Since the Organ Procurement and Transplantation Network (OPTN) began its registry in 1987 until mid-2014 over 575,000 solid organ transplantations have been performed in the United States [1]. Although there have been considerable improvements in patient treatment pre- and post-transplant surgery and immunosuppressant therapies (IST), various grades of rejection are observed in up to 40 % of transplanted individuals within the first year post transplant [2], and affects approximately 60 % of transplanted individuals over the course of the graft lifetime thereby representing a major risk factor for graft damage and eventual graft loss [3, 4]. There are also significant risks post transplant ranging from severe adverse events to side-effects of ISTs including nephrotoxicity, hyperlipidemia, and new onset of diabetes after transplantation (NODAT) [5, 6].

Recent advances in genomic technologies and large-scale human reference maps such as the International HapMap Project have led to the development of genome-wide association studies (GWAS) utilizing cost-effective arrays that allow for the rapid interrogation of several hundreds of thousands single nucleotide polymorphisms (SNPs) and copy number variants (CNV) across the human genome [7–9]. Large scale whole genome sequencing studies show that approximately 3.5 million and approximately 10 million common and rare polymorphisms are typically observed between two unrelated individuals of European and African ancestries, respectively [10]. Each donor-recipient (D-R) pair of genomes contains vast permutations of non-synonymous amino-acid differences and other potential sources for allogenicity, beyond the highly characterized human leukocyte antigen (HLA) region, conventionally considered to contain the main genetic factors underpinning allograft rejection. Rejection is observed in following HLA-matched transplantations between full sibling, suggesting that histocompatibility may depend on non-HLA genetic differences. This includes a number of minor histocompatibility antigens, such as the H-Y antigens [11], which have been studied in the context of renal transplantation. Such findings from these studies identifying non-HLA histocompatibility loci suggest that non-HLA genetic disparities exist between D-Rs, and that these differences may manifest as the presentation of polymorphic peptides that the recipient’s immune system recognizes as non-self even in the presence of IST. Indeed, analyses of overall 10-year kidney graft failure rates for cadaver donors showed that 18 % of graft failures were due to HLA factors, as observed through mismatched living donor grafts; and 43 % were attributable to non-immunological factors, and 38 % of the failures were due to immunological reactions against non-HLA factors as seen in HLA-identical sibling grafts [4]. The natural killer cell immunoglobulin-like receptor (KIR) region comprising a family of 13 genes on chr19q13.46 are known to interact with HLA Class I molecules, and many unique KIR haplotypes identified have been linked to transplantation outcomes [12, 13] Additional non-HLA/KIR polymorphisms have also been shown to impact transplantation outcomes since through the generation histo-incompatibilities [14–16]. Investigations of non-HLA genetic determinants of clinical outcomes following organ transplantation have yet to be performed in any systematic well powered fashion to date.

A recent genome-wide study of NODAT was conducted in a prospective cohort of 529 kidney transplant recipients, 57 of whom developed NODAT with 26 SNPs identified in the discovery stage (P <1 × 10⁻⁵), eight of which retained association on replication, of which seven intriguingly are in loci known to have a role in Beta-cell apoptosis [17]. A number of genetic variants impacting uptake, metabolism, and excretion of immunosuppressant drugs have been identified [18]. While there are examples of robust associations in a number of these studies, validation of a large number of other putative associations in independent studies are often not observed [19]. This is likely to contribute to publication bias, underpowered discovery cohorts, and failure to adjust for population stratification.

The use of current sequencing and dense genotyping data from reference populations also makes it feasible to further infer, or impute, tens of millions of additional genotypes, which were not directly genotyped on the initial platform [20–22], by the use of whole genome imputation using highly characterized genomic reference datasets such as the 1000 genomes project (1KGP) and the Genomes of the Netherlands (GoNL) [23, 24]. Array-based genotyping technologies that have enabled conventional GWAS analyses also permit flexibility in choosing the scope and density of SNPs for disease or trait-specific arrays geared toward particular research communities. Such arrays include platforms such as the ‘cardiochip’ [25] and more recently the Immunochip and Cardio-Metabochip arrays [26, 27] have unveiled hundreds of new genetic associations leading to deeper understanding of the genetic architecture of new regions underpinning biological and disease processes. These newest arrays, including the Axiom Biobank and the UK Biobank genotyping arrays enable more comprehensive capture of genetic diversity across populations [28].

To create a unique genome-wide platform to facilitate genomic research studies in transplant-related studies, we designed a genome-wide genotyping tool customized for known and potentially relevant loci in metabolic and pharmacological aspects of transplantation including content relevant for D-R genomic incompatibility. We describe here the design and implementation of a genome-wide 782,000 marker array herein termed the ‘TxArray’ with tailored deeper capture of variants in HLA, KIR, pharmacogenomic, and metabolic genes/loci important in transplantation while still allowing conventional hypothesis-free GWAS to be performed. The genome-wide coverage of this array was created using content from conventional GWAS arrays [29, 30] with transplant-specific content informed from a range of sources including comprehensive literature searching and by expert opinions on priority pharmacogenomic loci. Our targeted customized modules are also designed to provide improved coverage of functional variations based on updated content from the 1KGP [10] and powerful analyses of over 32,000 exomes [31, 32].

DNA from over 16,000 DNA samples has thus far been genotyped using this array allowing for more robustly powered in silico replication as well analyses of rare variants and loss of function (LoF) variants ablating all or parts or a given gene, and cross-cohort meta-analyses in diverse populations. The majority of these samples are contributed as a part of International Genetics & Translational Research in Transplantation Network (iGeneTRAiN), a major international collaboration on the genomics of transplantation [33]. The objectives of forming this consortium are: (1) to pool expertise for selection of genes and SNPs; (2) to reduce costs by producing a standardized genome-wide genotyping platform; (3) to facilitate ease of cross cohort meta-analyses and replication for a large set of SNPs in high priority candidate genes; and (4) to bolster statistical power by combining as many of these appropriately harmonized datasets to discover new genes involved in a range of phenotypes and outcomes relating to solid organ and hematopoietic stem cell transplantation (HCT). Here we formally describe the rationale, design and content of our transplant genotyping array, and describe the imputation process as well as evaluate its performance in capturing variation across major populations.