Faculty Bibliography

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 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: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.

BACKGROUND:Epilepsy, a serious chronic neurological condition effecting up to 100 million people globally, has clear genetic underpinnings including common and rare variants. In Saudi Arabia, the prevalence of epilepsy is high and caused mainly by perinatal and genetic factors. No whole-exome sequencing (WES) studies have been performed to date in Saudi Arabian epilepsy cohorts. This offers a unique opportunity for the discovery of rare genetic variants impacting this disease as there is a high rate of consanguinity among large tribal pedigrees. RESULTS:We performed WES on 144 individuals diagnosed with epilepsy, to interrogate known epilepsy-related genes for known and functional novel variants. We also used an American College of Medical Genetics (ACMG) guideline-based variant prioritization approach in an attempt to discover putative causative variants. We identified 32 potentially causative pathogenic variants across 30 different genes in 44/144 (30%) of these Saudi epilepsy individuals. We also identified 232 variants of unknown significance (VUS) across 101 different genes in 133/144 (92%) subjects. Strong enrichment of variants of likely pathogenicity was observed in previously described epilepsy-associated loci, and a number of putative pathogenic variants in novel loci are also observed. CONCLUSION:Several putative pathogenic variants in known epilepsy-related loci were identified for the first time in our population, in addition to several potential new loci which may be prioritized for further investigation.

BACKGROUND:Large-scale gut microbiome sequencing has revealed key links between microbiome dysfunction and metabolic diseases such as type 2 diabetes (T2D). To date, these efforts have largely focused on Western populations, with few studies assessing T2D microbiota associations in Middle Eastern communities where T2D prevalence is now over 20%. We analyzed the composition of stool 16S rRNA from 461 T2D and 119 non-T2D participants from the Eastern Province of Saudi Arabia. We quantified the abundance of microbial communities to examine any significant differences between subpopulations of samples based on diabetes status and glucose level. RESULTS:In this study we performed the largest microbiome study ever conducted in Saudi Arabia, as well as the first-ever characterization of gut microbiota T2D versus non-T2D in this population. We observed overall positive enrichment within diabetics compared to healthy individuals and amongst diabetic participants; those with high glucose levels exhibited slightly more positive enrichment compared to those at lower risk of fasting hyperglycemia. In particular, the genus Firmicutes was upregulated in diabetic individuals compared to non-diabetic individuals, and T2D was associated with an elevated Firmicutes/Bacteroidetes ratio, consistent with previous findings. CONCLUSION:Based on diabetes status and glucose levels of Saudi participants, relatively stable differences in stool composition were perceived by differential abundance and alpha diversity measures. However, community level differences are evident in the Saudi population between T2D and non-T2D individuals, and diversity patterns appear to vary from well-characterized microbiota from Western cohorts. Comparing overlapping and varying patterns in gut microbiota with other studies is critical to assessing novel treatment options in light of a rapidly growing T2D health epidemic in the region. As a rapidly emerging chronic condition in Saudi Arabia and the Middle East, T2D burdens have grown more quickly and affect larger proportions of the population than any other global region, making a regional reference T2D-microbiome dataset critical to understanding the nuances of disease development on a global scale.

UNLABELLED:genotype on clinical outcomes and financial expenditure. METHODS/UNASSIGNED:). Individuals were categorized as rapid metabolism phenotype without LoF alleles' intermediate phenotype for 1 LoF allele' and slow phenotype for 2 LoF alleles. KTx outcomes (patient/kidney survival and Medicare spending) were determined using linked transplant registry and claims data. RESULTS/UNASSIGNED: = 0.003). CONCLUSIONS/UNASSIGNED:functional variants appear to be associated with differential outcome and spending after transplant.

At the outset of solid organ transplantation, genetic variation between donors and recipients was recognized as a major player in mechanisms such as allograft tolerance and rejection. Genome-wide association studies have been very successful in identifying novel variant-trait associations, but have been difficult to perform in the field of solid organ transplantation due to complex covariates, era effects, and poor statistical power for detecting donor-recipient interactions. To overcome a lack of statistical power, consortia such as the International Genetics and Translational Research in Transplantation Network have been established. Studies have focused on the consequences of genetic dissimilarities between donors and recipients and have reported associations between polymorphisms in candidate genes or their regulatory regions with transplantation outcomes. However, knowledge on the exact influence of genetic variation is limited due to a lack of comprehensive characterization and harmonization of recipients' or donors' phenotypes and validation using an experimental approach. Causal research in genetics has evolved from agnostic discovery in genome-wide association studies to functional annotation and clarification of underlying molecular mechanisms in translational studies. In this overview, we summarize how the recent advances and progresses in the field of genetics and genomics have improved the understanding of outcomes after solid organ transplantation.