Biology subjects

Damrauer, S.

Publications and source records attributed to Damrauer, S..

6 recordsLinked to original sources

bioRxiv2024

Platelets are Protective in Early Abdominal Aortic Aneurysm Formation

BackgroundAbdominal aortic aneurysm (AAA) is a disease associated with the pathophysiologic degradation of the tunica media resulting in aortic dilatation, systemic inflammation, and dysregulated hemostasis. Beyond role its role in initiating primary hemostasis, platelets are a source of ROS, inflammatory cytokines and growth factors necessary for angiogenesis and vascular remodeling. Although platelets contribute to the progression of established aneurysms, their role in the initiation of AAA remains undefined. MethodsLow density lipoprotein receptor deficient (Ldlr-/-) mice were examined for platelet accumulation in the angiotensin II (AngII) model of AAA utilizing in vivo labeling techniques. Two platelet antagonists (clopidogrel and aspirin), a thrombin inhibitor (dabigatran) or genetic deficiencies (protease-activated receptor 4, P2Y12, Lnk) were administered to AngII-infused mice to determine the role of platelets in initiation of AAA. The effect of platelet depletion was examined in multiple mouse strains of AngII-induced AAA and two additional aneurysm models. PheWAS and meta-analysis was analyzed in humans for platelet gene SNPs associated with AAA. ResultsWe show that platelets are recruited rapidly to the aorta after the initiation of AngII infusion. Genetic deficiency of platelet receptors had no effect on abdominal aortic diameter, but augmented rupture-induced death in littermate versus placebo controls during AngII-induced AAA. Moreover, Ldlr-/- mice receiving anti-platelet inhibitors or a thrombin inhibitor also had augmented rupture-induced death. Platelet depletion preceding aneurysm formation resulted in pervasive rupture-induced death in several mouse strains and with three different mouse models of AAA. ConclusionsInhibition of platelet function is detrimental in an early expanding aortic lumen resulting in catastrophic rupture and hemodynamic failure in murine AAA models.

pathology↗

bioRxiv2024

Accelerating Genome- and Phenome-Wide Association Studies using GPUs - A case study using data from the Million Veteran Program

The expansion of biobanks has significantly propelled genomic discoveries yet the sheer scale of data within these repositories poses formidable computational hurdles, particularly in handling extensive matrix operations required by prevailing statistical frameworks. In this work, we introduce computational optimizations to the SAIGE (Scalable and Accurate Implementation of Generalized Mixed Model) algorithm, notably employing a GPU-based distributed computing approach to tackle these challenges. We applied these optimizations to conduct a large-scale genome-wide association study (GWAS) across 2,068 phenotypes derived from electronic health records of 635,969 diverse participants from the Veterans Affairs (VA) Million Veteran Program (MVP). Our strategies enabled scaling up the analysis to over 6,000 nodes on the Department of Energy (DOE) Oak Ridge Leadership Computing Facility (OLCF) Summit High-Performance Computer (HPC), resulting in a 20-fold acceleration compared to the baseline model. We also provide a Docker container with our optimizations that was successfully used on multiple cloud infrastructures on UK Biobank and All of Us datasets where we showed significant time and cost benefits over the baseline SAIGE model.

genetics↗

bioRxiv2024

An aortic hemodynamic fingerprint reduced order modeling analysis reveals traits associated with vascular disease in a medical biobank

