Different contributions of local‐ and distant‐regulatory changes to transcriptome divergence between stickleback ecotypes. (1st March 2017)

• Record Type: Journal Article
• Authors: Ishikawa, Asano; Kusakabe, Makoto; Yoshida, Kohta; Ravinet, Mark; Makino, Takashi; Toyoda, Atsushi; Fujiyama, Asao; Kitano, Jun
• Is Part Of: Evolution. Volume 71:Number 3(2017)
• Page Start: 565
• Page End: 581
• Publication Date: 2017-03-01
• Subjects: eQTL -- cis -- trans -- adaptation -- pleiotropy; Evolution -- Periodicals; Heredity -- Periodicals; Évolution (Biologie) -- Périodiques; Hérédité -- Périodiques; 338.47004094
• DOI: 10.1111/evo.13175
• Languages: English
• ISSNs: 0014-3820

Abstract

Abstract: Differential gene expression can play an important role in phenotypic evolution and divergent adaptation. Although differential gene expression can be caused by both local‐ and distant‐regulatory changes, we know little about their relative contribution to transcriptome evolution in natural populations. Here, we conducted expression quantitative trait loci (eQTL) analysis to investigate the genetic architecture underlying transcriptome divergence between marine and stream ecotypes of threespine sticklebacks ( Gasterosteus aculeatus ). We identified both local and distant eQTLs, some of which constitute hotspots, regions with a disproportionate number of significant eQTLs relative to the genomic background. The majority of local eQTLs including those in the hotspots caused expression changes consistent with the direction of transcriptomic divergence between ecotypes. Genome scan analysis showed that many local eQTLs overlapped with genomic regions of high differentiation. In contrast, nearly half of the distant eQTLs including those in the hotspots caused opposite expression changes, and few overlapped with regions of high differentiation, indicating that distant eQTLs may act as a constraint of transcriptome evolution. Finally, a comparison between two salinity conditions revealed that nearly half of eQTL hotspots were environment specific, suggesting that analysis of genetic architecture in multiple conditions is essential for predicting response to selection.