Around the year 2000 something similar happened in historical population genetics. The analysis of mtDNA lineages, passed from mother to daughter, had matured, and techniques for typing the Y chromosome had started to catch up, so that a symmetry between the sexes could arise. “Mitochondrial Eve” was now paired with “Y chromosomal Adam.” Though mtDNA and Y lineages were only two direct lines of ancestry, because there was no recombination across much of their sequence it was easy to analyze them within the context of coalescent theory. In contrast, the genealogy of autosomal regions of the genome were confounded by recombination, which mixed & matched the variation in a manner which made reconstruction of past history far more difficult. So we had the technology to extract the genetic variation from mtDNA and the Y chromosome, and, we knew how to model their evolution. The two together produced a genetic time machine.

The result was a swelling of papers utilizing uniparental markers. You can see the chronology to some extent at the frequency of postings at Stanford’s Human Population Genetics Laboratory online repository. Another byproduct was the emergence of public intellectuals who filled the need which arose to interpret and communicate the findings to a lay audience. Four books are emblematic of the era, Bryan Sykes’ The Seven Daughters of Eve: The Science That Reveals Our Genetic Ancestry, Spencer Wells’ The Journey of Man: A Genetic Odyssey, Stephen Oppenheimer’s The Real Eve: Modern Man’s Journey Out of Africa, and Steven Olson’s Mapping Human History: Genes, Race, and Our Common Origins. As a professional journalist Olson’s treatment was the odd one out, as much reportage as a personal interpretation. In contrast, Sykes, Wells, and Oppenheimer were making scholarly cases from their own vantage point. Oppenheimer and Wells also paired their books with television documentaries. Wells continues to remain in the public eye, he’s become a new sort of intellectual entrepreneur with the The Genographic Project. The age of uniparental markers then spawned careers and truisms. For example, the patterns of variation of mtDNA and Y chromosomes resulted in the consensus that ~75% of the ancestors of modern Europeans are descended from Paleolithic hunter-gatherers. The proportion of the ancestry contributed by Neolithic farmers decreased from southeast to northwest, converging upon zero in the far reaches of the British Isles and Norden.

This inference was made in large part based upon the contemporary patterns of genetic variation, by assigning modern haplogroups to putative ancient populations. To the left is a map of the frequency of haplogroup R1b, which is the most common Y chromosomal lineage in western Europe. The frequency is highest among the Basques, who were presumed to be the most pristine reservoir of the genetic substratum of Paleolithic Europe. The conception here was that the Basques were clearly indigenous to Iberia, they were already there before the arrival of outsiders such as the Celts, Phoenicians, and finally Romans (this has influenced modern Basque nationalism to some extent). Their non-Indo-European language was assumed to be a relic of many dialects which once existed before Indo-European swept over them. Using R1b, and other haplogroups at high frequency among the “indigenous peoples” of Europe, historical geneticists pegged the ancestral quanta of hypothetical prehistoric groups using these putative indigenes as modern references. But the inferences rested on assumptions, assumptions which couldn’t be directly tested. Until that is another methodological revolution arrived on the scene: the extraction of ancient DNA! These new waves of results, which came to the fore in the latter 2000s, have unsettled our preconceptions. It now seems that the past was likely more complex than we’d presumed, and the palimpsest of human genetic variation over time may have obscured and clouded our understanding of the map of what once was.

More recently some researches have gone back and looked at the variation within the R1b haplogroup, specifically the subclade which is very common in Western Europe, R1b1b2, and concluded that in fact it was most diverse in the eastern Mediterranean.  The most plausible inference to be made from this was that the R1b1b2 originated to the east, and spread to the west, rising in frequency due to genetic drift as populations went through bottlenecks and then rapidly expanded in size. Additionally, the last common ancestor of these lineages was on the order of ~10,000 years ago. This naturally upends the model which geneticists were confidently pushing forward in the early 2000s, shutting the door on debates as to the provenance of modern Europeans and their relationship to Ice Age hunter-gatherers. A follow up paper rebutted this new claim as to the origin and expansion of R1b1b2. What had been a stable and conventional area of historical population genetics has now been thrown into tumult, and researchers are looking more closely at the uniparental lineages which had had their time in the sun. Or so it seemed.

