Recent advances in genomics, including genomewide association studies (GWAS), second-generation sequencing (SGS), and their application within appropriately powered and carefully phenotyped studies, have yielded meaningful insights into the understanding of the molecular basis of a multitude of common and rare diseases.1-3 4 5-7 7 8
Association of transplant outcomes and polymorphisms in HLA and natural killer (NK) cell immunoglobulin-like receptor (KIR) regions are well established9-11 HLA Class I molecules are recognized to act as ligands for KIR on NK cells. The KIR plays essential roles in regulating the ability of NK cells to sense and respond to HLA Class I surface expression, which have been shown to be important in allorecognition.12 KIR haplotypes have been identified, and these are unevenly distributed across human populations in a manner similar to HLA .13 14
Next to the identification of immunological factors, genomic studies focusing on a priori gene regions of pharmacological relevance have led to the discovery of polymorphisms underpinning variance in trough blood concentrations of immunosuppressant therapies (ISTs)15-17 18
Although acute rejection (AR), graft survival, and the other posttransplant outcomes have known genetic underpinnings, they are also heavily influenced by nongenetic factors, including ischemia reperfusion injury, recipient waiting list time, noncompliance, and donor organ quality which limit the effect any given genetic variant will have on the identified phenotype. However, such covariates can be integrated into analyses and risk models. The transplant setting could greatly benefit from GWAS and SGS studies for a variety of reasons including discovery of additional HLA and non-HLA D-R genomic incompatibilities that may underpin rejection and insights gained into primary disease recurrence and important comorbidities, such as new onset of diabetes after transplant (NODAT). This is especially important as the majority of recipients require exposure to potent ISTs for the remainder of their lives and as such the identification of gene-drug interactions and development of strategies for avoiding harmful interactions in susceptible individuals is significant. Because transplant phenotypes, such as AR states, are highly complex traits with multifactorial components at the donor and recipient physiological and genomic level, as well as environmental and treatment settings, scientific advances are challenging. Such phenotyping issues further compound GWAS power constraints for discovery (reviewed by Stegall et al19 20,21
The International Genomics & Translational Research in Transplantation Network (iGeneTRAiN) was established to bring together genomic data and well-curated heart, kidney, liver, and lung transplant phenotype data sets. Genome-wide genotype data are available from more than 28000 individuals to date, of whom more than 5000 are of non-European ancestry and afford ample statistical power for meta-analyses of a number of key transplant phenotypes. The initial aims of iGeneTRAiN include the discovery and validation of genomic underpinnings of rejection and Tx complications, with the ultimate goal to translate this information into clinical applications, such as patient-specific IST selection and dosing, better genomic compatibility matching of D-R pairs and improved rejection monitoring. In this paper we present: the study characteristics of the iGeneTRAiN cohorts with existing genotype data; the strategies for harmonization of proposed GWAS using imputation; prioritization and harmonization of the initial phenotypes of interest and analytical strategies for association studies and functional annotation of findings from these studies. iGeneTRAiN provides a required standard framework for the curation of phenotypes, imputation of genotypes and analytical strategies across studies that will yield increased statistical power through aggregation of large-scale data sets compared to traditional smaller single independent studies.
METHODS
iGeneTRAiN Studies
The respective iGeneTRAiN study designs and characteristics for subjects recruited with existing GWAS data are outlined in Table 1 . In total, there are over 16494 recipients with 11669 donors across the 4 solid organs. The existing GWAS include participants with a wide geographic representation across the United States, The Netherlands, United Kingdom, Ireland, Spain, and Australia. The majority of transplant recipients in the studies are adults with the exception of 2 cohorts, which comprise approximately 1060 pediatric subjects. The study designs are primarily single or multisite prospective cohorts, and although a small number are retrospective in recruitment approach, most are continuing to accrue longitudinal transplant phenotypes and outcome events. Approximately 18900 samples were subjected to GWAS using a transplant-specific genomewide genotyping array (described below) and approximately 8600 additional samples genotyped using conventional GWAS arrays. The iGeneTRAiN studies range in size from less than 100 D-R pairs to several thousands of recipients. All kidney studies and 2 liver studies have varied proportions of living donor versus deceased donors. Of the 16494 recipients, approximately 81.3%, 12.1%, 1.7%, and 4% are of are of European, African, Asian, and Hispanic ancestry, respectively, with the remainder classified as “other,” and approximately 62% of D-R pairs have conventional 2- or 4-digit HLA typing available. Table S1 (SDC , https://links.lww.com/TP/B191
TABLE 1: Descriptive characteristics of iGeneTRAiN studies contributing genomewide association study data
Phenotypes and Disease Endpoints
Collation and Harmonization of Phenotypes
Four primary phenotypes have been selected for ease of harmonization and of greatest clinical impact: (1) graft survival, (2) AR, (3) NODAT, and (4) delayed graft function (DGF). Length of allograft survival after Tx is arguably the most important clinical outcome, and although many complex factors underpin it, graft survival is a hard outcome which can be unequivocally adjudicated. Acute rejection is an important cause of morbidity and mortality, irrespective of organ, in transplant recipients and has important subtypes: antibody-mediated, cellular, and mixed rejection. Patients who develop AR are at higher risk for chronic graft dysfunction development, which can progress to graft loss.34 35 36 37 38
The primary phenotypes described above were collected, arbitrated, and harmonized as follows:
(a) Graft survival is defined as the number of days of functioning organ (inclusive of retransplant metrics and return to dialysis for kidney recipients). We have also measured patient survival which we defined as the number of living days after Tx. Concordance was assessed in each site using internal data coordinating centers (DCC) and/or respective hospital records, and national registry databases.
