Human Genetics and Genomics Advances

Genome-wide association studies (GWAS) and polygenic score (PGS) development are typically constrained by the data available in biobank repositories in which European cohorts are vastly overrepresented. Here, we increase the utility of non-European participant data within the UK Biobank (UKB) by characterizing the genetic affinities of UKB participants who self-identify as Bangladeshi, Indian, Pakistani, "White and Asian" (WA), and "Any Other Asian" (AOA), towards creating a more robust South Asian sample size for future genetic analyses. We assess the relationships between genetic structure and self-selected ethnic identities and use consistent patterns of clustering in the dataset to train a support vector machine (SVM). The SVM was utilized to reassign n = 1,853 AOA and WA participants at the subcontinental level, and increase the sample size of the UKB South Asian group by 1,381 additional participants. We further leverage these samples to assess GWAS performance and PGS development. We include environmental covariates in the height GWAS by implementing a rigorous covariate selection procedure, and compare the outputs of two GWAS models: GWASnull and GWASenv. We show that PGS performance derived from both GWAS models yield comparable prediction to PGS models developed with an order of magnitude larger training, and environmentally-adjusted PGS models reduce the sex-bias in predictive performance. In summary, we demonstrate how GWAS performance can be improved by leveraging ambiguous ethnicity codes, ancestry matched imputation panels, and including environmental covariates.

PurposeIdiopathic hypogonadotropic hypogonadism (IHH) is characterized by impaired reproductive maturation, and approximately half of all cases lack an identified genetic cause. We investigated the genetic basis of IHH in two large cohorts to identify novel disease-causing genes. MethodsWe analyzed exome and genome sequencing data from 1,822 patients with IHH from two independent cohorts. Rare variants were filtered using pedigree-informed inheritance models. PLEKHA6 expression in the postmortem human hypothalamus were tested at the mRNA and protein level. Functional studies assessed kisspeptin secretion in cell-based assays. ResultsWe identified 18 distinct PLEKHA6 variants in 24 patients from 20 unrelated families (1.3% of cohort). Variants segregated with disease under autosomal recessive and autosomal dominant (with variable penetrance) inheritance patterns. PLEKHA6 was robustly expressed in the hypothalamus and showed clear colocalization with neurokinin B, which served as the marker for the GnRH pulse generator. Functional studies demonstrated that patient variants significantly impaired kisspeptin secretion. ConclusionPLEKHA6 is a novel IHH gene and the first reported regulator of kisspeptin secretion from the kisspeptin-neurokinin B-dynorphin (KNDy) neurons, which have recently been established as the GnRH pulse generator. These findings establish impaired kisspeptin release as a new disease mechanism in IHH and highlight the critical role of neuropeptide trafficking in reproductive function.

There are few therapeutic options to treat patients with sickle cell disease (SCD), a blood disorder caused by mutations in the {beta}-globin gene that affects >7M individuals worldwide. Combining human genetics and high-throughput proteomics can help identify new drug targets. Here, we present results from a proteogenomic analysis of the plasma proteome in SCD patients. We measured the levels of 5,411 plasma proteins and tested their associations with common genetic variation in 343 SCD patients. After conditional analyses, we identified 560 protein quantitative trait loci (pQTL), including 58 (10%) that are novel. Many of these pQTL are not specific to SCD patients and associate with clinically relevant traits in non-SCD African Americans from the Million Veteran Program (e.g. hemoglobin concentration, triglycerides). The effect sizes of the pQTL is largely concordant between SCD and non-SCD individuals, although we found examples (e.g. APOL1, haptoglobin) with evidence of heterogeneity that suggests an interaction between the plasma proteome and the SCD genotype. Finally, we combine pQTL and genome-wide association study results for fetal hemoglobin (HbF) in a Mendelian randomization analysis to prioritize five proteins that may increase HbF production (ENPP5, LBP, NAAA, PT3X, ZP3).

