Talk by Christina Papageorgopoulou on Mesolithic/Neolithic Greek DNA

The talk is in Greek but the slides are mostly in English. This is related to the BEAN Project which I have covered before in this blog.

She discusses various aspects of DNA preservation (poorer than Central Europe/Siberia due to higher temperature, and potentially because non-freshly excavated samples were tested) which appears to be variable across sites.

A screenshot of haplogroups detected so far (X, K, J, H, T). Interestingly, the X haplogroup only comes from Franchthi cave, although Dr. Papageorgopoulou cautions against very simple interpretations of this fact.

She mentions a complete absence of haplogroup U in all her samples so far (either Mesolithic or Neolithic), in contrast to Central European Mesolithic samples where it predominates.

Fst values between Greek Mesolithic/Neolithic samples and Central European Mesolithic ones are high (~0.2), but with Linearbandkeramik they are very low (0.03-0.05).

I'm looking forward to the first publication of these important results, and hopefully they will be soon supplemented with results from Bulgaria and Anatolia that are also to be studied as part of the BEAN Project.

D-statistics on ADMIXTURE components

One of the most persistent questions I get as admin of the Dodecad Project is whether some low level of admixture (e.g., 0.7%) of some ancestral component is "noise" or "real".

I have hitherto advised all those who contacted me about this issue to (i) treat low levels of admixture with suspicion, and (ii) to run DIYDodecad in byseg mode; this might show whether this type of admixture is concentrated in some specific long segments, and is thus more likely to be "real" recent ancestry than low-level noise sprinkled across the genome that is more difficult to interpret.

Nonetheless, this was always unsatisfying to me, because it did not provide a way of quantifying one's confidence on the "reality" of the admixture evidence. Thus, I developed admixtureDstat.r an R script which calculates D-statistics of the form:

D(Pop1, Individual; Pop3, Outgroup)

If the individual can be seen as being drawn from population Pop1 but with some admixture from population Pop3, then this statistic will take significant negative values. For example, suppose that your main admixture component is "North_European", but you also have 1% "Siberian" admixture. You would want to calculate the following statistic:

D(North_European, YOU; Siberian, Palaeo_African)

which would tell you whether the Siberian admixture is "real" or not. (Of course, things are more complicated for those who might have both Siberian and African admixture, in which case their Siberian admixture would tend to make the D-statistic negative, and the African one positive, with the end result being a balance of the two processes).

There are of course many subtleties in the interpretation of D-statistics and I refer you to Green et al. (2010), Durand et al. (2011), and Patterson et al. (2012) for some of the technical details.

Using the script is quite simple, and only requires that you have R installed on your computer:

• download standardize.r and admixtureDstat.r from here, saving them into some directory in your computer (henceforth, we will call this the "working directory"). If you have Genographic 2.0 data, you should also download hgdp.base.txt.
• unzip your raw genotype data (from 23andMe, Family Finder, or Genographic 2.0) into the working directory. 
• launch R and change the directory into the working directory (using the Menu in Windows, or setwd() in Unix-like operating systems). Enter in one line:

 source('admixtureDstat.r'); source('standardize.r')

• In R, enter the command:

standardize('johndoe.txt', company='23andMe') 

• The above command, will convert your data into a format understood by my script, writing a genotype.txt file in the working directory.  You should change johndoe.txt to whatever your unzipped raw data file is called, and the company should be one of '23andMe', 'ftdna', or 'geno2', or 'geno2new' depending on the source of your data. If you have used DIYDodecad before, you have already created a genotype.txt file, so you can skip this step.
• Finally, you should have the four calculator files (with endings .par, .txt, .alleles, and .F) in the working directory. You can, for example, use the calculator files of the globe13, or if you have experience working with ADMIXTURE, you may make your own using your dataset. The .txt file will contain the names of the ancestral populations that you can use, so make sure you type them correctly if you decide to choose "listfile" mode (see below).
• You are now all set to use the script! You can do this in either of two ways:

(1) outgroup mode:

In this mode, you specify an outgroup, i.e., one of the populations from the calculator, and the program cycles through all possible (Pop1, Pop3) pairs, outputs the D-statistics to the screen as it calculates them, and finally writes them to a dstat.txt file in the working directory.

To use this mode, you simply type:

admixtureDstat(parfile="globe13.par", outgroup="Palaeo_African")

The use of "Palaeo_African" as an outgroup is a reasonable choice for most non-Africans, since these are unlikely to have recent admixture from Sub-Saharan hunter-gatherer groups in which this component is represented.

Note that many of the D-statistics produced this way may have little meaning for you. For example, a person that is mostly European will get a very negative statistic of the form:

D(West_African, YOU; East_Asian, Palaeo_African)

But this will have little to do with your potential West_African or East_Asian ancestry, but rather with the relationships of populations (e.g., Europeans being more closely related to East Asians than West Africans). A little West_African/East_Asian ancestry will increase/decrease the value of this statistic, which will, however, remain strongly negative.

Instead, you should look at D-statistics that might be meaningful to you, e.g., if the following is negative:

D(North_European, YOU; East_Asian, Palaeo_African)

Then you might have some real East_Asian admixture.

(2) listfile mode:

In this mode, you write all the D-statistics you are interested in in a simple text file, e.g., listDstat.txt, in the order Pop1, Pop3, Outgroup, e.g.:

Mediterranean North_European Palaeo_African
North_European Siberian Palaeo_African
North_European West_Asian Palaeo_African

A reasonable choice is to calculate D-statistics where Pop1 is your most important component, e.g., North_European for someone from Finland, and Pop3 is a minor component whose "reality" you seek to investigate, e.g., Siberian.

Using the listfile mode will take less time (because you calculate a subset of D-statistics), and can be invoked as follows:

admixtureDstat(parfile="globe13.par", listfile="listDstat.txt")

Z-scores

The significance of D-statistics is assessed by the Z-scores, which are the last column of the output.    If they are greater than 3 in absolute value (i.e., less than -3 or greater than 3) then Z-scores are significant.

Other details: 

There are some additional options you might use. For example

   admixtureDstat(parfile="globe13.par", listfile="listDstat.txt", k=1000)

will use 1,000 SNPs for the block jackknife instead of the default 500. In general, there is little reason to mess with this parameter.

