Nature Genetics

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.

Chronic kidney disease is characterized by decreased glomerular filtration rate (eGFR, estimated from serum creatinine or cystatin C) or increased urinary albumin-to-creatinine-ratio (UACR). Genome-wide association studies provided the genetic make-up of these traits, but their overlap remained largely unknown. Our multi-trait GWAS (N=1M) identified 812 signals and multi-trait fine-mapping sharpened the identification of likely causal variants. Of 333 signals classified for filtration function or albuminuria, only 11 overlapped. Their effects on eGFR and UACR were directionally concordant, dominated by eGFR and independent of HbA1c or mean arterial pressure. Mapped genes pinpointed mechanisms related to glomerular filtration area (SHROOM3, EPB41L5) and sodium-mediated intraglomerular pressure (NRBP1, DPEP1/CHMP1A). Genetics of fluid intake resulted in shadow effects on UACR without albumin leakage into urine. Our multi-trait approach sharpened the identification of likely causal genes for kidney traits, demonstrated largely distinct genetics for filtration function versus albuminuria, and provided new biological insights into the overlap.

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.

Stimulant use disorder (StimUD) is a significant public health problem, but genetic studies have been limited by small sample sizes. We conducted genome-wide association studies (GWAS) of StimUD in the Million Veteran Program (MVP) and All of Us (AOU), followed by meta-analysis with FinnGen and 10 additional datasets, for a total of 709,369 individuals (Ncases=33,977, Ncontrols=675,392) in four broad ancestry groups: European (EUR) (Ncases=22,564, Ncontrols=624,672), African (AFR) (Ncases=7,574, Ncontrols=34,189), Admixed American (AMR) (Ncases=3,657, Ncontrols=15,698), and East Asian (EAS) (Ncases=182, Ncontrols=833). Population-specific SNP heritability was 6.1% in EUR and 2.4% in AFR. We discovered a total of 19 genome-wide-significant loci, six in EUR, including DRD2*rs5794864, P=7.32E-10, one in AFR, five in a multi-ancestry meta-analysis, including CHRNA5*rs55781567, P=3.27E-9, two in a male-only meta-analysis, including FTO*rs8057044, P=9.50E10-9, and five in a meta-analysis of sex-stratified results. In a hold-out AOU subsample (NEUR=18,841, NAFR=12,263, NAMR=9,739), ancestry-specific polygenic risk scores were significantly associated with StimUD in EUR (OR=3.28, 95% confidence interval (CI)=2.89-3.71) and AMR (OR=2.01, 95% CI=1.71-2.37). Transcriptome-wide association studies, fine-mapping, and colocalization analyses prioritized additional genes (e.g., GPX1, BSN). Genetic correlation, Mendelian randomization, and causal mixture analyses revealed relationships with other substance use and use disorder phenotypes, including cannabis use disorder (rg=0.94, P=5.43E-237) and opioid use disorder (rg=1.01, P=4.40E-107), and other psychiatric traits, including anxiety, depression, neuroticism, and attention-deficit/hyperactivity disorder. This is the first well-powered GWAS of StimUD, and it offers significant insights into disease biology.

People with HIV exhibit elevated inflammation and cardiovascular risk despite antiretroviral therapy. To define the genetic architecture of inflammasome-associated inflammation, we performed whole-genome sequencing and quantified plasma IL-6, IL-1{beta}, and IL-18 in 1,000 ART-suppressed PWH from the U.S. Military HIV Natural History Study. Genome-wide analyses identified 14 loci implicating antiviral defense (DDX17, DDX41, EEA1, BCL11A), lipid metabolism (ABCA1, ABCA12, ABCC1, AGMO), and vascular remodeling (KLHL29, RNF213, ETV1). Transcriptome-wide analyses across cardiovascular and immune tissues identified regulatory programs linking interferon signaling, immune activation, and vascular biology to circulating cytokine levels. Mendelian randomization analyses supported causal relationships between inflammasome-associated cytokines and vascular events. Functional integration with genome-wide CRISPR perturbation datasets in primary CD4 T cells linked cytokine-associated loci to HIV antiviral pathways and cytokine regulatory networks. External validation in cohorts without HIV demonstrated pathway-level convergence despite limited variant-level overlap. These findings define genetic mechanisms linking inflammasome signaling, antiviral defense, and cardiovascular risk.

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.

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.

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 [≤] 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.

