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PubMed2026

Characterizing the Uncertainty, Misclassification and Inconsistency of Polygenic Prediction.

Polygenic risk scores (PRSs) hold promise for precision medicine, yet their clinical translation is hindered by substantial uncertainty in individual risk estimates and often limited agreement in risk stratification across multiple PRSs for the same disease. We develop a unified inferential framework to calibrate PRS point estimates and uncertainties for both quantitative traits and binary phenotypes, and to characterize how PRS accuracy, uncertainty, pairwise correlation jointly determine misclassification and classification inconsistency. We show, both theoretically and empirically, that individual- and population-level misclassification and inconsistency rates are highly predictable in independent datasets. We further evaluate PRS integration and uncertainty-aware probabilistic thresholding strategies that reduce misclassification and improve concordance in risk stratification. Together, these results demonstrate that instability in PRS-based classification is a predictable statistical consequence of uncertainty and establish a principled foundation for incorporating uncertainty into PRS-based risk interpretation, communication, and clinical decision-making.

Journal Article↗

PubMed2026

Association of Genetic Liability to Psychiatric Disorders with Peripheral Metabolic Dysregulation.

IMPORTANCE: Individuals with psychiatric disorders face elevated cardiometabolic risk which is linked to increased mortality. The extent to which this reflects shared pathogenesis or the downstream effects of illness and treatment remains poorly understood. OBJECTIVE: To characterize the direct pleiotropic effects of psychiatric genetic liability on circulating metabolites and aggregate cardiometabolic risk, independent of psychiatric diagnosis and psychotropic medication use. DESIGN SETTING AND PARTICIPANTS: Cross-sectional analysis of Mass General Brigham Biobank participants with metabolomic profiling, genomic data, and linked electronic health records. EXPOSURES: Genetic liability to nine psychiatric disorders quantified using polygenic risk scores (PRS): attention deficit/hyperactivity disorder (ADHD), anorexia nervosa (ANO), anxiety disorder (ANX), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), PTSD, schizophrenia (SCZ), and substance use disorder (SUD). MAIN OUTCOMES AND MEASURES: 249 circulating metabolites and four metabolomic risk scores (MRS) for type 2 diabetes, myocardial infarction, ischemic stroke, and vascular dementia. PRS-metabolite associations were estimated using nested models adjusting for lifetime psychiatric diagnosis and psychotropic medication use. RESULTS: Across 25,290 participants, we identified 604 significant PRS-metabolite associations (Bonferroni p< 1.36 x 10-4), of which 89% persisted after adjustment for lifetime diagnosis and medication use, suggesting that the direct genetic effects on metabolism are largely independent of illness or treatment. PRS for MDD, PTSD, and ADHD showed the most extensive dysregulation, with a transdiagnostic pattern of elevated lipids and systemic inflammation, specifically triglycerides (β = 0.04 to 0.05, all p< 4.4 x10-13) and glycoprotein acetyls (β = 0.05, all p< 2.2 x10-16). Notably, PRS for SCZ and BD showed minimal metabolite dysregulation despite having the strongest association with their target diagnoses. PRS for MDD, PTSD, ADHD, and SUD were associated with increased MRS across cardiometabolic conditions (β = 0.03 to 0.08, all p< 2.1 x10-4). Sensitivity analyses controlling for BMI or excluding participants without any psychiatric history (N: 21,305 and 11,150, respectively) showed a similar pattern. CONCLUSIONS AND RELEVANCE: Psychiatric genetic liability is associated with systemic metabolic dysregulation independent of illness onset or treatment, supporting a partially pleiotropic basis for psychiatric-cardiometabolic comorbidity.

Journal Article↗

PubMed2026

Type 2 diabetes genetics in 125,000 admixed adults from Mexico City.

Type 2 diabetes (T2D) is a highly heritable, polygenic disease with over 600 loci identified through genome-wide association studies (GWAS). However, despite possessing unique genetic variation shaped by demographic history and admixture, Latin American populations remain markedly underrepresented in global genomic research. To address this gap, we conducted genome- and exome-wide analyses of 19,431 T2D cases and 105,611 controls from the Mexico City Prospective Study (MCPS). We identified 86 independent GWAS associations, including 21 novel signals, 15 of which replicated in external cohorts. Risk alleles at novel loci were enriched in individuals with Indigenous American ancestry. Exome analyses revealed rare and ultra-rare missense variants with substantial risk effects at HNF1A and GCK, as well as a protein-damaging variant in SLC30A8 that reduced T2D risk by 45% in carriers. Integrative analyses indicate that T2D genetic architecture in Mexico is predominantly driven by common regulatory variation acting in the endocrine pancreas. Polygenic risk scores strongly stratified T2D risk and transferred to Indigenous Mexican populations. These findings demonstrate the power of large-scale genetic discovery in diverse populations to refine disease architecture and identify loci with potential therapeutic relevance.

