patterns of divergent selection from combined dna barcode and phenotypic data tim barraclough,...
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![Page 1: Patterns of divergent selection from combined DNA barcode and phenotypic data Tim Barraclough, Imperial College London](https://reader036.vdocuments.site/reader036/viewer/2022081519/56649f165503460f94c2cea5/html5/thumbnails/1.jpg)
Patterns of divergent selection from combined DNA barcode and phenotypic data
Tim Barraclough, Imperial College London
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Goals:
1) Delimit evolutionary species
independent arenas for selection and drift
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Goals:
1) Delimit evolutionary species
independent arenas for selection and drift
2) Identify the processes generating diversity
separate demography
reproductive isolation
divergent selection
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DNA barcodes
DNA sequence data sampled at the individual level across an entire clade
• sample certain no. individuals per taxonomic species
• environmental samples irrespective of known species
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DNA barcodes
DNA sequence data sampled at the individual level across an entire clade
• sample certain no. individuals per taxonomic species
• environmental samples irrespective of known species
new resource linking macro- and population genetic questions
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DNA barcodes: limitations
• How to detect evolutionary species?
Rely on traditional species
Phenetic approaches, e.g. 2% sequence divergence.
Population models => prior guess on minimum units (c.f. bacteria)
=> multilocus, => parameter-rich
Sampling?
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DNA barcodes: limitations
• Single marker
1) Species tree versus gene treemultilocus approaches
2) Arbitrary or neutral markers
No information on adaptive variationniche traits, those involved in R.I.
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Goals:
1) Delimit independently evolving species
2) Identify the processes generating diversity
divergent selection
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1) Delimit independently evolving species
Prediction: genetic clusters separated by longer internal branches
Conservative - missrecent
Assumptions
Just uses DNA variation
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Statistical approach
Null model:Entire sample derives from single species, i.e. no independently evolving subsets of individuals
Single coalescent process
Likelihood of waiting times
L(x i) (ni(ni 1))pe (ni(ni1))p x i
1
2N e
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Statistical approach
Null model:Entire sample derives from single species, i.e. no independently evolving subsets of individuals
Single coalescent process
L(x i) (ni(ni 1))pe (ni(ni1))p x i
1
2N e
scaling parameter
p<1 excess of old branching events, p>1 excess of recent
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Statistical approach
Alternative model: separate independently evolving species
Within species branches coalescence
Between species speciation, extinction
Between species branchingWithin species branching
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Alternative model: separate populations
L(xi )bebxi
b* k1
(ni,k1
)pk1 ( j (n i,j (n i,j 1))
pj )
j1, k
1. Label which branches are within v. between species
2. Set of independent coalescent processes in each species
3. Generalized Yule model for between species branchingp=1 constant speciation rate modelp>1 increasing speciation rate, background extinctionp<1 slowdown, incomplete sample of species
(Mixed Coalescent Yule model, Pons et al. 2006. Syst Biol. 55:559-609)
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Alternative model: separate populations
Implementation
Optimize which nodes define separate species, e.g. sliding threshold or more complex
Confidence intervals on delimitation
Hypothesis testing
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Alternative model: separate populations
Example
Tiger beetles fromAustralian salt lakes
468 individuals
48 +2/-4 genetic clusters
Fitted parameters:Growing populationsor selective sweeps
Pons et al. 2006. Syst Biol. 55:559-609)
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Alternative model: separate populations
Limitations
Current implementation uses sliding threshold
Identical individuals
Sampling
Not exact (but generalized)
Conservative, but could focus e.g. multi-locus
Correcting for mtDNA rate variation
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Goals:
1) Delimit independently evolving species
2) Identify the processes generating diversity
divergent selection
Focus on a trait or traitsecomorphologyreproductive morphologybehaviour, defensive chemicals
etc.
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Divergent selection
Character under divergent selection displays greater ratio of
inter-group variationinter-group variation
Than neutrally evolving characters
Can compare variation in morphological traits to variation of arbitrary DNA markers
Qst-Fst Fontaneto et al. 2007. PLoS Biology 5:e87
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Divergent selection
Compared to clusters identified from mtDNA
1) Coincident with clusters
2) Acting at broader level (uniform selection across entire clade or sub-clades)
3) Acting within clusters - recently formed species or adaptive polymorphism
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Divergent selection
Example: divergent selection on feeding morphology in bdelloid rotifers
Fontaneto et al. 2007. PLoS Biology 5:e87
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Significant pattern of clustering
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Significant pattern of clustering
ML solution: 13 clustersCOI Pairwise within = 1.5%COI Pairwise between = 16%
H0:H1, p<0.0001
Some traditional species contain several clusters
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TraditionalSpecies are morphologicalclusters
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Mapped rate of evolution of trophi size and shape relative to silent mtDNA change
Null model: one rate across DNA tree
Alternatives: 1) Within versus between species2) Within versus between clusters3) Three rates
Schluter et al. 1997.
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Results
Significant evidence for divergent selection on trophi size and shape between taxonomic species, not clusters.
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Sexual organisms?
Assumptions
Assume additive genetic variation environmental variation might inflate intra
Limited to measurable traits, measurement error will inflate intra
Prospects?
DNA barcode data as framework to explore selection on morphological traits of voucher specimens