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  • Mailbag: Genetics of schizophrenia

    Sat, 2011-09-03 14:49 -- John Hawks

    Re: Schizophrenia

    I am watching/listening to your Teaching Co. DVD lecture series on Human Evolution and very much enjoying it. I graduated from Beloit College in '68 with a BA in Anthro, and while I have tried to keep up with new discoveries, it has been haphazard. Your lecture series really helps me appreciate what huge progress has been made in this field since 1968.

    I recently retired from a career in Mental Health. I have wondered why schizophrenia is so common amongst humans and have thought it might be like sickle cell anemia.
    A very small dose of the schizophrenia complex of genes might be connected to our use of symbolism and creativity. A large dose might create the dysfunction of psychosis.

    Thanks for your research and for being able to express the material with such clarity and energy.

    Thank you so much for your kind words! We put so much work into doing the best lectures possible, and I'm really proud of the result.

    Your question about schizophrenia is one that really strikes at what evolutionary biologists are thinking about the subject. We've been thinking with our work on recent selection in human populations that we might find some selected genes with side-effects on cognition. Many human geneticists have been looking for genes that explain the risk of schizophrenia, and we know that there are a few common gene variants that affect risk. But it appears that most of the risk must be explained by gene variations that are found in one or a few families. It seems to be a case of "every unhappy family is unhappy in its own way."

    That makes it hard to find and understand the genetic causes, but as we move toward whole-genome sequencing and more and more observations on different families, we will begin to understand more about the causes.

  • HLA class-I loci in Neandertals and Denisova

    Thu, 2011-08-25 21:08 -- John Hawks

    With draft sequences of genomes from several Neandertals and from Denisova, we can begin to investigate known human variations that affect phenotypes. In practice, this is a very simple approach -- take alleles that we know exist in recent human populations, and see if they are in the DNA sequences of these ancient people. My lab has been following this line of research, trying to get information about aspects of biology that are not evident from the skeleton. The immune system is one of the most fascinating, both because of its extensive variation in living people, and because we might be able to test hypotheses about the diseases and parasites that ancient humans faced.

    Today Science has released an early manuscript edition of a paper by Laurent Abi-Rached and colleagues (bibliographic information not yet available), which identifies the HLA class-I alleles present in the three highest-coverage Neandertal genomes from Vindija (Vi 33.16, 33.25, and 33.26) and the Denisova pinky genome. The paper is very brief and fairly straightforward, providing provisional HLA class 1 allele types for these individuals, discussing possible haplotype associations among these alleles that may have been in the ancient genomes, and providing the frequency of those alleles in present-day human populations.

    These archaic individuals carried HLA types that are presently rare in Africa and more common outside of Africa, supporting the hypothesis that these alleles in living people originated in those archaic populations. The linkage between alleles at different HLA class-I genes also supports that hypothesis. The present immune system biology of humans was strongly shaped by the interaction of different regional populations of archaic humans.

    The title of the paper calls this "multiregional admixture", and the word "introgression" appears 8 times. Good for us!

    (This is the point where I grumble about the lack of citations in this paper....OK, done grumbling.)

    Selected genes may have a very different pattern from neutral genes

    This paper is the first demonstration that gene variants of functional importance were not only inherited from Neandertals and Denisovans but were valuable and selected in later populations.

    We already knew that humans today have gene variants from these archaic humans. Neandertal genes presently account for around 3 percent of the genomes of people outside Subsaharan Africa. My lab has been studying the pattern of frequency of these genes ("Europe and China have different Neandertal genes"). Most of the genes shared between the Neandertal genome and living people outside Africa are presently very rare -- most occur only in a single individual in our sample of Europeans and Chinese people, for example.

    These HLA class-I alleles are different. Some of them are quite common today. If they came from the Neandertals and Denisovans -- that is, if they were not present in the African people who make up most of our ancestry genome-wide -- then these alleles must have increased quite a lot during the recent evolution of people outside Africa.

    The best explanation for the large increase in frequency of these genes in modern human populations is selection. If readers want to get an introduction to the scientific literature on the topic of functional genes, I can suggest a detailed review paper I wrote with Greg Cochran on the dynamics of introgression and selection as applied to Neandertals [1], and a review paper we wrote in Trends in Genetics about identifying genes in living humans that that may have come from archaic populations [2]. In both papers, we discuss the dynamics of functional genes that may be affected by selection in modern human populations and how they differ from the predictions for neutral loci affected only by genetic drift. The new paper by Abi-Rached and colleagues follows on that line of inquiry.

    I think the hypothesis of adaptive introgression is very likely, and that we shouldn't be at all surprised that the immune system might house many good examples of it.

    A look at the most extreme examples, involving the Denisova genome, shows the extent that these functional genes might reflect introgression well beyond that indicated by most of the genome. The HLA class-I alleles present in the Denisova genome are most common today in South Asia (HLA-C*12:02, HLA-C*15 which is also common in Australia) and Southeast Asia (HLA-A*11). These regions of the Old World have no substantial evidence of Denisova inheritance across their genomes. Yet they may very well have substantial frequencies (up to 48 percent for HLA-A*11) of HLA class-I alleles from the archaic Denisovan population.

    Reasons to be cautious

    This is the point where I have to make a note of caution. Even though I personally think it is likely that these HLA alleles really did introgress into the modern population from Neandertals and Denisovans, their geographic pattern really isn't enough to demonstrate this without question.

    Reports earlier this summer described some of the work this group was doing on HLA class-I loci, including a public lecture by PI Peter Parham. I noted at the time that the geographic distribution of the alleles mentioned in that lecture seemed a mismatch for the hypothesis of a Denisovan origin for the alleles ("The immune systems of archaic humans"). For example:

    HLA-A*11 is very common in Papua New Guinea, but it is also very common in north India and in China. These two areas otherwise show no significant evidence of Denisova ancestry. We might conclude that the HLA-A gene just has an unusually high level of introgression into Asian populations, not typical of the genome as a whole. That's certainly possible. But without finding any substantial number of derived mutations in the HLA-A*11 variant in the Denisova genome and in living Asians, it is hard to rule out that the sharing of HLA-A*11 in all these populations is just coincidence.

    Of course, if the allele were absent in Africa, that would weigh in favor of the idea it is shared by Late Pleistocene interbreeding outside Africa. But HLA-A*11 is in Africa, just very rare. And it's in Europe. This is the kind of locus that is difficult to interpret: if it has any tiny disadvantage against malaria, for instance, its rarity in Africa is easily explained as a function of recent evolution, while its presence almost everywhere outside Africa would be no surprise even if there were never any interbreeding.

    The story of HLA-C*12:02 is similar. It's common in PNG, but also broadly across South Asia and into Iran, areas where no substantial evidence of Denisovan ancestry has been demonstrated.

    Introgression under selection is a good hypothesis for why these alleles should be so much more broadly distributed than the evidence from the rest of the genome. But introgression isn't the only explanation, because the alleles might have been retained by balancing selection, with recombinant haplotypes suppressed by purifying selection. We might use haplotype age to test the hypothesis. If the alleles were retained by ILS, they would look much older than if they came in from an archaic population by introgression. But as I'll describe below, in this case we actually have the opposite problem: these haplotypes look too young to have come in by introgression, likely a consequence of selection long after the Neandertals and Denisovans had contributed their genes to us.

    The curious case of HLA-B*73

    If I agree that the results of this paper are pretty likely, why am I still cautious? Well, the most confusing thing in this paper is an allele described in great detail that they didn't find in the archaic genomes. And I know from experience that not finding things is a pretty common occurrence when we go looking for odd things that might have come from Neandertals.

    There's a detective story here, that probably explains the initial interest of this group in the Neandertal genome, but that just didn't pan out in their search through the archaic genomes. The allele is HLA-B*73.

