PLOS Genetics

Background: In prospective cohort studies, where an exposure is collected repeatedly, interest often lies in determining whether the timing of that exposure has a differential effect on a later outcome. The Structured Life Course Modeling Approach (SLCMA), where users select between temporal hypotheses of exposure specified a priori, provides one way to analyse such longitudinal data. However, few studies using SLCMA consider the effect of time-varying covariates (TVC) which may impact associations. Methods: We present a modified version of the SLCMA - called direct and mediated effects (DME)-SLCMA - which corrects for TVC. We first develop the DME-SLCMA method, test it through simulation, and apply it to psychosocial data from the Drakenstein Child Health Study (DCHS, n=336) to investigate relationships between maternal psychopathology, TVC of socioeconomic status, and offspring depressive symptoms. Results: We found that, on average, offspring depressive symptoms score increased by 3.9% (95% CI: 1.0%-6.9%, p = 0.039) for each unit of maternal psychopathology (SRQ) at 48 months whilst adjusting for time-varying socioeconomic status (at 18, 30, 42 and 54 months). Our simulations identified several realistic scenarios where selections ignoring TVC - with TVC mediated exposure effects present - were prone to be incorrect, including our DCHS example. Conclusion: DME-SLCMA is a robust new approach for life course modelling in the presence of time-varying covariates. We recommend adjusting for TVC whenever possible, and, when not possible, our simulation study identified that scenarios where mediated effects are comparable, or greater, in magnitude to direct effects are most prone to confounding.

Gene therapy is rapidly emerging as a transformative treatment for monogenic neurological disorders, including pediatric movement disorders such as aromatic L-amino acid decarboxylase (AADC) deficiency. However, its success critically depends on defining target cells and windows for therapeutic intervention. Here, we present an open-access single-nucleus transcriptomic atlas of the human basal ganglia spanning a therapy-relevant window from second/third trimester to the perinatal period and adulthood. Across 35,755 nuclei, we identify major (non-)neuronal cell types, retrace developmental trajectories, and characterize gene-regulatory networks. We identify so far unrecognized human-specific expression of key neuronal signaling genes, including GNAO1 and ADCY5, and discuss the implications for targeted gene replacement therapies. Unexpectedly, we found that the Huntingtin gene (HTT) is already expressed during prenatal stages of human brain development, supporting a previously proposed neurodevelopmental component of Huntington's disease, which should be considered in diagnostic and therapeutic strategies. Moreover, FOXG1 expression and regulon activity are predominantly located in a prenatal time window, suggesting constraints on the effectiveness of postnatal interventions. Our findings highlight the importance of datasets capturing human brain development in real time and provide a publicly available resource to guide precision gene therapy strategies in the future.

The contribution of multi-allelic CNVs (mCNVs) to disease risk has not been widely studied. This is largely because they have been difficult to characterise at a large-scale genome-wide, and are often not strongly associated with flanking SNVs, limiting imputation. Improved understanding of the role of mCNVs in disease risk could lead to novel insights into the pathobiology of disease. We robustly typed 69 mCNVs from UK Biobank whole exome sequences in discovery (n=150,682) and replication sets (n=269,317). Discovery and replication PheWAS used clinically-curated composite phenotypes by integrating self-report, primary and secondary health care data to interrogate these variants, for unrelated British individuals of African, European and Central/South Asian ancestries. 173 mCNV-phenotype associations were detected from 26 mCNVs, of which 114 associations replicated. One of eight potentially novel mCNV-phenotype signals was independent of neighbouring associated SNVs, the association of Sulfotransferase 1A1 and 1A2 genes (SULT1A1/SULT1A2) with estimated glomerular filtration rate (eGFR) in individuals of European ancestry (meta-analysed p=1.05x10-9, beta=0.016 [0.011; 0.021]). Other potentially novel associations include Golgi phosphoprotein 3 (GOLPH3) with the cardiovascular phenotype bundle branch block in individuals of South Asian ancestry (meta-analysed p=3.35x10-6, OR=2.13 [1.53, 2.96]) and alpha amylase 2B (AMY2B) with ventricular fibrillation and flutter in individuals of European ancestry (meta-analysed p=2.48x10-6, OR=1.50 [1.26; 1.78]). In summary, we show that accurate typing of biobank-scale sample sizes can identify associations between traits and mCNVs, acting through a gene dosage relationship. Our work provides several novel likely causative variants contributing to particular traits of clinical importance and immediately suggest a putative functional mechanism for the observed associations.

