recipient and an East Asian donor. GenCOLT also gathered three pairs with at least one individual (donor or recipient) with admixed ancestry and five pairs with Asian ancestry. The remaining 2% pairs included donors and recipients of non-European ancestry.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe GenCOLT biobank represents a unique and valuable resource in Europe for investigating genetic and immunogenetic factors in LT. It was established upon COLT, which has been a comprehensive source of clinical data on LT since 2009. Over a span of 10 years, we have created a robust and consistent multicentric cohort that accurately reflects the clinical practices and medical records of LT in France and Belgium. Of the hundreds of available clinical variables, only two are significantly different between GenCOLT and COLT (center and preserving liquid), which is why we consider GenCOLT to be a smaller but accurate representation of COLT, and therefore a snapshot of LT clinical practice between 2009 and 2018.\u003c/p\u003e \u003cp\u003eTo ensure data homogeneity and quality, the GenCOLT genomic data were generated using a standardized experimental pipeline, including the same genotyping chips, technological protocols and analytical pipelines. Rigorous quality controls were implemented to guarantee the highest data quality. Additional checks were conducted to verify sex and \u003cem\u003eHLA\u003c/em\u003e matching between genetically inferred information and medical records, minimizing the risk of sample mishandling. This was particularly important considering the retrieval of samples that were stored for more than 10 years, which increased the potential for human errors.\u003c/p\u003e \u003cp\u003eFurthermore, SNP to \u003cem\u003eHLA\u003c/em\u003e allele imputation was also performed to enhance the immunogenetic genotypes within the GenCOLT dataset. This imputation process addressed the issue of incomplete \u003cem\u003eHLA\u003c/em\u003e data in the COLT clinical records, particularly for \u003cem\u003eHLA-C\u003c/em\u003e, where 98.9% data was missing in both recipients and donors. By retrospectively filling these missing gaps, SNP to \u003cem\u003eHLA\u003c/em\u003e imputation provided consistent \u003cem\u003eHLA\u003c/em\u003e annotations for future immunogenetic association testing.\u003c/p\u003e \u003cp\u003eAdditionally, by projecting SNP genetic data from GenCOLT and individuals of the 1,000 Genomes Project, we were able to assess the genetic ancestry of donors and recipients without directly collecting ancestry background information, as it is prohibited in France and Belgium. This approach helped prevent population stratification and provided insights into the genetic ancestry within the GenCOLT dataset. Overall, the establishment of GenCOLT involved meticulous processes to ensure data conformity, sample traceability, and comprehensive genetic and immunogenetic information. It represents a valuable resource for studying LT outcomes and its associated molecular factors in a standardized and controlled manner.\u003c/p\u003e \u003cp\u003eHowever, the GenCOLT cohort has some limits. First of all, the sample size is limited compared with studies on other solid organ transplantation such as kidney transplantation(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), which might limit our statistical power for discovery in our future GWAS analyses. Similarly, GenCOLT is essentially composed of European individuals, and as such, we will only be able to draw conclusions on the genetic risk factors for lung graft rejection at the European level. To address this limitation, we are planning future collaborations with partners from the International Genetics and Translational Research in Transplantation Network (iGeneTRAiN)(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), an international collaborative effort and the largest genetic initiative focusing on better understanding transplant rejections and complications to date. Meta-analyses and additional patient recruitment will increase the statistical power for discoveries, while potentially increasing the genetic diversity of the study population.\u003c/p\u003e \u003cp\u003eIn conclusion, GenCOLT stands as one of the most extensive LT DNA cohorts worldwide, encompassing 387 donor-recipient pairs (n\u0026thinsp;=\u0026thinsp;775 individuals). This comprehensive cohort not only comprises clinical data but also genetic information for both donors and recipients, making it an invaluable resource for investigating the underlying mechanisms of CLAD, as well as other LT outcomes and complications. The genetic profiles of the recipients are likely to significantly impact a wide range of transplantation outcomes, from their susceptibility to experiencing rejection, response to medications (pharmacogenetics), to the development of various medical conditions linked to the prolonged use of immunosuppressive treatments(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Furthermore, the genetic makeup of the organ donors may also serve as a predictor for the function of the transplanted organ. Finally, the genetic compatibilities between the donor and recipient could play a crucial role in influencing both the likelihood of rejection and the long-term survival of transplanted organs. The identification of \u003cem\u003eHLA\u003c/em\u003e and non-\u003cem\u003eHLA\u003c/em\u003e genetic associations within GenCOLT would significantly enhance our understanding of CLAD and