For this whole lesson, we are going to look at some actual empirical data in the same general shape as the simulations you've been running. Our first stop involves the ground finches from Daphne Major, monitored from 1973 to 2012 by the Grants. In the 2000s, genetic sequencing became cheap enough that a lot of the specimens the Grants had been collecting, and especially the new birds they were still monitoring, could be sequenced. The genomes could be analyzed, and the specific alleles being passed on from parent to child could be correlated with the phenotypes that the Grants had been measuring the whole time.
A number of loci were found associated with each of these traits, as you might expect: the characters they were looking at are controlled by a great many loci. However, a few particular loci stuck out. HMGA2 had a strong effect on beak size, depending on which allele you had; ALX1 had a strong effect on beak shape; BMP4 had a strong effect on beak depth; and CALM1 had a strong effect on beak length. Multiple alleles for these and other loci (as, again, each trait was controlled by a large number) were then monitored over time. We're going to track one allele of HMGA2, controlling beak size.
In the simulations below, it's fundamentally the same as what we did before. Rainfall controls how much food is available; how much food is available controls births and deaths. If you don't die, you're a survivor. If you are born, you're a new chick. And the frequency of the big-beak allele is going to be a function of who lives (who survives, who doesn't die), and who is born, and who their parents are.
Where this gets into selection is that beak size is going to have an impact on how rainfall creates food. When the rain falls, seeds are made. All of the birds can eat small seeds, but only certain birds can eat the larger, harder seeds. As a result, how much food is made available to a bird by the falling rain is itself a function of the bird's beak. And so here you're going to run a few simulations to see how the frequency of an allele changes when you can mess with the association of the trait with food acquisition, relative to the amount of births and deaths that are random.
Essentially, in the graph of the model shown below, you're going to be able to influence drift directly, through the role of chance in who lives, who dies and who reproduces; and the role of selection, in terms of how specifically the allele affects food availability as a function of rain, and in what direction. You'll fit this a few times to simulated data, to get a feel for how your manipulations impact it. And then you'll get one chance to try and match the empirical data that was actually seen in the Grants' birds.
In Part A, you were predicting the frequency of an allele at a single locus. A diagram was given showing its context in the genome, amongst many other loci, for that chromosome and several others. Meiosis does not pass on individual alleles; rather, in sexually reproducing diploids, you pass on an entire haploid cell. Recombination scrambles some loci, but genomes are still inherited as chunks instead of individual pieces.
Here we're going to run some simulations where we're tracking the frequency of a new allele, but we're going to be concerned not only with its frequency over time, but also its genomic context. The model here will be extremely simple. We are just looking at the advantage of the allele, and the effects of chance (drift, basically), on the rate at which it spreads. The focus here is on how the rate at which it spreads affects the diversity in other places in the genome: places that have no effect whatsoever on the phenotype of interest.
This is based on the FGFR3 receptor, a protein that is related to fibroblast growth factor. If you look at this particular locus in dachshunds versus non-dachshunds, you'll find that dachshunds have almost zero genetic diversity in a region about 20 megabases, or about 20 million base pairs, on either side of FGFR3. All dachshunds are homozygous for all the same alleles. It's a very, very big region of zero diversity compared to normal dogs in the same area, even though leg length has been important to all dog breeds.
The size of the homozygous region is going to be related to how fast the allele went to fixation: how fast the allele drove all other alleles extinct at that locus. The faster this happens, the less time there is for recombination to separate one locus out from its neighbors, producing a situation where a strongly advantageous allele sweeps the area around it free of diversity, just based on the speed of its wiping out variation around it, relative to the speed of recombination.
Do you inherit traits? You don't inherit hair color. You inherit nucleotide sequences that code for proteins that, in the context of the environment and the other proteins you have, produce a trait. Does selection act on the nucleotide sequence, or does it act on the trait? It's the trait. There's a massive disconnect between what gets inherited and what gets selected.
Selection is fast — way faster than mutation, faster than drift in almost every circumstance, sometimes faster than recombination.
MHC-I is a billboard. Every cell sticks samples of what it's making on its surface, so killer T cells can check. Normal advertised? Fine. Wrong protein advertised? Killed.
Nothing is ever fixed in time. If scale-eating is rare, do the other fish look out for scale-eaters? No. As scale-eaters become more common, what do the other fish start to do? Watch out for them. Is scale-eating still as good? No. How good scale-eating is depends on how common it is. The fitness function itself changes — partly because environments change, but also because the fitness function changes because of the fitness function.
If I told you a car was driving at 50 mph and has gone 100 miles, how long ago did it leave? You have a distance and a speed — distance divided by speed gives you time. Number of mutations is your distance. The slope of mutations versus time is your speed. Number of mutations divided by slope gives you time to most recent common ancestor.