(b) Acute rejection is defined as biopsy proven rejection (defined by Banff or other international criteria) that was clinically treated with standard IST/steroid regimes. Borderline rejection episodes, mixed, and cellular rejection were systematically recorded in most sites.
(c) NODAT is defined as a new diagnosis of diabetes that occurred after Tx and required continued antidiabetic pharmacotherapy. Where possible, NODAT was strictly defined as a new requirement for oral hypoglycemic agents or insulin for the management of hyperglycemia at a 6-month, 12-month or 2-year period after Tx.
(d) DGF, limited in the first iteration of the iGeneTRAiN program to the kidney cohorts, is defined as the requirement for dialysis within the first 7 days after Tx. The DGF analysis includes the genomics of chronic renal allograft rejection, transplant-LINES Genetics, Vanderbilt, UPenn and The Long-Term Deterioration of Kidney Allograft Function Study Genomics/GEN-03 studies, with incidence rates of 20%, 33%, 9%, 16%, and 9%, respectively.
Analytical Approaches
Association Studies
Our primary analyses will test for genotype differences between recipients and D-R pairs, who encounter the clinical outcomes described above. For graft loss/patient survival, NODAT, and AR, survival analyses will be performed using Cox regression with genotype as the primary exposure, whereas for AR (anytime), NODAT, and DGF, analyses will be performed using logistic regression with genotype as the primary exposure. All models will be adjusted for phenotype-specific covariates (see below). For D-R paired analyses, we will analyze: (1) quantified total number of HLA allele mismatches across the genome; (2) the number and distributions of polymorphisms (and amino acid) discrepancies between D-R pairs; (3) nonsynonymous single-nucleotide polymorphisms; and (4) LoFs, grouped by functional implication. Covariates that will be considered in the regression models include recipient age, sex, body mass index, year of transplantation, diabetes status, primary diagnosis, previous transplant, peak PRA, preemptive Tx, HLA-A, -B, -DR mismatches; donor age, sex, graft status (deceased/living), cause of death (for deceased donors), principal components of ancestry computed from available genotype data (to identify and correct for population substructure), transplant center, cytomegalovirus status, and ischemic time. Where collected for clinical use, PRA measurements will be analyzed for between cohort and between center variation, with the prospective collection made using the Luminex technology where possible. For all D-R pairs, we will compute an HLA -based mismatch score to reflect the genetic compatibility between donor and recipient, which we will test in all models for each D-R pair for association with clinical outcomes.
For the AR analysis, we will examine cause-specific rejection as a categorical variable (antibody-mediated, cellular, and mixed rejection) by organ type and as a collapsed (any rejection) variable, stratified by organ, as well as combined across all organs. We will also perform sensitivity analyses in which borderline rejection, positive protocol, or surveillance biopsies positive for AR and “for-cause” biopsies assessed as nonrejecting are analyzed separately as well as collectively. A separate sensitivity analysis will also be conducted with organ-specific AR assessed alone and subtypes of rejection to determine if any GWAS signals are evident in the combined “all AR types” data sets and/or subsets analyses.
HLA Imputation and Interaction Analyses
The HLA imputation is carried out using SNP2HLA which has been described in detail elsewhere.39
where Y represents a binary phenotype, for example, indicator of transplant rejection; δ i C i δ a δ b
. To improve the statistical power to detect such interactions, the proposed method uses a screening-testing approach inspired by recent gene x environment detection methods.40-42
Functional Variant Analyses
To identify variants that may be potentially pathogenic, including LoF variants, we will use the Loss of Function Transcript Effect Estimator (http://github.com/konradjk/loftee 43 44 45
Organization and Governance
Informed and written consent was obtained independently for each iGeneTRAiN study participant, with appropriate oversight and approvals from respective local institutional review boards/Research Ethics Committees to use either summary-level or anonymized individual-level data.
The iGeneTRAiN network selected 4 solid-organs for collation and harmonization of the appropriate covariates, intermediate phenotypes, and outcomes to an acceptable standardization quality amongst the working groups. Each iGeneTRAiN Phenotyping Committee has been charged with: (a) fully harmonizing the genotype and phenotype data sets across all studies; (b) validating the integrity of these data sets; and (c) annual updates for event accruals and phenotypic measurements (where available). A hematopoietic cell Tx working group is planned for the near future.