So with that background, a paper in The European Journal of Human Genetics steps into the “R1b controversy,” leaning to the side of those who argue for its origin more recently among Neolithic farmers. A major Y-chromosome haplogroup R1b Holocene era founder effect in Central and Western Europe:

The phylogenetic relationships of numerous branches within the core Y-chromosome haplogroup R-M207 support a West Asian origin of haplogroup R1b, its initial differentiation there followed by a rapid spread of one of its sub-clades carrying the M269 mutation to Europe. Here, we present phylogeographically resolved data for 2043 M269-derived Y-chromosomes from 118 West Asian and European populations assessed for the M412 SNP that largely separates the majority of Central and West European R1b lineages from those observed in Eastern Europe, the Circum-Uralic region, the Near East, the Caucasus and Pakistan. Within the M412 dichotomy, the major S116 sub-clade shows a frequency peak in the upper Danube basin and Paris area with declining frequency toward Italy, Iberia, Southern France and British Isles. Although this frequency pattern closely approximates the spread of the Linearbandkeramik (LBK), Neolithic culture, an advent leading to a number of pre-historic cultural developments during the past 10 thousand years, more complex pre-Neolithic scenarios remain possible for the L23(xM412) components in Southeast Europe and elsewhere.

There’s aren’t incredibly novel techniques of analyses here. Rather, the confusion around R1b1b2 has prompted researchers to expand their population coverage and resequence the markers around the haplogroup. These phylogenetic trees are constructed by genealogies which are separated by mutational steps, with steps of daughter mutations down a particular branch and distinguishing various derived clades. The terminology can kind of get confusing, but R1b1b2 is equivalent to the M269 branch in this study. What they did was analyze the phylogenetic relationships of the branches of R1b1b2 and it sister clades, and plot their frequencies as a function of geography. Below are a set of figures which show the frequencies of various clades across Europe. The last figure has several panels because they’re all subclades, and of somewhat less interest to the big picture. The first figure has the various branches, so you can see how they relate before browsing the maps.

[View with PicLens]

Tree

M73

M269

L23

M412

L11

Other markers

M269 is really the one to focus on. It and its daughter branches are at the heart of the Paleolithic vs. Neolithic controversy. Compare the phylogenetic tree in the first image, and the distributions of the allele frequencies in the subsequent images. The Western European variants seem to be daughter branches from an ancestral variant which is found in Anatolia or thereabouts. The authors also confirm the coalescence back to the last common ancestor  ~10,000 years ago, though the methods have a bias toward inflating the value, so that’s an upper bound. They also used PCA analysis show how the haplogroup variation exhibited cluster patterns. The first panel has the haplogroups, with PC 1 separating the ancestral R1b variant from the daughters, and the second PC separating each daughter branch. The second panel inputs the various fractions of R1b haplogroups in populations. There’s an obvious recapitulation of the geographical map in the distribution of haplogroups.

What’s the moral of this story? I’m not going to get into the correlations they adduce between various archaeological groups and genetic lineages. That got us into trouble earlier as I implied. I don’t think the fine-grained results are solid enough that we should be taking that sort of interpretation too seriously. Rather, it’s telling us what we don’t know, and what we shouldn’t be clear on. I lean toward the proposition that R1b1b2 was brought by Neolithic farmers at this point, the paper which refuted that finding leaned strong on samples from Sardinia, which I suspect are more than not atypical and not representative (Sardinia tends to be an outlier on genetic plots because of its island isolation). But my confidence is hardly even modest at this point. There’s a lot we don’t know.

History begins in Sumeria with the written word ~5,000 years ago. But as history dawns agriculture was still new to Norden and the fringes of the Baltic and British Isles. By the time what the ancients called Thule came into some focus, after the fall of the Roman Empire, much had passed beyond our line of sight. The original geneticists and archaeologists who attempted to synthesize their disciplines and construct a plausible model of how Europeans and Europe in its linguistic, genetic, and cultural variation, came to be, followed the principle of parsimony. Cavalli-Sofrza, Ammerman, and Renfrew presented us with a model where Paleolithic Europeans, who hunted & gathered, and spoke non-Indo-European languages, were slowly replaced culturally, linguistically, and partially genetically, by Indo-Europeans who brought farming from the Middle East. This was the “demic diffusion” hypothesis. I don’t think anyone accepts this as likely at this point, at least in its total simplicity of explanatory power. We need to reconsider whether the Basques can even serve as models for Paleolithic European man anymore! It may be that the Basques themselves are culturally and genetically intrusive, bringing their language and folkways along Mediterranean shores with agriculture, eventually marginalizing the thin numbers of hunter-gatherers beyond the limes of their “civilization.” Additionally, we have to remember that there was history before history, that what we term prehistory is rich with many developments which are preserved only vaguely and in the mists of oral tradition (though that tradition rapidly decays in fidelity). The more recent expansion of the Bantu and Austronesian languages do not benefit from copious records, because they spread with preliterate societies. The expansion of Turkic and Indo-Iranian dialects can only be perceived in the outlines because these peoples were on the fringes of societies where writing was part of their culture. Europe’s shift to agriculture occurred over thousands of years, and those thousands of years were all preliterate. Stonehenge and the megaliths were constructed by societies which we can comprehend only through their most robust monuments. The stones speak to a complexity which genetics can not resolve. Sometimes admitting that you don’t know is an answer in and of itself.

