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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

Volume 7833 of the series Lecture Notes in Computer Science pp 23-34

Inferring Human Phenotype Networks from Genome-Wide Genetic Associations

  • Christian DarabosAffiliated withLancaster UniversityDepartment of Genetics, The Geisel Medical School at Dartmouth College
  • , Kinjal DesaiAffiliated withLancaster UniversityDepartment of Genetics, The Geisel Medical School at Dartmouth College
  • , Richard Cowper-Sal·lariAffiliated withLancaster UniversityDepartment of Genetics, The Geisel Medical School at Dartmouth College
  • , Mario GiacobiniAffiliated withCarnegie Mellon UniversityComputational Epidemiology Group, Department of Veterinary Sciences, and Complex Systems Unit, Molecular Biotechnology Center, University of Torino
  • , Britney E. GrahamAffiliated withLancaster UniversityDepartment of Genetics, The Geisel Medical School at Dartmouth College
  • , Mathieu LupienAffiliated withCarnegie Mellon UniversityOntario Cancer Institute, Princess Margaret Cancer Center-University Health Network, Ontario Institute for Cancer Research and the Department of Medical Biophysics, University of Toronto
  • , Jason H. MooreAffiliated withLancaster UniversityDepartment of Genetics, The Geisel Medical School at Dartmouth College

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Abstract

Networks are commonly used to represent and analyze large and complex systems of interacting elements. We build a human phenotype network (HPN) of over 600 physical attributes, diseases, and behavioral traits; based on more than 6,000 genetic variants (SNPs) from Genome-Wide Association Studies data. Using phenotype-to-SNP associations, and HapMap project data, we link traits based on the common patterns of human genetic variations, expanding previous studies from a gene-centric approach to that of shared risk-variants. The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. Additional network metrics hint that the HPN shares properties with social networks. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present a collection of documented clinical connections supported by the network. Furthermore, we highlight phenotypes modules and links that may underlie yet undiscovered genetic interactions. Despite its simplicity and current limitations the HPN shows tremendous potential to become a useful tool both in the unveiling of the diseases’ common biology, and in the elaboration of diagnosis and treatments.