The screen output might be too wide for your R window, and you can fix this prior to running admixtureDstat by entering something like options(width=300) which allows more characters per line of screen output. In any case, you can see the program's output nicely formatted in the dstat.txt file in the working directory after it completes its run.

AN EXAMPLE

I will give an example of program usage using globe13 results. Take individual DOD133 whose results are seen below:

This individual is mostly Mediterranean (52.5%) and North_European (42%), but with small percentages of Amerindian (1.1%), Southwest_Asian (1.5%), Arctic (0.3%), and South_Asian (1.7%).

First, I calculate D(Mediterranean, DOD133; North_European, Palaeo_African) and D(North_European, DOD133; Mediterranean, Palaeo_African) to confirm the major admixture between Mediterranean and North_European. In listfile mode, I put the following in the listDstat.txt file:

Mediterranean North_European Palaeo_African
North_European Mediterranean Palaeo_African

The results are as follows:

Pop1 Pop3 Outgroup Dstat Z

Mediterranean North_European Palaeo_African -0.02399 -11.2

North_European Mediterranean Palaeo_African -0.033 -15.06

Ok, this confirms that DOD133 does indeed appear to be a mixture of North_European and Mediterranean. Now, let's take one of the minor components, e.g., South_Asian, and put the following in the listDstat.txt file:


Mediterranean South_Asian Palaeo_African
North_European South_Asian Palaeo_African

The results are now:

Pop1 Pop3 Outgroup Dstat Z
Mediterranean South_Asian Palaeo_African -0.01268 -5.98
North_European South_Asian Palaeo_African 0.00202 0.96

A possible interpretation for this pattern is that the individual does have some South_Asian-like admixture that is lacking in his Mediterranean component. Perhaps this reflects an ancient Central Asian population that migrated into both northern Europe and south Asia; some alleles from this population were incorporated into the Northern European gene pool, thus becoming part of what it means to be "northern European", so the evidence for admixture does not exist in the {North_European, South Asian} pair, since both of these contain gene flow from our hypothetical Central Asian population. There are many ways to interpret the observed patterns, and using admixtureDstat you can explore some of them.

Now, let's take another minor component, Arctic (0.3%):

Pop1 Pop3 Outgroup Dstat Z
Mediterranean Arctic Palaeo_African -0.02413 -9.48
North_European Arctic Palaeo_African 0.01028 3.95

This is an interesting pattern; the individual appears admixed with Arctic relative to Mediterranean, but North_European appears to be more Arctic than DOD133. A possible explanation is that this Arctic component represents ancestry that was mediated by a north European population, that as Patterson et al. (2012) have shown contain some "north Eurasian" ancestry.

Finally, let's take the Southwest_Asian minor component (1.5%), where the reverse situation applies:

Pop1 Pop3 Outgroup Dstat Z

Mediterranean Southwest_Asian Palaeo_African 0.00496 2.4

North_European Southwest_Asian Palaeo_African -0.01075 -5.05

So, in this case, this might represent ancestry common between Mediterranean and Southwest_Asian that contrasts with the North_European portion of the individual's genome.

I won't pretend that interpreting D-statistics is easy, but they are certainly a nice exploratory tool to have in one's arsenal, and I hope that they will prove useful.

TERMS OF USE: You are free to use and modify this tool for any non-commercial purpose, as long as you provide a link to Dienekes' Anthropology Blog or this blog post when you do so. You should probably also cite one of the aforementioned papers where D-statistics were discussed, as well as the ADMIXTURE paper.

UPDATE (Dec 3): You might want to try D-statistics using globe13anc, a new calculator that includes an Ancestral (chimp) outgroup.

Quantifying an attractive body

An interesting study in which separate samples of young male and female students used software to "build" an attractive male and female body. It turns out that they both ended up creating quite similar physiques -with subtle differences- but while the ideal men's body had an "average" or even higher-than-average (accounting for mascularity) Body Mass Index, the ideal female's body had a lower BMI than almost all the women in the sample.

The figures on the left (A and B) were set by female participants, and on the right (C and D) by male ones. There is not much that is surprising in the paper, but here's an interesting finding:

An alternative explanation would be that the ideals are influenced by a common media environment which pushes them towards the same concept of the ideal body. However, there are subtle gender-specific differences in the media images seen in the magazines targeted at men and women. For the male body, magazines aimed at a male audience contain male models which are more muscular than those aimed at a female audience [31], [66]. For the female body, female models in women’s magazines are slimmer and have a smaller bust than female models in men’s magazines [53], [54]. This would suggest that there should be systematic differences between the ideals favoured by the two genders. 

This is partially what we find here. The male body selected by the male participants is indeed more muscular than the ideal male body chosen by the female participants. However, in the case of the ideal female body both men and women prefer a female body with the same low BMI, but the female participants prefer a larger bust size than the male participants. This directly contradicts what would be expected from the size and shape of the female models in their respective gender-specific media; the men should prefer a heavier female body than the women and a larger bust.

It might be interesting to repeat this experiment in a non-British sample as well as a sample that is not influenced by media. Anyone have an explanation to offer?

PLoS ONE 7(11): e50601. doi:10.1371/journal.pone.0050601

What Is an Attractive Body? Using an Interactive 3D Program to Create the Ideal Body for You and Your Partner

Kara L. Crossley

What is the ideal body size and shape that we want for ourselves and our partners? What are the important physical features in this ideal? And do both genders agree on what is an attractive body? To answer these questions we used a 3D interactive software system which allows our participants to produce a photorealistic, virtual male or female body. Forty female and forty male heterosexual Caucasian observers (females mean age 19.10 years, s.d. 1.01; 40 males mean age 19.84, s.d. 1.66) set their own ideal size and shape, and the size and shape of their ideal partner using the DAZ studio image manipulation programme. In this programme the shape and size of a 3D body can be altered along 94 independent dimensions, allowing each participant to create the exact size and shape of the body they want. The volume (and thus the weight assuming a standard density) and the circumference of the bust, waist and hips of these 3D models can then be measured. The ideal female body set by women (BMI = 18.9, WHR = 0.70, WCR = 0.67) was very similar to the ideal partner set by men, particularly in their BMI (BMI = 18.8, WHR = 0.73, WCR = 0.69). This was a lower BMI than the actual BMI of 39 of the 40 women. The ideal male body set by the men (BMI = 25.9, WHR = 0.87, WCR = 0.74) was very similar to the ideal partner set by the women (BMI = 24.5, WHR = 0.86, WCR = 0.77). This was a lower BMI than the actual BMI of roughly half of the men and a higher BMI than the other half. The results suggest a consistent preference for an ideal male and female body size and shape across both genders. The results also suggest that both BMI and torso shape are important components for the creation of the ideal body.

