Human Genetics and Genomics Advances

Standard polygenic risk scores (PRSs) are constructed based on additive genome-wide association study (GWAS) summary statistics. Nonlinear machine learning methods have been increasingly applied to construct PRSs directly from individual-level data, with the aim of improving predictive performance over standard PRSs through their ability to model non-additive genetic effects. However, their superiority across studies has been inconsistent, and the conditions under which they provide meaningful improvements remain unclear. We combined theoretical analysis, simulations and a real-world application to investigate when two widely used nonlinear machine learning methods, random forest and XGBoost, outperform standard PRSs. Theoretical analysis showed that standard PRSs can implicitly capture part of the genetic variance attributable to nonadditive genetic effects through their contributions to marginal SNP effects, thereby losing less information than commonly assumed. Although nonlinear models have a higher theoretical potential, their greater flexibility incurs a bias-variance trade-off that can limit predictive gains at finite sample sizes. Simulations showed that XGBoost outperformed the standard PRS only when the genetic architecture involves a sufficiently large proportion of interaction genetic variance concentrated across relatively few interaction effects and large training samples were available. Random forest consistently underperformed the standard PRS. In an application to ischemic heart disease prediction using UK Biobank data, XGBoost showed no meaningful improvement in predictive performance over the standard PRS, whereas random forest again performed worse. Together, these findings suggest that nonlinear machine learning do not uniformly outperform standard PRSs; rather, their relative performance depends jointly on genetic architecture and training sample size. Our study helps to reconcile the inconsistent results reported across previous studies and provides a framework for identifying settings in which more complex PRS models are likely to be beneficial.

Genotype imputation remains essential for large-scale human genetics studies, but its performance is limited by the size and ancestral diversity of available reference panels, reducing accuracy for rare variants and underrepresented populations. Here, we present a cloud-based imputation service built on a multi-ancestry reference panel derived from 515,579 jointly phased genomes from the All of Us (N=414,830) and National Human Genome Research Institute's Analysis, Visualization, and Informatics Lab-space (AnVIL, N=100,749) datasets. The All of Us + AnVIL reference panel is highly diverse and includes 261,163 participants most genetically similar to non-European reference populations, spanning 665,398,839 high-quality autosomal sites, representing a nearly 50% increase over TOPMed, the previous largest imputation service. Across multiple ancestry groups, the panel enables accurate imputation (empirical R2 0.8) for variants with allele frequencies as low as 0.2%, extending reliable imputation into the rare-variant frequency spectrum, including allele frequencies down to 0.002% and 0.006% for samples with European ancestry and African ancestry in the United States, respectively. Compared with TOPMed, the panel improves imputation accuracy across all ancestry groups except Africans, and recovers additional trait-associated variants not represented in existing reference panels. To facilitate broad community access while preserving participant privacy, we deploy the panel through a secure cloud-based imputation platform using privacy-preserving recombined haplotypes. This resource establishes a new foundation for genome-wide association studies (GWAS) and fine-mapping, especially in previously underrepresented populations.

Pathogenic variants in RUBCN, encoding the Run domain Beclin-1 interacting and cysteine-rich domain-containing protein (Rubicon) have been implicated in autosomal recessive spinocerebellar ataxia 15 (SCAR15). However, the molecular mechanisms underlying disease pathogenesis remain poorly understood. Here, we report 18 individuals from 15 unrelated families harbouring biallelic RUBCN variants, who present with an aggressive neurodevelopmental disorder variably characterized by seizures, developmental delay, intellectual disability and movement abnormalities that cause regression, progressive brain atrophy and neurodegenerative features. Through functional characterization, we demonstrate that a subset of disease-associated putative truncating variants disrupt autophagy regulation. In Caenorhabditis elegans models, loss-of-function RUBCN variants result in an increased autophagic flux and impaired neuronal function, recapitulating key features in humans. Correspondingly, cellular assays reveal that nonsense and frameshift RUBCN variants lead to defective autophagy inhibition, underscoring a crucial role for RUBCN as a key negative autophagy regulator. Molecular dynamics simulations rank the eleven missense variants by structural effect, with p.Arg813Trp alone altering the target protein at both the local and the regional level and lying within the RAB7A-binding module that the truncating alleles remove altogether. Our findings establish and expand the RUBCN-related disorders as a clinically and molecularly distinct subset of autophagy-related diseases. By delineating both the genetic landscape and cellular consequences of Rubicon dysfunction, this study enhances our understanding of autophagy-related neurodevelopmental disorders and provides a foundation for future therapeutic investigations.

