Bottom Up and Top Down
Perspectives on the Evolutionary
Process: From Mutations to
Phylogenetic Patterns
Charles B. Fenster
Acknowledgements: NSF, NFR, NGS, UMD, UVA and many colleagues
Four Modes of EVOLUTIONARY PROCESS:
Natural Selection1
Evolution
&
Phenotypic variation
Diversification5
Genetic Architecture
Genetic variation
Mutations2
GENETIC DRIFT3
GENE FLOW4
Population Genetic Structure
Maad, Armbruster
Ecological Context
and Evolutionary Process
Galloway
Dudash, Biere, Castillo, Dotterl, Holland, Kula Reynolds, Zhou
Flower size variation along an
altitudinal gradient (Alpine, Norway)
Erickson
Epistasis for fitness
(Prairie, Illinois)
Quantifying QTL effects
(Prairie, Kansas)
Silene stellata-Hadena ectypa interaction
(mutualism evolution, food web approaches,
sexual conflict)
Huang, Ree, Hereford, Eaton
Rutter, Lenormand, Imbert,
Agren, Weigel, Wright
Marten-Rodriguez
Quantifying Mutations
(Garrangue, France)
Pollination and breeding system evolution
in Gesnerieae (Caribbean)
Reproductive isolation and
community sorting in Tibetan Pedicularis
Outline
1) BOTTOM UP: Input of genetic variation
 Mutation parameters
2) TOP DOWN: Natural selection & species selection
 Quantifying role of natural selection in assembly of
complex traits
 Consequences of trait evolution for phylogenetic
patterns
3) CONSERVATION GENETICS (time permitting)
 Inbreeding, epistasis and outbreeding depression
Quantifying mutation parameters using Arabidopsis thaliana
mutation accumulation lines
Matthew Rutter, Jon Agren, Jeff Conner, Eric Imbert, Thomas Lenormand, Angie Roles,
Detlef Weigel, Stephen Wright & Charles Fenster
Funding by NSF and Max Planck Society
The values of mutation parameters for fitness
determine many evolutionary processes
Parameters: Rate, Effect & Size
• Evolution of Adaptation (Fisher, Kimura, Orr)
Beneficial mutation rate, size of effect (s)
• Evolution of Sex (Muller’s Ratchet)
Number of Asexual individuals without mutations
PROPORTIONAL to: 1/U (deleterious mutation rate); s
•Inbreeding Depression & Mating System Evolution
PROPORTIONAL to: U; 1/s
Mutation Rates at the Following Levels:
Nucleotide or Locus
10-8 - 10-9
10-5 - 10-6
ATGCATGCATGCATCCCAA
G
Whole Genome Sequence Level:
T
U ~ 0.7-2 (haploid)
(e.g. Keightley et al. 2007, Ossowski et al. 2010)
Frequency
Traits (fitness): h2m ~ 10-4 - 10-3 , U ~ 0.05-0.12 (haploid)
After mutation
Before mutation
Trait
Mutations have a distribution of fitness
effects
all/mostly deleterious
-
+
-1
-0.75 -0.5 -0.25
0
0.25 0.5 0.75
1
-
+
-1
-0.75 -0.5 -0.25
0
0.25 0.5 0.75
Selection coefficient
1
Mutation accumulation lines (MA lines) (Produced by Ruth Shaw)
Nearly homozygous
progenitor
Single seed
descent in
greenhouse
Traits (Fitness):
100 MA lines
25thgeneration
Columbia
MA lines
Sequence: 5 MA lines
1 . . .
