In my earlier posts where I gave a short intro to using Plink I distributed a data set termed PHLYO. One thing I did not mention is that I’ve also been running it on Admixture. But here’s an important point: I ran the data set 10 times from K = 2 to K = 15. Why? Because the algorithm produces somewhat different results on each run (if you use a different seed, which you should), and I wanted to not be biased by one particular result. Additionally, I also turned on cross-validation error, which gives me a better sense of which K’s to trust. But after I select the K which I want to visualize which replicate run will I then use to generate the bar plots? I won’t pick any specific one. Rather, I’ll merge them together with an off-the-shelf algorithm. Additionally, I also want to sort the individuals by their modal population cluster.
This sounds rather convoluted, and it is somewhat. I have a pipeline that I use, but it’s not too user friendly. One of my projects is to clean it up, document it, and publish it online. Though if you have your own pipeline all ready to go, please post it in the comments with a link! The general steps are as follows for me:
1) Convert Admixture Q files into Structure format, transform family identifications to numeric values, and generate a file with family identification and numeral pairs
2) Merge the results across runs using Clumpp
3) Sort the individual results within populations
4) The use Distruct to produce an output file
Before I show you the resultant bar plot, here are the cross-validation results with standard deviation ticks:
While reading The Founders of Evolutionary Genetics I encountered a chapter where the late James F. Crow admitted that he had a new insight every time he reread R. A. Fisher’s The Genetical Theory of Natural Selection. This prompted me to put down The Founders of Evolutionary Genetics after finishing Crow’s chapter and pick up my copy of The Genetical Theory of Natural Selection. I’ve read it before, but this is as good a time as any to give it another crack.
Almost immediately Fisher aims at one of the major conundrums of 19th century theory of Darwinian evolution: how was variation maintained? The logic and conclusions strike you like a hammer. Charles Darwin and most of his contemporaries held to a blending model of inheritance, where offspring reflect a synthesis of their parental values. As it happens this aligns well with human intuition. Across their traits offspring are a synthesis of their parents. But blending presents a major problem for Darwin’s theory of adaptation via natural selection, because it erodes the variation which is the raw material upon which selection must act. It is a famously peculiar fact that the abstraction of the gene was formulated over 50 years before the concrete physical embodiment of the gene, DNA, was ascertained with any confidence. In the first chapter of The Genetical Theory R. A. Fisher suggests that the logical reality of persistent copious heritable variation all around us should have forced scholars to the inference that inheritance proceeded via particulate and discrete means, as these processes do not diminish variation indefinitely in the manner which is entailed by blending.
The above image, and the one to the left, are screenshots from my father’s 23andMe profile. Interestingly, his mtDNA haplogroup is not particularly common among ethnic Bengalis, who are more than ~80% on a branch of M. This reality is clear in the map above which illustrates the Central Asian distribution my father’s mtDNA lineage. In contrast, his whole genome is predominantly South Asianform, as is evident in the estimate that 23andMe provided via their ancestry composition feature, which utilizes the broader genome. The key takeaway here is that the mtDNA is informative, but it should not be considered to be representative, or anything like the last word on one’s ancestry in this day and age.
The above map shows the population coverage for the Geno 2.0 SNP-chip, put out by the Genographic Project. Their paper outlining the utility and rationale by the chip is now out on arXiv. I saw this map last summer, when Spencer Wells hosted a webinar on the launch of Geno 2.0, and it was the aspect which really jumped out at me. The number of markers that they have on this chip is modest, only >100,000 on the autosome, with a few tens of thousands more on the X, Y, and mtDNA. In contrast, the Axiom® Genome-Wide Human Origins 1 Array Plate being used by Patterson et al. has ~600,000 SNPs. But as is clear by the map above Geno 2.0 is ascertained in many more populations that the other comparable chips (Human Origins 1 Array uses 12 populations). It’s obvious that if you are only catching variation on a few populations, all the extra million markers may not give you much bang for the buck (not to mention the biases that that may introduce in your population genetic and phylogenetic inferences).
