2014
DOI: 10.1038/ncomms5071
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Gene pleiotropy constrains gene expression changes in fish adapted to different thermal conditions

Abstract: Understanding the factors that shape the evolution of gene expression is a central goal in biology, but the molecular mechanisms behind this remain controversial. A related major goal is ascertaining how such factors may affect the adaptive potential of a species or population. Here we demonstrate that temperature-driven gene expression changes in fish adapted to differing thermal environments are constrained by the level of gene pleiotropy estimated by either the number of protein interactions or gene biologi… Show more

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Cited by 78 publications
(80 citation statements)
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“…We used one‐way best hits zebrafish orthologs to estimate pleiotropy for differentially expressed genes based on their number of associated GO biological processes, PPIs, and developmental stages when they are known to be expressed (Papakostas et al. ). We again used GO Consortium resources (Ashburner et al.…”
Section: Methodsmentioning
confidence: 99%
“…We used one‐way best hits zebrafish orthologs to estimate pleiotropy for differentially expressed genes based on their number of associated GO biological processes, PPIs, and developmental stages when they are known to be expressed (Papakostas et al. ). We again used GO Consortium resources (Ashburner et al.…”
Section: Methodsmentioning
confidence: 99%
“…Consequently, differences in patterns of G Â E variation will influence the ability of populations to evolve new adaptive responses following introduction or in situ environmental challenges. In addition, a better understanding of the genetic architectures underlying reaction norms, such as the effects of modularity versus pleiotropy of regulatory pathways [118], will better inform models of constraint on plasticity evolution in natural populations [67,119].…”
Section: (B) Plasticity and Evolutionary Potentialmentioning
confidence: 99%
“…were used for further analysis in the Chipster open source platform (version 1.4). 20 The arrays were normalized using the quantile method. 23 Probes with missing values and probes that varied least across all observations were filtered away based on the coefficient of variation value 0.2, leaving 18 256 probes for further analyses.…”
Section: Rna Sample Preparation and Array Hybridizationmentioning
confidence: 99%