Landscape genomics in sugar pines (Pinus lambertiana)

Exploring patterns of adaptive genetic variation along environmental gradients.

Carl Vangestel

Why associations with measures of aridity?

• Drought stress common cause mortality and annual yield loss

• Shortage of water is one of the strongest environmental constraints and abiotic selective forces in trees

• Geography directly affect water availability → clinal variation in adaptive traits

Spatial Genomics

Why associations with measures of aridity?

• Future climate change

→ affect local abiotic conditions and distribution of trees

→ higher temperatures and increased variability in

precipitation SW US

→ increase in frequency and intensity of drought

Spatial Genomics

Why sugar pine?

• Sugar pines are less tolerant to drought stress than other conifer species

→ expected to show strong clinal patterns in adaptive genetic variation along aridity gradient → very sensitive to future climate changes: alterations in current distribution range

• One of the most diverse genomes among conifers→ average heterozygosity of specific genes was 26 percent (upper range of pines studied so far)

Spatial Genomics

Climate Change

current 2030 2060 2090

(Source: USDA Forest Service, RMRS, Moscow Forestry Science Labaratory)

- Different scenarios- Hadley Climate Scenario

Spatial Genomics

Goal of this study:

• identify adaptive SNP’s associated with variation in temperature, precipitation, aridity index (precipitation/potential evapo-transpiration), elevation

• functionally annotate these genes • explore both neutral and adaptive variation across the sugar pine’s

range

Spatial Genomics

How adaptive variation is distributed over the range of environments is largely unknown

Detailed knowledge on adaptive variation may become crucial to mitigate impact global climate change

N= 338 individualsSpatial Genomics

• Transcriptome assembly: Sanger, 454 (pool) and Illumina (3 ind)

• Candidate SNPs selection Literature SNP Quality

• MYB proteins (stomatal closure, etc ...)

• heat shock proteins (prevention of protein denaturation during cellular dehydration)

• Trehalose-6-phosphate synthase (osmotic protection cell membranes during dehydration)

• LEA proteins (membrane and protein stabilisers, etc ...)• ...

• First screening: 67 genes selected • Second screening: 109 under review

Spatial Genomics

Generalized linear models

Fst Outlier Analysis

Bayesian Environmental

analyses

Spatial Genomics

Multi-analytical approach

Neutral SNPSpatial Genomics

Gene Flow (IBD) Genetic Drift

Neutral SNPSpatial Genomics

• “Separate” neutral patterns from selective ones

• Explore adaptive patterns while accounting for neutral population structure

‘Neutral SNP’ ‘Adaptive SNP’

Spatial Genomics

ENVi = Environmental value for tree i q1i .. q12n: first n principal components of Q-matrix for tree i

Generalized linear models

For each SNP j:

Spatial Genomics

iiiij

ij qqENV 1212110int ...1

log

Fst Outlier AnalysisArlequin

Spatial Genomics

FDR=0.2 FDR=0.05 FDR=0.001

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0.0

0.2

0.4

0.6

0.8

Alpha10 posterior distribution

Alpha10

Density

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

0.0

0.2

0.4

0.6

0.8

1.0

Alpha11 posterior distribution

Alpha11

Density

0 1 2 3 4

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Alpha66 posterior distribution

Alpha66

Density

Fst Outlier Analysis

SNP10[0.68,2.35]

SNP11: [0.92,2.52]

SNP66: [0.00,2.20]

HPDI

BayeScan

0 -1 -2 -3 -4

0.01

0.02

0.03

0.04

0.05

log10(q value)

fst

10

11

66

0 -1 -2 -3 -4

0.01

0.02

0.03

0.04

0.05

log10(q value)

fst

9

10

11

66

0 -1 -2 -3 -4

0.01

0.02

0.03

0.04

0.05

log10(q value)

fst

10

11

Spatial Genomics

ε 𝑙

𝑥 𝑙1 𝑥 𝑙4 𝑥 𝑙5𝑥 𝑙3𝑥 𝑙2

fancestral

Drift: fpopulation deviate

Gene flow: deviations covary

Spatial Genomics

𝑔 (θ 𝑙1) 𝑔 (θ 𝑙5)𝑔 (θ 𝑙3)𝑔 (θ 𝑙2) 𝑔 (θ 𝑙4)

Transformed variable )

Bayesian Environmental Analysis

(Coop et al., 2010)

Structure

Spatial Genomics

Heat map of var-cov matrix

[ 1 ρρ 1

ρ ² ρ ³ρ ρ ²

ρ4ρ ³

ρ ² ρρ ³ ρ ²

1 ρρ 1

ρ ²ρ

ρ4 ρ ³ ρ ² ρ 1]

← p

op1

← p

op2

← p

op3

← p

op4

← p

op5

← pop1

← pop2

← pop3

← pop4

← pop5

Ω =

Bayesian Environmental Analysis

Bayesian Environmental Analysis

• Selected 1 SNP per gene for var-cov matrix (excluded putative selective genes)

Correlation matrix BayEnv Pairwise Fst matrix

Spatial Genomics

• Formulate null model: drift/gene flow

• Alternative model: drift/gene flow + selection

Spatial Genomics

Null model: P(θl|Ω, εl) ~ N(εl, εl(1- εl) Ω)

Alternative model: P(θl|Ω, εl, β) ~ N(εl + βY, εl(1- εl) Ω)

• Bayes Factor: ratio of posterior probability under alternative to the one under null

• High BF indicative for SELECTION

Bayesian Environmental Analysis

Bayesian Environmental AnalysisSpatial Genomics