Daily Cardiology Research Analysis
Three studies reshape cardiometabolic and cardiovascular research: a multi-ancestry fine-mapping method (SuShiE) boosts causal variant resolution and TWAS/PWAS discovery; a Nature Medicine analysis shows BMI polygenic scores predict obesity trajectories from early childhood across ancestries; and a UK Biobank cohort links higher Life's Crucial 9 cardiovascular health to substantially lower atrial fibrillation risk regardless of genetic susceptibility.
Summary
Three studies reshape cardiometabolic and cardiovascular research: a multi-ancestry fine-mapping method (SuShiE) boosts causal variant resolution and TWAS/PWAS discovery; a Nature Medicine analysis shows BMI polygenic scores predict obesity trajectories from early childhood across ancestries; and a UK Biobank cohort links higher Life's Crucial 9 cardiovascular health to substantially lower atrial fibrillation risk regardless of genetic susceptibility.
Research Themes
- Genetics and multi-ancestry risk prediction
- Lifestyle cardiovascular health and arrhythmia prevention
- Methodological advances in fine-mapping of molecular QTLs
Selected Articles
1. Improved multiancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.
SuShiE leverages LD heterogeneity to sharpen cis-molQTL fine-mapping and cross-ancestry inference. Applied to >36k molecular phenotypes, it mapped 18.2% more genes with fewer variants and improved functional enrichment, and boosted TWAS/PWAS discovery by 25.4% for blood cell traits.
Impact: Methodological innovation with broad applicability improves causal variant resolution across ancestries, directly strengthening downstream gene-to-trait inference critical to cardiovascular genomics.
Clinical Implications: While not immediately practice-changing, more precise fine-mapping and ancestry-aware effect estimation can refine target discovery, biomarker development, and interpretation of TWAS/PWAS results for cardiovascular diseases.
Key Findings
- Introduces SuShiE, a model leveraging LD heterogeneity to improve cis-molQTL fine-mapping and cross-ancestry effect correlation inference.
- Across 36,907 molecular phenotypes (TOPMed-MESA, GENOA), SuShiE fine-mapped 18.2% more genes using fewer variants and with greater functional enrichment.
- Detected cross-ancestry consistency overall but heterogeneity at predicted loss-of-function–intolerant genes.
- Using SuShiE effect sizes, TWAS/PWAS in All of Us biobank identified 25.4% more associated genes for white blood cell traits.
Methodological Strengths
- Multi-ancestry modeling explicitly leveraging LD heterogeneity with extensive simulations and real-world multi-omics applications
- Improved precision with fewer prioritized variants and enhanced functional enrichment, plus independent validation via TWAS/PWAS gains
Limitations
- Clinical translation depends on tissue/context specificity of molQTL datasets and availability across diverse ancestries
- Method performance may vary with sample size imbalance and quality of underlying molecular measurements
Future Directions: Apply SuShiE to cardiovascular-relevant tissues (e.g., cardiomyocytes, vascular cells), integrate rare variants, and link fine-mapped signals to experimental perturbation for target validation.
Multiancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. Here we present the sum of shared single effects (SuShiE) model, which leverages linkage disequilibrium heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations and estimate ancestry-specific expression prediction weights. Through extensive simulations, we find that SuShiE consistently outperforms existing methods. We apply SuShiE to 36,907 molecular phenotypes including mRNA expression and protein levels from individuals of diverse ancestries in the TOPMed-MESA and GENOA studies. SuShiE fine-maps cis-molQTLs for 18.2% more genes compared with existing methods while prioritizing fewer variants and exhibiting greater functional enrichment. While SuShiE infers highly consistent cis-molQTL architectures across ancestries, it finds evidence of heterogeneity at genes with predicted loss-of-function intolerance. Lastly, using SuShiE-derived cis-molQTL effect sizes, we perform transcriptome- and proteome-wide association studies on six white blood cell-related traits in the All of Us biobank and identify 25.4% more genes compared with existing methods. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
2. Polygenic prediction of body mass index and obesity through the life course and across ancestries.
A multi-ancestry BMI PGS explains substantial variance (17.6% in UKB Europeans) and predicts early-life adiposity trajectories and adult weight dynamics. Adding PGS markedly improves BMI prediction from early childhood, but performance varies across ancestries and settings.
Impact: Large-scale multi-ancestry genetics provides actionable risk stratification for obesity—a key cardiovascular risk factor—enabling earlier, tailored prevention.
Clinical Implications: Integrate PGS-informed risk with lifestyle assessment to target prevention in early childhood; ensure equitable deployment given reduced performance in some ancestries (e.g., rural Ugandans).
Key Findings
- Multi-ancestry BMI PGS explained 17.6% of variance in UK Biobank Europeans; performance ranged from 16% (East Asian-Americans) to 2.2% (rural Ugandans).
- Higher PGS predicted accelerated BMI gain and earlier adiposity rebound from age 2.5 to adolescence (ALSPAC).
- Adding PGS to birth predictors nearly doubled variance explained for BMI from age 5 onward (e.g., 11% to 21% at age 8); adding to early-life BMI improved prediction of BMI at 18 (e.g., 22% to 35% at age 5).
