Identify segments from a given population after admixture #1887
Replies: 5 comments 7 replies
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Hi @leovallini, welcome 👋 I haven't run your code or thought too hard about it, but your approach basically seems right to me. I think it'd be helpful if you printed out the migration events that you get here, and a simulated tree ( |
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@jeromekelleher below is the list of migrations with
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I think the issue here is that your looking at the internal nodes of the tree - once the samples within a population have coalesced, they'll produce internal nodes, so you can't just match against the same IDs. This would be easier to see if there wasn't so much recombination. What do you get if you turn off recombination entirely? |
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with no recombination I guess individuals within the admixed population will migrate (backward in time) entirely (= all of their genome) either to population B (with prob=0.75) or to population C (with prob=0.25). |
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I understand that you won't be able to answer the question you're ultimately interested in @leovallini, I was just suggesting this as a path towards understanding what's going on and developing a solution. I usually find it best to start with a single tree to understand what's going on, and then generalising. What do you get if you print out the tree and migrations in this case? |
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ok sorry, I hadn't understood that.
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Great! Now, it's probably a good idea to add in a few more samples so we get more coalescence. Also, printing out the node table will be handy here |
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I am now simulating the following: Nodes Table
Migration Table
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Great! So, you're interested in the migrations with source=3 I think. Can you see where these nodes are on the tree, and how they relate to the samples you're interested in? Note the "population" column in the node table - this tells you the population that a node was "born" in. |
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Thanks, I think I finally got it. |
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Excellent! I think that because you have no migration in the model before the admixture you can just look directly at the migrating segments, but it's good to look at the trees as well just to confirm that this the right thing to do. |
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This is one more run where I restored recombination, I used This is one of the migrations I get, where the node is an internal node and not one of the sample nodes, then information about the node and the 5 trees describing the corresponding interval:
If I got this correctly the migrating chunk with coordinates (left=14252017.0, right=14270154.0) existed 898.57 generations ago but was later broken down by recombination (at least in some individuals) into two smaller chunks: one with coordinates (left=14252017.0, right=14261228.0 - the first two trees) and one with coordinates (left=14261228.0, right=14270154.0 - the last three trees). The first of the smaller segments is now present in three of my sampled individuals from pop D (with id 0, 2 and 3) while the second one can be found in four individuals: 0, 2, 4 and 3. So I should mark the segment(left=14252017.0, right=14270154.0) as beeing "inherited from pop C" in individuals 0, 2 and 3. |
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Hi msprime community,
I'm sorry for the noob question but I was not able to find the solution elsewhere:
I have a simple population demography where population D is the admixture of population B and C with proportion 0.75 and 0.25 (see figure).
What I want to do is simulate multiple individuals from the admixed population D and for each of them(crucial, I want to know that for single individuals, not population-wise) save the left and right coordinates of the chunks that come from population C.
So far I only managed to do that for a single individual using record_migrations = True and selecting migration events where the source population is C, when I try to do that for more than one individual in the admixed D population, however, I get many migration events where the node does not coincide with one of my samples nodes; I think the reason for that is that the segments referred to in these migrations are shared by more than one individual in the admixed population, but how do I know which ones?
Below is the code for what I have done so far, which works fine if I simulate 1 individual from population D, but not when there is more than 1.
Thanks for your help,
Leonardo
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