Genetic dissection of complex traits using hierarchical biological knowledge.
Journal:
PLoS computational biology
PMID:
34534210
Abstract
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.
Authors
Keywords
Benomyl
Chromosome Mapping
Computational Biology
Copper
Gene Ontology
Genome-Wide Association Study
Glucose
Glycine
Hydroxyurea
Knowledge Bases
Machine Learning
Metabolic Networks and Pathways
Models, Genetic
Multifactorial Inheritance
Mutation
Neural Networks, Computer
Nucleotidyltransferases
Phenotype
Polymorphism, Single Nucleotide
Saccharomyces cerevisiae
Systems Biology