- In lifestyle intervention trials, higher PGS was associated with slightly greater initial weight loss (0.55 kg per SD) but higher propensity to regain.
Methodological Strengths
- Sample size up to 5.1 million across diverse ancestries with ancestry-specific and multi-ancestry models
- Validation across life stages (childhood to adulthood) and in lifestyle intervention trials
Limitations
- Predictive performance varies markedly across ancestries and settings, highlighting portability challenges
- PGS informs risk but does not directly prescribe specific interventions; environmental modifiers remain critical
Future Directions: Improve portability via multi-ancestry training and local recalibration; test PGS-guided, equity-focused prevention programs and monitoring from early life.
Polygenic scores (PGSs) for body mass index (BMI) may guide early prevention and targeted treatment of obesity. Using genetic data from up to 5.1 million people (4.6% African ancestry, 14.4% American ancestry, 8.4% East Asian ancestry, 71.1% European ancestry and 1.5% South Asian ancestry) from the GIANT consortium and 23andMe, Inc., we developed ancestry-specific and multi-ancestry PGSs. The multi-ancestry score explained 17.6% of BMI variation among UK Biobank participants of European ancestry. For other populations, this ranged from 16% in East Asian-Americans to 2.2% in rural Ugandans. In the ALSPAC study, children with higher PGSs showed accelerated BMI gain from age 2.5 years to adolescence, with earlier adiposity rebound. Adding the PGS to predictors available at birth nearly doubled explained variance for BMI from age 5 onward (for example, from 11% to 21% at age 8). Up to age 5, adding the PGS to early-life BMI improved prediction of BMI at age 18 (for example, from 22% to 35% at age 5). Higher PGSs were associated with greater adult weight gain. In intensive lifestyle intervention trials, individuals with higher PGSs lost modestly more weight in the first year (0.55 kg per s.d.) but were more likely to regain it. Overall, these data show that PGSs have the potential to improve obesity prediction, particularly when implemented early in life.
3. Life's Crucial 9, Genetic Susceptibility, and the Risk of Atrial Fibrillation: A Prospective Study in the UK Biobank Cohort.
In UK Biobank, higher Life’s Crucial 9 cardiovascular health was associated with substantially lower atrial fibrillation risk over 12.9 years, independent of polygenic risk. The lowest risk occurred in high-CVH/low-PRS individuals, with evidence of additive interactions.
Impact: Establishes that comprehensive cardiovascular health confers protection against AF regardless of genetic predisposition, guiding prevention strategies that complement genomic risk profiling.
Clinical Implications: Use LC9-based cardiovascular health assessment to stratify AF prevention and counseling, integrating PRS when available, and prioritize lifestyle interventions even in high genetic risk individuals.
Key Findings
- Higher LC9 cardiovascular health associated with lower AF incidence: HR 0.75 (moderate) and 0.66 (high) vs low CVH.
- High-CVH and low-PRS individuals had the lowest AF risk (HR 0.55 vs low-CVH/high-PRS).
- Additive interactions observed between low-to-moderate CVH and high PRS, indicating combined risk effects.
Methodological Strengths
- Large prospective cohort with long median follow-up (12.87 years) and time-to-event modeling
- Integration of lifestyle-based cardiovascular health metric (LC9) with polygenic risk stratification
Limitations
- Observational design susceptible to residual confounding and measurement error in lifestyle metrics
- Generalizability beyond UK Biobank demographics may be limited
Future Directions: Test LC9-guided AF prevention interventions, evaluate cost-effectiveness of integrating PRS with lifestyle counseling, and assess applicability across diverse ancestries.
BACKGROUND: To date, the association between Life's Crucial 9 (LC9) and incidence of atrial fibrillation (AF) has not been explored. This study aimed to investigate the association between LC9 and the incidence of AF and evaluate the potential role of genetic risk. METHODS: LC9 consists of 9 elements categorized as low, medium, and high cardiovascular health (CVH). Polygenic risk scores (PRSs) were categorized as low, medium, or high. A Cox proportional hazards regression model was used to determine the association between LC9 and incidence of AF. The combined effects and interactions between LC9 and AF PRS on incidence of AF were also examined. RESULTS: During the median follow-up of 12.87 years, 11,141 patients developed AF. Moderate CVH (hazard ratio [HR], 0.75; 95% confidence interval [CI], 0.70, 0.79) and high CVH (HR, 0.66; 95% CI, 0.62, 0.71) were associated with a reduced risk of AF, respectively, compared with those with low CVH. Individuals with a high CVH and low PRS exhibited the lowest risk of AF compared with those with low CVH and high PRS (HR, 0.55; 95% CI, 0.47, 0.64). Additive interactions between low-to-moderate CVH and high PRS were found (relative excess risk caused by interaction: 95% CI, 1.45 [0.75, 2.15] and 0.60 [0.28, 0.92]; attributable proportion caused by interaction: 95% CI, 0.25 [0.15, 0.35] and 0.14 [0.06, 0.21], respectively). CONCLUSIONS: Higher LC9 scores were associated with a decreased risk of AF. Adherence to the LC9 guidelines may help reduce the incidence of AF, regardless of genetic risk.