Loeys-Dietz syndrome (LDS) is an autosomal dominant connective-tissue disorder caused by genetic variants in TGF-{beta} pathway genes, most often TGFBR1/2. While pathogenic TGFBR2 genetic mutations usually cluster in the kinase domain and disrupt SMAD signalling, distinguishing with confidence those with functional impact on TGFBR2 function from rare benign genetic alterations represents one of the most important ongoing challenges for accurate genetic testing. Therefore, there is a pressing need to develop methods that can improve functional variant interpretation. Here, we describe and characterize the functional impact of a novel genetic variant in the TGFBR2 kinase domain (E431K), in a patient with the clinical diagnosis of syndromic genetic aortopathy. We assessed the structural and functional consequences of this variant using AI-driven molecular modelling and in vitro cell-based assays. A high-quality homology-based model of TGFBR2 was generated and computational mutagenesis based on the structural context and evolutionary conservation was used to forecast variant pathogenicity. Relative to wild type, the variant affects protein stability by disrupting intramolecular interactions and likely induces conformational changes that may affect kinase activity and thus TGF-{beta} signalling. This was experimentally confirmed by showing abnormal protein level and alteration of canonical TGF-{beta} pathway activation. Overall, our results establish that the E431K variant leads to aberrant TGF-{beta} signalling and confirm the diagnosis of Loeys-Dietz syndrome type 2 in this patient.

Alternative-splicing events (ASE) increase transcriptomic variability and play key roles in biological functions. The contribution of ASE to bipolar disorder (BD) remains largely unexplored. We performed a Transcriptome-Wide Alternative-Splicing Analysis (TWASA) to identify ASEs and genes potentially involved in BD. The study comprised 635 individuals: a discovery sample (DS) of 31 individuals from eight multiplex BD families (16 BD cases; 15 unaffected relatives), and a replication sample (RS) of 604 subjects (372 BD cases; 232 controls). Sequencing was conducted on RNA from lymphoblastoid cell lines (DS) and whole blood (RS). TWASA was performed using VAST-TOOLS (VT), rMATS (RM), and MAJIQ/MOCCASIN (MCC). Gene-set association analyses of genes containing ASEs were performed across six psychiatric disorders. Novel ASE (nASE) were investigated in the DS using FRASER. Limited gene overlap was observed across TWASA tools. MCC identified 2,031 complex ASEs involving 1,508 genes, showing the strongest genetic association with BD across psychiatric phenotypes. Prioritization of MCC-identified ASE genes yielded 441 candidates, including DOCK2 as top candidate from the DS. Replication was obtained for 98 genes, five with an identical ASE, and four (RBM26, QKI, ANKRD36, and TATDN2) showing a concordant percentage-spliced-in direction with the DS. Finally, 578 nASE were identified in the DS, with no evidence of familial segregation or differences in ASE types. This first TWASA in BD reveals tool-specific variability, complex ASE for genes specifically associated with BD, and novel candidate genes for BD. Alternative transcript isoform abundance may represent a mechanism contributing to BD pathophysiology.

Genome-wide association studies (GWAS) have identified numerous genetic variants associated with complex traits. However, linkage disequilibrium (LD) confounds these associations, leading to false positives where non-causal variants appear associated because they are correlated with nearby causal variants. This is particularly the case in highly polygenic traits where the genome can be saturated in causal variants. To address this issue, we propose LDeconv a method based on truncated singular value decomposition (SVD) that adjust GWAS summary statistics without requiring individual-level genotype data. This approach accounts for LD structure, isolates causal variants in high-LD regions, and improve the reliability of effect size estimates. We assess its performance through simulations across various LD scenarios, conduct extensive sensitivity analyses, and apply them to real GWAS data from the UK Biobank. Our results demonstrate that LDeconv effectively reduces false discoveries while preserving true associations, offering a robust framework for post-GWAS analysis.

The APOE {varepsilon}4 allele is the strongest genetic risk factor for Alzheimers Disease. However, its distribution across Indian populations is poorly characterized. We analyze APOE allele frequencies in 9,524 individuals from 83 distinct populations in the GenomeIndia dataset. {varepsilon}4 frequencies show large variation across populations within India, ranging from 2.7% to 36.1%, with a median of 11%. Tribal populations have higher {varepsilon}4 frequencies compared to non-tribal groups, while Tibeto-Burman populations have significantly lower frequencies. One tribal population from the northern coastal highlands has {varepsilon}4 frequency of 0.36, with 59% of individuals being carriers. {varepsilon}4 carrier status correlates significantly with lipid phenotypes including LDL, HDL, total cholesterol, and triglycerides. Collectively, these findings reveal exceptional genetic diversity in Alzheimers Disease risk across India and have important implications for population-specific screening strategies, genetic counseling, and precision medicine approaches to dementia prevention.

