Interactions with polygenic background impact quantitative traits in the UK Biobank

Cholesteatoma recurrence is relatively common and often requires repeat surgical interventions, imposing a significant burden on patients and healthcare systems. Although clinical factors such as age, disease aggressiveness, surgical technique, and surgeon experience influence recurrence risk, accurate prediction remains challenging. Genetic Risk Scores (GRS), which aggregate the effects of multiple genetic variants, offer a promising approach to individualized recurrence risk estimation. To evaluate the contribution of specific genetic variants to cholesteatoma recurrence by constructing a GRS using data from the UK Biobank. A systematic review of PubMed identified six genes previously associated with cholesteatoma recurrence: KGF (FGF7), KGF-R (FGFR2), MMP9, KRT1, KRT10, and MIF. Corresponding single nucleotide polymorphisms (SNPs) were analyzed using the UK Biobank, a large-scale biomedical database of approximately 500,000 participants. Individuals with recurrent cholesteatoma were identified using ICD-10 code H95.0. SNPs with minor allele frequency <5% or in linkage disequilibrium were excluded. A weighted GRS was calculated by summing the number of risk alleles for each SNP, multiplied by their β coefficients (log odds ratios). A total of 39 SNPs were included in the final GRS calculation. Among 502,164 UK Biobank participants, 55 individuals were identified with recurrent cholesteatoma. The mean GRS for these individuals was 3.86, compared to 3.72 in the general population, indicating a 15.6% relative increase in genetically determined recurrence risk (OR ≈ 1.156). This study demonstrates a modest but measurable contribution of genetic variation to cholesteatoma recurrence. While the effect size is limited, future studies with larger cohorts and genome-wide data may improve predictive accuracy. Even at this stage, the GRS may help guide surgical decision-making and follow-up planning, moving toward more personalized management of cholesteatoma.

A Blueprint for Genetic Determinism

AimsTo evaluate the utility of coffee‐related genetic variants as proxies for coffee consumption in Mendelian randomization studies, by examining their association with non‐alcoholic beverage consumption (including subtypes of coffee and tea) and a range of socio‐demographic and life‐style factors.DesignObservational study of the association of genetic risk scores for coffee consumption with different types of non‐alcoholic beverage consumption.SettingUK general population.ParticipantsIndividuals of European ancestry aged 40–73 years from the UK Biobank between 2006 and 2010 (n = 114 316).MeasurementsGenetic risk scores were constructed using two, four and eight independent single nucleotide polymorphisms (SNPs) identified in genome‐wide association studies (GWAS) of coffee consumption. Drinks were self‐reported in a baseline questionnaire (all participants) and in detailed 24 dietary recall questionnaires in a subset (n = 48 692).FindingsGenetic risk scores explained up to 0.38, 0.19 and 0.76% of the variance in coffee, tea and combined coffee and tea consumption, respectively. Genetic risk scores demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption, and with most subtypes of coffee consumption, but only with standard tea consumption. There was no clear evidence for positive associations with most other non‐alcoholic beverages, but higher genetic risk for coffee consumption was associated with lower daily water consumption. The genetic risk scores were associated with increased alcohol consumption, but not consistently with other socio‐demographic and life‐style factors.ConclusionsCoffee‐related genetic risk scores could be used as instruments for combined coffee and tea consumption in Mendelian randomization studies. However, associations observed with alcohol consumption require further investigation to determine whether these are due to causal effects of coffee and tea consumption or biological pleiotropy.

