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.

Transdermal Delivery of Niacin Through Polysaccharide Films Ameliorates Cutaneous Flushing in Experimental Wistar Rats.

BACKGROUND: Niacin, an established therapeutic for dyslipidemia, is hindered by its propensity to induce significant cutaneous flushing when administered orally in its unmodified state, thereby constraining its clinical utility. OBJECTIVE: This study aimed to fabricate, characterize, and assess the in-vitro and in-vivo effectiveness of niacin-loaded polymeric films (NLPFs) comprised of carboxymethyl tamarind seed polysaccharide. The primary objective was to mitigate the flushing-related side effects associated with oral niacin administration. METHODS: NLPFs were synthesized using the solvent casting method and subsequently subjected to characterization, including assessments of tensile strength, moisture uptake, thickness, and folding endurance. Surface characteristics were analyzed using a surface profiler and scanning electron microscopy (SEM). Potential interactions between niacin and the polysaccharide core were investigated through X-ray diffraction experiments (XRD) and Fourier transform infrared spectroscopy (FTIR). The viscoelastic properties of the films were explored using a Rheometer. In-vitro assessments included drug release studies, swelling behavior assays, and antioxidant assays. In-vivo efficacy was evaluated through skin permeation assays, skin irritation assays, and histopathological analyses. RESULTS: NLPFs exhibited a smooth texture with favorable tensile strength and moisture absorption capabilities. Niacin demonstrated interaction with the polysaccharide core, rendering the films amorphous. The films displayed slow and sustained drug release, exceptional antioxidant properties, optimal swelling behavior, and viscoelastic characteristics. Furthermore, the films exhibited biocompatibility and non-toxicity towards skin cells. CONCLUSION: NLPFs emerged as promising carrier systems for the therapeutic transdermal delivery of niacin, effectively mitigating its flushing-associated adverse effects.

Diagnosis of the Initial Stage of Hepatocellular Carcinoma: A Review.

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths worldwide. There may be more than a million instances of hepatocellular carcinoma by 2025, making it a persistent concern for global health. The most common form of hepatocellular carcinoma accounts for more than 90% of cases. There is no known cure for hepatocellular carcinoma, which is usually detected late in life. Unlike most other common malignancies, such as lung, prostate, and breast cancers, where mortality rates are declining, rates of death are rising by around 2-3% every year. It is extremely difficult to diagnose hepatocellular carcinoma in its early stages. Alpha-fetoprotein serology studies and ultrasonography (US) monitoring were historically the primary methods for early detection of hepatocellular cancer. However, the sensitivity or specificity of ultrasonography/alpha-fetoprotein (US/AFP) is not high enough to detect hepatocellular carcinoma in its early stages. Alpha-fetoprotein, or AFP, is an amino acid that is normally produced by the liver or yolk sac of an embryonic baby. In adults, AFP levels are typically modest. Adults with high levels of AFP have been associated with several illnesses, the most well-known of which are certain types of cancer. It is still possible to diagnose hepatocellular carcinoma early because of current technological advancements. We address the advancements in the diagnosis of hepatocellular carcinoma in this article, with a focus on new imaging techniques and diagnostic markers for early-stage tumor identification.

Tunable Q-factor wavelet transform based identification of diabetic patients using ECG signals.

Diabetes is a chronic health condition that is characterized by increased levels of glucose (sugar) in the blood. It can have harmful effects on different parts of the body, such as the retina of the eyes, skin, nervous system, kidneys, and heart. Diabetes affects the structure of electrocardiogram (ECG) impulses by causing cardiovascular autonomic dysfunction. Multi-resolution analysis of the input ECG signal is utilized in this paper to develop a machine learning-based system for the automated detection of diabetic patients. In the first step, the input ECG signal is decomposed into sub-bands utilizing the tunable Q-factor wavelet transform (TQWT) technique. In the second step, four entropy-based characteristics are evaluated from each SB and elected using the K-W test method. To develop an automatic diabetes detection system, selected features are given as input with 10-fold validation to a SVM classifier using various kernel functions. The 3rd sub-band of TQWT with the Coarse Gaussian kernel function kernel of the SVM classifier yields a classification accuracy of 91.5%. In the same dataset, the comparative analysis demonstrates that the proposed method outperforms other existing methods.