Fetal hemoglobin (HbF) prevents the polymerization of sickle hemoglobin (HbS). HbF, measured usually as a percent of total hemoglobin (%HbF), is inversely associated with the severity of sickle cell disease (SCD) but fails to capture the distribution of HbF concentrations within red blood cells (RBCs). The relative proportion of HbF and HbS within a RBC is reflected by the HbF:HbS ratio whereas HbF/F-cell quantifies the absolute amount of HbF/RBC. While correlated, HbF:HbS ratio and HbF/F-cell are not interchangeable. In the context of mean corpuscular hemoglobin (MCH), HbF/F-cell approximates whether sufficient HbF is present to inhibit HbS polymerization. We examined the association of mean HbF/F-cell with sub-phenotypes of sickle cell disease in three independent cohorts. Both %HbF and HbF/F-cell were significantly associated with multiple clinical and laboratory features of SCD; however, HbF/F-cell demonstrated stronger associations with clinical severity measures across cohorts. Higher HbF/F-cell was associated with fewer clinical events, reduced hemolysis, and mortality. Changes in HbF/F-cell after hydroxyurea treatment were associated with ~11-13% reduction in acute events in patients with <1 pg increase and >60% reduction with a >5 pg increase in HbF/F-cell. For each pg increase in HbF/F-cell there was ~6% reduction in the rate of acute events. As a surrogate for the distribution of HbF concentrations among F-cells, HbF/F-cell adds physiologically relevant insights that could guide prognosis and treatment

Background Blood pressure (BP) regulation in individuals with sickle cell disease (SCD) is influenced by a complex interplay of genetic and physiological factors. While SCD has traditionally been associated with lower BP, there is an increased risk of hypertension. Emerging BP research suggests significant heterogeneity across genotypes, age groups, and sex. Objectives: This study investigated the longitudinal effects of population-level characteristics and continuous clinical and laboratory predictors on systolic (SBP) and diastolic blood pressure (DBP) in individuals with SCD, with emphasis on the interactions between baseline and predicted blood pressure slopes over time. Methods We retrospectively analyzed longitudinal data from a cohort of 2,739 patients with diverse SCD genotypes. Descriptive statistics were documented across sex, age range, genotype, health status and relative systemic hypertension risk categories (rHTN-risk). Linear mixed-effects models provided estimates of fixed- and random-effects of baseline BP and of time-related BP effects, respectively. Post-estimation margins provided contrasts of baseline-adjusted BP means and of pre-specified time effects on BP patterns. Results Males had significantly higher baseline SBP ({beta} = 6.64, p < 0.001) but lower baseline DBP ({beta} = -2.61, p < 0.001) compared with age-matched HbSS females. Baseline SBP was more unstable compared with baseline DBP and baseline DBP was more predictive of future BP trends than baseline SBP. Genotype was a consistent predictor of DBP (p < 0.05), but not of SBP. Similarly, we observed increased risks of relative diastolic hypertension across most genotypes, while the prevalence and magnitude of systolic hypertension was lower across all genotype compared with HbSS. Conclusions Blood pressure trajectories in SCD patients are not uniform and are significantly related to genotype, age group and sex over time. Baseline diastolic levels were less heterogenous and exhibited clear upward trajectories over time. These findings support the need for patient-specific BP surveillance in the care and management of SCD.