Journal Article↗

PubMed2026

Germline determinants of risk and molecular subtype in young-onset lung cancer.

Young-onset lung cancer is enriched for never-smoking and oncogene-driven tumors, yet its inherited genetic basis remains poorly defined. We performed germline whole-genome sequencing in 251 young-onset lung cancer cases (median age 37), which we jointly analyzed with never-smoking cases (n=196; median age 68) and cancer-free controls (n=1,883). We identified enrichments of rare deleterious coding variants across 55 cancer-related gene sets, including EGFR/ERBB2 signaling and genes implicated by prior lung cancer GWAS. Exome-wide analyses of rare coding variants affirmed TP53 as a penetrant lung cancer predisposition gene (odds ratio [OR]=36.1, p=1.02x10-7) and discovered two novel exome-wide significant tumor subtype-dependent associations: IREB2 in cases with fusion-driven tumors (p=1.39x10-6) and SMAD6 in fusion-negative tumors (p=2.05x10-6). Structural variants contributed distinct risk, with enrichment in constrained, lung-expressed genes (OR=5.79, p=5.8x10-5) and very large germline deletions being markedly enriched in cases with fusion-driven tumors. Polygenic risk scores for lung cancer were inversely correlated with rare variant burden, consistent with additive risk from rare and common variants. Collectively, these findings delineate a complex germline architecture underlying susceptibility and molecular subtype in young-onset lung cancer.

Journal Article↗

PubMed2025

Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.

Polygenic scores (PGSs) that can predict response to interventions can facilitate precision medicine and are detectable in observational datasets as PGS-by-exposure (PGS×E) interactions. PGSs based on interactions (iPGSs) or variance effects (vPGSs) may be more powerful than standard PGSs for detecting PGS×E, but these have yet to be systematically compared. We describe a generalized pipeline for developing and comparing these PGS types and apply it to detect genetic modification of the relationship between adiposity (measured by BMI) and a broad set of cardiometabolic risk factors. Our applied analysis in the UK Biobank identified significant PGS×BMI for 16/20 risk factors, most consistently for the iPGS approach. Many interactions replicated in All of Us (AoU); for example, we observed a 72% larger BMI-alanine aminotransferase association in the top iPGS decile in AoU. Our study provides a framework for the comparison of PGS×E strategies and informs efforts toward clinically useful response-focused PGSs.

Humans↗

PubMed2025

Polygenic scores for obstructive sleep apnoea reveal pathways contributing to cardiovascular disease.

BACKGROUND: Obstructive sleep apnoea (OSA) is a common chronic condition, with obesity its strongest risk factor. Polygenic scores (PGSs) summarise the genetic liability to phenotype and can provide insights into relationships between phenotypes. Recently, large datasets that include genetic data and OSA status became available, providing an opportunity to utilise PGS approaches to study the genetic relationship between OSA and other phenotypes, while differentiating OSA-specific from obesity-specific genetic factors. METHODS: Using race/ethnic diverse samples from over 1.2 million individuals from the Million Veteran Program, FinnGen, TOPMed, All of Us (AoU), Geisinger's MyCode, MGB Biobank, and the Human Phenotype Project, we developed and assessed PGSs for OSA, both without (BMIunadjOSA-PGS) and with adjustment for the genetic contributions of BMI (BMIadjOSA-PGS). FINDINGS: Adjusted odds ratios (ORs) for OSA per 1 standard deviation of the PGSs ranged from 1.38 to 2.75. The associations of BMIadjOSA- and BMIunadjOSA-PGSs with CVD outcomes in AoU shared both common and distinct patterns. Only BMIunadjOSA-PGS was associated with type 2 diabetes, heart failure, and coronary artery disease, while both BMIadjOSA- and BMIunadjOSA-PGSs were associated with hypertension and stroke. Sex stratified analyses revealed that BMIadjOSA-PGS association with hypertension was driven by females (OR = 1.1, p-value = 0.002, OR = 1.01 p-value = 0.2 in males). OSA PGSs were also associated with body fat measures with some sex-specific associations. INTERPRETATION: Distinct components of OSA genetic risk are related and independent of obesity. Sex-specific associations with body fat distribution measures may explain differing OSA risks and associations with cardiometabolic morbidities between sexes. FUNDING: R01AG080598.