Introduction: Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is a common congenital malformation with complex etiology involving both genetic and environmental factors. Epigenetic mechanisms may mediate environmental contributions, but separating genetic from environmental effects remains challenging. Methods: We present an epigenome-wide association study with 32 monozygotic and 22 dizygotic twin pairs discordant for NSCL/P on blood and saliva samples. Differential methylation analysis was conducted using linear models to identify CpG sites showing significant methylation differences between affected and unaffected twins followed by functional annotation and pathway enrichment analysis. Results: The top-ranked finding is a differentially methylated region comprising two CpG sites at the CYP26A1 locus, cg12110262 (P = 3.21x10-7) and cg15055355 (P = 1.39x10-3). CYP26A1 is essential for retinoic acid catabolism and craniofacial patterning. The chromatin regulator ANKRD11, which causes KBG syndrome featuring cleft palate was the second best hit. Differentially methylated CpG sites showed significant enrichment in craniofacial enhancers and overlap with multiple GWAS-validated cleft genes including VAX1, PVRL1, SMAD3, and PRDM16. Conclusions: Our findings implicate retinoic acid signaling and chromatin regulation in NSCL/P etiology and demonstrate the value of discordant twin designs for distinguishing environmental from genetic epigenetic contributions to complex malformations.

Body fat distribution is a strong predictor of cardiometabolic disease risk. Gene-environment and gene-gene interactions can affect body fat distribution, resulting in differential phenotypic variance across genotype groups that can be detected through variance quantitative trait loci (vQTLs). Using UK Biobank MRI data in conjunction with genetic data, we explored evidence for vQTLs for body fat distribution phenotypes aiming to uncover potential genetic interactions. We identified three vQTLs for liver fat distribution, including rs738408 (PNPLA3), rs4293458 (APOE), and rs58542926 (TM6SF2), and one vQTL region (FTO) for abdominal subcutaneous adipose tissue. To dissect putative gene-environment and gene-gene interactions underlying these signals, we identified multiple vQTL-environment interactions and one epistatic effect (rs58542926*rs429358) for liver fat. The vQTLs and interaction results were validated in multiple UK Biobank and external replication cohort datasets (Framingham Heart Study, All of Us, and TwinsUK), showing replication of the three liver vQTLs with the greatest reproducibility for vQTL rs738408. Our findings uncover vQTLs and underlying interaction effects on body fat distribution, especially liver fat, that may be useful for the development of precision medicine approaches.

Cortisol is a major endogenous glucocorticoid that regulates numerous physiological processes. In plasma, cortisol and its inactive metabolite cortisone bind to corticosteroid-binding globulin (CBG) and albumin, leaving only the unbound fraction available for receptor activation and metabolism. Changes in ligand or protein concentrations alter unbound fractions. Existing binding equations are difficult to extend to multi-ligand, multi-protein systems and do not readily capture competitive endogenous binding interactions. The goal of this study was to develop a plasma protein binding model that quantitatively describes binding species and predicts unbound concentrations across physiological states. Total and unbound cortisol and cortisone, CBG and albumin were measured in plasma from healthy premenopausal women (n=13) at baseline and after 7 days of 30 mg hydrocortisone treatment. Reversible 1:1 binding models were implemented in COPASI and MATLAB/Simulink, and dissociation constants (Kd) were estimated by fitting binding models to observed unbound concentrations. A model describing simultaneous binding of cortisol and cortisone to CBG and albumin yielded in vivo Kd values for cortisol:CBG, cortisone:CBG, cortisol:albumin, and cortisone:albumin of 0.0130 {micro}M, 0.169 {micro}M, 172 {micro}M, and 519 {micro}M, respectively. Model predictions agreed with observed unbound cortisol and cortisone, and bootstrap resampling confirmed stable Kd estimates. This work provides a quantitative framework for predicting unbound cortisol and cortisone across physiological and disease states by accounting for both changes in ligand and protein concentrations. This enables extrapolation without reparameterization and supports exploration of conditions such as pregnancy, adrenal insufficiency, and liver disease, informing interpretation of altered cortisol concentrations in these populations. Significance statementThis work establishes a framework to predict in vivo cortisol and cortisone binding. The developed model was applied to predict unbound cortisol and cortisone concentrations in physiological and pathophysiological states and can be integrated into pharmacokinetic models. Our analysis demonstrates that cortisol and cortisone binding affinities estimated in the native plasma environment differ from those measured using purified proteins. These differences have important implications for predicting and analyzing unbound cortisol concentrations. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/718600v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@4a6bc1org.highwire.dtl.DTLVardef@1e87515org.highwire.dtl.DTLVardef@5ed98eorg.highwire.dtl.DTLVardef@11d0a3c_HPS_FORMAT_FIGEXP M_FIG C_FIG Created in BioRender. Authement, A. (2026) https://BioRender.com/zl1bg0k

