Viral SARS-CoV-2 Rebound Rates in Linked Commercial Pharmacy-Based Testing and Health Care Claims.

Background: Viral SARS-CoV-2 rebound (viral RNA rebound) is challenging to characterize in large cohorts due to the logistics of collecting frequent and regular diagnostic test results. Pharmacy-based testing data provide an opportunity to study the phenomenon in a large population, also enabling subgroup analyses. The current real-world evidence approach complements approaches focused on smaller, prospective study designs. Methods: We linked real-time reverse transcription quantitative polymerase chain reaction test data from national pharmacy-based testing to health care claims data via tokenization to calculate the cumulative incidence of viral RNA rebound within 28 days following positive test results in nirmatrelvir/ritonavir (NMV-r)-treated and untreated individuals during the Omicron era (December 2021-November 2022) and prior to the Omicron era (October 2020-November 2021). Results: Among 30 646 patients, the rate of viral RNA rebound was 3.5% (95% CI, 2.0%-5.7%) in NMV-r-treated infections as compared with 1.5% (95% CI, 1.3%-1.7%) in untreated infections during the Omicron era and 1.9% (95% CI, 1.7%-2.1%) prior to the Omicron era. Viral RNA rebound in patients who were vaccinated (n = 8151), high risk (n = 4411), or older (≥65 years, n = 4411) occurred at comparable rates to the overall cohort (range, 1.1%-4.8%). Viral rebounds to high RNA levels in NMV-r-treated infections occurred in 8% of viral rebounds as compared with 5% to 11% in untreated infections. Rates of hospitalization were comparable between patients with NMV-r-treated infections with viral RNA rebound (0%) and untreated patients with viral RNA rebound (0%-1.2%). Conclusions: Our findings suggest viral RNA rebound is rare (< 5%), with rates that were consistent with those from the EPIC-HR trial (Evaluation of Protease Inhibition for COVID-19 in High-Risk Patients). Most occurrences of viral RNA rebound were associated with low viral RNA levels, and viral RNA rebound progression to severe disease was not observed.

SARS-CoV-2 Antiviral Prescribing Gaps Among Nonhospitalized High-Risk Adults.

Within a multistate clinical cohort, SARS-CoV-2 antiviral prescribing patterns were evaluated from April 2022-June 2023 among nonhospitalized patients with SARS-CoV-2 with risk factors for severe COVID-19. Among 3247 adults, only 31.9% were prescribed an antiviral agent (87.6% nirmatrelvir/ritonavir, 11.9% molnupiravir, 0.5% remdesivir), highlighting the need to identify and address treatment barriers.

The relationship between the epigenetic aging biomarker "grimage" and lung function in both the airway and blood of people living with HIV: An observational cohort study.

BACKGROUND: Age-related comorbidities such as chronic obstructive pulmonary disease (COPD) are common in people living with human immunodeficiency virus (PLWH). We investigated the relationship between COPD and the epigenetic age of the airway epithelium and peripheral blood of PLWH. METHODS: Airway epithelial brushings from 34 PLWH enrolled in the St. Paul's Hospital HIV Bronchoscopy cohort and peripheral blood from 378 PLWH enrolled in The Strategic Timing of Antiretroviral Treatment (START) study were profiled for DNA methylation. The DNA methylation biomarker of age and healthspan, GrimAge, was calculated in both tissue compartments. We tested the association of GrimAge with COPD in the airway epithelium and airflow obstruction as defined by an FEV1/FVC<0.70, and FEV1 decline over 6 years in blood. FINDINGS: The airway epithelium of PLWH with COPD was associated with greater GrimAge residuals compared to PLWH without COPD (Beta=3.18, 95%CI=1.06-5.31, P=0.005). In blood, FEV1/FVC

Epigenome-wide association study and epigenetic age acceleration associated with cigarette smoking among Costa Rican adults.

