Introduction to the Coalescent Theory - Lecture 2

Delve into the intricacies of coalescent theory in this comprehensive lecture from the Third Bangalore School on Population Genetics and Evolution. Explore key concepts such as coalescent variance, effective population size, and population structure. Examine models like the structured Wright-Fisher model and the two-deme model, and investigate the effects of migration rates. Learn about sex in evolutionary contexts, including hermaphroditism and self-fertilization. Discover the role of recombination in evolution, its backward-in-time perspective, and the ancestral recombination graph. Gain insights into simulating data, detecting recombination events, and understanding linkage disequilibrium. This in-depth presentation covers advanced topics in population genetics, providing a solid foundation for researchers and students in evolutionary biology, mathematics, and related fields.

Third Bangalore School on Population Genetics and Evolution
Introduction to the coalescent theory
The coalescent variance
The coa Review variance
Robustness of the coalescent
Example
Population genetics in and of itself only allows limited inference
Effective population size
Variable population size
In a constant population:
Exponential growth
Rule of thumb
Population structure
The structured Wright-Fisher model
This model can equally well be studied backward in time.
The two-deme model
Slow migration
Consequences of slow migration
Fast migration
Let 7; be the stationary probability that a lineage is in patch i.
Source-sink environments
Is the island model a good model?
Sex
Hermaphrodites and self-fertilization
Males and females
Stage structure
Stochastic demography
Why recombination?
Recombination backwards in time!
Recombination makes it possible for linked sites to
In the The ancestral recombination graph
In the limit as N - co:
A walk through tree space
Simulating data
How common is recombination?
Detecting recombination
It can be shown that a very large fraction of recombination events cannot be detected even with infinite mutation rate.
Overcoming the evolutionary variance
The number of SNPs in 20 copies of 10 kb, 1000 runs:
Same thing, with recombination:
Linkage disequilibrium LD
Why "linkage disequilbrium"?
Where does come from?
Assuming random mating, we have:
How does evolve? Well,
Decay of haplotype sharing
HapMap Project