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Dissecting dynamic genetic variation that controls temporal gene response in yeast.

Brodt A, Botzman M, David E, Gat-Viks I - PLoS Comput. Biol. (2014)

Bottom Line: Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells.We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern.Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Research and Immunology, Tel Aviv University, Tel Aviv, Israel.

ABSTRACT
Inter-individual variation in regulatory circuits controlling gene expression is a powerful source of functional information. The study of associations among genetic variants and gene expression provides important insights about cell circuitry but cannot specify whether and when potential variants dynamically alter their genetic effect during the course of response. Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells. We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern. The temporal genetic effects of some modules represented a single state-transitioning pattern; for example, at 10-30 minutes following stimulation, genetic effects in the phosphate utilization module attained a characteristic transition to a new steady state. In contrast, another module showed an impulse pattern of genetic effects; for example, in the poor nitrogen sources utilization module, a spike up of a genetic effect at 10-20 minutes following stimulation reflected inter-individual variation in the timing (rather than magnitude) of response. Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module. Our methodology provides a quantitative genetic approach to studying the molecular mechanisms that shape dynamic changes in transcriptional responses.

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Comparative performance analysis on synthetic data.Shown is the accuracy measure (scatter plots, left) and an example (histograms, right) across compared methods and different synthetic data parameters. Left: The accuracy measure (y-axis) using different patterns of genetic effects (impulse, single state-transitioning (sustained), linear, and complex sub-panels). Results are shown over genes that were measured in different numbers of time points (measures were averaged over effect sizes; x-axis, A), or over genes of different effect sizes (averaged over time points; x-axis B). Plots depict six alternative mapping methods (color coded). Right: Examples of performance (y-axis) using the four different dynamic effect patterns (color coded) across various methods (x-axis) for nine time points (A) or for genetic effect size 0.5 (B). The plots indicate that for non-linear genetic effect patterns, DyVER has an advantage over existing methods.
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pcbi-1003984-g003: Comparative performance analysis on synthetic data.Shown is the accuracy measure (scatter plots, left) and an example (histograms, right) across compared methods and different synthetic data parameters. Left: The accuracy measure (y-axis) using different patterns of genetic effects (impulse, single state-transitioning (sustained), linear, and complex sub-panels). Results are shown over genes that were measured in different numbers of time points (measures were averaged over effect sizes; x-axis, A), or over genes of different effect sizes (averaged over time points; x-axis B). Plots depict six alternative mapping methods (color coded). Right: Examples of performance (y-axis) using the four different dynamic effect patterns (color coded) across various methods (x-axis) for nine time points (A) or for genetic effect size 0.5 (B). The plots indicate that for non-linear genetic effect patterns, DyVER has an advantage over existing methods.

Mentions: DyVER showed good accuracy in all non-linear dynamic effect patterns (0.5 penalty; Fig. 3). Fig. 3A presents the accuracy metric for synthetic datasets of varying numbers of time points. Accuracy values are averaged across the eight collections of distinct effect size. In all non-linear dynamic effect patterns, DyVER displayed the best accuracy in all tested time points ranging between 3 and 27, with improved accuracy for a larger number of time points. Importantly, although DyVER was not designed for linear-like effect patterns, it still attains the second-best performance for this case. The ‘expression dynamics’ approach yielded the most accurate predictions for the linear case, but attained poor results in the non-linear case. The tradeoff between sensitivity and specificity in the accuracy measure across the different methods is further demonstrated in Figure S1A and B. Results were similar for varying effect sizes (Fig. 3B and Figure S1C and S1D) and for an additional synthetic dataset that is based on prototypical effects in C. elegans (Methods; Figure S2). Furthermore, although DyVER's accuracy is reduced in the case of missing data, it is still notably high in comparison to alternative methods (Figure S3). Taken together, our results indicated that DyVER performs well on a broad range of genetic effect patterns.