Smoking-associated DNA methylation (DNAm) signatures are reproducible among studies of mostly European descent, with mixed evidence if smoking accelerates epigenetic aging and its relationship to longevity. We evaluated smoking-associated DNAm signatures in the Costa Rican Study on Longevity and Healthy Aging (CRELES), including participants from the high longevity region of Nicoya. We measured genome-wide DNAm in leukocytes, tested Epigenetic Age Acceleration (EAA) from five clocks and estimates of telomere length (DNAmTL), and examined effect modification by the high longevity region. 489 participants had a mean (SD) age of 79.4 (10.8) years, and 18% were from Nicoya. Overall, 7.6% reported currently smoking, 35% were former smokers, and 57.4% never smoked. 46 CpGs and five regions (e.g. AHRR, SCARNA6/SNORD39, SNORA20, and F2RL3) were differentially methylated for current smokers. Former smokers had increased Horvath's EAA (1.69-years; 95% CI 0.72, 2.67), Hannum's EAA (0.77-years; 95% CI 0.01, 1.52), GrimAge (2.34-years; 95% CI1.66, 3.02), extrinsic EAA (1.27-years; 95% CI 0.34, 2.21), intrinsic EAA (1.03-years; 95% CI 0.12, 1.94) and shorter DNAmTL (- 0.04-kb; 95% CI - 0.08, - 0.01) relative to non-smokers. There was no evidence of effect modification among residents of Nicoya. Our findings recapitulate previously reported and novel smoking-associated DNAm changes in a Latino cohort.

Maternal Prenatal Anxiety and the Fetal Origins of Epigenetic Aging.

BACKGROUND: The fetal origins of mental health is a well-established framework that currently lacks a robust index of the biological embedding of prenatal adversity. The Pediatric-Buccal-Epigenetic (PedBE) clock is a novel epigenetic tool that associates with aspects of the prenatal environment, but additional validation in longitudinal datasets is required. Likewise, the relationship between prenatal maternal mental health and the PedBE clock has not been described. METHODS: Longitudinal cohorts from the Netherlands (Basal Influences on Baby Development [BIBO] n = 165) and Singapore (Growing Up in Singapore Towards Healthy Outcomes [GUSTO] n = 340) provided data on prenatal maternal anxiety and longitudinal assessments of buccal cell-derived genome-wide DNA methylation assessed at 6 and 10 years of age in BIBO, and at 3, 9, and 48 months of age in GUSTO. Measures of epigenetic age acceleration were calculated using the PedBE clock and benchmarked against an established multi-tissue epigenetic predictor. RESULTS: Prenatal maternal anxiety predicted child PedBE epigenetic age acceleration in both cohorts, with effects largely restricted to males and not females. These results were independent of obstetric, socioeconomic, and genetic risk factors, with a larger effect size for prenatal anxiety than depression. PedBE age acceleration predicted increased externalizing symptoms in males from mid- to late childhood in the BIBO cohort only. CONCLUSIONS: These findings point to the fetal origins of epigenetic age acceleration and reveal an increased sensitivity in males. Convergent evidence underscores the societal importance of providing timely and effective mental health support to pregnant individuals, which may have lasting consequences for both mother and child.

An integrated, modular approach to data science education in microbiology.

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.

DNA methylation is associated with airflow obstruction in patients living with HIV.

INTRODUCTION: People living with HIV (PLWH) suffer from age-related comorbidities such as COPD. The processes responsible for reduced lung function in PLWH are largely unknown. We performed an epigenome-wide association study to investigate whether blood DNA methylation is associated with impaired lung function in PLWH. METHODS: Using blood DNA methylation profiles from 161 PLWH, we tested the effect of methylation on FEV1, FEV1/FVC ratio and FEV1 decline over a median of 5 years. We evaluated the global methylation of PLWH with airflow obstruction by testing the differential methylation of transposable elements Alu and LINE-1, a well-described marker of epigenetic ageing. RESULTS: Airflow obstruction as defined by a FEV1/FVC<0.70 was associated with 1393 differentially methylated positions (DMPs), while 4676 were associated with airflow obstruction based on the FEV1/FVC

Occurrence of Accelerated Epigenetic Aging and Methylation Disruptions in Human Immunodeficiency Virus Infection Before Antiretroviral Therapy.

