sequencing

Today, Svante Pääbo's group at the Max Planck Institute for Evolutionary Anthropology released high-coverage sequence data from a toe bone from Denisova Cave. The new genome comes a year after the same group released the high-coverage genome of the Denisova finger bone, several months before they published the first high-coverage analysis of this ancient genome [1]. Today's announcement is here: "A high-quality Neandertal genome sequence". It adds a second high-coverage genome from Denisova Cave, this one from a toe bone. Unlike the first finger bone genome, this toe has produced a genome very much like Neandertal specimens from much further west, including the Vindija Neandertals.

Something interesting in these data: the presence of a Y chromosome.

There's not so terribly much we can say about a toe. This particular bone was first reported in 2011 by Mednikova [2], who described the specimen's anatomy. She found the toe similar in some respects to equivalent Neandertal toe bones, but also like recent humans in a couple of details. Still, the anatomy wouldn't be enough to conclude that the bone is a Neandertal, because we don't know much about the toes of other ancient human populations.

The genetics are fairly clear about the level of similarity of this new genome to other Neandertals. From the announcement:

The figure shows a tree relating this genome to the genomes of Neandertals from Croatia, from Germany and from the Caucasus as well as the Denisovan genome recovered from a finger bone excavated at Denisova Cave. It shows that this individual is closely related to these other Neandertals. Thus, both Neandertals and Denisovans have inhabited this cave in southern Siberia, presumably at different times.

This is a cluster diagram based on genome-wide similarity, which doesn't tell us about possible mixture among the populations. But it does show the high degree of similarity among the known Neandertals. This new specimen from Denisova (labeled "Altai") is a bit further from them than they are to each other, but not much. It will be interesting to assess this degree of similarity in comparison with the within-population similarity of more living human populations.

I'm reluctant to accept a dichotomy of "Denisovan" versus "Neandertal". Distinguishing the samples in that way invites a typological assumption about the ancient people, giving an impression of distinctness that I'm not yet convinced about. It remains to seriously investigate the hypothesis that one or both of these putative samples represents some amount of gene flow from each other, or from yet more ancient populations. But I suppose we're stuck with the "Neandertal from Denisova" and the "Denisovan from Denisova".

Unless we go for "manual genome" versus "pedal genome", which is admittedly unappealing.

There's not much meat in this announcement, that will wait for the full published analysis that we can expect later this year. The most important aspect of this, like the Denisova data availability from last year, is that we can now start working with the high-quality data. As someone who works with sequences, I cannot overstate the importance of having the best high-coverage data available for our work.

I have a paper in preparation where I make a relevant analogy, in this case noting last year's high-coverage Denisovan genome in comparison to the history of ancient DNA sequencing:

To put this into context: the original 360bp sequence from Feldhofer 1 has been memorialized on a cross-shaped plaque at the site outside Mettmann, Germany. This plaque is approximately 1 square meter in size. A similar monument to contain the Denisova high-coverage data would need to be more than 14 kilometers across. Compared to the first sequencing effort in 1997, today’s state of the art involves the generation of more than 200 million times more data.

It's a pretty awesome time for those of us exploring human evolution!

References

Alon Keinan and Andrew Clark have a short report in the current Science examining the effects of recent human population growth on the expected spectrum of human genetic variation [1]. Population growth skews the variation in a population so that there are many more rare alleles than would be expected in a constant-sized population.

Why is this? In a constant-sized population, individuals have an average of two offspring who survive to have offspring of their own. Many people have no children at all, or only one, while only a small proportion of people have more than four children. In the constant-sized population, a person born with a new mutation would have a 50% chance of passing it on to each child. In such a population, more than a third (36%) of mutations aren't passed on even once. The same fraction are inherited by only one child, and these face the same odds of extinction in the next generation. This isn't natural selection, it is random genetic drift -- and its net result is that most new mutations are lost.

In a growing population, individuals average more than two offspring. Every additional offspring increases the chance that a new mutation will be passed on to the next generation. In other words, more people means less genetic drift. As a population grows, new mutations begin to stack up at low frequencies in the population.