Dissecting dynamic genetic variation that controls temporal gene response in yeast.

Brodt A, Botzman M, David E, Gat-Viks I - PLoS Comput. Biol. (2014)

Comparative performance analysis on synthetic data.Shown is the accuracy measure (scatter plots, left) and an example (histograms, right) across compared methods and different synthetic data parameters. Left: The accuracy measure (y-axis) using different patterns of genetic effects (impulse, single state-transitioning (sustained), linear, and complex sub-panels). Results are shown over genes that were measured in different numbers of time points (measures were averaged over effect sizes; x-axis, A), or over genes of different effect sizes (averaged over time points; x-axis B). Plots depict six alternative mapping methods (color coded). Right: Examples of performance (y-axis) using the four different dynamic effect patterns (color coded) across various methods (x-axis) for nine time points (A) or for genetic effect size 0.5 (B). The plots indicate that for non-linear genetic effect patterns, DyVER has an advantage over existing methods.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256076&req=5

pcbi-1003984-g003: Comparative performance analysis on synthetic data.Shown is the accuracy measure (scatter plots, left) and an example (histograms, right) across compared methods and different synthetic data parameters. Left: The accuracy measure (y-axis) using different patterns of genetic effects (impulse, single state-transitioning (sustained), linear, and complex sub-panels). Results are shown over genes that were measured in different numbers of time points (measures were averaged over effect sizes; x-axis, A), or over genes of different effect sizes (averaged over time points; x-axis B). Plots depict six alternative mapping methods (color coded). Right: Examples of performance (y-axis) using the four different dynamic effect patterns (color coded) across various methods (x-axis) for nine time points (A) or for genetic effect size 0.5 (B). The plots indicate that for non-linear genetic effect patterns, DyVER has an advantage over existing methods.
Mentions: DyVER showed good accuracy in all non-linear dynamic effect patterns (0.5 penalty; Fig. 3). Fig. 3A presents the accuracy metric for synthetic datasets of varying numbers of time points. Accuracy values are averaged across the eight collections of distinct effect size. In all non-linear dynamic effect patterns, DyVER displayed the best accuracy in all tested time points ranging between 3 and 27, with improved accuracy for a larger number of time points. Importantly, although DyVER was not designed for linear-like effect patterns, it still attains the second-best performance for this case. The ‘expression dynamics’ approach yielded the most accurate predictions for the linear case, but attained poor results in the non-linear case. The tradeoff between sensitivity and specificity in the accuracy measure across the different methods is further demonstrated in Figure S1A and B. Results were similar for varying effect sizes (Fig. 3B and Figure S1C and S1D) and for an additional synthetic dataset that is based on prototypical effects in C. elegans (Methods; Figure S2). Furthermore, although DyVER's accuracy is reduced in the case of missing data, it is still notably high in comparison to alternative methods (Figure S3). Taken together, our results indicated that DyVER performs well on a broad range of genetic effect patterns.

Bottom Line: Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells.We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern.Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Research and Immunology, Tel Aviv University, Tel Aviv, Israel.

ABSTRACT
Inter-individual variation in regulatory circuits controlling gene expression is a powerful source of functional information. The study of associations among genetic variants and gene expression provides important insights about cell circuitry but cannot specify whether and when potential variants dynamically alter their genetic effect during the course of response. Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells. We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern. The temporal genetic effects of some modules represented a single state-transitioning pattern; for example, at 10-30 minutes following stimulation, genetic effects in the phosphate utilization module attained a characteristic transition to a new steady state. In contrast, another module showed an impulse pattern of genetic effects; for example, in the poor nitrogen sources utilization module, a spike up of a genetic effect at 10-20 minutes following stimulation reflected inter-individual variation in the timing (rather than magnitude) of response. Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module. Our methodology provides a quantitative genetic approach to studying the molecular mechanisms that shape dynamic changes in transcriptional responses.

Show MeSH