Regulatory Architecture of Gene Expression Variation in the Threespine Stickleback Gasterosteus aculeatus.
Victoria L PritchardHeidi M ViitaniemiR J Scott McCairnsJuha MeriläMikko NikinmaaCraig Robert PrimmerErica H LederPublished in: G3 (Bethesda, Md.) (2017)
Much adaptive evolutionary change is underlain by mutational variation in regions of the genome that regulate gene expression rather than in the coding regions of the genes themselves. An understanding of the role of gene expression variation in facilitating local adaptation will be aided by an understanding of underlying regulatory networks. Here, we characterize the genetic architecture of gene expression variation in the threespine stickleback (Gasterosteus aculeatus), an important model in the study of adaptive evolution. We collected transcriptomic and genomic data from 60 half-sib families using an expression microarray and genotyping-by-sequencing, and located expression quantitative trait loci (eQTL) underlying the variation in gene expression in liver tissue using an interval mapping approach. We identified eQTL for several thousand expression traits. Expression was influenced by polymorphism in both cis- and trans-regulatory regions. Trans-eQTL clustered into hotspots. We did not identify master transcriptional regulators in hotspot locations: rather, the presence of hotspots may be driven by complex interactions between multiple transcription factors. One observed hotspot colocated with a QTL recently found to underlie salinity tolerance in the threespine stickleback. However, most other observed hotspots did not colocate with regions of the genome known to be involved in adaptive divergence between marine and freshwater habitats.
Keyphrases
- gene expression
- genome wide
- dna methylation
- transcription factor
- poor prognosis
- copy number
- binding protein
- high resolution
- long non coding rna
- single cell
- oxidative stress
- electronic health record
- genome wide identification
- deep learning
- high throughput
- high density
- heat shock protein
- genetic diversity
- artificial intelligence
- machine learning