    Parham and colleagues [3] first characterized this allele, which is remarkably different from other HLA-B alleles. Homologs of HLA-B*73 are present in living apes, suggesting that the different human alleles originated before we diverged from gorillas. The retention of such an ancient allele in humans isn't a surprise in the HLA system, because many very divergent alleles have been kept in the population across evolutionary time by balancing selection. What's a bit surprising about HLA-B*73 is its limited diversity in living people. It appears to have persisted in humans throughout our evolution, but people today who carry the allele have very similar sequences, and it is nearly always linked to one single allele at the nearby gene, HLA-C (HLA-C*15). Also, the allele is very rare inside Africa and reaches its highest frequency in West Asia., where it occurs in only 4.5 percent of people. Because of this strange pattern, Parham and colleagues suggested that the allele may have been inherited from Neandertals.

    When I was in graduate school working on modern human origins, I took a special interest in genes that had this pattern of variation. HLA-B*73 was not the only one, there are others.

    The variation of the HLA-B*73 allele and its association with HLA-C*15 correspond very well to the predictions we presented in our paper on identifying introgression from archaic humans [2]. It's a highly divergent allele in humans compared to others, and it appears not to have recombined much with nearby genes, suggesting it was sequestered in another population through much of the diversification of present-day HLA alleles. But the HLA system is actually a rotten place to look for this kind of evidence, because there are many, many instances where ancient alleles have been retained in human populations by balancing selection. As we pointed out in 2008, a deep root to the gene tree and a rarity of recombination can be good evidence of introgression, but balancing selection and inhibitions to recombination are alternatives to introgression for explaining this pattern of variation.

    There's no necessary contradiction between the two processes, and ancient DNA in this case could establish that the allele was both under selection and came from archaic humans. The problem: they didn't find the allele in the archaic genomes.

    So why did they spend so much time in this paper discussing this allele? My guess is that they were surprised not to find it. But they did find HLA-C*15 in the Denisova genome, which is often linked to HLA-B*73 in living people who carry it. That makes for an indirect argument:

    C*12:02 and C*15 were formed before the Out-of-Africa migration (Fig. 2H and fig. S15) and exhibit much higher haplotype diversity in Asia than in Africa (fig. S16), contrasting with the usually higher African genetic diversity (20). These properties fit with C*12:02 and C*15 having been introduced to modern humans through admixture with Denisovans in west Asia, with later spreading to Africa (21, 22) (Fig. 1F and fig. S11 for C*15). Given our minimal sampling of the Denisovan population it is remarkable that C*15:05 and C*12:02 are the two modern HLA-C alleles in strongest LD with B*73 (Fig. 1E). Although B*73 was not carried by the Denisovan individual studied, the presence of these two associated HLA-C alleles provide strong circumstantial evidence that B*73 was passed from Denisovans to modern humans.

    I would go one simpler: Given that HLA-B*73 is most common today in West Asia, I suspect it came from West Asian Neandertals. There's no reason why the HLA genes of European Neandertals should have been identical to West Asian Neandertals. Today's Europeans are different from today's West Asians in the frequencies of these alleles, so why not in the past as well? For that matter, we really only have two alleles from European Neandertals for HLA-B (since the paper finds that

    Why do the Vindija Neandertals all have the same HLA types?

    It's a pretty good question. The paper cannot distinguish the genotypes from these three individuals. That's not the same as saying they're exactly the same type, since the sequences are very low coverage, but probably they were. Here's what the paper says:

    Genome-wide analysis showing three Vindija Neandertals exhibited limited genetic diversity (3) is reflected in our HLA analysis: each individual has the same HLA class I alleles (fig. S17). Because these HLA identities could not be the consequence of modern human DNA contamination of Neandertal samples, which is <1% (3), they indicate these individuals likely belonged to a small and isolated population (fig. S18).

    Still, I think this indicates a pretty high degree of inbreeding among these individuals. I wonder what the organ registry for Neandertals would have looked like.

    (Not so) final words

    I have more to write on the topic of linkage disequilibrium among these genes. The rate of recombination between HLA-B and HLA-C is high enough that a haplotype between these genes should have mostly decayed in the time since our mixture with archaic humans. HLA-C and HLA-A are an order of magnitude further apart, so linkage between alleles of these genes should have been totally erased in the time since any archaic admixture.

    That means that the extended haplotypes reported in this study must reflect selection in the period since the population mixture and introgression. The story isn't a simple case of inheritance from archaic humans, it is rather more complex. But more on that later.

    I think this paper confirms that it will be really productive to look at archaic genomes for variants present in living humans. Identifying modern human alleles in a Neandertal isn't really very exciting science, though. I've been doing this on my blog for a year now. It's a tricky job to type these HLA alleles, compared to genotyping many other genes, as we discovered. Still, I never really expected that reporting on genotypes in the public domain would be sufficient to get printed in Science.

    Still, this set of three genes is particularly interesting. And the paper does add evidence from one additional locus, KIR3DS1, which also has the pattern where an allele rare in Africa but common in Asia is present in the Denisova genome.

    If it turns out that we have widespread adaptive introgression in Asia today from Denisovans, that will change the game of studying the origins of these populations. Based on the genome-wide comparison, it looks like the genetic interaction that led to the habitation of Asia did not involve Denisovans, who contributed only to populations at the most eastern extreme of habitation in island Southeast Asia. But the only Denisovans we know about lived near the geographic center of the Asian landmass, not at the extreme southeastern extreme.

    The HLA pattern may suggest a more widespread pattern of mixture across Asia, which was later overwritten by population movements of people who didn't have Denisovan ancestry. That means that the habitation of Asia was a process of successive migrations and replacements, which imperfectly covered up the evidence of archaic intermixture. The genes that remain as signs of this intermixture are those that had selective advantages in later populations.


    References

    Synopsis: 
    Abi-Rached and colleagues report that the human system owes much to the Neandertals and Denisovans.
  • Y chronology awry

    Wed, 2011-08-24 09:57 -- John Hawks

    Dienekes links to and discusses a current paper by George Busby and colleagues [1] on the Y chromosome chronology for the settlement of Europe: "Back to the drawing board for R-M269 (Busby et al. 2011)." The main idea is that microsatellite loci on the Y chromosome have made up the majority of our information about biogeography using this marker, but the rate of mutational changes of these loci has been badly misapplied:

    A bad clock is not useless: it gives you some information about time. Moreover, you can often use several to iron out the inaccuracy of any single one of them.

    Unfortunately, better estimation through averaging of bad estimators works only in one case: when the estimators are unbiased.

    The inclusion of some fast-mutating STR loci tends to make all estimates too young. The paper finds that this problem is general, affecting most commonly-used datasets.

    Our analysis confirms that this phenomenon is not specific to the R-M269 haplogroup nor to methods using ASD. Figure 4b shows that STRs with high D produce larger estimates of T. What is clear is that estimates of T implicitly depend on the STRs that are selected to make this inference. Using BATWING on an HGDP population for which 65 Y-STRs are available, we have shown that the median estimate of TMRCA can differ by over five times when STRs are selected on the basis of the expected duration of linearity (electronic supplementary material, figure S4). While researchers take into account STR mutation rates when estimating divergence time with ASD, commonly used STRs do not have the specific attributes that allow linearity to be assumed further into the past. The majority of haplogroup dates based on such sets of STRs may therefore have been systematically underestimated.

    One weakness of the study is that its reliance on geographic patterns of the haplotypes depends on the assumption that they have evolved neutrally relative to each other. Selection might radically affect this pattern.


