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HIGH‐DIMENSIONAL VARIANCE PARTITIONING REVEALS THE MODULAR GENETIC BASIS OF ADAPTIVE DIVERGENCE IN GENE EXPRESSION DURING REPRODUCTIVE CHARACTER DISPLACEMENT

Overview of attention for article published in Evolution, June 2011
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Title
HIGH‐DIMENSIONAL VARIANCE PARTITIONING REVEALS THE MODULAR GENETIC BASIS OF ADAPTIVE DIVERGENCE IN GENE EXPRESSION DURING REPRODUCTIVE CHARACTER DISPLACEMENT
Published in
Evolution, June 2011
DOI 10.1111/j.1558-5646.2011.01371.x
Pubmed ID
Authors

Elizabeth A. McGraw, Yixin H. Ye, Brad Foley, Stephen F. Chenoweth, Megan Higgie, Emma Hine, Mark W. Blows

Abstract

Although adaptive change is usually associated with complex changes in phenotype, few genetic investigations have been conducted on adaptations that involve sets of high-dimensional traits. Microarrays have supplied high-dimensional descriptions of gene expression, and phenotypic change resulting from adaptation often results in large-scale changes in gene expression. We demonstrate how genetic analysis of large-scale changes in gene expression generated during adaptation can be accomplished by determining high-dimensional variance partitioning within classical genetic experimental designs. A microarray experiment conducted on a panel of recombinant inbred lines (RILs) generated from two populations of Drosophila serrata that have diverged in response to natural selection, revealed genetic divergence in 10.6% of 3762 gene products examined. Over 97% of the genetic divergence in transcript abundance was explained by only 12 genetic modules. The two most important modules, explaining 50% of the genetic variance in transcript abundance, were genetically correlated with the morphological traits that are known to be under selection. The expression of three candidate genes from these two important genetic modules was assessed in an independent experiment using qRT-PCR on 430 individuals from the panel of RILs, and confirmed the genetic association between transcript abundance and morphological traits under selection.

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The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 12%
Australia 1 2%
Switzerland 1 2%
Canada 1 2%
Brazil 1 2%
Unknown 40 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 28%
Student > Ph. D. Student 13 26%
Student > Master 5 10%
Professor > Associate Professor 3 6%
Other 3 6%
Other 8 16%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 82%
Biochemistry, Genetics and Molecular Biology 3 6%
Social Sciences 1 2%
Materials Science 1 2%
Unknown 4 8%