Genetic dissection of complex traits using hierarchical biological knowledge

• Main Authors: Ideker, T. (Author), Kreisberg, J.F (Author), Tanaka, H. (Author)
• Format: Article
• Language: English
• Published: Public Library of Science 2021
• Subjects: Article; benomyl; Benomyl; biological model; biology; chromosomal mapping; Chromosome Mapping; Computational Biology; controlled study; copper; Copper; drug effect; fatty acid; fatty acid metabolism; gene; gene locus; gene ontology; Gene Ontology; genetic analysis; genetic trait; genetics; genome-wide association study; Genome-Wide Association Study; glucose; Glucose; glycine; Glycine; hydroxyurea; Hydroxyurea; knowledge base; Knowledge Bases; machine learning; Machine Learning; Metabolic Networks and Pathways; metabolism; Models, Genetic; multifactorial inheritance; Multifactorial Inheritance; mutation; Mutation; Neural Networks, Computer; nonhuman; nucleotidyltransferase; Nucleotidyltransferases; phenotype; Phenotype; phenotypic variation; Polymorphism, Single Nucleotide; population genetics; procedures; purine; purine synthesis; reactive oxygen metabolite; Saccharomyces cerevisiae; single nucleotide polymorphism; systems biology; Systems Biology; UDPacetylglucosamine pyrophosphorylase; yeast
• Online Access: View Fulltext in Publisher

 Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use an ontology-guided ML system to map single nucleotide variants (SNVs) focusing on 6 classic phenotypic traits in natural yeast populations. The 29 identified loci are largely novel and account for ~17% of the phenotypic variance, versus <3% for standard genetic analysis. Representative results show that sensitivity to hydroxyurea is linked to SNVs in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. This work demonstrates a knowledge-based approach to amplifying and interpreting signals in population genetic studies. © 2021 Tanaka et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.