100
Sublines to control for maternal effects
Test in natural environments:
Any genetic difference between lines are due to mutation
Blandy Farm (UVA) Blue
Ridge of Virginia
Total plants:
48,000
100 lines
X
70/line
X
7 Environments
Total fruits:
> 600,000
Kellog Biological Station
(MSU), southern MI
Fall field planting (2x)
Fall seed field planting VA and MI
Spring field planting (2 x)
Greenhouse
Results:
1. MA lines diverged in fitness
2. Founder performance near average MA performance
Founder
Total fruit produced
= fruit # * survival
14
# of MA lines
12
10
8
6
4
2
0
9
10
11
12
13
Block P<0.0001
MA line P<0.029
Subline P<0.0051
14
15
16
17
18
19
20
21
22
23
24
25
26
Fruit number
MA line vs. Founder P= 0.8650
Rutter et al. 2010
Reaction Norm of Fitness Rank Across Seasons
100
Rank fitness of MA lines
90
80
70
40 MA lines
switch fitness
relative to parent
60
Founder
50
Fitness
40
30
20
10
0
Spring
Fall
Season
Mixed Model Analytical Approach to Quantify
G x E on Fitness
100 MA Lines & Founder Planted in 2 Spring & 2 Fall Experiments as Seedlings
Large Effect of Environmental Variables (Block, Season, Experiment, Year)
MA Line : (100)
MA Line x Experiment (4)
MA Line x Year (2)
MA Line x Season (2)
P = 0.053
P = 0.0006
P = 0.0015
P = 0.022
MA LINE PERFORMANCE SUGGESTS GENE EXPLORATION
Fall Fitness Ranking
100
90
80
70
GXE
Consistent beneficial
60
50 Consistent deleterious
GXE
40
30
20
10
0
0
10
20
30
40
50
60
70
Spring Fitness Ranking
80
90
100
Fitness Mutation Parameters in the FIELD:
(Rutter et al. 2010, 2012 & unpublished)
Whole genome mutation rate for fitness = 0.12 (haploid)
Mutation effects relative to the environment are small:
h2m for fitness ~ 1 x 10-4 (3/4 experiments)
High frequency of beneficial mutations
G X E:
variance G x E (MA line effects in 3/4 experiments)
MA line x Season
MA line x Year
MA line x Experiment
Mutations Contribute Substantially to
Population Genetic Variation of Fitness
Adaptive landscapes & mutation parameters
“The vast majority of mutations are deleterious… [a] wellestablished principle of evolutionary genetics”
Keightley and Lynch, 2003
Fisher, 1930
Beginning of a conceptual framework for the
prediction of mutation effects
NSF Arabidopsis 2010, Rutter and Fenster (with T. Lenormand, E. Imbert & J. Agren)
Ongoing: New MA lines developed from
French and Swedish genotypes
NSF Arabidopsis 2010 (Rutter and Fenster with Lenormand, Imbert & Agren)
Also EMS mutagenesis approaches
(Frank Stearns, graduate student)
“Mutation was the exchange of one kind of beans for
another…Beanbag genetics do not explain the physiological
interaction of genes and the interaction of genotype and
environment…But what precisely has been the contribution of
this mathematical school to evolutionary theory?
Mayr, 1959, 1963
Wright and Andolfatto 2008
Distinguishing between true signatures of adaptive evolution and
alternative non-adaptive models poses a challenge in future
studies
Nei 2013
Bean Bag Genetics: Fisher Wright Haldane have
not explained the evolution of major adaptations
We need a mechanistic understanding of the relationship
between mutations and fitness
Mutations Detected (Ossowski et al. 2010 ): Sequenced 5 MA lines vs. Founder
Dark blue = nonsynonymous or indel in coding region
Total =114 mutations detected
Synthesizing Sequence and Phenotype Results
(Rutter et al., 2012)
• Sequence experiment:
Mutation rate = 0.7/haploid
Nonsynonymous mutations and indels in coding
region = 0.1/haploid
• Field experiment:
0.12/haploid affecting fitness
Mean fruit production of 5 MA lines and the founder
premutation line in 6 natural environments and their
mutational profile
Rutter et al., 2012
Fitnesses were estimated using an aster model including survival (binomial) and fruit number
(Poisson). P-values (* P < 0.05, ** P < 0.01, *** P < 0.001) represent MA-founder comparisons. Pvalues were calculated by likelihood ratio tests, and validated using a parametric bootstrap. Means
in bold represent a significant difference following within experiment sequential Bonferroni
correction (P < 0.05). BEF = Blandy Experimental Farm; KBS = Kellogg Biological Station.
Significant GxE (aster model, P<0.05)
FYI: MA line 49: deletion includes DNA binding transcription factor
MA line 119: large deletion in a gypsy class retrotransposon
Conclusions
• Congruence of estimates for mutation rates for
fitness by the two methods
• Beneficial mutations occur at high frequency
• Initial understanding of relationship of specific
mutations with fitness
Funding from NSF and Max Planck Institute
Current Funding to Fully Sequence
Rutter, Weigel, Wright:
100 Columbia MA lines
320 Swedish and French
MA lines
>50 genotypes representing
one multilocus genotype
Sequence
Fitness
Mutation rates and spectrum and
interface with natural selection
1.
Precise estimates of mutation rate and spectrum
(including genetic variation for mutation rate)
2.
About 6500 natural mutations that can be related to
fitness
3.
