AoUPRS: A Cost-Effective and Versatile PRS Calculator for the All of Us Program.

Background: The All of Us (AoU) Research Program provides a comprehensive genomic dataset to accelerate health research and medical breakthroughs. Despite its potential, researchers face significant challenges, including high costs and inefficiencies associated with data extraction and analysis. AoUPRS addresses these challenges by offering a versatile and cost-effective tool for calculating polygenic risk scores (PRS), enabling both experienced and novice researchers to leverage the AoU dataset for significant genomic discoveries. Results: AoUPRS is implemented in Python and utilizes the Hail framework for genomic data analysis. It offers two distinct approaches for PRS calculation: the Hail MatrixTable (MT) and the Hail Variant Dataset (VDS). The MT approach provides a dense representation of genotype data, while the VDS approach offers a sparse representation, significantly reducing computational costs. In performance evaluations, the VDS approach demonstrated a cost reduction of up to 99.51% for smaller scores and 85% for larger scores compared to the MT approach. Both approaches yielded similar predictive power, as shown by logistic regression analyses of PRS for coronary artery disease, atrial fibrillation, and type 2 diabetes. The empirical cumulative distribution functions (ECDFs) for PRS values further confirmed the consistency between the two methods. Conclusions: AoUPRS is a versatile and cost-effective tool that addresses the high costs and inefficiencies associated with PRS calculations using the AoU dataset. By offering both dense and sparse data processing approaches, AoUPRS allows researchers to choose the approach best suited to their needs, facilitating genomic discoveries. The tool's open-source availability on GitHub, coupled with detailed documentation and tutorials, ensures accessibility and ease of use for the scientific community.

DNA Methylation Patterns Associated with Tinnitus in Young Adults-A Pilot Study.

PURPOSE: Tinnitus, the perception of sound without any external sound source, is a prevalent hearing health concern. Mounting evidence suggests that a confluence of genetic, environmental, and lifestyle factors can influence the pathogenesis of tinnitus. We hypothesized that alteration in DNA methylation, an epigenetic modification that occurs at cytosines of cytosine-phosphate-guanine (CpG) dinucleotide sites, where a methyl group from S-adenyl methionine gets transferred to the fifth carbon of the cytosine, could contribute to tinnitus. DNA methylation patterns are tissue-specific, but the tissues involved in tinnitus are not easily accessible in humans. This pilot study used saliva as a surrogate tissue to identify differentially methylated CpG regions (DMRs) associated with tinnitus. The study was conducted on healthy young adults reporting bilateral continuous chronic tinnitus to limit the influence of age-related confounding factors and health-related comorbidities. METHODS: The present study evaluated the genome-wide methylation levels from saliva-derived DNA samples from 24 healthy young adults with bilateral continuous chronic tinnitus (> 1 year) and 24 age, sex, and ethnicity-matched controls with no tinnitus. Genome-wide DNA methylation was evaluated for > 850,000 CpG sites using the Infinium Human Methylation EPIC BeadChip. The association analysis used the Bumphunter algorithm on 23 cases and 20 controls meeting the quality control standards. The methylation level was expressed as the area under the curve of CpG sites within DMRs.The FDR-adjusted p-value threshold of 0.05 was used to identify statistically significant DMRs associated with tinnitus. RESULTS: We obtained 25 differentially methylated regions (DMRs) associated with tinnitus. Genes within or in the proximity of the hypermethylated DMRs related to tinnitus included LCLAT1, RUNX1, RUFY1, NUDT12, TTC23, SLC43A2, C4orf27 (STPG2), and EFCAB4B. Genes within or in the proximity of hypomethylated DMRs associated with tinnitus included HLA-DPB2, PM20D1, TMEM18, SNTG2, MUC4, MIR886, MIR596, TXNRD1, EID3, SDHAP3, HLA-DPB2, LASS3 (CERS3), C10orf11 (LRMDA), HLA-DQB1, NADK, SZRD1, MFAP2, NUP210L, TPM3, INTS9, and SLC2A14. The burden of genetic variation could explain the differences in the methylation levels for DMRs involving HLA-DPB2, HLA-DQB1, and MUC4, indicating the need for replication in large independent cohorts. CONCLUSION: Consistent with the literature on comorbidities associated with tinnitus, we identified genes within or close to DMRs involved in auditory functions, chemical dependency, cardiovascular diseases, psychiatric conditions, immune disorders, and metabolic syndromes. These results indicate that epigenetic mechanisms could influence tinnitus, and saliva can be a good surrogate for identifying the epigenetic underpinnings of tinnitus in humans. Further research with a larger sample size is needed to identify epigenetic biomarkers and investigate their influence on the phenotypic expression of tinnitus.

Zoish: A Novel Feature Selection Approach Leveraging Shapley Additive Values for Machine Learning Applications in Healthcare.