Link

Recent origin of protein-coding variants in humans (Fu et al. 2012)

From the paper:

We estimated the age of all 1,146,401 SNVs using 6 different demographic models 5,6,8–11, 3 of which considered recent explosive population growth5,6,8 (Supplementary Table 2). Estimates of allele age were generally robust across different demographic models, with the largest discrepancies resulting in a twofold difference in average age across all SNVs (Supplementary Table 3 and Supplementary Fig. 8a). However, because most SNVs arose recently (see below), differences among demographic models were highly concordant (Supplementary Information). Accordingly, we report results based on a modified Out-of-Africa model9 in which accelerated population growth began 5,115 years ago with a per-generation growth rate of 1.95% and 1.66% for European Americans and African Americans, respectively6. 

The six models considered are:

None of them are very satisfactory, because 5/6 place the Out-of-Africa event at 87.5kya or later, and it now seems likely that this event took place before 100kya. On the other hand, this parameter may not be as critical in this case, because it appears that while the earliest colonization of Eurasia by modern humans did indeed take place prior to 100kya, the Eurasian population stems from a post-70kya bottleneck. The OOA event may not have caused the Eurasian bottleneck; the latter may be a consequence of environmental deterioration in North Africa-Arabia belt c. 70kya.

The modified Out-of-Africa model used in the main paper is by Gravel et al. (2011). That model used a mutation rate of 2.38x10-8 mutations/bp/gen to convert into times in years, which is a little less than twice the slower mutation rate inferred recently with a variety of methods. Thus, its age estimates ought to be pushed back by a factor of two, and this would resolve the inferred 23ky differentiation between Europeans and Asians without invoking any special mechanism, simply as a consequence of the peopling of West and East Eurasia in the last 40-50 thousand years.

But, in the current paper (Fu et al.) a mutation rate of 1.5x10-8 has been used, which is a fairly slow one, albeit a little faster than the 1.2x10-8 reported in a number of studies. It's not entirely clear to me what the consequence would be of mixing a model (Gravel et al.'s) whose parameters have been inferred using a 2.38x10-8 mutation rate with a mutation rate of 1.5x10-9. Overall, I'd think that the main effect would be to bias age estimates downwards, although the analysis should probably be repeated.

Table S3 provides some idea of how different demographic models affect age estimates:

I took a look at Nelson et al. (which has the higher time estimates) in which a median mutation rate of 1.38x10-8 was inferred. Tennessen et al. estimate OOA at 51kya, so they are probably using the faster (and probably outdated) rate.

When did population growth (which meant more bodies, more mutations, higher chance of mildly deleterious mutations to survive) begin? One possibility is that this was a response to deglaciation and temperature rises after the end of the last Ice Age. Another is that it was a consequence of population growth facilitated by agriculture which increased the land's carrying capacity. Perhaps ancient DNA may inform this discussion by measuring the number of deleterious SNVs in individuals across the last 10 thousand years or so.


Nature (2012) doi:10.1038/nature11690

Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants

Wenqing Fu et al.

Establishing the age of each mutation segregating in contemporary human populations is important to fully understand our evolutionary history1, 2 and will help to facilitate the development of new approaches for disease-gene discovery3. Large-scale surveys of human genetic variation have reported signatures of recent explosive population growth4, 5, 6, notable for an excess of rare genetic variants, suggesting that many mutations arose recently. To more quantitatively assess the distribution of mutation ages, we resequenced 15,336 genes in 6,515 individuals of European American and African American ancestry and inferred the age of 1,146,401 autosomal single nucleotide variants (SNVs). We estimate that approximately 73% of all protein-coding SNVs and approximately 86% of SNVs predicted to be deleterious arose in the past 5,000–10,000 years. The average age of deleterious SNVs varied significantly across molecular pathways, and disease genes contained a significantly higher proportion of recently arisen deleterious SNVs than other genes. Furthermore, European Americans had an excess of deleterious variants in essential and Mendelian disease genes compared to African Americans, consistent with weaker purifying selection due to the Out-of-Africa dispersal. Our results better delimit the historical details of human protein-coding variation, show the profound effect of recent human history on the burden of deleterious SNVs segregating in contemporary populations, and provide important practical information that can be used to prioritize variants in disease-gene discovery.

Link

Using Genographic 2.0 data with DIYDodecad

I have released a converter for Genographic 2.0 data at the Dodecad blog. This will allow you to use DIYDodecad with your Genographic 2.0 raw data download.

Pinpointing Roma origins: Out of Northwestern India

Interestingly, besides H-M82, there has been recent evidence that R-Z93 might also represent a second founder haplogroup of the European Roma populations; it will be interesting to study it in the future in order to confirm the scenario presented in this new paper.

From the paper:

This first genetic evidence of this nature allows us to develop a more detailed picture of the paternal genetic history of European Roma, revealing that the ancestors of present scheduled tribes and scheduled caste populations of northern India, traditionally referred to collectively as the Ḍoma, are the likely ancestral populations of modern European Roma. Our findings corroborate the hypothesized cognacy of the terms Rroma and Ḍoma and resolve the controversy about the Gangetic plain and the Punjab in favour of the northwestern portion of the diffuse widespread range of the Ḍoma ancestral population of northern India.

A paper about Roma origins based on autosomal DNA is also apparently in the works, so it will be interesting to see how it might tie in with the Y-chromosome evidence.

PLoS ONE 7(11): e48477. doi:10.1371/journal.pone.0048477

The Phylogeography of Y-Chromosome Haplogroup H1a1a-M82 Reveals the Likely Indian Origin of the European Romani Populations

Niraj Rai et al.