    References

  • Selection for smaller brains in Holocene human evolution

    Mon, 2011-08-22 18:32 -- John Hawks
    Research authors: 
    Publication information: 

    This a pre-publication manuscript. Please contact the author for information about citation.

    Work status: 

    This is a completed manuscript in the process of submission and review. The findings have not been peer-reviewed, but I am confident in the analysis and quality of citations.

    Abstract: 

    Background: Human populations during the last 10,000 years have undergone rapid decreases in average brain size as measured by endocranial volume or as estimated from linear measurements of the cranium. A null hypothesis to explain the evolution of brain size is that reductions result from genetic correlation of brain size with body mass or stature. Results: The absolute change of endocranial volume in the study samples was significantly greater than would be predicted from observed changes in body mass or stature. Conclusions: The evolution of smaller brains in many recent human populations must have resulted from selection upon brain size itself or on other features more highly correlated with brain size than are gross body dimensions. This selection may have resulted from energetic or nutritional demands in Holocene populations, or to life history constraints on brain development.

    Background

    An increase in brain size was one of the major trends of human evolution [1][2]. At the beginning of the Pleistocene, the average endocranial volume of fossil Homo specimens was approximately 750 ml [3]. By 30,000 years ago, this average value had increased to nearly 1500 ml [1][2]. Much of this increase occurred within the period following 800,000 years ago [1][2], during which mean endocranial volume in \emph{Homo} increased by approximately 70 ml per 100,000 years. This trend occurred in all regions of the Old World [2], which may have included either a single [4][2] or multiple species of archaic Homo [5][3].

    Less well known is that the terminal Pleistocene and Holocene (ca. 30,000 years ago to present) witnessed a substantial decline in endocranial volume [6][7][1]. This decrease occurred within modern \emph{Homo sapiens}, and has been observed in many parts of the world [6][7][8]. The scope of this decrease is remarkable: for example, within the past 10,000 years the average endocranial volume in European females reduced from a mean of 1502 ml to a recent value of 1241 ml [7]. This decrease of approximately 240 ml in 10,000 years is nearly 36 times the rate of increase during the previous 800,000 years.

    Brain size is related to body size both across higher taxa [9] and within humans [10]. This suggests the hypothesis that changes in human brain size may result from changes in body size. For example, the larger brain size in early Homo compared to Australopithecus may reflect the simple expansion in body size from earlier hominids [11]. This explanation cannot explain every change in brain size in humans: for example, the long increase in brain size during the Pleistocene did not coincide with increases in body size [3].

    What about the reduction in brain size during the last 10,000 years—can it be explained by a reduction in the size of the body? Human body size, as measured from skeletal dimensions, did reduce during the past 30,000 years, at least in some populations [6][7][1][12]. This reduction influenced both mass and stature [7][1][12]. A reduction in overall body size may have resulted from Late Pleistocene and Holocene subsistence strategies, which replaced close-contact ambush hunting of large mammals with projectile weapons, intensive collection of small animals, fish, and shellfish, and ultimately sedentary pastoralism and agriculture [13]. Nutritional inadequacies and disease during the Holocene also may explain reductions in body size [14]. Within Europe, where the trend has been most closely studied, body size rebounded within the past 1000 years as manifested by increases in stature [7].

    Several workers have suggested that recent reductions in brain size may have been caused by reductions in body size [6][7][1][15]. A coincidence of reduction in both these measures would lend some support to that hypothesis. However, for a reduction in body size to be a sufficient explanation for reduction in brain size, it is not enough that the reductions occurred at the same time. Natural selection on one character (like body size) will affect a correlated character (like brain size) only to the extent that the two characters are heritable and are genetically correlated. Therefore, to test the hypothesis that selection on body size accounts for reductions in brain size in recent human evolution, we must consider the relationship and genetics of these characters within human populations.

    Here, I apply a quantitative genetic model to test the hypothesis that Holocene evolution of brain size may be explained by reductions in body size. The reasons for reduction in body size are unclear, so I consider both body mass and stature as candidates for the target of selection in recent populations. This is a very limited approach, constrained to published estimates of endocranial volume in archaeological populations and estimates of phenotypic correlations and heritability from samples of living humans. No attempt is made to correlate brain size and body size in the same samples of archaeological specimens, as such data are not available at present. Instead, I estimate the amount of body size change that would be necessary to explain the observed change in endocranial volume. This estimate is then assessed for credibility as applied to archaeological samples.

    Results and Discussion

    Body mass

    Body mass is related to brain size in humans with a phenotypic correlation of r≈0.29. The standard deviation of male body mass within recent human populations ranges around 11 kg, a value near the midpoint of within-sex variation in other primate species [16]. Using these values along with the others listed in Table 1, selection on body mass would be expected to reduce the mean endocranial volume by 4.3 ml for each kilogram of reduction in body mass.

    The decline in body mass in human populations during the last 10,000 years has been estimated as less than 5 kg, or less than a 10 percent reduction in mass from a Late Upper Paleolithic mean of some 63 kg [1]. A decline of 5 kg would predict a decrease in endocranial volume only around 22 ml. The observed decline in several regions (including Europe, China, Southern Africa, and Australia) is between 100 and 150 ml during the past 10,000 years. Therefore, the reduction in body mass would be expected to have decreased brain size by only one-fifth to one-seventh the observed decline.

    We can look at the inverse question: how much reduction in body mass would be required to cause a 150 ml reduction in endocranial volume? Using the same ratio (4.3 ml per kilogram body mass), the endocranial volume contrast would predict a reduction of 34 kg. This value is implausibly high, by more than a factor of five.

    The reduction of endocranial volume in these populations is not well explained by body mass according to equation 1. Selection for smaller mass is insufficient to account for reduction in brain size or vault dimensions.

    Stature

    Applying equation 1 to the parameters for stature and its correlation to brain size, endocranial volume would be expected to change approximately 9.5 ml per centimeter change in stature. This value is less extreme than the reduction in body mass that would be necessary to achieve the same reduction in brain size. But the skeletal record is inconsistent with any great decrease of mean male stature, particularly during the post-Neolithic time period.

    Stature estimates exist for a broad sample of ancient European populations, showing approximate stasis in stature during the last 4000–6000 years. Over the same time period, the estimated endocranial volume declined slightly more than 100 ml in Europe from an estimated 1496 ml to 1391 ml. This decline cannot be explained by decreases in stature, because the stature did not change. Additionally, although these early samples are small, Mesolithic Europeans had larger endocranial volumes than Upper Paleolithic Europeans, across the same interval when they underwent a substantial decline in stature. That Mesolithic change in endocranial volume is in the opposite direction expected from the change in stature.

    Likewise, the femur lengths of foragers in Southern Africa showed no net decrease over the last 10,000 years. From 5500 to 2500 years ago, both femur length and femur head diameter declined in this region, but they rebounded within the last 2500 years [17]. Across the same 10,000-year time period, Henneberg and Steyn [8] documented a decline in external and internal cranial module. The sample of LSA foragers (before 2000 years BP) had a mean external cranial module of 154.7, Iron Age (2000--200 years BP) had a mean of 149.6, while recent foragers had a mean of 150.3 --- roughly a standard deviation lower than the pre-2000 BP value. Under the hypothesis that change in endocranial volume is predicted by the change in stature, we should predict no net change in endocranial volume in this population. But the reduction in external module corresponds to a reduction in endocranial volume between 100 and 150 ml [8]. However, the LSA sample in that study is very small (n=12) and temporally dispersed.

    Early Holocene populations in Australia have produced a substantial sample of crania, but postcrania from this time period are rare or poorly preserved [18]. The net change in endocranial volume, roughly 130 ml from the terminal Pleistocene to late Holocene skeletal sample [19] would predict a reduction in stature of 13 cm, if the brain size had changed only because of correlated changes in stature. That degree of stature reduction is not biologically impossible although it would be extreme. Further investigation of the evolution of body size in recent Australian hunter-gatherers may be necessary to answer the question.