Introduction: APOL1 high-risk variants markedly increase susceptibility to kidney disease among individuals of African ancestry; however, only a subset of carriers develops clinically significant CKD or ESKD. This discrepancy highlights a gap between genetic risk and clinical trajectory. Current prognostic tools rely primarily on eGFR and albuminuria, which incompletely reflect the underlying biological processes driving APOL1-associated kidney injury. We hypothesized that plasma biomarkers reflecting inflammatory and tubular injury pathways could identify biologically active disease states within this genetically high-risk population and improve prognostic stratification. Methods: Participants from the Mount Sinai BioMe Biobank carrying two APOL1 high-risk alleles (G1, G1; G1, G2; or G2 G2) were followed for a median of 6 years. Baseline plasma biomarkers of inflammation and tubular injury (TNFR1, TNFR2, KIM-1, MCP-1, YKL-40, IL-18, suPAR) were measured. The composite outcome was sustained 40% decline in eGFR or ESKD. Multivariable Cox models assessed associations between biomarkers and outcomes. A weighted biomarker risk score was derived from tertile-based hazard ratios and categorized into low-, moderate-, and high-risk groups. Results: Among 498 participants (median eGFR 83 ml/min/1.73 m2), 80 (16.1%) reached the composite outcome. Higher concentrations of TNFR1, TNFR2, suPAR, KIM-1, and IL-18 were independently associated with kidney events after multivariable adjustment. Event rates were 7% in the low-risk group, 16% in the moderate-risk group, and 36% in the high-risk group. Conclusions: Plasma biomarkers reflecting inflammatory and tubular injury pathways reveal marked heterogeneity in kidney outcomes among individuals with high-risk APOL1 genotypes. Integration of these signals into a biology-weighted score identifies distinct prognostic phenotypes beyond genotype and traditional clinical measures, supporting multidomain biomarker frameworks for risk stratification and potential trial enrichment in APOL1-associated kidney disease.

Although the Y chromosome represents roughly 2% of the male genome, it is often ignored in genome-wide association studies (GWAS). Subsequently, the potential health impacts of Y-chromosomal genetic variation remain incompletely understood. To fill this gap, we performed a phenome-wide association study (PheWAS) in FinnGen across 1,426 binary and quantitative traits using Y-chromosomal variation (frequency [&ge;] 1%) in 104,334 genotyped men. As Y chromosome variation is prone to population stratification, we performed carefully adjusted association analyses and further examined these through kin-based validation in 19,275 female and 24,712 male 1st degree relatives. We found 121 suggestive (p < 5.6x10-3) phenotypic associations in the Y chromosome, yet none of these were strong enough to reach phenome-wide significance (p < 3.9x10-6). While only 38 associations were supported in the kin-based validation, intriguingly we found support for a previously suggested link between haplogroup I1 and coronary heart disease (CHD; OR=1.06, 95%CI=1.02-1.11, p=3.7x10-3; male validation OR=1.05; female validation OR=0.97). The I1-CHD association was detected across distinct geographical areas within Finland and was independent from Loss of Y (LOY) and the autosomal risk to CHD, proposing a link between germline Y-chromosomal variation and heart disease risk. Overall, this study presents a comprehensive phenome-wide analysis of Y-chromosomal associations, highlighting the potential relevance of Y-chromosomal variation beyond sex determination. Our findings further emphasize the need for improved capture of Y-chromosomal variants and further analyses in biobank-scale data to allow for deeper exploration of male-specific genetic architecture of complex diseases.

Background: Chronic low-grade inflammation drives cardiovascular-kidney-metabolic (CKM) syndrome. Clonal hematopoiesis of indeterminate potential (CHIP), an age-related driver of systemic inflammation, is linked to several cardiometabolic disorders. However, whether CHIP modifies CKM progression and contributes to heterogeneity in cardiovascular disease (CVD) risk within the CKM framework remains uninvestigated. Methods: This cohort study included 307,025 UK Biobank participants at CKM stages 0-3 free of baseline CVD. CHIP status was identified via whole-exome sequencing (WES). The association between CHIP and baseline CKM severity was examined, along with the independent and joint effects of CHIP and CKM stages on incident CVD risk. The joint effects of CHIP and polygenic risk scores (PRS) were further assessed, and the incremental predictive value of incorporating CHIP into the AHA PREVENT equations was evaluated. Results: CHIP carriers were more likely to present with advanced CKM stages [OR 1.14 (1.09-1.20), P < 0.001] and exhibited higher incident CVD risk during follow-up [HR 1.13 (1.08-1.18), P < 0.001]. Significant joint effects between CHIP and CKM stages were observed, with the highest risk among CHIP carriers at CKM stage 3 [HR 1.63 (1.50-1.78), P < 0.001]. Large or multiple CHIP mutations conferred greater hazards, with distinct gene-specific effects observed. Moreover, CHIP and high genetic risk also jointly amplified CVD susceptibility. Most importantly, incorporating CHIP into AHA PREVENT significantly improved risk discrimination. Conclusions: CHIP is a significant risk factor associated with more advanced CKM stages and amplifies incident CVD risk. Integrating CHIP into existing prevention strategies may refine CVD risk stratification.