Citation: Myres NM, Rootsi S, Lin AA, Järve M, King RJ, Kutuev I, Cabrera VM, Khusnutdinova EK, Pshenichnov A, Yunusbayev B, Balanovsky O, Balanovska E, Rudan P, Baldovic M, Herrera RJ, Chiaroni J, Di Cristofaro J, Villems R, Kivisild T, & Underhill PA (2010). A major Y-chromosome haplogroup R1b Holocene era founder effect in Central and Western Europe. European journal of human genetics : EJHG PMID: 20736979

Image Credit: Frédéric Vincent, Matthew Field, National Geographic, Wikimedia

One of the things that always bothered me about George Bush’s revolutionary rhetoric was how he identified the expansion of political freedom with God’s design for man, which makes God’s plan one of narrow political deliverance rather than deliverance from death. These claims that representative government and separation of powers have some grounding in Christianity bother me in a different way. Probably the most thoroughly Christianized state in the medieval world was Byzantium, but it retained a late Roman autocratic system of government for its entire existence, so what is the connection between political structures and Christianity? Because the experience of most of Christian history in most parts of the world does not fit this picture of Christianity as the foundation of modern constitutional government, these claims have to privilege the Christianity of certain parts of western Europe and North America as the norm when it was clearly the exception. Furthermore, the reason for privileging Christianity from these parts of the world becomes an expressly political one. In other words, the quality or acceptability of one’s Christianity becomes dependent on the extent to which it complements the political values of modern Western states. Tying the importance of Christianity to the instrumental claim that Christianity is necessary because it created or undergirded our political culture takes us closer to defending Christianity in terms of little more than “Christian-flavored civic religion.” Even if it were true, I’m not sure that Christians should want to make that argument.

The American Radical Reformation tradition of evangelical Protestant Christianity is particularly prone to making really extreme conflations between Christianity and a specific concrete temporal order (or, at the other extreme reject the temporal order altogether as illegitimate) . I think it has to do with the sectarian and often parochial nature of American evangelical pastors, as opposed to more internationalist Roman Catholic clerics. This tendency is not necessarily good, or bad, as such. But it does lead to strange assertions of necessary entailments from Christian religious affiliation which would render most pre-modern Christians imperfectly Christian, and many non-Western Christians imperfectly Christian today (the attempts by American Protestants to convert Oriental Orthodox Christians in the Near East, traditions with a 2,000 year history, is a practical outcome of this mode of thinking). The Mormon church explicitly interjects Americocentrism into their religious system, taking these tendencies to their logical extreme, and arguably out of mainstream Christianity.

Interestingly, this way of thinking is not limited to Christians. I have observed American Muslims state that the United States is the most Islamic nation, the nation where Islam is practiced most freely and in its truest, pure, form. There were similar strands in 19th century Reform Judaism, which saw in America a nation where the Jewish religion could flourish without the impediments and historical baggage which had characterized Judaism in Europe, and so ushering in the Messianic era.

The common thread then is Americanism, not any particular religion.

I believe much of the difference among white non-Hispanics by ancestry is caused by other variables. In particular, the low values for Italians is probably a function of their disproportionately urban residence patterns in comparison to German Americans. Other white ethnic groups (the sample sizes were smaller for these constraints) with a strong urban bias show the same pattern.

To replicate, go to http://sda.berkeley.edu/cgi-bin/hsda?harcsda+gss08.

Under “Analysis” at the top left select “Comparison of Means.”

