title: Predicting phenotypic variation in yeast from individual genome sequences.
abstract: A central challenge in genetics is to predict phenotypic variation from individual genome sequences. Here we construct and evaluate phenotypic predictions for 19 strains of Saccharomyces cerevisiae. We use conservation-based methods to predict the impact of protein-coding variation within genes on protein function. We then rank strains using a prediction score that measures the total sum of function-altering changes in different sets of genes reported to influence over 100 phenotypes in genome-wide loss-of-function screens. We evaluate our predictions by comparing them with the observed growth rate and efficiency of 15 strains tested across 20 conditions in quantitative experiments. The median predictive performance, as measured by ROC AUC, was 0.76, and predictions were more accurate when the genes reported to influence a trait were highly connected in a functional gene network.
authors.affiliation: European Molecular Biology Laboratory, Centre for Genomic Regulation, Systems Biology Research Unit, Barcelona, Spain.
authors.forename: Rob
authors.initials: R
authors.lastname: Jelier
authors.forename: Jennifer I
authors.initials: JI
authors.lastname: Semple
authors.forename: Rosa
authors.initials: R
authors.lastname: Garcia-Verdugo
authors.forename: Ben
authors.initials: B
authors.lastname: Lehner
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D003198:Computer Simulation; D017124:Conserved Sequence; D005787:Gene Frequency; D014644:Genetic Variation; D016681:Genome, Fungal; D054643:INDEL Mutation; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000254:growth & development; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D029701:Saccharomyces cerevisiae Proteins
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