Mailund on the Internet

We have just published a new paper in PLoS Genetics:

A New Isolation with Migration Model along Complete Genomes Infers Very Different Divergence Processes among Closely Related Great Ape Species

We present a hidden Markov model (HMM) for inferring gradual isolation between two populations during speciation, modelled as a time interval with restricted gene flow. The HMM describes the history of adjacent nucleotides in two genomic sequences, such that the nucleotides can be separated by recombination, can migrate between populations, or can coalesce at variable time points, all dependent on the parameters of the model, which are the effective population sizes, splitting times, recombination rate, and migration rate. We show by extensive simulations that the HMM can accurately infer all parameters except the recombination rate, which is biased downwards. Inference is robust to variation in the mutation rate and the recombination rate over the sequence and also robust to unknown phase of genomes unless they are very closely related. We provide a test for whether divergence is gradual or instantaneous, and we apply the model to three key divergence processes in great apes: (a) the bonobo and common chimpanzee, (b) the eastern and western gorilla, and (c) the Sumatran and Bornean orang-utan. We find that the bonobo and chimpanzee appear to have undergone a clear split, whereas the divergence processes of the gorilla and orang-utan species occurred over several hundred thousands years with gene flow stopping quite recently. We also apply the model to the Homo/Pan speciation event and find that the most likely scenario involves an extended period of gene flow during speciation.

It is mainly a methods paper; we could have done a lot more interesting stuff with the data analysis but since the main message of the paper, in our view, is the new inference model we really only did proof-of-concept analysis. So don't expect exciting new biological insight (well, there is one thing that's a bit exciting, but I'll get back to that).

It's the modelling that is the main result.

A bit of background for CoalHMMs

We've been working with inferring parameters of speciation - split times, effective population sizes, amount of incomplete lineage sorting - using the sequential Markov coalescence model for several years now. We just haven't called our models SMC models, mainly because we didn't think in terms of actually modelling the coalescence process in our first paper in 2007. There we just had a hidden Markov model and figured out how to get coalescence parameters out of its transition matrix.

We did parameterise the model directly from the coalescence process in 2009 - in the process figuring out that some of the symmetries we thought we needed the first model shouldn't be there, while others should. I still prefer to distinguish between the population genetics model, SMC, and the inference model model, CoalHMM (or SMCHMM maybe, but that is harder to pronounce).

SMC (and SMC' for that matter) assumes that there is at most one recombination between any two neighbouring genealogies.  This is mathematically correct in those models since they work with continuous space. No two recombinations can ever hit the same point there.

It is also perfectly sensible to assume that only one recombination occurs between any pair of nucleotides when having discrete space, in most cases, since the recombination rate is very small compared to the coalescence rate.

We were comparing genomes from different species, though, where the divergence between two genomes can be several millions of years, and there it turns out that you introduce a large bias in estimating the recombination rate if you don't take this into account. Although the largest problem with the model from 2009 was not actually multiple recombinations but not allowing "back-coalescence" from a lineage into itself (which is the difference between the SMC and SMC' models).

Not realising that SMC' would probably solved most of the problems we had, and probably also because I didn't want to have to derive the equations needed to use it, we wanted a model that didn't make any assumptions about the number of recombinations and where lineages would coalesce. We of course need the Markov assumption to make any inference on a genome scale feasible, but aside from that we didn't want any other assumptions.

I was playing around with this for several months until, in a discussion with Carsten Wiuf, I had a Eureka moment. Once you accept that you have the Markov assumption, you need only worry about pairs of neighbouring nucleotides, and although the state space of all possible ancestral configurations is large it is not impossible to compute and manipulate.

You can build the state space for ancestral configurations, it is finite if you only have two nucleotides (it is as long as you have discrete space, really), and this is just one big continuous time Markov chain.  You can easily compute the probability of any run of this CTMC, and if you discretise time you get a finite number of runs (or equivalence classes of runs, really, but who cares?).

As a side note I should say that if I had know the literature better I should have known that this is the way to do it. Simonsen and Churchill did it already back in 1997 and Slatkin and Pollack something very similar to what we implemented in 2006. I just didn't know about this until long after we published our method. Anyway...

Getting a transition matrix for the HMM from this CTMC is relatively straightforward when you only look at two genomes, and we published a model doing this last year. It is very similar to PSMC in that it uses discrete time intervals as hidden states and makes inference in the coalescence process by building the transition matrix from a model of the coalescence process.