Dependent: CHILDS

Row: WORDSUM(r:0-4″Low intelligence”;5-7″Average intelligence”;8-10″Above average intelligence”) DEGREE GOD(r:1-2″Atheist and Agnostic”;3″Higher Power”;4-6″Believe in God”) polviews(r:1-3″Liberal”;4″Moderate”;5-7″Conservative”) RELIG ETHNIC(r:2,11″German”;3,10″French”;7,9,19,26″Nordic”;14″Irish”;15″Italian”;8,24″British”)

German = Germany + Austria
French = France + French Canadian
Nordic = Norway, Denmark, Sweden and Finland
British = Scottish, English and Welsh

Immunity under natural selection. Focusing on three of the genes which showed signatures of selection in the HapMap 3 paper. It’s interesting to note that only a generation ago many scholars were spending time debating about the adaptive value of blood group antigens and the like because so little was known about the impact of natural selection on the genome (since genomics as such didn’t really exist). Now the swell of results requires labor hours to filter.

Y Chromosomal Variation Tracks the Evolution of Mating Systems in Chimpanzee and Bonobo. Basically, common chimpanzees exhibit more “sperm competition” than bonobos. I think this is a case where common chimpanzees are the outgroup to bonobos and humans. It also puts the spotlight on the necessary nuance in traits like “polyandry.” Imported from human mating systems, they don’t necessarily inform very well for our primate cousins.

The Crystal Ball’s Labor Day Predictions. High probability of Republicans winning the House, modest probability of them winning the Senate. Notes that the two are not independent probabilities. Assuming that the Republicans win the House, the probability of them winning the Senate goes up. 100% probability that the “pundits” are going to discuss the “Death of the Democratic Party” for a few months.

Quants merge with humans. There’s a lot of money to be made in the business of money, so where there’s a will, there’s always a way. The Cowles’ Commission’s finding that stock market newsletters 1) are not effective 2) but will continue to be produced, illustrates the power of human psychology within the context of the rational market.

Burger King Has Suitors From Brazil. Another indication of Brazil’s “ascension graphic.” I still don’t get why Russia is in the list of BRICs. Its demographics look like Japan.

The really sad events around the Discovery Channel hostage situation, and the subsequent death of James Lee, made me wonder a little bit about total fertility rates. Lee seems to have had sentiments very similar to some of the anti-humanists within the Deep Ecology movement. Unlike the mainstream Left these elements of Deep Ecology are greatly concerned about the high fertility rates of non-developed societies and of the immigrants from those societies in a proximate sense (that is, the fertility is of a concern foremost because of the direct immediate environmental footprint, rather than a symptom of broader societal underdevelopment). I’ve probed the international differences in total fertility rates before, but what about in the United States? You probably won’t be surprised which segment of the population has the highest fertility, but what about the lowest? The US Census Bureau tracks these sorts of things in detail. So according to the Census Bureau the TFR’s in 2008 were:

2.09 – All races
1.84 – Non-Hispanic white
2.11 – Non-Hispanic black
1.84 – American Indian, Native Alaskan
2.06 – Asian & Pacific Islander
2.99 – Hispanic

Yes, Native Americans have the same TFR as non-Hispanic whites! Total surprise to me. The profile in relation to within wedlock and out-of-wedlock differs between the groups a fair amount. Here are the percents born to unmarried mothers:

40.6% – All races
28.6% – Non-Hispanic white
72.3% – Non-Hispanic black
65.8% – American Indian, Native Alaskan
16.9% – Asian & Pacific Islander
52.5% – Hispanic

So Native Americans combine low total fertility with a high proportion of out-of-wedlock births. I actually found births per 1,000 trend lines broken down by Hispanic ethnic group. There’s an interesting pattern here:

As you can see, the “high Hispanic fertility rate” is really the high Mexican American fertility rate. Cuban Americans have fertility below non-Hispanic whites (Cuba’s TFR is 1.6, though Cuban Americans are highly unrepresentative of Cuba demographically and culturally somewhat isolated from the island), and Puerto Ricans seem to track black Americans. The latter aligns with the sociology I’ve seen that indicates American Puerto Ricans share many norms and vital stats with the blacks next to whom they settled in Manhattan (Puerto Ricans in Puerto Rico have a TFR which is the same as non-Hispanic whites). Interestingly, the Native and non-Hispanic white fertility has only converged in the last generation or so. One issue to consider is that there’s been a large increase in the number of Americans identifying as Native American since the 1980s, so this may have altered the fertility simply because of a change in the demographic composition of those who identify as Native American. Another point is that Mexican Americans have a TFR which is much higher than in Mexico, which is now around ~2 (though if you look up the data, you’ll see some underdeveloped southern Mexican states are around ~3).