Compare spectrum of mutations to standing genetic
variation & to genetic differences between species (e.g.,
trend for genome size reduction)
4.
M vs. G
Natural Selection (top down)
“From the observations of various botanists and my own
I am sure that many other plants offer analogous
adaptations of high perfection…” (Darwin, 1877)
Fenster et al. 2004
22 23 24 25 26 27 28 29 30 31 32 33 34 35
The Adaptive Landscape
Simpson 1944
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Adaptations reflect adaptive trait combinations
-
Does natural selection act on trait combinations?
Documenting Patterns of Natural Selection
Responsible for Silene Floral Evolution
S. caroliniana
S. virginica
S. stellata
M. Dudash, R. Reynolds, A. Kula, S. Konkel, J. Zhou & many NSF REU’s
Funding: NSF, National Geographic Society, UVA Pratt Fund
How to document the pattern of
natural selection on a multivariate
character (the flower)?
• Quantify Phenotypic Selection
• Experimental Manipulation Approaches
• Comparative Approaches
Phenotypic Selection in the Field: Silene virginica
8 year study (1992-95, 2002-06)
Female Reproductive Success
(Total Fruit & Seed)
Attraction
Petal Size (Length x Width)
Display Height
Display Size (# Flowers)
Mechanics of Pollen Deposition
Corolla Tube Length
Stigma Exsertion
Corolla Tube Diameter
Covariates
Flower Number
Various Vegetative Traits
150-300 individuals/year
(Reynolds et al., Evolution 2010)
Mtn. Lake Biol. Station
Phenotypic Selection: Analytical Approach (6 Traits)
Female Reproductive Success
Lande-Arnold (1983), Phillips & Arnold (1989)
Corolla tube length, nectar-stigma distance, corolla tube diameter, petal length, petal width, inflor. Ht.
6
1.
2.
wi / w    f   z ij  j  
6
6 j 1
6
j 1
j 1
j 1 k 1
6
1st Approach
wi / w    f   zij  j  1 / 2 zij2 j    jk z j z k ( j  k )  
3.
w      z '   z ' z  
MM '  
4.
5.
6.
y  zM '
2nd Approach
(Canonical Analysis)
w    y '  y ' y
(Reynolds et al., Evolution 2010)
Experimental Approach:
3 Array Design
4
2
1
*Trial = approx. 25 plant visits in a block or flowers were empty of nectar
*30 minutes - 90 minutes (four observers)
*Total of 28 Trials run
2072 Plant visits
Response Variables:
# of visits per
plant
= proxy of fitness
S. virginica:
Red, Tall, Diffuse, Horizontal, Narrow
S. caroliniana:
Pink, Short, Clump, Vertical, Narrow
S. stellata:
White, Tall, Clump, Horizontal, Wide
+ 45 other combinations
Model Selection Approach:
Best-subsets regression analysis of main and interactive effects
Response variable:
Plant visits by hummingbirds
Intercept
FLHT
FC2
PRES
IA
TW.FLHT
FLHT.FC2
FLHT.PRES
FLHT.IA
FC2.PRES
FC1.IA
FC2.IA
FLHT.FC1.IA
FLHT.FC2.IA
FLHT.FC2.PRES.IA
PRES.IA.FLHT.TW.FC1
1
X
2
X
X
AIC score comparisons
Number of Variables in Best Model
3
4
5
6
7
8
9
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
10
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Fenster, Reynolds, Markowski, & Dudash in prep
Does selection act on trait combinations?
Yes
Contemporary selection on S. virginica is correlational
(Reynolds et al. Evolution 2010)
And Yes
Experimental manipulation of floral traits demonstrate
hummingbirds visit based on floral trait combinations
Can we use the phylogeny of the
angiosperm to document multitrait
selection?
NESCent Working Group:
“Floral Assembly: Quantifying the composition of a complex adaptation”
Charlie Fenster (PI), Pam Diggle (coPI) Scott Armbruster (coPI) , Pam Diggle,
Lawrence Harder, Stephen Smith, Amy
2 Litt, Lena Heilman, Chris Hardy,
Peter Stevens, Larry Hufford, Susanna Magallon
AND….
Brian O’Meara
Stacey Dewitt Smith
The Angiosperm Flower is Highly Labile:
Convergence through multiple developmental origins
Attractive Features in the Core Caryophyllales
Sepals
Stamens
Leaves
Stamens
Sepals
Sepals
Sepals
Sepals, Bracts
Stamens
Sepals
Sepals
Sepals, bracts
Brockington et al., 2009
Intl J Plt Sci.