Four overarching working groups, whose interests span all solid organ phenotypes, were also formed: (a) HLA working group, (b) genomic and other omics groups (including genomic analysis of SGS/GWAS/other genetic data), (c) pharmacogenomics, and (d) a steering committee comprised of representatives from the working groups (see www.igenetrain.org Table 1 ; collaborates in manuscript preparation; and provides overall coordination and quality assurance, including coordination of the activities of the data monitoring, the Statistical Group and Phenotyping Committees, and other iGeneTRAiN solid-organ and working groups. In addition, DCC provides continued support of our website (http://www.igenetrain.org
RESULTS
Tx SNP Array
To maximize power to identify novel loci associated with transplant-related outcomes, we have designed a cost-effective genotyping array to facilitate genomic research studies among the Tx community. The “Tx Array” contains approximately 782,000 genetic variants, and the design was tailored to maximize precise and/or denser coverage in exonic, pharmacogenomic, hdCNV, LoF, MHC, and KIR , CVD/metabolic-related loci while still maintaining strong genomewide content that is compatible with conventional GWAS arrays. The genome-wide coverage was designed to allow accurate imputation of ungenotyped markers using sequencing-based reference panels, such as the 1000 genomes project. We have genotyped 85 HapMap samples (including 8 trios) and show that indeed the genotyping quality is high with a concordance of 99.6%, and ungenotyped SNPs can be imputed with an average accuracy of 96.2%. A more detailed description of the Tx Array can be found in a dedicated design article.46
Genotyping and Imputation
Of the 22 independent genotype data sets, 17 studies were primarily genotyped using the Tx Array (n = 18338 samples were genotyped in a single site). The remaining iGeneTRAiN studies were genotyped across 5 genotyping sites using conventional GWAS arrays (see Table 2 ).
TABLE 2: Existing iGeneTRAiN genomics data sets with HLA and new onset of diabetes after transplant and acute rejection outcomes
Genotype imputation is the process of inferring unobserved genotypes in a study sample based on the linkage disequilibrium and haplotypes observed in more densely genotyped reference samples of similar genetic background.47 Table 2 ) of the genomewide content using the following population-based reference data sets: The 1000 genomes project (1KGP), Genome of The Netherlands Consortium,48 49 46
Because of the highly complex linkage disequilibrium and polymorphic nature of HLA class I/II genes, we will use SNP2HLA, a tool specifically for imputation of HLA Class I/II.39
We performed a validation of the SNP2HLA imputation pipeline using clinical HLA typing data from European individuals within the Utrecht Heart Transplant Study, using the HLA panel from the TxSNP array platform.46 50 39 HLA-A , -B , -C , DRB1 , and DQB1 , respectively, as shown in Table S2 (SDC , https://links.lww.com/TP/B191 HLA Class I/II alleles are imputed to similar accuracies.39
Sample Size and Power
Figure 1 shows our power to detect main effect association signals across a 5-Mb region, such as the MHC. The statistical power calculations based on different MAFs for the first wave of transplant phenotypes of interest: graft survival, AR, NODAT, and DGF (in kidney only) under an additive model in recipients, only assuming approximately 20000 tests for the MHC region. For graft failure, we have very high power to detect a 25% minor allele frequency with a conservatively estimated effect size of 1.2-fold increased risk per allele. Figure 2 shows our power to detect main effect association signals across the entire human genome assuming a similar main effect association signals but using ∼500000 SNP tests. We also have excellent power to detect modest effect sizes across the four initial phenotypes of interest. This indicates the overall iGeneTRAiN data set is well-powered for common genetic variation with modest effect sizes as can be expected in the MHC and across the genome.
FIGURE 1: Statistical power calculations to detect main effects for the four main iGeneTRAiN phenotypes across the Major Histocompatibility Complex . Graft Survival in 4800 cases and 11,250 controls (top-left); AR in 3250 cases and 12,400 controls (bottom-left); NODAT in 1400 cases and 14,500 controls (top-right); DGF in 1,020 cases and 8,900 controls (kidney only) (bottom-right). The X-axes shows the OR effect size, and the Y-axes illustrate the statistical power to detect the main effects under different MAFs: 5%, 10%, 20%, and 30% shown in blue, red, green, and purple, respectively. The models are additive model in recipients and assume approximately 20,000 tests (Bonferroni correction 0.05). OR indicates odds ratio; MAFs, minor allele frequencies.
FIGURE 2: Genome-wide statistical power calculations to detect main effects for the four main iGeneTRAiN phenotypes. Graft Survival in 4800 cases and 11,250 controls (top-left); AR in 3250 cases and 12,400 controls (bottom-left); NODAT in 1400 cases and 14,500 controls (top-right); DGF in 1020 cases and 8900 controls (kidney only) (bottom-right). The X-axes shows the OR effect size, and the Y -axes illustrate the statistical power to detect the main effects under different MAFs: 5%, 10%, 20%, and 30% shown in blue, red, green, and purple, respectively. Significance is assessed at 5% level using Bonferroni correction, assuming 500,000 SNP tests
DISCUSSION
Discovery of genetic factors associated with graft loss and patient survival can generate fundamental insights into the biological and immunological factors underpinning posttransplant diseases and graft survival. Although barriers exist for harmonizing retrospective and prospective transplant study designs beyond single-site independent studies, international guidelines and hard outcomes allow for standardization of phenotypes, such as graft and patient survival, AR, NODAT, and DGF. In addition, where feasible, participating studies will share genotype and phenotype information between centers to fully maximize power to detect novel biological findings with the greater goal of conferring clinical impact. The collapsing of phenotypes across solid-organ types may not be ideal for organ-specific phenotypes such as kidney DGF, for other phenotypes, in particular NODAT and pharmacogenomic-related phenotypes, aggregation will collectively improve statistical power. The current absence of adequately powered data sets makes our consortium a unique force for GWAS efforts in Tx. The data sets in iGeneTRAiN constitute the largest genomic and phenotypic Tx data sets aggregated to date, with genomewide genotyping and phenotypes collected for more than 27500 subjects (with >11300 D-R pairs) recruited from 1993 to 2014.