I mention some of these populations regularly on this weblog, the HGDP, HapMap and POPRES being three prominent data sets with a diverse range. These groups cover only a small subset of human populations, and of those populations only a small proportion of the genomes of individuals (albeit, the component which is likely to vary within the population). A new paper in Nature takes a close look at the expansion of the HapMap to a new set of populations. Since it’s out of the HapMap consortium the list of authors themselves gives us a large set of individuals who might be of population genetic interest! (though not a representative set of human population variation; where are the Papuan employees of the Broad Institute?) Some of the data coming out of the next stage of the HapMap has been found in several papers already (often in the supplements), but this looks to be an overview and taste of what’s to come (the paper was submitted last fall). Integrating common and rare genetic variation in diverse human populations:

Despite great progress in identifying genetic variants that influence human disease, most inherited risk remains pa lained. A more complete understanding requires genome-wide studies that fully examine less common alleles in populations with a wide range of ancestry. To inform the design and interpretation of such studies, we genotyped 1.6 million common single nucleotide polymorphisms (SNPs) in 1,184 reference individuals from 11 global populations, and sequenced ten 100-kilobase regions in 692 of these individuals. This integrated data set of common and rare alleles, called ‘HapMap 3’, includes both SNPs and copy number polymorphisms (CNPs). We characterized population-specific differences among low-frequency variants, measured the improvement in imputation accuracy afforded by the larger reference panel, especially in imputing SNPs with a minor allele frequency of ≤5%, and demonstrated the feasibility of imputing newly discovered CNPs and SNPs. This expanded public resource of genome variants in global populations supports deeper interrogation of genomic variation and its role in human disease, and serves as a step towards a high-resolution map of the landscape of human genetic variation.

Since the supplements are free to all I recommend you download them if you don’t have academic access. The main difference is that they’re not as pithy in the supplements, and the graphics are lower quality. The populations (the original HapMap populations bold):

Centre d’Etude du Polymorphisme Humain collected in Utah, USA, with ancestry from northern and western Europe (CEU)
Han Chinese in Beijing, China (CHB)
Japanese in Tokyo, Japan (JPT)
Yoruba in Ibadan, Nigeria (YRI)
African ancestry in the southwestern USA (ASW)
Chinese in metropolitan Denver, Colorado, USA (CHD)
Gujarati Indians in Houston, Texas, USA (GIH)
Luhya in Webuye, Kenya (LWK)
Maasai in Kinyawa, Kenya (MKK)
Mexican ancestry in Los Angeles, California, USA (MXL)
Tuscans in Italy (Toscani in Italia, TSI)

So memorize some of those abbreviations! One particular difference across these populations is that some are parent-offspring trios, and some are not. So the CEU sample are trios, while the TSI are not. This obviously matters since you’re going to have clusters of relatedness within the CEU sample that you wouldn’t have within TSI. There are analytic upsides and downsides to having trios or not having trios, but, for a major purpose of this sort of data set, covering world wide human variation, you probably would want unrelated individuals with a population. These are the samples with trios: CEU, ASW, MXL, MKK, and YRI.

To get the SNPs and CNPs they merged the results from Affymetrix and Illumina chips, and came out with ~1.5 million variants across ~1,000 individuals. In terms of exploring big picture questions which are on a coarse scale this is pretty good, though I’m not sure that it’s that much better than the HGDP, which has so many populations (though about half the number of SNPs). Rather, one of the primary issues focused on in this paper is finding enough of the rarer variants, which may not have shown up in the initial panel because of its narrow population coverage, so as to perform imputation for purposes of statistical analysis in GWAS. So, for example, they compare the CEU vs. the CEU+TSI in imputing to a British study group. Here’s what they found (MAF = major allele frequency):

For common SNPs (MAF ≥5%), the larger HapMap 3 reference panel made only a slight difference to the already excellent performance (mean r² increased from 0.946 to 0.961). However, as expected there was greater improvement for rare (MAF <0.5%) and low-frequency SNPs (MAF = 0.5–5%). Their combined mean r² increased from 0.60 to 0.76, driven by a large subset of rare SNPs (41%) and low-frequency SNPs (25%) where r² increased by at least 0.1, yielding mean r² improvement for these subsets of 0.62 and 0.49 respectively…