This is a very basic point in population genetic theory, and it interacts in a troubling way with the current generation of sequencing technology. Short-read shotgun sequencing yields a high number of false positive mutations, which must be aggressively filtered out of whole genome data. If we don't filter these out, we will arrive at incorrect conclusions about many aspects of human biology. The simplest means of filtering require some understanding of how many rare mutations you expect to find, in particular how many should be found in only one person in a sample of people. That expectation is different in a growing population, resulting in a potentially large bias.

Despite an improvement in the accuracy of sequencing technologies, some errors remain unavoidable. For example, with a sequencing error rate of 1 in 10,000 bases, in a sample of 10,000 individuals, each base pair will exhibit two errors on average across the sample and the majority of monomorphic sites will appear polymorphic (most often as a singleton or a doubleton; i.e., with the rare allele present in one or two copies in the sample). On the other hand, strict filtering of the data will lead to missing many rare variants because they are not observed as reliably. Hence, any analysis of large sample sizes must account for the uncertainty inherent in sequencing by considering the variant calls probabilistically, and secondary validation of rare variants by an alternate sequencing procedure is essential.

Keinan and Clark present some models that show how much it matters to consider a growing population compared to the usual null model of constant population size.

It's so interesting to me to see human geneticists catching up to where anthropologists have been for a long time. Of course, we wrote about the effects of recent population expansions in 2007, noting the apparent acceleration of positive selection in post-agricultural populations ("Why human evolution accelerated") [2].

Large-scale sequencing projects have moved beyond simply categorizing common genetic variation. They are now at a stage where thousands of individuals need to be examined, to find increasingly rare genetic variations and determine their collective effects on phenotypes. That means that the next version of the 1000 Genomes Project really needs to be involve many of us who are directly concerned with human population history. The growth and dynamics of actual historic human populations are going to matter to how we understand their genetic variation and its effects on phenotypes. Fortunately, archaeology and written history can help -- if anthropologists are involved in this work from the start!

References

Holly Dunsworth, at the University of Rhode Island, is undertaking a unique project with her undergraduate course this semester, providing 23andMe genotyping for every student. She describes some of her thoughts on the "cans of worms" that this may create for her: "First we were snapped, now we're SNP'd".

Part of what students have to do this semester is form a 'plan of action.' That's what I've called their assignment where they predict what their SNPs will hold and where they explain what they will do if they find out they're at high risk for a disease or even, yes, that they might not be related to their father. (This discovery doesn't require paternal DNA. Since half of your genome is from your father, and since a few traits are pretty simple, the rare participant with the rare SNP can deduce that they did not get their DNA from their father who doesn't show the trait in question.)

I've been discussing this issue a lot with people lately. In a few years, most of my students will have whole-genome genotyping or sequencing done for routine medical purposes, because that's how cheap it will be. Interpretation of the results will not (necessarily) be cheap, and it may not be appreciably better than it is now.

Some readers will say, "Well, if the interpretation isn't a lot better than now, nobody will want the results anyway, so it won't happen."

I disagree. Take Mendelian disorders. Today, every child in Wisconsin is tested for a few dozen genetic disorders at birth. It is already possible to screen parents for every Mendelian disorder with a frequency of more than one in a thousand. In a short time, that genotyping will be cheaper than the current postnatal testing. Prenatal care already includes a score of tests, and fetal cell genotyping may eventually replace postnatal testing for genetic disorders. Moreover, companies (for example, Counsyl) are already providing genotyping and interpretive services for the couples prenatal testing market. As genotyping becomes cheaper, it will pull in a broader and broader fraction of my students, future college graduates and professionals.

So I've taken it as my attitude that my biological anthropology courses must educate them for this future. Our curricula can provide the students useful information about health and ancestry, including both the promise and limits of genetic information. The beauty of the new genetic approaches is that they provide better illustrations of most of the classic topics in human biology and variation. You can see some of that at play in my Principles of Biological Anthropology lectures this semester.