PurposeTo determine the clinical relevance of reduced order model (ROM) aortic hemodynamic imaging-derived phenotypes (IDPs) for a range of flow conditions applied to computed tomography (CT) scan data in the Penn Medicine Biobank (PMBB). MethodsThe human thoracic aorta was automatically segmented in 3,204 chest CT scans from patients in the Penn Medicine Biobank (PMBB) patients using deep learning. Thoracic aorta anatomic IDPs such as aortic diameter and length were computed. Resistance, and flow boundary conditions, were varied, resulting in 125,000 ROM simulations, producing a fingerprint of aortic hemodynamics IDPs for a range of flow conditions. To determine the clinical relevance of the aortic hemodynamic fingerprint, untargeted phenome wide association studies (PheWAS) for disease conditions were performed using aortic geometries and pulse pressure as IDPs. ResultsBy utilizing patient metadata from the PMBB, the human aortic radius for different age groups over a normalized radius was visualized, showing how the vessel deforms with age, as well as other characteristic geometric information. The average radius of the ascending thoracic aortic data set was 26.6 {+/-} 3.1 mm, with an average length of 310 {+/-} 37 mm. A combination of pathology codes (phecodes) and hemodynamic simulations were utilized to develop a relationship between them, showing a strong relationship between the resulting pulse pressure and diseases relating to aortic aneurysms and heart valve disorders. The average pulse pressure calculated by the model was 22.5 {+/-} 8.5 mmHg, with the maximum pressure modeled by the system being 201 mmHg, with the minimum being 63.6 mmHg. The pulse pressures of the most significant phecodes were examined for patients with and without the condition, showing a slight separation between the two cases. The pulse pressure was also slightly negatively correlated with the calculated tapering angle of the ascending thoracic aorta. ConclusionsROM hemodynamic simulations can be applied to aortic imaging traits from thoracic imaging data in a medical biobank. The derived hemodynamic fingerprint, describing the response of the aorta to a range of flow conditions, shows clinically relevant associations with disease.

bioengineering↗

bioRxiv2023

Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes

The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce Multi-response Mendelian randomization (MR2), a novel MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and viceversa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases.

genetics↗

bioRxiv2019

Polygenic Risk Scores for Cardio-renal-metabolic Diseases in the Penn Medicine Biobank

Cardio-renal-metabolic (CaReMe) conditions are common and the leading cause of mortality around the world. Genome-wide association studies have shown that these diseases are polygenic and share many genetic risk factors. Identifying individuals at high genetic risk will allow us to target prevention and treatment strategies. Polygenic risk scores (PRS) are aggregate weighted counts that can demonstrate an individuals genetic liability for disease. However, current PRS are often based on European ancestry individuals, limiting the implementation of precision medicine efforts in diverse populations. In this study, we develop PRS for six diseases and traits related to cardio-renal-metabolic disease in the Penn Medicine Biobank. We investigate their performance in both European and African ancestry individuals, and identify genetic and phenotypic overlap within these conditions. We find that genetic risk is associated with the primary phenotype in both ancestries, but this does not translate into a model of predictive value in African ancestry individuals. We conclude that future research should prioritize genetic studies in diverse ancestries in order to address this disparity.

genomics↗

bioRxiv2019

Genome-wide Association Study of Alcohol Consumption and Use Disorder in Multiple Populations (N = 274,424)

Although alcohol consumption level and alcohol use disorder (AUD) diagnosis are both moderately heritable, their genetic risks and overlap are not well understood. We conducted genome-wide association studies of these traits using longitudinal Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) scores (reflecting alcohol consumption) and AUD diagnoses from electronic health records (EHRs) in a single, large multi-ancestry Million Veteran Program sample. Meta-analysis across population groups (N = 274,424) identified 18 genome-wide significant loci, 5 of which were associated with both traits and 13 with either AUDIT-C (N = 8) or AUD (N = 5). A significant genetic correlation between the traits reflects this overlap. However, downstream analyses revealed biologically meaningful points of divergence. Cell-type group partitioning heritability enrichment analyses indicated that central nervous system was the most significant cell type for AUDIT-C and the only significant cell type for AUD. Polygenic risk scores (PRS) for both traits were associated with alcohol-related disorders in two independent samples. Genetic correlations for 188 non-alcohol-related traits were significantly different for the two traits, as were the phenotypes associated with the traits polygenic risk scores. We conclude that EHR-derived, longitudinal, repeated measures of alcohol consumption level and AUD diagnosis can facilitate genetic discovery and help to elucidate the relationship between drinking level and AUD risk. Finally, although heavy drinking is a key risk factor for AUD, it is not a sufficient cause of the disorder.

genetics↗