So the older HapMap data set was fine with more common variants, but a larger sample set really gave some returns with less common variants. This makes intuitive sense. What is interesting to me is that the CEU sample of Utah Whites is presumably genetically close to a group of British whites born in 1958, and yet adding a Tuscan sample was still useful. To get a sense of how the power of this sort of imputation drops off between populations, as the further the genetic distance the fewer rare variants are shared, they imputed in a pairwise fashion, or, comparing a population to putative admixtures. So African Americans, who have a substantial proportion of European admixture with West African primary ancestry, are best modeled once you combine CEU+YRI with appropriate weights. This is especially true for rare alleles, r² was 83% and 86.5% for common SNPs for African Americans and Yoruba, and Africans Americans and Yoruba & Utah Whites. For rare SNPs, it was 45.5% vs. 71.7%! Models which added the other HapMap 3 populations were actually less effective at imputation. East Eurasians have different genetic variants which simply confuse the picture.

It is intuitively obvious why rare alleles show up as you increase sample size. But why are rare alleles more distinctive across populations? If they’re common alleles they’re likely to have been around a long time, and so may be ancestral variants, or have had time to spread via gene flow. In contrast, rare alleles may be new, and so more distinctive across populations. Similarly, there are alleles which surely are passed down through families.

Figure 3 shows the impact of sample size on SNPs discovered:

Note the two groups of curves: African vs. non-African. This paper confirms the findings that Africans have more genetic diversity than other populations, while East Asians have less (presumably if Amerindians were in the sample they would round out the bottom). From the text:

As judged by this measure, informativeness varied greatly for different population pairs. Consistent with the observation that non-African diversity is largely a subset of African diversity…African samples provided a more complete discovery resource for variant sites in non-African samples than the converse…Focusing only on low-frequency variants in the original sample of 30 A individuals (one or two copies, corresponding to allele frequencies of 3.3% or less), even African samples were highly incomplete for diversity outside of Africa, with informativeness ratios dropping to 40–60% in LWK and YRI…In general, for low-frequency variants only closely related populations did an adequate job of capturing variation…probably reflecting the recent origins of low-frequency variants. Two populations, LWK and GIH, stand out as being poorly captured by any of our other populations, the result of admixture with an ancestral population not closely related to any in our regional sequencing data….

So again, African genetic diversity can inform on other populations, but with low frequency allelic variants even Africans don’t have enough to account for non-African groups. As a historical matter much of that might be due to the fact that the non-African variants have emerged more recently since the out of Africa event. Figure 2a shows the pairwise relationships between and within the populations measured by low frequency SNPs. More precisely, they took 30 random individuals from a population, and compared them to 30 random individuals from within the same population (without overlap), as well as 30 random individuals from other populations. The black bar is the same population comparison, while the colored bars represent across population comparisons. The higher the bar the better the across sample concordance; SNPs in one sample set map on well to those in the other sample set. First, observe the minimal difference between CEU & TSI. Europeans are relatively genetically homogeneous, and as far back as History and Geography of Human Genes it was evident that there was relatively minimal within continental variance. Next in line in relation to a CEU reference is GIH, the Gujaratis. This makes sense from all the other studies we know. South Asians are closer to West Eurasians than any other populations. Similarly, YRI are closest in correspondence with LWK, the Bantu sample from Kenya. But though the rank order of population relatedness is roughly similar to what you’d find in Fst, the authors note that the pairwise comparisons are not symmetrical. GIH was informative for 71% of TSI low frequency SNPs, but TSI was only informative for 55% of GIH. Why? GIH is more diverse, but it is also probably the Gujaratis are a compound of a European-like and non-European population, so what your’e seeing is overlap across the European fractions. Since the Tuscans lack the non-European fraction the Gujaratis will have alleles which aren’t found within them.

Speaking of the Gujaratis, there are some interesting results in the supplements which I want to highlight. They illustrate again the importance of context in PCA charts. They’re representations of reality, but only as good as your ability to interpret them and the inputs you’re giving them. Below are a set of images from the supplements, and you can skim them quickly. I’ve labelled them by population and context. Note how the populations shift positions based on the population set of variation you plug into the analysis. These are all the two largest components of variance.