Pathogenic variants in ATP13A2, which encodes an endolysosomal polyamine exporter, cause Kufor-Rakeb syndrome and are associated with early-onset parkinsonism and related neurodegenerative disorders, however, the mechanisms by which ATP13A2 dysfunction drives disease remain incompletely defined. In Atp13a2 knockout mice, we identified an early, transient reduction in brain polyamines that precedes overt gliosis and behavioural abnormalities. Pharmacological polyamine depletion exacerbates phenotypes, whereas oral supplementation of spermidine, but not spermine, rescues parkinsonian symptoms establishing metabolic polyamine deficiency as a pathogenic driver. Mechanistically, spermidine counteracts microglia lysosomal dysfunction in the brain and exerts mitochondrial antioxidant and anti-inflammatory effects in primary mouse microglia, thereby improving neuronal integrity. In the absence of Atp13a2, microglial spermidine import relies on the related polyamine transporter Atp13a3. Importantly, these findings translate to human systems, whereby spermidine attenuates inflammation in ATP13A2-deficient human differentiated microglia, while postmortem ATP13A2-deficient brain analysis confirms increased microglia reactivity. Spermidine also rescues motor deficits and dopaminergic neuron loss in ATP13A2-deficient Drosophila and other fly parkinsonism models. Together, these findings identify early polyamine dysregulation as a mechanistic contributor to ATP13A2-associated parkinsonism and nominate spermidine supplementation as a potential therapeutic strategy for ATP13A2-driven pathology and possibly a broader range of parkinsonian sub-types.

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.

Whole-genome sequencing comprehensively captures coding, non-coding and structural variation in families with suspected inherited disorders, yet its clinical utility remains constrained by an interpretation bottleneck: selecting a handful of relevant variants from millions of candidates. Current rule-based pipelines, anchored in ACMG/AMP criteria, excel at identifying highly penetrant Mendelian alleles but frequently miss variants of low-to-moderate penetrance, non-coding alterations and germline-somatic interactions. Here we introduce PolyCLIP-T, a topology-guided multimodal framework that transforms variant selection from a classification problem into a geometric discovery task. By contrastively aligning DNA-sequence embeddings with functional annotations, PolyCLIP-T constructs a unified latent space in which the displacement between reference and alternate embeddings quantifies the molecular perturbation induced by each variant. Persistent homology then identifies stable topological components - coherent variant groups shared among affected relatives - that transcend single-variant scoring logic. Applied to six families with multi-morbid cancer, autoimmune and cardiovascular disease, PolyCLIP-T recovered non-coding and structural candidates overlooked by conventional pipelines and revealed pleiotropic networks spanning disease categories. This approach provides an interpretable, scalable solution for genome-first investigations of disorders driven by polygenic architectures that evade single-variant analysis. The framework was developed and benchmarked on deeply characterised familial cohorts selected for transgenerational multimorbidity; validation in larger, independent populations will be essential to establish its generalisability. An interactive web tool is freely available at https://www.polyclip-t.uma.es/.

Mood and anxiety disorders emerge predominantly in adolescence, yet they are usually identified only once symptoms have consolidated, when intervention can only be reactive. A marker that registers the loss of healthy brain function before symptoms crystallise would allow earlier and more targeted treatment, much as caged canaries once warned miners of danger before it became apparent. Here we report such a marker using a single baseline resting-state functional MRI scan in 150 adolescents in the Human Connectome Project Boston Adolescent Neuroimaging of Depression and Anxiety (HCP BANDA) cohort, allowing us to prospectively predict depression and anxiety symptoms one year later in held-out participants at r = 0.60, substantially above the effect-size ceiling reported for functional connectivity in the same data. The marker is not computed from raw functional connectivity but read out from a whole-brain generative model fitted to each individual's dynamics, which gives access to interference structure that covariance-based features cannot represent. The regions driving the prediction, including precuneus, ventromedial prefrontal and anterior cingulate cortices, are among those previously implicated in internalising disorders, and the same signature tracks cognitive variation in healthy participants and is mechanistically linked to the efficiency of task-related computation. These findings establish a mechanistically interpretable and prospectively predictive marker of adolescent mental health and define a clear path towards external validation and clinical use.

Cardiovascular diseases (CVDs) remain the primary global health burden, motivating the search for robust, non-invasive risk biomarkers. We harness a foundation model pretrained on over 10 million recordings, to evaluate ECG-derived age deviation as a cross-cohort biomarker of CVD burden. A predictive model, trained exclusively on healthy subjects, achieved accurate age prediction. Diseased subjects exhibited significant positive age acceleration across multiple categories, with structural and ischemic heart diseases showing the largest effects. External validation in a hospital-based cohort (n=160,493) confirmed that age acceleration independently predicts all-cause mortality, with the strongest prognostic value in patients under 65 years. Furthermore, we demonstrated that disease discrimination and mortality prediction are preserved across 6-lead and single-lead configurations, supporting potential deployment in wearable or mobile devices. Our analysis also revealed a striking morphological confound from the complete left bundle branch block, leading us to propose absolute age deviation as a more robust, universal risk marker. These findings establish ECG-derived biological age deviation as a highly generalizable and clinically actionable biomarker for assessing cardiovascular risk. We have also developed a web application at https://bioinformatics.mdc-berlin.de/ECGage that allows users to easily test our framework.