The results from iGeneTRAiN will create clinical knowledge and applications in a number of specific areas: (1) Knowledge gained from genomewide as well as MHC/KIR variants, which may be highly penetrant, will facilitate novel insight into the biology of genomic incompatibility of D-R pairings. This may inform better patient care through improved risk assessment and monitoring of higher risk HLA D-R pairs, and/or more tailored IST. (2) More appropriate D-R matching before Tx may be possible based on MHC/HLA and KIR genotype combinations, or LoF compatibility in D-R pairs (eg, where 0 gene copies exist, ie, hdCNV, in the recipient and 1 or 2 gene copies exist in the respective donor), especially in the living donor transplant setting where multiple potential donors may exist. Consortia such as The Electronic Medical Records and Genomics (eMERGE) Network have very effective models for the development of genotype/phenotype algorithms from electronic medical records. The eMERGE is currently implementing dissemination of clinical genomic tests into electronic medical records and returning results back to physicians and patients in a clinical care setting,51 15 52,53
The potential limitations of our study are also common to many GWAS efforts, including complex phenotypes with nongenetic confounders as well as limited power to identify novel loci and variants which may have modest effect sizes. We aim to overcome these issues by setting up large, well-powered studies with manually curated and harmonized phenotypes. Nongenetic factors such as cold ischemic time and use of immunosuppressive drugs heavily influence clinical outcomes, and we will use all available modelling techniques to account for these covariates where available. However, evidence from many phenotypes has taught us that genetic polymorphisms with even modest independent effect sizes can uncover key biological mechanisms in solid organ Tx outcomes.
The iGeneTRAiN aggregation of existing GWAS efforts is essential to amass and harmonize large numbers of highly curated genotype and phenotype data sets,54
The concept of the iGeneTRAiN network structure as a model in Tx research is extremely powerful on a number of fronts. First, the ability to harmonize cohorts within the same solid-organ studies affords the ability to perform replication look-ups of putative genetic associations in independent cohorts, which is an absolute requirement for any large-scale genomic study. There are minimal transplant genomic studies in existence to date for such replication efforts. The ability to discover cross-organ as well as organ-specific associations using such large sample sizes is a unique strength of iGeneTRAiN. We wish to encourage other studies to join this consortium and use genome-wide genotyping arrays with well-phenotyped samples, which can be harmonized with the networks data sets. More appropriately phenotyped studies will increase the statistical power through meta-analysis to discover new loci underpinning phenotypes such as NODAT, immunosuppression-related outcomes, DGF and rejection. A major focus of the consortium is in-depth analyses of HLA and KIR polymorphisms with a range of transplant outcomes. A number of the iGeneTRAiN studies including A-WISH, CTOT-3, Deterioration of Kidney Allograft Function Genomics/Gen03, genomics of chronic renal allograft rejection have also performed functional biomarker studies using micro RNA, messenger RNA, proteomics, and/or metabolomics as follow-up surveillance studies to detect rejection and other complications of Tx.
In conclusion, iGeneTRAiN is a large consortium of solid-organ transplant studies that aims to lead genomic investigations into clinical outcomes after organ Tx. Our consortium seeks to significantly advance our understanding of the genetic architecture of transplant-related outcomes and in doing so, improve outcomes for these high-risk patients. The GWAS analyses will include recipient-only analyses, D-R mismatches with focus on LoF variants and nonsynonymous single-nucleotide polymorphisms, and interaction analyses between D-R pairs. Findings from this consortium are expected to provide unique insights into genomic incompatibility of D-R pairs, and fundamental incremental improvements in knowledge of the biology underpinning rejection and complications of Tx, with the ultimate goal of novel therapeutic targets, informing personalized prescribing of immunosuppressive therapies, and prolonging graft and patient survival.
ACKNOWLEDGMENTS
The authors thank the patients and their families for their participation in the genotyping studies. The authors are also very thankful for the contributions of the all of the study coordinators and clinicians from the respective studies that made collection of these DNA samples and phenotyping possible.
