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Using genomics to identify adaptive variation at the mountain pine beetle expansion front Janes

1 JK ,

Li

2 Y ,

Yuen

2 M ,

Keeling

2 CI ,

1 University

Jones

of Alberta,

2 SJM ,

Murray

2 University

3 BW ,

Huber

3 DPW ,

Bohlmann

2 J ,

Cooke

1 JEK ,

Coltman

1 DW ,

Sperling

1 FAH

3

of British Columbia, University of Northern British Columbia

Background

Implications

The mountain pine beetle (Dendroctonus ponderosae – MPB) is an irruptive forest pest of high ecological and economic importance throughout much of western North America. MPB’s have a well documented history of recurrent population irruptions that are capable of decimating millions of hectares of pine forest (Pinus spp.)1. This destruction results from two sources: 1) MPBs construct breeding galleries within which larvae feed on the phloem layers 1, 2) the transmission of saproprobic and pathogenic blue-stain fungi 2. In recent years the MPB has increased its outbreak frequency, range and numbers to an unprecedented extent3. While the causes of these increases remain uncertain, they may be the result of altered forest management regimes and accelerated global climate change, thereby providing a unique opportunity to directly study a non-model pest species at the forefront of its range and host expansion.

Forest management: • If MPB becomes better adapted to attack “healthy” trees, as outlying loci associated with detoxification suggest, forest management may have to be altered. • If MPB continues to spread north and east in lodgepole pine, new areas of forest will need to implement management plans. • If MPB takes hold in jack pine, these forests will require management plans, as will provinces further east. Our results, coupled with data from Cullingham et al. 20118 suggest this transition is occurring. Implications for economic policy: • Alterations to current forest management practices have considerable economic implications. • Sustainability of both lodgepole and jack pine forest need to be considered as MPB range expands, this has economic implications for forestry – plant more trees/alter management vs. accept losses. Implications for ecosystem dynamics: • The impacts on stand succession and regeneration from large-scale MPB induced forest destruction are unclear. If large areas of jack pine are killed, then we may see shifts in forest succession to more aspen and white pine dominated forests in the east. This in turn has implications for boreal bird communities, insect communities etc. • MPB is not always successful in colonizing a host, but it may significantly weaken the host’s defences which may contribute to an increase in other insect or fungal attacks.

Examples of graphical outputs from BayesFST (left) and LOSITAN (right) outlier detection tests

The importance of outlying loci Typically, the loci detected as being potential subjects of adaptive selection are considered outliers on the basis that they exhibit higher genetic differentiation than expected under a neutral hypothesis. This is the underlying assumption of the programs LOSITAN4, BayeScan5 and BayesFST6. An alternative approach is to establish allele distribution models for each locus in order to directly correlate allelic frequency with the variation observed in explanatory variables such as temperature. This is the methodological basis of MatSAM7. Loci identified by these programs can then be BLAST searched to provide a direct estimate of the associated gene function. From here, inferences are made relating to the basis and effects of directional or balancing selection on these genes. A total of 379 unique SNPs (24.6%) have been identified as potentially informative signatures of adaptive selection. Summary of results from outlier detection programs

Left: Lindgren funnel trap with pheromone lure; Top right: SEM image of a mountain pine beetle; Bottom right: actual size of mountain pine beetle

Applying genomics to ecological and population genetics questions 1. Are adaptive selection signatures detectable in MPBs in response to changes in environment, host and/or altered population dynamics? 2. Can signatures detected in MPB be integrated with comparable pine and fungal signatures to explain adaptive processes within the mountain pine beetle system across the landscape? To answer these questions we adopted a genome-wide SNPs approach. Using Illumina sequences from eight separate pooled populations across the Canadian (and one South Dakota) range of MPB, and a draft MPB genome as a reference, we detected SNPs across the genome. SNPs were compared to a cDNA library of candidate genes in order to reduce and focus the number of SNPs utilized. A total of 1536 SNPs were GoldenGate genotyped across 537 MPB samples that represented historical and expansion forefront populations throughout Canada. SNPs were assessed such that high quality SNPs were utilized in the adaptive selection detection processes that identify outliers.

Loci per method Highly significant Repeated detection across populations Repeated detection in genetic methods Repeated detection across all methods

LOSITAN

BayeScan

BayesFST

MatSAM

144 2

334 2

85 8

8 6

4

3

5

7

17 (1.1 %) 6 (0.4 %)

Outlier detection methods LOSITAN – infinite island model, simulates neutral FST distributions conditional on heterozygosity using frequentist-like tests to infer outlying loci and selection. Confidence interval = 99.5%. BayesFST & BayeScan – island model of migration, uses a hierarchical Bayesian model to decompose FST values into locus and population-specific components shared by all populations; selection is assumed when locus-specific component is required to explain the pattern. Confidence intervals = 95%. MatSAM - uses univariate logistic regression to find correlations between individual allele frequencies and environmental variation. Confidence interval = 99%.

Genetic function of significant outliers A total six loci were consistently detected across all methods and could be directly correlated with environmental variables. Several of these loci represent genes with various functions, for example, ion transport and cardiac regulation. These loci have strong correlations with winter temperatures and summer precipitation, suggesting that the MPB is adapting to harsher winter temperatures and physiological changes within host trees that are induced by summer drought conditions.

Examples of high (left) and low quality (right) SNP scores

Heat maps illustrating the direction of change in allelic frequency for biallelic SNPs identified as outlying loci across western Canada

Some loci have not been associated with a particular gene based on BLAST searches against Tribolium castaneum and other organisms however, these loci may represent novel genes with an important role in MPB.

References Safranyik, L., Wilson B (2006). The mountain pine beetle : a synthesis of biology, management, and impacts on lodgepole pine. L. Safranyik, Wilson B. Victoria, British Columbia, Natural Resources Canada: 1-317. 2 Solheim, H. (1995). "Early stages of blue-stain fungus invasion of lodgepole pine sapwood following mountain pine beetle attack." Canadian Journal of Botany, 73: 70-74. 3 Samarasekera GDN, Bartell NV, Lindgren BS, Cooke JEK, Davis CS, James PMA, Coltman DW, Mock KE, Murray BW (2012). Spatial genetic structure of the mountain pine beetle (Dendroctonus ponderosae) outbreak in western Canada: Historical patterns and contemporary dispersal. Molecular Ecology, 21: 2931-2948. 4 Antao T, Lopes A, Lopes RJ, Beja-Pereira A, Luikart G (2008) LOSITAN: A workbench to detect molecular adaptation based on a Fst-outlier method. BMC Bioinformatics 9, 323. 5 Foll M, Gaggiotti OE (2008). A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics, 180: 977993. 6 Beaumont MA, Balding DJ (2004). Identifying adaptive genetic divergence among populations from genome scans. Molecular Ecology, 13: 969-980. 7 Joost S, Bonin A, Bruford MW, Despres L, Conord C, Erhardt G, Taberlet P (2007). A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Molecular Ecology, 16: 3955-3969. 8 Cullingham CI, Cooke JEK, Dang S, Davis CS, Cooke BJ, Coltman DW (2011). Mountain pine beetle host-range expansion threatens boreal forest. Molecular Ecology, 20: 2157-2171. 1

Poster using%20genomics  

https://foothillsri.ca/sites/default/files/Poster-Using%20genomics.pdf

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