john hawks weblog

paleoanthropology, genetics and evolution

Holocene

  • My review of "Paleofantasy"

    Thu, 2013-03-14 16:22 -- John Hawks

    I have a review of Marlene Zuk's new book, Paleofantasy, in this week's Nature: "Evolutionary biology: Twisting the tale of human evolution" [1].

    I can't replicate my review here, but for people who have access to Nature I thought I'd bring attention to it. And if you don't have access, I wanted to share a couple of my reactions.

    It was a fun book for me to read. Zuk brings a light-hearted skepticism to a broad array of topics in human evolution. She took as her focus a collection of "paleo-advice" ideas: barefoot running, paleo diet, back-to-nature parenting advice. She then added some uncritically-accepted scientific notions about our evolution, such as the idea that agriculture was "the worst invention ever devised". To each of these topics, she brings an array of recent science questioning or disproving the assumptions. The result is not to debunk ideas, but to give a fuller (and more nuanced) perspective on how much we know (and don't know) about our evolution.

    The serious issue underlying all these topics, which Zuk recognizes, is the difficulty of reconstructing Pleistocene environments. Some hypotheses assume a fairly detailed model of ancient environments -- the so-called "environment of evolutionary adaptedness". But ancient humans lived in an array of environments, more different than each other in many ways than different parts of today's globalized world. We are unquestionably living in environments no ancient humans knew, in population size, density, disease, lifespan, and many other ways. But in other ways, our difference from some ancient people is trivial compared to their diversity. Are we well-adapted to live in cities? Perhaps not in some ways, but maybe in others.

    Probably the best part of my review to share is the end:

    As an anthropologist, I observe that Zuk's use of the term 'fantasy' is just an emphatic way of describing the hypothesis-forming that is essential to evolutionary science. We play with hypotheses, explore their predictions and try very hard to falsify them. So it is, in a way, unremarkable that so many hypotheses proposed by anthropologists about ancient environments now seem to be wrong — and, in a few cases, even ridiculous.

    It means that science is working. Genomics, high-resolution climate records, and microscopic and isotopic evidence have changed our understanding of what the past has to offer. With that in mind, let the next round of palaeofantasies begin.

    Zuk's "very brief" overview of human evolution is a lot shorter than in other recent books on the topic. I found this to be a merciful change -- how many times do I really need to read about the Australopithecus-to-humans timeline? Readers who don't already know the basic timeline are unlikely to pick up the book, I would guess. Still, if you're looking for a "latest news" about early humans, this book is not directed that way. Where it excels is its coverage of recent evolutionary changes and the shifts in Holocene environments and genetics.

    The book is not without its weak points. Without quite enough of the "paleo-advice" topics to carry the whole story, there were some real differences in tone across the chapters, with some a bit drier than others.

    People coming to this book for "the right answer" about ancient environments are not going to find it. There is no right answer, at least not a scientific one, for many of the topics covered here. Zuk has done well to talk to a range of scientists, covering these different aspects of our evolutionary history, and discuss the reasons for their disagreement.

    I wish scientists would do that for themselves more often!


    References

    Synopsis: 
    A new book by Marlene Zuk challenges some paleo advice mongers.
  • Coprolite microbial ecology

    Thu, 2013-02-28 00:25 -- John Hawks

    The advent of metagenomic analysis of microbial communities has led to some unexpected insights about human biology. These techniques have quietly been leading to new discoveries from old archaeological contexts. One example is Alan Cooper's work demonstrating long-term changes in oral microbiota from ancient dental calculus ("Tracing teeth troubles with fossil bacteria").

    Another is a recent paper from Cecil Lewis' lab, "Insights from characterizing extinct human gut microbiomes." [1]. The paper is open access in PLoS ONE. In it, Raul Tito and colleagues recover DNA data from ancient coprolites, from three archaeological sites in the Americas. As discussed in the paper, they obtain good data from a 1400-year-old site in Mexico. Those people, who lived near present-day Durango, were contemporaries of the classic Maya and Teotihuacanos. As such, their gut microbiomes may provide a really interesting picture of health and diet from a key period in the prehistory of the Americas.

    Coprolites may seem simple, but each represents a unique history of deposition and subsequent preservation. The microbial community may shift during the early stages of this history, and subsequent DNA damage may shift estimates of microbial abundances away from their true values. They found one of their sites appeared to preserve a good signal, while the others were degraded:

    Most striking, both Rio Zape coprolites exhibited a gut microbiome signature with similarities to the children from a rural African village with the exclusion of a sample of U.S. modern adult gut microbiomes (see Figure S4 for a heat map of these data and Figure S5 for the variability in the source proportion estimates). ZA04 also harbored similarities to non-human primate gut. The coprolites from Caserones and Hinds Cave showed little similarity to a gut microbiome environment. A portion of Caserones coprolite microbial community was similar to compost, which may be explained by the post-mortem gut serving as an organic bioreactor filled with carbon and nitrogen from decaying food detritus. The microbial community assignment for Hinds Cave failed to assign well to any source environment.

    From this, we can see that any interpretation of data from a sample of ancient coprolites must be cautious. We're generally interested in how microbial communities may have changed in ancient populations, particularly in response to other factors such as shifts in diet. But as yet it's not very clear what kinds of changes we should predict in association with diet or other changes. That makes it hard to develop a convincing test of a hypothesis.

    This paper is more of a proof of principle. And in its discussion, Tito and colleagues present different ways to explain the kinds of differences that they found in the ancient coprolite microbiota. To me, the most provocative hypothesis is that changes may have more to do with parasite load than diet:

    Information from Rio Zape also supports a current hypothesis about the composition of human microbiomes in traditional communities, potentially revealing an important aspect of the ancestral human microbiome. Spirochaetes are atypical of gut microbiomes in cosmopolitan communities. However, Treponema was reported by Filippo et al. [21] in their comparative study of modern microbiota in children from Europe and rural Africa. In their study, Treponema was observed in the rural African children but was absent in the European children. They hypothesized that the Treponema may enhance the hosts ability to extract nutrients from fibrous foods and may provide anti-inflammatory capability. They raise the hypothesis that microbiota coevolved with ancient diets and that changes in food production greatly impacted the intestinal microbiota. Treponema was also observed in the published rural data for Malawi and Venezuela [22]. The results from Rio Zape provide further support for Treponema as part of the rural human microbiome. Specifically, Treponema now is observed in four rural communities from different continents, three extant communities and one community that has been extinct for over a thousand years.

    As we uncover more comparative data from living people, we will begin to have a better picture of the covariates of microbial community structure. Today's oral bacterial populations in "cosmopolitan" post-industrial peoples are uncharacteristic of past variation. The gut microbiota of cosmopolitan peoples may be just as uncharacteristic. The diversity may have had great importance to ancient health, especially at key times when pathogens were spreading through post-agricultural populations.


    References

    Synopsis: 
    A look within the gut microbiota of ancient Americans
  • Recent evolution of coding variants

    Wed, 2012-12-05 01:00 -- John Hawks

    How did I get myself quoted in a story as the skeptic about recent human evolution? ("Human Evolution Enters an Exciting New Phase"). After all, I've been a huge advocate of the idea that recent human evolution was a lot faster and more interesting than anthropologists used to think ("Why human evolution accelerated").