APPENDIX
The International Genetics & Translational Research in Transplantation Network (iGeneTRAiN):
Brendan J. Keating , DPhil , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA, Department of Pediatrics, Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, and The Children's Hospital of Philadelphia, Philadelphia, PA; Jessica van Setten, PhD , Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands; Pamala A. Jacobson, PharmD, FCCP , College of Pharmacy, University of Minnesota, Minneapolis, MN; Michael V. Holmes, MD, PhD , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Shefali S. Verma, MSc , Center for SystemsGenomics, The Pennsylvania State University, University Park, PA; Hareesh R. Chandrupatla, MSc , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA and The Children's Hospital of Philadelphia, Philadelphia, PA; Nikhil Nair, BSc , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA and The Children's Hospital of Philadelphia, Philadelphia, PA; Hui Gao, PhD , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA and The Children's Hospital of Philadelphia, Philadelphia, PA; Yun R. Li, PhD , The Children's Hospital of Philadelphia, Philadelphia, PA and Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Bao-Li Chang, PhD , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA and The Children's Hospital of Philadelphia, Philadelphia, PA; Chanel Wong, BSc , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA and The Children's Hospital of Philadelphia, Philadelphia, PA; Randy Phillips, MD, Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Brian S. Cole, PhD , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA and Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, PA; Eyas Mukhtar, BSc , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA and The Children's Hospital of Philadelphia, Philadelphia, PA; Weijia Zhang, PhD , Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Hongzhi Cao, PhD , BGI-Shenzhen, Shenzhen, China and Department of Biology, University of Copenhagen,Copenhagen, Denmark; Maede Mohebnasab, MD , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Cuiping Hou, MSc , The Children's Hospital of Philadelphia, Philadelphia, PA; Takesha Lee, BSc , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA and The Children's Hospital of Philadelphia, Philadelphia, PA; Laura Steel, BSc , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA and The Children's Hospital of Philadelphia, Philadelphia, PA; Oren Shaked, MD , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; James Garifallou, BSc , The Children's Hospital of Philadelphia, Philadelphia, PA; Michael B. Miller, PhD , Department of Psychology, University of Minnesota, Minneapolis, MN; Konrad J. Karczewski, PhD , Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA; Abdullah Akdere , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Ana Gonzalez, BSc , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Kelsey M. Lloyd, BA , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Daniel McGinn , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Zach Michaud , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Abigail Colasacco , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Monkol Lek, PhD , Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA and PrograminMedical and PopulationGenetics, Broad Institute of Harvard and MIT, Cambridge, MA; Yao Fu, BSc , Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT; and Mayur Pawashe, BSc , Molecular Biophysics and Biochemistry Department, Yale University, New Haven, CT; Toumy Guettouche, PhD , The Children's Hospital of Philadelphia, Philadelphia, PA; Aubree Himes, BSc , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA and The Children's Hospital of Philadelphia, Philadelphia, PA; Leat Perez , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Weihua Guan, PhD , Division of Biostatistics, University of Minnesota, Minneapolis, MN; Baolin Wu, PhD , Division of Biostatistics, University of Minnesota, Minneapolis, MN; David Schladt, MS , Minneapolis Medical Research Foundation, Hennepin County Medical Center, Minneapolis, MN; Madhav Menon, MD , Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Zhongyang Zhang, PhD , Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY; Vinicius Tragante, PhD , Division of Heart and Lungs, Department of Cardiology, University, Medical Center Utrecht, Utrecht, The Netherlands; Nicolaas de Jonge, MD PhD , Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands; Henny G. Otten, PhD , Department of Immunology, University Medical Center, Utrecht, the Netherlands; Roel A. de Weger, PhD , Department of Pathology, University Medical Center, Utrecht, The Netherlands; Ed A. van de Graaf, MD, PhD , Department of Pulmonary Disease, University Medical Center Utrecht, The Netherlands; Carla C. Baan, PhD , Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands; Olivier C. Manintveld, MD, PhD , Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands; Iwijn De Vlaminck, PhD , Department of Bioengineering and Applied Physics, Stanford University, Stanford, CA and Howard Hughes Medical Institute, Stanford, CA; Brian D. Piening, PhD , Howard Hughes Medical Institute, Stanford, CA and Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA; Calvin Strehl, BSc , Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA; Mary Shaw, RN, BBA , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Harold Snieder, PhD , Department of Epidemiology, Unit of Genetic Epidemiology and Bioinformatics, University of Groningen, University Medical Center Groningen, The Netherlands; Goran B. Klintmalm, MD, PhD, FACS , Baylor Simmons Transplant Institute, Baylor University Medical Center, Dallas, TX; Jacqueline G. O'Leary, MD, MPH , Division of Hepatology, Annette C. & Harold C. Simmons Transplant Institute, Baylor University Medical Center, Dallas, TX; Sandra Amaral, MD, MHS , Division of Nephrology, The Children's Hospital of Philadelphia, Philadelphia, PA and Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA; Samuel Goldfarb, MD , Division of PulmonaryMedicine, The Children's Hospital of Philadelphia, Philadelphia, PA; Elizabeth Rand, MD , Division of Gastroenterology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA; Joseph W. Rossano, MD, MS, FAAP, FAAC , Division of Pediatric Cardiology, Children's Hospital of Philadelphia, Philadelphia, PA; Utkarsh Kohli, MD ¸ Division of Pediatric Cardiology, Children's Hospital of Philadelphia, Philadelphia, PA; Peter Heeger, MD , Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY and Department of Medicine, Icahn School of Medicine at Mount Sinai, NY; Eli Stahl, PhD , Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY; Jason D. Christie, MD, MSCE , Division of Pulmonary, Allergy & Critical Care, University of Pennsylvania, Philadelphia, PA; Maria Hernandez Fuentes, PhD , Division of Transplantation Immunology and Mucosal Biology, Guy’s Hospital & King’s College