G protein-coupled receptors rely on dynamic conformational changes to coordinate G protein activation and recruitment of regulatory transducers such as G protein-coupled receptor kinases and {beta}-arrestins. The chemotactic receptor GPR183 has been implicated in a context-dependent role in hematological malignancies. Here, we investigated the impact of A338V mutation located within the C-terminal tail of GPR183. This mutation is associated with acute myeloid leukaemia. Using bioluminescence resonance energy transfer-based assays in HEK293A cells, we assessed receptor-proximal signaling events. The A338V variant displayed preserved agonist potency and comparable agonist-induced Gi activation relative to wild type, although constitutive activity towards Gi was modestly reduced. In contrast, recruitment of GRK2 and {beta}-arrestin2 was consistently impaired across multiple assay configurations. These differences were not attributable to altered receptor abundance, as the C-tail untagged mutant exhibited increased plasma membrane expression despite reduced regulatory transducer engagement. While intramolecular conformational biosensor measurements revealed subtle differences in global receptor conformation between WT and A338V, extensive molecular dynamics simulations supported the altered conformational sampling of the C-terminal tail in the A338V variant. Together, these data support a model in which the A338V substitution selectively alters C-terminal structural dynamics, impairing GRK2 and {beta}-arrestin2 recruitment while preserving G protein activation.

PIGA-CDG is a congenital disorder of glycosylation caused by pathogenic partial loss-of-function variants in the PIGA gene. PIGA encodes an enzyme responsible for the catalytic transfer of N-acetylglucosamine to phosphatidylinositol during the first step of glycosylphosphatidylinositol anchor biosynthesis. Loss of this enzyme has a widespread phenotypic impact, but primarily results in neurological symptoms including seizures, intellectual disability, and developmental delay. Currently, treatments are limited and focus on symptom management. We developed an eye model of PIGA-CDG that has a reduced eye size. We screened a library of 98% 1,520 FDA/EMA-approved compounds to find drugs that improved the small eye phenotype. This screen revealed numerous drugs that improved eye size, including those that targeted dopamine signaling and cyclooxygenases. Using pharmacological and genetic approaches, we show that modulating dopamine signaling improves the eye size. Genetic inhibition of dopamine 2 receptor signaling and dopamine reuptake improve both the eye model and neurologically relevant PIGA-CDG phenotypes, including seizures and locomotor deficits. We also pharmacologically and genetically validate cyclooxygenase targeting drugs in the eye model. These findings reveal novel biology underlying PIGA-CDG and point towards candidate therapeutic approaches. AUTHOR SUMMARYPIGA-CDG is a rare neurodevelopmental disorder caused by pathogenic variants in the gene PIGA. Patients primarily display neurological symptoms, including seizures, developmental delay, and intellectual disability. Fewer than 100 patients have been identified, and treatment strategies are limited. In the context of rare diseases, de novo drug development is difficult due to the high cost, lengthy development times, and often too small of a patient population to conduct a clinical trial. Our lab leverages drug repurposing screening to circumvent many of the hurdles associated with de novo drug development. Here, we develop and screen FDA- or EMA-approved compounds on a Drosophila model of PIGA-CDG, uncovering novel biology underlying PIGA-associated pathophysiology. We use pharmacological and genetic tools to demonstrate that modifying dopamine signaling and abundance, as well as cyclooxygenase-mediated pathways, contribute to PIGA associated phenotypes. This work highlights promising therapeutic targets for PIGA-CDG.

Assessing the contribution of ultra-rare variants (minor allele frequency <0.01%) to the heritability of complex traits remains challenging due to limited understanding of potential biases. Here, we focus on singletons (that is, variants observed only once in the study sample), the most abundant class of ultra-rare variants, to showcase various confounders of heritability estimates and underline pitfalls in their interpretation. We show through theory, simulations, and analysis of 5,330,210 exome-sequenced singletons in 305,813 unrelated European-ancestry individuals in the UK Biobank that (i) population stratification induces both upward and downward biases in singleton-based heritability estimates (), (ii) estimates capture non-additive genetic effects, and (iii) asymptotic standard errors of estimates from likelihood-based procedures are generally mis-calibrated when traits are not normally distributed. We further showcase these biases in real-data analyses of 22 quantitative phenotypes and report, after accounting for these pitfalls, significant estimate for number of children (3.4%), peak expiratory flow (1.9%), red blood cell count (2.5%), white blood cell count (1.9%) and heel bone mineral density (2.4%). Overall, our study provides recommendations for robust inference of heritability from ultra rare variants and underscores that reliable estimates for ordinal and binary traits will require far larger sample sizes and improved methods, given that confounding in these traits remains difficult to detect and correct