In the intricate landscape of healthcare analytics, effective feature selection is a prerequisite for generating robust predictive models, especially given the common challenges of sample sizes and potential biases. Zoish uniquely addresses these issues by employing Shapley additive values-an idea rooted in cooperative game theory-to enable both transparent and automated feature selection. Unlike existing tools, Zoish is versatile, designed to seamlessly integrate with an array of machine learning libraries including scikit-learn, XGBoost, CatBoost, and imbalanced-learn.The distinct advantage of Zoish lies in its dual algorithmic approach for calculating Shapley values, allowing it to efficiently manage both large and small datasets. This adaptability renders it exceptionally suitable for a wide spectrum of healthcare-related tasks. The tool also places a strong emphasis on interpretability, providing comprehensive visualizations for analyzed features. Its customizable settings offer users fine-grained control over feature selection, thus optimizing for specific predictive objectives.This manuscript elucidates the mathematical framework underpinning Zoish and how it uniquely combines local and global feature selection into a single, streamlined process. To validate Zoish's efficiency and adaptability, we present case studies in breast cancer prediction and Montreal Cognitive Assessment (MoCA) prediction in Parkinson's disease, along with evaluations on 300 synthetic datasets. These applications underscore Zoish's unparalleled performance in diverse healthcare contexts and against its counterparts.

Polygenic Risk Score-Based Association Analysis Identifies Genetic Comorbidities Associated with Age-Related Hearing Difficulty in Two Independent Samples.

PURPOSE: Age-related hearing loss is the most common form of permanent hearing loss that is associated with various health traits, including Alzheimer's disease, cognitive decline, and depression. The present study aims to identify genetic comorbidities of age-related hearing loss. Past genome-wide association studies identified multiple genomic loci involved in common adult-onset health traits. Polygenic risk scores (PRS) could summarize the polygenic inheritance and quantify the genetic susceptibility of complex traits independent of trait expression. The present study conducted a PRS-based association analysis of age-related hearing difficulty in the UK Biobank sample (N = 425,240), followed by a replication analysis using hearing thresholds (HTs) and distortion-product otoacoustic emissions (DPOAEs) in 242 young adults with self-reported normal hearing. We hypothesized that young adults with genetic comorbidities associated with age-related hearing difficulty would exhibit subclinical decline in HTs and DPOAEs in both ears. METHODS: A total of 111,243 participants reported age-related hearing difficulty in the UK Biobank sample (> 40 years). The PRS models were derived from the polygenic risk score catalog to obtain 2627 PRS predictors across the health spectrum. HTs (0.25-16 kHz) and DPOAEs (1-16 kHz, L1/L2 = 65/55 dB SPL, F2/F1 = 1.22) were measured on 242 young adults. Saliva-derived DNA samples were subjected to low-pass whole genome sequencing, followed by genome-wide imputation and PRS calculation. The logistic regression analyses were performed to identify PRS predictors of age-related hearing difficulty in the UK Biobank cohort. The linear mixed model analyses were performed to identify PRS predictors of HTs and DPOAEs. RESULTS: The PRS-based association analysis identified 977 PRS predictors across the health spectrum associated with age-related hearing difficulty. Hearing difficulty and hearing aid use PRS predictors revealed the strongest association with the age-related hearing difficulty phenotype. Youth with a higher genetic predisposition to hearing difficulty revealed a subclinical elevation in HTs and a decline in DPOAEs in both ears. PRS predictors associated with age-related hearing difficulty were enriched for mental health, lifestyle, metabolic, sleep, reproductive, digestive, respiratory, hematopoietic, and immune traits. Fifty PRS predictors belonging to various trait categories were replicated for HTs and DPOAEs in both ears. CONCLUSION: The study identified genetic comorbidities associated with age-related hearing loss across the health spectrum. Youth with a high genetic predisposition to age-related hearing difficulty and other related complex traits could exhibit sub-clinical decline in HTs and DPOAEs decades before clinically meaningful age-related hearing loss is observed. We posit that effective communication of genetic risk, promoting a healthy lifestyle, and reducing exposure to environmental risk factors at younger ages could help prevent or delay the onset of age-related hearing difficulty at older ages.

A genome-wide association study reveals a polygenic architecture of speech-in-noise deficits in individuals with self-reported normal hearing.

