Psoriasis and coronary artery disease (CAD) are related comorbidities that are well established, but whether a genetic basis underlies this is not well studied. We apply trans-disease meta-analysis to 11,024 psoriasis and 60,801 CAD cases, along with their associated controls, identifying one opposing and three shared genetic loci, which are confirmed through colocalization analysis. Combining results from Bayesian credible interval analysis with independent information from genomic, epigenomic, and spatial chromatin organization, we prioritize genes (including IFIH1 and IL23A) that have implications for common molecular mechanisms involved in psoriasis and CAD inflammatory signaling. Chronic systemic inflammation has been associated with CAD and myocardial infarction, and Mendelian randomization analysis finds that CAD as an exposure can have a significant causal effect on psoriasis (OR = 1.11; p = 3×10−6) following adjustment for BMI and waist-hip ratio. Together, these findings suggest that systemic inflammation which causes CAD can increase the risk of psoriasis.

Polygenic risk scores (PRS), developed as the sum of single-nucleotide polymorphisms (SNPs) weighted by the risk allele effect sizes as estimated by published genome-wide association studies, have recently received much attention for genetics risk prediction. While successful for the Caucasian population, the PRS based on the minority population cohorts suffer from limited event rates, small sample sizes, high dimensionality and low signal-to-noise ratios, exacerbating already severe health disparities. Due to population heterogeneity, direct trans-ethnic prediction by utilizing the Caucasian model for the minority population also has limited performance. As a result, it is desirable to design a data integration procedure to measure the difference between the populations and optimally balance the information from them to improve the prediction stability of the minority populations. A unique challenge here is that due to data privacy, the individual genotype data is not accessible for either the Caucasian population or the minority population. Therefore, new data integration methods based only on encrypted summary statistics are needed. To address these challenges, we propose a BRegman divergence-based Integrational Genetic Hazard Transethnic (BRIGHT) estimation procedure to transfer the information we learned from PRS across different ancestries.The proposed method only requires the use of published summary statistics and can be applied to improve the performance of PRS for ethnic minority groups, accounting for challenges including potential model misspecification, data heterogeneity and data sharing constraints. We provide the asymptotic consistency and weak oracle property for the proposed method. Simulations show the prediction and variable selection advantages of the proposed method when applied to heterogeneous datasets. Real data analysis constructing psoriasis PRS scores for a South Asian population also confirms the improved model performance.