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Higher-fitness yeast genotypes are less robust to deleterious mutations

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Science  25 Oct 2019:
Vol. 366, Issue 6464, pp. 490-493
DOI: 10.1126/science.aay4199
  • Fig. 1 Schematic of mutagenesis and fitness-assay pipeline.

    Plasmids with different colors indicate different regions of homology from the yeast genome. BC, barcode; Seg., segregant; Tn7, transposon 7.

  • Fig. 2 DFEs of insertion mutations.

    (A) DFEs in the large library experiment. Segregants are organized by background fitness. Color represents the fraction of mutations for each segregant in each fitness-effect bin (scale bar at right). (B) Relationship between background fitness and the mean of the DFE for the large library experiment (P = 0.03, two-sided t test). (C) DFEs in the small library experiment. Color represents the fraction of mutations for each segregant in each fitness-effect bin (scale bar at right). (D) Combined DFE of most-fit and least-fit quartile of segregants in the small library experiment. (E to G) Relationship between background fitness and DFE statistics for the small library experiment (P = 4.13 × 10−31, P = 7. 83 × 10−14, P = 4.08 × 10−12, respectively, two-sided t test).

  • Fig. 3 Patterns of epistasis for individual mutations.

    Fitness effects of 12 representative insertion mutations are plotted against segregant background fitness. The top-left mutation is one of five putatively neutral insertions used as controls. Allelic state at the largest-effect QTL for the fitness effect of each mutation is shown by yellow (BY) or blue (RM) color; allelic state at the second-largest–effect QTL is shown by closed (BY) or open (RM) symbol. If no significant QTLs were detected, all data points are black. Analogous plots for all insertion mutations are shown in fig. S7. Error bars represent SEs (21).

  • Fig. 4 Genetic determinants of fitness effects.

    (A) Histogram of regression slopes between fitness effect and background fitness for each mutation. Significant negative and positive correlations are shown in blue and yellow, respectively. (B) For each mutation, the standard deviation (std dev) of fitness effect across segregants and the square root of the variance explained by each of the three models. For each mutation, the variance explained by models that were not significantly better than a no-epistasis model are not plotted. Mutations shown in red or black are insertions in or near the corresponding gene, respectively; stars indicate the mutations shown in Fig. 3. Only mutations with fitness-effect measurements in at least 50 segregants are shown.

Supplementary Materials

  • Higher-fitness yeast genotypes are less robust to deleterious mutations

    Milo S. Johnson, Alena Martsul, Sergey Kryazhimskiy, Michael M. Desai

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    • Materials and Methods
    • Supplementary Text
    • Figs. S1 to S12
    • Tables S1 to S6
    • Captions for Data Tables S1 to S3
    • References
    Data Table S1
    Inferred fitness effects for mutations in E1 and E2 along with mutation annotation information and the results of the modeling described in "Modeling genetic determinants of fitness effects," DFE statistics from E1 and E2 along with the results of the modeling described in "Modeling genetic determinants of DFE statistics," inferred fitness effects for mutations during different stages of growth during the GC experiment, and cell density estimates and inferred growth rates for each segregant during the GC experiment.
    Data Table S2
    Inferred QTLs for individual mutations in E1, for individual mutations in E2, and for DFE statistics in E2 (No QTLs were detected for DFE statistics in E1), and information on multi-hit QTL regions including whether they were observed in previous work.
    Data Table S3
    Inferred fitness effects and significance testing results from our initial analysis of E1, which we used to choose mutations to include in E2.

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