Purpose: Aicardi-Goutieres syndrome (AGS) is a type I interferonopathy presently associated with nine genes. PTPN1 is a negative regulator of the interferon pathway previously associated with chronic inflammation and recently type 1 IFN autoinflammation. Methods: Genomic data from undiagnosed individuals with suspected AGS were interrogated for PTPN1 variants, and predicted loss-of-function (pLOF) and damaging missense variants in PTPN1 were sought in two additional academic databases as well as the All of Us database. Results: We identified 13 cases with ultra-rare heterozygous pLOF or highly damaging missense variants in PTPN1. Nine cases were identified in a cohort of 53 individuals (~ 17%) with clinical, imaging and persistent biochemical features of AGS. Median age of onset is 1.75 years (IQR 0.67), significantly later (p< 0.0001) than other AGS genotypes. Four additional cases were identified in academic datasets with variable clinical features suggestive of autoinflammation. Additionally, 49 individuals with ultra-rare, damaging PTPN1 variants were identified in the All of Us database, none had features suggestive of AGS, but autoimmunity was highly prevalent (~21.6%). Conclusion: Our data implicate PTPN1 as a cause of later-onset presentations of AGS within a broader spectrum of autoinflammatory phenotypes. Segregation and biobank data demonstrate reduced penetrance, with carriers being enriched for autoimmune disorders.

Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of SignificanceTo understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.

Genome-wide association studies have led to the identification of more than 150 genomic regions that are associated with breast cancer risk. Translating these findings into a greater understanding of that risk requires identification of functional variants and target genes. Breast cancer progression and metastasis does not depend solely on cancer cell autonomous defects; the stroma, of which fibroblasts comprise a dominant component, also has a functional role. We generated promoter capture Hi-C data in primary and immortalized mammary fibroblasts and identified 28 interaction peaks involving 116 credible causal breast cancer variants and 26 target genes that were exclusive to fibroblasts. Integrating these data with H3K27ac CUT&Tag peaks identified a potentially functional variant (rs17393059) and target gene (filamin A interacting protein 1 like (FILIP1L)) at the 3q12.1 breast cancer risk locus. Using genome-wide functional data in breast-relevant cell types we demonstrate that perturbation of gene expression in mammary fibroblasts may impact risk of breast cancer by a cell non-autonomous mechanism.

Rheumatoid arthritis (RA) is a heritable and common autoimmune condition. To date, most genetic associations were derived from individuals with either European or East Asian ancestries. Here, we applied a multimodal automated phenotyping strategy to define RA and performed a genome-wide association study (GWAS) of RA in the Million Veteran Program (MVP), including underrepresented African American (AFR) and Admixed American (AMR) populations. Meta-analyses with previous RA cohorts identified 152 autosomal genome-wide significant loci, of which 31 were novel. Inclusion of multi-ancestry data dramatically improved fine-mapping resolution. Functional characterization of these loci using single-cell transcriptomic and chromatin data suggested new RA genes such as CHD7 and CD247. We identified underappreciated functional roles of fine-grained immune cell states other than T cells, such as B cell and myeloid cell states. We observed that multi-ancestry polygenic risk scores using our data demonstrated better predictive ability, especially for AFR and AMR populations.

Abstract Background Spontaneous coronary artery dissection (SCAD) is a well-recognised cause of acute coronary syndrome particularly among women without conventional cardiovascular risk factors. Increasing evidence indicates a genetic contribution; however, the underlying genetic architecture of SCAD remains insufficiently understood. Objective The aim of this study was to assess the prevalence of rare variants in previously reported SCAD associated genes and to explore the potential presence of novel genetic alterations in well-characterised Swedish patients with SCAD. Methods The study comprised 201 patients enrolled in SweSCAD, a national project examining the clinical characteristics, aetiology, and outcomes of SCAD. All individuals had a confirmed diagnosis based on invasive coronary angiography. Comprehensive exome sequencing was performed to identify rare variants contributing to disease susceptibility. Results Genetic variants that have been associated with SCAD according to current clinical genetics practice for variant reporting were identified in approximately 4 % of patients. In addition, rare potentially relevant variants were detected in almost 60 % of patients in genes associated with vascular integrity and vascular remodelling. Conclusion This study supports SCAD as a genetically complex arteriopathy, driven by rare high?impact variants together with broader polygenic susceptibility. Variants in collagen, vascular extracellular matrix, and oestrogen?responsive pathways provide biologically plausible links to female?predominant disease. Although the diagnostic yield of clearly actionable variants is modest, these findings support broader genomic evaluation beyond overt syndromic presentations and highlight the need for larger integrative genomic and functional studies to refine risk stratification and management.