    The story, by Brandom Keim, is a good account of a new paper in Nature by Wenqing Fu and colleagues, "Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants" [1]. It's a pretty cool study, which has identified protein-coding alleles in large samples of European-American and African-American individuals.

    Fu and colleagues compared all the coding variants they found in large samples of European-Americans and African-Americans, and discovered that the European-ancestry people have a higher fraction of rare coding variants. They propose that the rate of new coding variants entering and persisting within the population actually accelerated in the ancestral European population. Why would this happen? In their view, demography is the most likely explanation. As European populations expanded during the Neolithic and later time periods, the rate by which new mutations are lost by genetic drift began to decline. These new mutations have pooled up within the European population, giving them a glut of new changes to protein-coding sequences. Many of these mutations may be deleterious, just not bad enough for natural selection to have weeded them out in the growing ancient population.

    I think in large part this explanation is correct. In some ways it is incomplete.

    The effect of population history on our evolution was the theme of our 2007 paper on positive selection in recent humans [2]. We relied on exactly the same mathematical relations used in this new paper: More people means more different mutations entering the population. In our case, the increase in the total number of mutations meant that we could expect more potential adaptive mutations to be selected within a growing population. In this case, the increase in the total number of mutations means more mutations remain to be picked up by resequencing rare neutral or deleterious variations in present samples.

    One of the senior authors of the study, Joshua Akey, commented:

    Most of the mutations that we found arose in the last 200 generations or so. There hasn’t been much time for random change or deterministic change through natural selection. We have a repository of all this new variation for humanity to use as a substrate. In a way, we’re more evolvable now than at any time in our history.

    (this is quoted by Punnett Square, not sure about the original source)

    That's a cool concept. These rare protein-coding variations may be mostly unimportant to fitness today, and many are slightly deleterious. Still they provide a store of variability that increases the potential range of responses to future adaptive challenges. Or, they give us room to examine the effects of small differences, which will help us to understand better how genes work. For the past few thousand years, a small proportion of those have come under positive selection, the part that we have been studying in my lab since 2007.

    The current study has some drawbacks. For one, it isn't evident from the results how these new coding mutations are distributed among individuals. Under population growth alone, we should expect that the number of these new coding variants carried by any one individual should be approximately the same as any other individual, regardless of the population size. Where a big population differs from a small population is in the variety of mutations carried by different individuals, with the average number per individual being equal. That may be true in this study, but it isn't possible to tell from the results presented.

    To the extent that some of these mutations are deleterious, their distribution matters. In Europeans, there may be a greater number of deleterious mutations that are on average more rare; all things being equal, this pattern should make it harder to find statistical evidence for association of these rare variants with complex disorders. By contrast, in Africans, the higher average frequencies of such variants should make them easier to tie to phenotypic variation. All this can be concluded from frequencies alone, without a need to relate frequency to age.

    Probably the biggest shortcoming of the paper is in its estimation of ages for these rare mutational variants. Estimating the ages of mutations in human populations has been a real problem for those of us working with genotyping or sequencing data from small samples. When we depend on the linkage between a rare allele and nearby genetic loci, we run into a sampling problem: Estimating the proportion of recombinants in a population fundamentally has a lot of error when you are working with a sample of 10 copies of the rare allele.

    Estimating dates by LD is bad enough, but this paper doesn't even go that far. Instead, it estimates the ages of alleles from their frequency.

    Frequency estimation of age is OK if the genome sequences have come from a Wright-Fisher population (that is, a random-mating, constant size population). More common alleles tend to be older, new alleles tend to be very rare. This isn't a very accurate means of dating any particular mutation, because the relationship of age and frequency under genetic drift has a tremendous variance. But when pooling large sets of alleles into frequency classes, the age-by-frequency approach gives a rough idea of whether mutations have accelerated or stayed at a constant rate over time.

    But there's one obvious thing missing from the model that may have a large effect on the frequencies of rare coding variants: Introgression from Neandertals! If we want to know why Europeans have a large store of rare coding variants relative to Africans, their ancient mixture of a small fraction of a very divergent human population is one obvious reason. None of the Neandertal alleles in Europeans today are new, they are all old. But a method that estimates their ages by allele frequency alone will always conclude that these rare Neandertal alleles are very young.

    In the current paper, the relation of frequency and age is derived from simulations that are based on a model of human population history. Like all recent papers that apply a model of human population history, this one is both overcomplicated (lots of parameters to which we have no good estimates) and oversimplified (too few events to accommodate known historical phenomena). Here's the population model used to derive allele ages in the paper:

    Population model from Fu et al. 2012

    Population model from Figure S5 in the supplementary information from Fu et al. 2012

    The parameters for population divergence times and ancient population sizes are estimated from genetic data, so any systematic error will propagate through to the estimation of allele ages. The exclusion of Neandertal introgression in the model really does bias the allele age estimates badly, as Neandertal genes today are mostly rare, and mostly very old. This year's shift in our assumptions about mutation rates (to a much slower rate than previously assumed) will also affect the estimates of the demographic parameters in the model. An older coalescence time for most genes means a larger ancestral effective size for these populations, and much older allele ages when frequency is the estimator.

    Our lab is working very hard on allele ages, and I hope to be able to share some of that work soon.

    This study is not alone in demonstrating the real importance of rare coding variation in human populations. This line of research has substantial value, as it helps to show why so much of the additive genetic variation underlying variation in human phenotypes has not yet been assigned to genes. We know that many traits are heritable by comparing genetic relatives with each other. Finding the genetic loci that explain similarity among relatives is relatively easy when the genes involved are common, because the same gene variants will be shared across many families. But pooling many families doesn't help us find very rare mutations, as these are likely carried only by a few pedigrees even in a very large sample. By showing the large store of rare coding variation, these studies help to establish that much of the genetic variation underlying disease may be there for us to discover, if we change our discovery approach.


    References

    Synopsis: 
    Probing the pattern of noncoding rare variation in whole exome data.
  • Indus health

    Tue, 2012-05-01 23:30 -- John Hawks

    Anthropology News interviews Gwen Robbins Schug and Veena Mushrif Tripathy on their work documenting health and mortality in the Indus Valley civilization: "South Asian Bioarchaeology: Human-Environment Interaction and Paleopathology in Indus Valley Civilization".

    It was argued that Harappa was a rare example of a peaceful, heterarchical state. The human skeletal material was never consulted to address this question. Based on our evidence for both exclusion and social differentiation in the mortuary practices at Harappa, we argue that Harappa was not entirely peaceful and social differentiation was part of life....

    We are using the human skeletons as artifacts of the social experience. We used the concept of structural violence in our most recent work because it accounts for the clear distinctions we see in the burial practices, ritual aspects, prevalence of trauma and infection. The mortuary and bioarchaeological evidence at Harappa suggests that the social experience in South Asia was not exceptionally different from other early urban civilizations; the kinds of suffering and the patterns of violence present at Harappa suggests structural violence—unequal power, uneven access to resources, and oppression that leads to denial of basic needs and even violence.

  • 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.