BACKGROUND: Whether accelerated aging develops over the course of chronic human immunodeficiency virus (HIV) infection or can be observed before significant immunosuppression on is unknown. We studied DNA methylation in blood to estimate cellular aging in persons living with HIV (PLWH) before the initiation of antiretroviral therapy (ART). METHODS: A total of 378 ART-naive PLWH who had CD4 T-cell counts >500/µL and were enrolled in the Strategic Timing of Antiretroviral Therapy trial (Pulmonary Substudy) were compared with 34 HIV-negative controls. DNA methylation was performed using the Illumina MethylationEPIC BeadChip. Differentially methylated positions (DMPs) and differentially methylated regions (DMRs) in PLWH compared with controls were identified using a robust linear model. Methylation age was calculated using a previously described epigenetic clock. RESULTS: There were a total of 56 639 DMPs and 6103 DMRs at a false discovery rate of <0.1. The top 5 DMPs corresponded to genes NLRC5, VRK2, B2M, and GPR6 and were highly enriched for cancer-related pathways. PLWH had significantly higher methylation age than HIV-negative controls (P = .001), with black race, low CD4 and high CD8 T-cell counts, and duration of HIV being risk factors for age acceleration. CONCLUSIONS: PLWH before the initiation of ART and with preserved immune status show evidence of advanced methylation aging.

TWIST1 DNA methylation is a cell marker of airway and parenchymal lung fibroblasts that are differentially methylated in asthma.

BACKGROUND: Mesenchymal fibroblasts are ubiquitous cells that maintain the extracellular matrix of organs. Within the lung, airway and parenchymal fibroblasts are crucial for lung development and are altered with disease, but it has been difficult to understand their roles due to the lack of distinct molecular markers. We studied genome-wide DNA methylation and gene expression in airway and parenchymal lung fibroblasts from healthy and asthmatic donors, to identify a robust cell marker and to determine if these cells are molecularly distinct in asthma. RESULTS: Airway (N = 8) and parenchymal (N = 15) lung fibroblasts from healthy individuals differed in the expression of 158 genes, and DNA methylation of 3936 CpGs (Bonferroni adjusted p value < 0.05). Differential DNA methylation between cell types was associated with differential expression of 42 genes, but no single DNA methylation CpG feature (location, effect size, number) defined the interaction. Replication of gene expression and DNA methylation in a second cohort identified TWIST1 gene expression, DNA methylation and protein expression as a cell marker of airway and parenchymal lung fibroblasts, with DNA methylation having 100% predictive discriminatory power. DNA methylation was differentially altered in parenchymal (112 regions) and airway fibroblasts (17 regions) with asthmatic status, with no overlap between regions. CONCLUSIONS: Differential methylation of TWIST1 is a robust cell marker of airway and parenchymal lung fibroblasts. Airway and parenchymal fibroblast DNA methylation are differentially altered in individuals with asthma, and the role of both cell types should be considered in the pathogenesis of asthma.

Maternal antenatal depression and child mental health: Moderation by genomic risk for attention-deficit/hyperactivity disorder.

Maternal antenatal depression strongly influences child mental health but with considerable inter-individual variation that is, in part, linked to genotype. The challenge is to effectively capture the genotypic influence. We outline a novel approach to describe genomic susceptibility to maternal antenatal depression focusing on child emotional/behavioral difficulties. Two cohorts provided measures of maternal depression, child genetic variation, and child mental health symptoms. We constructed a conventional polygenic risk score (PRS) for attention-deficit/hyperactivity disorder (ADHD) (PRSADHD) that significantly moderated the association between maternal antenatal depression and internalizing problems at 60 months (p = 2.94 × 10-4, R2 = .18). We then constructed an interaction PRS (xPRS) based on a subset of those single nucleotide polymorphisms from the PRSADHD that most accounted for the moderation of the association between maternal antenatal depression and child outcome. The interaction between maternal antenatal depression and this xPRS accounted for a larger proportion of the variance in child emotional/behavioral problems than models based on any PRSADHD (p = 5.50 × 10-9, R2 = .27), with similar findings in the replication cohort. The xPRS was significantly enriched for genes involved in neuronal development and synaptic function. Our study illustrates a novel approach to the study of genotypic moderation on the impact of maternal antenatal depression on child mental health and highlights the utility of the xPRS approach. These findings advance our understanding of individual differences in the developmental origins of mental health.

The PedBE clock accurately estimates DNA methylation age in pediatric buccal cells.