Sub-Saharan African populations carry pharmacogenomic alleles poorly represented in the European-derived reference panels underlying most clinical genotyping tools. We present a curated, machine-readable catalog of nine actionable alleles across six pharmacogenes (CYP2D6, CYP2B6, CYP2C9, CYP2C19, CYP3A5, NAT2) with African-specific frequency ranges, functional annotations, and evidence levels derived from reanalysis of 661 high-coverage whole-genome sequences across seven 1000 Genomes Project African populations. Direct comparison against PharmCAT v3.4.0 shows that CYP2D6 produces zero diplotype calls (0/661 samples callable) due to monomorphic reference positions absent from standard variant-only VCF output, a known limitation whose consequences for African allele carriers had not been reported. afripharmagen's reduced-position strategy identifies 243 CYP2D617 and 134 CYP2D629 carriers from the same input. For CYP2B6, CYP2C9, CYP2C19, and NAT2, both tools show concordance of 95-100%. Frequency gradients (CYP2B66: 30-50%; CYP2D617: 15-35% in West Africa; CYP3A5*1: 60-95%) translate directly into prescribing risk for efavirenz, tramadol, tacrolimus, and isoniazid. Pharmacogenomic decision support in African settings must incorporate population-specific allele definitions and input-format-aware strategies.

Confounding is a central challenge in observational studies. Here, we propose a framework for identifying confounders of two non-causally related traits by employing cross-trait pleiotropy analysis to detect genetic loci that affect both traits and multi-trait colocalisation to identify molecular phenotypes mediating these effects. We apply this approach to the analysis of C-reactive protein (CRP) - a non-specific marker of inflammation - and 10 inflammation-related cancers. In UK Biobank, higher pre-diagnostic CRP levels are associated with increased risk of multiple cancers, but bidirectional Mendelian randomization provides little evidence for a causal relationship. Cross-trait genetic analyses identify 92 loci with shared CRP-cancer effects including those with established roles in cancer and 50 novel loci such as RSPO3 (breast cancer) and GCKR (colorectal cancer). Integration with proteomic and single-cell transcriptomic data identified putative molecular mediators at 24 loci including plasma TLR1 levels in breast cancer and CD4+ T cell IRF5 expression in kidney cancer. Notably, 15 candidate effector genes encode targets of approved or investigational medications, including IL6, PDE4D, and CASP8, indicating potential opportunities for their repurposing for cancer prevention. The proposed approach provides a generalisable framework for leveraging non-causal phenotypic relationships to yield insights into disease mechanisms and therapeutic targets for disease prevention.