BackgroundDespite advances in therapy, optimal management of juvenile idiopathic arthritis (JIA) remains challenging. The ability to predict disease progression in JIA can improve personalized treatment decisions, but few reliable clinical predictors have been identified. We developed machine learning approaches to predict disease trajectories in children with JIA. MethodsUsing data from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry (years 2015-2024), we developed machine learning models to predict attainment of inactive disease in children with non-systemic JIA. We applied Dynamic Bayesian Networks (DBN) to model temporal dependencies and causal relationships, and Convolutional Neural Networks (CNN) to capture complex non-linear patterns. Model input included demographic factors, longitudinal clinical factors, and medication use in the preceding 12 months. FindingsA total of 8,093 participants were included. When tested on an independent test cohort, both DBN (AUC:0.76; precision:0.73; recall:0.83; F1-score:0.78; accuracy:0.71) and CNN (AUC:0.76; precision:0.71; recall:0.63; F1-score:0.67; accuracy:0.70) models achieved comparable performance in predicting inactive disease. Disease activity levels in the preceding 12 months, presence of enthesitis and uveitis were the strongest predictors. Causal relationships captured in the DBN model revealed suboptimal care patterns, likely shaped by insurance constraints and a predominantly reactive approach to JIA management. InterpretationOur study demonstrates that machine learning approaches can predict disease trajectories in JIA with good discriminative performance. Unlike prior studies that predict outcomes at single timepoints, our models are the first to predict inactive disease longitudinally. However, suboptimal care patterns in retrospective data limit models capacity to learn treatment-outcome relationships, underscoring critical opportunities to improve JIA care and the need for prospective comparative studies to better inform prediction models. FundingPatient-Centered Outcomes Research Institute (PCORI) Award (ME-2022C2-25573-IC). RESEARCH IN CONTEXT Evidence before this studyNumerous studies have sought to identify clinical predictors of JIA progression and outcomes. However, few reliable predictors have emerged and existing prediction models demonstrate limited performance. As a result, our ability to personalize treatment decisions based on individual risk of severe disease course remains limited. Added value of this studyWe developed novel machine learning models that predict individualized disease trajectories in children with polyarticular and oligoarticular JIA using data from their preceding 12-month clinical course. These models demonstrated strong discriminative performance and outperformed previously published machine learning approaches in JIA. Unlike prior studies limited to single time-point predictions, our models are the first to predict inactive disease longitudinally, enabling a patient-specific projection of disease progression over time. Importantly, our findings also bright to light patterns of suboptimal care, likely driven by insurance constraints and a reactive treatment paradigm, underscoring critical opportunities to improve JIA management. Implications of all the available evidenceOur models have the potential to support clinical decision-making by enabling early identification of children with JIA at risk for unfavorable disease trajectories. In addition, the suboptimal care patterns and systems-level barriers identified through our analyses highlight priority areas for quality improvement initiatives and policy interventions to reduce gaps in JIA care delivery.

Klotho KL is an aging factor that has been associated with Alzheimers Disease (AD) risk. Two common alleles circulate in human populations: the major allele FC and the minor allele VS, which is defined by two SNPs that cause two amino acid substitutions (F352V and C370S) in KLs second exon. To investigate the possibility that human KL variants influence brain aging and cognition, we developed a novel mouse model with humanized KL alleles. We used RNA-Seq to measure the whole brain transcriptome in four-and 12-month-old male and female C57Bl/6J mice carrying either the FC or the VS KL allele. We found that FC and VS carriers had widespread differences in gene expression in the brain at 12 months old, but not at four months old. The largest differences were in genes annotated to mitochondrial, ribosomal, and synaptic functions. Differential exon usage analysis identified differential splicing of synaptic genes, further supporting a role for KL on neuronal function. A more focused analysis of differential expression identified variation in glutamate receptors and amyloid precursor (APP) processing in particular, thereby linking human KL haplotypes to biological processes integral to AD pathogenesis. These results provide evidence that the human FC and VS KL haplotypes affect the function of the KL protein product in a manner that has widespread effects on gene expression in the brain and supports the hypothesis that these haplotypes may influence AD risk and pathogenesis.