Phenotype inference in an Escherichia coli strain panel

Elife. 2017 Dec 27;6:e31035. doi: 10.7554/eLife.31035.


Understanding how genetic variation contributes to phenotypic differences is a fundamental question in biology. Combining high-throughput gene function assays with mechanistic models of the impact of genetic variants is a promising alternative to genome-wide association studies. Here we have assembled a large panel of 696 Escherichia coli strains, which we have genotyped and measured their phenotypic profile across 214 growth conditions. We integrated variant effect predictors to derive gene-level probabilities of loss of function for every gene across all strains. Finally, we combined these probabilities with information on conditional gene essentiality in the reference K-12 strain to compute the growth defects of each strain. Not only could we reliably predict these defects in up to 38% of tested conditions, but we could also directly identify the causal variants that were validated through complementation assays. Our work demonstrates the power of forward predictive models and the possibility of precision genetic interventions.

Keywords: E. coli; computational biology; genotype to phenotype; infectious disease; microbiology; phenotypic diversity; reference panel; systems biology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biological Variation, Population
  • Escherichia coli K12 / genetics*
  • Escherichia coli K12 / physiology*
  • Genetic Complementation Test
  • Genetic Variation*
  • Genotype
  • Phenotype

Grant support

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.