Background: Many at-risk patients lack access to genetic services due to a genetic counselor (GC) workforce shortage. Little is known about how digital alternatives impact patients with and without cancer who meet criteria for genetic testing. Methods: eREACH2 is a randomized 4-arm non-inferiority trial where pre-test (visit 1) and/or return of results (visit 2) GC counseling was replaced with a patient-centered digital intervention. Arms include: A (GC/GC), B (GC/digital), C (digital/GC) and D (digital/digital). Primary outcomes were non-inferiority in uptake of genetic services and change in genetic knowledge and general anxiety from baseline to post-disclosure of results (T0-T2). Secondary cognitive and affective outcomes were assessed using non-inferiority ANOVAs and equivalency chi-squared tests in intention-to-treat and per-protocol analyses. Findings: 773 participants were recruited nationwide; 46.6% from rural areas. Mean age was 51 years (range 20-87), 13% male, 12% non-white, 29% had less than a college education, and 33% had a personal history of cancer. 584 (76%) patients completed testing (14% had a positive result, 16% had a VUS). In the primary ITT analyses, we met the non-inferiority for uptake of genetic services and anxiety, but results were inconclusive for knowledge. Secondary outcomes were heterogeneous across arms. Arm C demonstrated consistently favorable effects, while Arms B and D showed less favorable outcomes in select domains (e.g. satisfaction and MICRA). Patients who received positive or VUS results via digital disclosure had significantly higher MICRA scores - indicating greater negative response to testing. Interpretation: In this large, randomized trial of patients with and without cancer, the eREACH intervention was effective for pre-test counseling, but inconclusive for digital disclosure of results. Exploratory analyses suggest that digital delivery could be a reasonable alternative for individuals receiving negative results, while those receiving positive or VUS results may derive some short-term psychosocial benefit from GC disclosure.

Recent large-scale studies have enabled new knowledge about genetic underpinnings of morphological and electrophysiological alterations of the retina. Variation in retinal traits, often of neurodevelopmental origin, have been linked to major psychiatric disorders (MPDs). Here, we investigate the genetic overlap between MPDs and key retinal traits to identify underlying molecular mechanisms. We obtained genome-wide associations studies data for bipolar disorder (BD), major depression (MD), schizophrenia (SCZ), and the retinal traits retinal nerve fibre layer thickness (RNFL), ganglion cell inner plexiform layer thickness (GCIPL), and vertical cup-disc ratio (VCDR). We estimated the number of trait-influencing variants shared between traits with MiXeR and identified shared genetic loci with condFDR. Subsequently, we examined the biological pathways of the genes mapped to shared loci. This revealed that GCIPL shared the most genetic variants with MPDs (~60%), followed by RNFL (~40%), and VCDR (~20%). The genetic variants shared between retinal traits and MPDs showed disorder-specific patterns with more pronounced overlaps of SCZ and BD with RNFL, and MD negatively correlated with GCIPL. Gene-pathway analysis highlighted the importance of GABAergic neurotransmission and a two-stage neurodevelopmental process in SCZ, whereas the role of mitochondria and a weaker developmental component were observed in BD. The results also implicated synaptic functioning and gene-expression processes in MD. Furthermore, polygenic analysis suggested that the genetic architecture of retinal traits can distinguish between MPDs. Our findings indicate shared genetic underpinnings between retinal traits and SCZ, BD, and MD, implicating altered neurodevelopment and neurotransmission underlying the retinal link to major psychiatric disorders.

Unmeasured confounding threatens causal inference and replicability in observational multi-omic studies across variable environments. Genetic instrumental variables (Mendelian randomization) and negative-control calibration each address complementary sources of unmeasured confounding, yet no existing framework unifies them for omics-scale mediation analysis. We introduce ICONIC, an R package that embeds genetic instruments and negative controls within a proximal causal inference framework for total-effect and mediation analysis. ICONIC implements eight estimators spanning five confounding-control strategies, supports continuous, binary, and time-to-event outcomes, and provides extensive diagnostics including sensitivity analyses that map estimator performance across plausible assumptions. Ground-truth benchmarks are calibrated to real-omics covariance structures via a hybrid generative model (GAN + feature-level Gaussian copula) rather than parametric simulation, and a companion planning tool predicts performance gains from collecting additional omic data. We demonstrate ICONIC in two case studies: identifying placental transcriptomic mediators of gestational diabetes on birth weight (n = 164), and tumor-expression mediators of smoking intensity on lung cancer survival (n = 494). Notably, ICONIC's diagnostics recommended different estimation strategies across the two scenarios, reflecting differences in the likely influence of unmeasured confounding. ICONIC is freely available at https://github.com/sbresnahan/iconic/.