London, United Kingdom; John E. Levine, MD, MS , Blood and Marrow Transplant Program, University of Michigan, Ann Arbor, MI; Richard Aplenc, MD, PhD , Division of Oncology, The Children's Hospital of Philadelphia, Philadelphia, PA; Eric E. Schadt, PhD , Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY; Barbara E. Stranger PhD , Section of Genetic Medicine, University of Chicago, Chicago, IL; Jolanda Kluin, MD, PhD , Department of Cardiothoracic Surgery, University Medical Center Utrecht, The Netherlands; Luciano Potena MD, PhD , Heart Failure and Heart Transplant Unit, Cardiovascular Department, University of Bologna, Italy; Andreas Zuckermann, MD , Department of Cardiac Surgery, Medical University of Vienna, Austria; Kiran Khush, MD, MAS , Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA; Alhusain J. Alzahrani, PhD , College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia; Fahad A. Al-Muhanna, MD , College of Medicine, University of Dammam, Dammam, Kingdom of Saudi Arabia; Amein K. Al-Ali, PhD , College of Medicine, University of Dammam, Dammam, Kingdom of Saudi Arabia; Rudaynah Al-Ali, MBBS , Department of Internal Medicine, King Fahd Hospital of the University, University of Dammam, Dammam, Kingdom of Saudi Arabia; Abdullah M. Al-Rubaish, MD , College of Medicine, University of Dammam, Dammam, Kingdom of Saudi Arabia; Samir Al-Mueilo, MBBS , College of Medicine, University of Dammam, Dammam, Kingdom of Saudi Arabia; Edna M. Byrne, PhD , The University of Queensland, St. Lucia, Brisbane, Australia; David Miller, PhD , Queensland Centre for Medical Genomics, IMB, University of Queensland, Queensland, Australia and Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia; Stephen I. Alexander, MB, BS, MPH , Centre for Kidney Research, The Children's Hospital at Westmead, University of Sydney, NSW, Australia; Suna Onengut-Gumuscu, PhD ; Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, VA; Stephen S. Rich, PhD , Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, VA; Manikkam Suthanthiran, MD , Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY; Helio Tedesco, MD , Division of Nephrology, Hospital do Rim e Hipertensão, São Paulo, Brazil; Chee L. Saw, PhD, HCLD , HLA Laboratory, Hematology Division, McGill University Health Centre, Montreal, Canada; Jiannis Ragoussis, PhD , McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada; Abdallah G. Kfoury, MD , Intermountain Heart Institute, Murray, UT; Benjamin Horne, MPH, PhD , Intermountain Heart Institute, Murray, UT; John Carlquist, PhD , Intermountain Heart Institute, Murray, UT; Mark B. Gerstein, PhD , The University of Queensland, St. Lucia, Brisbane, Australia and Intermountain Heart Institute, Murray, UT; Roman Reindl-Schwaighofer, MD , Division of Nephrology Dialysis, Department of Internal Medicine III, University of Vienna, Austria; Rainer Oberbauer, MD , Division of Nephrology Dialysis, Department of Internal Medicine III, University of Vienna, Austria; Cisca Wijmenga, PhD , University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands; Scott Palmer, MD, MHS , Department of Medicine, Duke University School of Medicine, Durham, NC; Alexandre C. Pereira, MD, PhD , Laboratory of Genetics & Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, Brazil; Javier Segovia, MD, PhD , Heart Transplant Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro Majadahonda,Madrid, Spain; Luis A. Alonso-Pulpon, MD, PhD , Heart Transplant Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro Majadahonda,Madrid, Spain; Manuel Comez-Bueno, MD , Heart Transplant Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro Majadahonda,Madrid, Spain; Carlos Vilches, MD , Department of Immunology, Hospital Universitario Puerta de Hierro Majadahonda,Madrid, Spain; Natalia Jaramillo, MD , Heart Transplant Unit, Department of Cardiology, Hospital Universitario Puerta de HierroMajadahonda, Madrid, Spain; Martin H. de Borst, MD, PhD , Division of Nephrology, Department of Internal Medicine, University Medical Center, University of Groningen, Groningen, The Netherlands; Maarten Naesens, MD, PhD , Department of Nephrology and Renal Transplantation, University Hospitals Leuven, Leuven, Belgium; Ke Hao, PhD , Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY; Daniel G. MacArthur, PhD , Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA; Suganthi Balasubramanian, PhD , Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT and Molecular Biophysics and Biochemistry Department, Yale University, New Haven, CT; Peter J. Conlon, MD , Department of Transplantation and Renal Medicine, Beaumont Hospital, Dublin, Ireland; Graham M. Lord, MD, PhD , Division of Transplantation Immunology and Mucosal Biology, Guy’s Hospital & King’s College London, United Kingdom; Marylyn D. Ritchie, PhD , Center for Systems Genomics, The Pennsylvania State University, University Park, PA; Michael Snyder, PhD , Department of Genetics, Stanford University School of Medicine, Stanford, CA and Stanford Cardiovascular Insti tute, Stanford University School of Medicine, Stanford, CA; Kim M. Olthoff, MD, FACS , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Jason H. Moore, PhD , Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, VA; Effie W. Petersdorf, MD , Division of Medical Oncology, Fred Hutchinson Cancer Research Center, University of Washington, School of Medicine Seattle, WA; Malek Kamoun, MD, PhD , Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Jun Wang, PhD , BGI-Shenzhen, Shenzhen, China; Dimitri S. Monos, PhD , The Children's Hospital of Philadelphia, Philadelphia, PA and Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Paul I.W de Bakker, PhD , Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands and Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands; Hakon Hakonarson, MD, PhD , The Children's Hospital of Philadelphia, Philadelphia, PA; Barbara Murphy, MD , Division of Nephrology and Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Matthew B. Lankree, MD, PhD , Department of Medicine, McMaster University, Hamilton, Ontario, Canada; Pablo Garcia-Pavia, MD, PhD , Heart Transplant Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain; William S. Oetting, PhD , Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN; Kelly A. Birdwell, MD, MSCI , Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Stephan J. L Bakker, MD, PhD , Division of Nephrology, Department of Internal Medicine, University Medical Center, University of Groningen, Groningen, The Netherlands; Ajay K. Israni, MD , Hennepin County Medical Center, University of Minnesota, Minneapolis, MN; Abraham Shaked, MD PhD , Penn Transplant Institute, Hospital of the University of Pennsylvania, Philadelphia, PA; Folkert W. Asselbergs, MD, PhD , Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands, Institute of Cardiovascular Science, University College London, London, United Kingdom, and Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands.