To understand nature in all its complexity we have to cut down the riotous variety down to size. For ease of comprehension we formalize with math, verbalize with analogies, and visualize with representations. These approximations of reality are not reality, but when we look through the glass darkly they give us filaments of essential insight. Dalton’s model of the atom is false in important details (e.g., fundamental particles turn out to be divisible into quarks), but it still has conceptual utility.
Likewise, the phylogenetic trees popularized by L. L. Cavalli-Sforza in The History and Geography of Human Genes are still useful in understanding the shape of the human demographic past. But it seems that the bifurcating model of the tree must now be strongly tinted by the shades of reticulation. In a stylized sense inter-specific phylogenies, which assume the approximate truth of the biological species concept (i.e., little gene flow across lineages), mislead us when we think of the phylogeny of species on the microevolutionary scale of population genetics. On an intra-specific scale gene flow is not just a nuisance parameter in the model, it is an essential phenomenon which must be accommodated into the framework.
There’s an interesting piece in Slate, The Great Schism in the Environmental Movement, which seems to be a distillation of trends which have been bubbling within the modern environmentalist movement for a generation now (I’ve read earlier manifestos in a similar vein). I can’t assess the magnitude of the shift, but here’s the top-line:
But that is a false construct that scientists and scholars have been demolishing the past few decades. Besides, there’s a growing scientific consensus that the contemporary human footprint—our cities, suburban sprawl, dams, agriculture, greenhouse gases, etc.—has so massively transformed the planet as to usher in a new geological epoch. It’s called the Anthropocene.
Modernist greens don’t dispute the ecological tumult associated with the Anthropocene. But this is the world as it is, they say, so we might as well reconcile the needs of people with the needs of nature. To this end, Kareiva advises conservationists to craft “a new vision of a planet in which nature—forests, wetlands, diverse species, and other ancient ecosystems—exists amid a wide variety of modern, human landscapes.”
In the post below I offered up my supposition that Dan MacArthur’s ancestry is unlikely to be Northwest Indian, which precludes a Romani origin for his South Asian ancestry. Indeed this is almost certainly so, Dienekes Pontikos followed up my crude analyses with IBD-sharing calculations (IBD = ‘identity by descent,’ which is basically what you would think it is). The South Asian population which MacArthur has the closest affinity to is from Karnataka, which is one of the Dravidian speaking states of the South. This does not necessarily refute my earlier contention, as aside from Brahmins most Bengalis seem to have broad South Indian affinities, except for the fact that they often have more East Asian ancestry.
A few days ago I suggested that Dr. Daniel MacArthur might have South Asian ancestry. Now, when confronted with surprise the best option is to stick with your prior assumption, unless that surprise is powerful enough for you to “update” your model. After a few days of further analysis I will update: I do think Dan MacArthur has South Asian ancestry. Dienekes dug further, and noticed that there are hallmarks of “Ancestral South Indian” ancestry along the first 2/3 or so of chromosome 10. Now, you do have to remember that this genomic region is only half South Asian. The other half is European.
But in any case, one question that some people brought up: perhaps MacArthur has Romani heritage? I’m skeptical of this partly because:
1) there weren’t that many Romani in Britain in the 19th century
2) The British Romani are already very highly admixed
The New Republic has a piece up, How Older Parenthood Will Upend American Society, which won’t have surprising data for readers of this weblog. But it’s nice to see this sort of thing go “mainstream.” My daughter was born when her parents were in their mid-30s, so I know all the statistics. They aren’t good bed-time reading (she’s healthy and robust so far!). If I had to do it over again I definitely wouldn’t have waited this long. After becoming a father it brought home to me that waiting was one of the worst decisions of my life. Why postpone something this incredible for the more far more prosaic pleasures of an extended adolescence? Granted, I’m not sure that I would have been the best father at 25, but I don’t think there’s much I can say in reply to the argument that I should have become a father by 30.