    References

    1. Ruff CB, Trinkaus E, Holliday TW. Body mass and encephalization in {Pleistocene} \\emph{Homo}. Nature. 1997;387:173–176.
    2. Lee S-H, Wolpoff MH. The Pattern of Evolution in {Pleistocene} Human Brain Size. Paleobiology. 2003;29:186–196.
    3. Rightmire GP. Brain size and encephalization in early to Mid-Pleistocene Homo. Am. J. Phys. Anthropol. [Internet]. 2004;124:109–123. Available from: http://dx.doi.org/10.1002/ajpa.10346
    4. Hawks J, Wolpoff MH. The Accretion Model of {Neandertal} Evolution. Evolution. 2001;55:1474–1485.
    5. Leigh SR. Cranial capacity evolution in \\emph{Homo erectus} and early \\emph{Homo sapiens}. American Journal of Physical Anthropology. 1992;87:1–14.
    6. Beals KL, Smith CL, Dodd SM. Brain size, cranial morphology, climate and time machines. Current Anthropology. 1984;25:301–330.
    7. Henneberg M. Decrease of human skull size in the {Holocene}. Human Biology. 1988;60:395–405.
    8. Henneberg M, Steyn M. Trends in Cranial Capacity and Cranial Index in Subsaharan {Africa} During the {Holocene}. American Journal of Human Biology. 1993;5:473–479.
    9. Jerison HJ. Evolution of the Brain and Intelligence. New York: Academic Press; 1973.
    10. Holloway RL. Within-Species Brain-Body Weight Variability: A Reexamination of the {Danish} Data and Other Primate Species. American Journal of Physical Anthropology. 1980;53:109–121.
    11. McHenry HM, Coffing K. Australopithecus to Homo: Tranformations in Body and Mind. Annual Review of Anthropology. 2000;29:125–146.
    12. Ruff C. Variation in Human Body Size and Shape. Annual Review of Anthropology [Internet]. 2002;31. Available from: http://dx.doi.org/10.2307/4132878
    13. Frayer DW. Body size, weapon use and natural selection in the European {Upper} {Paleolithic} and {Mesolithic}. American Anthropologist. 1981;83:57–73.
    14. Armelagos GJ, Goodman AH, Jacobs KH. The Origins of Agriculture: Population Growth During a Period of Declining Health. Population and Environment [Internet]. 1991;13:9–22. Available from: http://dx.doi.org/10.1007/BF01256568
    15. Leach HM. Human Domestication Reconsidered. Current Anthropology [Internet]. 2003;44:349–368. Available from: http://dx.doi.org/10.1086/368119
    16. Smith RJ, Jungers WL. Body mass in comparative primatology. Journal of Human Evolution [Internet]. 1997;32:523–559. Available from: http://dx.doi.org/10.1006/jhev.1996.0122
    17. Pfeiffer S, Sealy J. Body Size Among {Holocene} Foragers of the {Cape} Ecozone, {Southern Africa}. American Journal of Physical Anthropology [Internet]. 2006;129:1–11. Available from: http://dx.doi.org/10.1002/ajpa.20231
    18. Brown P. {Pleistocene} homogeneity and {Holocene} size reduction: the {Australian} human skeletal evidence. Archaeology and Physical Anthropology in Oceania. 1987;22:41–67.
    19. Brown P. Recent human evolution in {East Asia} and {Australasia}. Philosophical Transactions of the Royal Society of London, Series B. 1992;337:235–242.
    20. Carlson DS. Temporal variation in prehistoric Nubian crania. Am. J. Phys. Anthropol. [Internet]. 1976;45:467–484. Available from: http://dx.doi.org/10.1002/ajpa.1330450308
    21. {Van Gerven} DP, Armelagos GJ, Rohr A. Continuity and Change in Cranial Morphology of Three {Nubian} Archaeological Populations. Man. 1977;12:270–277.
    22. Brown P, Maeda T. {Post-Pleistocene} Diachronic Change in {East Asian} Facial Skeletons: The Size, Shape and Volume of the Orbits. Anthropological Science [Internet]. 2004;112:29–40. Available from: http://dx.doi.org/10.1537/ase.00072
    23. Henneberg M, Steyn M. Diachronic Variation of Cranial Size and Shape in the {Holocene}: A Manifestation of Hormonal Evolution?. Rivista di Anthropologia. 1995;73:159–164.
    24. Schwidetsky I. {Postpleistocene} Evolution of the Brain?. American Journal of Physical Anthropology. 1977;45:605–611.
    25. Pakkenberg H, Voigt J. Brain weight of the {Danes}: forensic material. Acta Anatomica. 1964;56:297–307.
    26. Stynder DD, Ackermann RR, Sealy JC. Craniofacial Variation and Population Continuity During the {South African} Holocene. American Journal of Physical Anthropology [Internet]. 2007;134:489–500. Available from: http://dx.doi.org/10.1002/ajpa.20696
    27. Wu X, Liu W, Zhang QC, Zhu H, Norton C. Craniofacial morphological microevolution of Holocene populations in northern China. Chinese Science Bulletin [Internet]. 2007;52:1661–1668. Available from: http://dx.doi.org/10.1007/s11434-007-0227-8
    28. Koepke N, Baten J. The biological standard of living in Europe during the last two millennia. European Review of Economic History [Internet]. 2005;9:61–95. Available from: http://dx.doi.org/10.1017/S1361491604001388
    29. Steckel RH. New Light on the "Dark Ages": The Remarkably Tall Stature of Northern European Men during the Medieval Era. Social Science History [Internet]. 2004;28:211–229. Available from: http://dx.doi.org/10.1215/01455532-28-2-211
    30. Frayer DW. Biological and cultural change in the European late {Pleistocene} and early {Holocene}. In: Smith FH, Spencer F The Origins of Modern Humans: A World Survey of the Fossil Evidence. The Origins of Modern Humans: A World Survey of the Fossil Evidence. New York: Alan R. Liss; 1984. pp. 211–250.
    31. Sealy J, Pfeiffer S. Diet, Body Size, and Landscape Use among {Holocene} People in the {Southern Cape}, {South Africa}. Current Anthropology [Internet]. 2000;41:642–655. Available from: http://dx.doi.org/10.1086/317392
    32. Lande R. Quantitative genetic analysis of multivariate evolution, applied to brain:body size allometry. Evolution. 1979;33:402–416.
    33. Peper JS, Brouwer RM, Boomsma DI, Kahn RS, {Hulshoff Pol} HE. Genetic Influences on Human Brain Structure: A Review of Brain Imaging Studies in Twins. Human Brain Mapping [Internet]. 2007;28:464–473. Available from: http://dx.doi.org/10.1002/hbm.20398
    34. Pennington BF, Filipek PA, Lefly D, Chhabidas N, Kennedy DN, Simon JH, Filley CM, Galaburda A, DeFries JC. A Twin {MRI} Study of Size Variations in Human Brain. Journal of Cognitive Neuroscience. 2000;12:223–232.
    35. Bartley AJ, Jones DW, Weinberger DR. Genetic variability of human brain size and cortical gyral patterns. Brain. 1997;120:257–259.
    36. Baaré WFC, Hulshoff Pol HE, Boomsma DI, Posthuma D, de Geus EJC, Schnack HG, van Haren NEM, van Oel CJ, Kahn RS. Quantitative Genetic Modeling of Variation in Human Brain Morphology. Cerebral Cortex [Internet]. 2001;11:816–824. Available from: http://dx.doi.org/10.1093/cercor/11.9.816
    37. Wallace GL, Eric Schmitt J, Lenroot R, Viding E, Ordaz S, Rosenthal MA, Molloy EA, Clasen LS, Kendler KS, Neale MC, et al. A pediatric twin study of brain morphometry. Journal of Child Psychology and Psychiatry [Internet]. 2006;47:987–993. Available from: http://dx.doi.org/10.1111/j.1469-7610.2006.01676.x
    38. Wright IC, Sham P, Murray RM, Weinberger DR, Bullmore ET. Genetic Contributions to Regional Variability in Human Brain Structure: Methods and Preliminary Results. NeuroImage [Internet]. 2002;17:256–271. Available from: http://dx.doi.org/10.1006/nimg.2002.1163
    39. Ankney DC. Sex differences in relative brain size: The mismeasure of woman, too?. Intelligence [Internet]. 1992;16:329–336. Available from: http://dx.doi.org/10.1016/0160-2896(92)90013-H
    40. Ho KC, Roessmann U, Straumfjord JV, Monroe G. Analysis of brain weight. I. Adult brain weight in relation to sex, race, and age. Archives of pathology & laboratory medicine [Internet]. 1980;104:635–639. Available from: http://view.ncbi.nlm.nih.gov/pubmed/6893659
    41. Silventoinen K, Sammalisto S, Perola M, Boomsma DI, Cornes BK, Davis C, Dunkel L, de Lange M, Harris JR, Hjelmborg JVB, et al. Heritability of adult body height: a comparative study of twin cohorts in eight countries. Twin Research. 2003;6:399–408.
    42. Mathias RA, Roy-Gagnon M-H\`{n}e, Justice CM, Papanicolaou GJ, Fan YT, Pugh EW, Wilson AF. Comparison of year-of-exam- and age-matched estimates of heritability in the Framingham Heart Study data. BMC Genetics. 2003;4.
    43. Rogers J, Kochunov P, Lancaster J, Shelledy W, Glahn D, Blangero J, Fox P. Heritability of Brain Volume, Surface Area and Shape: An {MRI} Study in an Extended Pedigree of Baboons. Human Brain Mapping [Internet]. 2007;28:576–583. Available from: http://dx.doi.org/10.1002/hbm.20407
    44. Jaquish CE, Dyer T, Williams-Blangero S, Dyke B, Leland M, Blangero J. Genetics of Adult Body Mass and Maintenance of Adult Body Mass in Captive Baboons (\\emph{Papio hamadryas}). American Journal of Primatology [Internet]. 1997;42:281–288. Available from: http://dx.doi.org/10.1002/(SICI)1098-2345(1997)42:4%3C281::AID-AJP3%3E3.0.CO;2-T
    45. Falk D, Froese N, Donald Stone Sade BC. Sex differences in brain/body relationships of Rhesus monkeys and humans. Journal of Human Evolution. 1999;36:233–238.
  • The sign of four

