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https://foothillsri.ca/sites/default/files/Poster-Application%20of%20genomics.pdf
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Application of Genomics to Understand Forest-Pest Interactions
Catherine I. Cullingham, Janice E.K. Cooke, David W. Coltman Department of Biological Sciences, University of Alberta, Edmonton
Pine forests in western North America have been devastated by an unprecedented outbreak of mountain pine beetle-fungal complex. Primarily, lodgepole pine forests in Canada have been affected resulting in economic loss and ecosystem damage. More concerning is the range expansion of the beetle/fungi both north and east resulting in exposure to a new host species, jack pine (Cullingham et al. 2011), a sister species to lodgepole pine. This naïve host is distributed throughout the Boreal forest across Canada and into the eastern United States putting an entire ecosystem at risk.
Are there genetic differences within and between lodgepole and jack pine?
Do these genetic differences affect mountain pine beetle colonizing or reproductive success (i.e. spread potential)?
How do environmental factors interact with the host-insect relationship?
Questions
Mountain pine beetle attack data in British Columbia and Alberta in three time periods: 1959-1998, 1999-2005 and 2006-2009 to demonstrate the magnitude of the recent outbreak. Attack data is overlaid pine distribution and density.
Introduction
Results
USE GENOMICS TO IDENTIFY AND UNDERSTAND HOST-GENETIC RISK FACTORS
Wound Drought
Genes of potential importance are expressed
in response to the change in environment
Extract RNA which represents the expressed
portion of the genome (transcriptome)
Needle
Stem
Root
RNA used as a source for high-throughput
sequencing
Methods Lodgepole Jack pine
1 597 295 fragments 1 558 772 fragments
Combined
Assembly (MIRA, Newbler)
SNP detection/selection Minimum coverage: 15X
MAF: 10% SNP > 100bp
Flanking region
1536 SNPs
399 457 362
Many thanks to all of the technical and intellectual support from Sophie Dang, Matt Bryman, Cory Davis, Stephanie Boychuk, Joël Fillon, Dominik Royko, Jasmine Janes, Patrick James, Jill Hamilton, Adriana Arango, Josh Miller, René Malenfant, William Peachman, and Lucky Chauhan.
Acknowledgements
References Balding (2003) Theor. Popul. Biol. 63: 221-230. Beaumont & Balding (2004) Mol. Ecol. 13: 969-980. Cullingham et al. (2011) Mol. Ecol. 20, 2157-2171. Foll & Gaggiotti (2008) Genetics, 180, 977-993. Gompert & Buerkle (2009) Mol. Ecol. 18, 1207-1224.
Implications
Characterize SNPs in lodgepole, jack pine and their hybrids across naïve and non-naïve stands
OUTLIERS
Identify loci that deviate from neutral expectations within and between species, “outlier analysis” (Beaumont & Balding 2004)
Spatially informed o MatSAM (Joost et al. 2008)
Spatially uninformed o BayeSCAN (Foll & Gaggiotti
2008) o BayesFST (Balding 2003) o Introgress (Gompert &
Buerkle 2009)
Across data-sets, 136 SNP loci showed patterns of non-neutral inheritance. The majority of which were found when comparing lodgepole and jack pine (“All”). These include transcription factors, protein degradation, growth regulators, synthesis, energy production and water transport.
All
Jack
8 130
2
0 0 0
Lodge
4
Interpolated surfaces of three SNP loci that that encode for proteins of potential interest that displayed patterns of non-neutral inheritance, and an association with the environment.
This research addresses knowledge gaps that exist given the range-expansion of the beetle to novel environments. From here we will assess the function of these candidate loci to identify whether they influence spread potential. We can then use our interpolated surfaces as inputs in spread-risk and economic models to mitigate the impacts of this outbreak, and assess potential future outbreaks.
Population Genomics
Physiology & Function
Ecology
MPB vector Fungal Pathogen
Pine host
Spread-risk models
Millions of DNA fragments were generated and processed using a bioinformatic work-flow