Speech-in-noise (SIN) perception is a primary complaint of individuals with audiometric hearing loss. SIN performance varies drastically, even among individuals with normal hearing. The present genome-wide association study (GWAS) investigated the genetic basis of SIN deficits in individuals with self-reported normal hearing in quiet situations. GWAS was performed on 279,911 individuals from the UB Biobank cohort, with 58,847 reporting SIN deficits despite reporting normal hearing in quiet. GWAS identified 996 single nucleotide polymorphisms (SNPs), achieving significance (p < 5*10-8) across four genomic loci. 720 SNPs across 21 loci achieved suggestive significance (p < 10-6). GWAS signals were enriched in brain tissues, such as the anterior cingulate cortex, dorsolateral prefrontal cortex, entorhinal cortex, frontal cortex, hippocampus, and inferior temporal cortex. Cochlear cell types revealed no significant association with SIN deficits. SIN deficits were associated with various health traits, including neuropsychiatric, sensory, cognitive, metabolic, cardiovascular, and inflammatory conditions. A replication analysis was conducted on 242 healthy young adults. Self-reported speech perception, hearing thresholds (0.25-16 kHz), and distortion product otoacoustic emissions (1-16 kHz) were utilized for the replication analysis. 73 SNPs were replicated with a self-reported speech perception measure. 211 SNPs were replicated with at least one and 66 with at least two audiological measures. 12 SNPs near or within MAPT, GRM3, and HLA-DQA1 were replicated for all audiological measures. The present study highlighted a polygenic architecture underlying SIN deficits in individuals with self-reported normal hearing.

Meta-Prediction of Coronary Artery Disease Risk.

Coronary artery disease (CAD) remains the leading cause of mortality and morbidity worldwide. Recent advances in large-scale genome-wide association studies have highlighted the potential of genetic risk, captured as polygenic risk scores (PRS), in clinical prevention. However, the current clinical utility of PRS models is limited to identifying high-risk populations based on the top percentiles of genetic susceptibility. While some studies have attempted integrative prediction using genetic and non-genetic factors, many of these studies have been cross-sectional and focused solely on risk stratification. Our primary objective in this study was to integrate unmodifiable (age / genetics) and modifiable (clinical / biometric) risk factors into a prospective prediction framework which also produces actionable and personalized risk estimates for the purpose of CAD prevention in a heterogenous adult population. Thus, we present an integrative, omnigenic, meta-prediction framework that effectively captures CAD risk subgroups, primarily distinguished by degree and nature of genetic risk, with distinct risk reduction profiles predicted from standard clinical interventions. Initial model development considered ~ 2,000 predictive features, including demographic data, lifestyle factors, physical measurements, laboratory tests, medication usage, diagnoses, and genetics. To power our meta-prediction approach, we stratified the UK Biobank into two primary cohorts: 1) a prevalent CAD cohort used to train baseline and prospective predictive models for contributing risk factors and diagnoses, and 2) an incident CAD cohort used to train the final CAD incident risk prediction model. The resultant 10-year incident CAD risk model is composed of 35 derived meta-features from models trained on the prevalent risk cohort, most of which are predicted baseline diagnoses with multiple embedded PRSs. This model achieved an AUC of 0.81 and macro-averaged F1-score of 0.65, outperforming standard clinical scores and prior integrative models. We further demonstrate that individualized risk reduction profiles can be derived from this model, with genetic risk mediating the degree of risk reduction achieved by standard clinical interventions.

The expression patterns of MALAT-1, NEAT-1, THRIL, and miR-155-5p in the acute to the post-acute phase of COVID-19 disease

ABSTRACT Introduction: One of the hallmarks of COVID-19 is overwhelming inflammation, which plays a very important role in the pathogenesis of COVID-19. Thus, identification of inflammatory factors that interact with the SARS-CoV-2 can be very important to control and diagnose the severity of COVID-19. The aim of this study was to investigate the expression patterns of inflammation-related non-coding RNAs (ncRNAs) including MALAT-1, NEAT-1, THRIL, and miR-155-5p from the acute phase to the recovery phase of COVID-19. Methods: Total RNA was extracted from Peripheral Blood Mononuclear Cell (PBMC) samples of 20 patients with acute COVID-19 infection and 20 healthy individuals and the expression levels of MALAT-1, NEAT-1, THRIL, and miR-155-5p were evaluated by real-time PCR assay. Besides, in order to monitor the expression pattern of selected ncRNAs from the acute phase to the recovery phase of COVID-19 disease, the levels of ncRNAs were re-measured 6-7 weeks after the acute phase. Result: The mean expression levels of MALAT-1, THRIL, and miR-155-5p were significantly increased in the acute phase of COVID-19 compared with a healthy control group. In addition, the expression levels of MALAT-1 and THRIL in the post-acute phase of COVID-19 were significantly lower than in the acute phase of COVID-19. According to the ROC curve analysis, these ncRNAs could be considered useful biomarkers for COVID-19 diagnosis and for discriminating between acute and post-acute phase of COVID-19. Discussion: Inflammation-related ncRNAs (MALAT-1, THRIL, and miR-150-5p) can act as hopeful biomarkers for the monitoring and diagnosis of COVID-19 disease.