    Thu, 2011-06-16 18:30 -- John Hawks

    Gene Expression this morning is worth some thought, a post about the mtDNA of Andaman Islanders and their connections to mainland Asian populations. "Present genetic variation is a weak guide to past genetic variation". In a nutshell, some anthropologists and geneticists had hoped that Andaman Island people were a kind of "time capsule" of the original migration of people out of Africa. The mtDNA lineages are inconsistent with that hypothesis.

    On a final note, if the Andaman Islanders arrived ~20 thousand years before the present from the South Asian mainland they don’t tell us very much about the “Out of Africa” people. They’re not “living fossils,” and it was frankly somewhat stupid probably to think they would be.

    I don't have time at the moment to do my own review but definitely there is a deeper issue at play. It is extremely interesting that we're finding the Andaman Island population fits into the genetic landscape of South Asia at the Last Glacial Maximum, and not earlier. Even if the islands were first inhabited at the LGM, we might expect early inhabitants to preserve variation that had later been supplanted within South and Southeast Asia by the spread of agriculturalists. Apparently, they don't. It is likewise extremely interesting that Neolithic European mtDNA is predominated by haplogroups that are rare or absent in earlier Europeans. With a fuller review, I think we could likely come up with several more instances where fairly large pre-agricultural turnover was happening...I have two or three in mind.

    These observations show that the present distribution of genetic variation is in some ways completely unrepresentative of the patterns in the past. The thing that strikes me: It takes a pretty massive demographic turnover to make this happen. And what we're looking at in today's populations is many, many instances of such turnovers during the last 20,000 years.

    I've spent a good part of my career as a voice in the wilderness, saying that things just aren't simple enough to use genetics and a Wright-Fisher population model to reconstruct events before the Neolithic. But in many ways, mainstream geneticists weren't making an unreasonable assumption that one might reconstruct those events in a straightforward way using mtDNA or the Y chromosome. It's just that reality is stranger than they expected.

  • Spatial dispersal, parallel adaptation, and the "Stooge effect"

    Thu, 2010-10-14 00:06 -- John Hawks

    Peter Ralph and Graham Coop have an interesting paper in the current Genetics, titled, "Parallel Adaptation: One or Many Waves of Advance of an Advantageous Allele?" [1]

    Fisher [2] famously considered the case in which an advantageous allele is dispersing through a spatially dispersed population, showing that the dispersal forms a "wave of advance". This work was the foundation for a lot of progress in understanding spatial dynamics of organisms.

    As I discussed in 2008 ("Overstating the obvious"), one of the consequences of the Fisher wave model for human evolution is that advantageous alleles will spread very slowly through the population. During the course of the Holocene, a strongly selected mutation might move only across a radius of a thousand or so kilometers. That provides one explanation for why new advantageous alleles haven't spread very far beyond their points of origin -- they just haven't had time yet.

    Another reason why an allele might not have spread widely is interference from other alleles with similar effects. I mentioned this process last year ("Spatial variation and near-fixed selected alleles"):

    Greg Cochran and I have been discussing this idea for some time. We call it the "Stooge effect". Think of the Three Stooges all trying to run through a door at the same time and getting stuck in the middle. That's what these genes are doing -- all of them are competing to respond to selection, but each is slowed by the presence of the others.

    Ralph and Coop have cleverly combined the "Stooge effect" phenomenon with spatial dispersal. They suppose a case in which two separate advantageous mutations arise in different geographic locations, each affecting the same trait. Each begins to spread independently as a Fisher wave of advance. What happens when they meet?

    As they show, the dynamics in this case give rise to a static equilibrium -- once the "waves of advance" meet, they stop moving, forming a stable boundary. A new favorable mutation makes headway only so long as it has no equally favorable mutation to compete against.

    I like the way they used both analytical approaches and simulations to come to this outcome. The appearance of stable boundaries in a reaction-diffusion system has long been known (demonstrated first by Alan Turing, actually!). But to my knowledge, no one has considered this specific case from an analytical perspective.

    The Fisher equation is not all that simple for most students to work with. If you become familiar with the equation, you will notice the key aspect is that it has two separate components -- a logistic (or reaction) component representing the increase in frequency at a single point in space, and a diffusion component representing the dispersal across space.

