Explore the vast range of titles available.

Pulmonary hypertension (PH) is associated with significant morbidity and mortality. RASA3 is a GTPase activating protein integral to angiogenesis and endothelial barrier function. In this study, we explore the association of RASA3 genetic variation with PH risk in patients with sickle cell disease (SCD)‐associated PH and pulmonary arterial hypertension (PAH). Cis‐expression quantitative trait loci (eQTL) were queried for RASA3 using whole genome genotype arrays and gene expression profiles derived from peripheral blood mononuclear cells (PBMC) of three SCD cohorts. Genome‐wide single nucleotide polymorphisms (SNPs) near or in the RASA3 gene that may associate with lung RASA3 expression were identified, reduced to 9 tagging SNPs for RASA3 and associated with markers of PH. Associations between the top RASA3 SNP and PAH severity were corroborated using data from the PAH Biobank and analyzed based on European or African ancestry (EA, AA). We found that PBMC RASA3 expression was lower in patients with SCD‐associated PH as defined by echocardiography and right heart catheterization and was associated with higher mortality. One eQTL for RASA3 (rs9525228) was identified, with the risk allele correlating with PH risk, higher tricuspid regurgitant jet velocity and higher pulmonary vascular resistance in patients with SCD‐associated PH. rs9525228 associated with markers of precapillary PH and decreased survival in individuals of EA but not AA. In conclusion, RASA3 is a novel candidate gene in SCD‐associated PH and PAH, with RASA3 expression appearing to be protective. Further studies are ongoing to delineate the role of RASA3 in PH.

Cardiopulmonary complications are major drivers of mortality caused by the SARS-CoV-2 virus. Interleukin-18, an inflammasome-induced cytokine, has emerged as a novel mediator of cardiopulmonary pathologies but its regulation via SARS-CoV-2 signaling remains unknown. Based on a screening panel, IL-18 was identified amongst 19 cytokines to stratify mortality and hospitalization burden in patients hospitalized with COVID-19. Supporting clinical data, administration of SARS-CoV-2 Spike 1 (S1) glycoprotein or receptor-binding domain (RBD) proteins into human angiotensin-converting enzyme 2 (hACE2) transgenic mice induced cardiac fibrosis and dysfunction associated with higher NF-κB phosphorylation (pNF-κB) and cardiopulmonary-derived IL-18 and NLRP3 expression. IL-18 inhibition via IL-18BP resulted in decreased cardiac pNF-κB and improved cardiac fibrosis and dysfunction in S1- or RBD-exposed hACE2 mice. Through in vivo and in vitro work, both S1 and RBD proteins induced NLRP3 inflammasome and IL-18 expression by inhibiting mitophagy and increasing mitochondrial reactive oxygenation species. Enhancing mitophagy prevented Spike protein-mediated IL-18 expression. Moreover, IL-18 inhibition reduced Spike protein-mediated pNF-κB and EC permeability. Overall, the link between reduced mitophagy and inflammasome activation represents a novel mechanism during COVID-19 pathogenesis and suggests IL-18 and mitophagy as potential therapeutic targets.

Genome-wide association studies (GWAS) in admixed populations such as African Americans (AA) have limited sample sizes, resulting in poor performance of polygenic risk scores (PRS). Based on the observations that many disease-causing genes are shared between AA and European ancestry (EA) populations, and some disease-causing variants are located within the boundaries of these genes, we proposed a novel gene-based PRS framework (PRS gene ) by using variants located within disease-associated genes. Using the AA GWAS of alcohol use disorder (AUD) from the Million Veteran Program and the EA GWAS of problematic alcohol use as the discovery GWAS, we identified 858 variants from 410 genes that were AUD-related in both AA and EA. PRS gene calculated using these variants were significantly associated with AUD in three AA target datasets ( P -values ranged from 7.61E−05 to 6.27E−03; Betas ranged from 0.15 to 0.21) and outperformed PRS calculated using all variants ( P -values ranged from 7.28E−03 to 0.16; Betas ranged from 0.06 to 0.18). PRS gene were also associated with AUD in an EA target dataset ( P -value = 0.02, Beta = 0.11). In AA, individuals in the highest PRS gene decile had an odds ratio of 1.76 (95% CI: 1.32–2.34) to develop AUD compared to those in the lowest decile. The 410 genes were enriched in 54 Gene Ontology biological processes, including ethanol oxidation and processes involving the synaptic system, which are known to be AUD-related. In addition, 26 genes were targets of drugs used to treat AUD or other diseases that might be considered for repurposing to treat AUD. Our study demonstrated that the gene-based PRS had improved performance in evaluating AUD risk in AA and provided new insight into AUD genetics.

