Genomewide Identification of Genes Under Directional Selection: Gene Transcription Q ST Scan in Diverging Atlantic Salmon Subpopulations

• Published in: Genetics
• Main Authors: Roberge, C, Guderley, H, Bernatchez, L
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2007
• Subjects: Atlantic salmon
• Online Access: http://dx.doi.org/10.1534/genetics.107.073759; https://academic.oup.com/genetics/article-pdf/177/2/1011/49502805/genetics1011.pdf

Abstract Evolutionary genomics has benefited from methods that allow identifying evolutionarily important genomic regions on a genomewide scale, including genome scans and QTL mapping. Recently, genomewide scanning by means of microarrays has permitted assessing gene transcription differences among species or populations. However, the identification of differentially transcribed genes does not in itself suffice to measure the role of selection in driving evolutionary changes in gene transcription. Here, we propose and apply a “transcriptome scan” approach to investigating the role of selection in shaping differential profiles of gene transcription among populations. We compared the genomewide transcription levels between two Atlantic salmon subpopulations that have been diverging for only six generations. Following assessment of normality and unimodality on a gene-per-gene basis, the additive genetic basis of gene transcription was estimated using the animal model. Gene transcription h2 estimates were significant for 1044 (16%) of all detected cDNA clones. In an approach analogous to that of genome scans, we used the distribution of the QST values estimated from intra- and intersubpopulation additive genetic components of the transcription profiles to identify 16 outlier genes (average QST estimate = 0.11) whose transcription levels are likely to have evolved under the influence of directional selection within six generations only. Overall, this study contributes both empirically and methodologically to the quantitative genetic exploration of gene transcription data.