Single-cell technologies enable linking disease-risk variants to gene regulatory effects in specific cell-state contexts. However, most so called "single-cell eQTL" studies use a "pseudobulking" strategy to identify expression Quantitative Trait Loci (eQTLs), obscuring subtle dynamic regulatory effects of disease alleles. Here, we propose Dynema (Dynamic eQTL mapping in single cells) for fast and accurate genome-wide mapping of context-dependent and independent eQTL effects at true single-cell resolution. To identify eQTLs, Dynema uses a Poisson model with cluster robust variance estimators (CRVEs) to account for correlation of single-cell profiles from the same individual. In contrast to other common methods, Dynema achieves statistical calibration and scales to genome-wide analysis in large single-cell datasets in realistic timeframes. We applied Dynema to two independent T cell datasets and identified reproducible cell-state-dependent eQTL effects. Some cell-state-dependent eQTLs are missed by pseudobulking approaches, and many others are conditionally independent from lead eQTL effects. We show that TSPAN32 and other autoimmune loci colocalize with cell-state-dependent eQTLs. Mapping context-dependent eQTLs at single-cell resolution enables the definition of the molecular effects of complex disease alleles.

Chemoradiotherapy (CRT) is the standard-of-care therapy for many solid malignancies, yet predictive biomarkers of treatment response remain limited. We identified a germline single nucleotide polymorphism (SNP) in an intrinsically disordered region of the lysine demethylase KDM3C/JMJD1C (p.S464T) that is associated with CRT outcomes in locally advanced rectal cancers (LARC) and head and neck squamous cell carcinoma (LA-HNSCC). In silico modeling with AlphaFold predicted S464T substitution influenced interaction between phosphorylated KDM3C and RNF8 FHA domain. In cellular models, conversion of S464 to T464 increased sensitivity to DNA-damaging agents. S464T substitution impaired damage-induced MDC1-RAP80 signaling and downstream RAP80-BRCA1 colocalization. SNP carrying cells impaired DNA repair causing genotoxic stress that is associated with increased cGAS-cGAMP innate immune signaling and increased apoptosis. Population analyses with the SNP highlighted an increase incidence of UV-induced skin and other cancers, linking inherited variation in the chromatin regulatory gene KDM3C to genome instability, cancer risk, and therapeutic vulnerability.

Indonesian rice cultivars represent valuable genetic resources, yet many remain poorly characterized at the genomic level. Here, we generated 95.40 Gb of PacBio HiFi sequence data from seven Indonesian rice cultivars and constructed cultivar-specific consensus genomes using the telomere-to-telomere Nipponbare reference AGIS1.0. Sequencing coverage ranged from 27.92x to 41.58x, and the resulting consensus genomes spanned 387.93-390.54 Mb, with BUSCO completeness of approximately 98.3-98.5%. OrthoFinder assigned 99.1% of predicted proteins to 40,737 orthogroups, including 27,514 core orthogroups represented across all seven cultivars, indicating a highly conserved predicted gene space within the reference-guided framework. Targeted analysis recovered 278 of 280 cultivar-by-locus combinations representing 40 genes or gene family entries associated with grain pigmentation, nitrogen and amino-acid metabolism, and starch properties. Comparative predicted protein analysis prioritized ANS1, SBE2b, SSIIa/ALK, Wx/GBSSI, OsAAP6/qPC1, and SSI as candidates for further investigation. Among 269 completed AGIS1.0-anchored promoter comparisons, 159 passed quality-control criteria, whereas 110 were flagged for gene-model, boundary, synteny, or structural concerns. Notably, these flagged comparisons accounted for more than 90% of the alignment-derived sequence variation, emphasizing the importance of rigorous quality control when interpreting apparent promoter divergence. Collectively, these reference-guided genomic resources provide a standardized framework for investigating sequence variation in Indonesian rice germplasm and prioritize testable coding and regulatory candidates for functional validation and future genomics-assisted crop improvement.