Explicitly modelling the ARG for two nucleotides, however, makes it very easy to model any kind of demographic scenarios. If you can model it with the coalescence process, you can also model it with the coalescence process limited to two nucleotides, and then use this framework to translate that into an HMM for inference.

In theory, at least. There are some practical challenges in automatically building the CTMC from a demographic scenarios - and you definitely do not want to do that by hand! - and in picking out which runs of the CTMC are equivalent and how they are mapped into the transition matrix of the HMM.

None of it is really that complicated in hindsight. There were just a bunch if ideas that were only simple once we worked them out. Like using a coloured Petri net to specify the models or using strongly connected component graphs to get the transition matrix ... none of it is stuff you really brute force your way to the solution, but it is simple enough once you have the idea.  We published how we did it in a paper earlier this year.

We've had the framework coded up for almost a year now, but it has taken surprisingly long to bugfix and validate. I'm always surprised at how long it takes from having something that works in theory until you've validated how it works in various scenarios where your assumptions are likely to be violated and we did a lot of that for this paper. You can read all about it in the supplemental material.

An isolation-with-migration CoalHMM

The model we present in this new paper is probably the simplest extension you can make in the framework to the model we published last year.

The model from last year (well, it is a bit older than that really, but it was a companion for the orangutan genome paper and so wasn't published until that paper came out last year) is a simple isolation model.  Given one genome from each of two species, it uses the distribution of "time to the most recent common ancestor" (TMRCA) to infer the ancestral effective population size and the time since the speciation.

In the new model, we add to that model a period of gene flow after the initial split in two populations until it eventually stops. This means that we work with three CTMCs in the model. One from the present day back to when gene flow is possible (looking backwards in time, as we do for coalescence models, confusing as that can be), then another where gene flow is possible, and then the final one where we have one single panmictic population.

Connecting these three CTMCs the right way, we get a single (non-homogeneous) CTMC, and from the SCC graph of this we get the transition matrix for the HMM.

Where the model really gets its information for inference is the distribution of TMRCA both in time and in space.  The distribution for the TMRCA is very different in an isolation model and an isolation-with-migrath model. For the time distribution Rosenberg and Feldman wrote a very nice review in 2002, and we looked a bit at it in a paper last year. Dutheil and Hobolth (co-authors on most of the CoalHMM papers, including the new one) also discusses it a bit in a chapter from this year. But it is not just the time distribution that is different between the two models, also how the TRMCA is distributed along a sequence alignment, and our model uses both the temporal and spatial distribution in its inference.

Using the model, we can infer when the ancestral population initially split up, how long gene flow still occurred, and how much gene flow there was. There are some symmetries in the model that makes the direction of gene flow non-identifiable, so we only estimate symmetric gene flow, though.

Comparing models with and without gene flow, we can also determine which fits the data best, and in that way determine if there was a clean allopatric split, or if there was a period of gene flow.

We cannot, though, determine if the gene flow was continuous over that time interval, or if it was a series of isolations and admixture events ... whether we can get to that with the framework we have is something we are currently working on. It looks a bit like we can see different levels of gene flow in different time intervals with it, but results are very preliminary still.

Speciation in the Great Apes

As you probably know, there are two species in three of the four great ape species; humans are the only lonely ones.  For Pan there are common chimpanzees and bonobos, for the gorillas the eastern and western gorillas are different species, and for orang-utans the Bornean and the Summatran orang-utans are different species.

From earlier studies we already have a good idea about whether those speciations were allopatric or had gene flow. There are a very few results seeing very limited gene flow between bonobos and chimpanzees, but most results show that there was a clean split, which is also the result we got in the bonobo genome paper. For the orang-utans we showed in the orang-utan genome paper we saw gene flow between the two orang-utans, and for the gorillas we saw the same in the gorilla genome paper.

So we sort of knew what to expect and used these three speciations as a proof-of-concept analysis, and got the results we expected there: for bonobos and chimpanzees, the clean split model is preferred, and for the other two the migration model is preferred.

Our estimates of the initial population split and end of gene flow are also in good concordance with estimates from the literature, where we have them.

