The phylogenetic trees falling on the tundra

A massive new ancient DNA preprint just dropped, The population history of northeastern Siberia since the Pleistocene:

…Here, we report 34 ancient genome sequences, including two from fragmented milk teeth found at the ~31.6 thousand-year-old (kya) Yana RHS site, the earliest and northernmost Pleistocene human remains found. These genomes reveal complex patterns of past population admixture and replacement events throughout northeastern Siberia, with evidence for at least three large-scale human migrations into the region. The first inhabitants, a previously unknown population of “Ancient North Siberians” (ANS), represented by Yana RHS, diverged ~38 kya from Western Eurasians, soon after the latter split from East Asians. Between 20 and 11 kya, the ANS population was largely replaced by peoples with ancestry from East Asia, giving rise to ancestral Native Americans and “Ancient Paleosiberians” (AP), represented by a 9.8 kya skeleton from Kolyma River. AP are closely related to the Siberian ancestors of Native Americans, and ancestral to contemporary communities such as Koryaks and Itelmen. Paleoclimatic modelling shows evidence for a refuge during the last glacial maximum (LGM) in southeastern Beringia, suggesting Beringia as a possible location for the admixture forming both ancestral Native Americans and AP. Between 11 and 4 kya, AP were in turn largely replaced by another group of peoples with ancestry from East Asia, the “Neosiberians” from which many contemporary Siberians derive. We detect additional gene flow events in both directions across the Bering Strait during this time, influencing the genetic composition of Inuit, as well as Na Dene-speaking Northern Native Americans, whose Siberian-related ancestry components is closely related to AP. Our analyses reveal that the population history of northeastern Siberia was highly dynamic, starting in the Late Pleistocene and continuing well into the Late Holocene. The pattern observed in northeastern Siberia, with earlier, once widespread populations being replaced by distinct peoples, seems to have taken place across northern Eurasia, as far west as Scandinavia.

The preprint is very interesting and thorough, and the supplements are well over 100 pages. I read the genetics and linguistics portions. They make for some deep reading, and I really regret making fun of Iosif Lazaridis’ fondness for acronyms now.

I will make some cursory and general observations. First, the authors got really high coverage (so high quality) genomes from the Yana RS site. Notice that they’re doing more data-intense analytic methods. Second, they did not find any population with the affinities to Australo-Melanesian that several research groups have found among some Amazonians. Likely they are hiding somewhere…but the ancient DNA sampling is getting pretty good. We’re missing something. Third, I am not sure what to think about the very rapid bifurcation of lineages we’re seeing around ~40,000 years ago.

The ANS population, ancestral by and large to ANE, seems to be about ~75% West Eurasian (without much Basal Eurasian) and ~25% East Eurasian. Or at least that’s one model. Did they then absorb other peoples? Or, was there an ancient population structure in the primal ur-human horde pushing out of the Near East? That is, are the “West Eurasians” and “East Eurasians” simply the descendants of original human tribes venturing out of Africa ~50,000 years ago? Also, rather than discrete West Eurasian and East Eurasian components, perhaps there was a genetic cline where the proto-ANS occupied a position closer to the former, as opposed to some later pulse admixture?

Without more ancient DNA we probably won’t be able to resolve the various alternative models.

Chinese and Indian American population genetic structure

In Who We Are and How We Got Here: Ancient DNA and the New Science of the Human Past David Reich makes the observation that India is a nation of many different ethnicities, while China is dominated by a single ethnicity, the Han. This is obviously true, more or less. Even today the vast majority of Indians seem to be marrying with their own communities, jati.

Over the years I’ve collected many different genotypes of Americans of various origins who have purchased personal genomics kits, and given me their raw results. I decided to go through my collection and strip detailed ethnic labels and simply group together all those individuals from India, and China, who have had their genotypes done from one of the major services.

I suspect that these individuals are representative of “Indian Americans” and “Chinese Americans.” So what’s their genetic structure?

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Nomads, cosmopolitan predators, and peasants, xenophobic producers

Ten years ago when I read Peter Heather’s Empires and Barbarians, its thesis that the migrations and conquests of the post-Roman period were at least in part folk wanderings, where men, women, and children swarmed into the collapsing Empire en masse, was somewhat edgy. Today Heather’s model has to a large extent been validated. The recent paper on the Lombard migration, the discovery that the Lombards were indeed by and large genetically coherent as a transplanted German tribe in Pannonia and later northern Italy, confirms the older views which Heather attempted to resurrect. Additionally, the Lombards also seem to have been defined by a dominant group of elite male lineages.