Parkinsons disease (PD) arises through disruption of multiple interconnected cellular processes, but the genetic contributions to these processes may differ across ancestries. We investigated functional convergence among genes harboring pathogenic or likely pathogenic (P/LP) variants and variants of uncertain significance (VUS) in a multicenter Indian cohort recruited through the Genetics of Parkinsons Disease in India Young Onset Parkinsons Disease project (GOPI YOPD). The cohort included 668 participants (463 males 69.3%) with a mean age at motor onset of 39.4+/-8.8 years. P/LP variants and VUS identified through previously reported whole-exome or whole genome sequencing were retained as separate evidential categories. The P/LP-associated gene set comprised 11 unique genes and the VUS associated set comprised 40 unique genes. Separate STRING functional-enrichment analyses evaluated Gene Ontology Biological Process, Molecular Function and Cellular Component terms, KEGG pathways, WikiPathways and STRING local network clusters. Terms meeting a Benjamini Hochberg false discovery rate threshold of <0.05 were organized into eight non-mutually-exclusive ontology/pathway categories. Gene to pathway mappings were subsequently projected to individual participants to estimate pathway representation and examine clinical associations. At least one reportable P/LP variant or VUS was identified in 336/668 participants (50.3%): 35 had a P/LP variant alone, 282 had VUS alone and 19 had a P/LP variant together with VUS in one or more additional genes. The most frequently represented categories were mitochondrial organization (247/336, 73.5%), autophagy related processes (228/336, 67.9%) and regulation of synaptic vesicle transport (201/336, 59.8%). PRKN was the most frequent P/LP-associated gene, occurring in 29/54 P/LP carriers, followed by PLA2G6 and PINK1. Lysosomal transport was represented exclusively by VUS-associated genes, particularly GBA1, VPS13C and LRRK2. Among P/LP carriers, additional VUS in distinct genes were not associated with age at onset (P = 0.81) or family history (52.6% versus 31.4%; P = 0.15). No pathway phenotype association remained significant after correction for multiple testing. Genetic findings in this Indian cohort converged across an interconnected mitochondrial autophagic lysosomal vesicular network, with different contributions from P/LP-associated and VUS associated gene sets. This study provides the first pathway resolved South Asian genetic profile and a framework for comparative studies across populations.

Background. The imprecision of current metrics to capture the complex genetic admixture and racial identity among Hispanic/Latino individuals in the United States [US] is a concern. We examined the relationship of self-reported race and genetic ancestry with hypertension [HTN] among Hispanics/Latinos. Methods. Cross-sectional study of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), including 10,586 Hispanic/Latino unrelated adults. Genetic ancestry: West African [AA], Amerindian [AI], and European [EA]. Self-reported race: White, Black, Native American, or Multiple/Missing (More than one race or Unknown/Not reported/Refused). HTN: systolic (SBP) [&ge;]130 mmHg, diastolic blood pressure (DBP) [&ge;]80 mmHg, and/or use of HTN medications. Age- and sex adjusted models were used. Results. Self-reported race was White (38{middle dot}6%), Black (3{middle dot}6%), Native American (4{middle dot}1%), and Multiple/Missing (53{middle dot}7%), with Unknown/Not reported/Refused representing 32{middle dot}7%. Black and White Hispanics/Latinos had the greatest AA (55{middle dot}7%) and EA (69{middle dot}3%) ancestries, respectively. Each 10% AA increase was associated with OR 1{middle dot}15, SBP beta +0{middle dot}9 mmHg, and DBP beta +0{middle dot}7 mmHg. Conversely, each 10% AI increase was associated with OR 0{middle dot}83, SBP beta -0{middle dot}4 mmHg, and DBP beta -0{middle dot}6 mmHg. HTN prevalence was highest among those with Black race or in the highest AA quantile (45{middle dot}6% and 48{middle dot}0%, respectively), and lowest among those with Native American race or in the highest AI quantile (37{middle dot}6% and 26{middle dot}7%, respectively). Conclusion. One-third of Hispanics/Latinos did not self-report race. Black or White self-reporting race did somewhat relate to AA or EA ancestry, respectively. HTN profiles were related to self-reported race and genetic ancestry in this admixed population.