    Why did brain size reduce during the Holocene?

    The evidence suggests substantial reductions in brain size in some recent human populations, more than can be explained by correlated changes in body size. It is worth discussing two related points concerning the distribution and causes of this pattern of brain size evolution.

    First, was the change global or local in scope? The samples here cover several far-flung geographic areas, but they do not cover all regions of the world. Beals, Smith and Dodd [6] reviewed the global evidence for endocranial volume and showed a decline in the available terminal Pleistocene to Holocene skeletal sample. The Late Pleistocene skeletal sample was in that case strongly biased toward Europe, an area that in contemporary humans has a relatively large average endocranial volume. Thus, it was not obvious whether geographic differences in sampling might explain the reduction in endocranial volume noted in the study. This problem also characterizes the somewhat more course sampling by Ruff and colleagues [1]. Here, the samples of endocranial volumes and body sizes are matched in region to the extent possible; they do represent probable evolutionary trends within these populations. But there are few other comparable sequences of skeletal samples, so it may not be possible to conclude strongly that the reduction in brain size generalizes outside these regions.

    A large series of crania from ancient Nubia covers the period from roughly 3400 years ago to 600 years ago [20][21]. Samples show a slight trend toward decrease in the major length, breadth and height measurements from Iron Age (Meroitic, external cranial module 145.2) to Medieval (Christian, external cranial module 143.9) times, but the intermediate series of crania (X-Group, external cranial module 147.1) is somewhat larger in these dimensions than either of the other groups. In this context it would be misleading to speak of a reduction in cranial vault size in this region. Across the same time interval, these samples show a substantial reduction in facial and dental measurements [21].

    Second, given that the pattern is widespread if not global, how can we explain the reduction in brain size? Several hypotheses have been presented that may help to explain recent brain evolution. It is beyond the scope of this paper to test these hypotheses but here I review several of the adaptive and non-adaptive alternatives with some notes relating to the observed pattern.

    1. Chance. Genetic drift may be considered a null hypothesis for any slight morphological change. However, in the case of brain size evolution during the last 10,000 years, genetic drift is a markedly unlikely hypothesis. Endocranial volume changed by a standard deviation or more, rapidly and directionally, within some very numerous and growing post-agricultural populations.
    2. Plasticity. Somatic development in humans is plastic to some degree, depending on uterine and childhood nutritional and disease environments. This plasticity underlies most of the recent secular trend in body mass and stature. However, the brain size reaches 90 percent of its adult value very early in development and most of the variance in living populations is additive. This suggests that brain size may be less plastic than other components of body size. The pattern of decrease does not match stature or mass across the last several thousand years in these populations, suggesting that environmental effects were probably mediated by genetic factors.
    3. Climate. Beals, Smith and Dodd [6] presented correlations between endocranial volumes of populations and their local climate, as reflected by latitude or temperature. Smaller-brained populations live in warmer climates, and this relation cannot be explained entirely in terms of body size of contemporary populations. They proposed that post-glacial climate change may have favored smaller brains. However, if the link between climate and brain volume is not mediated through body mass (following Bergmann's rule), it is not obvious why climate should cause brain size reduction.
    4. Nutrition. The diets of early agriculturalists were nutritionally challenging in several ways: low in protein content, sometimes low in essential vitamins, and subject to fluctuating supply. The brain is an energetically expensive organ and nutritionally costly to develop. Smaller brains on balance should be advantageous under energetic or nutritional constraint, if they are functionally equivalent. Larger Holocene populations may have been selected for smaller brians for energetic reasons.
    5. Function. Smaller brains may have some functional implications, as white matter tracts are shorter and functional areas of the cortex may be more compact. Given the social and ecological changes of the Holocene, it is possible that a different mix of mental and cognitive functions was the target of selection. Despite the long Pleistocene history of human brain evolution, it would be fallacious to assume that larger brains were always adaptive in the context of cognitive changes.
    6. Development. Although adult brain size is attained relatively early in development compared to adult body size, brain development continues during adolescence and early adulthood. It is possible that the life history evolution of recent humans has involved changes in the maturation schedule that would impact the ontogeny of brain maturation. If so, then the schedule of brain development after it attains adult size might have been constrained by earlier events, in such a way that faster development or smaller completed size was advantageous.

    These hypotheses are not mutually exclusive. To assess them, it will be necessary to collect systematic data from a large sample of crania representing these and other regions of the world. This study represents only an early step toward understanding the cross-regional record of brain size evolution in the Holocene.

    Comparative data may also be useful to resolve these hypotheses. The decline of human endocranial volume during the last 10,000 years is paralleled most obviously by the reductions of brain size in domesticated animal species, including dogs, cattle and sheep, compared to their wild progenitors. Nutritional, developmental, and functional issues are all possible explanations for these parallel cases of brain size reduction. Humans are different in many ways from these domesticated species, but exhibit other parallel trends such as decreased skeletal robusticity.

    At present, the literature presents a relative hodge-podge of estimates of endocranial volume, based on different original measurements. Estimates taken from the same method are compatible with each other, but it is not obvious that estimates based on different methods can be reconciled. It would be valuable to replace this mixture of measurements with a standard morphometric profile. The size of the endocranial cavity is interesting because of the developmental and energetic aspects of brains. But size is only one aspect of recent brain evolution. A full accounting of the shape of the cranial vault or endocast will be necessary to test hypotheses about why and how the brain reduced in size in these Holocene populations.

    Conclusions

    The available skeletal samples show a reduction in endocranial volume or vault dimensions in Europe, southern Africa, China, and Australia during the Holocene. This reduction cannot be explained as an allometric consequence of reductions of body mass or stature in these populations. The large population numbers in these Holocene populations, particularly in post-agricultural Europe and China, rule out genetic drift as an explanation for smaller endocranial volume. This is likely to be true of African and Australian populations also, although the demographic information is less secure. Therefore, smaller endocranial volume was correlated with higher fitness during the recent evolution of these populations. Several hypotheses may explain the reduction of brain size in Holocene populations, and further work will be necessary to uncover the developmental and functional consequences of smaller brains.

    Methods

    Endocranial volume

    Studies of skeletal samples from different regions of the world are very consistent in finding reductions of endocranial volume during the last 10,000 years [6][22][7] [19] [23] [8][24]. However, there are discrepancies among studies in the both the method of estimation and the time periods for which skeletal samples are available. These are listed in Table 1.

    Estimation methods

    The literature on brain size in archaeological specimens refers to several different measurements:

    1. Endocranial volume: directly measured by mustard seed, shot or water displacement of endocasts, or estimated from tomographic (CT) or magnetic resonance (MRI) methods. These different measurement methods can lead to systematically different results and so should not be combined without accounting for the measurement bias. The endocranial volume is larger than the brain volume (because of the intervening fluid and meningeal membranes).
    2. Brain weight: directly measured from cadavers or estimated from CT or MRI based on brain volume and estimated tissue density.

      Some notable large-sample studies of variation within contemporary human populations have examined brain weight [25]. Brain weight and endocranial volume are strongly correlated but not identical. The volume of the skull includes fluid and tissue components that are not included with cadaver brain weights, while different means of preservation of cadaver brains may inflate the variability of some brain weight datasets. The problems of brain weight measurement are not directly relevant to archaeological samples, where there are no brains to weigh. But brain weight remains important because of the present-day samples in which we can estimate the phenotypic correlation of brain and body size. Where possible, I have included present-day samples that include either endocranial volume or cranial measurements, for direct comparability with the archaeological samples.