Humans↗

PubMed2026

African genomes are not a subset.

Africa harbors more genetic variation than the rest of the world combined, but approximately 1% of genomes in major databases derive from individuals of African ancestry. This is not an equity problem; it is a scientific error that distorts drug dosing, degrades risk scores, and undermines precision medicine globally.

African pangenome↗

PubMed2026

Unravelling sex differences in the genetic architecture of anxiety.

BACKGROUND: Anxiety disorders show striking sex differences in prevalence, symptoms, and clinical characteristics, shaping how they manifest and are experienced. METHODS: Here, we report the first sex-specific meta-analysis of genome-wide association studies (GWAS) of anxiety, leveraging two of the largest biobank datasets, UK Biobank and All of Us, comprising 85,042 female cases with 196,789 controls and 36,732 male cases with 136,924 controls. Functional annotation, sex-specific polygenic scores (PGS), and genetic correlations were performed to assess genetic differences and functional implications. RESULTS: In females, 21 lead SNPs were significantly associated with anxiety, compared to five in males. Although the genetic correlation between sexes was high, it was significantly different from one, indicating partially distinct genetic architectures. In addition, both the SNP-based observed and liability-scale heritabilities (assuming a 2:1 female-to-male prevalence ratio) were significantly higher in females. Gene-based tests and functional prioritization identified different genes associated with anxiety in females and males. Moreover, genetic correlation analyses revealed stronger associations of female anxiety with attention-deficit/hyperactivity disorder (ADHD) and body mass index (BMI), whereas male anxiety showed stronger correlations with waist-hip-ratio-adjusted BMI. CONCLUSIONS: While the overall genetic architecture of anxiety is largely shared, our findings reveal distinct sex-specific genetic associations and correlations, highlighting the value of analyzing the sexes separately to uncover genetic signals that may be masked in sex-combined samples.

Female↗

PubMed2026

Osteoporosis genetic risk prediction using bone mineral density polygenic scores in Japanese: TMM CommCohort study.

Osteoporosis and fractures are major health concerns. We developed and validated a polygenic score (PGS) for quantitative ultrasound (QUS)-defined osteoporosis risk in Japanese individuals using heel QUS-derived T-scores. Genome-wide association study summary statistics from up to 10,794 participants in the Tohoku Medical Megabank Community-Based Cohort identified genome-wide significant loci, including MBL2, TMEM135, and WNT16. PGS models were constructed and evaluated using independent datasets for model selection (n = 1419) and validation (n = 8711). Adding the PGS to age and sex yielded only modest improvements in discrimination, whereas PGS quintiles supported genetic risk stratification. Compared with the intermediate group, individuals in the lowest PGS quintile had higher odds of the outcome (1.22, 95% confidence interval [CI]: 1.07-1.40), whereas those in the highest quintile had lower odds (0.85, 95% CI: 0.74-0.98). During prospective follow-up (mean 3.5 years), a similar gradient was observed, with higher incidence rate ratios in the lowest quintile (1.42, 95% CI: 1.17-1.73) and lower incidence rate ratios in the highest quintile (0.70, 95% CI: 0.54-0.89). No statistically significant interaction between age and PGS was observed, and age-T-score regression analyses showed no differences in age-related T-score decline across genetic risk groups. However, analyses in young adults (20-44 years) and extrapolation to age 20 suggested lower bone status around peak bone mass in individuals at high genetic risk for QUS-defined osteoporosis. These findings suggest that a Japanese-specific PGS may help identify individuals at elevated genetic risk earlier in adulthood.

Journal Article↗

PubMed2026

The association between GLP-1R expression and cardiovascular-kidney-metabolic-related diseases in non-diabetic and non-obese population: evidence triangulation using Mendelian randomization, observational and polygenic score association analysis.

