Genomic and chromosomal context is valuable in interpreting many aspects of genetics, including genome-wide association studies, population genetics, and gene expression. Salmonids underwent an ancestral whole genome duplication but are now back to a diploid state although some regions remain semi-tetraploid. In my first postdoc with Prof. Louis Bernatchez and Céline Audet, we developed a high-density genetic map for Brook Charr Salvelinus fontinalis and integrated this map with all other salmonids, providing insight in the rediploidization process (Sutherland et al. 2016). Chromosomal correspondence enabled investigation into changing sex chromosomes in the salmonids in the context of recombination differences between the sexes (Sutherland et al. 2017). Using a full-sib family of Brook Charr, we are characterizing the gene co-expression networks of this species to better understand gene regulatory architecture. This information with the genetic map will subsequently enable expression QTL analysis, providing information on the evolution of gene regulation in this salmonid.
Oyster Genomics and Population Health
In my second postdoc term, in the laboratory of Kristi Miller at the Pacific Biological Station (DFO-MPO) I am part of a collaboration with interdisciplinary academic and governmental researchers funded by the Moore Foundation and Hakai to investigate the causes of observed oyster die-offs in the Pacific northwest. We hope to advance this field by looking into the genetic factors associated with life on a farm, identifying microbes and genetic signatures associated with large-scale die-offs, and exploring the genetic differences that can be identified throughout the range of Pacific Oysters throughout coastal British Columbia.
Environmental DNA (eDNA) and Fisheries
eDNA is a promising new tool for any biologist interested in biodiversity, disturbances, species range changes, or introductions of invasive species. Although metabarcoding (sequence-based eDNA evaluation) remains only semi-quantitative, there are some interesting new directions to push this technique into a more quantitative state. With the ease of use and the power that outpaces traditional methods, this technique holds great promise. So far, I have been able to sample and analyze eDNA as part of one of the largest projects across all three coasts of Canada with CanadaC3 and early results of this work has been highlighted in a Vancouver Sun article. I am currently writing up this work in an initial preprint for release early 2018. More information on how we hope that eDNA will improve fisheries management will be discussed at a theme session of the 2018 ICES meeting on incorporating genetic techniques into fisheries stock assessment and monitoring.
Transcriptomics provides a broad view of the response of an organism to its biotic and abiotic environment. In the lab of Prof. Ben Koop I have applied this to the question of salmon and infective agents such as sea lice or viruses. We have demonstrated species-specific (Sutherland et al. 2014) and life stage-specific variation (Sutherland et al. 2011) in infection rates and responses of salmonids to sea lice. We have also applied these approaches to explain the variation observed from the louse side in response to Pacific and Atlantic salmonids, highlighting the possibility of co-evolution resulting in different infection dynamics in the responses of lice to different species of hosts (Braden, Sutherland et al., 2017). Transcriptomics work has also indicated energetic trade-offs in responses, for example the suppression of the innate immune system during smoltification (Sutherland et al. 2014), or during responses of sea lice to different levels of abiotic stress (Sutherland et al., 2012). Sea lice transcriptomics has also been used to investigate the differences in sexual dimorphism in lice (Poley et al. 2016), different expression patterns associated with antiparasiticide drug resistance (Sutherland et al. 2015) and sea lice responses to a naturally occurring microsporidian parasite (Poley , Sutherland et al. 2017).
Method Development: MapComp
Method Development: GO Trimming
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