The development of biological markers of aging has primarily focused on adult samples. Epigenetic clocks are a promising tool for measuring biological age that show impressive accuracy across most tissues and age ranges. In adults, deviations from the DNA methylation (DNAm) age prediction are correlated with several age-related phenotypes, such as mortality and frailty. In children, however, fewer such associations have been made, possibly because DNAm changes are more dynamic in pediatric populations as compared to adults. To address this gap, we aimed to develop a highly accurate, noninvasive, biological measure of age specific to pediatric samples using buccal epithelial cell DNAm. We gathered 1,721 genome-wide DNAm profiles from 11 different cohorts of typically developing individuals aged 0 to 20 y old. Elastic net penalized regression was used to select 94 CpG sites from a training dataset (n = 1,032), with performance assessed in a separate test dataset (n = 689). DNAm at these 94 CpG sites was highly predictive of age in the test cohort (median absolute error = 0.35 y). The Pediatric-Buccal-Epigenetic (PedBE) clock was characterized in additional cohorts, showcasing the accuracy in longitudinal data, the performance in nonbuccal tissues and adult age ranges, and the association with obstetric outcomes. The PedBE tool for measuring biological age in children might help in understanding the environmental and contextual factors that shape the DNA methylome during child development, and how it, in turn, might relate to child health and disease.

Airway epithelial cell isolation techniques affect DNA methylation profiles with consequences for analysis of asthma related perturbations to DNA methylation.

The airway epithelium forms the interface between the inhaled environment and the lung. The airway epithelium is dysfunctional in asthma and epigenetic mechanisms are considered a contributory factor. We hypothesised that the DNA methylation profiles of cultured primary airway epithelial cells (AECs) would differ between cells isolated from individuals with asthma (n = 17) versus those without asthma (n = 16). AECs were isolated from patients by two different isolation techniques; pronase digestion (9 non-asthmatic, 8 asthmatic) and bronchial brushings (7 non-asthmatic and 9 asthmatic). DNA methylation was assessed using an Illumina Infinium HumanMethylation450 BeadChip array. DNA methylation of AECs clustered by isolation technique and linear regression identified 111 CpG sites differentially methylated between isolation techniques in healthy individuals. As a consequence, the effect of asthmatic status on DNA methylation was assessed within AEC samples isolated using the same technique. In pronase isolated AECs, 15 DNA regions were differentially methylated between asthmatics and non-asthmatics. In bronchial brush isolated AECs, 849 differentially methylated DNA regions were identified with no overlap to pronase regions. In conclusion, regardless of cell isolation technique, differential DNA methylation was associated with asthmatic status in AECs, providing further evidence for aberrant DNA methylation as a signature of epithelial dysfunction in asthma.

A biologically-informed polygenic score identifies endophenotypes and clinical conditions associated with the insulin receptor function on specific brain regions.

BACKGROUND: Activation of brain insulin receptors modulates reward sensitivity, inhibitory control and memory. Variations in the functioning of this mechanism likely associate with individual differences in the risk for related mental disorders (attention deficit hyperactivity disorder or ADHD, addiction, dementia), in agreement with the high co-morbidity between insulin resistance and psychopathology. These neurobiological mechanisms can be explored using genetic studies. We propose a novel, biologically informed genetic score reflecting the mesocorticolimbic and hippocampal insulin receptor-related gene networks, and investigate if it predicts endophenotypes (impulsivity, cognitive ability) in community samples of children, and psychopathology (addiction, dementia) in adults. METHODS: Lists of genes co-expressed with the insulin receptor in the mesocorticolimbic system or hippocampus were created. SNPs from these genes (post-clumping) were compiled in a polygenic score using the association betas described in a conventional GWAS (ADHD in the mesocorticolimbic score and Alzheimer in the hippocampal score). Across multiple samples (n = 4502), the biologically informed, mesocorticolimbic or hippocampal specific insulin receptor polygenic scores were calculated, and their ability to predict impulsivity, risk for addiction, cognitive performance and presence of Alzheimer's disease was investigated. FINDINGS: The biologically-informed ePRS-IR score showed better prediction of child impulsivity and cognitive performance, as well as risk for addiction and Alzheimer's disease in comparison to conventional polygenic scores for ADHD, addiction and dementia. INTERPRETATION: This novel, biologically-informed approach enables the use of genomic datasets to probe relevant biological processes involved in neural function and disorders. FUND: Toxic Stress Research network of the JPB Foundation, Jacobs Foundation (Switzerland), Sackler Foundation.