Stebbins 1951 in a nutshell:
“A flower is … a harmonious unit.”
For 8 floral traits examined two states.
Expect 28 different combinations found in angiosperms.
But observed only 86/256 possible combinations & 200 of the
400 families represented 12 different combinations!
Uneven distribution.
“The characteristic [combinations] of many genera and
families [represent] peaks.”
-
Natural Selection:
Is there a bias in trait transitions?
Species Selection:
Do some combinations lead to greater net
diversification than others?
Binary State Dependent Speciation
& Extinction (Markov Models)
Maddison et al. 2007
q01
r0
1
0
r1
q10
Ancestral (root)
Derived
 two states are 0 and 1
 r is the diversification rate for each (speciation minus extinction)
 q01 and q10 = transition rates between character states
Extending BiSSE to Trait Combinations:
Six Major Angiosperm Traits Scored
(26 combinations = 64 combos)
Ancestral Trait State
Derived Trait State
Combo Score
for Derived
Trait State
Corolla present
Corolla absent
1*****
Petals separate
Petals fused
*1****
Symmetry radial
Symmetry bilateral
**1***
Stamens many
Stamens few
***1**
Carpels separate
Carpels fused
****1*
Ovary superior
Ovary inferior
*****1
e.g.: Corolla present, Symmetry bilateral, Stamens few: 0*11**
Tree construction methods and
character mapping:
Generated branch lengths by randomly sampling 500 species from
GenBank based on clade size
1.7 megabases of sequence data (7 genes)
Supermatrix constructed with PHLAWD
RAxML
Constrained tree (APG and group knowledge)
40 fossils for calibration points
Determined trait states and trait state combinations
Mapped character states
Binary State Dependent Speciation
& Extinction (Markov Models)
Maddison et al. 2007
q01
r0
1
0
r1
q10
Ancestral (root)
Derived
Series of bipartitions for 6 traits each with 2 character states:
For any character the state could be: 0, 1, or *
36 bipartitions x 5 rate models x 6 transition models
Focal Groups and Bi-partitions
(developed by Brian O’Meara)
Corolla present, bilateral symmetry, few stamens
combined with 23 = 8 other character states
Phenotypic Space
Bi-partitioning Phenotypic Space
Transition Rate models for Focal and Non-Focal States
K is the number of free parameters in the model
Model
K
qNF
1.
Equal
1
2.
Inflow unique
2
3.
Outflow unique
2
4.
Inflow and
outflow
different
3
qNF
5.
Free
4
qNF
qNN
qFF
qFN
qNF = qNN = qFF = qFN
qNF
qNN = qFF = qFN
qNF = qNN = qFF
qFN
qNN = qFF
qNN
qFF
qFN
qFN
Each model contains up to four transition rates (qNF, qNN, qFF, qFN),
where “N” denotes the non-focal state and “F” the focal state. The rate
qNF is thus the rate of transitions from the non-focal to the focal state.
Diversification rate models for focal and non-focal states
K is the number of free parameters (rates) in the model
Each model contains up to four rates (λF, λN, μF, μN) where λ is the
speciation rate (units?), μ is the extinction rate (units?) and “N” and “F”
denote the non-focal and focal states, respectively.
36 bipartitions x 5 rate models x 6 transition models =
729 bipartitions x 5 rate models x 6 transition models =
= 19, 567 unique models
Models were ranked with AIC
Frequency of Trait Combination
in Sample of Angiosperm
Character evolution & diversification
across the Angiosperms
Trait combination
space
0*11**
High Diversification
Focal state:
Corolla present
Symmetry bilateral
Stamens few
Null Distribution
Observed Trait Combinations & Unordered Null Distribution
O’Meara, Smith et al.
Top ten models (ranked by AIC weight) from maximum
likelihood focal combination analysis.