other LT outcomes, and may uncover potential therapeutic targets. Moreover, the identification of predictive biomarkers in GenCOLT would help stratifying individual risk at an early stage, before the onset of symptoms, ultimately improving personalized and predictive medicine in the field of lung transplantation. The wealth of information within GenCOLT therefore opens up new avenues for research and has the potential to advance our knowledge and management of lung transplant patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARAD:\u0026nbsp;azithromycin responsive allograft dysfunction\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u003c/p\u003e\n\u003cp\u003eBOS: bronchiolitis obliterans syndrome\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCLAD: chronic lung allograft dysfunction\u003c/p\u003e\n\u003cp\u003eCOLT: cohort in lung transplantation\u003c/p\u003e\n\u003cp\u003eCOPD: chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eDNA: deoxyribonucleic acid\u003c/p\u003e\n\u003cp\u003eGWAS:\u0026nbsp;genome-wide association study\u003c/p\u003e\n\u003cp\u003eHLA: human leukocyte antigen\u003c/p\u003e\n\u003cp\u003eLT: lung transplantation\u003c/p\u003e\n\u003cp\u003eMAF: minor allele frequency\u003c/p\u003e\n\u003cp\u003ePCA: principal component\u0026nbsp;analysis\u003c/p\u003e\n\u003cp\u003ePMRA:\u0026nbsp;precision medicine research array\u003c/p\u003e\n\u003cp\u003eQC: quality control\u003c/p\u003e\n\u003cp\u003eRAS: restrictive allograft syndrome\u003c/p\u003e\n\u003cp\u003eSNP: single nucleotide polymorphism\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe GenCOLT data are available from our CR2TI team. Data and summary statistics are available upon request following approval from the GenCOLT 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 GenCOLT data or through enriching the existing cohort with new patients. Potential collaborators are invited to contact the primary investigator Sophie Limou: [email protected].\u003c/p\u003e\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank the participating patients and their families, whose trust, support, and cooperation were essential for the collection of the data used in this study. We thank the COLT Consortium for their organizational achievement and the LUNG innovation (LUNG O2) cluster. The authors thank everyone who helped in the design, collection, genotyping experiments, data cleaning and analyses. We are grateful to the Genomics Core Facility GenoA, member of Biogenouest and France Genomique (ANR-11-INBS-0013), for the use of its resources and technical support. Finally, we thank the biological resource center for biobanking (CHU Nantes, H\u0026ocirc;tel Dieu,\u0026nbsp;\u003cem\u003eCentre des ressources biologique\u003c/em\u003es (CRB, BRIF: BB-0033-00040)).\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eData curation and statistical analysis, S.B.; Methodology and software, S.L., M.M., O.R., V.M., N.D., A.D. and N.V.; Writing original draft, S.B.; Writing review and editing, S.B., S.L., A.T. and M.S.; Providing samples and monitoring patients, B.R., B.C., X.D., L.F., J.L., A.R., T.V., C.K., JF.M., M.S., V.B., A.M.; Provided genomics expertise, P.A.G. All authors have read and agreed to the published version of the manuscript.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe Cohort in Lung Transplantation was funded by\u0026nbsp;Vaincre La Mucoviscidose, l\u0026rsquo;Association Gr\u0026eacute;gory Lemarchal, the French Research Ministry (Agence Nationale de la Recherche grant), the French Government (Programme Hospitalier de Recherche Clinique \u0026ndash;\u0026nbsp;DGOS 20-11), the European Union (FP7 collaborative project HEALTH.2012.2.1.2-1 \u0026ndash; grant agreement number: 305457) and Nantes Metropole.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study sponsor(s) or funder(s) had no role in the study design, in the collection, analysis, and interpretation of data, in the report writing and, in the decision, to submit the article for publication. Researchers were independent from funders and all authors, external and internal, had full access to all data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis.\u0026nbsp;S. Brocard benefits from the support of IMT Atlantique, Centrale Nantes and Institut de Recherche en Sant\u0026eacute; Respiratoire des Pays de la Loire for his PhD research.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eThis study protocol was approved by ethics committee (\u003cem\u003eComit\u0026eacute; de Protection des Personnes\u003c/em\u003e, 2009-A00036-51) the study and all participants provided a written informed consent (CNIL French data protection authority, #911142).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interest\u003c/p\u003e\n\u003cp\u003ePA Gourraud is the founder of Methodomics (2008) and a co-founder of Big data Sant\u0026eacute; (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, Lek, Methodomics, Merck, M\u0026eacute;rieux, Octopize, Sanofi-Genzyme). 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. The other authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChambers DC, Yusen RD, Cherikh WS, Goldfarb SB, Kucheryavaya AY, Khusch K, et al. The Registry of the International Society for Heart and Lung Transplantation: Thirty-fourth Adult Lung And Heart-Lung Transplantation Report\u0026mdash;2017; Focus Theme: Allograft ischemic time. 