The distribution of fat-soluble compounds (including antioxidants) in follicular fluid (FF) remains sparsely documented in relation to in vitro fertilization (IVF) outcomes and existing studies have reported diverging associations. This study aimed to describe plasma and FF concentrations of fat-soluble micronutrients in women undergoing IVF and to analyze their adjusted associations with ovarian function, embryo development and pregnancy outcomes. In 2021-2022, plasma and FF samples were collected from 82 women (first IVF cycle) at oocyte puncture, along with lifestyle data covering the three preceding months. Eleven compounds (two tocopherols, three xanthophylls, five carotenes and retinol) were quantified. All compounds were detected in both compartments (lowest in FF) except phytoene, undetectable in FF. Plasma and FF -tocopherol concentrations were positively associated with plasma estradiol levels before oocyte puncture (both p<0.01) while FF -carotene and lycopene were inversely associated with plasma progesterone concentrations (p=0.01 and 0.02, respectively). Plasma phytofluene and phytoene were positively associated with mature oocyte rate (p=0.03 and p=0.01, respectively), while FF retinol was negatively associated (p=0.03). Carotenes, tocopherols and retinol were inversely associated with later IVF outcomes: fertilization rate (p<0.001 for plasma g-tocopherol, 0.02 for FF retinol), top-quality embryo (p=0.02 for plasma phytofluene), biochemical pregnancy at day 7 post-embryo transfer (p=0.05 for plasma -tocopherol, 0.02 for plasma -carotene), clinical pregnancy (p=0.03 for plasma -tocopherol, 0.01 for plasma phytoene) and live birth (p=0.04 for plasma -tocopherol, 0.02 for plasma phytoene). Plasma and FF g-tocopherol were positively associated with embryo fragmentation (both p<0.05). Finally, among xanthophylls, only plasma {beta}-cryptoxanthin was positively associated with plasma progesterone concentrations (p=0.02). Our findings of heterogeneous associations between tocopherols, carotenes, retinol and IVF outcomes across the stages of IVF suggest a beneficial effect limited to early outcomes and support a complex and context-dependent role of these compounds in female reproduction. This manuscript has been submitted to PlosOne on August 19, 2026.

Isoform-resolved transcriptomics is fundamental to decoding the molecular complexity of the human brain, yet population-scale long-read RNA sequencing has remained inaccessible due to labor-intensive library preparation, sensitivity to RNA degradation in postmortem tissue, and the absence of integrated, reproducible analysis pipelines. Here we present SALRR (Scalable Analysis of Long-Read RNA-seq), an integrated wet-lab and computational platform designed to overcome these barriers. Automated ONT long-read cDNA library preparation on the Hamilton Microlab NGS STAR platform reduces hands-on time by 67% and enables 24 libraries per operator per day while maintaining performance across RNA integrity values. A modular, Snakemake-based pipeline performs end-to-end processing from ONT signal data to isoform-level quantification, incorporating SIRV spike-in calibration, multi-stage quality control, and stringent isoform validation. Applied to 10 postmortem frontal cortex samples from the North American Brain Expression Consortium, SALRR identified 31,607 high-confidence isoforms from 10,075 genes, including 8,532 novel splice variants absent from GENCODE v49, and complex splicing events systematically missed by short-read sequencing at neurodegeneration-relevant loci, including GBA1, CCNF, CHCHD10, and TREM2. All protocols and code are openly available, providing a scalable, community-ready framework for isoform-resolved transcriptomics in neurodegeneration, aging, and complex brain disease.