A Review on Lipid-Based Nanoformulations for Targeting Brain through Non-invasive Nasal Route.

The nasal method for administering nanoformulations to the brain has been examined and proven successful by prior investigators. For the treatment of central nervous system (CNS) disorders such as neuropsychiatric, depression, Alzheimer and anxiety, intranasal administration has become more popular for delivering drugs to the brain. This method offers direct transport through neuronal pathways. The lipid-based nanocarriers like nanostructured lipid carriers (NLC) appear more favorable than other nanosystems for brain administration. The nanostructured lipid carriers (NLC) system can quickly transform into a gelling system to facilitate easy administration into the nasal passages. The various compatibility studies showed that the other lipid structured-based formulations may not work well for various reasons, including a low drug filing capacity; during storage, the formulation showed changes in the solid lipid structures, which gives a chance of medication ejection. Formulations containing NLC can minimize these problems by improving drug solubility and permeation rate by incorporating a ratio of liquid lipids with solid lipids, resulting in improved stability during storage and drug bioavailability because of the higher drug loading capacity. This review aimed to find and emphasize research on lipid-based nanocarrier formulations that have advanced the treatment of central nervous system illnesses using nasal passages to reach the targeted area's drug molecules.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network.

MOTIVATION: Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. RESULTS: Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities. AVAILABILITY AND IMPLEMENTATION: The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation.

Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.

Emulgel: A Cutting Edge Approach for Topical Drug Delivery System.

Emulgel is considered an advanced leading form of topical drug delivery system. It possesses the quality of a dual control drug mechanism for drug release as it holds the properties of both gel as well as emulsion. Emulgel is capable of overcoming the problems of the conventional route of topical drug delivery, like low spreadability and stickiness with the delivery of hydrophobic drugs, enhanced bioavailability at the local site of action, no greasy texture, and ensuring patient compliance. An emulsion is used either w/o or o/w, and the drug can be incorporated into the suitable phase of the emulsion. After that, the emulsion is incorporated into the gel phase. Several factors like oil phase, gelling agent, and emulsifier can affect the efficacy and stability. This advancement is beneficial not only for dermatology but also for cosmetology as well. Currently, emulgel-based formulations are used for the delivery of anti-inflammatory, analgesic, anti-acne, and antifungal drugs with a wide array of exploration.

Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis.

The objective of this study is to define CT imaging derived phenotypes for patients with hepatic steatosis, a common metabolic liver condition, and determine its association with patient data from a medical biobank. There is a need to further characterize hepatic steatosis in lean patients, as its epidemiology may differ from that in overweight patients. A deep learning method determined the spleen-hepatic attenuation difference (SHAD) in Hounsfield Units (HU) on abdominal CT scans as a quantitative measure of hepatic steatosis. The patient cohort was stratified by BMI with a threshold of 25 kg/m2 and hepatic steatosis with threshold SHAD ≥ - 1 HU or liver mean attenuation ≤ 40 HU. Patient characteristics, diagnoses, and laboratory results representing metabolism and liver function were investigated. A phenome-wide association study (PheWAS) was performed for the statistical interaction between SHAD and the binary characteristic LEAN. The cohort contained 8914 patients-lean patients with (N = 278, 3.1%) and without (N = 1867, 20.9%) steatosis, and overweight patients with (N = 1863, 20.9%) and without (N = 4906, 55.0%) steatosis. Among all lean patients, those with steatosis had increased rates of cardiovascular disease (41.7 vs 27.8%), hypertension (86.7 vs 49.8%), and type 2 diabetes mellitus (29.1 vs 15.7%) (all p < 0.0001). Ten phenotypes were significant in the PheWAS, including chronic kidney disease, renal failure, and cardiovascular disease. Hepatic steatosis was found to be associated with cardiovascular, kidney, and metabolic conditions, separate from overweight BMI.