More concretely, we would have had sperm and egg “banked” if we had been smart delaying parenthood. The article notes that storage of sperm costs $850 up front, and $300 to $500 per year after that, and that many balk at the cost. And how much do you spend on your cell phone every year? The issue here seems to be time preference.
Most people are aware that altitude imposes constraints on individual performance and function. Much of this is flexible; athletes who train at high altitudes may gain a performance edge. But over the long term there are costs, just as there are with computers which are ‘overclocked.’ This is the point where you make the transition from physiology to evolution. Residence at high altitude entails strong selective pressures on populations. Over the past few years there has been a great deal of exploration of the genetics of long resident high altitude groups, the Tibetans, Peruvians, and Ethiopians.
My initial inclination in this post was to discuss a recent ordering snafu which resulted in many of my friends being quite peeved at 23andMe. But browsing through their new ‘ancestry composition’ feature I thought I had to discuss it first, because of some nerd-level intrigue. Though I agree with many of Dienekes concerns about this new feature, I have to admit that at least this method doesn’t give out positively misleading results. For example, I had complained earlier that ‘ancestry painting’ gave literally crazy results when they weren’t trivial. It said I was ~60 percent European, which makes some coherent sense in their non-optimal reference population set, but then stated that my daughter was >90 percent European. Since 23andMe did confirm she was 50% identical by descent with me these results didn’t make sense; some readers suggested that there was a strong bias in their algorithms to assign ambiguous genomic segments to ‘European’ heritage (this was a problem for East Africans too).
Here’s my daughter’s new chromosome painting:
One aspect of 23andMe’s new ancestry composition feature is that it is very Eurocentric. But, most of the customers are white, and presumably the reference populations they used (which are from customers) are also white. Though there are plenty of public domain non-white data sets they could have used, I assume they’d prefer to eat their own data dog-food in this case. But that’s really a minor gripe in the grand scheme of things. This is a huge upgrade from what came before. Now, it’s not telling me, as a South Asian, very much. But, it’s not telling me ludicrous things anymore either!
But in regards to omission I am curious to know why this new feature rates my family as only ~3% East Asian, when other analyses put us in the 10-15% range. The problem with very high values is that South Asians often have some residual ‘eastern’ signal, which I suspect is not real admixture, but is an artifact. Nevertheless, northeast Indians, including Bengalis, often have genuine East Asia admixture. On PCA plots my family is shifted considerably toward East Asians. The signal they are picking up probably isn’t noise. Almost every apportionment of East Asian ancestry I’ve seen for my family yields a greater value for my mother, and that holds here. It’s just that the values are implausibly low.
In any case, that’s not the strangest thing I saw. I was clicking around people who I had “shared” genomes with, and I stumbled upon this:
As you can guess from the screenshot this is Daniel MacArthur’s profile. And according to this ~25% of chromosome 10 is South Asian! On first blush this seemed totally nonsensical to me, so I clicked around other profiles of people of similar Northern European background…and I didn’t see anything equivalent.
What to do? It’s going to take more evidence than this to shake my prior assumptions, so I downloaded Dr. MacArthur’s genotype. Then I merged it with three HapMap populations, the Utah whites (CEU), the Gujaratis (GIH), and the Chinese from Denver (CHD). The last was basically a control. I pulled out chromosome 10. I also added Dan’s wife Ilana to the data set, since I believe she got typed with the same Illumina chip, and is of similar ethnic background (i.e., very white). It is important to note that only 28,000 SNPs remained in the data set. But usually 10,000 is more than sufficient on SNP data for model-based clustering with inter-continental scale variation.