Background and Objectives: Deep sequencing of brain tissue in the research setting has established that mosaic variants are a major cause of malformations of cortical development (MCDs) and epilepsy. However, genetic testing in the clinical setting primarily detects germline variants using clinically accessible samples. We aimed to determine the diagnostic yield and clinical utility of deep sequencing in the clinical setting to identify pathogenic mosaic variants for this population. Methods: We performed a retrospective cohort analysis of individuals at Boston Children's Hospital with MCDs with or without epilepsy who received clinical deep sequencing between September 2017 and February 2026. Demographic, clinical, and genetic testing data were abstracted from the medical record. For individuals without systemic features, we classified brain tissue as an affected tissue sample. For individuals with systemic features, we classified brain or relevant non-brain tissue as affected. The primary outcome was the diagnostic yield of clinical deep sequencing performed using affected vs unaffected tissue samples. The secondary outcome was the clinical utility of genetic diagnoses. Results: Our cohort included 37 individuals (19/37 (51%) female, 18/37 (49%) male) with MCDs, of whom 35/37 (95%) had epilepsy (25 with brain tissue samples available from epilepsy surgery) and 8/37 (22%) had systemic features. Most (35/37 (95%)) had dysplasia phenotypes on MRI and 12/27 (44%) with pathology available had Focal Cortical Dysplasia Type I or II. The diagnostic yield was 53% (17/32; 16 mosaic and 1 germline variant) when clinical deep sequencing was performed using an affected tissue sample vs 0% (0/6) using an unaffected tissue sample (p=0.016). Of the diagnosed cases, 13/17 (76%) had testing performed on brain tissue (1 with systemic features) and 4/17 (24%) on non-brain tissue (3 buccal and 1 duodenal tissue, all with systemic features). All but one diagnosis involved the mTOR pathway. All diagnoses had clinical utility. Discussion: Clinical deep sequencing, when performed using an affected tissue sample, has high diagnostic yield and clinical utility for individuals with MCDs, especially dysplasia phenotypes, and epilepsy. Our findings support implementation of clinical deep sequencing for this population, especially as the genetic diagnoses have implications for emerging precision therapies.

Migraine is a leading cause of disability, yet preventive treatment remains largely empirical despite the availability of several mechanistically distinct therapies. Genetic data can clarify mechanisms and therapeutic hypotheses when association signals are integrated with molecular and clinical data. We meta-analyzed migraine GWAS data from 12 European ancestry cohorts (206,893 cases and 2,093,175 controls) and four African ancestry cohorts (22,115 cases and 178,626 controls). We identified 311 lead variants in European-ancestry analyses and 316 lead variants in trans-ancestry analysis. Fine-mapping and transcriptome-wide analyses prioritized variants and genes implicated in sensory neuronal signaling, vascular tone, and immune regulation, with convergent evidence at several established loci including TRPM8 and PHACTR1. Drug-repurposing analyses identified therapeutic targets and compounds, including established migraine treatments and candidates requiring experimental validation. Genetic correlations, Mendelian randomization, and a phenome-wide scan linked migraine liability to psychiatric, pain, and gastrointestinal phenotypes. Together, these findings expand the known genetic architecture of migraine across ancestries and provide a genetics-led map connecting association signals with biological pathways, multimorbidity and candidate therapeutic mechanisms, providing a foundation for future functional and translational studies.

Autosomal dominant gain-of-function mutations in platelet-derived growth factor receptor beta (PDGFRb) cause overgrowth of the skeleton and other connective tissue in Kosaki overgrowth syndrome. However, the target cell type and signaling pathways underlying PDGFRb-driven overgrowth are unknown. Normal postnatal growth is controlled by pituitary-secreted growth hormone (GH), which activates the STAT5 transcriptional factor to upregulate insulin-like growth factor 1 (IGF1). To investigate the role of the GH-STAT5-IGF1 pathway in PDGFRb-related overgrowth, we generated mice with a PDGFRb gain-of-function mutation in skeletal and fibroblast lineages, which resulted in STAT5 activation and gigantism. Conditional deletion of Stat5ab in connective tissue lineages rescued skeletal overgrowth and keloid-like fibrosis in the skin. Conditional deletion of GH receptor (Ghr) did not rescue overgrowth, indicating the physiological activator of STAT5 is not required for overgrowth. However, deletion of Igf1, the STAT5 target gene, and its receptor, Igf1r, in connective tissue, rescued the overgrowth phenotype. These findings demonstrate a GHR-independent STAT5-IGF1 signaling pathway in mutant connective tissue cells, which mediates PDGFRb-driven overgrowth in mice and potentially in humans with similar PDGFRB mutations.

