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Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models

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dc.contributor.authorMomen, Mehdien
dc.contributor.authorMehrgardi, Ahmad Ayatollahien
dc.contributor.authorRoudbar, Mahmoud Amirien
dc.contributor.authorKranis, Andreasen
dc.contributor.authorPinto, Renan Mercurien
dc.contributor.authorValente, Bruno D.en
dc.contributor.authorMorota, Gotaen
dc.contributor.authorRosa, Gullherme J. M.en
dc.contributor.authorGianola, Danielen
dc.contributor.departmentAnimal and Poultry Sciencesen
dc.date.accessioned2019-10-28T16:47:15Zen
dc.date.available2019-10-28T16:47:15Zen
dc.date.issued2018-10-09en
dc.description.abstractNetwork 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 (Btu), 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.en
dc.description.notesMM wishes to acknowledge the Ministry of Science, Research and Technology of Iran for financially supporting his visit to the University of Wisconsin-Madison. Work was partially supported by the Wisconsin Agriculture Experiment Station under hatch grant 142-PRJ63CV to DG.en
dc.description.sponsorshipMinistry of Science, Research and Technology of Iran; Wisconsin Agriculture Experiment Station [142-PRJ63CV]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fgene.2018.00455en
dc.identifier.issn1664-8021en
dc.identifier.other455en
dc.identifier.pmid30356716en
dc.identifier.urihttp://hdl.handle.net/10919/95191en
dc.identifier.volume9en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectcausal structureen
dc.subjectGWASen
dc.subjectmultiple traitsen
dc.subjectpath analysisen
dc.subjectSEMen
dc.subjectSNP effecten
dc.titleIncluding Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Modelsen
dc.title.serialFrontiers in Geneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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