I did two things:
1) I ran ADMIXTURE at K = 3, unsupervised
2) I ran an MDS, which visualized the genetic variation in multiple dimensions
Before I go on, I will state what I found: these methods supported the inference from 23andMe, on chromosome 10 Dr. MacArthur seems to have an affinity with South Asians (i.e., this is his ‘curry chromosome’). Here are the average (median) values in tabular format, with MacArthur and his wife presented for comparison.
|ADMIXTURE results for chromosome 10|
|K 1||K 2||K 3|
You probably want a distribution. Out of the non-founder CEU sample none went above 20% South Asian. Though it did surprise me that a few were that high, making it more plausible to me that MacArthur’s results on chromosome 10 were a fluke:
And here’s the MDS with the two largest dimensions:
Again, it’s evident that this chromosome 10 is shifted toward South Asians. If I had more time right now what I’d do is probably get that specific chromosomal segment, phase it, and then compare it to various South Asian populations. But I don’t have time now, so I went and checked out the results from the Interpretome. I cranked up the settings to reduce the noise, and so that it would only spit out the most robust and significant results. As you can see, again chromosome 10 comes up as the one which isn’t quite like the others.
Is there is a plausible explanation for this? Perhaps Dr. MacArthur can call up a helpful relative? From what recall his parents are immigrants from the United Kingdom, and it isn’t unheard of that white Britons do have South Asian ancestry which dates back to the 19th century. Though to be totally honest I’m rather agnostic about all this right now. This genotype has been “out” for years now, so how is it that no one has noticed this peculiarity??? Perhaps the issue is that everyone was looking at the genome wide average, and it just doesn’t rise to the level of notice? What I really want to do is look at the distribution of all chromosomes and see how Daniel MacArthur’s chromosome 10 then stacks up. It might be a random act of nature yet.
Also, I guess I should add that at ~1.5% South Asian that would be consistent with one of MacArthur’s great-great-great-great grandparents being Indian. Assuming 25 year generation times that puts them in the mid-19th century. Of course, at such a low proportion the variance is going to be high, so it is quite possible that you need to push the real date of admixture one generation back, or one generation forward.
In many cases there are questions of a historical and ethnographic nature which are subject to controversy and debate. Scholarly arguments are laid out, and further dispute ensues. For decades progress seems fleeting, as one hypothesis is accepted, only to be subject to later revision. This sort of pattern gives succor to the most cynical and jaded of ‘Post Modern’ set, especially when the ‘discourse’ in question is in the domain of science.
But thankfully these debates can come to an end in some cases. So it is with the origins of the European Romani, better known as ‘Gypsies’ (though the Roma are the most well known of the Romani, other groups within Europe have different ethnonyms). Obviously many of the basic elements have long been there, but I think the most recent genetic work now establishes a level of closure. Taking a step back, what do we know?
1) The Romani language seems to be Indo-Aryan, with a likely affinity with the northwest group of Indo-Aryan languages
2) The Romani presence in Europe only dates to the past ~1,000 years, with an entry point in the Byzantine Empire
3) They are an admixture between an ancestral Indian element, and local populations
4) Their history of endogamy has resulted in a strong genetic drift effect
The two papers which seem to nail the coffin shut on these questions use somewhat different methodologies. One relies on Y chromosomal STRs (hypervariable repeat regions) to generate a paternal phylogeny. Focusing just on the paternal phylogeny allows for one to make very robust genealogical inferences. Additionally, the authors had a very large data set across India. Their goal was to ascertain the exact region of origin of the Romani before they left India. As noted in bullet #1 there is already some evidence from their language that this must be in northwest India. The second paper uses a SNP-chip; hundreds of thousands of autosomal markers. This has been done to death for other populations, so the method isn’t new. Rather, it is that it is now being applied to the Romani.
First, the Y chromosomal paper. The Phylogeography of Y-Chromosome Haplogroup H1a1a-M82 Reveals the Likely Indian Origin of the European Romani Populations:
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.