Background: Endometriosis affects approximately 10% of reproductive-age women and is associated with substantial diagnostic delay and heterogeneous symptom presentation. Prior machine-learning prediction models have relied on comorbidity data alone or on small candidate-variant genetic scores, with inconsistent or incompletely reported performance. No study has combined a well-powered, multi-ancestry polygenic risk score (PRS) with environmental, reproductive, and symptom data in a single hybrid model. We developed and evaluated hybrid risk-prediction models integrating a genome-wide, multi-ancestry PRS with clinical and symptom data for endometriosis in the US-based All of Us Research Program. Methods: Among 69,376 participants (15,382 endometriosis cases, 53,994 controls) across six genetically inferred ancestry groups, we computed individual-level PRS values using PRS-CS weights derived from an independent, multi-ancestry GWAS. Five nested logistic regression, random forest, and XGBoost models progressively added age, ancestry, and within-ancestry genetic principal components (Model 1), environmental and reproductive factors (Model 2), symptom and comorbidity indicators (Model 3), all covariates combined (Model 4), and PRS x environment interactions (Model 5). Performance was assessed by AUROC in a held-out test set and 5-fold cross-validation, with class-weighted, Youden-optimized thresholds used for sensitivity, specificity, and predictive values; permutation importance identified top contributors. Pairwise AUROC differences were tested with a Holm-corrected DeLong-type test. Results: Discrimination improved from AUROC 0.63 (PRS, age, ancestry, principal components) to 0.72 for the full model, driven mainly by symptom and comorbidity data. XGBoost consistently outperformed logistic regression and random forest. The PRS ranked among the top individual predictors by permutation importance in nearly every model, alongside age, while genetic and demographic information alone gave only modest discrimination, and PRS x environment interactions did not improve on environmental factors alone. Threshold optimization yielded balanced sensitivity and specificity (~0.67/0.65) versus near-zero sensitivity at a default threshold. Conclusions: Combining the PRS with symptom and comorbidity data gave the best discrimination compared to solely a well-powered, multi-ancestry PRS as a predictor of endometriosis. This study clarifies both the promise and current limits of hybrid genetic-clinical prediction for endometriosis and points to symptom-based phenotyping, molecular subtyping, and external validation as priorities.

To sustain blood formation, hematopoietic stem and progenitor cells (HSPCs) coordinate a multitude of cell biological processes, from cell cycle control and stress responses to lineage priming. While many genetic regulators of high-level HSPC function have been identified, how HSPCs coordinate more basal cell biological programs, and how such programs relate to stem cell function, remains incompletely understood. Here we use Perturb-seq to profile the transcriptional consequences of targeting 520 genes by CRISPRi in primary mouse HSPC cultures. We developed an analytical strategy to separate perturbation-induced changes in cell-state abundance and clonal heterogeneity from cell-state-local transcriptional effects. From these local perturbation signatures, we identified 19 gene regulatory programs (GRPs) that are defined by co-regulation in response to genetic perturbation, in contrast to co-expression or human curation, and align well with cell biological processes. By decomposing gene expression data from functional and clinical studies into program activity, we show that GRP activities associate with, and predict, phenotypes such as clonal output after transplantation, as well as survival and drug response in retrospective acute myeloid leukemia (AML) cohorts. Together, our study establishes perturbation-derived co-regulation programs as an interpretable framework for linking genetic regulators, cell-biological processes and stem-cell-associated phenotypes.