Background Hypertrophic cardiomyopathy (HCM) is an important cause of mortality, caused by rare pathogenic variants (sarcomere-positive) in around one third of cases. Recent large genome-wide association studies (GWAS) highlight the important contributions of common genetic variation on HCM risk and heritability1-3. Polygenic scores (PGS) quantify the cumulative individual risk from common genetic variation, and may provide important clinical utility. Purpose The aim of this study is to generate PGS for HCM, and evaluate its performance in predicting HCM in the general population, HCM penetrance in sarcomere-positive carriers, and risk of adverse outcomes in individuals with HCM. Methods The PGS was generated using a Bayesian framework (PRS-CS) with individual SNP effect estimates derived from the largest published HCM GWAS (5900 cases and 68359 controls of European ancestry from 7 cohorts) and multi-trait analysis of GWAS (MTAG) (incorporating GWAS of genetically correlated cardiac magnetic resonance imaging (CMR) traits from 36203 White British individuals in the UK Biobank [UKB])1. Genome-wide PGS were calculated using an additive model for participants in two cohorts (UKB and 100,000 Genomes Project [GeL]). To evaluate the effect of PGS on penetrant HCM in sarcomere-positive carriers, we identified individuals with pathogenic or likely pathogenic variants in 8 definitive HCM-causing genes (MYBPC3, MYH7, TNNT2, TNNI3, TPM1, ACTC1, MYL3, and MYL2) in UKB and GeL. Results In 343,182 unrelated White British ancestry participants from the UK Biobank (UKB), PGS was associated with an increased risk of HCM (OR per PGS SD 2.3, P&amp;lt;2x10-16), with 75% of HCM cases having a PGS above the population mean (Figure 1A). Individuals with PGS in the top centile had a substantially increased risk of HCM compared with those in the median (OR 14.5, P&amp;lt;2x10-16) and bottom centile (OR 36.6, 3x10-25) (Figure 1B). PGS had significant effects on stratifying HCM penetrance in 640 sarcomere-positive carriers in the UKB (top vs. middle quintile HCM OR: 3.7, P 0.009), and risk of being a HCM case in 599 sarcomere-positive carriers in GeL (top vs. middle quintile HCM OR 9.5, P 4x10-5) (Figure 1C), highlighting the important interactive effect of common and rare genetic variants. Finally, PGS predicted risk of all-cause mortality and major adverse cardiovascular events (MACE) after HCM diagnosis in 382 cases in UKB (all-cause mortality: top vs. bottom quintile: HR 3.9, P 0.013; MACE: top vs. bottom quintile: HR 3.5, P 4.x10-4), and all-cause mortality in 683 cases in GeL (top vs. bottom quintile: HR 6.3, P 1x10-6) (Figure 1D). Conclusions We derive a PGS for HCM risk prediction, and demonstrate potential clinical utility in stratifying risk of penetrant HCM in sarcomere-positive carriers, and in predicting risk of adverse outcomes in individuals with HCM.Figure 1

Polygenic Risk Scores for Follow Up After Colonoscopy and Polypectomy: Another Tool for Risk Stratification and Planning Surveillance?

Background/Objectives: We aimed to predict the incidence of obesity in a Korean population using a genetic risk score (GRS) constructed with obesity-related single nucleotide polymorphisms (SNPs) along with an oxidative stress score (OSS). Subjects/Methods: A total of 9,460 Korean subjects and 356,974 SNPs were included. The GRS was constructed using three significant obesity-related SNP loci, and the OSS was calculated with three reliable oxidative stress biomarkers. Results: In an association study, the top three SNPs, rs2846412, rs58424360, and rs591120, showed profound associations with obesity in a Korean population (all FDR &amp;lt; 0.1). The GRS showed a more significant association with increased obesity (OR = 2.879, 95% CI = 2.283-3.630) than individual SNPs did after adjusting for age and sex. Three oxidative stress biomarkers, including malondialdehyde (MDA), oxidized low-density lipoprotein (ox-LDL), and 8-epi-prostaglandin F2α (8-epi-PGF2α), showed significantly high levels in the obese group. Moreover, the OSS, which was the sum of each oxidative stress biomarker score, showed a markedly high association with the incidence of obesity, with an OR of 3.213 (p = 3.57E-18). Based on the results from the regression tests and a receiver-operating characteristic (ROC) curve analysis, we found that HOMA-IR, hs-CRP, the GRS, and the OSS were the most relevant factors for the incremental risk of obesity and were significantly associated with the incidence of obesity. The area under the ROC curve (AUROC) was improved when the GRS was added to the model with the OSS and two obesity-related parameters (from 74.2% to 75.1%). Conclusions: We first identified that subjects with an obesity GRS and a high OSS may have a higher risk of obesity. Our findings and weighting approaches were effective in predicting the incidence of obesity; furthermore, the GRS is a relevant factor that significantly predicts the risk of obesity. Disclosure M. Kim: None. H. Yoo: None. Y. Han: None. J. Lee: None. Funding Korean Ministry of Health and Welfare (HI13C0715); Korean National Research Foundation (NRF-2017R1C1B2007195, NRF-2012M3A9C4048762)