Diabetic peripheral neuropathy (DPN) is a common and disabling condition for which no disease-modifying therapies are available. Glycemic and metabolic drivers do not fully explain why only a subset of individuals with diabetes develop DPN, and genetic contributors remain poorly defined. We aimed to perform a multi-population genome-wide association study (GWAS) of DPN to highlight potential new etiological pathways and therapeutic targets. Methods We performed a multi-population GWAS of neuropathy in people with and without diabetes using the VA Million Veteran Program and UK Biobank, followed by replication in the All of Us Research Program (AoU), and gene-based and gene-set analyses to identify implicated pathways. Causal relationships between circulating serine levels and DPN were further tested using two sample Mendelian randomization. To further evaluate pathogenic potential, we analyzed rare, high impact variants in GWAS implicated genes among individuals with unresolved inherited neuropathies using the GENESIS platform. Findings Among individuals with type 2 diabetes, we identified seven genome wide significant loci (p<5x10-): PHGDH and PSPH (key serine synthesis genes), TEAD1, CYP4F11, LARGE1, FTO, and COBLL1. No loci were significant in individuals without diabetes or with type 1 diabetes. Four loci (PHGDH, TEAD1, FTO and CYP4F11) replicated in AoU (p <0.05). Mendelian randomization demonstrated that higher genetically predicted serine levels were associated with lower DPN risk, consistent with a causal role of serine metabolism in disease pathogenesis. Rare-variant burden analyses revealed associations of predicted deleterious variants with inherited neuropathy case status in PHGDH (odds ratio [OR] 12.7 [95% CI 7.9, 20.4]), PSPH (OR 8.5 [7.2, 10.2]), PHKG1 (OR 4.8 [3.7, 6.3]), and LARGE1 (OR 0.007 [0.0004, 0.1]). Interpretation Convergent genetic evidence across common and rare variation implicates serine synthesis as a key pathway in DPN. These findings link diabetic and inherited neuropathies through a shared metabolic mechanism, identifying serine metabolism as a potential therapeutic target.

Consanguinity is a reproductive union between individuals who share a recent common ancestor. These unions are common in many regions of the world and increase the burden of rare recessive disorders by elevating autozygosity in offspring. Current reproductive genetic screening focuses on a limited set of known pathogenic variants, leaving most recessive risk unaddressed. Here we argue that embryo-level autozygosity, quantified as the fraction of the genome in long runs of homozygosity (FROH), is a potentially actionable genomic biomarker that can be integrated into routine preimplantation genetic testing as a homozygosity-informed embryo-prioritization framework (PGT-H) that can be layered onto existing embryo biopsy workflows when couples are already undergoing IVF with PGT-A or PGT-M. Using forward simulations of first-cousin and double-first-cousin couples, we show that siblings conceived by the same couple span a wide range of FROH; selecting the lowest-FROH candidate from a cohort of five embryos reduces FROH by approximately 40% on average. Combining these reductions with empirical effect-size estimates, we estimate that for first-cousin couples this strategy could reduce risk of intellectual disability by roughly 35-45% (corresponding to an absolute risk reduction of about 1.8-2.2%) and potentially reduce excess recessive disease burden, while also modestly reducing risk of common diseases such as type 2 diabetes. We outline how existing PGT-A and PGT-M workflows could potentially be extended to report embryo-level FROH and discuss ethical and counseling considerations. Autozygosity-based embryo prioritization offers a principled way to address a component of recessive risk that current variant-centric approaches miss.

