Among‐family variation in survival and gene expression uncovers adaptive genetic variation in a threatened fish
Harder, Avril M.; Willoughby, Janna R.; Ardren, William R.; Christie, Mark R. (2020), Among‐family variation in survival and gene expression uncovers adaptive genetic variation in a threatened fish, Dryad, Dataset, https://doi.org/10.5061/dryad.stqjq2bzz
Variation in among‐family transcriptional responses to different environmental conditions can help to identify adaptive genetic variation, even prior to a selective event. Coupling differential gene expression with formal survival analyses allows for the disentanglement of treatment effects, required for understanding how individuals plastically respond to environmental stressors, from the adaptive genetic variation responsible for differential survival. We combined these two approaches to investigate responses to an emerging conservation issue, thiamine (vitamin B1) deficiency, in a threatened population of Atlantic salmon (Salmo salar). Thiamine is an essential vitamin that is increasingly limited in many ecosystems. In Lake Champlain, Atlantic salmon cannot acquire thiamine in sufficient quantities to support natural reproduction; fertilized eggs must be reared in hatcheries and treated with supplemental thiamine. We evaluated transcriptional responses (via RNA sequencing) to thiamine treatment across families and found 3,616 genes differentially expressed between control (no supplemental thiamine) and treatment individuals. Fewer genes changed expression equally across families (i.e., additively) than exhibited genotype × environment interactions in response to thiamine. Differentially expressed genes were related to known physiological effects of thiamine deficiency, including oxidative stress, cardiovascular irregularities and neurological abnormalities. We also identified 1,446 putatively adaptive genes that were strongly associated with among‐family survival in the absence of thiamine treatment, many of which related to neurogenesis and visual perception. Our results highlight the utility of coupling RNA sequencing with formal survival analyses to identify candidate genes that underlie the among‐family variation in survival required for an adaptive response to natural selection.
Mortality counts taken for families spawned in 2016 and 2017. Censored data indicate that individuals were removed (alive) for other scientific studies, disease testing, or USFWS broodstock efforts.
Data in 2017_survival_analysis_data_R.csv can be used as input for survival analysis in R (see survival_analysis.R at
Department of Biological Sciences, Purdue University