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.

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.

Severe preeclampsia (sPE) is a major cause of maternal and fetal morbidity worldwide, yet its placental molecular heterogeneity remains poorly defined by current clinical diagnosis. To resolve the molecular architecture of sPE, here we integrated DNA methylation and proteomic profiling from a multi-ethnical cohort of 444 placentas from the Hawaiian Biorepository (HiBR), including 169 sPE cases, matched preterm controls and full-term controls. To address cellular heterogeneity in bulk placental tissue, we developed HOMED (Hierarchically Optimized Methylation Deconvolution), a single-cell-guided hierarchical framework for inferring placental cell-type composition from DNA methylation data. HOMED-adjusted integrative analyses identified extensive subtype-specific alterations involving hypoxia, angiogenesis, immune activation, trophoblast differentiation and metabolic remodeling. Molecular stratification revealed two reproducible sPE subtypes with divergent placental aging trajectories. One subtype exhibited a pre-mature placental state marked by accelerated placental aging, whereas the other displayed slower accelerated placental aging but a substantially increased risk of small-for-gestational-age birth (P = 0.028). These subtypes were independently replicated across six external cohorts and further supported by proteomic signatures achieving a classification accuracy of 0.88. Integrative epigenomic and proteomic analyses linked the growth-restricted subtype to hypoxia-associated glycolytic remodeling, suggesting distinct pathogenic mechanisms underlying clinically diagnosed sPE. Together, our findings redefine severe preeclampsia as a biologically heterogeneous placental disorder composed of molecularly distinct subtypes with divergent aging trajectories and fetal growth outcomes, providing a framework for mechanism-based stratification and precision obstetric medicine.

Cancer treatment has shifted toward personalized therapy based on molecular profiling, particularly in advanced disease. Existing circulating tumor DNA panels are often broad, generating many non-actionable variants and incurring costs that limit routine use in molecular tumor boards. We developed and validated a manufacturer-independent, 109-gene liquid biopsy-centered pan-cancer open next generation sequencing panel (LION panel), combined with an in-house bioinformatic pipeline to support clinical decision-making. A total of 87 samples were analyzed, including 17 reference samples, 21 healthy blood donor controls, and 49 patient samples including nine tumor entities. The LION panel achieved 92% sensitivity and 99% specificity in reference samples, with high concordance to digital droplet PCR (r = 0.99). It detected variant allele frequencies as low as 0.05% (tumor-informed) and 0.5% (tumor-uninformed). Clinical concordance reached 82% with blood-based digital droplet PCR and 75% with whole exome tissue sequencing. In representative cases, variant dynamics correlated with disease progression and revealed additional targetable variants. Overall, the LION panel supports clinical decision-making by enabling identification of targetable variants, disease monitoring, and detection of treatment resistance, particularly when tumor tissue is unavailable.

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.

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.

Background: Unstructured data represent about 80% of total electronic health records (EHR) data. Structuring this free text is essential for advancing clinical research, including cohort selection for trials, retrospective studies, and the development of disease registries. While manual chart review (MCR) remains the gold standard for extracting this clinical data, the process is inherently slow, resource-intensive, and susceptible to errors from human fatigue. We evaluated the extraction accuracy, safety, and efficiency of the HeLIX (Hepatology Logic-Integrated Extraction) framework, a Large Language Model (LLM) protocol using Google Gemini 3 Pro, compared to a gold-standard Manual Chart Review (MCR). Methods: A prospective validation study was conducted using 50 high-complexity, simulated hepatology discharge summaries designed to replicate the real-world heterogeneity of EHRs. The HeLIX framework employed a Zero-Shot, Structured Chain-of-Thought (CoT) prompting strategy enforced by a three-layer architecture: Clinical Reasoning Trace, Schema Enforcement, and Evidence Verification. The model extracted 45 distinct clinical variables. Performance was benchmarked against a consensus MCR. Results: Across 2,250 evaluated data points, the model achieved an overall Extraction Accuracy of 99.24% (95% CI: 98.8%-99.5%), with perfect concordance in 35/45 (77.8%) variables. For binary diagnostic variables, the model demonstrated an overall F1-score of 0.98, Recall of 0.99 and substantial inter-rater reliability (Cohens {kappa} = 0.97). Hallucinations were exceptionally rare (2/2250; 0.08%). Critical errors affecting clinical management occurred in only 2 instances (<0.1% of total data), both involving etiological misattribution in complex multifactorial diagnoses. The AI workflow was 13.4-fold faster and 95.1% more cost-effective than manual extraction. Conclusion: The HeLIX framework demonstrates physician-level accuracy and reliability in extracting complex hepatology data. It offers a scalable, efficient, and economical alternative to manual chart review. Such frameworks could accelerate clinical research, enabling healthcare systems globally to build comprehensive patient registries for a fraction of the traditional cost.