%A Momen,Mehdi %A Ayatollahi Mehrgardi,Ahmad %A Amiri Roudbar,Mahmoud %A Kranis,Andreas %A Mercuri Pinto,Renan %A Valente,Bruno D. %A Morota,Gota %A Rosa,Guilherme J. M. %A Gianola,Daniel %D 2018 %J Frontiers in Genetics %C %F %G English %K causal structure,GWAS,multiple traits,Path analysis,SEM,SNP effect %Q %R 10.3389/fgene.2018.00455 %W %L %M %P %7 %8 2018-October-09 %9 Original Research %# %! Structural equation modeling for association studies %* %< %T Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models %U https://www.frontiersin.org/articles/10.3389/fgene.2018.00455 %V 9 %0 JOURNAL ARTICLE %@ 1664-8021 %X 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.