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Eye regression in blind Astyanax cavefish may facilitate the evolution of an adaptive behavior and its sensory receptors

Overview of attention for article published in BMC Biology, July 2013
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Title
Eye regression in blind Astyanax cavefish may facilitate the evolution of an adaptive behavior and its sensory receptors
Published in
BMC Biology, July 2013
DOI 10.1186/1741-7007-11-81
Pubmed ID
Authors

Richard Borowsky

Abstract

The forces driving the evolutionary loss or simplification of traits such as vision and pigmentation in cave animals are still debated. Three alternative hypotheses are direct selection against the trait, genetic drift, and indirect selection due to antagonistic pleiotropy. Recent work establishes that Astyanax cavefish exhibit vibration attraction behavior (VAB), a presumed behavioral adaptation to finding food in the dark not exhibited by surface fish. Genetic analysis revealed two regions in the genome with quantitative trait loci (QTL) for both VAB and eye size. These observations were interpreted as genetic evidence that selection for VAB indirectly drove eye regression through antagonistic pleiotropy and, further, that this is a general mechanism to account for regressive evolution. These conclusions are unsupported by the data; the analysis fails to establish pleiotropy and ignores the numerous other QTL that map to, and potentially interact, in the same regions. It is likely that all three forces drive evolutionary change. We will be able to distinguish among them in individual cases only when we have identified the causative alleles and characterized their effects.

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Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Italy 1 3%
Unknown 36 95%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 11 29%
Researcher 5 13%
Student > Ph. D. Student 4 11%
Student > Master 4 11%
Professor 3 8%
Other 8 21%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 55%
Neuroscience 4 11%
Biochemistry, Genetics and Molecular Biology 3 8%
Computer Science 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 7 18%