Disentangling horizontal and vertical Pleiotropy in genetic correlation estimation: introducing the HVP model.

Abstract

Genome-wide genetic correlation studies have demonstrated widespread shared genetic architecture between complex traits, yet the impact of vertical pleiotropy on these genetic correlation estimates remains unclear. To address this, we propose the Horizontal and Vertical Pleiotropy (HVP) model, designed to disentangle horizontal from vertical pleiotropy effects. This approach provides unbiased genetic correlation estimates specifically attributed to horizontal pleiotropy. Through simulations, we verify that the HVP model corrects biases introduced by vertical pleiotropy-particularly the causal influence of exposure on outcomes-across various scenarios, improving the accuracy of heritability and genetic correlation estimates. Vertical pleiotropy biases genetic variances and covariances, influencing essential estimates such as SNP-based heritability and genetic correlation in traditional methods. By addressing these biases, the HVP model enhances accuracy in parameter estimation. Real data analysis shows that horizontal pleiotropy significantly contributes to genetic correlations between metabolic syndrome (MetS) and traits such as type 2 diabetes, C-reactive protein (CRP), sleep apnoea, and cholelithiasis, whereas vertical pleiotropy is more relevant between body mass index (BMI) and MetS, and MetS and cardiovascular diseases. These findings suggest that action on modifiable factors like lowering BMI may effectively reduce MetS risk, while CRP-though not causative-serves as a useful marker in risk prediction through horizontal pleiotropic genes. These results confirm the HVP model's relevance and utility in revealing the complex genetic architecture underlying traits such as metabolic syndrome, highlighting its potential to inform precision healthcare.