Heterozygous carriers of autosomal recessive disease variants are conventionally considered unaffected, yet population-scale genomic datasets reveal subclinical carrier phenotypes. MMACHC encodes a cobalamin-processing protein whose biallelic loss causes cobalamin C deficiency, an inborn error of intracellular cobalamin metabolism. We performed an unbiased quantitative phenome-wide association screen in All of Us Research Program v8 to identify phenotypes associated with rare heterozygous MMACHC burden variants. Serum/plasma vitamin B12 was the top quantitative association. Carriers had higher circulating B12 than non-carriers in adjusted analyses, but also higher homocysteine, suggesting that elevated circulating B12 does not reflect improved intracellular cobalamin function. Carriers were less likely to fall below conventional B12 insufficiency thresholds, indicating a potential diagnostic blind spot. A pathway-wide rare-variant gene-burden (All-by-All) gene-burden analysis placed this finding in broader biological context. Burdens in genes related to circulating B12 binding or intestinal absorption were associated with lower circulating B12. In contrast, burdens in several genes involved in cellular delivery and intracellular cobalamin handling were associated with higher circulating B12. This step-specific directionality supports a model in which elevated circulating B12 can reflect impaired cellular handling and consequent systemic accumulation rather than improved cellular cobalamin availability. Because EHR-derived B12 is shaped by heterogeneous clinical and medication contexts, prospective carrier-enriched studies with standardized methylmalonic acid, homocysteine, diet, supplement, medication, comorbidity, and symptom ascertainment are needed to evaluate functional-marker-based screening.

Background: In the absence of high-resolution response data, exposure-response modelling often relies on aggregated low-frequency exposure data, leading to loss of high-resolution information. Mixed Data Sampling (MIDAS) from econometrics offers an alternative but is limited due to its inability to make high-resolution predictions, inflexible likelihoods and penalised nonlinear functions, and limited visualization options. We propose a mixed-frequency Distributed Lag Non-linear Model (mf-DLNM) which can eliminate the need to aggregate exposure data in environmental epidemiology and provide high resolution predictions for time series studies. Methods: We evaluated the inference and predictive performance of the mf-DLNM. To evaluate its ability to estimate exposure-response relationships, we applied mf-DLNM and same-frequency (sf)-DLNM using data from the West Midlands, UK. Additionally, we compared the predictive performance of mf-DLNM with sf-DLNM and MIDAS across nine regions of England. As MIDAS cannot predict at the resolution of the predictor (daily), we compared the predictive performance of mf-DLNM and MIDAS at weekly resolution. To test the model's ability to predict high temporal resolution risk (daily), we compared sf-DLNM (with access to daily mortality counts) with mf-DLNM (with access only to weekly mortality counts). Results: In the West Midlands example, mf-DLNM performed comparably to sf-DLNM in estimating daily risk of temperature on respiratory mortality. Furthermore, mf-DLNM and MIDAS exhibited similar performance for weekly predictions. For high-resolution predictions, mf-DLNM and sf-DLNM showed nearly similar performance, despite mf-DLNM having access only to low-resolution response data. Conclusion: This mixed-frequency approach in environmental epidemiology overcomes the limitations of predicting health risks using aggregated exposure data and provides estimates of high-resolution outcomes in the absence of high-frequency health outcome datasets.

Abstract Background Alport Syndrome (AS), caused by pathogenic variants in type IV collagen genes COL4A3/4/5, is a leading monogenic cause of Kidney Failure (KF). Clinical course varies widely, and disease specific predictors of progression relevant to clinical care and trial design remain incompletely defined. Methods In this retrospective cohort study of individuals with AS in the UK National Registry of Rare Kidney Diseases, patients were classified as having AS or heterozygous genotypes and followed to assess proteinuria progression, eGFR slope and kidney survival. Proteinuria and eGFR trajectories were analysed using mixed effects regression models; kidney survival using Kaplan Meier analysis. Results Among 1032 participants (median follow up 11.6 years; 47% female), 475 (46%) had AS genotypes (Male XLAS or autosomal recessive AS). eGFR decline accelerated with advancing CKD stage across all genotypes (p<0.001). Proteinuria increased as eGFR declined and occurred earlier in AS genotypes. After reaching proteinuria thresholds of more than 1.0 and 3.0g/g, kidney survival over the subsequent 5 years did not differ significantly between genotypes (logrank p=0.14, p=0.17, respectively), although modest differences emerged over longer follow-up. Across eGFR thresholds (90, 60, and 45mL/min/1.73m2), higher proteinuria was associated with shorter time to KF; for example, at eGFR 45mL/min/1.73m2, median time to KF was 3.0 years (IQR, 1.6-5.4) for above-median vs 6.5 years (5.1-not estimable) for below-median proteinuria (p<0.0001). Almost all patients who reached KF had developed proteinuria of more than 0.3g/g. Conclusion In this national cohort, eGFR decline accelerated with CKD stage and proteinuria was strongly associated with progression to KF across genotypes. The non linearity of eGFR decline may inform its interpretation in clinical practice and use as a trial endpoint. Once comparable proteinuria levels were reached, differences in outcomes by genotype were attenuated, supporting proteinuria as a key prognostic marker and strengthening rationale for its use as a surrogate endpoint in AS clinical trials