REFERENCES
1. Hindorff LA, Sethupathy P, Junkins HA, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits.
Proc Natl Acad Sci U S A . 2009; 106: 9362–9367.
2. Bamshad MJ, Ng SB, Bigham AW, et al. Exome sequencing as a tool for Mendelian disease gene discovery.
Nat Rev Genet . 2011; 12: 745–755.
4. MacArthur DG, Balasubramanian S, Frankish A, et al. A systematic survey of loss-of-function variants in human protein-coding genes.
Science . 2012; 335: 823–828.
5. Sudmant PH, Kitzman JO, Antonacci F, et al. Diversity of human copy number variation and multicopy genes.
Science . 2010; 330: 641–646.
6. Handsaker RE, Korn JM, Nemesh J, et al. Discovery and genotyping of genome structural polymorphism by sequencing on a population scale.
Nat Genet . 2011; 43: 269–276.
7. McCarroll SA, Bradner JE, Turpeinen H, et al. Donor-recipient mismatch for common gene deletion polymorphisms in graft-versus-host disease.
Nat Genet . 2009; 41: 1341–1344.
8. Li R, Li Y, Zheng H, et al. Building the sequence map of the human pan-genome.
Nat Biotechnol . 2010; 28: 57–63.
9. Venstrom JM, Pittari G, Gooley TA, et al. HLA-C-dependent prevention of leukemia relapse by donor activating KIR2DS1.
N Engl J Med . 2012; 367: 805–816.
10. Vampa ML, Norman PJ, Burnapp L, et al. Natural killer-cell activity after human renal transplantation in relation to killer immunoglobulin-like receptors and human leukocyte antigen mismatch.
Transplantation . 2003; 76: 1220–1228.
11. Zou Y, Stastny P. The role of major histocompatibility complex class I chain-related gene A antibodies in organ transplantation.
Curr Opin Organ Transplant . 2009; 14: 414–418.
12. Moretta A, Bottino C, Vitale M, et al. Receptors for HLA class-I molecules in human natural killer cells.
Annu Rev Immunol . 1996; 14: 619–648.
13. Norman PJ, Stephens HA, Verity DH, et al. Distribution of natural killer cell immunoglobulin-like receptor sequences in three ethnic groups.
Immunogenetics . 2001; 52: 195–205.
14. Terasaki PI. Deduction of the fraction of immunologic and non-immunologic failure in cadaver donor transplants.
Clin Transpl . 2003: 449–452.
15. Birdwell KA, Grady B, Choi L, et al. The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients.
Pharmacogenet Genomics . 2012; 22: 32–42.
16. Jacobson PA, Schladt D, Israni A, et al. Genetic and clinical determinants of early, acute calcineurin inhibitor-related nephrotoxicity: results from a kidney transplant consortium.
Transplantation . 2012; 93: 624–631.
17. Clatworthy MR, Matthews RJ, Doehler B, et al. Defunctioning polymorphism in the immunoglobulin G inhibitory receptor (FcγRIIB-T/T232) does not impact on kidney transplant or recipient survival.
Transplantation . 2014; 98: 285–291.
19. Stegall MD, Park WD, Dierkhising R. Genes and transplant outcomes: the search for “associations”.
Transplantation . 2014; 98: 257–258.
20. O'Brien RP, Phelan PJ, Conroy J, et al. A genome-wide association study of recipient genotype and medium-term kidney allograft function.
Clin Transplant . 2013; 27: 379–387.
21. McCaughan JA, McKnight AJ, Maxwell AP. Genetics of new-onset diabetes after transplantation.
J Am Soc Nephrol . 2014; 25: 1037–1049.
22. Damman J, Daha MR, Leuvenink HG, et al. Association of complement C3 gene variants with renal transplant outcome of deceased cardiac dead donor kidneys.
Am J Transplant . 2012; 12: 660–668.
23. Gourishankar S, Leduc R, Connett J, et al. Pathological and clinical characterization of the ‘troubled transplant’: data from the DeKAF study.
Am J Transplant . 2010; 10: 324–330.
24. Cantu E, Lederer DJ, Meyer K, et al. Gene set enrichment analysis identifies key innate immune pathways in primary graft dysfunction after lung transplantation.
Am J Transplant . 2013; 13: 1898–1904.
25. Olthoff KM, Kulik L, Samstein B, et al. Validation of a current definition of early allograft dysfunction in liver transplant recipients and analysis of risk factors.
