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Tim Barraclough_ Imperial College London_ Ascot_ United Kingdom

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Tim Barraclough_ Imperial College London_ Ascot_ United Kingdom Powered By Docstoc
					Patterns of divergent selection from combined
DNA barcode and phenotypic data




Tim Barraclough, Imperial College London
Goals:

1) Delimit evolutionary species

     independent arenas for selection and drift
Goals:

1) Delimit evolutionary species

     independent arenas for selection and drift


2) Identify the processes generating diversity

     separate demography

     reproductive isolation

     divergent selection
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
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
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?
DNA barcodes: limitations

•   Single marker

    1) Species tree versus gene tree
           multilocus approaches

    2) Arbitrary or neutral markers


      No information on adaptive variation
      niche traits, those involved in R.I.
Goals:

1) Delimit independently evolving species


2) Identify the processes generating diversity

           divergent selection
1) Delimit independently evolving species

Prediction:
genetic clusters
separated by longer
internal branches

Conservative - miss
recent

Assumptions

Just uses DNA variation
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
Statistical approach

Null model:
Entire sample derives from
single species, i.e. no
independently evolving
subsets of individuals

Single coalescent process




        scaling parameter

 p<1 excess of old branching events, p>1 excess of recent
                                                     Within species
Statistical approach       Between species branching branching


Alternative model:
separate
independently
evolving species

Within species branches
Þ coalescence

Between species
Þ speciation, extinction
Alternative model: separate populations




•    Label which branches are within v. between species

•    Set of independent coalescent processes in each species

•    Generalized Yule model for between species branching
                 p=1 constant speciation rate model
                 p>1 increasing speciation rate, background extinction
                 p<1 slowdown, incomplete sample of species


    (Mixed Coalescent Yule model, Pons et al. 2006. Syst Biol. 55:559-609)
Alternative model: separate populations

Implementation

Optimize which nodes define
separate species, e.g. sliding
threshold or more complex

Confidence intervals on
delimitation

Hypothesis testing
Alternative model: separate populations
Example

Tiger beetles from
Australian salt lakes

468 individuals

48 +2/-4 genetic clusters

Fitted parameters:
Growing populations
or selective sweeps

Pons et al. 2006.
Syst Biol. 55:559-609)
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
Goals:

1) Delimit independently evolving species


2) Identify the processes generating diversity

           divergent selection

Focus on a trait or traits
           ecomorphology
           reproductive morphology
           behaviour, defensive chemicals etc.
Divergent selection

Character under divergent selection displays
  greater ratio of

          inter-group variation
          inter-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
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
Divergent selection

Example: divergent selection on feeding
  morphology in bdelloid rotifers




                          Fontaneto et al. 2007.
                          PLoS Biology 5:e87
Significant pattern of clustering
Significant pattern of clustering

ML solution:
13 clusters
COI Pairwise within = 1.5%
COI Pairwise between = 16%

H0:H1, p<0.0001

Some traditional species
contain several clusters
Traditional
Species are
morphological
clusters
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 species
     2) Within versus between clusters
     3) Three rates

Schluter et al. 1997.
Results
Significant evidence for divergent selection on trophi size
and shape between taxonomic species, not clusters.
Sexual organisms?

Assumptions

Assume additive genetic variation
Þ 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

				
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posted:5/19/2014
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