    3. Cranial module: The external cranial module is the arithmetic mean of three external measurements of the skull: maximum length (glabella-opisthocranion), maximum breadth (euryon-euryon) and cranial height (basion-bregma). These external measurements include not only the brain but also the thickness of cranial bones.

      In some populations considered here, the thickness of cranial vault bones declined during the Holocene. This means that a decrease in the external module may be explained in part by a decrease in thickness, and some correction must be made to consider endocranial volume. The effect of thickness can be quite substantial; a decrease of 5 mm of thickness around a skull with an external module of 160 mm would increase its endocranial volume by around 180 ml. Where measurements of thickness are available, one approach is to subtract twice the vault thickness from the external module, resulting in an internal cranial module. This is the approach taken by Henneberg [7], for example, who reports both internal cranial module and resulting estimates of endocranial volume derived from regression on internal module.

    The current paper uses the generic term ``brain size'' to refer to any of these estimation methods. Each of the four regions considered here is represented by at least one study that uses consistent estimation methods within the region. Even though different regions may be characterized by different methods of estimation, these differences should not bias the results within each region. But when different regions produce a common result, it remains possible that the magnitude of changes may actually diverge from each other due to differences in estimation methods.

    One fundamental problem remains. Estimates of heritability and brain-body phenotypic correlation within human samples typically involve brain weight (for autopsy studies) or brain volume (for MRI or CT studies). Estimates from skeletal samples typically involve endocranial volume or cranial module. We cannot know that the heritability of the skeletal measures is equal to that of the soft-tissue measures.

    Regions

    The literature includes sufficient data to consider the reduction of brain size in four regions of the world.

    The greatest temporal detail is available from Europe, reviewed by Henneberg [7]. Samples of up to several thousand skulls have estimates of endocranial volume. The largest set of these are based on external measurements, corrected for average vault thickness. The literature also includes a substantial number of direct measurements of endocranial volume by seed or water displacement. Henneberg [7] reports a Mesolithic mean endocranial volume for males of 1567 ml (based on internal cranial module of 144.1). This estimate is based on a relatively small sample of 35 individuals. For Neolithic and Eneolithic samples, with 1017 individuals, the mean endocranial volume estimate reduced to 1496 ml (internal cranial module 141.9), Bronze and Iron Age samples had a mean estimate of 1468 ml (internal cranial module 141.0), Roman period mean estimate 1452 ml (internal cranial module 140.5), and Early Middle Ages 1449 (internal cranial module 140.4). Late Middle Ages had a mean estimate 1418 (internal cranial module 139.4), and ``Modern Times'' (which comprises post-Medieval samples) corresponded to a mean estimate of 1391 ml (internal cranial module 138.5). Female samples across this time period exhibited a similar degree of size change; from a Neolithic mean of 1373 ml to 1210 ml in the ``Modern Times'' sample.

    Henneberg's study was notable for its discussion of the limitations of these data, which are compiled from many sources. The reliance on external dimensions does tend to increase the interstudy comparability of the values, but necessitates relying on regression predictions of endocranial volume, which necessarily involve some error. The overall change is substantial enough to overcome the plausible methodological inconsistencies, but it is appropriate to be cautious between time intervals (e.g., Early to Late Middle Ages) where the amount of change is minimal.

    Endocranial volume in southern Africa was considered by Henneberg and Steyn [8], estimating from measurements of external and internal cranial module. The sample covers the time period after 30,000 radiocarbon years BP, however, the vast majority of specimens date to the last 2000 years. Henneberg and Steyn [8] showed a statistically significant decline in both male and female crania, separated by morphological criteria.

    Much of this sample, together with a larger selection of archaeological crania, were included in a later study by Stynder and colleagues [26] using morphometric methods. This study demonstrated an increase in craniofacial size during the last 4000 years, which appears to contradict the findings of Henneberg and Steyn [8]. The resolution between these two results is twofold. Most obviously, Stynder and colleagues [26] did not include landmarks that would indicate cranial breadth across the parietals, as these are not easily digitized. The breadth values are those showing the most consistent decreases in the sample studied by [8]. Secondarily, Stynder et al. [26] included facial measurements in their sample, so that the centroid size of crania was determined by both facial and vault dimensions. The allometric shape analyses in this paper demonstrated that larger centroid size was associated with allometric increase in the face and relative decrease in the vault. The implications of this allometry for the absolute vault dimensions are not clear, although the direct measurements indicate a reduction in vault size for the sample measured by Henneberg and Steyn [8]. It would be valuable to look at these allometric questions comprehensively with both landmark and caliper measurements in the southern African sample.

    Brown and Maeda [22][19] reported on diachronic change of skeletal measurements in Holocene north China and Australia. They showed that the endocranial volume of males decreased from a mean of 1510 ml in early Neolithic (5500--6000 year old) samples down to 1400 ml in present-day Chinese. The change is consistent with a trend toward decrease across time intervals, despite relatively small sample sizes (n=10 to n=20 in the archaeological samples). Present-day Chinese people appear to vary in cranial size from north to south, possibly by more than 100 ml [19][6], and it is not obvious which samples of contemporary Chinese make the most relevant comparisons. So a decrease of 100 ml over the last 6000 years may either overstate or understate the actual change in endocranial volume in this population.

    Wu and colleagues [27] confirmed the trend toward smaller cranial size from Bronze Age to recent northern Chinese populations. The study included a much larger sample of crania than examined by Brown and Maeda [22], but endocranial volume itself was not measured. The length, breadth and height of the skull all underwent significant reductions from the Bronze Age, roughly 3000 years ago, to the present.

    Brown [19] presented a comparison of 19 male Australian crania from the terminal Pleistocene and 23 contemporary crania of Aboriginal Australians. The terminal Pleistocene sample stretches across a substantial range of dates, the earliest specimens possibly older than 30,000 years, to as little as 9000 for the large Coobool Creek sample. The Pleistocene people were larger in body size than recent Australians, and exhibit larger teeth and greater skeletal robusticity. The mean endocranial volume of the terminal Pleistocene males is 1405 ml; the recent mean is 1272 ml, for a decrease of just over 130 ml.

    In qualitative terms, the strongest documentation of the decline in endocranial volume is from Europe, due to both sample size and sample preservation. The other three skeletal samples show a comparable magnitude of decrease. In China, this decline occurred over roughly the same time interval as in Europe; in South Africa and Australia the reductions may have unfolded over a longer period of time. In all cases, the estimated reduction of endocranial volume was greater than 100 ml within males, roughly 7 percent of the mean.

    Mass and stature

    Like brain size, stature and body mass provide challenges in the archaeological record.

    Mass is a parameter of fundamental biological interest, but it depends strongly on soft tissue body composition and is therefore estimated only with substantial error from skeletal samples. In a global survey of the Pleistocene human skeletal record, Ruff and colleagues [1] estimated a mean body mass for Late Upper Paleolithic humans as 62.9 kg; this estimate was derived from 71 skeletal specimens, mostly from Europe. The ``living worldwide'' value cited in that study was 58.2 kg, a reduction of less than 5 kg from the Late Upper Paleolithic value, although the samples are geographically inconsistent.

    Stature should be a better proxy for body size in the archaeological record, because it exhibits less phenotypic plasticity and because it relates more directly to measurable skeletal quantities such as long bone lengths. This increases the geographic sample available to test hypotheses of temporal change, because either long bone lengths or stature estimates exist for Europe, Southern Africa, and China.

    Frayer [13] reported an Upper Paleolithic male mean stature of 174 cm with a standard deviation of 9.4 cm. The Mesolithic male mean stature in that study was 165 cm with a standard deviation of 6.6 cm. The reduction in female stature values was concordant with the male values, with roughly half the number of sampled individuals. Maximum femur length reduced from 466 to 446 mm in male individuals between these time periods, with standard deviations of 38 and 29 mm, respectively.

