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

Area of Science:

• Genetics
• Bioinformatics
• Statistical Genetics

Background:

• Genome-wide studies reveal shared genetic architecture among complex traits.
• The influence of vertical pleiotropy on genetic correlation estimates is not well understood.
• Existing methods may be biased by vertical pleiotropy, affecting heritability and genetic correlation accuracy.

Purpose of the Study:

• To develop and validate a model that disentangles horizontal and vertical pleiotropy.
• To provide unbiased genetic correlation estimates attributed to horizontal pleiotropy.
• To improve the accuracy of heritability and genetic correlation estimation.

Main Methods:

• Introduction of the Horizontal and Vertical Pleiotropy (HVP) model.
• Simulations to verify the HVP model's ability to correct biases from vertical pleiotropy.
• Real data analysis to assess pleiotropic effects on metabolic syndrome (MetS) and related traits.

Main Results:

• The HVP model successfully corrects biases caused by vertical pleiotropy, enhancing parameter estimation accuracy.
• Horizontal pleiotropy significantly contributes to genetic correlations between MetS and type 2 diabetes, CRP, sleep apnoea, and cholelithiasis.
• Vertical pleiotropy is more associated with the genetic correlation between BMI and MetS, and MetS and cardiovascular diseases.

Conclusions:

• The HVP model accurately reveals complex genetic architecture, particularly for metabolic syndrome.
• Findings suggest targeting modifiable factors like BMI for MetS risk reduction.
• Horizontal pleiotropic genes associated with CRP may aid in risk prediction for MetS.