Electronic Physician Profiles: Developing an Interactive Web-Based Report for Physicians at Island Health.

Evidence based measurement is accepted as an important aspect of quality improvement. Making this data available to physicians to support them in quality improvement and to inform their practice presents a broad range of challenges. Addressing these challenges requires a solution that allows physicians to leverage the data repositories in the Enterprise Data Warehouse to develop and use evidence based clinical measures using an interactive, user-friendly, and secure infrastructure. In this talk, we discuss key aspects of the development of an interactive web-based report for physicians at Island Health, and present the final product. Given the privacy and security issues surrounding this type of data, a mock profile will be presented, rather than true results for actual physicians.Here, we have developed an initial prototype of a physician level report delivered via Microsoft Power BI as a possible dynamic tool to share these important data.

Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation.

BACKGROUND: DNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, their genetic determinants, and their respective causal impact on immune gene regulation remain elusive. RESULTS: We report extensive population differences in DNA methylation between 156 individuals of African and European descent, detected in primary monocytes that are used as a model of a major innate immunity cell type. Most of these differences (~ 70%) are driven by DNA sequence variants nearby CpG sites, which account for ~ 60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~ 20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation. CONCLUSION: Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.

Systematic evaluation of DNA methylation age estimation with common preprocessing methods and the Infinium MethylationEPIC BeadChip array.

BACKGROUND: The capacity of technologies measuring DNA methylation (DNAm) is rapidly evolving, as are the options for applicable bioinformatics methods. The most commonly used DNAm microarray, the Illumina Infinium HumanMethylation450 (450K array), has recently been replaced by the Illumina Infinium HumanMethylationEPIC (EPIC array), nearly doubling the number of targeted CpG sites. Given that a subset of 450K CpG sites is absent on the EPIC array and that several tools for both data normalization and analyses were developed on the 450K array, it is important to assess their utility when applied to EPIC array data. One of the most commonly used 450K tools is the pan-tissue epigenetic clock, a multivariate predictor of biological age based on DNAm at 353 CpG sites. Of these CpGs, 19 are missing from the EPIC array, thus raising the question of whether EPIC data can be used to accurately estimate DNAm age. We also investigated a 71-CpG epigenetic age predictor, referred to as the Hannum method, which lacks 6 probes on the EPIC array. To evaluate these epigenetic clocks in EPIC data properly, a prior assessment of the effects of data preprocessing methods on DNAm age is also required. METHODS: DNAm was quantified, on both the 450K and EPIC platforms, from human primary monocytes derived from 172 individuals. We calculated DNAm age from raw, and three different preprocessed data forms to assess the effects of different processing methods on the DNAm age estimate. Using an additional cohort, we also investigated DNAm age of peripheral blood mononuclear cells, bronchoalveolar lavage, and bronchial brushing samples using the EPIC array. RESULTS: Using monocyte-derived data from subjects on both the 450K and EPIC, we found that DNAm age was highly correlated across both raw and preprocessing methods (r > 0.91). Thus, the correlation between chronological age and the DNAm age estimate is largely unaffected by platform differences and normalization methods. However, we found that the choice of normalization method and measurement platform can lead to a systematic offset in the age estimate which in turn leads to an increase in the median error. Comparing the 450K and EPIC DNAm age estimates, we observed that the median absolute difference was 1.44-3.10 years across preprocessing methods. CONCLUSIONS: Here, we have provided evidence that the epigenetic clock is resistant to the lack of 19 CpG sites missing from the EPIC array as well as highlighted the importance of considering the technical variance of the epigenetic when interpreting group differences below the reported error. Furthermore, our study highlights the utility of epigenetic age acceleration measure, the residuals from a linear regression of DNAm age on chronological age, as the resulting values are robust with respect to normalization methods and measurement platforms.

The biological embedding of early-life socioeconomic status and family adversity in children's genome-wide DNA methylation.