[View with PicLens]

Gujarati + Mexican (World Context)

Gujarati + Mexican + European

Gujarati + European + Chinese

Gujarati + European

Mexican + European + Chinese

Notice how Gujaratis and Mexican Americans overlap on the world wide PCA plot. Why? Because their gene frequencies are a linear combination of East and West Eurasian genetic variance, to a first approximation. I’ve indicated before that the overlap disappears when you look at other components of variation. But as the second image shows, you don’t have have to do that. Use only Mexican Americans, Europeans, and Gujaratis, and you see that Mexican Americans have a component of variance which is different from the other two. That’s because the non-European ancestry of Gujaratis is very different from that of the Mexican Americans, though both cluster to together when set next to Europeans, East Asians, and Africans. Remember that in the world wide set PC 1 is African vs. non-African, so removing Africans immediately frees up a dimension for the plot. The last figure shows Mexican Americans with Chinese and Europeans, and again, you see that there’s variation which isn’t simply a linear combination of Chinese and Europeans, Amerindians have their own uniqueness not found in either. In contrast, African Americans are a rather straightforward combination of West Africans and Europeans. Thankfully for African American genetics their parental populations were in the original HapMap. For Gujaratis and Mexican Americans you have only half the picture in the original HapMap, and you’d have to use the imperfect substitute of East Asians (very imperfect for Gujaratis, and somewhat so for Mexican Americans).

One final issue on phylogenetic relationships: the strange pattern among Gujaratis which I perceived among other South Asians as well is still evident. In the plot with Mexican Americans + Europeans + Gujaratis, the Gujaratis seem a linear combination of European + something else. What Reich et al. would term “Ancestral North Indian” + “Ancestral South Indian.” But the Gujarati + European plot shows that in the second component of variation there’s a difference between two clusters of Gujaratis. There’s something going on with the Gujarati group which is a touch closer to Europeans on the largest component of variance, because on the second dimension they’re deviated from the other Gujarati cluster and Europeans. This is similar in quality to the pattern with the South Asian data set with an orthogonal component of variation to the European-South Indian axis. The orthogonal component is striking among those which are between the Europeans and South Indians. The CEU + GHI + CHB plot doesn’t indicate to us that it’s East Asian either.

Of course the paper wasn’t just about validating the power of expanding the data set for medical genetics and clarifying phylogenetic relationships. There are several subsections, but I thought I’d jump to the end where they allude to detecting natural selection. This seems preliminary at least. They didn’t really go that much further for populations in the original HapMap, but found some interesting stuff for the new groups. To the left is a table from the supplements (I reedited it a bit) which shows loci which popped out of the CMS test for natural selection for Tuscans, Masai, and Luhya (the second are Nilotic and Bantu from Kenya). I present the results for readers with an interest in particular loci who might seem something in the list that does, or doesn’t, make sense to them. It seems that this part of the paper is primarily about showing that the new populations have some utility in fleshing out evolutionary phenomena which may have been missing in the original analyses of the HapMap because of constrained population coverage. Comparing Tuscans to CEU, and the Masai to Luhya, should tell us something about the evolution of lactase persistence. These pairs consist of populations which are rather close to each other in terms of ancestry (especially the European groups), but local ecological and cultural conditions have no doubt applied different selection pressures (the majority of Tuscans seem to lack the lactase persistence allele common in northern Europe last I checked).

Finally, from the conclusion:

With improvements in sequencing technology, low-frequency variation is becoming increasingly accessible. This greater resolution will no doubt expand our ability to identify genes and variants associated with disease and other human traits. This study integrates CNPs and lower-frequency SNPs with common SNPs in a more diverse set of human populations than was previously available. The results underscore the need to characterize population-genetic parameters in each population, and for each stratum of allele frequency, as it is not possible to extrapolate from past experience with common alleles. As expected, lower-frequency variation is less shared across populations, even closely related ones, highlighting the importance of sampling widely to achieve a comprehensive understanding of human variation.

Intrepid readers can poke around the data themselves at the HapMap website.

Citation: The International HapMap 3 Consortium (2010). Integrating common and rare genetic variation in diverse human populations Nature : 10.1038/nature09298

Announcing PLoS Blogs. This looks to be a season of shakeups and transitions in the science blogosphere. Expect some more in the near future from what I’ve been told.