Polygenic risk scores for cardiometabolic traits demonstrate importance of ancestry for predictive precision medicine.

Polygenic risk scores (PRS) have predominantly been derived from genome-wide association studies (GWAS) conducted in European ancestry (EUR) individuals. In this study, we present an in-depth evaluation of PRS based on multi-ancestry GWAS for five cardiometabolic phenotypes in the Penn Medicine BioBank (PMBB) followed by a phenome-wide association study (PheWAS). We examine the PRS performance across all individuals and separately in African ancestry (AFR) and EUR ancestry groups. For AFR individuals, PRS derived using the multi-ancestry LD panel showed a higher effect size for four out of five PRSs (DBP, SBP, T2D, and BMI) than those derived from the AFR LD panel. In contrast, for EUR individuals, the multi-ancestry LD panel PRS demonstrated a higher effect size for two out of five PRSs (SBP and T2D) compared to the EUR LD panel. These findings underscore the potential benefits of utilizing a multi-ancestry LD panel for PRS derivation in diverse genetic backgrounds and demonstrate overall robustness in all individuals. Our results also revealed significant associations between PRS and various phenotypic categories. For instance, CAD PRS was linked with 18 phenotypes in AFR and 82 in EUR, while T2D PRS correlated with 84 phenotypes in AFR and 78 in EUR. Notably, associations like hyperlipidemia, renal failure, atrial fibrillation, coronary atherosclerosis, obesity, and hypertension were observed across different PRSs in both AFR and EUR groups, with varying effect sizes and significance levels. However, in AFR individuals, the strength and number of PRS associations with other phenotypes were generally reduced compared to EUR individuals. Our study underscores the need for future research to prioritize 1) conducting GWAS in diverse ancestry groups and 2) creating a cosmopolitan PRS methodology that is universally applicable across all genetic backgrounds. Such advances will foster a more equitable and personalized approach to precision medicine.

Neuropathy, its Profile and Experimental Nerve Injury Neuropathic Pain Models: A Review.

Neuropathy is a terrible disorder that has a wide range of etiologies. Drug-induced neuropathy, which happens whenever a chemical agent damages the peripheral nerve system, has been linked here to the iatrogenic creation of some drugs. It is potentially permanent and causes sensory impairments and paresthesia that typically affects the hands, feet, and stockings; motor participation is uncommon. It might appear suddenly or over time, and the long-term outlook varies. The wide range of chronic pain conditions experienced by people has been one of the main obstacles to developing new, more effective medications for the treatment of neuropathic pain. Animal models can be used to examine various neuropathic pain etiologies and symptoms. Several models investigate the peripheral processes of neuropathic pain, whereas some even investigate the central mechanisms, such as drug induce models like vincristine, cisplatin, bortezomib, or thalidomide, etc., and surgical models like sciatic nerve chronic constriction injury (CCI), sciatic nerve ligation through spinal nerve ligation (SNL), sciatic nerve damage caused by a laser, SNI (spared nerve injury), etc. The more popular animal models relying on peripheral nerve ligatures are explained. In contrast to chronic sciatic nerve contraction, which results in behavioral symptoms of less reliable stressful neuropathies, (SNI) spared nerve injury generates behavioral irregularities that are more feasible over a longer period. This review summarizes the latest methods models as well as clinical ideas concerning this mechanism. Every strongest current information on neuropathy is discussed, along with several popular laboratory models for causing neuropathy.

Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program.

Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide P<4.6×10-11 significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.

A critical review on the role of nanotheranostics mediated approaches for targeting ß amyloid in Alzheimer's.

