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Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models

Mehdi Momen, Ahmad Ayatollahi Mehrgardi, Mahmoud Amiri Roudbar, Andreas Kranis, Renan Mercuri Pinto, Bruno D. Valente, View ORCID ProfileGota Morota, Guilherme J. M. Rosa, Daniel Gianola
doi: https://doi.org/10.1101/251421
Mehdi Momen
1Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman (SBUK), Kerman, Iran
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Ahmad Ayatollahi Mehrgardi
1Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman (SBUK), Kerman, Iran
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  • For correspondence: mehrgardi@uk.ac.ir momenmehdi@yahoo.com mahmoud.amiri225@gmail.com morota@unl.edu andreas.kranis@roslin.ed.ac.uk rpinto@wisc.edu bvalente@wisc.edu grosa@wisc.edu gianola@ansci.wisc.edu
Mahmoud Amiri Roudbar
1Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman (SBUK), Kerman, Iran
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Andreas Kranis
2Roslin Institute, University of Edinburgh, Midlothian, UK, EH25 9PS
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Renan Mercuri Pinto
3Department of Exact Sciences, University of São Paulo - ESALQ, Piracicaba-SP, Brazil
4Department of Animal Sciences, University of Wisconsin, Madison, WI, USA
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Bruno D. Valente
4Department of Animal Sciences, University of Wisconsin, Madison, WI, USA
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Gota Morota
5Department of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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  • ORCID record for Gota Morota
Guilherme J. M. Rosa
4Department of Animal Sciences, University of Wisconsin, Madison, WI, USA
6Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
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Daniel Gianola
4Department of Animal Sciences, University of Wisconsin, Madison, WI, USA
6Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
7Department of Dairy Science, University of Wisconsin, Madison, WI, USA
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Abstract

Background Phenotypic networks describing putative causal relationships among multiple phenotypes can be used to infer single-nucleotide polymorphism (SNP) effects 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.

Methods We applied SEM to GWAS (SEM-GWAS) in chickens, taking into account putative causal relationships among body weight (BW), breast meat (BM), 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).

Results 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 SEMGWAS.

Conclusions Although MTM-GWAS and SEM-GWAS use the same probabilistic models, we provide evidence that SEM-GWAS captures complex relationships 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted April 25, 2018.
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Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models
Mehdi Momen, Ahmad Ayatollahi Mehrgardi, Mahmoud Amiri Roudbar, Andreas Kranis, Renan Mercuri Pinto, Bruno D. Valente, Gota Morota, Guilherme J. M. Rosa, Daniel Gianola
bioRxiv 251421; doi: https://doi.org/10.1101/251421
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Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models
Mehdi Momen, Ahmad Ayatollahi Mehrgardi, Mahmoud Amiri Roudbar, Andreas Kranis, Renan Mercuri Pinto, Bruno D. Valente, Gota Morota, Guilherme J. M. Rosa, Daniel Gianola
bioRxiv 251421; doi: https://doi.org/10.1101/251421

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