Two trees illustrate the results succinctly:
The bottom line:
– This particular Y chromosomal lineage which is highly diagnostic of South Asian origin in the Romani shows that the Romani seem to derive from the populations of northwest India
– Additionally, within these populations the Romani Y chromosomal lineages derive from the lower caste elements, the scheduled castes and scheduled tribes
But the above results don’t get directly at genome-wide admixture. The second paper does, using hundreds of thousands of markers to explore the Romani affinity to other populations. Reconstructing the Population History of European Romani from Genome-wide Data:
The Romani, the largest European minority group with approximately 11 million people…constitute a mosaic of languages, religions, and lifestyles while sharing a distinct social heritage. Linguistic…and genetic…studies have located the Romani origins in the Indian subcontinent. However, a genome-wide perspective on Romani origins and population substructure, as well as a detailed reconstruction of their demographic history, has yet to be provided. Our analyses based on genome-wide data from 13 Romani groups collected across Europe suggest that the Romani diaspora constitutes a single initial founder population that originated in north/northwestern India ∼1.5 thousand years ago (kya). Our results further indicate that after a rapid migration with moderate gene flow from the Near or Middle East, the European spread of the Romani people was via the Balkans starting ∼0.9 kya. The strong population substructure and high levels of homozygosity we found in the European Romani are in line with genetic isolation as well as differential gene flow in time and space with non-Romani Europeans. Overall, our genome-wide study sheds new light on the origins and demographic history of European Romani.
The plot to the left illustrates the relationship of the Romani to world-wide populations using multi-dimensional scaling, where genetic variation is decomposed into dimensions, and individuals are plotted on those dimensions. In short, the Romani exhibit a classic admixture cline pattern.That is, they are the products of a two-way admixture between populations which occupy distinct positions along a cline, and Romani individuals and populations are distributed along the cline in proportion to their admixture. One notable aspect is that the Romani are actually two clusters; one which manifests a strong ‘east’-‘west’ distribution, and another which seems located purely within the European cluster. The latter seems to be the Welsh Romani, who in the neighbor-joining tree (see the supplements) fall on the same branch as European populations, as opposed to the other Romani, who form their own clade.
To drill down further you need to ascertain admixture with a model-based clustering algorithm. Ergo, ADMIXTURE. I’ve reedited the figure to illustrate the salient points. In particular, it is clear that the Roma populations except the Welsh have significant South Asian ancestry. The question is how much? To answer this question you need to know the source population in South Asia. A peculiar aspect of this plot is that the Romani have very little of the green ancestral component, which happens to be modal in the Middle East (not shown). This element happens to be highly enriched in many Pakistani populations, but not necessarily northwest Indian ones. Nevertheless, the issue that leaves me suspicious of this particular finding is that many of the European populations, in particular those groups (e.g., Balkans) which may have admixed with the Romani, have this element to extent not evident in one of their presumed ‘daughter’ populations. I wonder if perhaps the peculiarities of Romani inbreeding has skewed the allele frequency distribution so much that you get strangeness like this. I am not showing higher K’s because those break out with a Romani-cluster. Just like the Kalash-cluster this is to a great extent a feature of the long term endogamy of these communities. With high levels of drift the allele frequency of these groups moves into a very peculiar space in relation to their parental populations, but one must not become confused and assume that the Romani or Kalash are themselves appropriate independent clusters in the same way that Europeans or East Asians are.
Using various forms of admixture analysis the authors seem to conclude that the Balkan Romani are 30-50% South Asian. This seems in line with intuition. But that still leaves open the question of who those South Asians were. As I noted above the most thorough Y chromosomal data point to the lower caste elements of northwest India. What do the autosomes say?
I don’t want get into the technical details of how they tested the models, but it seems that one of the likely parental populations to the Romani had a close relationship to the Meghwal, a scheduled caste from northwest India. In other words, the autosome results align very well with the Y chromosomal inferences. Additionally, the models tested imply that the Romani likely left South Asian ~1,000 years before the present, which aligns well with what is known from the historical record (though this is a case where I put much more stock in the historical record than inferences from population genetic models; look at the intervals).