One thing that might be a bit odd is the timing of the bonobo-chimpanzee split. Even using a lower mutation rate than we normally would (for very good reasons), the split is recent compared to the formation of the Congo River (from what I can find out about that, it seems very hard to figure out). The formation of the Congo River has been the argument for why that particular split was instant rather than having a long period of gene flow, but that doesn't quite fit what we see - or what earlier estimates have seen either, for that matter.

The split between humans and chimpanzees

Not surprisingly we were asked in reviews to also look at the human and chimpanzee split. As a race, we are quite self absorbed so that split is of course more interesting than what was going on in the other great apes...

Looking at this split, our gene flow model is preferred over the clean split, and we actually see quite a long period with limited gene flow.

Now whether that surprises you or not, I don't know, but it doesn't seem that far fetched to me, considering that we have seen gene flow in most of the other great ape splits and we now know that there were a at least two or three admixture events between modern humans and archaic humans.

Statistically, the simpler model should be the null model, but my prior is now preferring gene flow over a clean split.

Admixture between protohumans and protochimpanzees was proposed by Patterson et al. in their "complex speciation" scenario in 2006. Most papers that have looked at this scenario since have preferred a clean split, and the evidence for the complex speciation scenario can be explained in other ways.  The large variation in TMRCA can be explained just by a large effective population size and the difference between X and autosomes by sperm competition or just selection being different on X as we think it is in central chimpanzees.

We cannot really see if the gene flow we estimate between chimpanzees and humans is any kind of complex scenario with long isolation followed by admixture - we need to model and test that explicitly - but we do see gene flow, and the time between the initial split and the end of gene flow aren't far from the initial split and then the admixture proposed by Patterson et al.

I've been away for a long time. RSI really keeps me away from blogging and I'm not planning to get back to blogging full time, but I really miss it so I hope to get back to writing a bit here and there...

Anyway, there is one thing that's been on my mind for a while, and I'd like input on it.

Should reviews be open or blind?

I've discussed this with colleagues for a while and I see all the points for or against, but really haven't made up my mind. Ideally I think that reviews are such an important part of the publication process that we cannot ignore it, but I see a lot of good arguments for blind reviews as well.

First some personal background: about five-ten years ago, I decided to accept all papers I was asked to review. I figured that it was 1) important to do my duty for the scientific community to review papers and 2) I would learn a lot from reviewing as many papers as I could get my hands on.

Okay, that was a very bad idea. The more papers I reviewed the more papers I was asked to review, and I ended up reviewing tens of papers a month. That means that my reviews ended up crappy since I simply did not have the time to give the papers any serious thought. I could spend two-three hours on a review max, and that is just not enough.

I felt really bad the first time I started refusing reviews, but I simply had to.

Recently I have limited the number of papers I can handle as an editor as well.  I just cannot handle too many papers and give them a fair judgment.

I feel bad about this, but is a question of survival. I cannot handle as many papers as I think I should, so I have to limit it.

This brings me back to the title of this post. Should reviews be blind or not?

When I was overwhelmed by papers I dropped signing my reviews. I didn't feel comfortable with the quality of my reviews and I am sorry to say that it meant that I could do the reviews but not be honest and stand behind them.

I can't really live with that. I don't want to give people criticism that I cannot be man enough to stand behind, so I have seriously limited the number of papers I accept to review and now I sign my reviews again.

This now leads me to a serious question: how does me signing the reviews affect my reviews?

I would like to say that my reviews have always been of high quality, but obviously they haven't been of equal quality since I now handle fewer manuscripts when I sign them. I obviously feel more reason to write better reviews when I sign them.

Having to sign my reviews clearly, for me at least, means that I am much more careful about the report I write.

Now I never write a report unless I've been thinking about the paper for a couple of days. And I think my reviews are much better for it.

That sounds like I am all in favour for open reviews (and generally I am) but I also see the problems with this.

At the pub in Cambridge my last visit there we discussed this. It is easy to sign a positive review but you can get in trouble when you sign negative reports, so why bother?

If open reviews means anything it means that you always sign reviews even if they are negative, and that can be a problem since you will make enemies when you are giving negative reviews.

I really don't know.

I know that my reviews are better now that I sign them, but I am also much more selective on the papers I want to review, so that puts a limit on what I will review. I still spend about a day a week on reviewing but I am much more selective on what papers I accept and I only accept papers I am likely to be positive about.