The heritability of arterial hypertension ranges from 25 to 68%, but the underlying genetic determinants remain elusive. Thousands of variants with small effects on systolic blood pressure (SBP) from European Ancestry Genome-Wide Association Studies have been used to derive genetic risk scores, whose predictive power in samples from diverse ancestries has yet to be determined. We used the LDPred2 algorithm and 423 thousand genetic variants from UK Biobank that were common to an admixed sample population from Brazil to derive SBP polygenic risk scores (PRS). We selected the best model that displayed a score with hyper-parameters ρ = 0.056, h 2 = 0.0739 and without sparsity, which presented the highest correlation coefficient with observed SBP (0.206) and SBP change per standard deviation increase (4.04mmHg) in a different set of data from the UK Biobank prior to test its predictive power on SBP and hypertension in a third set of the UK Biobank and two admixed Brazilian population samples. All three populations displayed similar normal distribution of PRS, with the UK Biobank PRS distribution slightly shifted to the right, presenting higher PRS average (7.8 vs. 5.6 and 6.4 in Baependi and Pelotas, respectively), but with similar standard deviation (3.3 vs. 3.2 for both Baependi and Pelotas). We used the PRS deciles to stratify the populations and observed a significant difference on SBP average between the top and bottom deciles in all three populations (14.43 mmHg, 9.34 mmHg and 7.51 mmHg for the UK Biobank, Baependi and Pelotas, respectively). We then compared the top 10% PRS versus the remaining 90% of the PRS distribution, and observed that high PRS is associated with 1.8, 2 to 3 and 3 to 4-fold increase in prehypertension, hypertension stage I and hypertension stage II risk, respectively, demonstrating that the increased risk is independently of age and ancestry. Considering the highly admixed nature of the Brazilian population, we provide evidence for the generalization of PRS between populations with different genetic structure.

Tumor Mutational Load and Germline Polygenic Risk Score Predicts Time-to-First Treatment in Chronic Lymphocytic Leukemia (CLL) and High-Count Monoclonal B Cell Lymphocytosis (MBL)

BackgroundEpidemiological studies have established that autoimmune diseases (AD) increase cardiovascular disease (CVD) risk. We aimed to elucidate their shared genetic architecture and clinical implications.MethodsWe conducted a cross-trait multivariate genome-wide association study (GWAS) for three autoimmune diseases (type 1 diabetes, systemic lupus erythematosus, and rheumatoid arthritis) and four cardiovascular diseases (coronary artery disease, heart failure, stroke, and peripheral artery disease). We performed both genome-wide and locus-based analysis including fine-mapping, functional annotation, enrichment analyses, transcriptome-wide association studies (TWAS), and proteome-wide and drug-repurposing Mendelian randomization (MR). We constructed a polygenic risk score (PRS) and explored its pleiotropic effects beyond AD and CVD in the UK Biobank population.ResultsIn the multivariate GWAS, we identified 259 genome-wide significant association signals that were enriched primarily in arterial tissues and lipid metabolism pathways. Through convergent evidence from TWAS and MR analyses, we prioritized 15 therapeutic targets including TGFB1 and IL6R, and identified histone deacetylase inhibitors as candidate drugs. The polygenic risk score showed discriminative ability in cardiovascular risk stratification among autoimmune patients, with individuals in the highest PRS decile exhibiting significantly elevated CVD risk compared to those in the 2nd-9th deciles (hazard ratios: 1.60 [95% CI: 1.19–2.15] for type 1 diabetes, 2.28 [95% CI: 1.14–4.56] for systemic lupus erythematosus, and 2.33 [95% CI: 1.94–2.80] for rheumatoid arthritis). Additionally, the PRS revealed pleiotropic associations with risk of various health conditions, including polyneuropathy, chronic renal failure, and depressive episodes.ConclusionOur study unveils the shared genetic architecture between autoimmune and cardiovascular diseases, providing insights for therapeutic development and risk stratification.Graphical abstractIn this multivariate genome-wide association study, we derived a shared genetic factor (Faid_cvd) using seven autoimmune and cardiovascular diseases: rheumatoid arthritis, systemic lupus erythematosus, type 1 diabetes, coronary artery disease, heart failure, stroke, and peripheral artery disease to describe the shared genetic liability of autoimmune and cardiovascular diseases. We identified the genomic loci, cells, tissues and pathways associated with Faid-cvd, and 15 potential therapeutic target genes or drug repurposing opportunities. We examined the clinical risk stratification performance of the Faid-cvd PRS in individuals of European ancestry in UK biobank. Additionally, we examined the association between Faid-cvd PRS and various health outcomes. PRS, polygenic risk score. MR, Mendelian randomization. transcriptome-wide association studies, TWAS.Graphical abstractSupplementary InformationThe online version contains supplementary material available at 10.1186/s12933-025-03041-8.