Alzheimer's is one of the most common neurodegenerative illnesses that affect brain cellular function. In this disease, the neurons in the brain are considered to be decaying steadily but consistently by the accumulation of amyloid mass, particularly the ß-amyloids, amyloid proteins, and Tau proteins. The most responsible amyloid-proteins are amyloid-40 and amyloid-42, which have a high probability of accumulating in excess over the brain cell, interfering with normal brain cell function and triggering brain cell death. The advancement of pharmaceutical sciences leads to the development of Nanotheranostics technology, which may be used to diagnose and treat Alzheimer's. They are the colloidal nanoparticles functionalised with the therapeutic moiety as well as a diagnostic moiety. This article discusses the prognosis of Alzheimer's, various nanotheranostics approaches (nanoparticles, quantum dots, aptamers, dendrimers, etc), and their recent advancement in managing Alzheimer's. Also, various in-vitro and in-vivo diagnostic methodologies were discussed with respect to nanotheranostics.

Describing the pathophysiology of Alzheimer's with respect to amyloid ß in the prognosis of the diseasePresenting the various nanotheranostics techniques for the detection and treatment of Alzheimer's diseaseNanoparticles, Aptamers, and Dendrimers used as diagnostic and treatment entitiesIn-vivo (MRI, OI) and In-vitro (STM, TRPS) diagnostic approaches for detecting Alzheimer's disease.

DETECT: Feature extraction method for disease trajectory modeling in electronic health records.

Modeling with longitudinal electronic health record (EHR) data proves challenging given the high dimensionality, redundancy, and noise captured in EHR. In order to improve precision medicine strategies and identify predictors of disease risk in advance, evaluating meaningful patient disease trajectories is essential. In this study, we develop the algorithm DiseasE Trajectory fEature extraCTion (DETECT) for feature extraction and trajectory generation in high-throughput temporal EHR data. This algorithm can 1) simulate longitudinal individual-level EHR data, specified to user parameters of scale, complexity, and noise and 2) use a convergent relative risk framework to test intermediate codes occurring between specified index code(s) and outcome code(s) to determine if they are predictive features of the outcome. Temporal range can be specified to investigate predictors occurring during a specific period of time prior to onset of the outcome. We benchmarked our method on simulated data and generated real-world disease trajectories using DETECT in a cohort of 145,575 individuals diagnosed with hypertension in Penn Medicine EHR for severe cardiometabolic outcomes.

Development of complemented comprehensive networks for rapid screening of repurposable drugs applicable to new emerging disease outbreaks.

BACKGROUND: Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease. METHODS: We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses. RESULTS: The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype. CONCLUSION: We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network.

Many diseases exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

The genetic and phenotypic correlates of mtDNA copy number in a multi-ancestry cohort.

Mitochondrial DNA copy number (mtCN) is often treated as a proxy for mitochondrial (dys-) function and disease risk. Pathological changes in mtCN are common symptoms of rare mitochondrial disorders, but reported associations between mtCN and common diseases vary across studies. To understand the biology of mtCN, we carried out genome- and phenome-wide association studies of mtCN in 30,666 individuals from the Penn Medicine BioBank (PMBB)-a diverse cohort of largely African and European ancestry. We estimated mtCN in peripheral blood using exome sequence data, taking cell composition into account. We replicated known genetic associations of mtCN in the PMBB and found that their effects are highly correlated between individuals of European and African ancestry. However, the heritability of mtCN was much higher among individuals of largely African ancestry (h2=0.3) compared with European ancestry individuals(h2=0.1). Admixture mapping suggests that there are undiscovered variants underlying mtCN that are differentiated in frequency between individuals with African and European ancestry. We show that mtCN is associated with many health-related phenotypes. We discovered robust associations between mtDNA copy number and diseases of metabolically active tissues, such as cardiovascular disease and liver damage, that were consistent across African and European ancestry individuals. Other associations, such as epilepsy and prostate cancer, were only discovered in either individuals with European or African ancestry but not both. We show that mtCN-phenotype associations can be sensitive to blood cell composition and environmental modifiers, explaining why such associations are inconsistent across studies. Thus, mtCN-phenotype associations must be interpreted with care.