Linguistic and genetic studies on Roma populations inhabited in Europe have unequivocally traced these populations to the Indian subcontinent. However, the exact parental population group and time of the out-of-India dispersal have remained disputed. In the absence of archaeological records and with only scanty historical documentation of the Roma, comparative linguistic studies were the first to identify their Indian origin. Recently, molecular studies on the basis of disease-causing mutations and haploid DNA markers (i.e. mtDNA and Y-chromosome) supported the linguistic view. The presence of Indian-specific Y-chromosome haplogroup H1a1a-M82 and mtDNA haplogroups M5a1, M18 and M35b among Roma has corroborated that their South Asian origins and later admixture with Near Eastern and European populations. However, previous studies have left unanswered questions about the exact parental population groups in South Asia. Here we present a detailed phylogeographical study of Y-chromosomal haplogroup H1a1a-M82 in a data set of more than 10,000 global samples to discern a more precise ancestral source of European Romani populations. The phylogeographical patterns and diversity estimates indicate an early origin of this haplogroup in the Indian subcontinent and its further expansion to other regions. Tellingly, the short tandem repeat (STR) based network of H1a1a-M82 lineages displayed the closest connection of Romani haplotypes with the traditional scheduled caste and scheduled tribe population groups of northwestern India.

Link

South Indian Y chromosomes (+ a little complaining about methods)

The table of haplogroup frequencies (left) may prove quite useful, but I am fairly disappointed with what appears to be the state of the art in recent published research on Y chromosome variation. This is not to belittle the tremendous amount of labor and money needed to collect and genotype large representative samples of individuals; only to express hope that better use of the collected samples could be achieved.

First of all, it is inconceivable to me how scientists can continue to use the 3x slower "evolutionary mutation rate" for their analyses of Y-chromosome ages on the basis of Y-STR markers. I have done my small part in my Y-STR series to show that this mutation rate is applicable only for a rather specific demographic history, and completely unsuitable to real growing human populations where Y-STR variance accumulates at close to the genealogical rate. And, my observations merely elaborated quantitatively what was already present in Zhivotovsky et al. (2006) but has been completely ignored since:

In simulations of a neutral process with average rate of increase m = 1, the number of surviving haplogroups rapidly decreased with time and corresponded well with the theory of mutant survival (Li 1955, p. 242), and the average size of the surviving haplogroups increased each generation by a value rapidly approaching 0.5 (data not shown), which agrees with asymptotic fraction of 2/t of haplotypes that survive at generation t (Athreya and Ney 1972, p. 19). The accumulated variance increased almost linearly (fig. 1), at a rate of increase about 0.00028 per generation; that is, the actual rate of accumulation microsatellite variation was about 3.6 times less than that predicted from the germ line mutation rate. This corresponds perfectly to the 3- to 4-fold difference observed between germ line and evolutionarily effective mutation rate.

The issue is all but resolved in the amateur "genetic genealogy" community, but even professional geneticists often use either genealogical or evolutionary rate, or take an agnostic stance by reporting results based on both rates. To arrive at strong conclusions about a topic on the basis of a mutation rate that is, to say the least, controversial, without even acknowledging the existence of a controversy is unsatisfactory. Y-chromosome researchers ought to copy the attitude of those working with autosomal DNA, where a corresponding mutation rate controversy was not swept under the carpet, but acknowledged (e.g., in the recent Meyer et al. high-coverage Denisova paper), with the implications of the uncertainty during the present "transitional" period quantified in the form of wider confidence intervals.

This "mutation rate" issue  notwithstanding, it was also recently shown that by Busby et al. that Y-STR based estimates have a dependence on the set of Y-STRs used, with markers exhibiting linear behavior across different time spans. This does not invalidate their use as molecular clocks, but highlights the need to not only select a bunch of Y-STRs, but also either (i) demonstrate that the selected set exhibits linear behavior for the time span of interest, or (ii) correct for deviations from linearity. Again, this type of modelling of microsatellite behavior was recently achieved for autosomal STRs by Sun et al.  Note that such deviations result in a slower rate than the genealogical one, but the mechanism whereby this is produced is completely different than the one proposed by Zhivotovsky et al.: it is not drift in a non-growing (m=1) population that reduces the effective rate, but rather "saturation" of the mutation process, whereby the variance at fast-mutating markers grows sub-linearly with time, because of physical constraints on their possible range of values.

I don't hope that Y-STR based age estimation will have much to offer in the coming years. But the third set of the 1000 Genomes Project is on its way, and this will include a variety of South Asian samples. Very soon we will be in a good position to study the time depth of common ancestry between e.g., European and South Asian Y-chromosomes within various haplogroups using point mutations, and these are not plagued by many of the problems associated with Y-STR variation and its interpretation.

Finally, I can't help but notice that this paper has not acknowledged the tremendous progress in resolving the Y chromosome phylogeny done by non-academic researchers. With the current state of our knowledge, the claim that haplogroup R1a1 is "autochthonous" in India is not tenable. Even if one discounts all the evidence made by SNP discoveries in the commercial testing world (and why should they?), finer-scale structure within this haplogroup has now been officially published and appears to be inconsistent with a South Asian origin of this haplogroup.

Certainly, not all is resolved; for example, the representation of tribal populations in commercial DNA testing is almost non-existent, and a sampling of their Y-SNP diversity is urgently needed. A very useful paradigm of research is that of recent work on the most basal clade of the Y-chromosome phylogeny (A00) in which the identification of very unique Y-chromosomes by genetic genealogists was combined with academic samples of "indigenous" peoples to produce new knowledge.

Much of population genetic research will benefit from such consilience between academics and amateurs. This is not an idle hope, but a recognition that this field is one in which the public not only has a substantial interest but can also do something about it. Many might be interested in Mars exploration, but without Elon Musk's bank account, most are consigned to being consumers of information about the Red Planet. Hopefully, better ways of combining the efforts of research scientists and the educated public can be identified and used in the near future.

PLoS ONE 7(11): e50269. doi:10.1371/journal.pone.0050269

Population Differentiation of Southern Indian Male Lineages Correlates with Agricultural Expansions Predating the Caste System

GaneshPrasad ArunKumar et al.