Even if we don't require reviews to be open, would we make better reviews if reviews were published together with papers, perhaps as supplemental material?  I don't know

I would love your thoughts.

I've been wanting to write about our paper on the orangutan genome for a while, but I've just been too busy so far, so a little late I finally get to it.

Besides the Nature paper, where we contributed to the analysis of the two sub-species of orangutans, we have two companion papers. One is already out in "early access" at Genome Research and the other will be out later in PLoS Genetics. Since the latter paper is not out yet, this post will be about the Genome Research paper.

Coalescent in an isolation model

Since all our work is based on coalescent theory and in particular CoalHMMs, I'll start there.

Imagine we have two species, and we sample a gene in each. We can then ask, what is the divergence between the two genes? This divergence will be determined by 1) the divergence of the two species, let's call that T, and 2) the coalescence time between the two genes within the ancestral species, let's call that C.

The species divergence we assume is fixed for all genes, so while it is unknown it is not a stochastic variable. The coalescence time, however, is stochastic, and from coalescence theory we expect it to be exponentially distributed with a rate determined by the effective population size in the ancestral species.

We call this setup an isolation model, and we will use the distribution of divergence times to make inference about the speciation time and the effective population size in the ancestral species.

The figure below illustrates the setup.

If C is exponentially distributed, and the divergence is given by D=C+T, then we can make inference about both parameters as follows: We sample a number of independent genes and get their divergence time. For the exponential distribution, the mean is equal to the standard deviation, so looking at the standard deviation of the divergences we can get the parameter for the exponential distribution. That gives us the mean value of C, and if we then look at D-E[C] we get an estimate for T.

Below is an example of this, where I've estimated the coalescence rate and divergence time from 50 divergence samples.

Complications

This is all very simple, but there are a few problems.

First, you don't really get independent samples of the divergence time between two species. If you sample n individuals from the first species and m from the second, the n in the first species will all have found a common ancestor before that lineage reach the ancestral species, and the same goes for the m samples in the other species. So no matter how many individuals you look at, you end up with a sample of two in the ancestral species. I've written about this before here.

It is not a show-stopper, though, since genes in different parts of the genome are close enough to independent. So if you sample different loci instead of different individuals, you get your independent samples. So while adding more individuals won't help, having an entire genome to look at gives you plenty of samples.

The second problem is that we cannot actually get samples of the divergence time. You cannot look at two pieces of DNA and from that get their divergence. You need to estimate it. It isn't really that hard, since you can get a good estimate from the number of differences between the two sequences. That is, if the entire alignment of sequences have the same divergence time.

If there is a recombination somewhere in the sequences, they do not have the same divergence time, and you cannot estimate the divergence.

You can get around this by looking at short DNA segments, where you expect few if any recombinations. You won't get a good estimate of the divergence then, but you can maybe alleviate this by having a lot of genes (but estimating the coalescence rate based on a standard deviation that have contributions from both the coalescence process and the estimation problems is, well, problematic).

You'd also have to throw most of your data away if you are looking at short segments scattered along the genome (and you cannot have them too close to each other, because then they will no longer be independent).

The CoalHMM approach

The models we develop to deal with this are based on hidden Markov models.

Using these models, we can estimate the divergence time for single nucleotides. Normally you cannot, since they are either equal or difference, and that doesn't tell you much about their divergence (is it zero for equal and infinity for different?). We can do this, because the flanking DNA contains information about this, whether recombinations have occurred or not, and we can capture this information through the Markov model.

It is a rough approximation to the coalescence process, but as far as we can tell, it works reasonably well.

We are getting pretty close to being able to estimate the distribution of divergence times using hidden Markov models, but the model we use is the one that will be published in PLoS Genetics soon and not the model we used in the Genome Research paper, so I'll wait a bit with describing how that works.

The model we used in the Genome Research paper is the one described in this paper.

In this model, we do not attempt to estimate the actual divergence times, but instead use something called incomplete lineage sorting. The idea here is, that if we have a third species closely related to the other two, then sometimes the two sister species have such deep divergence times, that one of them can end up being closer related to the third species than its sister species.

This leaves a stronger signal in the DNA and is thus easier to model and make inference about.