BACKGROUND: Glucagon-like peptide-1 receptor (GLP-1R) agonists are emerging as promising therapies for cardiovascular-kidney-metabolic (CKM) related diseases in individuals with type 2 diabetes mellitus (T2DM) or obesity. But their effects in non-obese and non-diabetic individuals are unclear. This study triangulates evidence using Mendelian randomization (MR), polygenic scores (PGS) and observational analyses to estimate the associations of GLP-1R expression with chronic kidney disease (CKD), heart failure (HF) and metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS: For the MR analysis, instruments mimicking GLP-1R expression were identified using pancreas-specific cis-expression quantitative trait loci from GTEx (N ≤ 305). MR-Robust method was used as the primary MR approach. PGS and observational analyses were performed both in non-diabetic and non-obese individuals separately. A genome-wide association study (GWAS) for MASLD (14,231 cases and 348,091 controls) was performed in the general population using data from UK Biobank. RESULTS: GLP-1R expression showed robust effects on CKD (odds ratio [OR] 0.96, 95%CI 0.95 to 0.97, q = 1.7 × 10- 10 ), HF (OR = 0.96, 95%CI 0.94 to 0.97, q = 2.5 × 10- 8) and MASLD (OR = 0.96, 95%CI 0.93 to 0.98, q = 1.3 × 10- 3) in the general population. Consistent results were observed in validation analyses. Furthermore, PGS and observational analyses among non-T2DM and non-obese individuals found little evidence to support its association with CKD, HF or MASLD. GWAS analysis identified eight conditionally independent variants associated with MASLD, in which rs563199662 was a new signal located at TFPI region. CONCLUSIONS: This study provides multilayered evidence for GLP-1R expression in mitigating CKD, HF and MASLD risks in the general population, while de-prioritized its effect on CKM-related diseases in non-obese and non-diabetic individuals. Further clinical trials are needed to validate the effects of GLP-1R agonists in relative health population.

Humans↗

PubMed2024

Polygenic burden and its association with baseline cognitive function and postoperative cognitive outcome in temporal lobe epilepsy.

OBJECTIVE: Demographic and disease factors are associated with cognitive deficits and postoperative cognitive declines in adults with pharmacoresistant temporal lobe epilepsy (TLE), but the role of genetic factors in cognition in TLE is not well understood. Polygenic scores (PGS) for neurological and neuropsychiatric disorders and IQ have been associated with cognition in patient and healthy populations. In this exploratory study, we examined the relationship between PGS for Alzheimer's disease (AD), depression, and IQ and cognitive outcomes in adults with TLE. METHODS: 202 adults with pharmacoresistant TLE had genotyping and completed neuropsychological evaluations as part of a presurgical work-up. A subset (n = 116) underwent temporal lobe resection and returned for postoperative cognitive testing. Logistic regression was used to determine if PGS for AD, depression, and IQ predicted baseline domain-specific cognitive function and cognitive phenotypes as well as postoperative language and memory decline. RESULTS: No significant findings survived correction for multiple comparisons. Prior to correction, higher PGS for AD and depression (i.e., increased genetic risk for the disorder), but lower PGS for IQ (i.e., decreased genetic likelihood of high IQ) appeared possibly associated with baseline cognitive impairment in TLE. In comparison, higher PGS for AD and IQ appeared as possible risk factors for cognitive decline following temporal lobectomy, while the possible relationship between PGS for depression and post-operative cognitive outcome was mixed. SIGNIFICANCE: We did not observe any relationships of large effect between PGS and cognitive function or postsurgical outcome; however, results highlight several promising trends in the data that warrant future investigation in larger samples better powered to detect small genetic effects.

Adult↗

PubMed2025

The impact of polygenic score and socioeconomic status in predicting risk for 19 complex diseases.

Both socioeconomic circumstances and genetic predisposition shape disease risk, yet their joint contribution across diseases has not been systematically examined. We studied 19 high-burden diseases in 743,194 participants (729,928 European; 13,266 non-European ancestry) from FinnGen, the UK Biobank, and Generation Scotland. Higher educational attainment was associated with lower risk of most conditions, but with higher risk of most common cancers. These associations were largely independent of disease-specific polygenic scores (PGSs). For seven out of 19 diseases, PGSs showed stronger effects among individuals with high education. Joint inclusion of education and PGSs modestly improved prediction for 14 and 10 out of 19 diseases in FinnGen and the UK Biobank, respectively. PGS associations were consistent across ancestries, whereas education effects were less stable; results using an alternative socioeconomic measure were directionally similar but smaller. Our findings highlight the distinct and partly interacting contributions of socioeconomic and genetic factors to disease risk.