Finally, there is the question of inbreeding. One aspect of the Romani genome is jumps out you is that they have many long “runs-of-homozygosity” (ROH). This is totally expected, as decades of uniparental analyses suggested a great deal of population bottleneck events as the Romani spread throughout Europe. But the ROH patterns also unearth an interesting fact: some of the Balkan Romani clearly have recent European admixture, while the non-Balkan Romani had an initial period of admixture followed by endogamy. The latter scenario seems to resemble Askhenazi Jews, while the former would suggest that the boundary between Romani and non-Romani in the Balkans is more fluid than is sometimes portrayed.
So there we have it. The Romani derive from lower castes populations from the northwest Indian subcontinent who seem to have left ~1,000 years ago. Over time they admixed with local populations, and are now 50-70% non-South Asian, with some groups being ~90% European (e.g., Welsh Romani). And, they have a long history as an endogamous group, judging by their inbreeding.
As a follow up to my post from yesterday, I decided to run TreeMix on a data set I happened to have had on hand (see Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data for more on TreeMix). Basically I wanted to display a tree with, and without, gene flow.
The technical details are straightforward. I LD pruned ~550,000 SNPs down to ~150,000. I ran TreeMix without and with migration parameters with the Bantu Kenya population being the root. Finally, when I did turn on the migration parameter I set it for 5. You can see the results below.
Most of the flows are pretty expected. The West Eurasian flow from the Turks to the Uygurs makes sense, because there is a large West Asian component to what the Uygurs have (from East Iranians?). The Chuvash are a Turkic group with minor, but significant, Turkic component. The HGDP Russian sample does have some East Eurasian ancestry. And the Moroccans also have African ancestry. But your guess is as good as mine with the Bantu flow in. These are I think Kenya, so it might be trying to interpret Nilotic admixture as generalized Eurasian.
A minor note: installing TreeMix and generating the appropriate files from pedigree format is not to difficult. But you might have confusion in how to generate the pedigree input file. You do it like so in PLINK:
./plink --noweb --bfile YourFile --freq --within YourGroupNamesFile --out YourOutPutFile
It’s the last you want to put into TreeMix’s python conversion script. The YourGroupNamesFile is basically the .fam file with an extra column, the population names for each individual.
I mentioned this in passing on my post on ASHG 2012, but it seems useful to make explicit. For the past few years there has been word of research pointing to connections between the Khoisan and the Cushitic people of Ethiopia. To a great extent in the paper which is forthcoming there is the likely answer to the question of who lived in East Africa before the Bantu, and before the most recent back-migration of West Eurasians. On one level I’m confused as to why this has to be something of a mystery, because the most recent genetic evidence suggests a admixture on the order of 2-3,000 years before the past.* If the admixture was so recent we should find many of the “first people,” no? As it is, we don’t. I think these groups, and perhaps the Sandawe, are the closest we’ll get.
Publication is imminent at this point (of this, I was assured), so I’m going to just state the likely candidate population (or at least one of them): the Sanye, who speak a Cushitic language with possible Khoisan influences. There really isn’t that much information on these people, which is why when I first heard about the preliminary results a few years back and looked around for Khoisan-like populations in Kenya I wasn’t sure I’d hit upon the right group. But at ASHG I saw some STRUCTURE plots with the correct populations, and the Sanye were one of them. I would have liked to see something like TreeMix, but the STRUCTURE results were of a quality that I could accept that these populations were not being well modeled by the variation which dominated their data set. Though Cushitic in language the Sanye had far less of the West Eurasian element present among other Cushitic speaking populations of the Horn of Africa. Neither were their African ancestral components quite like that of the Nilotic or Bantu populations. The clustering algorithm was having a “hard time” making sense of them (it seemed to wanted to model them as linear combinations of more familiar groups, but was doing a bad job of it).
