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The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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.