Outline of the talk

1. Big picture
2. Tools
3. Applications

G6PD deficiency allele frequencies

Human G6PD variants (Howes et al 2013)

• over 130 G6PD deficiency alleles; 34 variants at high frequency
• provide protection against malaria but increase risk of anemia
• Estimated ages 40-400 generations

• Dark-pigmented mammals and reptiles on volcanic outcrops in the Southwest. (Dice, Benson 1936)
• ‘Dark’ allele beneficial on outcrops, deleterious elsewhere.
• MC1R: basis is shared between species but not between populations (Nachman, Hoekstra)

“isolation by distance”

• genetic versus geographic distance between pairs of 272 desert tortoises (McCartney-Melstad, Shaffer)
• clouds are comparisons within/between the two colors

How much of the genome is under selection?

Diversity correlates with recombination rate

Hudson 1994; Cutter & Payseur 2013; Corbett-Detig et al 2015

linked selection

The indirect effects of selection on genomic locations that are linked to the sites under selection by a lack of recombination.

The Mimulus aurantiacus species complex

The data:

• chromosome-level genome assembly
• \(20\times\) coverage of 8 taxa and outgroup (M.clevelandii)
• diversity (\(\pi\)), divergence (\(d_{xy}\)), and differentiation (\(F_{ST}\)) in windows
• 36 pairwise comparisons among 9 taxa
• estimates of recombination rate and gene density from map and annotation

\[ \begin{aligned} \pi &= \text{ (within-pop genetic distance) } \\ d_{xy} &= \text{ (between-pop genetic distance) } \end{aligned} \]

Ok, then: selection.

But: what kind of selection?

• newly adaptive variants?
• purifying selection?
• local adaptation?
• selection for introgression?

To test theories and fit models, we need simulations with realistic

1. population sizes,
2. genomes,
3. selective pressures,
4. histories, and
5. geography.

History is a sequence of trees

For a set of sampled chromosomes, at each position along the genome there is a genealogical tree that says how they are related.

The tree sequence is a way to describe this, er, sequence of trees.

Kelleher, Etheridge, and McVean introduced the tree sequence data structure for a fast coalescent simulator, msprime.

• stores sequence and genealogical data very efficiently

• tree-based sequence storage closely related to haplotype-matching compression

• tskit : python/C tools

jerome kelleher

File sizes

Computation run time

The main idea

If we record the tree sequence that relates everyone to everyone else,

after the simulation is over we can put neutral mutations down on the trees.

This means recording the entire genetic history of everyone in the population, ever.

A 100x speedup!

What else can you do with tree sequences?

• recorded pedigree and migration history
• true ancestry assignment
• recapitation: fast, post-hoc initialization with coalescent simulation
• fast, convenient computation

For example:

• genome as human chr7 (\(1.54 \times 10^8\)bp)
• \(\approx\) 10,000 diploids
• 500,000 overlapping generations
• continuous, square habitat
• selected mutations at rate \(10^{-10}\)
• neutral mutations added afterwards

Runtime: 8 hours

The data

Simulations

Conclusions:

• neutral
• background selection
• selection against introgressed alleles
• positive selection
• local adaptation

Bigger picture

How strongly does selection enhance or constrain genetic variation?

Other uses for population simulation

• train inference methods

• predict management outcomes

• do power analyses

• develop intuition