AIM: To examine variation in child DNA methylation to assess its potential as a pathway for effects of childhood social adversity on health across the life course. MATERIALS & METHODS: In a diverse, prospective community sample of 178 kindergarten children, associations between three types of social experience and DNA methylation within buccal epithelial cells later in childhood were examined. RESULTS: Family income, parental education and family psychosocial adversity each associated with increased or decreased DNA methylation (488, 354 and 102 sites, respectively) within a unique set of genomic CpG sites. Gene ontology analyses pointed to genes serving immune and developmental regulation functions. CONCLUSION: Findings provided support for DNA methylation as a biomarker linking early-life social experiences with later life health in humans.

The early care environment and DNA methylome variation in childhood.

Prenatal adversity shapes child neurodevelopment and risk for later mental health problems. The quality of the early care environment can buffer some of the negative effects of prenatal adversity on child development. Retrospective studies, in adult samples, highlight epigenetic modifications as sentinel markers of the quality of the early care environment; however, comparable data from pediatric cohorts are lacking. Participants were drawn from the Maternal Adversity Vulnerability and Neurodevelopment (MAVAN) study, a longitudinal cohort with measures of infant attachment, infant development, and child mental health. Children provided buccal epithelial samples (mean age = 6.99, SD = 1.33 years, n = 226), which were used for analyses of genome-wide DNA methylation and genetic variation. We used a series of linear models to describe the association between infant attachment and (a) measures of child outcome and (b) DNA methylation across the genome. Paired genetic data was used to determine the genetic contribution to DNA methylation at attachment-associated sites. Infant attachment style was associated with infant cognitive development (Mental Development Index) and behavior (Behavior Rating Scale) assessed with the Bayley Scales of Infant Development at 36 months. Infant attachment style moderated the effects of prenatal adversity on Behavior Rating Scale scores at 36 months. Infant attachment was also significantly associated with a principal component that accounted for 11.9% of the variation in genome-wide DNA methylation. These effects were most apparent when comparing children with a secure versus a disorganized attachment style and most pronounced in females. The availability of paired genetic data revealed that DNA methylation at approximately half of all infant attachment-associated sites was best explained by considering both infant attachment and child genetic variation. This study provides further evidence that infant attachment can buffer some of the negative effects of early adversity on measures of infant behavior. We also highlight the interplay between infant attachment and child genotype in shaping variation in DNA methylation. Such findings provide preliminary evidence for a molecular signature of infant attachment and may help inform attachment-focused early intervention programs.

PRS-on-Spark (PRSoS): a novel, efficient and flexible approach for generating polygenic risk scores.

BACKGROUND: Polygenic risk scores (PRS) describe the genomic contribution to complex phenotypes and consistently account for a larger proportion of variance in outcome than single nucleotide polymorphisms (SNPs) alone. However, there is little consensus on the optimal data input for generating PRS, and existing approaches largely preclude the use of imputed posterior probabilities and strand-ambiguous SNPs i.e., A/T or C/G polymorphisms. Our ability to predict complex traits that arise from the additive effects of a large number of SNPs would likely benefit from a more inclusive approach. RESULTS: We developed PRS-on-Spark (PRSoS), a software implemented in Apache Spark and Python that accommodates different data inputs and strand-ambiguous SNPs to calculate PRS. We compared performance between PRSoS and an existing software (PRSice v1.25) for generating PRS for major depressive disorder using a community cohort (N = 264). We found PRSoS to perform faster than PRSice v1.25 when PRS were generated for a large number of SNPs (~ 17 million SNPs; t = 42.865, p = 5.43E-04). We also show that the use of imputed posterior probabilities and the inclusion of strand-ambiguous SNPs increase the proportion of variance explained by a PRS for major depressive disorder (from 4.3% to 4.8%). CONCLUSIONS: PRSoS provides the user with the ability to generate PRS using an inclusive and efficient approach that considers a larger number of SNPs than conventional approaches. We show that a PRS for major depressive disorder that includes strand-ambiguous SNPs, calculated using PRSoS, accounts for the largest proportion of variance in symptoms of depression in a community cohort, demonstrating the utility of this approach. The availability of this software will help users develop more informative PRS for a variety of complex phenotypes.