The model based on this needs only four states: one state where the two sister species coalesce early, and three states with deep divergence. If the divergence is deep, the topology of relationships between the species should be uniform -- each topology is seen with one third probability -- and how often we see deep divergences is given by the two speciation times together with the effective population size of the ancestor of the sister species.

As we scan along a genome alignment, we can infer how often we see recent divergences and how often we see deep divergences, and how the deep divergences are distributed along the three topologies.

Below is a figure that Julien made for illustrating this.

With this model, you don't extract as much information from the genomes as you would if you could estimate the divergence times, but with full genomes to work with, you have plenty of information to get good estimates.

You need three closely related species to work with, though.

Incomplete lineage sorting patterns among human, chimpanzee and orangutan suggest recent orangutan speciation and widespread selection

And now, finally, we get to the paper.

Incomplete lineage sorting patterns among human, chimpanzee and orangutan suggest recent orangutan speciation and widespread selection
Asger Hobolth, Julien Y. Dutheil, John Hawks, Mikkel H. Schierup and Thomas Mailund

Abstract

We search the complete orangutan genome for regions where humans are more closely related to orangutans than to chimpanzees due to incomplete lineage sorting (ILS) in the ancestor of human and chimpanzees. The search uses our recently developed coalescent HMM framework. We find ILS present in ~1% of the genome, and that the ancestral species of human and chimpanzees never experienced a severe population bottleneck. The existence of ILS is validated with simulations, site pattern analysis, and analysis of rare genomic events. The existence of ILS allows us to disentangle the time of isolation of humans and orangutans (the speciation time) from the genetic divergence time, and we find speciation to be as recent as 9-13 mya (contingent on the calibration point). The analyses provide further support for a recent speciation of human and chimpanzee at ~4 mya and a diverse ancestor of human and chimpanzee with an effective population size of ~50,000 individuals. Posterior decoding infers ILS for each nucleotide in the genome and we use this to deduce patterns of selection in the ancestral species. We demonstrate the effect of background selection in the common ancestor of humans and chimpanzees. In agreement with predictions from population genetics, ILS found to be reduced in exons and gene dense regions when we control for confounding factors such as GC content and recombination rate. Finally, we find the broad scale recombination rate to be conserved through the complete ape phylogeny.

In this paper we used humans, chimpanzees and orangutans.

The first question to ask is then, are these three species close enough that we see incomplete lineage sorting?

Without it, we don't have the signal in the data that we need for the model.

Based on previous estimates of the species divergence times and ancestral effective population size of humans and chimpanzees we could work out that some was expected. So that is a good start. To make sure, though, we used some simpler approaches. We looked at indels to check if there would be signals in these supporting clustering of human and orangutan or chimp and orangutan and found that. We also looked at the distribution of alignment columns and again found some signals for alternative topologies of the three species. So with that checked, we applied the model.

From the model we estimate three things: 1) The speciation times for humans and chimps, and from the African apes and orangutan, 2) the effective population size of the ancestral species, and 3) in which regions of the genome humans and chimps, humans and orangutan, and chimp and orangutan are closest related.

I won't say much about number two. The effective population size is a weird parameter that can be affected by so many things, that it is really hard to interpret, and right now we just don't know what really is important, so I'd rather not make any claims (but I'll say a few things about local effective population sizes towards the end of the post).

Number one is interesting because it tells us something about when humans diverged from the other two apes. Our estimates are measured in the number of substitutions since the divergence, but assuming a molecular clock and assuming we have a good estimate of the rate we can get an estimate in years.

Assuming a rate of around 1 substitution per nucleotide per billion years -- an estimate based on several earlier papers that get this number from calibrations with the fossil record -- we get a human/chimp speciation around 4-4.5 million years ago, and a human/orangutan speciation around 11-13 million years ago.

I really don't know how reasonable this is, in relation to the fossil record, so this is when we got John Hawks involved. I have my fingers crossed that he will blog about this at some point.

There are good reasons to be a bit skeptical, though. From recent studies, we know that the substitution rate is lower in humans today, and if that is also true in the past, the estimates should be moved further back in time. We cannot get too far back, though, without running into inconsistencies in the deeper past, but how this will all play out once we do more analysis I cannot say yet. It is something we look into for the gorilla genome (and I'll just leave that as a cliff hanger for now, I'll get back to it when we have published that genome).

For number three, I don't really know. You might be surprised that we are sometimes closer related to the orangutan than the chimpanzee, or you might not. It depends on your prior assumptions, I guess.