    The muscle of the dispersal process comes from the logistic component. Without the intrinsic growth of the selected allele, the dispersal of individuals along the boundary would not carry many copies of the selected allele into new geographic areas. If the local selective advantage dies, the wave of advance rapidly stalls. A static equilibrium arises, with the frequency of the selected allele forming a cline that correlates with the local selection pressure.

    Ralph and Coop's model approximates this case, in a dynamical sense. Each new selected mutation forms an increasing zone in which the selective advantage of other mutations is zero. When those other mutations encounter this zone, they form a stable cline. The cline is stable in the short term, but the diffusion component still disperses copies of an allele; they just lack the muscle to continue their deterministic expansion.

    The most interesting simulations by Ralph and Coop show the two-dimensional case, in which the stable boundaries emerge in a "tesselation" pattern.

    Tesselations

    Figure 6 from Ralph and Coop (2010), showing "tesselations" in 2-d simulations of waves of advance.

    The lower three panes in the figure show the stability of the boundaries between the selected alleles. They proceed to fixation locally, but their dispersal stops where they come into contact with other adaptive alleles. Over the very long term, the population will mix -- the diffusion process will slowly carry all these alleles throughout the species' range. Look at the process after a million generations and the entire zone will be gray. But this dispersal occurs at the neutral rate, where the diffusion term is the only factor driving the dispersal.

    What about humans?

    My graduate student Zach Throckmorton and I have been working in this area for a while now. One of the things that impresses us is the way that much more interesting dynamics can emerge when you alter the assumptions. I learned some of this stuff by talking to Frank Livingstone, who gave a lot of thought to these issues of spatial dispersal and selection as applied to malaria resistance alleles.

    In particular, Frank thought about the case where one allele has a slightly larger advantage than another. In some contexts, this allows the "better" allele to overtake and swamp the expansion of the "weaker" (but nonetheless adaptive) one. In others, the two come to a near standstill, one displacing the other only very gradually. Much depends on the timing of the two mutations and the local conditions controlling their initial dispersal.

    Ralph and Coop briefly consider this case in their paper, noting that the difference in fitness advantage of two alleles will allow one to advance into the range of the other, albeit at a slower rate. In humans, we may be seeing a smaller subset of cases, where one or more of the alleles have not yet established a wavefront. In these cases, the arrival of another wave can disrupt the spatial pattern of the rarer allele. The diploid case gives rise to the possibility of more complex epistases. Well-defined boundaries between selected alleles are rare, and where they occur (as may be the case with HbC and HbS in Africa), many have focused on negative epistasis as an explanation.

    Also, alleles are unlikely to substitute perfectly for each other. In many cases, they may work synergistically -- individuals carrying two selected alleles that affect the same function may outperform those carrying only one such allele. At some point, new selected mutations may start to have diminishing returns, even on a trait like skin pigmentation where dozens of alleles may have been selected in widespread human populations. So the current distribution may to some extent be "frozen", but by a more complicated dynamic than the simple intersection of waves of advance.

    As Coop and colleagues showed last year [3], and we discussed in 2007 [4], there are really only few genes that have approached local fixation in recent human evolution. The current spatial pattern of recently selected alleles doesn't look like a tesselation with many alleles near local fixation. Over most of the Old World, it looks like populations have a very large number of very new alleles, far from fixation, and few up over 70 percent in frequency.

    So the specific scenario in this paper by itself probably does not explain the overall empirical pattern in humans. But if we consider the current pattern as a transient, approximating the early stages of dispersal for many selected alleles, we may not be terribly far off the mark.

    Mutation-limited evolution

    This is a long dense paper and there's a lot in it. One further aspect of the paper that I think is essential is the way that Ralph and Coop reiterate the basic point that more people means more mutations. In their case, they focus on population density over space (population number, when you multiply them) as a constraint on the number of possible adaptive mutations. They apply this idea as a hypothesis to account for parallel adaptations that may have emerged in recent human evolution.

    Multiple mutational origins are likely if the characteristic length is shorter than the physical dimensions of the region. Eurasia measures >8000 km across, and so Table 1 suggests that multiple origins at a single base pair are very unlikely at the lower population density. On the other hand, if the mutational target is large, then multiple origins are likely at low densities, while at high densities independent origins are ubiquitous. The complementary cases of (rho = 2, µ = 10–8) and (rho = 0.002, µ = 10–5) give identical characteristic lengths of 3000 km, although the timescale on which the mutations spread differs. Thus for these two parameter combinations we can expect a few mutations to dominate within continents and for multiple mutations to be common in a population spread across an area the size of Eurasia. Obviously these calculations are very crude, as population densities vary through space and time, and dispersal across continents is not simply a function of geographic distance and individual dispersal. Nevertheless, these calculations suggest that it is plausible that for adaptive traits with reasonable mutational targets (e.g., a change anywhere within a gene or pathway) even low population densities can lead to parallel adaptation across an area the size of Eurasia, and higher densities almost certainly will.

    We note that as human population densities have increased dramatically over time, so too has the probability of parallel adaptation. It is interesting therefore to note that a number of recent human adaptations (e.g., sickle cell alleles) involve repeated changes at very small mutational targets in relatively small geographic areas, while older adaptations from single changes (e.g., skin pigmentation) are more broadly spread.

    They are describing a scenario in which small human populations would have been mutation-limited -- that is, the number of new mutations is small, making it unlikely that adaptive mutations will happen in any given generation. In such populations, the rate of adaptation is limited by the availability of new mutations. In an extreme -- in the very small effective sizes of Pleistocene human populations -- the rate of adaptation may be extremely slow and regional populations may come to differ at many weakly selected loci, which spread very slowly.

    As the population grows, strongly adaptive mutations become more and more likely to happen somewhere in the species' range. Yet they are still relatively rare -- meaning that they have an opportunity to spread fairly far before encountering another equally strongly selected mutation affecting the same trait.

    This process can give rise to very large differences on a continental scale, even when the selection pressures in different regions do not differ. In humans, the dispersal of selected alleles across space may have been significantly accelerated by actual dispersals of populations. It is not a mere coincidence that very widespread alleles in Eurasia also tend to be much older than 20,000 years old -- long-distance dispersals prior to that time had a higher chance of leaving a lasting influence on subsequent populations.

    But as the population gets bigger and bigger, parallel mutations are more and more likely to happen. As Ralph and Coop point out, at the extreme of large population size and likely mutations, you shouldn't see any new mutations emerging and spreading over very large areas. Any of these mutations would be very likely to encounter other new mutations that do the same thing.

    Is this likely in humans? Clearly some mutations have happened recurrently. Making a broken gene is easy -- there's a large mutational target, since a large fraction of nonsynonymous substitutions might do the job. So if there's a net selective advantage to breaking a gene, we ought to see that happen recurrently in human populations.

    In contrast, if the mutational target is very small, then mutations will still be rare even in a very large population. If only one base change can have an adaptive effect, that precise change will happen less than once in 109 births (remember that not just any mutation at a site, but some particular mutation is what we may need). If a rare duplication or gene conversion is the necessary change, then it may be much rarer.