Here is an interesting article on these groups: Little known tribe that census forgot. Like the Sandawe this is a population which seems to have been hunter-gatherers very recently, and to some extent still engage in this lifestyle. In this way I think they are fundamentally different from Indian tribal populations, who are often held up to be the “first people” of the subcontinent. More and more it seems that the tribes of India are less the descendants of the original inhabitants of the subcontinent, at least when compared to the typical Indian peasant, and more simply those segments of the Indian population which were marginalized and pushed into less productive territory. Over time they naturally diverged culturally because of their isolation, but the difference was not primal. In contrast, groups like the Sanye and Sandawe may have mixed to a great extent with their neighbors (and lost their language like the Pygmies), but evidence of full featured hunting & gathering lifestyles implies a sort of direct cultural continuity with the landscape of eastern Africa before the arrival of farmers and pastoralists from the west and north.
* I understand some readers refuse to accept the likelihood of these results because of other lines of information. I am just relaying the results of the geneticists. I am not interested in re-litigating prior discussions on this. We’ll probably have a resolution soon enough.
While I was at Spencer Wells’ poster at ASHG I was primarily curious about bar plots. He’s got really good spatial coverage, so I’m moderately excited about the paper (though I didn’t see much explicit testing of phylogenetic hypotheses, which I think this sort of paper has to do now; we’re beyond PCA and bar plots only papers). That being said, Spencer was more interested in me promoting the Scientific Grants Program. Here’s some more information:
The Genographic Project’s Scientific Grants Program awards grants on a rolling basis for projects that focus on studying the history of the human species utilizing innovative anthropological genetic tools. The variety of projects supported by the scientific grants will aim to construct our ancient migratory and demographic history while developing a better understanding of the phylogeographic structure of world populations. Sample research topics could include subjects like the origin and spread of the Indo-European languages, genetic insights into Papua New Guinea’s high linguistic diversity, the number and routes of migrations out of Africa, the origin of the Inca, or the genetic impact of the spread of maize agriculture in the Americas.
Recipients will typically be population geneticists, students, linguists, and other researchers or scientists interested in pursuing questions relevant to the Genographic Project’s broad goal of exploring our migratory history. Recipients of Genographic scientific grant funds will become members of the Genographic Consortium, and will be expected to act as agents of the greater Genographic mission, participating in and reporting on multiple aspects of Genographic fieldwork, in addition to their own proposed and mission‐aligned pilot projects. Openness and transparency within the Consortium are the key values of the project’s research team, and grantees will be expected to abide by this code of conduct.
– Life Technologies/Ion Torrent apparently hires d-bag bros to represent them at conferences. The poster people were fine, but the guys manning the Ion Torrent Bus were total jackasses if they thought it would be funny/amusing/etc. Human resources acumen is not always a reflection of technological chops, but I sure don’t expect organizational competence if they (HR) thought it was smart to hire guys who thought (the d-bags) it would be amusing to alienate a selection of conference goers at ASHG. Go Affy & Illumina!
– Speaking of sequencing, there were some young companies trying to pitch technologies which will solve the problem of lack of long reads. I’m hopeful, but after the Pacific Biosciences fiasco of the late 2000s, I don’t think there’s a point in putting hopes on any given firm.
– I walked the poster hall, read the titles, and at least skimmed all 3,000+ posters’ abstracts. No surprise that genomics was all over the place. But perhaps a moderate surprise was how big exomes are getting for medically oriented people.
– Speaking of medical/clinical people, I noticed that in their presentations they used the word ‘Caucasian‘ a lot. This was not evident in the pop-gen folks. It shows the influence of bureaucratic nomenclature in modern medicine, as they have taken to using somewhat nonsensical US Census Bureau categories.