    Henneberg [7] lists a series of stature estimates from rural Poland since the 13th century. Both male and female statures were in approximate stasis over that time period, until the 19th century. Koepke and Baten [28] put together a broader sample of anthropometric measures from across Europe during the last 2000 years, and also concluded that heights had been ``stagnant'' across that interval. Brief excursions of stature in some parts of Europe may nevertheless have occurred. Steckel [29] collated a series of stature estimates from Northern European skeletal samples dating from the 9th to the 19th centuries. Across this region, the mean male stature declined from roughly 173.4 to a low of 166.2 cm during the 18th century, a reduction of 7 cm. That decline may have been presaged by an increase in the post-classical period suggested by the data of Koepke and Baten [28]. Neither trend was noted in the samples considered by Frayer [30] or Henneberg [7].

    Sealy and Pfeiffer [31] measured and performed stable isotope composition analysis of femora from the Cape region of South Africa, dating to the last 10,000 years. The male-attributed femora with measurable lengths in this study date to the period between 6000 and 1000 years ago. They show no significant decline in maximal length across this period. Femoral head diameter reduced slightly and significantly between the earlier male sample (before 4000 years ago) and later males (between 1000 and 4000 years ago). Pfeiffer and Sealy [17] revisited this sample and added evidence from more recnet skeletal individuals. The results showed that stature tended to rebound to a larger mean within the last 2000 years, roughly equal to the initial sample before 6000 years ago. Across this entire time period, the stature and mass of the archaeological population was within the range exhibited by present-day Khoisan peoples.

    The documentation of stature by long bone lengths is the best available source of data on body size in archaeological samples. Conservatively, we can conclude that the skeletal record documents a modest reduction of stature since the Upper Paleolithic in Europe, most of which had occurred by the Mesolithic. In Europe and China, the skeletal record is consistent with approximate stasis of stature during the last 5000 years, with some geographic and temporal excursions from the broad pattern.

    Body mass is unlikely to have changed is a very different pattern from stature. Fatness is poorly documented skeletally and is at present the largest component of variation in within-sex mass in industrial populations, but this varied much less substantially in pre-industrial peoples.

    Quantitative genetic model

    For both body size parameters, the error of skeletal estimates is substantial. Therefore, here I adopt a very conservative test of the null hypothesis: (1) Determine the amount of change in body size that would minimally be required to explain the observed change in brain size; and (2) Evaluate whether that amount of change in body size is credible given the skeletal record. The skeletal record addresses point (2), but for point (1) we must turn to a quantitative genetic model relating the evolutionary dynamics of correlated characters.

    The allometry of brain and body size has been investigated extensively among both living and fossil organisms. From a quantitative genetic perspective, Lande [32] developed mathematical expectations for allometric change in the population mean of a single phenotypic character in response to selection on a correlated character. This change is given by Equation 2b in Lande (1979) [32]:

    Equation 1

    [note: HTML is difficult to represent bar over letters; these are z-bar in the manuscript]

    Δzizb indicates the change in the population mean zi of one character (here, endocranial volume) with a correlated change in the mean zb of a selected character (here, body size). The genetic correlation between the two characters is γib, while hiσi is the square root of the additive genetic variance of character i.

    For this study, the null hypothesis is that brain volume should be predicted by equation 1, given the parameter estimates and the change in body size. This is equivalent to the hypothesis that brain size has changed entirely due to its genetic correlation with body size. The parameters in equation 1 have all been estimated in one or more contemporary human populations.

    It is important to note that parameter estimates may be conservative or nonconservative in their effects under the null hypothesis. The genetic correlation of the two traits must be less than 1. So measuring change in units of standard deviations, the null hypothesis predicts that brain size should change relatively less than body size. However, the absolute change must be considered relative to heritability and variance of the two phenotypic traits. Brain size should change more relative to a given change in body size if:

    1. the genetic correlation of brain and body sizes is higher,
    2. the heritability of brain size is higher,
    3. the phenotypic variation of brain size is higher,
    4. the heritability of body size is lower, or
    5. the phenotypic variation of body size is lower.

    If the parameter estimates are in error in these directions, the test of the null hypothesis will be conservative to some degree—that is, the null hypothesis will be accepted in cases where the true parameter values would lead to rejection.

    Estimates of heritability and variances are available for humans and for some other species of primates, both for brain volume and for body mass and stature. The availability of different estimates makes it possible to consider their consistency with each other and the likely effects of error.

    Mass and stature are considered separately as independent variables in the analysis.

    Brain size variation

    The skeletal samples above allow estimates of standard deviations for each sample. However, because of the limited sizes of archaeological samples, these estimates of variability may either overstate or understate the variation of ancient populations.

    There is substantial sexual dimorphism of both brain and body size in humans. The simplest way to correct for variation due to sex is to consider males and females separately. All estimates of parameter values in living humans are reported from male- or female-specific samples. Archaeological samples often permit assessment of individual sex, although there is necessarily some error in these assessments. Where possible, this study reports values for males, and assumes that variation is distributed like that of males in living human populations.

    Additionally, phenotypic estimates in humans may include confounding age effects. A few cited studies use age-controlled samples, but many rely on postmortem measures in samples with a broad range of age-at-death. Archaeological samples always include age-related variability, although this is likely distributed differently than in many surveys of living humans.

    Peper and colleagues [33] reviewed heritability estimates for total and regional brain volume based on MRI studies of twins. Most studies have yielded high estimates for the heritability of total brain volume, ranging from 0.97 [34], 0.94 [35], 0.90 [36] and 0.89 [37]. One outlier study reported a lower estimate of heritability (0.66), but this came from a sample of only 10 MZ and 10 DZ twin pairs [38]. In the current study, the use of a high estimate of heritability will tend to bias the result toward accepting the null hypothesis, since a more heritable character will be expected to change more under the effect of correlation with body size.

    Brain-body genetic correlation

    The genetic correlation between brain size and body size is not known for humans. However, the phenotypic correlations between brain volume or mass and body mass or stature have been extensively studied. The largest sample of these metrics was published from Danish autopsies by Pakkenberg and Voight [25]. Holloway [10] computed correlations between brain mass, stature and body mass in this dataset; these are reported in Table 1.

    Ankney [39], using the data from Ho et al. [40], reports phenotypic correlations between brain mass and stature as r=0.20 for white males and r=0.24 for white females, r=0.20 for black males and r=0.15 for black females. These values are lower than those computed from the Danish data. Both sets of estimates should be regarded as underestimates because of the confounding effect of age variation in the sample. On the other hand, these are phenotypic correlations, and the genetic correlation may be lower than the phenotpic values due to effects induced by the environment or gene-environment interactions. Here, I employ the higher reported estimates of correlations because they have a conservative effect on the hypothesis test: A higher correlation predicts a more substantial change in brain size.

    Parameter Value Source
    Brain volume heritability (h2 0.94 [35]
    Stature heritability (h2) 0.80 a [41]
    Body mass heritability (h2) 0.52 b [42]
    Brain size--stature correlation 0.47 c [10]
    Brain size--body mass correlation 0.29 [10]

    Table 1 - Estimates of quantitative genetic parameters. Correlations and heritabilities of human brain and body dimensions used in this study. Values are from combined-sex samples. a Based on a range of estimates from several countries. b Age-matched sample. c Correlations taken from [10] based on original data from [25] and other sources cited therein.

    Parameter values in nonhuman primates

    Estimates of brain-body correlations and heritabilities in humans have mostly been taken in European or American population samples. These estimates may therefore be biased dietary Westernization and concomitant changes in body mass index. To address this possibility, we can consider these relationships in non-human primates.