We didn't really find anything cool correlated to the patterns of relatedness, so we don't have much of a story to tell about this.

Ancestral selection

The final thing we looked at in the paper was correlations between incomplete lineage sorting and gene density.

Why this is interesting gets a bit technical but has to do with the effective population size.  As I mentioned above, it is a bit of a weird parameter, but one that is affected by selection. If you have a selective sweep the genetic diversity is reduced, and you see this as a reduction in the effective population size. The same effect is seen with purifying selection, where again the genetic diversity is reduced and so is the effective population size.

Incomplete lineage sorting is positively correlated with the effective population size, so if you observe a correlation between incomplete lineage sorting and gene density, it is a signal for selection.

We observe this, and take it as a signal that selection rather than just drift has been a major player in the evolution of our genome.

How much of a surprise this is depends on your prior assumptions again, I guess, but it does indicate that neutrality may not always be the obvious null model for genome analysis.

It is a pretty weak signal for this, though, in this analysis. We see so little incomplete lineage sorting for these three species that it is really hard to analyse it in detail.

When we get human, chimp and gorilla, there is a lot more incomplete lineage sorting, and we can do a lot more. We are seeing some cool signals there, but I'll let that be the second cliff hanger for the gorilla genome paper.

--
Hobolth, A., Dutheil, J., Hawks, J., Schierup, M., & Mailund, T. (2011). Incomplete lineage sorting patterns among human, chimpanzee and orangutan suggest recent orangutan speciation and widespread selection Genome Research DOI: 10.1101/gr.114751.110

There's a paper that came out yesterday in PLoS ONE on visualising LD structure:

The Textile Plot: A New Linkage Disequilibrium Display of Multiple-Single Nucleotide Polymorphism Genotype Data

Kumasaka, Nakamure and Kamatani

Linkage disequilibrium (LD) is a major concern in many genetic studies because of the markedly increased density of SNP (Single Nucleotide Polymorphism) genotype markers. This dramatic increase in the number of SNPs may cause problems in statistical analyses, such as by introducing multiple comparisons in hypothesis testing and colinearity in logistic regression models, because of the presence of complex LD structures. Inferences must be made about the underlying genetic variation through the LD structure before applying statistical models to the data. Therefore, we introduced the textile plot to provide a visualization of LD to improve the analysis of the genetic variation present in multiple-SNP genotype data. The plot can accentuate LD by displaying specific geometrical shapes, and allowing for the underlying haplotype structure to be inferred without any haplotype-phasing algorithms. Application of this technique to simulated and real data sets illustrated the potential usefulness of the textile plot as an aid to the interpretation of LD in multiple-SNP genotype data. The initial results of LD mapping and haplotype analyses of disease genes are encouraging, indicating that the textile plot may be useful in disease association studies.

An example of this new kind of plots looks like this:

At a quick glance it looks like it is displaying haplotype blocks, like you can get in HaploView (although in a nicer graphics).

It isn't quite that, though.

The textile plot is showing LD between genotypes and not haplotype blocks, so you always have three "blocks" per column, and so you don't know the phase of the genotypes you are looking at.

The plot simply visualises the genotype LD structure, and I am sure that with a bit of practice they can be used to explore that.

I don't have that practice, though, so I find them a bit hard to interpret.  They are beautiful, though.

If you are interested in phylogenomics and primate evolution -- including human evolution -- this new review in Genome Research is a must read.

Phylogenomics of primates and their ancestral populations

Adam Siepel

Genome assemblies are now available for nine primate species, and large-scale sequencing projects are underway or approved for six others. An explicitly evolutionary and phylogenetic approach to comparative genomics, called phylogenomics, will be essential in unlocking the valuable information about evolutionary history and genomic function that is contained within these genomes. However, most phylogenomic analyses so far have ignored the effects of variation in ancestral populations on patterns of sequence divergence. These effects can be pronounced in the primates, owing to large ancestral effective population sizes relative to the intervals between speciation events. In particular, local genealogies can vary considerably across loci, which can produce biases and diminished power in many phylogenomic analyses of interest, including phylogeny reconstruction, the identification of functional elements, and the detection of natural selection. At the same time, this variation in genealogies can be exploited to gain insight into the nature of ancestral populations. In this Perspective, I explore this area of intersection between phylogenetics and population genetics, and its implications for primate phylogenomics. I begin by “lifting the hood” on the conventional tree-like representation of the phylogenetic relationships between species, to expose the population-genetic processes that operate along its branches. Next, I briefly review an emerging literature that makes use of the complex relationships among coalescence, recombination, and speciation to produce inferences about evolutionary histories, ancestral populations, and natural selection. Finally, I discuss remaining challenges and future prospects at this nexus of phylogenetics, population genetics, and genomics.

