Transcript Adaptationism and the Adaptive Landscape

Adaptationism and the
Adaptive Landscape
Genomic imprinting, mathematical
models, and notions of optimality
in evolution
Overview
• Adaptationism
• Zoom and Grain in the adaptive
landscape
• Mathematical models of genomic
imprinting
Adaptationism
• Primary role for natural selection in evolution
– versus drift, historical and developmental constraints,
etc.
• Modern debate framed by the Sociobiology wars
(Wilson, Dawkins, Lewontin, Gould, etc.)
• Continuation with Evolutionary Psychology, but
• Partial reconciliation in most fields
– Tests of selection, contemporary systematics
Types of adaptationism
• Empirical
– Central causal role for selection
• Explanatory
– Selection answers the big questions
• Methodological
– Selection is a good organizing concept
– Godfrey-Smith (2001)
The Adaptive Landscape
• Natural selection is conceived of as a hillclimbing algorithm
Caveats
• Units (genotype vs.
phenotype, population vs.
individual fitness)
• High dimensionality
• Topology of the landscape
• Dependence on other
organisms
• Hill-climbing metaphor
implies a deterministic
process
Zoom level 1
• High level analyses invoke rugged landscapes, which
emphasize the role of historical contingency
Zoom level 2
• Intermediate levels of analysis focus on local regions with a
small number of peaks, emphasizing optimization
Zoom level 3
• Low-level analyses reveal the discontinuities in the fitness
landscape, emphasizing drift, recombination, etc.
Zoom level 3
• Low-level analyses reveal the discontinuities in the fitness
landscape, emphasizing drift, recombination, etc.
Sickle-cell anemia
• HbA / HbA
Resistant
parents
– Susceptible
• HbA / HbS
– Resistant
• HbS / HbS
– Sickle-cell
Susceptible
Resistant
Sickle-cell
Population-genetic timescale
HbA / HbS
HbA / HbA
HbS / HbS
~100 generations
•
Mendelian segregation recreates sub-optimal phenotypes every
generation
Mutation timescale
HbA + HbS
HbA
~104 generations
•
The mutation giving rise to the HbS allele represents a partial
adaptation to malaria
Chromosomal rearrangement
timescale
HbAS
HbA + HbS
HbA
~108 generations
•
A (hypothetical) rearrangement could give rise to a single chromosome
containing both the HbA and HbS alleles. This new allele should
sweep to fixation.
Immune-system evolution
timescale
IgM
IgA
IgG
IgE
HbAS
Ig-
HbA + HbS
HbA
~1010+ generations
•
In principle, we could ask why our immune system is susceptible to
malaria at all.
Genomic Imprinting
• Non-equivalence of
maternal and
paternal genomes
• Normal
development in
mammals requires
both
Genomic Imprinting
• Epigenetic
differences result
in differences in
expression
Oogenesis
gene 1
gene 2
Spermatogenesis
gene 1
• DNA methylation
– reversible
chemical
modification of the
DNA
gene 1
gene 2
gene 1
gene 2
gene 2
Reciprocal heterozygotes are
non-equivalent
≠
Conflict over resources
Inclusive fitness
Asymmetries in relatedness
Maternal
optimum
Paternal
optimum
Fitness increases as
more resources are
acquired for self
Fitness decreases as
cost to siblings
becomes too great
Growth factor expression level
Conflict over resources
Maternal expression
Growth-enhancing locus
Unimprinted
gene
Cis-acting
maternal
modifiers
Maternal optimum
Paternal optimum
Paternal expression
Cis-acting
paternal
modifiers
Conflict over resources
Maternal expression
Growth-suppressing locus
Unimprinted
gene
Cis-acting
maternal
modifiers
Paternal optimum
Maternal optimum
Paternal expression
Cis-acting
paternal
modifiers
Game-theoretic / stability
analysis models of imprinting
•
•
•
•
•
•
•
•
X - expression level
Wm - matrilineal fitness
Wp - patrilineal fitness
U - individual fitness
V - fitness of other offspring 

G - resource demand
C - cost of gene expression 
2p - fraction of mother’s
offspring with the same father 
X  xm  xp
Wm U 1 V G C



G 2 G 
X X
X 
W p U
V G C
   p  
X G
G X X
Wp Wm  1 V G

 p  
X
X  2 G X
Growth enhancer:
V
G
 0,
0
G
X
W p W W m


X
X
X
unimodality Xˆ p  Xˆ m
Population-genetic models
•
Two sibs, paternal imprinting
•
Fitness of unimprinted sibs: 1
– e.g., AA, AA
• A - unimprinted allele
• a - imprintable allele
• a = A when maternally
inherited
• a -> (a) when paternally
inherited
•
•
AA = aA
a(a) = A(a)
•
Fitness if both imprinted: 1+u
– e.g., a(a), A(a)
•
If only one is imprinted:
– e.g., AA & A(a)
– Imprinted fitness: 1-s for A(a)
– Unimprinted fitness: 1+t for AA
Population-genetic models
• Parameters: allele frequencies, fitnesses, frequency
of multiple paternity
– Spencer, Feldman, and Clark 1998 Genetics
Population-genetic models
•
Two sibs, paternal imprinting
•
Fitness of unimprinted sibs: 1
– e.g., AA, AA
• A - unimprinted allele
• a - imprintable allele
• a = A when maternally
inherited
• a -> (a) when paternally
inherited
•
•
AA = aA
a(a) = A(a)
•
Fitness if both imprinted: 1+u
– e.g., a(a), A(a)
•
If only one is imprinted:
– e.g., AA & A(a)
– Imprinted fitness: 1-s for A(a)
– Unimprinted fitness: 1+t for AA
•
Monandrous females:
– a invades A if u > s
– a stable if u > t/2
•
Polyandrous females:
– a invades A if s < 0
– a stable if u > t/2
Predictions and contradictions
• Game-theoretic
• Population-genetic
• Imprinting requires multiple
paternity (p < 1/2)
• Allele favoring lower
expression will be
completely silenced
• Particular combinations of s, t,
and u can produce stable
polymorphisms
• Multiple paternity is not
required
• Maternal silencing for growth
enhancers is more likely, but
paternal silencing can occur
– maternal silencing of
growth enhancers
– paternal silencing of growth
suppressors
Paternally silenced
growth enhancer
Maternal expression
Growth-enhancing locus
Unimprinted
gene
Reduced paternal
expression would
be favored from
these points
Maternal optimum
Paternal optimum
Paternal expression
Cis-acting
maternal
modifiers
Cis-acting
paternal
modifiers
Key assumption
• Game-theoretic models assume that the unimprinted
expression level is at its optimum before the
introduction of an imprinted allele
• Is this assumption a good one?
• Gene expression array analyses of population-level
variation reveal a high level of variation
• This implies a good opportunity for selection to find
the optimum
Separation of timescales in the
evolution of imprinting
Imprinting opens up a new
dimension in strategy space
Unimprinted alleles are
restricted to a subspace in
the fitness landscape
If mutations that quantitatively
change gene expression are
much more common than those
that give rise to imprinting,
imprinting will always arise in
the context of an optimized
expression level
Take-home message
• Choice of a particular modeling framework implies
certain assumptions that can affect your
interpretation of your results
• When smart people doing reasonable things
disagree, there is probably something interesting
going on