Momen Mehdi, Ayatollahi Mehrgardi Ahmad, Amiri Roudbar Mahmoud, Kranis Andreas, Mercuri Pinto Renan, Valente Bruno D, Morota Gota, Rosa Guilherme J M, Gianola Daniel
Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.
Roslin Institute, University of Edinburgh, Midlothian, United Kingdom.
Front Genet. 2018 Oct 9;9:455. doi: 10.3389/fgene.2018.00455. eCollection 2018.
Network based statistical models accounting for putative causal relationships among multiple phenotypes can be used to infer single-nucleotide polymorphism (SNP) effect which transmitting through a given causal path in genome-wide association studies (GWAS). In GWAS with multiple phenotypes, reconstructing underlying causal structures among traits and SNPs using a single statistical framework is essential for understanding the entirety of genotype-phenotype maps. A structural equation model (SEM) can be used for such purposes. We applied SEM to GWAS (SEM-GWAS) in chickens, taking into account putative causal relationships among breast meat (BM), body weight (BW), hen-house production (HHP), and SNPs. We assessed the performance of SEM-GWAS by comparing the model results with those obtained from traditional multi-trait association analyses (MTM-GWAS). Three different putative causal path diagrams were inferred from highest posterior density (HPD) intervals of 0.75, 0.85, and 0.95 using the inductive causation algorithm. A positive path coefficient was estimated for BM → BW, and negative values were obtained for BM → HHP and BW → HHP in all implemented scenarios. Further, the application of SEM-GWAS enabled the decomposition of SNP effects into direct, indirect, and total effects, identifying whether a SNP effect is acting directly or indirectly on a given trait. In contrast, MTM-GWAS only captured overall genetic effects on traits, which is equivalent to combining the direct and indirect SNP effects from SEM-GWAS. Although MTM-GWAS and SEM-GWAS use the similar probabilistic models, we provide evidence that SEM-GWAS captures complex relationships in terms of causal meaning and mediation and delivers a more comprehensive understanding of SNP effects compared to MTM-GWAS. Our results showed that SEM-GWAS provides important insight regarding the mechanism by which identified SNPs control traits by partitioning them into direct, indirect, and total SNP effects.
基于网络的统计模型考虑了多个表型之间的假定因果关系，可用于推断在全基因组关联研究（GWAS）中通过给定因果路径传递的单核苷酸多态性（SNP）效应。在具有多个表型的GWAS中，使用单一统计框架重建性状和SNP之间的潜在因果结构对于理解整个基因型-表型图谱至关重要。结构方程模型（SEM）可用于此目的。我们将SEM应用于鸡的GWAS（SEM-GWAS），考虑了胸肉（BM）、体重（BW）、鸡舍生产性能（HHP）和SNP之间的假定因果关系。我们通过将模型结果与传统多性状关联分析（MTM-GWAS）获得的结果进行比较，评估了SEM-GWAS的性能。使用归纳因果算法从0.75、0.85和0.95的最高后验密度（HPD）区间推断出三种不同的假定因果路径图。在所有实施的情景中，估计BM→BW的路径系数为正，而BM→HHP和BW→HHP的路径系数为负。此外，SEM-GWAS的应用能够将SNP效应分解为直接效应、间接效应和总效应，确定SNP效应是直接还是间接作用于给定性状。相比之下，MTM-GWAS仅捕获对性状的总体遗传效应，这相当于将SEM-GWAS中的直接和间接SNP效应结合起来。虽然MTM-GWAS和SEM-GWAS使用类似的概率模型，但我们提供的证据表明，与MTM-GWAS相比，SEM-GWAS在因果意义和中介方面捕获了复杂关系，并对SNP效应提供了更全面的理解。我们的结果表明，SEM-GWAS通过将已识别的SNP效应划分为直接、间接和总SNP效应，为所识别的SNP控制性状的机制提供了重要见解。