Educational attainment-related polygenic scores have been implicated in autism spectrum disorder (ASD), but how parental polygenic scores shape offspring phenotypes remains unclear. Using genotyping and exome-sequencing data from 142,357 individuals (55,252 ASD cases) in a large ASD cohort, we dissected the direct and indirect genetic effects of educational attainment-related polygenic scores on ASD phenotypes. Trio-model analyses showed that parental polygenic scores for educational attainment (PGSEA ) were associated with milder core ASD symptoms, including social deficits and repetitive behaviors, predominantly through indirect genetic effects, whereas their associations with comorbidities were driven predominantly by direct genetic effects. PGSEA was also significantly negatively associated with rare variant burden and prenatal factors, although these factors contributed largely independently to most phenotypes. Adjustment for full-scale intelligence quotient (FSIQ) and socioeconomic status (SES) partially attenuated the indirect effects of PGSEA on offspring phenotypes. Finally, higher parental PGSEA was associated with later age at diagnosis in offspring, partly through its protective effects on ASD phenotypes. These findings indicate that indirect genetic effects of parentalPGSEA contribute substantially to phenotypic variation in ASD and highlight family-mediated pathways as an important component of ASD heterogeneity.

Background Differential contributions to transmission across age groups have been reported for many respiratory infections, including SARS-CoV-2. They are crucial for estimating the impact of age-specific interventions. Disentangling these age-dependent contributions remains challenging, as they may reflect differences in contact rates, biological susceptibility, or infectiousness. Aim We aim to jointly estimate age-specific per-contact infectiousness and susceptibility and their effect on the impact of age-specific interventions. Methods The age-specific infectiousness and susceptibility were jointly estimated in a Bayesian framework by combining contact data with transmission pair data (who-infected-whom). We applied this approach to 197,840 self-reported household transmission pairs collected in the Netherlands during the COVID-19 pandemic. Using these estimates, we projected the expected impact of school closure and work-from-home measures during the early stages of an epidemic in the absence of other interventions. Results Both infectiousness and susceptibility to SARS-CoV-2 infection were lowest in children aged 0-9 years and highest in adults over 30 years old, with 2- to 4.5-fold differences between these groups. Projected impacts of age-specific interventions indicated that school closures would reduce the reproduction number by 8% or 29% when age-specific susceptibility and infectiousness were or were not considered, respectively. Conversely, working-from-home policies would lead to reductions of 41% with and 20% without age-specific infectiousness and susceptibility. Conclusion Our method enables robust estimation of age-specific infectiousness and susceptibility. Accounting for these age heterogeneities is essential for projecting the impact of age-targeted interventions. Our approach is adaptable to other respiratory infections and can guide more tailored public health responses.