Oh, No, It’s a Girl! South Asians Flock to Sex-Selection Clinics in U.S.. There’s variation in sex ratio bias within India, and it is notable one of the women they highlight in the article flew in from Vancouver, Canada. Vancouver of course has a huge Punjabi community, and this is the ethnic group which has made the most use of sex selective abortion within India (with sex ratio imbalances in rural Punjab resulting in the movement, licit and illicit, of women from eastern South Asia to be brides for Punjabi farmers). Smell that Canadian diversity! On the other hand, please note that in Japan, and then South Korea, the strong preference for males shifted to females with economic development and smaller families. This seems a clear case where economic development results in an uplift from barbarism. In my own extended family in Bangladesh the move has been to a two child ideal, and often there is a preference for daughters first because sons are perceived to be a riskier proposition, and if you have only one or two children you want to avoid possible problems (this is the avowed rationale at least).

Reading Arabic Isn’t Easy, Brain Study Suggests. They’re suggesting here that Arabic script is just harder to learn than alphabets. If you’ve tried to read Arabic you know why. The implication here is that this retards development of literacy, but from what I can recall the same general class of issues arises with Chinese characters. There are supposed benefits to this slower and more labored development though.

Vitamin D Is a Prognostic Marker in Heart Failure, Study Finds. Just an observational study. A correlation if you will. Stop the presses when they do a randomized trial and find something. Heart disease is still killer #1.

Weight Index Doesn’t Tell the Whole Truth. The tension between BMI’s population wide informativeness and the large error on an individual level is problematic in terms of public understanding of the underlying issues.

Guardian science blogs: We aim to entertain, enrage and inform. They don’t have many science blogs. Yet. But I’m sure they’ll add more, and other “big media” outfits will be adding/expanding in the near future.

Wolves Are Smart, but Dogs Look Back. Makes me think of the empathizing–systemizing theory. Dogs empathize, wolves systemize.

Scientists Square Off on Evolutionary Value of Helping Relatives. Carl Zimmer has a good overview of the controversy which emerged out of the Nowak et al. paper, which seems to take a maximalist position against the utility of kin selection in explaining euosociality. Apparently some “big names” are going to be writing a response, so I’ll be curious. I haven’t bothered going through Nowak et al.’s supplements, so I really can’t say much more than that.

Outlines Emerge of Future State in the West Bank. The thing that stood out was the relative quiescence engendered by economic growth. Idle hands and all.

The New World is similar. The initial migration out of Africa by modern humans resulted in the range expansion of the human lineage into a region which had been untouched by earlier hominins, Australasia. But after that point tens of thousands of years passed before our species pushed into virgin territory, in this case North America. The when and the how of this though is still up for debate. A new paper PLoS One attempts to construct a plausible scenario by taking archaeological data points and inputing them into a diffusion model. Archaeological Support for the Three-Stage Expansion of Modern Humans across Northeastern Eurasia and into the Americas:

We use diffusion models…to quantify these dynamics. Our results show the expansion originated in the Altai region of southern Siberia ~46kBP , and from there expanded across northern Eurasia at an average velocity of 0.16 km per year. However, the movement of the colonizing wave was not continuous but underwent three distinct phases: 1) an initial expansion from 47-32k calBP; 2) a hiatus from ~32-16k calBP, and 3) a second expansion after the LGM ~16k calBP. These results provide archaeological support for the recently proposed three-stage model of the colonization of the Americas….Our results falsify the hypothesis of a pre-LGM terrestrial colonization of the Americas and we discuss the importance of these empirical results in the light of alternative models.

It’s an interesting paper because it seems to have been triggered in part by inferences made from the genetic data. I don’t know how confident archaeologists are about their radiometric dates, but I think some of the molecular clock results from the genetics of Amerindians need to be taken with a grain of salt (I don’t see many people repeating some of the really ancient coalescence dates for Amerindian Y lineages at this point).

These data seem to indicate that modern humans made it no further than previous hominin groups for several tens of thousands of years. But something happened within the last 20,000 years, and our species made the leap across Beringia. The bottleneck here is certainly not the Bering Strait, which was spanned by land much of the time in any case. Rather, our species didn’t have the biological or cultural capacity to survive in extremely frigid environments. I’ve read modern humans pushed the boundaries of their range in northern Europe further than Neandertals ever did, indicating our flexibility and plasticity. Since the human lineage had been resident in Eurasia for at least one million years that suggests to me that it was behavioral modernity that was key. In particular, how quickly our cultures evolve and shift. Though that flexibility itself may be a function of our biological competencies.