Why is this even surprising? Because to a great extent, the ethnic and tribal character of the post-Roman power transfer between Late Antique elites and the newcomers was diminished and dismissed for decades. I can still remember the moment in 2010 when I was browsing books on Late Antiquity at Foyles in London and opened a page on a monograph devoted to the society of the Vandal kingdom in North Africa. The author explained that though the Vandals were defined by a particular set of cultural codes and mores, they were to a great extent an ad hoc group of mercenaries and refugees, whose ethnic identity emerged de novo on the post-Roman landscape.

In the next few years, we will probably get Vandal DNA from North Africa. I predict that they will be notably German (though with admixture, especially as time progresses). Additionally, I predict most of the males will be haplogroup R1b or I1. But the Vandal kingdom was actually one where there was a secondary group of barbarians: the Alans. It was Regnum Vandalorum et Alanorum. I predict that Alan males will be R1a. In particular, R1a1a-z93.

But this post is not about the post-Roman world. Rather, it’s about the Inner Asian forest steppe. The sea of grass, stretching from the Altai to the Carpathians. A new paper in Science adds more samples to the story of the Srubna, Cimmerians, Scythians, and Sarmatians. Ancient genomes suggest the eastern Pontic-Caspian steppe as the source of western Iron Age nomads. The abstract is weirdly nonspecific, though accurate:

For millennia, the Pontic-Caspian steppe was a connector between the Eurasian steppe and Europe. In this scene, multidirectional and sequential movements of different populations may have occurred, including those of the Eurasian steppe nomads. We sequenced 35 genomes (low to medium coverage) of Bronze Age individuals (Srubnaya-Alakulskaya) and Iron Age nomads (Cimmerians, Scythians, and Sarmatians) that represent four distinct cultural entities corresponding to the chronological sequence of cultural complexes in the region. Our results suggest that, despite genetic links among these peoples, no group can be considered a direct ancestor of the subsequent group. The nomadic populations were heterogeneous and carried genetic affinities with populations from several other regions including the Far East and the southern Urals. We found evidence of a stable shared genetic signature, making the eastern Pontic-Caspian steppe a likely source of western nomadic groups.

The German groups which invaded the Western Roman Empire were agropastoralists. That is, they were slash and burn farmers who raised livestock. Though they were mobile, they were not nomads of the open steppe. Man for man the Germans of Late Antiquity had more skills applicable to the military life than the Roman peasant. This explains in part their representation in the Roman armed forces in large numbers starting in the 3rd century. But the people of the steppe, pure nomads, were even more fearsome. Ask the Goths about the Huns.

Whole German tribes, like the Cimbri, might coordinate for a singular migration for new territory, but for the exclusive pastoralist, their whole existence was migration. Groups such as the Goths and Vandals might settle down, and become primary producers again, but pure pastoralists probably required some natural level of predation and extortion upon settled peoples to obtain a lifestyle beyond marginal subsistence. Which is to say that some of the characterizations of Late Antique barbarians as ad hoc configurations might apply more to steppe hordes.

There has been enough work on these populations over the past few years to admit that various groups have different genetic characteristics, indicative of a somewhat delimited breeding population. But, invariably there are outliers here and there, and indications of periodic reversals of migration and interactions with populations from other parts of Eurasia.

Earlier I noted that Heather seems to have been correct that the barbarian invasions of the Roman Empire were events that involved the migration of women and children, as well as men. The steppe was probably a bit different. Here are the Y and mtDNA results for males from these data that are new to this paper:

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How related should you expect relatives to be?

Like many Americans in the year 2018 I’ve got a whole pedigree plugged into personal genomic services. I’m talking from grandchild to grandparent to great-aunt/uncles. A non-trivial pedigree. So we as a family look closely at these patterns, and we’re not surprised at this point to see really high correlations in some cases compared to what you’d expect (or low).

This means that you can see empirically the variation between relatives of the same nominal degree of separation from a person of interest. For example, each of my children’s’ grandparents contributes 25% of their autosomal genome without any prior information. But I actually know the variation of contribution empirically. For example, my father is enriched in my daughter. My mother is my sons.