Background: Chronic diseases and symptom syndromes often develop after prolonged biological changes that may precede formal diagnosis. RNA-based metatranscriptomics captures active microbial and human gene expression and may provide a functional layer for disease risk evaluation. To address this translational gap, we developed and validated a Disease Risk Score (DRS) framework that integrates metatranscriptome-derived pathway activity scores from stool, saliva, and blood samples, and evaluated its potential clinical utility as an adjunct risk-evaluation tool. Methods: DRS uses disease-specific sets of pathway activity scores derived from stool and saliva microbial functions, stool and saliva microbial taxa, and blood human gene expression. For each disease, 'not optimal' pathway scores are aggregated into a normalized cumulative odds ratio, or cOR, using score-level odds ratios, statistical significance, and literature-supported biological relevance derived from a Development Cohort of 22,369 individuals. A cOR [&ge;] 5 is defined as high risk. Performance is evaluated in an independent Validation Cohort of 15,908 individuals using self-reported diseases as the reference. Disease support requires both significant cOR separation between self-reported and not-reported (Cohen's d [&ge;] 0.2) and risk ratio enrichment of self-reported disease among individuals classified as high risk (95% CI of Risk Ratio > 1). Results: Of 20 initially evaluated diseases, 15 meet the prespecified validation criteria on the independent validation cohort: ADHD, anxiety, chronic fatigue syndrome, depression, GERD, hypertension, inflammatory bowel disease, IBS-C, IBS-D, insomnia, MASLD, obesity, obstructive sleep apnea, Sjogren's syndrome, and type 2 diabetes. Five selected clinical scenarios illustrate how DRS can support clinician-mediated decision making, including IBS subtype reclassification, improved diagnostic acceptance in IBS-D, personalized lifestyle counseling in MASLD and early type 2 diabetes, and diagnostic uncertainty in atypical GERD. Conclusions: DRS is a metatranscriptomics-based risk-stratification framework that aggregates active microbial and human pathway signals into interpretable disease-specific risk estimates across a wide range of disease conditions. Validation against self-reported disease labels in an independent cohort shows significant risk enrichment for each of 15 diseases. DRS is intended as an adjunct to clinical evaluation: a decision support tool in situations where routine care encounters uncertainty, delay, or low patient engagement. Future prospective studies using clinically adjudicated endpoints are needed to assess calibration and clinical outcomes.

Seroprevalence surveys reveal the extent of humoral immunity against pathogens such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and under some circumstances represent cumulative incidence of prior infection. However, antibody waning - or seroreversion - biases these estimates by reducing assay sensitivity in a time-varying manner. Because assay sensitivity decays over time, naively using serosurveys can substantially bias estimates of SARS-CoV-2 cumulative incidence and fatality rates. The Bayesian assay-specific, time-varying sensitivity adjustment developed in this paper can reliably correct for this bias and account for the delay between infection and serosurvey. In seroprevalence studies conducted in the United States in 2020, adjusting for time-varying sensitivity increased cumulative incidence by up to 1.4-fold, with an adjustment of 1.08 for a national study. Our estimates contrast with a previously published 2-fold adjustment that did not account for assay design. This suggests that previous analyses overestimated cumulative incidence by applying seroreversion corrections that did not account for assay-specific effects, or underestimated cumulative incidence by not applying seroreversion corrections. These biases imply fatality rate underestimation and overestimation, respectively. Our model provides a framework for design-specific time-varying sensitivity corrections in seroprevalence surveys for other pathogens.

Whole-exome and whole-genome sequencing technology has enabled the discovery of rare genetic variants associated with human health and diseases. However, existing statistical methods used for rare variant association testing are not well-suited for building genetic risk prediction models that jointly incorporate rare and common variants. We propose STELLAR, a flexible ensemble learning-based approach to compute rare variant polygenic risk scores (PRS) using association summary statistics to enhance conventional common variant PRS. Our method combines burden-based and penalty-based rare variant analysis and leverages functional annotation information to prioritize potentially causal variants within the prediction models. In simulation studies, PRS using STELLAR consistently showed the highest prediction accuracy compared to models using common variants alone or rare variant burdens. Applied to UK Biobank whole-exome sequencing data (n=310,831) across eight continuous and five binary traits, STELLAR significantly improved prediction accuracy, refined stratification of individuals at the highest genetic risk beyond common variants, and prioritized biologically relevant genes. STELLAR provides a scalable strategy to incorporate rare variants into PRS in addition to common variants, advancing precision risk prediction and enabling more comprehensive assessment of genetic contributions to complex diseases.

