Amente Lamessa Dube, Mills Natalie T, Le Thuc Duy, Hyppönen Elina, Lee S Hong
Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
Hum Genet. 2025 Aug;144(8):861-876. doi: 10.1007/s00439-025-02762-w. Epub 2025 Sep 16.
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.
全基因组遗传相关性研究表明复杂性状之间存在广泛的共享遗传结构，但垂直多效性对这些遗传相关性估计的影响仍不清楚。为了解决这个问题，我们提出了水平和垂直多效性（HVP）模型，旨在区分水平多效性和垂直多效性效应。这种方法提供了专门归因于水平多效性的无偏遗传相关性估计。通过模拟，我们验证了HVP模型在各种情况下校正了由垂直多效性引入的偏差，特别是暴露对结果的因果影响，提高了遗传力和遗传相关性估计的准确性。垂直多效性使遗传方差和协方差产生偏差，影响传统方法中基于单核苷酸多态性（SNP）的遗传力和遗传相关性等重要估计。通过解决这些偏差，HVP模型提高了参数估计的准确性。实际数据分析表明，水平多效性对代谢综合征（MetS）与2型糖尿病、C反应蛋白（CRP）、睡眠呼吸暂停和胆结石等性状之间的遗传相关性有显著贡献，而垂直多效性在体重指数（BMI）与MetS以及MetS与心血管疾病之间更为相关。这些发现表明，对降低BMI等可改变因素采取行动可能有效降低MetS风险，而CRP虽然不是因果关系，但通过水平多效性基因在风险预测中是一个有用的标志物。这些结果证实了HVP模型在揭示代谢综合征等性状潜在的复杂遗传结构方面的相关性和实用性，突出了其为精准医疗提供信息的潜力。