Liver Transpl . 2010; 16: 943–949.
26. Li L, Khush K, Hsieh SC, et al. Identification of common blood gene signatures for the diagnosis of renal and cardiac acute allograft rejection.
PLoS One . 2013; 8: e82153.
27. De Vlaminck I, Valantine HA, Snyder TM, et al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection.
Sci Transl Med . 2014; 6: 241ra77.
28. Zijlstra LE, Constantinescu AA, Manintveld O, et al. Improved long-term survival in Dutch heart transplant patients despite increasing donor age: the Rotterdam experience.
Transpl Int . 2015; 28: 962–971.
29. Garcia-Pavia P, Syrris P, Salas C, et al. Desmosomal protein gene mutations in patients with idiopathic dilated cardiomyopathy undergoing cardiac transplantation: a clinicopathological study.
Heart . 2011; 97: 1744–1752.
30. Garcia-Pavia P, Vazquez ME, Segovia J, et al. Genetic basis of end-stage hypertrophic cardiomyopathy.
Eur J Heart Fail . 2011; 13: 1193–1201.
31. Holweg CT, Balk AH, Uitterlinden AG, et al. Functional heme oxygenase-1 promoter polymorphism in relation to heart failure and cardiac transplantation.
J Heart Lung Transplant . 2005; 24: 493–497.
32. Shaked A, Ghobrial RM, Merion RM, et al. Incidence and severity of acute cellular rejection in recipients undergoing adult living donor or deceased donor liver transplantation.
Am J Transplant . 2009; 9: 301–308.
33. Diamond JM, Akimova T, Kazi A, et al. Genetic variation in the prostaglandin E2 pathway is associated with primary graft dysfunction.
Am J Respir Crit Care Med . 2014; 189: 567–575.
34. Meier-Kriesche HU, Ojo AO, Hanson JA, et al. Increased impact of acute rejection on chronic allograft failure in recent era.
Transplantation . 2000; 70: 1098–1100.
35. Matas AJ, Smith JM, Skeans MA, et al. OPTN/SRTR 2012 Annual Data Report: kidney.
Am J Transplant . 2014; 14 (Suppl 1): 11–44.
36. Chakkera HA, Pham PT, Pomeroy J, et al. Response to comment on: chakkera et al. can new-onset diabetes after kidney transplant be prevented? Diabetes Care 2013;36:1406–1412.
Diabetes Care . 2013; 36: e183.
37. Rakel A, Karelis AD. New-onset diabetes after transplantation: risk factors and clinical impact.
Diabetes Metab . 2011; 37: 1–14.
38. Kamoun M, Holmes JH, Israni AK, et al. HLA-A amino acid polymorphism and delayed kidney allograft function.
Proc Natl Acad Sci U S A . 2008; 105: 18883–18888.
39. Jia X, Han B, Onengut-Gumuscu S, et al. Imputing amino acid polymorphisms in human leukocyte antigens.
PLoS One . 2013; 8: e64683.
40. Hsu L, Jiao S, Dai JY, et al. Powerful cocktail methods for detecting genome-wide gene-environment interaction.
Genet Epidemiol . 2012; 36: 183–194.
41. Murcray CE, Lewinger JP, Conti DV, et al. Sample size requirements to detect gene-environment interactions in genome-wide association studies.
Genet Epidemiol . 2011; 35: 201–210.
42. Kooperberg C, Leblanc M. Increasing the power of identifying gene x gene interactions in genome-wide association studies.
Genet Epidemiol . 2008; 32: 255–263.
43. McLaren W, Pritchard B, Rios D, et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor.
Bioinformatics . 2010; 26: 2069–2070.
44. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome.
Nature . 2012; 489: 57–74.
45. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project.
Nat Genet . 2013; 45: 580–585.
46. Yun R, Li JvS, Verma SS, et al. Concept and design of a genome-wide association genotyping array tailored for transplantation-specific studies.
Genome Med . 2015; 7: 90.
47. Li Y, Willer C, Sanna S, et al. Genotype imputation.
Annu Rev Genomics Hum Genet . 2009; 10: 387–406.
48. Genome of the Netherlands Consortium. Whole-genome sequence variation, population structure and demographic history of the Dutch population.
Nat Genet . 2014; 46: 818–825.
49. Muddyman D, Smee C, Griffin H, et al. Implementing a successful data-management framework: the UK10K managed access model.
Genome medicine . 2013; 5: 100.
50. Marsh SG, Albert ED, Bodmer WF, et al. Nomenclature for factors of the HLA system, 2010.
Tissue Antigens . 2010; 75: 291–455.
51. Gottesman O, Kuivaniemi H, Tromp G, et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future.
Genet Med . 2013; 15: 761–771.
52. Makley LN, Gestwicki JE. Expanding the number of ‘druggable’ targets: non-enzymes and protein-protein interactions.
Chem Biol Drug Des . 2013; 81: 22–32.
53. Tragante V, Barnes MR, Ganesh SK, et al. Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci.
Am J Hum Genet . 2014; 94: 349–360.
54. Panagiotou OA, Willer CJ, Hirschhorn JN, et al. The power of meta-analysis in genome-wide association studies.
Annu Rev Genomics Hum Genet . 2013; 14: 441–465.