AIC
weight
Cumulative
AIC weight
Focal combination
0.274
0.274
0x11xx = Corolla present, Symmetry bilateral, Stamens few
0.245
0.519
xx1xxx =
Symmetry bilateral
0.202
0.721
xx1xxx =
Symmetry bilateral
0.128
0.849
xx11xx =
Symmetry bilateral, Stamens few
0.090
0.939
0.024
Effect of Trait(s) on
Diversification & Transition Rates
(R>B, G=Equal)
A
D
A
D
A
D
A
D
0x1xxx = Corolla present, Symmetry bilateral
A
D
0.963
xx1xxx =
A
D
0.014
0.977
0x11xx = Corolla present, Symmetry bilateral, Stamens few
A
D
0.009
0.985
xx1xxx =
Symmetry bilateral
A
D
0.003
0.988
0x1xxx = Corolla present, Symmetry bilateral
A
D
0.003
0.991
0x11xx = Corolla present, Symmetry bilateral, Stamens few
A
D
Symmetry bilateral
… approximately 19,500 models evaluated
No effect: petals separate/fused, carpels separate/fused, ovary superior/inferior
Corolla Present, Bilateral Symmetry Stamens:
Influence Diversification and Transition Rates O’Meara, Smith et al.
Simulated time to first appearance of each
combination of the three characters.
O’Meara, Smith et al.
Tall bars and short bars indicate the median and 95%
confidence interval, respectively, based on 50 simulations.
Stochastic simulations of angiosperm evolution using four
different models for two character combinations:
M. grandiflora
Contemporary
Frequency
Contemporary
Frequency
A. sesquipedale
O’Meara, Smith et al.
Net Diversification
(Speciation – Extinction)
Conclusions
BiSSE (O’Meara et al. ):
time
Flower Symmetry:
Radial
Radial
Bilateral
Stamen Number:
Many
Few
Few
Character Combinations
 Trait combinations are important
 Selection within species assembles the traits but this is rate-limiting
 Selection among species based on trait combinations generates
angiosperm diversity
O’Meara, Smith et al.
Synthesis
Input of genetic variation
- maintenance of genetic variation
- future insight on adaptive walks via mutation
 Elegance of natural selection in its ability to
pick out trait combinations
 Multi-trait evolution has consequences for
diversification and species selection
~ 250 endemic Pedicularis spp.
in Hengduan Mountains, Tibet
Eaton et al. 2012
Inbreeding increases with habitat
fragmentation & isolation:
inbreeding vs outbreeding depression
shawneeaudobon.org
Ohiodnr.com
Prairie Chicken
Lakeside Daisy
Should we be concerned
about outbreeding depression?
Florida panther
floridapanther.com
Quantitative Genetic Model to Measure Epistasis:
F2 or F3 <,=,> (Midparents +F1)/2
Parental and F1 performance reveals adaptation and heterosis/inbreeding
Galloway and Fenster 1999, 2000, Fenster and Galloway, 2000a, Erickson and Fenster 2006
Home - Away Parent
LOCAL ADAPTATION
1.5
* *
1
0.5
F1 - Home Parent
F1 HETEROSIS
MD
0
-0.5
Series1
-1
*
-1.5
-2
-2.5
2
F3 - Home Parent
OUTBREEDING
DEPRESSION
Relative Performance
Local Adaptation, F1 Heterosis, & F3 Outbreeding Depression in
Chamaecrista fasciculata: Consequences for Conservation Genetics
1.5
*
1
* *
* *
*
*
Series1
MD
* P<0.05
0.5
0
0.2
0
-0.2
KS
-0.4
Series1
*
-0.6
-0.8
*
-1
0.1 1
10 100 1000 2000 Km
Transplant (Parents) Distance or Crossing Distance (F1 and F3)
Predicting the Probability of Outbreeding Depression
(Frankham et al. 2011 Conservation Biology)
Genetic management following fragmentation:
Inbreeding:
genetic erosion of fitness
population extirpation
Genetic Rescue:
X
heterozygosity restored
population persistence
Outbreeding Depression
Genetic Rescue Decision Tree:
1. Similar karyotype
2. Recent history of fragmentation (post 1492)
3. No evidence of local adaptation ~ similar habitat
YES, cross populations!
Black-footed Rock Wallaby Recovery Program
Mark Eldridge, Australian Museum
“To be too similar is bad, to be different is good”
“All are recently related”
Translated from Pitjinjara
Implications of Different Species
Concepts for Genetic Rescue
Reviewed the many species concepts:
Phylogenetic Species Concept can result in over
delimitation preventing genetic rescue, e.g., mtDNA
Biological Species Concept most suitable for promoting
preservation of biodiversity
(Frankham et al. 2012 Biological Conservation)
Future Directions
Conservation & Restoration arena:
• Conservations Genetics textbook on Genetic Rescue
• Primer for land managers on Genetic Rescue
• Quantify the effects of breeding schemes on
inbreeding and performance
• Quantify the consequences of adapting to a captive
environment