Whole transcriptome profiling of prospective endomyocardial biopsies reveals prognostic and diagnostic signatures of cardiac allograft rejection.

BACKGROUND: Heart transplantation provides a significant improvement in survival and quality of life for patients with end-stage heart disease, however many recipients experience different levels of graft rejection that can be associated with significant morbidities and mortality. Current clinical standard-of-care for the evaluation of heart transplant acute rejection (AR) consists of routine endomyocardial biopsy (EMB) followed by visual assessment by histopathology for immune infiltration and cardiomyocyte damage. We assessed whether the sensitivity and/or specificity of this process could be improved upon by adding RNA sequencing (RNA-seq) of EMBs coupled with histopathological interpretation. METHODS: Up to 6 standard-of-care, or for-cause EMBs, were collected from 26 heart transplant recipients from the prospective observational Clinical Trials of Transplantation (CTOT)-03 study, during the first 12-months post-transplant and subjected to RNA-seq (n = 125 EMBs total). Differential expression and random-forest-based machine learning were applied to develop signatures for classification and prognostication. RESULTS: Leveraging the unique longitudinal nature of this study, we show that transcriptional hallmarks for significant rejection events occur months before the actual event and are not visible using traditional histopathology. Using this information, we identified a prognostic signature for 0R/1R biopsies that with 90% accuracy can predict whether the next biopsy will be 2R/3R. CONCLUSIONS: RNA-seq-based molecular characterization of EMBs shows significant promise for the early detection of cardiac allograft rejection.

Rare copy number variants in over 100,000 European ancestry subjects reveal multiple disease associations.

Copy number variants (CNVs) are suggested to have a widespread impact on the human genome and phenotypes. To understand the role of CNVs across human diseases, we examine the CNV genomic landscape of 100,028 unrelated individuals of European ancestry, using SNP and CGH array datasets. We observe an average CNV burden of ~650 kb, identifying a total of 11,314 deletion, 5625 duplication, and 2746 homozygous deletion CNV regions (CNVRs). In all, 13.7% are unreported, 58.6% overlap with at least one gene, and 32.8% interrupt coding exons. These CNVRs are significantly more likely to overlap OMIM genes (2.94-fold), GWAS loci (1.52-fold), and non-coding RNAs (1.44-fold), compared with random distribution (P < 1 × 10-3). We uncover CNV associations with four major disease categories, including autoimmune, cardio-metabolic, oncologic, and neurological/psychiatric diseases, and identify several drug-repurposing opportunities. Our results demonstrate robust frequency definition for large-scale rare variant association studies, identify CNVs associated with major disease categories, and illustrate the pleiotropic impact of CNVs in human disease.

Genome-Wide Study Updates in the International Genetics and Translational Research in Transplantation Network (iGeneTRAiN).

The prevalence of end-stage renal disease (ESRD) and the number of kidney transplants performed continues to rise every year, straining the procurement of deceased and living kidney allografts and health systems. Genome-wide genotyping and sequencing of diseased populations have uncovered genetic contributors in substantial proportions of ESRD patients. A number of these discoveries are beginning to be utilized in risk stratification and clinical management of patients. Specifically, genetics can provide insight into the primary cause of chronic kidney disease (CKD), the risk of progression to ESRD, and post-transplant outcomes, including various forms of allograft rejection. The International Genetics & Translational Research in Transplantation Network (iGeneTRAiN), is a multi-site consortium that encompasses >45 genetic studies with genome-wide genotyping from over 51,000 transplant samples, including genome-wide data from >30 kidney transplant cohorts (n = 28,015). iGeneTRAiN is statistically powered to capture both rare and common genetic contributions to ESRD and post-transplant outcomes. The primary cause of ESRD is often difficult to ascertain, especially where formal biopsy diagnosis is not performed, and is unavailable in ∼2% to >20% of kidney transplant recipients in iGeneTRAiN studies. We overview our current copy number variant (CNV) screening approaches from genome-wide genotyping datasets in iGeneTRAiN, in attempts to discover and validate genetic contributors to CKD and ESRD. Greater aggregation and analyses of well phenotyped patients with genome-wide datasets will undoubtedly yield insights into the underlying pathophysiological mechanisms of CKD, leading the way to improved diagnostic precision in nephrology.

Genetic sharing and heritability of paediatric age of onset autoimmune diseases.

Autoimmune diseases (AIDs) are polygenic diseases affecting 7-10% of the population in the Western Hemisphere with few effective therapies. Here, we quantify the heritability of paediatric AIDs (pAIDs), including JIA, SLE, CEL, T1D, UC, CD, PS, SPA and CVID, attributable to common genomic variations (SNP-h(2)). SNP-h(2) estimates are most significant for T1D (0.863±s.e. 0.07) and JIA (0.727±s.e. 0.037), more modest for UC (0.386±s.e. 0.04) and CD (0.454±0.025), largely consistent with population estimates and are generally greater than that previously reported by adult GWAS. On pairwise analysis, we observed that the diseases UC-CD (0.69±s.e. 0.07) and JIA-CVID (0.343±s.e. 0.13) are the most strongly correlated. Variations across the MHC strongly contribute to SNP-h(2) in T1D and JIA, but does not significantly contribute to the pairwise rG. Together, our results partition contributions of shared versus disease-specific genomic variations to pAID heritability, identifying pAIDs with unexpected risk sharing, while recapitulating known associations between autoimmune diseases previously reported in adult cohorts.

Concept and design of a genome-wide association genotyping array tailored for transplantation-specific studies.

BACKGROUND: In addition to HLA genetic incompatibility, non-HLA difference between donor and recipients of transplantation leading to allograft rejection are now becoming evident. We aimed to create a unique genome-wide platform to facilitate genomic research studies in transplant-related studies. We designed a genome-wide genotyping tool based on the most recent human genomic reference datasets, and included customization for known and potentially relevant metabolic and pharmacological loci relevant to transplantation. METHODS: We describe here the design and implementation of a customized genome-wide genotyping array, the 'TxArray', comprising approximately 782,000 markers with tailored content for deeper capture of variants across HLA, KIR, pharmacogenomic, and metabolic loci important in transplantation. To test concordance and genotyping quality, we genotyped 85 HapMap samples on the array, including eight trios. RESULTS: We show low Mendelian error rates and high concordance rates for HapMap samples (average parent-parent-child heritability of 0.997, and concordance of 0.996). We performed genotype imputation across autosomal regions, masking directly genotyped SNPs to assess imputation accuracy and report an accuracy of >0.962 for directly genotyped SNPs. We demonstrate much higher capture of the natural killer cell immunoglobulin-like receptor (KIR) region versus comparable platforms. Overall, we show that the genotyping quality and coverage of the TxArray is very high when compared to reference samples and to other genome-wide genotyping platforms. CONCLUSIONS: We have designed a comprehensive genome-wide genotyping tool which enables accurate association testing and imputation of ungenotyped SNPs, facilitating powerful and cost-effective large-scale genotyping of transplant-related studies.