Journal Article↗

PubMed2024

Plasticity of Human Microglia and Brain Perivascular Macrophages in Aging and Alzheimer's Disease.

The complex roles of myeloid cells, including microglia and perivascular macrophages, are central to the neurobiology of Alzheimer's disease (AD), yet they remain incompletely understood. Here, we profiled 832,505 human myeloid cells from the prefrontal cortex of 1,607 unique donors covering the human lifespan and varying degrees of AD neuropathology. We delineated 13 transcriptionally distinct myeloid subtypes organized into 6 subclasses and identified AD-associated adaptive changes in myeloid cells over aging and disease progression. The GPNMB subtype, linked to phagocytosis, increased significantly with AD burden and correlated with polygenic AD risk scores. By organizing AD-risk genes into a regulatory hierarchy, we identified and validated MITF as an upstream transcriptional activator of GPNMB, critical for maintaining phagocytosis. Through cell-to-cell interaction networks, we prioritized APOE-SORL1 and APOE-TREM2 ligand-receptor pairs, associated with AD progression. In both human and mouse models, TREM2 deficiency disrupted GPNMB expansion and reduced phagocytic function, suggesting that GPNMB's role in neuroprotection was TREM2-dependent. Our findings clarify myeloid subtypes implicated in aging and AD, advancing the mechanistic understanding of their role in AD and aiding therapeutic discovery.

Journal Article↗

PubMed2026

The Progress of Gout Prediction Models Based on Multi-source Data.

INTRODUCTION: Gout, a highly serious inflammatory disease that is caused by monosodium urate crystals, is becoming an increasingly significant health concern. Artificial Intelligence and multi-omics-based research have made significant gains for the early detection and prevention of gout based on diverse approaches. This review intends to summarize current advances in forecasting gout susceptibility and gout-related symptoms, evaluate the predictive efficacy of different features, and ascertain which clinical and omics characteristics are most effective in these prediction models. METHODS: We explored the PubMed database after 2010 using keywords such as "gout", "predictive model", "risk prediction", and "machine learning", and confined our search to Englishlanguage articles. The original peer-reviewed research articles that developed gout models were selected. Research that was not original or lacked internal validation was excluded. RESULTS: Clinical features, genomics, microbiomics, radiomics, and metabolomics have been utilized to construct models related to gout and have demonstrated excellent predictive performance. Multisource data prediction models usually exhibit better effectiveness. DISCUSSION: Gout-oriented models performed excellently in predictive performance but present limitations in certain clinical and omics domains. However, if they are to affect actual patient care, they must overcome some external confirmation roadblocks and the fiscal and practical implications they will face ahead of time. CONCLUSION: This review indicates that clinical and multi-omics models of gout are significant instruments for clinical decision-making. The models constructed in these studies may be crucial for the treatment of gout and its practical benefits.

Gout↗

PubMed2026

Integrating rare and common variation in epilepsy genetics: from genetic architecture to penetrance and clinical expressivity.

Epilepsy genetics has often been interpreted through a useful but simplified dichotomous framework in which severe epilepsies, particularly developmental and epileptic encephalopathies, are attributed mainly to rare, high-effect variants, whereas more common epilepsies are viewed as arising largely from the cumulative effects of common, small-effect variation. Although this framework has been instrumental for gene discovery, molecular diagnosis, and mechanism-based treatment, it does not fully explain incomplete penetrance, intrafamilial phenotypic heterogeneity, or marked differences in severity among individuals sharing the same molecular diagnosis. Evidence from exome sequencing, copy number variant (CNV) studies, and genome-wide association studies increasingly suggests that rare SNVs/indels, CNVs, and common variant should not be interpreted as entirely independent risk sources, but may partially converge on shared genes, pathways, cell types, and neurobiological processes relevant to neuronal excitability, network stability, and seizure susceptibility. Here, we review evidence across epilepsy subtypes, focusing on convergence and divergence across the allelic spectrum, and discuss how polygenic background and other modifiers may influence penetrance and clinical expressivity among carriers of rare pathogenic variants and CNVs. We also consider implications for variant interpretation, genetic counseling, risk stratification, and precision medicine, while emphasizing that most rare-common integrated models remain insufficiently validated for routine clinical decision-making.

common variants↗

PubMed2026

Polygenic score for sleep duration in relation to the risk of Alzheimer's disease: results from the UK biobank.

