Literature DB >> 31593884

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

Yuichi Eguchi1, Gaurav Bilolikar1, Kerry Geiler-Samerotte2.   

Abstract

The phenotypic impacts of a genetic change can depend on genetic background (e.g. epistasis), as well as other contexts including environment, developmental stage, cell type, disease state, and higher-order combinations thereof. Recent advances in high-throughput phenotyping are uncovering examples of context dependence faster than genotype-phenotype maps and other core concepts are changing to reflect the dynamic nature of biological systems. Here, we review several approaches to study context dependence and their findings. In our opinion, these findings encourage more studies that examine the spectrum of effects a genetic change may have, as opposed to studies that exclusively measure the impact of a genetic change in a particular context. Studies that elucidate the mechanisms that cause the effects of genetic change to vary with context are of special interest. Previous studies of the mechanisms underlying context dependence have improved predictions of phenotype from genotype and have provided insight about how biological systems function and evolve.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Year:  2019        PMID: 31593884     DOI: 10.1016/j.gde.2019.08.003

Source DB:  PubMed          Journal:  Curr Opin Genet Dev        ISSN: 0959-437X            Impact factor:   5.578


  5 in total

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

Authors:  Grant Kinsler; Kerry Geiler-Samerotte; Dmitri A Petrov
Journal:  Elife       Date:  2020-12-02       Impact factor: 8.140

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

Authors:  Kerry A Geiler-Samerotte; Shuang Li; Charalampos Lazaris; Austin Taylor; Naomi Ziv; Chelsea Ramjeawan; Annalise B Paaby; Mark L Siegal
Journal:  PLoS Biol       Date:  2020-08-17       Impact factor: 8.029

3.  Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond.

Authors:  Avery G A Selberg; Eric A Gaucher; David A Liberles
Journal:  J Mol Evol       Date:  2021-01-24       Impact factor: 2.395

Review 4.  Constructive Neutral Evolution 20 Years Later.

Authors:  Jeremy G Wideman; Kerry Geiler-Samerotte; Sergio A Muñoz-Gómez; Gaurav Bilolikar
Journal:  J Mol Evol       Date:  2021-02-19       Impact factor: 2.395

Review 5.  Decoding 'Unnecessary Complexity': A Law of Complexity and a Concept of Hidden Variation Behind "Missing Heritability" in Precision Medicine.

Authors:  Rama S Singh
Journal:  J Mol Evol       Date:  2021-08-02       Impact factor: 2.395

  5 in total

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