    Looking across the last few million years, when human population numbers were much smaller than the Holocene, we can be pretty sure that some aspects of our evolution were mutation-limited. The changes that took hold in our ancestors were the ones that happened, and that survived the winnowing of genetic drift. Many changes that would have been adaptive didn't happen in our ancestors. They just weren't lucky enough.

    But some of those changes would still be adaptive now, if we could get them. And we have had much larger numbers in the last 10,000 years. Homo erectus needed these mutations, but we only now are seeing them selected in the human population.

    Malaria adaptation

    Hemoglobinopathies are among the cases of easy mutations -- where breaking a gene is adaptive. It's not just any broken version of alpha- or beta-globin that does the job, though. The hemoglobin needs to be impaired in certain ways to impede the parasites while maintaining blood function. This provides many of the classic cases of human adaptation, and Ralph and Coop turn to this system for examples of parallel adaptation:

    The sickle cell allele HbS at the β-globin gene in humans provides a particularly interesting case of putative parallel adaptation. The HbS allele (β6 Glu-Val) has been driven to intermediate frequencies by selection within the past 10,000 years due to increased resistance to malaria of heterozygotes for the allele (HALDANE 1949; ALLISON 1954; CURRAT et al. 2002; KWIATKOWSKI 2005). The HbS allele is present on at least four major distinct haplotypes in Africa, each at intermediate frequency within a different geographic region; the haplotypes are named after the population sample where they were first discovered (Central African Republic, Senegal, Benin, and Cameroon). This is consistent with multiple origins of this single-base-pair change. Note that a distinct, malaria resistance allele, HbC (β6 Glu-Lys), has also arisen in Africa at the same codon as the HbS allele (TRABUCHET et al. 1991; AGARWAL et al. 2000; WOOD et al. 2005a), increasing our confidence that the mutational input was high enough to allow multiple types to arise. However, FLINT et al. (1998) thought the hypothesis of multiple new mutations arising at a single base pair was extremely unlikely and proposed that it was more likely that gene conversion had spread a single mutation across multiple haplotypes.

    The theory we have developed can be used to assess the plausibility of the multiple mutational origins of the sickle cell allele, by exhibiting parameter combinations that yield characteristic lengths consistent with the separation of the sample locations. [Recall that the wave of advance, and thus also our model, works in the case of heterozygote advantage (ARONSON and WEINBERGER 1975).] The different HbS haplotypes co-occur within a few thousand kilometers of each other (see Table 5 of FLINT et al. 1998) (noting that these locations are unlikely to reflect the geographic mutational origins, and mutations will have been spread by large population movements). As the HbS changes occur at a single base pair, the mutation rate would have been 10–8, and we take an s = 0.05 (as in CURRAT et al. 2002). If human dispersal at that time was well approximated by a Gaussian kernel with sigma = 100 km, then a characteristic length of 1000 km would require an effective density of individuals of rho = 25 km–2, while if sigma = 10 km, then we would require only rho = 2.5 km–2. This latter set of parameters does not seem unrealistic, considering our knowledge of population density and dispersal parameters, so our model suggests that the hypothesis of multiple origins is not unreasonable.

    I think they've got the basic idea correct here, but there are some additional details to consider. The distribution of HbE is not quite so easy to understand if parallel mutations are really so likely, and of course there is the negative epistasis of different alleles (and the thalassemias) which impacts their dispersal ability when they become moderately common. The dynamic may be of similar form to the one described here, but boundaries between alleles may be reinforced by the fitness costs of carrying multiple ones.

    This situation raises the issue of path dependence. Some mutations have "first mover" advantages. Once they are common, other adaptive mutations may still occur -- even mutations that are better from the standpoint of fitness -- but be lost or grow very slowly because their net fitness advantage over the common mutant is slight. Where HbE is common, new HbS alleles are unlikely to invade quickly. Where HbS is common, new HbE mutants are similarly unlikely to invade -- even though HbE has a higher fitness.

    Network effects among genes may also dominate the spatial dynamics. HbS spread most widely in the context of populations that were already Duffy null, and in which G6PD deficiency was rapidly increasing. The first conditioned the parasite environment -- P. vivax had a strong disadvantage in Duffy null populations, P. falciparum made up most of the parasite load. G6PD deficiency should have impacted the relative advantage of HbS, more and more as it became more common. Those are two loci among many that alter malaria dynamics in Africa compared to South and Southeast Asia.

    Conclusions

    There is much more to say about this paper -- it's 22 journal pages. But I think I've given an impression of what's there and how the ideas may impact our interpretation of recent human evolution. Many of the central concepts were presaged by earlier work in 2007 and 2008, as reviewed here on the blog. The new analytical and simulation work, I really like.

    Hopefully we can get out some shorter papers that will focus on aspects of these problems as applied to humans. A message that comes across very clearly in our work and this new paper is that different time periods in our evolutionary history must have had very different selection dynamics. Pleistocene humans were not only in a different ecology than us, they experienced a radically lower potential for adaptation.


    References

  • Mailbag: The teacher who wouldn't believe in shrinking brains

    Wed, 2010-09-15 15:54 -- John Hawks

    My son, a student at [redacted university], was recently ridiculed by his professor in class when my son suggested that the human brain has been shrinking for the last 20,000 years (the teacher insists that it has not changed). When my son cited the article in September Discover, he was (somewhat understandably) further ridiculed for such a source. My question is: can you provide me with some links to credible sources for this information? He has started to work on this, but wading through so many sources that mention brain size, etc. has proven difficult. We would appreciate any help you could provide.

    Thank you for writing -- that is indeed sad but not surprising; I hear all too often from people who have teachers that can't be bothered to read.

    The reduction in brain size during the last 10,000 years is a really well-known fact in anthropology, it is not at all controversial. It is, for example, discussed in the introductory-level textbook that I assign my students. I have attached several papers that include primary data from archaeological or historical samples. In Europe the trend is most clearly documented, because of the large number (many thousands) of skeletons that have been studied, but the trend is also apparent in South Africa, China, and Australia. Some of the papers include many other characters that also changed during the same time span.

    We cannot rule out that other locations might have had stasis instead of reduction in brain size, but there is not yet a well-documented example of it.

    The most common explanations for a reduction in brain size are (a) diet and the consequent reduction in body size; and (b) warmer Holocene climate. Larger brains are always bad, in that they require more development and time, so what we are looking at in the Holocene is a change in the stabilizing selection -- either an intensification of selection against larger brains, or a relaxation in selection against small brains.

    Body size also did get smaller during the past 50,000 years, which gives rise to the question of whether the brain has reduced *more than should be expected* from the reduction in body size. My research indicates that it has done so (this was one topic covered in the Discover article), but I would not say this is yet a consensus view.

  • More on Tibet, demography and selection

    Tue, 2010-07-06 12:30 -- John Hawks

    My post about the Tibetan high altitude selection story last Friday summarized the research and included some criticism of the demographic model applied in the paper by Yi and colleagues. This weekend, I had some correspondence from study coauthor Rasmus Nielsen.