Large language models embedded in autonomous agents process trusted instructions and untrusted data in one context window, leaving them open to direct and indirect prompt injection. In healthcare this is not hypothetical: a 2025 JAMA Network Open study found commercial medical LLMs followed injected instructions in 94.4% of simulated patient encounters, including life threatening recommendations . Yet the clinically decisive problem we quantify here is different. Most real clinical threats protected health information PHI exfiltration, cross patient access, bulk export, out of scope advice are fluent, legitimate looking requests that carry no attack signal, so even a state of the art injection detector passes them. Existing runtime guardrails trade safety against latency: model based auditors are accurate but add hundreds of milliseconds of Python inference, while lexical filters are fast but blind to obfuscated or semantically disguised payloads. We present QFIRE, an inline, provider agnostic prompt firewall implemented as a single self contained Rust toolchain proxy, CLI, and benchmark harness. QFIRE combines three mechanisms: (i) positive security scope constraints, which restrict a model call to a declared natural language purpose and block out of scope drift even when no overt attack token is present; (ii) an asynchronous detector graph that runs N rules and their detector nodes concurrently, cheapest checks first; and (iii) a de obfuscation pass that decodes Base64 hex ROT13, folds homoglyphs and leetspeak, and strips zero width characters before detection. QFIRE ships 106 versioned firewall rules and a dedicated HIPAA Safe Harbor 18 identifier PHI panel, and runs a local DeBERTa v3 injection classifier via embedded ONNX Runtime. On 1968 public prompt injection and jailbreak prompts QFIREs deterministic hybrid attains F1 0.86, statistically tied with Metas state of the art PromptGuard 2 0.86 and above protectai DeBERTa v3 0.83; lexical baselines lag 0.16 to 0.50. Our central result is on QFIRE HealthBench, a new 2000 prompt healthcare benchmark we build and release with real garak and Microsoft PyRIT payloads. There the same PromptGuard-2 recovers only 0.40 recall DeBERTa v3 0.57, because most clinical threats carry no injection signal; QFIREs combined scope plus PHI chain reaches 0.83 recall F1 0.87 at a calibrated 0.08 false positive rate. Generic injection detection, even state of the art, is therefore necessary but not sufficient for healthcare agents. A bare LLM judge also closes most of this static corpus gap F1 0.90; QFIREs contribution beyond static accuracy is auditable determinism, bounded latency, and adaptive robustness, where the bare judge falls to 34 to 59% recall section 5.5. End to end, placing QFIRE in front of a tool using agent over a mock EHR sandbox cuts the agents harmful action rate from 0.38 to 0.00 at a 0.13 benign utility cost. All code, rules, corpora snapshots, and scripts are released, and every table regenerates from a single make paper target against local models with no paid API keys.

Cardiovascular diseases (CVDs) are highly heritable, but pathogenesis at the organ and physiological level is still poorly defined. Polygenic risk scores (PRSs), which estimate individual genetic susceptibility to a disease, may allow for the identification of associated abnormal organ structures. Ultimately, identifying where cardiovascular polygenic risk manifests can guide early interventions, shape mechanistic hypotheses, and motivate prevention trials for cardiac remodelling. This study investigated the association between PRSs for five common CVDs [heart failure (HF), coronary artery disease (CAD), atrial fibrillation (AF), abdominal aortic aneurysm (AAA) and ischaemic stroke (IS)] and 28 imaging-derived phenotypes (IDPs) from cardiac magnetic resonance imaging of ~62,000 participants in UK Biobank. To investigate the cardiac features associated with elevated polygenic risk of CVDs, we tested CVD PRSs against cardiac IDPs and identified 97 significant associations (FDR [&le;] 0.05). We further identified 32 significant putative mediators between CVD PRSs and incident disease events, revealing that across CVDs, polygenic risk manifested as distinct patterns in cardiac structures. HF implicated all cardiac chambers, including left ventricular and left atrial dysfunction alongside enlarged aorta. AF was characterised by biatrial enlargement and reduced ejection fractions, most prominently in the left atrium but also involving left ventricular wall thickness. IS exhibited left ventricular hypertrophy and left atrial dysfunction, while CAD predominantly involved left ventricular hypertrophy. AAA was primarily characterised by enlarged descending aorta. Overall, cardiac IDPs mediated a substantial proportion of polygenic risk for CVDs, in particular for HF. Taken together, our results show that cardiac structure and function lie on the pathway between polygenic risk and cardiovascular events.