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Normothermic Ex Vivo Lung Perfusion in Clinical Lung Transplantation. N Engl J Med. 2011 Apr 14;364(15):1431\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eSommer W, K\u0026uuml;hn C, Tudorache I, Avsar M, Gottlieb J, Boethig D, et al. Extended criteria donor lungs and clinical outcome: Results of an alternative allocation algorithm. J Heart Lung Transplant. 2013 Nov 1;32(11):1065\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eYoung KA, Dilling DF. The Future of Lung Transplantation. Chest. 2019 Mar;155(3):465\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eHofstetter E, Boerner G. Survival After Lung Transplantation in Europe - Clad as Major Cause of Death. J Heart Lung Transplant. 2022 Apr 1;41(4, Supplement):S286. \u003c/li\u003e\n\u003cli\u003eVerleden GM, Raghu G, Meyer KC, Glanville AR, Corris P. A new classification system for chronic lung allograft dysfunction. 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Curr Opin Organ Transplant. 2020 Feb;25(1):35\u0026ndash;41. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-human-genetics","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejhg","sideBox":"Learn more about [European Journal of Human Genetics](http://www.nature.com/ejhg/)","snPcode":"41431","submissionUrl":"https://mts-ejhg.nature.com/cgi-bin/main.plex","title":"European Journal of Human Genetics","twitterHandle":"@ejhg_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lung Transplantation, Donor-recipient pairs, Cohort, DNA biocollection, GWAS Genomics, HLA, CLAD, chronic lung allograft dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-3999519/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3999519/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe main limitation to long-term lung transplant (LT) survival is chronic lung allograft dysfunction (CLAD), which leads to irreversible lung damage and significant mortality. Individual factors can impact CLAD, but no large genetic investigation has been conducted to date. We established the multicentric Genetic COhort in Lung Transplantation (GenCOLT) biobank upon the rich and homogeneous COLT cohort. GenCOLT collected DNA, high-quality GWAS (genome-wide association study) genotyping and robust \u003cem\u003eHLA\u003c/em\u003e data for donors and recipients to supplement COLT clinical data. GenCOLT closely mirrors the global COLT cohort without significant variations in variables like demographics, initial disease and survival rates (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The GenCOLT donors were 45 years-old on average, 44% women, and primarily died of stroke (54%). The recipients were 48 years-old at transplantation on average, 45% women, and the main underlying disease was chronic obstructive pulmonary disease (45%). The mean follow-up time was 67 months and survival at 5 years was 57.3% for the CLAD subgroup and 97.4% for the stable subgroup. After stringent quality controls, GenCOLT gathered more than 7.3\u0026nbsp;million SNP and HLA genotypes for 387 LT pairs, including 91% pairs composed of donor and recipient of European ancestry. Overall, GenCOLT is an accurate snapshot of LT clinical practice in France and Belgium between 2009 and 2018. It currently represents one of the largest genetic biobanks dedicated to LT with data available simultaneously for donors and recipients. This unique cohort will empower to run comprehensive GWAS investigations of CLAD and other LT outcomes.\u003c/p\u003e","manuscriptTitle":"Description and first insights on a large genomic biobank of lung transplantation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 15:44:01","doi":"10.21203/rs.3.rs-3999519/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-07-09T14:44:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-07-01T15:01:34+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-12T13:48:26+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-04-12T04:57:54+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-04-01T23:59:16+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-03-22T10:44:58+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-03-20T13:01:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-29T14:24:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-29T11:33:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Human Genetics","date":"2024-02-29T11:33:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-human-genetics","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejhg","sideBox":"Learn more about [European Journal of Human Genetics](http://www.nature.com/ejhg/)","snPcode":"41431","submissionUrl":"https://mts-ejhg.nature.com/cgi-bin/main.plex","title":"European Journal of Human Genetics","twitterHandle":"@ejhg_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e5d97156-50f8-45b3-bc9d-3e212e4f8e54","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-04T17:10:23+00:00","versionOfRecord":{"articleIdentity":"rs-3999519","link":"https://doi.org/10.1038/s41431-024-01683-y","journal":{"identity":"european-journal-of-human-genetics","isVorOnly":false,"title":"European Journal of Human Genetics"},"publishedOn":"2024-08-20 04:00:00","publishedOnDateReadable":"August 20th, 2024"},"versionCreatedAt":"2024-03-22 15:44:01","video":"","vorDoi":"10.1038/s41431-024-01683-y","vorDoiUrl":"https://doi.org/10.1038/s41431-024-01683-y","workflowStages":[]},"version":"v1","identity":"rs-3999519","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3999519","identity":"rs-3999519","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}