Data_Sheet_3_Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships.PDF (1.13 MB)

Data_Sheet_3_Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships.PDF

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posted on 2019-05-10, 07:44 authored by Deborah Weighill, Piet Jones, Carissa Bleker, Priya Ranjan, Manesh Shah, Nan Zhao, Madhavi Martin, Stephen DiFazio, David Macaya-Sanz, Jeremy Schmutz, Avinash Sreedasyam, Timothy Tschaplinski, Gerald Tuskan, Daniel Jacobson

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.