    Rogers and colleagues [43] measured brain volume and body mass in captive free-ranging baboons (Papio hamadryas) with known pedigrees. They found brain-body phenotypic correlation of r=0.29 (r2=0.086) for males and r=0.16 (r2=0.026) for females. The heritability of brain volume was estimated as 0.52. The heritability of body mass in this captive population was previously estimated as 0.50 [44].

    Falk and colleagues [45] found phenotypic correlations in rhesus macaques (Macaca mulatta) between brain volume and body mass to be r=0.54 for males and r=0.40 for females.

    Stature is not strictly comparable between humans and other primates, because of the obvious difference in locomotor anatomy.

    These comparisons allow several conclusions:

    1. The heritability of body mass is approximately the same in humans as in other primates.
    2. Heritability of brain size in humans is substantially higher than reported in other primates. Using a high estimate should bias against rejection of the null hypothesis.
    3. The phenotypic correlations between brain size and mass in these primates are within the range reported for humans.

    Thus, as near as possible, using the human values for these parameter estimates will provide an appropriate test of the null hypothesis, that changes in brain size were caused by changes in body size in recent human populations.


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  • Mailbag: Could autism genes be adaptive?

    Wed, 2011-08-17 22:53 -- John Hawks
    I have always wondered if autism could be an adaptive mutation. However, since I myself have autism, and specifically one of the more fortunate types of autism. I've figured it would make me a monumental bleep to take such a notion seriously. But when I saw your article, I figured why not go out on a limb and run this fleck of curiosity by an expert. So could it be?

    P.S. Love your Faq #6! An induction schema that compliments the contributer once, but insults him an unlimited number of times. LOL. Unfortunately, I highly doubt those types of people would get the irony.

    Thanks!

    It's hard to say without knowing many of the genes that increase the probability of being on the spectrum. If you read in genetics now about the "hidden heritability", this is one of the cases -- we know that the trait has a strong genetic influence, but in large samples we don't find strong evidence for any single gene.

    It's likely that the heritability is explained by many different genes, each of which is rare in the population. That pattern would make it less likely that the genes that influence autism are adaptive -- many (but not all) adaptive traits are cases where a relatively small number of common genes influence the trait. But we won't really know until we have a better account of the genes involved.

  • Did Denisovans have genetic adaptations to high altitude?

    Tue, 2011-06-21 12:26 -- John Hawks

    We don't really know the extent of territory that might have been occupied by the population represented by the Denisova genome. The signs of mixture into the Melanesian/New Guinea population suggests that the Denisova individual shared many genes with people who lived somewhere along the South or Southeast Asian coast. Denisova itself, however, is in the Altai Mountains.

    Last week I wrote some thoughts about the possible introgression of HLA alleles from Denisovans into more recent populations. HLA genes pose many problems for testing this hypothesis -- including the difficulty of identifying the alleles in a low-coverage genome and the high chance of incomplete lineage sorting of ancient alleles in recent populations. Other parts of the genome in principle may be much easier to find evidence of introgression.

    If an allele that originated in Denisovans had some advantage in later populations, it might today be found very widely spread across Asian populations, even if the amount of Denisovan ancestry in most of these populations is very small. This was the theme of my paper with Gregory Cochran several years ago [1] ("The inevitability of introgression"). The probability that a single copy of an advantageous allele will survive and increase in the population is roughly 2s, where s is the fitness advantage in a heterozygote carrying the allele. A relatively small number of copies of an allele might have entered a recent human population by introgression from some ancient population, but these few copies would have a high likelihood of surviving and increasing in frequency, possibly toward fixation. HLA alleles could easily be in this category, but the challenges identifying them and high chance of ILS make the hypothesis hard to test.

    Another strategy is to identify genes that have been selected in recent populations and see if the linked haplotype shows up in the Denisova genome. Recently, several studies have attempted to identify genes related to high altitude adaptation in Tibetans. At least some Denisovans lived in the mountainous areas of central Asia, and so I'm curious whether they might have some alleles adapted to this environment. The Altai are not nearly as high as the Tibetan plateau (in fact Denisova itself is not much higher than western Kansas), and we don't know how long Denisovan people might have been resident in Central Asia, but if we're looking for selected alleles there are some strong candidates in this category of genes.

    So let's look at some of them. All positions here are mapped to the hg18 human genome assembly.

    Yi and colleagues [2] find a strong frequency difference between China and Tibet for a SNP in EPAS1, at chr2:46441523. The derived allele, G, has a frequency of 87% in their Tibetan sample but only 9% in their Chinese sample (and zero in Denmark). The Denisova genome is represented by two reads at this site, both C, the ancestral allele. We don't necessarily have to accept that this is a functional site, but as the marker most strongly differentiating the high altitude population it would likely be closely linked to any functional variant. So the Denisova allele suggests that this ancient individual lacked whatever functional variant might currently be common in Tibetans for this gene.

    Simonson and colleagues [3] took a different approach, focusing on candidate genes that they argued a priori were likely to be involved in adaptation to hypoxia because of their physiological role. They evaluated these genes for evidence of positive selection in Tibetans, finding several candidate haplotypes for recent adaptive evolution to high altitude.

    For each of five genes, they identified a three-locus "core selection haplotype" that shows signs of selection within Tibet. The purpose of these three-SNP haplotypes was to examine the correlation of haplotypes and phenotypes in a sample of people where physiological data were taken. So they are intended as tags, not as comprehensive and unique identifiers of the candidates at the genetic level. But the three-locus haplotypes are the only ones reported in the supplement to the paper, so that's what I have to compare.

    EGLN1: The three-allele candidate selected haplotype consists of A at chr1:229793717, T at chr1:229667980 and T at chr1:229665156. Denisova apparently has the selected haplotype with A at chr1:229793717 (2/2 reads), T at chr1:229667980 (3/3 reads) and T at chr1:229665156 (1/1 reads). However, it is not obvious whether this is significant. All three alleles on the candidate selected haplotype are the ancestral (present in chimpanzees and gorillas) alleles, which are much more likely to show up in the archaic genomes than derived alleles. These ancestral alleles are also present in several of the whole genomes provided along with the Denisova sequence reads. So it's not clear to me how good a candidate for selection the haplotype really is.

    CYP17A1: Here the three-allele candidate selected haplotype includes G at chr10:104568521, G at chr10:104594906, and C at chr10:104517420. Denisova has C (5/5 reads, ancestral), T (4/4 reads, ancestral), and C (3/3 reads, ancestral). Again, Denisova has the all-ancestral haplotype here, but in this case it is not the selection candidate.

    PTEN: The selected candidate haplotype is G at chr10:89770364, C at chr10:89790851 and C at chr10:89778618. Denisova has G (5/5 reads, ancestral), T (2/2 reads, derived), and C (4/4 reads, ancestral). Not selected.

    I always find it interesting when the Denisova genome has a derived allele at an interesting site -- it is the shared derived alleles between these archaic genomes and living people that constitute evidence of genetic persistence of the archaic people. No single site carries that information (any one allele may be shared by incomplete lineage sorting), but I still like to note them. The Papuan and half the Native American, Sardinian and Mongolian reads share the derived T at chr10:89790851 with Denisova.

    HMOX2: The candidate selected haplotype has C at chr16:4456093, T at chr16:4465266, T at chr16:4442515. Denisova has this candidate selected haplotype: C (3/3 reads, ancestral), T (4/4 reads, ancestral), T (5/5 reads, ancestral). That haplotype may also be in the Cambodian whole genome accompanying the Denisova data, and can't be ruled out for the Mongolian. Again, the all-ancestral haplotype and wider distribution argue against the hypothesis that this haplotype was specifically selected in Tibet.