Studies have suggested that sleep duration may be associated with Alzheimer's disease risk; however, findings based on self-reported sleep duration are likely to be influenced by reverse causation and residual confounding bias. We derived weights for genetic variants associated with wearable-derived sleep duration using the LDpred2-auto method in 77,770 white British participants from the UK Biobank, following the generation of new genome-wide association summary statistics. We then used these weights to generate polygenic scores (PGSs) for the remaining 264,746 white British participants for the association analysis, independent of the sample used to develop PGS weights. We assessed the association of fifths between genetically predicted sleep duration and the risk of Alzheimer's disease (1,451 cases/264,746 individuals over a median 12.5 years of follow-up). The PGS explained approximately 2% of the variation in device-measured sleep duration. Compared with individuals in the middle fifth of PGSs, those in the highest fifth (indicating approximately 15 min/day longer sleep) had a lower risk of Alzheimer's disease (hazard ratio (HR) = 0.79[95%CI, 0.67-0.94]). Our results indicate that genetic predisposition to relatively long sleep duration is associated with a lower Alzheimer's disease risk.

Alzheimer’s disease↗

PubMed2026

Effects of quetiapine on cognitive functioning in schizophrenia: evidence for the remyelination hypothesis?

Postmortem findings, neuroimaging data, and in-vitro models suggest a decrease in number and density of oligodendrocytes is driving cognitive deficits in schizophrenia (SCZ). Second-generation antipsychotics are discussed to improve oligodendrocyte dysfunction with most conclusive evidence available for quetiapine (QET). We postulate that sustained QET treatment leads to cognitive improvement in SCZ, particularly, in tests with high demands for working memory function. We further hypothesize that these effects are moderated by polygenic factors associated with hippocampus-related brain volumes, general white matter integrity, and/or oligodendroglia-related SCZ risk. Using data of the prospective PsyCourse study, we identified 166 patients with SCZ spectrum disorder receiving QET at one or two consecutive visits plus 166 matched patients without QET. Polygenic scores were calculated for subcortical brain volumes, measures of white matter integrity, and for cell type-specific genetic SCZ risks. QET treatment was consistently associated with improved cognitive function independent of time, specifically, in tests with high, but not with low to medium working memory load. Polygenic analyses did not reveal significant moderation effects. In contrary, low genetic SCZ risk specific for genes related to human oligodendrocyte function was associated with higher cognitive performance independent from QET. While we observed improved cognitive performance under QET in high working memory tests, we did not find evidence that polygenic factors associated with hippocampus-related brain volumes, white matter integrity, or oligodendroglia-related SCZ risk moderate this association. Thus, our tentative findings do not provide evidence for the hypothesis that polygenic estimates of hippocampal remyelination capacities influence the association between QET and cognitive performance in SCZ.

Humans↗

PubMed2026

Variant harmonization critically determines polygenic score transferability for lipid traits in Samoan populations.

Dyslipidemia is a significant risk factor for cardiovascular disease (CVD), the leading cause of death in Samoa. Polygenic scores (PGSs) for lipid traits offer promise for improved CVD risk prediction; however, their performance in Pacific Islander populations-comprising only 0.002% of genome-wide association study (GWAS) participants as of 2024-remains unknown. We evaluated the transferability of multi-ancestry PGS for LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), triglycerides (TGs), and total cholesterol (TC) in 4,342 Samoan adults across five cohorts spanning 1990-2010. PGSs from Graham et al. and Kanoni et al. multi-ancestry meta-analyses were harmonized with genome-wide imputed genotypes using a Samoan-specific reference panel, and performance was assessed via incremental R2 from linear mixed models with bootstrapped confidence intervals. HDL-C showed the highest performance (incremental R2 5.0%-15.0%), followed by TC (5.0%-10.7%), LDL-C (5.7%-8.6%), and TG (3.5%-7.0%). Critically, meaningful LDL-C performance was achieved only with the genome-wide PRS-CS score (99.6%-99.7% variant matching), while a curated pruning-and-thresholding score achieved ∼9% matching and near-zero performance. These findings establish systematic lipid PGS benchmarks in Samoans, demonstrating meaningful transferability when genome-wide variant coverage is ensured, and highlight variant harmonization as a critical precondition for PGS deployment in underrepresented populations.

Pacific Islanders↗