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.
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.
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.