Bayesian regression methods that incorporate different mixture priors for marker effects are used in multi-trait genomic prediction. These methods can also be extended to genome-wide association studies (GWAS). In multiple-trait GWAS, incorporating the underlying causal structures among traits is essential for comprehensively understanding the relationship between genotypes and traits of interest. Therefore, we develop a GWAS methodology, SEM-Bayesian alphabet, which, by applying the structural equation model (SEM), can be used to incorporate causal structures into multi-trait Bayesian regression methods. SEM-Bayesian alphabet provides a more comprehensive understanding of the genotype-phenotype mapping than multi-trait GWAS by performing GWAS based on indirect, direct and overall marker effects. The superior performance of SEM-Bayesian alphabet was demonstrated by comparing its GWAS results with other similar multi-trait GWAS methods on real and simulated data. The software tool JWAS offers open-source routines to perform these analyses.

在多特征基因组预测中使用了结合了先验标记效果的不同混合先验的贝叶斯回归方法。这些方法也可以扩展到全基因组关联研究（GWAS）。在多性状GWAS中，将性状之间的潜在因果结构结合在一起对于全面理解基因型与目标性状之间的关系至关重要。因此，我们开发了GWAS方法，即SEM-贝叶斯字母表，通过应用结构方程模型（SEM），可以将因果结构纳入多特征贝叶斯回归方法中。SEM贝叶斯字母通过基于间接，直接和整体标记作用执行GWAS，比多特征GWAS提供了对基因型-表型作图的更全面的了解。通过将SEM-贝叶斯字母表的GWAS结果与其他类似的多特征GWAS方法在真实和模拟数据上进行比较，证明了SEM-贝叶斯字母表的优越性能。JWAS软件工具提供了开源例程来执行这些分析。