    PPARA: The core candidate selected haplotype has A at chr22:44827140, C at chr22:44832376 and T at chr22:44842095. Denisova has A (8/8 reads, ancestral), A (5/5 reads, ancestral), and C (2/2 reads, ancestral). Notice again, Denisova has the all-ancestral haplotype. As an ancient sequence, we are finding this is the usual case, human-derived alleles are just rarer in this genome.

    OK, where are we? Out of six genes that are candidates for selection on altitude adaptation in Tibetans, the Denisova genome has two -- at ELGN1 and HMOX2. In both cases, the core selected haplotype consists entirely of ancestral alleles, and so I think they are actually poor evidence of introgression on the surface. I would test them by looking at more SNPs linked to the presumed selected haplotype, hoping to find some derived SNPs shared by the Denisovan genome and the presumed selected haplotypes. Unfortunately, publications do not yet routinely report long haplotypes, so it will take some more digging to test these cases.


    References

    Synopsis: 
    Noodling through the Denisova genome data for signs of candidate altitude adaptations.
  • Rats in the radiocarbon (or vice versa)

    Wed, 2008-06-11 09:12 -- John Hawks

    The story of the New Zealand rat bones is a bit deeper than the press reports (e.g., this AP report). The main idea is that the rat radiocarbon dates support an initial habitation of New Zealand that was relatively late, around 1200 AD. That's not a big surprise, since no human archaeological site or remains have been found to have earlier dates.

    I don't have any opinion about New Zealand prehistory, really. It seems to me that the rats are a very good source of evidence, because their population growth is potentially much much faster than human population growth. If rats arrive on an island, there's a good chance of finding them early. I could imagine that humans might escape leaving archaeology for some time. I doubt very much that they could remain invisible for over a thousand years, but that depends on the intensity of archaeological research. But rats are not going to stay invisible. When you have extinct predators who ate rats, and they leave rat bones in their feces that you can sample, and none of those rat bones are more than 800 years old, well that's a sign.

    So what's the real story here? The Oxford Radiocarbon Accelerator Unit keeps changing sample preparation protocols! These changes have brought in a number of new ways to take contamination and recent carbon out of the sample. I noted the redating of Vindija G1, which was based on a new sample preparation method using filtration to purify collagen from the bone. At the time, this was one among several new methods attempting to improve the accuracy of AMS dates. The cumulative effect of the advent of AMS dating, coupled with these later improvements, has added substantial precision to our knowledge of Europe during the last 40,000 years, as I reviewed here. Tom Higham, who was behind the new dates in the New Zealand paper, also worked out the Vindija G1 redating.

    The problem is that every new sampling method raises the prospect that a lot of currently accepted dates are actually wrong. That is what has happened in the case of the New Zealand rats. The rat case demonstrates the depth of the problem: Holdaway (1996) presented seven AMS dates on rat bones whose confidence intervals are significantly older than 1000 AD (calibrated), two that are significantly older than 500 AD. The present study by Wilmhurst et al. must claim that all those rats were contaminated with old carbon.

    Since the half-life of carbon-14 is 5730 years, an elevation of more than 500 years in a date represents a very substantial deficit of carbon-14 -- on the order of five percent of the maximum amount. Such deficits might be possible, either due to conditions after burial or consumption of marine carbon by the animals during their lives. But in his original study, Holdaway closely considered these effects:

    Potential sources of error include the addition of 'old' or reservoir carbon to the bone gelatin before death in the diet, or after deposition via unremoved humics or diagenetic processes in carbonate sedimentary environments, especially for small specimens.

    Dietary influences were not apparent. Two individuals of known death date give calibrated ages that include their death dates. In addition, 14C dates on bone gelatin from two herbivorous birds (equilibrium carbon consumers) are not significantly different from those on rat bones from comparable levels. Humic contamination is unlikely, most being removed by gelatinization, but must still be considered fro earlier 'collagen' dates. Environmental carbonates were removed by an acid pre-wash, eliminating carbonate contamination. Measured ages were not related to whole-sample mass.

    Longer-term diagenetic changes do not appear to have a significant effect. Samples of moa eggshell (species unknown) and bird bone from close proximity in sediment enclosed by two undisturbed volcanic tephras give indistinguishable ages.... These materials were prepared using different treatments. Finally, a rat dentary excavated from beneith the Taupo Tephra gives an age of 1,775±93 yr BP. In addition to the radiocarbon age being consistent with that of the covering tephra, the bone's position beneath the undisturbed layer provides independent evidence that Pacific rats were established in the North Island before the Taupo eruption (Holdaway 1996:226).

    Yes, you read that right. He had a rat under a well-dated volcanic tephra.

    The current paper claims that all the oldest dates for rat remains have come from a single lab, all before a single date:

    Subsequent dating of Pacific rat bones sampled from both laughing owl (32) and archaeological sites (33-35) failed to duplicate the early series of old rat bone dates (35-38). The most telling criticism of the original dates is that they fall into two distinct groups according to when the bones were processed in the same dating laboratory (22, 36, 37) (see Fig. 1). The early series of rat bone dates processed in 1995 and 1996 are all older than the oldest-dated archaeological evidence (1280 A.D.), but all bones dated after 1996 are younger (36, 37) (Fig. 1). Moreover, some rat bones from archaeological assemblages that were processed in 1995 and 1996 are significantly older than consistent dates on diverse materials from the same stratigraphic contexts (34, 35). Critics argued that this unusual bimodal distribution of ages according to when the bones were processed was due to inadequate pretreatment of small bones (33, 35-37). It has also been argued that some of the old 1995-1996 rat bone dates are older than their "true" age because of dietary uptake of carbon depleted in 14C (e.g., refs. 39-40).

    Well, there you have it. The argument has to be that the dates are wrong due to the different sample preparation methods. The "dietary carbon-14" argument can't be the explanation, because some of the more recently dated samples ought to show the same deficit, and they don't. I personally don't see how they deal with the rat under the tephra -- they don't address the question. The only possibility that makes sense with their argument is that the samples were technically processed in a way that led to older dates.

    Again, I have no opinion about New Zealand settlement. The recent chronology proposed here sounds reasonable to me, but mainly because people in a massively expanding population shouldn't remain archaeologically invisible.

    I just want to point out how much our knowledge of the archaeological sequence depends on the technical details of dating methods, known only to a small number of researchers. To be sure, technology advances. But we have thrown out an awfully large number of radiocarbon dates in the last few years, due to small but important changes in methods. And the New Zealand case shows that this problem is not confined to the upper limits of AMS dating, where the preserved carbon-14 fraction is at its lowest. In the European case, the biggest problem has been supposed Aurignacian specimens that turned out to be Holocene in age.

    This raises the obvious question: how much weight should we give to current date estimates?

    References:

    Wilmhurst JM, Anderson AJ, Higham TFG, Worthy TH. 2008. Dating the late prehistoric dispersal of Polynesians to New Zealand using the commensal Pacific rat. Proc Nat Acad Sci 105:7676-7680. doi:10.1073/pnas.0801507105

    Holdaway RN. 1996. Arrival of rats in New Zealand. Nature 384:225-226. doi:10.1038/384225b0

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Neandertals

For years, I've worked on their bones. Now I'm working on their genes. Read more about the science studying these ancient people.

Denisova

From a finger bone of an ancient human came the record of a completely unexpected population. My lab is working on the science of the Denisova genome.

Acceleration

The advent of agriculture caused natural selection to speed up greatly in humans. We're uncovering some of the ways that populations have rapidly changed during the last 10,000 years.

Malapa

Just outside Johannesburg, the Malapa site is producing some of the most exciting finds in human evolution. This site is the headquarters of the Malapa Soft Tissue Project.