Abstract
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.
Highlights
Genome-wide association studies (GWAS) have become a standard approach for investigating relationships between common genetic variants in the genome and phenotypes of interest in human, plant, and animal genetics (Hayes and Goddard, 2010; Brachi et al, 2011; Wang et al, 2012)
We considered the following GWAS models with their causal structures were recovered by the inductive causation (IC) algorithm (Pearl, 2009): (1) MTM-GWAS with pedigree-based kinship A (MTM-A) or marker-based kinship G (MTM-G), and (2) structural equation model (SEM)-GWAS with A (SEM-A) or G (SEM-G)
SEM-A85 detected a direct effect of breast meat (BM) on body weight (BW), the direct effect of BW on hen-house production (HHP), and the indirect effect of BM on HHP mediated by BW
Summary
Genome-wide association studies (GWAS) have become a standard approach for investigating relationships between common genetic variants in the genome (e.g., single-nucleotide polymorphisms, SNPs) and phenotypes of interest in human, plant, and animal genetics (Hayes and Goddard, 2010; Brachi et al, 2011; Wang et al, 2012). Multi-trait model GWAS (MTM-GWAS) can be used to test for genetic associations among a set of traits (Korte et al, 2012; O’Reilly et al, 2012; Zhou and Stephens, 2012). It has been established that MTM-GWAS reduces false positives and increases the statistical power of association tests, explaining the recent popularity of this method. MTM-GWAS can be used to study genetic associations among a set of traits. It does not consider various cryptic biological signals that may affect a trait of interest, either directly or indirectly through other intermediate traits
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