    Nielsen was kind enough to provide a lot of information about how they arrived at their demographic model. Also, his comments are of substantial interest as a perspective on science journalism. I have posted them in their entirety, and have added my own perspective below them. Click through to read on:

    Nielsen:

    I read your blog on the EPAS1 gene. You write that my answers to Nicholas Wade in the NYT article are lame. I couldn't agree more. Reading the quotes Wade put together from a long phone interview and two replies to follow-up requests by email for further information - I could get quite convinced about my lameness myself. Let me give you our side of the story:

    (1) Regarding effective population size estimation: we fit several different demographic models to the data. The best fitting one according to the Akaike information criterion was chosen in the paper to use for the coalescence simulations. But notice that we made no strong claims about population sizes in the paper. They appear in the supplementary information to ensure that other people could reproduce our study. The main objective for fitting a demographic model was to allow us to perform coalescence simulations under a model that fit the data well. The model described in the paper fits the data very well and was the best fitting model we could find. As such - it was our best option for how to calculate p-values - and was certainly, in our opinion, better than providing no p-values, or use p-values based on some simpler model that did not fit the data. Had we used another model with different values of Ne, we would have obtained less accurate p-values.

    However, we did not interpret the effective population size estimates strongly - mostly because we do not believe they have very much to do with census population sizes. I would argue that this is true for both this study and other similar studies on other populations. Estimated effective population sizes are not only a function of changes in population size, natural selection, male/female ratios and variance in offspring number. They also rely on the structure of the populations. A population organized into many small sub-populaiton might have an Ne that is substantially larger than N, while a population without sub-structure might have a much smaller Ne than the census size if there has been fluctuations in the population size or higher variance in offspring number than that expected from a Poisson. Therefore, it is wrong to interpret estimates of Ne as estimates of actual number of individuals - or to believe that there is some simple general relationship between effective population size and true number of individuals. For this reason, we did purposefully not provide an interpretation of the estimates of Ne in terms of actual values of N and I feel that our work is not being represented accurately by arguing that we obtained estimates of the number of Han individuals or Tibetans living 3000 years ago. That does not mean that we cannot try to understand why we get such a small Ne for Hans 3000 years ago and such a large estimate for Tibetans. The most likely explanation for the Hans is that there have been other bottlenecks that we have not modeled - before or after. If we estimate Ne for Europeans today using a model that does not take all the bottlenecks into account, we get estimates of about 5-15,000 individuals. I don't think anybody would claim that there are only 5-15,000 Europeans alive today. Similarly, our estimate for Ne for the Hans 3000 years ago is in the hundreds presumably because there were some previous bottlenecks that we have not modeled. Ancestral bottlenecks can be extremely hard to date from frequency spectrum data - and you end up getting the same likelihood for a long time period with small population sizes and a short time period with extremely small population sizes. The have been several published papers making this point, the first one I believe to be Adams and Hudson. 2004. Genetics 168:1699-171. Changing our model to having a larger population size 3000 years ago but with an appropriately modeled preceding bottleneck would produce more or less the same p-values - because it would produce the same expected frequency spectrum (or at least something very similar).

    Regarding the large Tibetan population size, it may likely be affected by population structure within Tibet and/or by admixture with other individuals. Both of these factors would inflate the estimate of Ne. We did try some other models - but ended choosing this particular model because if fit the data the best. It seemed, therefore, most appropriate for the coalescence simulations. Again, I want to emphasize that we did not attempt to estimate number of individuals living in particular places during particular times - we were interested in finding a model which fit the distribution of allele frequencies well so that we at least could make some attempt at estimating relevant p-values. We never claimed that there were just a few hundred Han individuals alive 3000 years ago - in the same way that we are not arguing that there are only 5-15,000 Europeans alive today.

    (2) Regarding the divergence time: none of the models we fitted could explain the data with a divergence time much larger than 3000 years. If you look at the figure in the paper, you can see that there is an extremely strong correlation between the allele frequencies in Hans and in Tibetans. This is very difficult to explain with a long divergence time of genetically separated populations. To maintain such a strong correlation for a large amount of time, the Tibetan population (and the Han population) would have to be enormously large - and this is incompatible with the observed levels of variation in the population. We could not find a model that fit the data and which included a large divergence time no matter what we did. But there are of course many factors going into these estimates - including a calibration of number of mutations with the chimp, a number of demographic assumptions, and assumptions regarding generation times. If we are making errors on these assumptions - the estimates could change in one way or another. For that reason I feel it is most conservative to avoid arguing that our analysis definitely rejects that the divergence time could be 6000 years. The main objective of the paper was after all to investigate the evolution of altitude adaptation. The demographic analysis was there mostly to allow us to do the coalescence simulations - but we also used them to make the argument that this selection has occurred quite recently - and not say 10k or 20k years ago. It is quite clear from the data that such long divergence times cannot be supported by the data

    This being said, we of course want to know if this short genetic divergence time is compatible with other evidence. I would argue that it is. There has been several migrations into Tibet. It is entirely compatible with the archaeological record that individuals living in Tibet today genetically mostly are descendants of migrants arriving around 3000 years ago even though the first migrants appeared much earlier. In terms of the selection - and when it has been acting - we want to determine when selection acted to increase the frequency on EPAS1 mutations in the ancestry of the individuals living in Tibet today. If they are genetically descendants of individuals migrating into Tibet just a few thousand years ago - then this is the relevant data for describing when selection has been acting on the EPAS1 mutations. As an aside I should also say that this has nothing to do with when the mutation(s) arose. Selection has in this case most likely been acting on standing variation.

    You argue in your blog that more could be done with this data in terms of demography. We agree. The paper was about altitude adaptation not demography. We are still working on the data and are hoping to produce a follow-up paper on the demographic analyses. We weren't sure how much interest there would be in the results - but the interest from you and other people in this is certainly a motivation to keep working on it as hard as possible.

    I hope you will post this reply on your blog and comment on it. If you do so - I would ask that you post it in its entirety. I learned a lot from the interview with Wade. I certainly now understand why politicians keep giving the same 2-line reply over and over again to journalists asking them questions. If a journalist talks sufficiently long with an interviewee - it will be possible for them to find some sentences that they can put together in some way to make the interviewee look foolish - if that's what they want to do.

    Me:

    Thanks so much for writing with this! I will of course post your comments, and I appreciate very much the time you spent detailing the work, especially on a holiday weekend.

    What you've written here basically agrees with my take on the text of your paper; the demographic model is useful as a test because it is conservative, it is not an attempt at population history. I've reviewed effective size at some length [readers can find a review that I wrote, and I can forward reprints on request]. As you write, this study does not differ substantially from many others in the use of effective size estimates.

    As an anthropologist I am very concerned at the proliferation of population models that are nonsensical from a demographic standpoint. Yes, the p-value will be much the same for EPAS1, but the model is hugely conservative with respect to anything with less extreme differentiation. Other studies are essentially alike; lowball demographic numbers are useful in their conservatism but give an incorrect view about the relation of demography and selection.

    Besides, you have to consider the mechanism by which the best-fit model has come to be so extreme. As you note, the effective size estimated under the assumption of neutrality actually will reflect the non-neutral dynamics across the exome. The HapMap doesn't give rise to anything like the model of an extreme and recent bottleneck that the exome data yield, yet of course both these genome-wide sets must have undergone the same demography. The difference is that the exome is limited to the coding fraction of the genome, pointing to selection on some (probably large) fraction of coding loci. The small effective size within the last 3000 years is mathematically equivalent to a statement that the data include genealogies with many coalescences in those 3000 years. Again, this doesn't happen in a population of hundreds of thousands of individuals unless there was rapid selection.

