Drosophila transcriptomics with and without ageing

Barter, Thomas T.; Greenspan, Zachary S; Phillips, Mark; Mueller, Laurence D.; Rose, Michael R.; Ranz, José M.

doi:10.1007/s10522-019-09823-4

Cited by 15 publications

(35 citation statements)

References 37 publications

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“…It is conceivable that the measurement FLAM’s accuracy is masked by the nature of our populations. Our populations are highly differentiated for each transcript that is included in these analyses (12). With this substantial differentiation between groups of populations, it is simple to achieve a moderate correlation between the actual and predicted values using any type of genetic variation, so long as such variation is correlated with treatment.…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, the 10 A-type populations are clearly differentiated from the 10 C-type populations across the genome (1), across the transcriptome (12), and in age-specific mortality and fecundity (13). Conversely, there is little to no differentiation between the populations within a single set of 10 populations for all three sets of data.…”

Section: Discussionmentioning

confidence: 99%

“…Having two clearly defined sets of ten experimentally evolved D. melanogaster populations in conjunction with a full suite of genomic, transcriptomic, and phenotypic data for both sets of populations has enabled us to piece together how these three levels of biological machinery interact with one another. Specifically, the 10 A-type populations are clearly differentiated from the 10 C-type populations across the genome (1), across the transcriptome (12), and in age-specific mortality and fecundity (13). Conversely, there is little to no differentiation between the populations within a single set of 10 populations for all three sets of data.…”

Section: Discussionmentioning

confidence: 99%

“…We used previously published stranded PE 75 RNA-seq data corresponding to day 14 and day 21 from egg (12). Details about extraction, sequencing, and read mapping are given in Barter et al (12).…”

Section: Methodsmentioning

confidence: 99%

“…1). For all populations, we previously sequenced their genomes (1) and female transcriptomes (12) and collected data on their life history traits (13). In this study, we use genomic and transcriptomic data to infer which gene regions might be causally linked to the phenotypes of age-specific mortality and fecundity, the life-history traits that numerically define biological fitness.…”

Section: Introductionmentioning

confidence: 99%

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Genome-Wide Architecture of Adaptation in Experimentally EvolvedDrosophila

Barter¹,

Greenspan²,

Phillips

et al. 2020

Preprint

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The molecular basis of adaptation remains elusive even with the current ease of sequencing the genome and transcriptome. We used experimentally evolved populations of Drosophila in conjunction with statistical learning tools to explore interactions between the genome, the transcriptome, and phenotypes. Our results indicate that transcriptomic measures from adult samples can predict phenotypic characters at many adult ages. Importantly, when comparing the genome and transcriptome in predicting phenotypic characters, we find that the two types of data are comparably useful. When using genome sites as predictors for the expression of the transcriptome, we find that gene expression is influenced by genomic regions across all large chromosome arms. Conversely, we found many genomic regions influencing the expression of numerous genes, which is consistent with widespread pleiotropy. Our results also highlight the power of the combination of experimental evolution, next-generation sequencing, and statistical learning tools in exploring the molecular basis of adaptation.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%