Department of Animal Science, University of California, Davis.
Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University.
G3 (Bethesda). 2020 Dec 3;10(12):4439-4448. doi: 10.1534/g3.120.401618.
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.
贝叶斯回归方法，将不同的混合先验用于标记效应，用于多性状基因组预测。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，纳入性状之间的潜在因果结构对于全面理解基因型与感兴趣性状之间的关系至关重要。因此，我们开发了一种 GWAS 方法 SEM-Bayesian alphabet，通过应用结构方程模型（SEM），可以将因果结构纳入多性状贝叶斯回归方法中。SEM-Bayesian alphabet 通过基于间接、直接和整体标记效应进行 GWAS，提供了比多性状 GWAS 更全面的基因型-表型映射理解。通过在真实和模拟数据上比较其 GWAS 结果与其他类似的多性状 GWAS 方法，证明了 SEM-Bayesian alphabet 的优越性能。软件工具 JWAS 提供了执行这些分析的开源例程。
