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

2018 ◽  
Author(s):  
Mehdi Momen ◽  
Ahmad Ayatollahi Mehrgardi ◽  
Mahmoud Amiri Roudbar ◽  
Andreas Kranis ◽  
Renan Mercuri Pinto ◽  
...  
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Association Studies ◽  
Genome Wide Association ◽  
Path Coefficient ◽  
Genome Wide Association Studies ◽  
Causal Relationships ◽  
Multiple Phenotypes ◽  
Genome Wide ◽  
Highest Posterior Density

AbstractBackgroundPhenotypic 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.MethodsWe 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).ResultsThree 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.ConclusionsAlthough 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.

scholarly journals Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Mehdi Momen ◽  
Malachy T. Campbell ◽  
Harkamal Walia ◽  
Gota Morota
Keyword(s):  
Water Use Efficiency ◽  
Water Use ◽  
Indirect Effects ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Use Efficiency

Abstract Background Plant breeders seek to develop cultivars with maximal agronomic value, which is often assessed using numerous, often genetically correlated traits. As intervention on one trait will affect the value of another, breeding decisions should consider the relationships among traits in the context of putative causal structures (i.e., trait networks). While multi-trait genome-wide association studies (MTM-GWAS) can infer putative genetic signals at the multivariate scale, standard MTM-GWAS does not accommodate the network structure of phenotypes, and therefore does not address how the traits are interrelated. We extended the scope of MTM-GWAS by incorporating trait network structures into GWAS using structural equation models (SEM-GWAS). Here, we illustrate the utility of SEM-GWAS using a digital metric for shoot biomass, root biomass, water use, and water use efficiency in rice. Results A salient feature of SEM-GWAS is that it can partition the total single nucleotide polymorphism (SNP) effects acting on a trait into direct and indirect effects. Using this novel approach, we show that for most QTL associated with water use, total SNP effects were driven by genetic effects acting directly on water use rather that genetic effects originating from upstream traits. Conversely, total SNP effects for water use efficiency were largely due to indirect effects originating from the upstream trait, projected shoot area. Conclusions We describe a robust framework that can be applied to multivariate phenotypes to understand the interrelationships between complex traits. This framework provides novel insights into how QTL act within a phenotypic network that would otherwise not be possible with conventional multi-trait GWAS approaches. Collectively, these results suggest that the use of SEM may enhance our understanding of complex relationships among agronomic traits.

scholarly journals Generalization of Cortical Multivariate Genome-Wide Associations Within and Across Samples

2021 ◽  
Author(s):  
Robert J. Loughnan ◽  
Alexey A. Shadrin ◽  
Oleksandr Frei ◽  
Dennis van der Mer ◽  
Weiqi Zhao ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
High Dimensional ◽  
Multivariate Techniques ◽  
Genome Wide Association Studies ◽  
Independent Data ◽  
Uk Biobank ◽  
Replication Rate ◽  
Multiple Phenotypes ◽  
Genome Wide

AbstractGenome-Wide Association studies have typically been limited to single phenotypes, given that high dimensional phenotypes incur a large multiple comparisons burden: ~1 million tests across the genome times the number of phenotypes. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci – a 10-fold increase in discovered loci compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 -imes cross validation within 35,644 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 428-1,037, replication rate: 95-96%) in independent data when compared with the established min-P approach (# replicated loci: 30-71, replication rate: 70-84%). An out-of-sample generalization of discovered loci was conducted with a sample of 8,336 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest compared to min-P. This finding underscores the importance of using multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

scholarly journals A Multiple-trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies

2019 ◽  
Author(s):  
Zigui Wang ◽  
Deborah Chapman ◽  
Gota Morota ◽  
Hao Cheng
Keyword(s):  
Association Studies ◽  
Bayesian Variable Selection ◽  
Regression Method ◽  
Bayesian Regression ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Trait ◽  
Genome Wide ◽  
Highest Posterior Density ◽  
Causal Structures

ABSTRACTBayesian regression methods that incorporate different mixture priors for marker effects are used in multi-trait genomic prediction. These methods can also be extended to genome-wide association studies (GWAS). In multiple-trait GWAS, incorporating the underlying causal structures among traits is essential for comprehensively understanding the relationship between genotypes and traits of interest. Therefore, we develop a GWAS methodology, SEM-BayesCΠ, which, by applying the structural equation model (SEM), can be used to incorporate causal structures into a multi-trait Bayesian regression method using mixture priors. The performance of SEM-BayesCΠ was demonstrated by comparing its GWAS results with those from multi-trait BayesCΠ. Through the inductive causation (IC) algorithm, three potential causal structures were inferred of 0.9 highest posterior density (HPD) interval. SEM-BayesCΠ provides a more comprehensive understanding of the genotype-phenotype mapping than multi-trait BayesCΠ by performing GWAS based on indirect, direct and overall marker effects. The software tool JWAS offers open-source routines to perform these analyses.

scholarly journals Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies

2018 ◽  
Vol 137 (3) ◽  
pp. 247-255 ◽  
Author(s):  
Xiang-He Meng ◽  
Hui Shen ◽  
Xiang-Ding Chen ◽  
Hong-Mei Xiao ◽  
Hong-Wen Deng
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Causal Relationships ◽  
Genome Wide
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