Why and how to study genetic changes with context-dependent effects

Eguchi, Yuichi; Bilolikar, Gaurav; Geiler-Samerotte, Kerry

doi:10.1016/j.gde.2019.08.003

Cited by 28 publications

(20 citation statements)

References 60 publications

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“…These results support the idea that predicting the phenotypic impact of a genetic change requires a paradigm shift [88,89], away from merely mapping the relationships between traits and toward unfurling the range of contexts across which those relationships persist. Future work in this direction will not only advance understanding of the evolution of complex traits but will have practical benefits.…”

Section: Plos Biologysupporting

confidence: 80%

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Geiler-Samerotte

Lazaris

et al. 2020

PLoS Biol

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Pleiotropy-when a single mutation affects multiple traits-is a controversial topic with farreaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (quantitative trait loci [QTL]) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background, and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context.

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Section: Plos Biologysupporting

confidence: 80%

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Geiler-Samerotte

Lazaris

et al. 2020

PLoS Biol

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“…The strong contribution that batch effects make on variation in fitness highlights the importance of a careful experimental design where the effects of treatment are not confounded with the effects of batch ( Box 1 ). It also highlights how strongly the impact of mutations depends on context, including even subtle contextual changes that are present across batches (Eguchi et al 2019;Geiler-Samerotte et al 2020; Kinsler et al 2020).…”

Section: Resultsmentioning

confidence: 99%

Extreme sensitivity of fitness to environmental conditions; lessons from #1BigBatch

Kinsler

Schmidlin

Newell

et al. 2022

Preprint

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The phrase 'survival of the fittest' has become an iconic descriptor of how natural selection works. And yet, precisely measuring fitness, even for single-celled microbial populations growing in controlled laboratory conditions, remains a challenge. While numerous methods exist to perform these measurements, including recently developed methods utilizing DNA barcoding, all methods seem limited in their precision to differentiate strains with small fitness differences. This limit on precision is relevant in many fields, including the field of experimental evolution. In this study, we hone in on the factors that contribute to noisy fitness measurements and suggest solutions to avoid certain sources of noise. Surprisingly, even when common sources of technical noise are controlled for, we find that fitness measurements are still very noisy. Our data suggest that subtle environmental differences among replicates create substantial variation across fitness measurements. We conclude by providing best practices for obtaining precise fitness measurements and by discussing how these measurements should be interpreted given their extreme context dependence. This work was inspired by the scientific community who followed us and gave us tips as we live-tweeted a high-replicate fitness measurement experiment at #1BigBatch.

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“…personalized medicine) and to investigate the structure of biological systems (e.g. their degree of modularity and pleiotropy) (Collet et al, 2018;Eguchi et al, 2019;Exposito-Alonso et al, 2019;Zan and Carlborg, 2020). Our model allows us to do both of these things.…”

Section: Discussionmentioning

confidence: 99%

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

Kinsler

Geiler-Samerotte

Petrov

2020

eLife

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103

208

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Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular—affecting a small number of phenotypes that matter to fitness in the environment where they evolved—yet globally pleiotropic—affecting additional phenotypes that may reduce or improve fitness in new environments.

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Why and how to study genetic changes with context-dependent effects

Cited by 28 publications

References 60 publications

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Extreme sensitivity of fitness to environmental conditions; lessons from #1BigBatch

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

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