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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.

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Adaptationism and the Adaptive Landscape

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  1. Adaptationism and the Adaptive Landscape Genomic imprinting, mathematical models, and notions of optimality in evolution

  2. Overview • Adaptationism • Zoom and Grain in the adaptive landscape • Mathematical models of genomic imprinting

  3. 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

  4. 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)

  5. The Adaptive Landscape • Natural selection is conceived of as a hill-climbing algorithm

  6. 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

  7. Zoom level 1 • High level analyses invoke rugged landscapes, which emphasize the role of historical contingency

  8. Zoom level 2 • Intermediate levels of analysis focus on local regions with a small number of peaks, emphasizing optimization

  9. Zoom level 3 • Low-level analyses reveal the discontinuities in the fitness landscape, emphasizing drift, recombination, etc.

  10. Zoom level 3 • Low-level analyses reveal the discontinuities in the fitness landscape, emphasizing drift, recombination, etc.

  11. Sickle-cell anemia • HbA / HbA • Susceptible • HbA / HbS • Resistant • HbS / HbS • Sickle-cell Resistant parents Susceptible Resistant Sickle-cell

  12. Population-genetic timescale • Mendelian segregation recreates sub-optimal phenotypes every generation HbA / HbS HbA / HbA HbS / HbS ~100 generations

  13. Mutation timescale • The mutation giving rise to the HbS allele represents a partial adaptation to malaria HbA + HbS HbA ~104 generations

  14. HbAS HbA + HbS HbA Chromosomal rearrangement timescale • A (hypothetical) rearrangement could give rise to a single chromosome containing both the HbA and HbS alleles. This new allele should sweep to fixation. ~108 generations

  15. Immune-system evolution timescale • In principle, we could ask why our immune system is susceptible to malaria at all. IgM IgA IgG IgE HbAS Ig- HbA + HbS HbA ~1010+ generations

  16. Genomic Imprinting • Non-equivalence of maternal and paternal genomes • Normal development in mammals requires both

  17. gene 1 gene 1 gene 2 gene 2 gene 1 gene 1 gene 2 gene 2 Genomic Imprinting Oogenesis Spermatogenesis • Epigenetic differences result in differences in expression • DNA methylation • reversible chemical modification of the DNA

  18. Reciprocal heterozygotes are non-equivalent ≠

  19. Conflict over resources

  20. Maternal optimum Paternal optimum Fitness increases as more resources are acquired for self Inclusive fitness Fitness decreases as cost to siblings becomes too great Growth factor expression level Asymmetries in relatedness

  21. Conflict over resources Growth-enhancing locus Unimprinted gene Cis-acting maternal modifiers Maternal expression Maternal optimum Cis-acting paternal modifiers Paternal optimum Paternal expression

  22. Conflict over resources Growth-suppressing locus Unimprinted gene Cis-acting maternal modifiers Maternal expression Paternal optimum Cis-acting paternal modifiers Maternal optimum Paternal expression

  23. 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 Growth enhancer:

  24. Population-genetic models • Two sibs, paternal imprinting • A - unimprinted allele • a - imprintable allele • a = A when maternally inherited • a -> (a) when paternally inherited • AA = aA • a(a) = A(a) • Fitness of unimprinted sibs: 1 • e.g., AA, AA • 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-sfor A(a) • Unimprinted fitness: 1+tfor AA

  25. Population-genetic models • Parameters: allele frequencies, fitnesses, frequency of multiple paternity • Spencer, Feldman, and Clark 1998 Genetics

  26. Population-genetic models • Two sibs, paternal imprinting • A - unimprinted allele • a - imprintable allele • a = A when maternally inherited • a -> (a) when paternally inherited • AA = aA • a(a) = A(a) • Fitness of unimprinted sibs: 1 • e.g., AA, AA • 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-sfor A(a) • Unimprinted fitness: 1+tfor 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

  27. Predictions and contradictions • Game-theoretic • Imprinting requires multiple paternity (p < 1/2) • Allele favoring lower expression will be completely silenced • maternal silencing of growth enhancers • paternal silencing of growth suppressors • Population-genetic • 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

  28. Paternally silenced growth enhancer Growth-enhancing locus Unimprinted gene Cis-acting maternal modifiers Reduced paternal expression would be favored from these points Maternal expression Cis-acting paternal modifiers Maternal optimum Paternal optimum Paternal expression

  29. 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

  30. 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

  31. 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

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