Previous studies that pooled Indian populations from a wide variety of geographical locations, have obtained contradictory conclusions about the processes of the establishment of the Varna caste system and its genetic impact on the origins and demographic histories of Indian populations. To further investigate these questions we took advantage that both Y chromosome and caste designation are paternally inherited, and genotyped 1,680 Y chromosomes representing 12 tribal and 19 non-tribal (caste) endogamous populations from the predominantly Dravidian-speaking Tamil Nadu state in the southernmost part of India. Tribes and castes were both characterized by an overwhelming proportion of putatively Indian autochthonous Y-chromosomal haplogroups (H-M69, F-M89, R1a1-M17, L1-M27, R2-M124, and C5-M356; 81% combined) with a shared genetic heritage dating back to the late Pleistocene (10–30 Kya), suggesting that more recent Holocene migrations from western Eurasia contributed less than 20% of the male lineages. We found strong evidence for genetic structure, associated primarily with the current mode of subsistence. Coalescence analysis suggested that the social stratification was established 4–6 Kya and there was little admixture during the last 3 Kya, implying a minimal genetic impact of the Varna (caste) system from the historically-documented Brahmin migrations into the area. In contrast, the overall Y-chromosomal patterns, the time depth of population diversifications and the period of differentiation were best explained by the emergence of agricultural technology in South Asia. These results highlight the utility of detailed local genetic studies within India, without prior assumptions about the importance of Varna rank status for population grouping, to obtain new insights into the relative influences of past demographic events for the population structure of the whole of modern India.

Link

Paleoamerican Odyssey conference, ~a year from now

Here is a list of abstracts from a conference that will take place in October 2013. A small sampling of interesting titles:

• Yana RHS site, earliest occupation of Siberia
• Late Pleistocene Siberia: Setting the Stage for the Peopling of the Americas
• Three Stage Colonization Model for the Peopling of the Americas
• The Younger Dryas Boundary (YDB) Cosmic Impact Hypothesis, 12.9 ka: A Review
• Bioarchaeological Biographies of Ancient Americans
• Paisley Caves: 14,500 Years of Human Occupations in the Northern Great Basin
• The Mammoth Steppe Hypothesis: The Mid Wisconsin (OIS 3) Peopling of the Americas
• North America Before Clovis: Variance in Temporal/Spatial Cultural Patterns, 24,000 to 13,000 BP 

On the archaeogenetics side, an intriguing abstract of Eske Willerslev's talk:

A Genomic Sequence of a Clovis Individual  

Eske Willerslev  

The Clovis complex is by some scientists considered being the oldest unequivocal evidence of humans in the Americas, dating between ca. 11,050 to 10,800 14C yr B.P. Only one human skeleton has been directly AMS dated to Clovis age and found associated with Clovis technology namely the Anzick human remains from Montana. We are currently sequencing the nuclear and mitochondrial genome from this human skeleton in order to address the origins and descendents of Clovis. I will present the results obtained by our international consortium.

In terms of the "three-migration" model, Clovis ought to be "First American". But, there is evidence that at least archaeologically Clovis had company and predecessors, so it will be interesting to see how closely the sample will match our expectation of what "First American" DNA looked like.

There is also the issue of the Solutrean hypothesis; if early North Americans had European ancestors, and the early population was diluted by subsequent population movements from Asia, this ought to show up. Additionally, there is the hypothesis of a common North Eurasian ancestry affecting both Europe and Amerindians, which would predict that the Clovis individual would be an early descendant possessing that type of ancestry.

In about a year we might know much more about the identity of early New World populations, and, by implication, adapt our views about the settling of the Old World itself.

Ancestry Mapper (Magalhães et al. 2012)

The idea of Ancestry Mapper is fairly simple: each individual is represented as a vector of similarity to a fixed number of a priori chosen reference populations. These vectors can then be processed (e.g., with clustering) as any other type of high-dimensional data (e.g., PC co-ordinates).

The following figure should appear familiar to readers familiar with my MDS/MCLUST "Clusters Galore" methodology:

This was produced by applying PAM clustering to AMids. I don't think that this is a better way to do clustering than PCA/MDS+MCLUST, both because "partition around medoids" is a less expressive model than the suite of models that MCLUST may consider and choose from, and also because the AMids assume a priori assignment of individuals to populations, which is not necessary for the "Galore" approach that uses MDS/PCA for dimensionality reduction of individuals and is agnostic about their population labels. In any case, it is useful to know that with both a different dimensionality reduction method and a different clustering algorithm, a large number of meaningful clusters can be inferred.

PLoS ONE 7(11): e49438. doi:10.1371/journal.pone.0049438

HGDP and HapMap Analysis by Ancestry Mapper Reveals Local and Global Population Relationships

Knowledge of human origins, migrations, and expansions is greatly enhanced by the availability of large datasets of genetic information from different populations and by the development of bioinformatic tools used to analyze the data. We present Ancestry Mapper, which we believe improves on existing methods, for the assignment of genetic ancestry to an individual and to study the relationships between local and global populations. The principle function of the method, named Ancestry Mapper, is to give each individual analyzed a genetic identifier, made up of just 51 genetic coordinates, that corresponds to its relationship to the HGDP reference population. As a consequence, the Ancestry Mapper Id (AMid) has intrinsic biological meaning and provides a tool to measure similarity between world populations. We applied Ancestry Mapper to a dataset comprised of the HGDP and HapMap data. The results show distinctions at the continental level, while simultaneously giving details at the population level. We clustered AMids of HGDP/HapMap and observe a recapitulation of human migrations: for a small number of clusters, individuals are grouped according to continental origins; for a larger number of clusters, regional and population distinctions are evident. Calculating distances between AMids allows us to infer ancestry. The number of coordinates is expandable, increasing the power of Ancestry Mapper. An R package called Ancestry Mapper is available to apply this method to any high density genomic data set.

Link

Skull trauma in Neolithic Scandinavia

I would be interested to know how this Neolithic sample might differ from more recent ones. My limited understanding suggests that between-male violence often has a signalling component whereby an individual's or group's dominance over another is asserted, so the fight often does not go all the way to death, but only until the status quo is manifested by the controlling party or toppled by a challenger.

