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Title: Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships

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.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1];  [2]; ORCiD logo [1];  [1];  [3];  [4];  [5]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Tennessee, Knoxville, TN (United States)
  3. West Virginia Univ., Morgantown, WV (United States)
  4. USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States)
  5. Hudson Alpha Inst. of Biotechnology, Huntsville, AL (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1559645
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Frontiers in Genetics
Additional Journal Information:
Journal Volume: 10; Journal Issue: n/a
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Weighill, Deborah A., Jones, Piet C., Bleker, Carissa R., Ranjan, Priya, Shah, Manesh B., Zhao, Nan, Martin, Madhavi Z., Difazio, Stephen, Macaya-Sanz, David, Schmutz, Jeremy, Sreedasyam, Avinash, Tschaplinski, Timothy J., Tuskan, Gerald A., and Jacobson, Daniel A. Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships. United States: N. p., 2019. Web. doi:10.3389/fgene.2019.00417.
Weighill, Deborah A., Jones, Piet C., Bleker, Carissa R., Ranjan, Priya, Shah, Manesh B., Zhao, Nan, Martin, Madhavi Z., Difazio, Stephen, Macaya-Sanz, David, Schmutz, Jeremy, Sreedasyam, Avinash, Tschaplinski, Timothy J., Tuskan, Gerald A., & Jacobson, Daniel A. Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships. United States. doi:10.3389/fgene.2019.00417.
Weighill, Deborah A., Jones, Piet C., Bleker, Carissa R., Ranjan, Priya, Shah, Manesh B., Zhao, Nan, Martin, Madhavi Z., Difazio, Stephen, Macaya-Sanz, David, Schmutz, Jeremy, Sreedasyam, Avinash, Tschaplinski, Timothy J., Tuskan, Gerald A., and Jacobson, Daniel A. Fri . "Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships". United States. doi:10.3389/fgene.2019.00417. https://www.osti.gov/servlets/purl/1559645.
@article{osti_1559645,
title = {Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships},
author = {Weighill, Deborah A. and Jones, Piet C. and Bleker, Carissa R. and Ranjan, Priya and Shah, Manesh B. and Zhao, Nan and Martin, Madhavi Z. and Difazio, Stephen and Macaya-Sanz, David and Schmutz, Jeremy and Sreedasyam, Avinash and Tschaplinski, Timothy J. and Tuskan, Gerald A. and Jacobson, Daniel A.},
abstractNote = {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.},
doi = {10.3389/fgene.2019.00417},
journal = {Frontiers in Genetics},
number = n/a,
volume = 10,
place = {United States},
year = {2019},
month = {5}
}

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