Abstract
Various patterns of multi-phenotype associations (MPAs) exist in the results of Genome Wide Association Studies (GWAS) involving different topologies of single nucleotide polymorphism (SNP)-phenotype associations. These can provide interesting information about the different impacts of a gene on closely related phenotypes or disparate phenotypes (pleiotropy). In this work we present MPA Decomposition, a new network-based approach which decomposes the results of a multi-phenotype GWAS study into three bipartite networks, which, when used together, unravel the multi-phenotype signatures of genes on a genome-wide scale. The decomposition involves the construction of a phenotype powerset space, and subsequent mapping of genes into this new space. Clustering of genes in this powerset space groups genes based on their detailed MPA signatures. We show that this method allows us to find multiple different MPA and pleiotropic signatures within individual genes and to classify and cluster genes based on these SNP-phenotype association topologies. We demonstrate the use of this approach on a GWAS analysis of a large population of 882 Populus trichocarpa genotypes using untargeted metabolomics phenotypes. This method should prove invaluable in the interpretation of large GWAS datasets and aid in future synthetic biology efforts designed to optimize phenotypes of interest.
Highlights
Unraveling the complex genetic patterns underlying complex phenotypes has previously been challenging
Multi-phenotype associations (MPAs) decomposition is a post-Genome Wide Association Studies (GWAS)/post-Phenome-Wide Association Studies (PheWAS) approach with is designed to take the results of a multi-phenotype genome-wide association-type analysis (such as a standard, univariate GWAS run on several phenotypes or a multi-phenotype approach such as SCOPA (Mägi et al, 2017) and provides a framework allowing the precise mathematical representation of the architecture of variant-phenotype associations within regions (MPA/pleiotropic signatures), and allows these regions to be clustered based on these complex signatures
The results of a standard GWAS analysis were represented as a bipartite single nucleotide polymorphism (SNP)-phenotype network, connecting SNP nodes to phenotype nodes between which there were significant associations
Summary
Unraveling the complex genetic patterns underlying complex phenotypes has previously been challenging. Multi-phenotype associations (MPAs) can be detected in the results of Genome Wide Association Studies (GWASs) as Single Nucleotide Polymorphisms (SNPs) within genes/functional regions having multiple significant phenotype associations This can be considered to be a pleiotropic pattern when the two phenotypes are seemingly unrelated. MPA decomposition is a post-GWAS/post-PheWAS approach with is designed to take the results of a multi-phenotype genome-wide association-type analysis (such as a standard, univariate GWAS run on several phenotypes or a multi-phenotype approach such as SCOPA (Mägi et al, 2017) and provides a framework allowing the precise mathematical representation of the architecture of variant-phenotype associations within regions (MPA/pleiotropic signatures), and allows these regions (such as genes) to be clustered based on these complex signatures
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