This type of "signalling" aspect of violent behavior does not apply to male-to-female violence because of the physical strength inequality between the sexes. Indeed, as with violence towards children or the elderly, male-to-female violence may have a "reverse signalling" effect, because it suggests that the perpetrator is unable to fight with "the strong" and is only able to assert physical dominance in "easy fights". On the other hand, such "easy fights" might be more abundant if perpetrators tend to enter fights they can win.

Fight-to-the-death, on the other hand, may occur either by accident (e.g., when the aim is to assert dominance, but the killer underestimates the tolerance of the victim), or by intent (when the aim is physical annihilation, either because reconciliation with the victim is perceived to be impossible, or because the victim's death may help keep other challengers in check).

There may be lots to learn about gender roles and social hierarchy from large palaeoanthropological samples. For example, how much did ideology affect secular patterns of interpersonal violence, and how much did changes in weapon technology (e.g., from Neolithic to Bronze, Iron, and more recently firearms).

Am J Phys Anthropol DOI: 10.1002/ajpa.22192

Patterns of violence-related skull trauma in neolithic southern scandinavia

Linda Fibiger et al.

This article examines evidence for violence as reflected in skull injuries in 378 individuals from Neolithic Denmark and Sweden (3,900–1,700 BC). It is the first large-scale crossregional study of skull trauma in southern Scandinavia, documenting skeletal evidence of violence at a population level. We also investigate the widely assumed hypothesis that Neolithic violence is male-dominated and results in primarily male injuries and fatalities. Considering crude prevalence and prevalence for individual bones of the skull allows for a more comprehensive understanding of interpersonal violence in the region, which is characterized by endemic levels of mostly nonlethal violence that affected both men and women. Crude prevalence for skull trauma reaches 9.4% in the Swedish and 16.9% in the Danish sample, whereas element-based prevalence varies between 6.2% for the right frontal and 0.6% for the left maxilla, with higher figures in the Danish sample. Significantly more males are affected by healed injuries but perimortem injuries affect males and females equally. These results suggest habitual male involvement in nonfatal violence but similar risks for both sexes for sustaining fatal injuries. In the Danish sample, a bias toward front and left-side injuries and right-side injuries in females support this scenario of differential involvement in habitual interpersonal violence, suggesting gendered differences in active engagement in conflict. It highlights the importance of large-scale studies for investigating the scale and context of violence in early agricultural societies, and the existence of varied regional patterns for overall injury prevalence as well as gendered differences in violence-related injuries.

Link

Medieval signal of Swedish (?) admixture in Finland

I took the FIN (Finnish), GBR (British), and CDX (Chinese Dai) samples of the 1000 Genomes Project, each of which has a sample size of 100 in order to investigate the signal of East-West Eurasian admixture in Finns. While neither Britons nor Dai could be imagine of having contributed to Finns directly, they ought to make useful proxies of a NW European population lacking recent East Eurasian ancestry, and an East Eurasian population lacking recent West Eurasian ancestry respectively.

In the following, I will assume a generation length of 29 years and a sample birthyear of 1980 as in previous experiments.

First, the 1-reference analysis of FIN using GBR produced an admixture proportion lower bound of 37.4 +/- 5.1 percent.

The corresponding analysis of FIN using CDX produced an admixture proportion lower bound of 4.4 +/- 1.0 percent.

The 2-ref admixture test with {GBR,CDX} reported success:

Test SUCCEEDS (z=2.76, p=0.0057) for FIN with {GBR, CDX} weights

But, the decay rates were inconsistent, a situation which might occur when major admixture from different sources took place at different times. In particular, the one using CDX corresponded to 65.57 +/- 8.36 generations, and the one using GBR to 25.48 +/- 4.93 generations.

In calendar dates, Finns are estimated to have mixed with an East Eurasian CDX-like population between 170BC-320AD and with a NW European GBR-like population between 1100-1380AD.

The central date of the latter estimate is 1,240AD, which corresponds quite closely to the beginning of Swedish rule and is in the middle of the 13th. century, between the time when Finland was initially claimed for western Christendom (12th c.) and the time when the conflict between Sweden and Russia was settled (14th c.).

Irish Travellers are Irish

Am J Phys Anthropol DOI: 10.1002/ajpa.22191

Genetic drift and the population history of the Irish travellers

John H. Relethford, Michael H. Crawford

Abstract

The Irish Travellers are an itinerant group in Ireland that has been socially isolated. Two hypotheses have been proposed concerning the genetic origin of the Travellers: (1) they are genetically related to Roma populations in Europe that share a nomadic lifestyle or (2) they are of Irish origin, and genetic differences from the rest of Ireland reflect genetic drift. These hypotheses were tested using data on 33 alleles from 12 red blood cell polymorphism loci. Comparison with other European, Roma, and Indian populations shows that the Travellers are genetically distinct from the Roma and Indian populations and most genetically similar to Ireland, in agreement with earlier genetic analyses of the Travellers. However, the Travellers are still genetically distinct from other Irish populations, which could reflect some external gene flow and/or the action of genetic drift in a small group that was descended from a small number of founders. In order to test the drift hypothesis, we analyzed genetic distances comparing the Travellers to four geographic regions in Ireland. These distances were then compared with adjusted distances that account for differential genetic drift using a method developed by Relethford (Hum Biol 68 (1996) 29–44). The unadjusted distances show the genetic distinctiveness of the Travellers. After adjustment for the expected effects of genetic drift, the Travellers are equidistant from the other Irish samples, showing their Irish origins and population history. The observed genetic differences are thus a reflection of genetic drift, and there is no evidence of any external gene flow.

Link

LAMP-LD paper and software

On a similar topic as the recent MULTIMIX software, this paper describes the performance of LAMP-LD software on Latinos with ancestry from Europe, Africa, and the Americas. The software can be obtained from this site.

From the paper itself, this figure highlights a problem I have previously identified:

In this experiment, the authors "European-ized" East Asian reference panels by introducing TSI (Tuscan) segments into them. From the paper:

Current day Native American haplotypes used as proxy for the Native American component of Latinos are presumed to contain European gene flow. In order to test the effect of this phenomenon on ancestry inference, we introduced TSI segments into the Asian haplotypes of a reference set composed of 117 CEU, 169 (CHB+CHD) and 115 YRI haplotypes. We performed 10 experiments, in each choosing at random a 5 Mb region along the chromosome, and replacing a percentage of the (CHB+CHD) haplotypes with TSI haplotypes along the chosen region. 