...and if you are wondering why my blog is so quiet these days, it is because I am swamped with four of the genome projects mentioned in the paper: orangutan, bonobo, gorilla and macaque...

Any summary of this paper that I write will not really do justice to it -- you really should read it yourself and you will be happy you did -- so I'll just briefly summarize the topics that Adam covers.

First he covers basic phylogenetics, that is figuring out species relationships.  This is, by now, a well known field and essentially boils down to modeling sequence evolution as Markov chains so you can estimate divergence times and tree relationships from the substitutions between sequences.

For closely related species, though, that is only a small part of the picture, and the more interesting part of the paper involves introducing population genetics to phylogenetics.  You have to remember that speciation somehow involves populations; two species do not just split up, rather groups of individuals diverge and their genomes start diverging as groups rather than individuals.  That leads to varying sequence divergence as you scan along the genomes, and under certain conditions to incomplete lineage sorting, where gene trees are different from species trees.

This doesn't just cause complications in genomic inference, though.  It provides valuable information about ancestral species and about speciation processes, which is the next topic Adam covers.  For primates, this is especially important.  The time intervals between speciations are short, and the ancestral effective population sizes are large *, so 1) if you ignore this your results will be way off, but 2) if you embrace it you have a lot of information to learn about the ancestry of the primates.

This then leads us to speciation models.  There are plenty of those, where the simplest (allopatric speciation) just assumes that some barrier appears between two populations after which they evolve independently to the point where they can no longer reproduce as hybrids.  That is probably a good model for the chimp/bonobo split, where the Congo River got in the way (chimps can't swim), but it is a bit simple so more complex scenarios are worth considering for most speciation events.  The point here just is that different scenarios will leave different signals in the genomes, and we should be able to work this out by looking at the extant genomes.

There's a nice review of the work done so far in the paper, but honestly we are still only at the starting phase of modeling this, and a lot of work remains before we can say anything conclusively about any of the primate speciations.

Next we get to selection.  With the whole neutral theory we have turned to believe that we can explain most of genome evolution with neutral mutations -- well I have anyway, but that might just be me.  Recent results, though, hints at selection being a major force in genome evolution anyway. My older colleagues tells me that selection was much more important in theory years back, but my background gave me the intuition that it could pretty much be ignored when comparing genomes; maybe I was wrong on that.

Perhaps the null model when we look at entire genomes shouldn't be neutrality after all, I don't know... We are seeing signals to that effect in our own work, anyway, but I'll tell you all about that later when those papers are out, for now let's just read Adam's paper that is much more interesting anyway!

The last part of the paper is on Future Prospects.  Well, most papers are, so no surprise there, but if you are getting into the field there are some interesting areas to start thinking about in this review.

How do we incorporate the ancestral recombination graph (ARG) into phylogenetic analysis?  How do we model it without the combinatorial state space explosion?  How do we infer anything usable from the weak signals that is in the data for this? How do we combine model sophistication with computational efficiency to alleviate the state space explosion? Which model assumptions are essential and which can we get away with approximating?

Let me add a few of my own: How do we model this complex system without too much complex math so that when we have results we can actually interpret the results?  How do we check if deviations from our model actually shows evidence for some model over another, and are not just showing that we have the wrong model?

Go read the paper!  Seriously, it is a great read!

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* Yeah, about ancestral population sizes... there are consistent estimates of very large ancestral effective population sizes, using very different methods, but generally it seems like the ancestral species were more diverge than the extant species are.  The consistent results, with different methods, indicates that this might be true, but it still is somewhat suspicious, but I guess we will learn more over the coming years as we get more data and more sophisticated methods.

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Siepel, A. (2009). Phylogenomics of primates and their ancestral populations Genome Research, 19 (11), 1929-1941 DOI: 10.1101/gr.084228.108

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