ArticleMolecular ecology2021
Innovative mark-recapture experiment shows patterns of selection on transcript abundance in the wild.
Article in Molecular ecology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
- Detecting signatures of selection on gene expression.Nature ecology & evolution · 2022Article
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Abstract
A fundamental aspect of evolutionary biology is natural selection on trait variation. Classically, selection has been estimated primarily on external morphological traits such as beak size and coloration, or on easily assayable physiological traits such as heat-tolerance. As technologies and methods improved, evolutionary biologists began examining selection on molecular traits such as protein sequences and cellular processes. In a From the Cover paper in this issue of Molecular Ecology, Ahmad et al. continue this trend by estimating parasite-driven selection on the molecular trait of transcript abundance in a wild population of brown trout (Salmo trutta) by uniquely combining a mark-recapture experimental design with noninvasive RNA sampling. Using transcript abundance to estimate selection allows for many different traits (each unique gene's transcript counts) to be tested in a single experiment, providing the opportunity to examine trends in selection. Ahmad et al. find directional selection strength on transcript counts is generally low and normally distributed. Surprisingly, transcripts under nonlinear selection showed a disruptive selection bias, contradicting previous comparative studies and theoretical work. This highlights the importance of within-generation selection studies, where mechanisms may differ from longer time frames. Their paper also highlights the benefits of a cost-effective 3' RNA sequencing technique to measure gene expression.
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