Faculty Bibliography

Background Cardiometabolic diseases are highly comorbid, but their relationship with female-specific or overwhelmingly female-predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross-trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross-trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score-based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross-trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age. We observed 27 significant associations between cardiometabolic polygenic scores and obstetrical/gynecological conditions (body mass index and endometrial cancer, body mass index and polycystic ovarian syndrome, type 2 diabetes and gestational diabetes, type 2 diabetes and polycystic ovarian syndrome). Mendelian randomization analysis provided additional evidence of independent causal effects. We also identified an inverse association between coronary artery disease and breast cancer. High cardiometabolic polygenic scores were associated with early development of polycystic ovarian syndrome and gestational hypertension. Conclusions We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female-specific health conditions.

BACKGROUND:Endomyocardial biopsy (EMB) is currently considered the gold standard for diagnosing cardiac allograft rejection. However, significant limitations related to histological interpretation variability are well-recognized. We sought to develop a methodology to evaluate EMB solely based on gene expression, without relying on histology interpretation. METHODS:Sixty-four EMBs were obtained from 47 post-heart transplant recipients, who were evaluated for allograft rejection. EMBs were subjected to mRNA sequencing, in which an unsupervised classification algorithm was used to identify the molecular signatures that best classified the EMBs. Cytokine and natriuretic peptide peripheral blood profiling was also performed. Subsequently, we performed gene network analysis to identify the gene modules and gene ontology to understand their biological relevance. We correlated our findings with the unsupervised and histological classifications. RESULTS:Our algorithm classifies EMBs into three categories based solely on clusters of gene expression: unsupervised classes 1, 2, and 3. Unsupervised and histological classifications were closely related, with stronger gene module-phenotype correlations for the unsupervised classes. Gene ontology enrichment analysis revealed processes impacting on the regulation of cardiac and mitochondrial function, immune response, and tissue injury response. Significant levels of cytokines and natriuretic peptides were detected following the unsupervised classification. CONCLUSION/CONCLUSIONS:We have developed an unsupervised algorithm that classifies EMBs into three distinct categories, without relying on histology interpretation. These categories were highly correlated with mitochondrial, immune, and tissue injury response. Significant cytokine and natriuretic peptide levels were detected within the unsupervised classification. If further validated, the unsupervised classification could offer a more objective EMB evaluation.

Ischemic stroke represents a significant societal burden across the globe. Rare high penetrant monogenic variants and less pathogenic common single nucleotide polymorphisms (SNPs) have been described as being associated with risk of diseases. Genetic studies in Saudi Arabian patients offer a greater opportunity to detect rare high penetrant mutations enriched in these consanguineous populations. We performed whole exome sequencing on 387 ischemic stroke subjects from Saudi Arabian hospital networks with up to 20,230 controls from the Saudi Human Genome Project and performed gene burden analyses of variants in 177 a priori loci derived from knowledge-driven curation of monogenic and genome-wide association studies of stroke. Using gene-burden analyses, we observed significant associations in numerous loci under autosomal dominant and/or recessive modelling. Stroke subjects with modified Rankin Scale (mRSs) above 3 were found to carry greater cumulative polygenic risk score (PRS) from rare variants in stroke genes (standardized PRS mean > 0) compared to the population average (standardized PRS mean = 0). However, patients with mRS of 3 or lower had lower cumulative genetic risk from rare variants in stroke genes (OR (95%CI) = 1.79 (1.29-2.49), p = 0.0005), with the means of standardized PRS at or lower than 0. In conclusion, gene burden testing in Saudi stroke populations reveals a number of statistically significant signals under different disease inheritance models. However, interestingly, stroke subjects with mRS of 3 or lower had lower cumulative genetic risk from rare variants in stroke genes and therefore, determining the potential mRS cutoffs to use for clinical significance may allow risk stratification of this population.

BACKGROUND:Loss-of-Function (LoF) variants in human genes are important due to their impact on clinical phenotypes and frequent occurrence in the genomes of healthy individuals. The association of LoF variants with complex diseases and traits may lead to the discovery and validation of novel therapeutic targets. Current approaches predict high-confidence LoF variants without identifying the specific genes or the number of copies they affect. Moreover, there is a lack of methods for detecting knockout genes caused by compound heterozygous (CH) LoF variants. RESULTS:We have developed the Loss-of-Function ToolKit (LoFTK), which allows efficient and automated prediction of LoF variants from genotyped, imputed and sequenced genomes. LoFTK enables the identification of genes that are inactive in one or two copies and provides summary statistics for downstream analyses. LoFTK can identify CH LoF variants, which result in LoF genes with two copies lost. Using data from parents and offspring we show that 96% of CH LoF genes predicted by LoFTK in the offspring have the respective alleles donated by each parent. CONCLUSIONS:LoFTK is a command-line based tool that provides a reliable computational workflow for predicting LoF variants from genotyped and sequenced genomes, identifying genes that are inactive in 1 or 2 copies. LoFTK is an open software and is freely available to non-commercial users at https://github.com/CirculatoryHealth/LoFTK .

INTRODUCTION/UNASSIGNED:Genome-wide association studies have discovered common polymorphisms in regions associated with schizophrenia. No genome-wide analyses have been performed in Saudi schizophrenia subjects. METHODS/UNASSIGNED:Genome-wide genotyping data from 136 Saudi schizophrenia cases and 97 Saudi controls in addition to 4,625 American were examined for copy number variants (CNVs). A hidden Markov model approach was used to call CNVs. RESULTS/UNASSIGNED: = 0.04). The analyses focused on extremely large >250 kilobases CNVs or homozygous deletions of any size. One extremely large deletion was noted in a single case (16.5 megabases on chromosome 10). Two cases had an 814 kb duplication of chromosome 7 spanning a cluster of genes, including circadian-related loci, and two other cases had 277 kb deletions of chromosome 9 encompassing an olfactory receptors gene family. CNVs were also seen in loci previously associated with schizophrenia, namely a 16p11 proximal duplication and two 22q11.2 deletions. DISCUSSION/UNASSIGNED:Runs of homozygosity (ROHs) were analyzed across the genome to investigate correlation with schizophrenia risk. While rates and sizes of these ROHs were similar in cases and controls, we identified 10 regions where multiple cases had ROHs and controls did not.

Post-transplant diabetes mellitus (PTDM) reduces allograft and recipient life span. Polygenic risk scores (PRSs) show robust association with greater risk of developing type 2 diabetes (T2D). We examined the association of PTDM with T2D PRS in liver recipients (n = 1,581) and their donors (n = 1,555), and kidney recipients (n = 2,062) and their donors (n = 533). Recipient T2D PRS was associated with pre-transplant T2D and the development of PTDM. T2D PRS in liver donors, but not in kidney donors, was an independent risk factor for PTDM development. The inclusion of a combined liver donor and recipient T2D PRS significantly improved PTDM prediction compared with a model that included only clinical characteristics: the area under the curve (AUC) was 67.6% (95% confidence interval (CI) 64.1-71.1%) for the combined T2D PRS versus 62.3% (95% CI 58.8-65.8%) for the clinical characteristics model (P = 0.0001). Liver recipients in the highest quintile of combined donor and recipient T2D PRS had the greatest risk of PTDM, with an odds ratio of 3.22 (95% CI 2.07-5.00) (P = 1.92 × 10^-7) compared with those in the lowest quintile. In conclusion, T2D PRS identifies transplant candidates with high risk of PTDM for which pre-emptive diabetes management and donor selection may be warranted.

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.