Background: Alcohol-associated liver disease (ALD) is the most common disorder of prolonged drinking. Mechanisms underlying cirrhosis in such patients remain unclear. MicroRNAs play regulatory role in several diseases, are affected by alcohol and may be important players in alcohol use disorders, such as cirrhosis. Methods: We investigated serum samples from heavy chronic alcohol users (80 g/day (male) and 50 g/day (female) for ≥10 years) that were available from our previously reported GenomALC study. A subset of GenomALC drinkers with liver cirrhosis (cases, n = 24) and those without significant liver disease (drinking controls, n = 23) were included. Global microRNA profiling was performed using high-throughput real-time quantitative PCR to identify the microRNA signatures associated with cirrhosis. Ingenuity Pathway Analysis (IPA) software was utilized to identify target mRNAs of significantly altered microRNAs, and molecular pathways were analysed. Identified microRNAs were analysed for correlation with traditional liver disease biomarkers and risk gene variants previously reported from GenomALC genome-wide association study. Results: The expression of 21 microRNAs was significantly downregulated in cases compared to drinking controls (p < 0.05, ∆∆Ct > 1.5-fold). Seven microRNAs (miR-16, miR-19a, miR-27a, miR-29b, miR-101, miR-130a, and miR-191) had a highly significant correlation (p < 0.001) with INR, bilirubin and MELD score. Three microRNAs (miR-27a, miR-130a and miR-191) significantly predicted cases with AUC-ROC 0.8, 0.78 and 0.85, respectively (p < 0.020); however, INR performed best (0.97, p < 0.001). A different set of six microRNAs (miR-19a, miR-26a, miR-101, miR-151-3p, miR-221, and miR-301) showed positive correlation (ranging from 0.32 to 0.51, p < 0.05) with rs10433937:HSD17B13 gene variant, associated with the risk of cirrhosis. IPA analysis revealed mRNA targets of the significantly altered microRNAs associated with cell death/necrosis, fibrosis and increased steatosis, particularly triglyceride metabolism. Conclusions: MicroRNA signatures in drinkers distinguished those with liver cirrhosis from drinkers without liver disease. We identified mRNA targets in liver functions that were enriched for disease pathogenesis pathways.

Imputation, the process of inferring genotypes for untyped variants, is used to identify and refine genetic association findings. Inaccuracies in imputed data can distort the observed association between variants and a disease. Many statistics are used to assess accuracy; some compare imputed to genotyped data and others are calculated without reference to true genotypes. Prior work has shown that the Imputation Quality Score (IQS), which is based on Cohen's kappa statistic and compares imputed genotype probabilities to true genotypes, appropriately adjusts for chance agreement; however, it is not commonly used. To identify differences in accuracy assessment, we compared IQS with concordance rate, squared correlation, and accuracy measures built into imputation programs. Genotypes from the 1000 Genomes reference populations (AFR N = 246 and EUR N = 379) were masked to match the typed single nucleotide polymorphism (SNP) coverage of several SNP arrays and were imputed with BEAGLE 3.3.2 and IMPUTE2 in regions associated with smoking behaviors. Additional masking and imputation was conducted for sequenced subjects from the Collaborative Genetic Study of Nicotine Dependence and the Genetic Study of Nicotine Dependence in African Americans (N = 1,481 African Americans and N = 1,480 European Americans). Our results offer further evidence that concordance rate inflates accuracy estimates, particularly for rare and low frequency variants. For common variants, squared correlation, BEAGLE R2, IMPUTE2 INFO, and IQS produce similar assessments of imputation accuracy. However, for rare and low frequency variants, compared to IQS, the other statistics tend to be more liberal in their assessment of accuracy. IQS is important to consider when evaluating imputation accuracy, particularly for rare and low frequency variants.

Genetic testing for Parkinson’s disease (PD) is infrequently performed due to perceptions of low utility. We investigated the personal utility in PD GENEration and how results lead to enrollment in additional research studies. Participants ( n  = 972) underwent genetic testing, results disclosure, genetic counseling, and completed a survey examining the perceived personal utility of their results and interest in participating in additional studies. Most participants found their genetic test results useful, including satisfying curiosity (81%), feeling good about helping the medical community (80%), and having information to share with family (77%). There were no significant differences in responses based on result type. Forty-five percent of participants expressed interest in participating in research studies; whereas 16% of participants confirmed enrollment. Our results suggest that participants find personal utility in genetic testing regardless of results. Although participants may be interested in enrolling in additional research, they may need support and resources.

Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify genetic signals. Here we train a deep convolutional neural network on noisy self-reported and International Classification of Diseases labels to predict COPD case–control status from high-dimensional raw spirograms and use the model’s predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls, and predicts COPD-related hospitalization without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival and exacerbation events. A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci and also identifies 67 new loci. Lastly, our method provides a general framework to use ML methods and medical-record-based labels that does not require domain knowledge or expert curation to improve disease prediction and genomic discovery for drug design. A deep convolutional neural network calculates liability scores for chronic obstructive pulmonary disease (COPD) from raw spirogram traces and noisy medical-record-based labels in the UK Biobank. Genome-wide analyses using these scores replicate known loci for lung function and identify 67 new disease loci.

Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD—spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction. Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE) uses machine learning to generate low-dimensional representations of healthcare data. Applied to lung spirograms and blood volume photoplethysmograms, REGLE factors capture additional information beyond expert-defined features, suggesting the utility of this approach.

We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis. A multi-ancestry genome-wide association study of liver cirrhosis and its associated endophenotypes identifies and validates 14 risk variants. Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis.

A hallmark of fetal alcohol spectrum disorders (FASD) is neurobehavioral deficits that still do not have effective treatment. Here, we present that reduction of Apolipoprotein E (APOE) is critically involved in neurobehavioral deficits in FASD. We show that prenatal alcohol exposure (PAE) changes chromatin accessibility of Apoe locus, and causes reduction of APOE levels in both the brain and peripheral blood in postnatal mice. Of note, postnatal administration of an APOE receptor agonist (APOE-RA) mitigates motor learning deficits and anxiety in those mice. Several molecular and electrophysiological properties essential for learning, which are altered by PAE, are restored by APOE-RA. Our human genome-wide association study further reveals that the interaction of PAE and a single nucleotide polymorphism in the APOE enhancer which chromatin is closed by PAE in mice is associated with lower scores in the delayed matching-to-sample task in children. APOE in the plasma is also reduced in PAE children, and the reduced level is associated with their lower cognitive performance. These findings suggest that controlling the APOE level can serve as an effective treatment for neurobehavioral deficits in FASD.