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

Authors

• Mehdi Momen, Shahid Bahonar University of KermanFollow
• Ahmad Ayatollahi Mehrgardi, Shahid Bahonar University of KermanFollow
• Mahmoud Amiri Roudbar, Shahid Bahonar University of KermanFollow
• Andreas Kranis, University of EdinburghFollow
• Renan Mercuri Pinto, University of Wisconsin-MadisonFollow
• Bruno D. Valente, University of Wisconsin-MadisonFollow
• Gota Morota, University of Nebraska- LincolnFollow
• Guilherme J.M. Rosa, University of Wisconsin-MadisonFollow
• Daniel Gianola, University of Wisconsin-MadisonFollow

Document Type

Article

Date of this Version

2018

Citation

bioRxiv preprint first posted online Jan. 22, 2018; doi: http://dx.doi.org/10.1101/251421.

Comments

The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY-ND 4.0 International license.

Abstract

Background: Phenotypic networks describing putative causal relationship 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 purpose.

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

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 IC 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 decomposition of SNP effects into direct, indirect, and total effects, identifying whether a SNP effect is acting directly or indirectly on 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.

Conclusions: Our results suggested that SEM-GWAS provides insights into mechanisms by which SNPs affect traits through partitioning effects into direct, indirect, and total components. Thus, we provide evidence that SEM-GWAS captures complex relationships and delivers a more comprehensive understanding of SNP effects compared to MTM-GWAS.