We observed that the typical effect of increasing the number of TSI segments present in the Native American reference panels is an increase in the estimated proportion of the Native American ancestry along the modified region, at the expense of the estimated European proportion.

In the case of Native American admixture, the occurrence of European segments in the reference panels is a problem, because we can be fairly sure that prior to 1492 there was no recent European ancestry in the Americas.

But, the problem also arises in other cases where this is less certain, for example the arrival of East Eurasian ancestry via Uralic and Turkic speakers from Siberia and Central Asia and into West Eurasia. In that case, we cannot be entirely certain whether the presence of European haplotypes in reference populations (e.g., present-day Siberians/Central Asians) is due to post- or pre-migration contact in the eastern source areas.

To make my observation clearer: suppose that an eastern population X, contributes to a European population Y. We can then estimate how much "X ancestry" population Y has absorbed. But, if X today is "more East Asian" than X when it contributed to Y, then the proportion of admixture will be underestimated, and in the converse case it will be overestimated.

This was made evident in my recent analysis of Turks where substantially different admixture estimates was obtained using different eastern populations. The evidence of that analysis suggests that major admixture occurred in Central Asia after it did in Anatolia.


Bioinformatics (2012) 28 (10): 1359-1367. doi: 10.1093/bioinformatics/bts144

Fast and accurate inference of local ancestry in Latino populations

Yael Baran et al.

Motivation: It is becoming increasingly evident that the analysis of genotype data from recently admixed populations is providing important insights into medical genetics and population history. Such analyses have been used to identify novel disease loci, to understand recombination rate variation and to detect recent selection events. The utility of such studies crucially depends on accurate and unbiased estimation of the ancestry at every genomic locus in recently admixed populations. Although various methods have been proposed and shown to be extremely accurate in two-way admixtures (e.g. African Americans), only a few approaches have been proposed and thoroughly benchmarked on multi-way admixtures (e.g. Latino populations of the Americas).

Results: To address these challenges we introduce here methods for local ancestry inference which leverage the structure of linkage disequilibrium in the ancestral population (LAMP-LD), and incorporate the constraint of Mendelian segregation when inferring local ancestry in nuclear family trios (LAMP-HAP). Our algorithms uniquely combine hidden Markov models (HMMs) of haplotype diversity within a novel window-based framework to achieve superior accuracy as compared with published methods. Further, unlike previous methods, the structure of our HMM does not depend on the number of reference haplotypes but on a fixed constant, and it is thereby capable of utilizing large datasets while remaining highly efficient and robust to over-fitting. Through simulations and analysis of real data from 489 nuclear trio families from the mainland US, Puerto Rico and Mexico, we demonstrate that our methods achieve superior accuracy compared with published methods for local ancestry inference in Latinos.

Availability: http://lamp.icsi.berkeley.edu/lamp/lampld/

Link

Lack of β thalassemia mutations in Minoan Cretans

Blood Cells, Molecules, and Diseases Volume 48, Issue 1, 15 January 2012, Pages 7–10

A search for β thalassemia mutations in 4000 year old ancient DNAs of Minoan Cretans

Jeffery R. Hughey et al.

Ancient DNA methodologies can be applied in the investigation of the genetics of extinct populations. A search for beta thalassemia mutations was performed on 49 Minoan individuals from the Bronze Age who were living in the island of Crete approximately 4000 Years Before Present (YBP). Standard precautionary measures were employed in the laboratory to ensure authenticity of the DNA extracted from the ancient bones, resulting in the successful analysis of DNA of 24 Minoans. DNA sequencing focused on the Intervening Sequence 1 (IVS-1) of the beta globin gene and its splicing junctions. 63% of the thalassemia mutations observed among modern Cretans reside in beta IVS-1. None of the Minoan individuals carried one of the IVS-1 mutations known to cause beta thalassemia; however, only one was expected to be observed if the average frequency of beta thalassemia heterozygotes in the Minoan population was the same with that of modern day Cretans (7.6%). One individual contained a C to G substitution in position 91 of the IVS-1, located 40 bp 5′ to the intron 1/exon 2 junction. Functional studies indicated that the mutation did not affect mRNA splicing or stability, and most likely represented an innocent single nucleotide polymorphism.

Link

Assessment of Totonac and Bolivian samples using 'globe13'

I was on the lookout for some Affy 6.0 samples recently, and I discovered the data of the recent Watkins et al. (2012) paper, so I decided to run them through my globe13 calculator. A total of 49,233 SNPs were in common between that and my globe13 set, which is not much, but ought to be sufficient to discover the main features of these two population samples.

It appears that both samples are mainly "Amerindian", with the Bolivian sample having some more European admixture than the Totonac one.

Here are the population portraits, clearly showing that the "European" admixture in Bolivians comes from a subset of individuals.

For comparison, here are the ADMIXTURE results from the original paper that appear quite similar to my own. (Note that the individual ordering is probably not the same as my own):

The Mediterranean/North_European ratio of my own analysis suggests the likely "southern" (probably Spanish) origin of the European admixture in these populations.

UPDATE:

I also combined the two Amerindian populations with HGDP Karitiana, Sardinian, and French to calculate f3-statistics. Here are the significant ones:

So, admixture in the Bolivian sample is confirmed, while in the Totonac one it is not. I do think it's possible that the Totonac might have a little European admixture though which might be masked by their history of drift. Also notice the evidence for admixture in the French using all three Amerindian samples, with lowest f3(French; Amerindian, Sardinian) using the Karitiana reference.

The comings and goings of Near Eastern and European domestic pigs (Ottoni et al. 2012)

This is an excellent paper whose findings re: pig domestication seem to parallel many of my own observations regarding the flow of human populations. It is open access, so you can read it for yourselves, but the following figure illustrates the situation admirably:

The left-right arrangement of the columns corresponds to a west-east longitude across West Asia. It can be easily seen that some of the early domestic samples (yellow, bottom row) are concentrated in the west (Y1 haplotype), while others (blue, Arm1T) in the east.