    So it seems to me that the data must reflect the high incidence of recent selection within mainland China. This is exactly what we expect based on the real demography of massive population growth across the same interval and adaptation to post-agricultural ecologies. Although the headline of the paper is about high altitude adaptation in Tibet, the real story is the massive selection in China of other genes.

    If this is correct, then I think there is much promising work to do by using real demographic estimates. Deriving the demographic model from the data themselves is really just throwing away useful information that is abundantly documented archaeologically and historically.

  • Fast selection in high altitude, but how fast?

    Fri, 2010-07-02 15:56 -- John Hawks

    Did the altitude of the Tibetan plateau lead to the fastest instance of human adaptation yet known?

    That's the claim in the new paper by Xin Yi and colleagues [1]:

    Given our estimate that Han and Tibetans diverged 2750 years ago and experienced subsequent migration, it appears that our focal SNP at EPAS1 may have experienced a faster rate of frequency change than even the lactase persistence allele in northern Europe, which rose in frequency over the course of about 7500 years (26). EPAS1 may therefore represent the strongest instance of natural selection documented in a human population, and variation at this gene appears to have had important consequences for human survival and/or reproduction in the Tibetan region.

    I have a significant criticism of that conclusion, but first I want to say I think this is really cool work. They sequenced 50 whole exomes of people of Tibetan ancestry. An exome is the coding fraction of the genome, leaving out the non-coding stuff. This let them do a genome-wide association including every SNP they found. As it turns out, the key gene (EPAS1) has no coding SNPs that differentiate strongly in these samples. It's an intronic SNP that shows a really large frequency difference (87% in Tibetans, 9% in Han Chinese). That's a really big difference.

    And it takes a big difference to test neutrality in this sample. Fifty exomes is a whole lot of sequencing, but it's really a small sample for finding selection. It takes a really big frequency change to exceed chance. Besides that, most new adaptive mutations will be missed because they haven't gotten off the ground yet. Finding one major allele that correlates strongly with population, and then doing the work to show its association with red blood cell production, that's all pretty neat stuff. This paper should be added to the paper last month by Cynthia Beall and colleagues [2], who also found an association with Tibetans and made a functional link with high altitude adaptation. This gene is part of the system that adapts people to hypoxia in the Tibet/Nepal area, although it certainly does not act alone and we don't yet know how the system works. It's a solid first step.

    OK, so what's my problem with the paper? Hypoxia is a strong selective agent, affecting performance, health, and -- maybe most important -- birth weight. As soon as people began living on the Tibetan Plateau, they were in a compromised environment. That makes this a really great example of recent selection associated with a novel environment. But the archaeological evidence suggests that people have been living in this environment for a lot longer than 3000 years. The population model in the paper is a mess.

    People have been living on the Tibetan Plateau for more than 15,000 years. They may have occupied the area intermittently before the Last Glacial Maximum, and certainly were in nearby medium-altitude areas of northwestern China before that time. The Paleolithic-era occupation of northeastern highland Tibet was reviewed by Madsen and colleagues [3] and Brantingham and colleagues [4]. Aldenderfer [5] reviewed what is known about Neolithic-era occupation of highland Tibet. Sites with ceramics, evidence of sedentary village occupation and domesticated animals occur between 4000 and 6500 calendar years B.P. That doesn't mean that today's Tibetan population derives entirely from these early Neolithic settlers or the even earlier Paleolithic occupants. But the archaeological record does show that the opportunity for genetic adaptation would have been present long before 3000 years ago.

    So there's a potential inconsistency. The inconsistency could be resolved by recognizing that selection is stochastic. Selection cannot start changing the frequency of an allele until after the mutation has occurred.

    The following passage comes from Nicholas Wade's account of the research, in the NY Times. Wade also picked up on the problem with the demography in the paper, and probed the authors about it:

    Geneticists have a more elastic view of dates than do archaeologists, and the estimate of a Han-Tibetan population split at 3,000 years ago could probably have been adjusted to 6,000 if the geneticists had taken any account of any other kind of evidence.

    Rasmus Nielsen, a Danish researcher at the University of California, Berkeley, did the statistical calculations for the Beijing study. “We feel fairly confident that something on the order of 3,000 years is correct,” he said. But in a later e-mail message, Dr. Nielsen said, “I cannot with confidence rule out that the divergence time is 6,000 instead of 3,000.”

    There is similar flexibility in the estimates of population sizes. The Beijing team calculates that at the time of divergence there were only 288 Han Chinese and 22,642 Tibetans. These estimates have bewildered archaeologists, given that rice cultivation in southern China started 10,000 years ago and that there was an extensive civilization by 3,000 years ago. Dr. Nielsen said that the figure of 288 people was meant simply to indicate a bottleneck in the Han population, meaning a time when it was very small, and that this bottleneck could just as easily have occurred 10,000 years ago.

    I think that's totally remarkable. "Geneticists have a more elastic view of dates than do archaeologists"! I think that phrase should be framed and hung in every classroom teaching anthropological genetics.

    Look at the expansion model. In what universe were there only 288 ancestors of Han Chinese people in the last 3000 years? We're talking about the late Bronze Age, here! This is just after the end of the Shang Dynasty, whose capital at Anyang had a walled area of 1000 hectares. That's 1000 soccer pitches full of city, within an empire that spanned the northern half of China.

    It is completely lame to claim that the model could represent a bottleneck as long ago as 10,000 years. You see, the size of the population determines the rate of differentiation under genetic drift. If the population was big, it shouldn't have changed very fast, so the present populations shouldn't be very different. Putting it into numbers, if there hasn't been a bottleneck for 10,000 years, then the divergence must be a lot older than 3000 years. Probably older than 10,000 years.

    These hypotheses can be tested directly with genetics, and the data are certainly rich enough now to do it. If they point to a genetic bottleneck in China during the last 10,000 years, we should be very, very surprised. Because then who was farming all the millet and rice, and domesticating pigs?

    Does it matter? For EPAS1, the timing really doesn't affect the interpretation of selection -- there's no way that drift made the populations as different as they are for this one locus. But it seems clear that this is not a new mutation because it has no long, linked haplotype around it that also differs in frequency in the two populations. Selection on a standing variant is indeed newsworthy, as these are hard to find. Since we don't have a long haplotype to date, the only way that we can estimate the timing of selection is with the population model. Use the wrong model, and you get the wrong time. That is probably what has happened here.

    Also, using this weird population model vastly increases the chance that genetic drift could cause large frequency changes in Tibet or China. This makes us much less likely to recognize genes that really have been subject to selection in either population. With respect to EPAS1 the test is conservative, but the genome-wide comparison will miss a lot of genes and give less significant p-values to others. It's a waste, because it means that we have to collect that much more data to get the same result.

    UPDATE (2010-07-06): Rasmus Nielsen has written me to clarify his remarks to the Times and give more information about the demographic model in the paper. I have posted his full remarks along with some comments of my own. It is well worth reading.


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