Abstract:
: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.
journal_name
Elifejournal_title
eLifeauthors
Galardini M,Koumoutsi A,Herrera-Dominguez L,Cordero Varela JA,Telzerow A,Wagih O,Wartel M,Clermont O,Denamur E,Typas A,Beltrao Pdoi
10.7554/eLife.31035subject
Has Abstractpub_date
2017-12-27 00:00:00issn
2050-084Xpii
31035journal_volume
6pub_type
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