Neolithic European samples possessed the Y1 haplotype, but lacked the Arm1T one. So, the authors conclude that:

The ancient Anatolian data presented here reveal that both wild and possibly domestic Neolithic pigs (identified using traditional metrics) possessed Y1 haplotypes ... The presence of these lineages corroborates the supposition that the earliest domestic pigs in Europe originated from populations originally domesticated in the Near East, conclusively linking the Neolithization of Europe with Neolithic cultures of western Anatolia (Larson et al. 2007a; Haak et al. 2010).

I have repeatedly highlighted the "puzzle" of the early European Neolithic: the signature Y-haplogroup G2a was unaccompanied by other common Near Eastern lineages, and the modal "West Asian" ancestral component in present-day West Asian populations seems to have been absent in early Neolithic samples, which were dominated by a "Sardinian-like" population. I have argued that this meant that the European Neolithic was drawn from a limited founder source that was more "Mediterranean/Southern" autosomally than "West Asian", at least in terms of the components identified by the Dodecad Project.

In Europe itself, the early Near Eastern domestic pigs were replaced by European ones:

Ancient DNA extracted from early Neolithic domestic pigs in Europe resolved this paradox by demonstrating that early domestic pigs in the Balkans and central Europe shared haplotypes with modern Near Eastern wild boar (Larson et al. 2007a). The absence of Near Eastern haplotypes in pre-Neolithic European wild boar suggested that early domestic pigs in Europe must have been introduced from the Near East by the mid 6th millennium BC before spreading to the Paris basin by the early 4th millennium BC (Larson et al. 2007a). 

By 3,900 BC, however, virtually all domestic pigs in Europe possessed haplotypes  from an indigenous European domestication process (Larson et al. 2007a) only found in European wild boar. This genetic turnover may have resulted from the accumulated introgression of local female wild boar into imported domestic stocks, or from an indigenous European domestication process (Larson et al. 2007a).

We have seen that early Neolithic domestic pigs came from Western Anatolia, but apparently these did not last, but were replaced in Europe by pigs carrying mtDNA of European wild boar. An additional possibility is that the European wild boar were better adapted to local conditions in Europe, so the stock of European farmers gradually became "local" due to artificial/natural selection favoring the local "European" type. It might also be that in accordance with Bergmann's rule, European-descended pigs were simply bigger, and thus more economically productive.

In any case, the interesting thing is that pigs carrying the "European" haplotype went the other way, crossing from Europe to Asia. The beginning of this process seems to have occurred in the Middle Bronze Age:

The temporal and geographic distribution of genetic haplotypes presented in our study demonstrates that the first AMS dated pig with European ancestry (haplotype A) appeared almost 1,000 years earlier than the Armenian samples in a Late Bronze Age context (~1,600-1,440 BC) at Lidar Höyük (fig. 1). An even earlier Middle Bronze Age specimen from the same site also possessed a European signature, but a direct
AMS date for this specimen could not be obtained.

I have written how increased mobility and long-range networks associated with the new metallurgical class facilitated commerce during the Bronze Age. The authors suggest the possibility of Minoan-Mycenaean/Hittite involvement during the Bronze Age, which are certainly plausible conduits for European pigs to have crossed the Aegean at this time. But, as you can see from the figure, the "European" pigs are still outliers during the Middle and Bronze Ages, but become common in the Iron Age sample from Lidar Höyük, and eventually replacing local types throughout Anatolia and Armenia, but, apparently, not Iran:

The frequency of pigs with European ancestry increased rapidly from the 12th century BC, and by the 5th century AD domestic pigs exhibiting a Near Eastern genetic signature had all but disappeared across Anatolia and the southern Caucasus. Though we did not detect European signatures in the ancient Iranian samples (fig. 1), the eastward spread of European lineages may have continued into Iran later than the Iron Age since European lineages have been found in wild caught modern Iranian samples (Larson et al. 2007a).

Of course a 12th century BC increase in European domestic pigs is entirely consistent -chronologically- with the Phrygian/Armenian settlement in Anatolia, and this association is further reinforced by the lack of European signatures in pigs from Iran where Phrygo-Armenians did not settle. The increase in European pigs could later be mediated by the Greek colonization, and the increase in trade during antiquity, just as trade would later introduce East Asian pig DNA into Europe.

The beautiful temporal transect presented in the Figure may also prove useful for students of ancient human DNA. I'd love to see how humans living close to sites #14-16, dominated by Arm1T haplotypes throughout history might differ from those of Neolithic West Anatolia, and whether the "mixed" Iron Age sample from Lidar Höyük shows evidence of the arrival of European-like human populations to accompany the European pigs.

Mol Biol Evol (2012) doi: 10.1093/molbev/mss261

Pig domestication and human-mediated dispersal in western Eurasia revealed through ancient DNA and geometric morphometrics

Claudio Ottoni et al.

Zooarcheological evidence suggests that pigs were domesticated in Southwest Asia ∼8,500 BC. They then spread across the Middle and Near East and westward into Europe alongside early agriculturalists. European pigs were either domesticated independently or appeared so as a result of admixture between introduced pigs and European wild boar. These pigs not only replaced those with Near Eastern signatures in Europe, they subsequently also replaced indigenous domestic pigs in the Near East. The specific details of these processes, however, remain unknown. To address questions related to early pig domestication, dispersal, and turnover in the Near East, we analyzed ancient mitochondrial DNA and dental geometric morphometric variation in 393 ancient pig specimens representing 48 archeological sites (from the Pre-Pottery Neolithic to the Medieval period) from Armenia, Cyprus, Georgia, Iran, Syria and Turkey. Our results firstly reveal the genetic signature of early domestic pigs in Eastern Turkey. We also demonstrate that these early pigs differed genetically from those in western Anatolia that were introduced to Europe during the Neolithic expansion. In addition, we present a significantly more refined chronology for the introduction of European domestic pigs into Asia Minor that took place during the Bronze Age, nearly 1,000 years earlier than previously detected. By the 5th century AD, European signatures completely replaced the endemic lineages possibly coinciding with the demographic and societal changes during the Anatolian Bronze and Iron Ages.

Link