Polygenic risk scores (PRS) have gained substantial attention for complex traits prediction in genome-wide association studies (GWAS). Motivated by the polygenic model of complex traits, we study the statistical properties of PRS under the high-dimensional but sparsity free setting where the triplet with being the sample size, the number of assayed single-nucleotide polymorphisms (SNPs), and the number of assayed causal SNPs, respectively. First, we derive asymptotic results on the out-of-sample (prediction) R-squared for PRS. These results help understand the widespread observed gap between the in-sample heritability (or partial R-squared due to the genetic features) estimate and the out-of-sample R-squared for most complex traits. Next, we investigate how features should be selected (e.g., by a p-value threshold) for constructing optimal PRS. We reveal that the optimal threshold depends largely on the genetic architecture underlying the complex trait and the sample size of the training GWAS, or the ratio. For highly polygenic traits with a large ratio, it is difficult to separate causal and null SNPs and stringent feature selection in principle often leads to poor PRS prediction. We numerically illustrate the theoretical results with intensive simulation studies and real data analysis on 33 complex traits with a wide range of genetic architectures in the UK Biobankdatabase.

Plasma Proteomic Signature Predicts Risk of Myeloid Neoplasm

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Impact of psychological status on cardiovascular diseases: Is it time for upgrading risk score charts?

Background: Genome-wide association studies (GWAS) have advanced our understanding of the polygenic basis of common, complex disorders. However, translating GWAS findings into clinical applications remains challenging. Polygenic risk scores (PRS) hold promise for clinical use with recent methodologies enhancing predictive accuracy. This study compared two PRS (LDpred-funct which incorporates functional annotations into the score and a traditional linear scoring system) for traits with varying heritability in European- (EA) and African-ancestry (AA) populations, addressing a need to evaluate novel approaches across diverse conditions and populations. Hypothesis: This study hypothesized that integrating functional annotations into PRS derivation will enhance performance compared to assessing the genetic load for a trait. The addition will provide improvement for (1) traits with high (height) compared to moderate (systolic blood pressure, SBP) heritability and (2) individuals with EA relative to AA. Methods: This study used multi-ancestry summary statistics representing data from 4,080,687 individuals for height (GIANT consortium) and 436,099 individuals for SBP adjusted for antihypertensive medication use (UK Biobank). Two PRS for these traits were constructed using LDpred-funct: PRS funct which incorporates the functional annotation and PRS raw which is a simple linear scoring system. Optimization was performed in 8,916 AA and 1,716 EA participants from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. PRS predictive performance was evaluated by calculating the proportion of trait variance explained by the PRS (R 2 ) after accounting for age, sex, and genetic ancestry. Results: For height, the PRS funct outperformed PRS raw among both race groups, with a greater improvement among EA (PRS funct R 2 =0.110 versus PRS raw R 2 =0.060, 5% difference) compared to AA (PRS funct R 2 =0.042 versus PRS raw R 2 =0.034, 0.8% difference). Similar findings were observed for medication-adjusted SBP, where PRS funct had better predictive performance than PRS raw , and with greater improvement observed in the EA (PRS funct R 2 =0.042 versus PRS raw R 2 =0.027, 1.5% difference) compared to AA participants (PRS funct R 2 =0.014 versus PRS raw R 2 =0.010, 0.4% difference). Conclusions: Incorporating functional priors into the PRS enhanced the predictive accuracy for both traits. A greater improvement was observed for the higher heritability (height) versus the moderate heritability (SBP) trait, as well as within individuals of EA compared to AA. These findings are currently undergoing validation, using data from the Electronic Medical Records and Genomics (eMERGE) Network, highlighting the potential of incorporating functional annotations into PRS development for broader clinical applications.

BackgroundImmune checkpoint inhibitor (ICI)-induced hepatitis is a relatively common immune-related adverse event (irAE).1–3 Understanding the underlying genetic factors contributing to the development of irAEs is crucial for identifying individuals at...