– Twitter was a pretty big deal. There were so many interesting sessions that I found myself checking my feed constantly for the #ASHG2012 hashtag. It was also an easy way to figure out who else was at the same session (e.g., in my case, very often Luke Jostins).
– If you could track the patterns of movements of smartphones at the conference it would be interesting to see a network of clustering of individuals. For example, the evolutionary and population genomics posters were bounded by more straight-up informatics (e.g., software to clean your raw sequence data), from which there was bleed over. But right next to the evolution and population genomics sections (and I say genomics rather than genetics, because the latter has been totally subsumed by the former) you had some type of pediatric disease genetics aisles. I wasn’t the only one to have a freak out when I mistakenly kept on moving (i.e., you go from abstruse discussions of the population structure of Ethiopia, to concrete ones about the likely probability of death of a newborn with an autosomal dominant disorder, with photos of said newborn!).
Last week Luke Jostins (soon to be Dr. Luke Jostins) published an interesting paper in Nature. To be fair, this paper has an extensive author list, but from what I am to understand this is the fruit of the first author’s Ph.D. project. In any case, you may know Luke because I have used his loess curve on hominin encephalization for years. His bread & butter is statistical genetics, and it shows in this Nature paper. God knows how he managed to cram so much density into ~5.5 pages of plain text. Luke is also a contributor to Genomes Unzipped, and has put up a post over there on one implication of the paper, Dozens of new IBD genes, but can they predict disease? The short answer is that for individual prediction complex traits are going to be a hard haul over the long term.*
They are subject to what Jim Manzi would term “high causal density.” A simple way to state this is that outcome X is dependent on a host of variables, and if you capture only a small number of variables, you aren’t going to be explaining much in a general fashion. This is obvious from the text of Luke’s paper. Let’ look at the abstract, Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease:
There is a high likelihood that you know of which ABO blood group you belong to. I am A. My daughter is A. My father is B. My mother is A. I have siblings who are A, O, B, and AB. The inheritance is roughly Mendelian, with O being “recessive” to A and B (which are co-dominant with each other, ergo, AB). It is also generally common knowledge that O is a “universal donor,” while A and B can only give to individuals within their respective blood group and AB.
Because ABO was easy to assay it was one of the earliest Mendelian markers utilized in human genetics. In the first half of the 20th century while some anthropologists were measuring skulls, others were mapping out the frequency of A, B, and O. Today with much more robust genetic methods ABO has lost its old luster as a genetic marker, especially since there is a strong suspicion that the variants are strongly shaped by natural selection. This makes them only marginally useful for systematics, which rely upon loci which are honest mirrors of demographic history.
I have mentioned the PLoS Genetics paper, The Date of Interbreeding between Neandertals and Modern Humans, before because a version of it was put up on arXiv. The final paper has a few additions. For example, it mentions the generally panned (at least in the circles I run in) PNAS paper which suggested that ancient population structure could produce the same patterns which were earlier used to infer admixture with Neandertals (the authors also point to Yang et al. as a support for the proposition of admixture rather than structure). The primary result, dating the admixture between Neandertals and anatomically modern humans ~40-80,000 years before the present, is reiterated.
An interesting aspect is that their method is to utilize linkage disequilibrium (LD) decay. It’s interesting because tens of thousands of years is a hell of a long time to be able to detect an admixture event via LD! In particular because there’s likely a palimpsest effect where there are intervening admixtures and other assorted demographic events (e.g., bottlenecks and selective sweeps can also generate LD). So how’d they do it? Basically the authors figured out a way to ascertain which pairs of SNPs may have introgressed from Neandertals by comparing the frequency in modern humans to Neandertals at those given SNPs (in particular, by looking at variants at low frequency in Africans and derived in Neandertals). A major technical problem here is the “genetic map” which allows one to assess what the nature of recombination over time is going to be which breaks apart the associations which are the hallmark of LD is not particular precise enough to robustly allow them to make the inferences that they want.