The sample principle applies to siblings. Though they should be 50% related on their autosomal genome, it turns out there is variation. I’ve seen some papers large data sets (e.g., 20,000 sibling pairs) which gives a standard deviation of 3.7% in relatedness. But what about other degrees of relation?

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David Burbridge’s 10 questions for A. W. F. Edwards In 2006

A few years ago I watched a documentary about the rise of American-influenced rock music in Britain in the 1960s. At some point, one of the Beatles, probably Paul McCartney, or otherwise Eric Clapton, was quoted as saying that they wanted to introduce Americans to “their famous people.” Though patronizing and probably wrong, what they were talking about is that there were particular blues musicians who were very influential in some British circles were lingering in obscurity in the United States of America due to racial prejudice. The bigger picture is that there are brilliant people who for whatever reason are not particularly well known to the general public.

This is why I am now periodically “re-upping” interviews with scientists that we’ve done on this weblog over the past 15 years. These are people who should be more famous. But aren’t necessarily.

In 2006 David Burbridge, a contributor this weblog and a historian of things Galtonian, interviewed the statistical geneticist A. W. F. Edwards. Edwards was one of R. A. Fisher’s last students, so he has a connection to a period if history that is passing us by.

I do want to say that his book, Foundations of Mathematical Genetics, really gave me a lot of insights when I first read it in 2005 and began to be deeply interested in pop gen. It’s dense. But short. Additionally, I have also noticed that there is now a book out which is a collection of Edwards’ papers, with commentaries, Phylogenetic Inference, Selection Theory, and a History of Science. Presumably, it is like W. D. Hamilton’s Narrow Roads of Gene Land series. I wish more eminent researchers would publish these sorts of compilations near the end of their careers.

There have been no edits below (notice the British spelling). But I did add some links!

David’s interview begins after this point:

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My interview of James F. Crow in 2006

Since the death of L. L. Cavalli-Sforza I’ve been thinking about the great scientists who have passed on. Last fall, I mentioned that Mel Green had died. There was a marginal personal connection there. I had the privilege to talk to Green at length about sundry issues, often nonscientific. He was someone who been doing science so long he had talked to Charles Davenport in the flesh (he was not complimentary of Davenport’s understanding of Mendelian principles). It was like engaging with a history book!

A few months before I emailed Cavalli-Sforza, I had sent a message on a lark to James F. Crow. It was really a rather random thing, I never thought that Crow would respond. But in fact he emailed me right back! And he answered 10 questions from me, as you can see below the fold. The truth is I probably wouldn’t have thought to try and get in touch with Cavalli-Sforza if it hadn’t been so easy with Crow.

If you are involved in population genetics you know who Crow is. No introduction needed. Some of the people he supervised, such as Joe Felsenstein, have gone on to transform evolutionary biology in their own turn.

Born in 1916, Crow’s scientific career spanned the emergence of population genetics as a mature field, to the discovery of the importance of DNA, to molecular evolution & genomics. He had a long collaboration with Motoo Kimura, the Japanese geneticist instrumental in pushing forward the development of “neutral theory.”

He died in 2012.

Below are the questions I asked 12 years ago. My interests have changed somewhat, so it’s interesting to see what I was curious about back then. And of course fascinating to read Crow’s responses.
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Tutorial to run supervised admixture analyses

ID Dai Gujrati Lithuanians Sardinian Tamil
razib_23andMe 0.14 0.26 0.02 0.00 0.58
razib_ancestry 0.14 0.26 0.02 0.00 0.58
razib_ftdna 0.14 0.26 0.02 0.00 0.57
razib_daughter 0.05 0.14 0.29 0.18 0.34
razib_son 0.07 0.17 0.28 0.19 0.30
razib_son_2 0.06 0.19 0.29 0.19 0.27
razib_wife 0.00 0.07 0.55 0.38 0.00

This is a follow-up to my earlier post, Tutorial To Run PCA, Admixture, Treemix And Pairwise Fst In One Command. Hopefully, you’ll be able to run supervised admixture analysis with less hassle after reading this. Here I’m pretty much aiming for laypeople. If you are a trainee you need to write your own scripts. The main goal here is to allow people to run a lot of tests to develop an intuition for this stuff.

The above results are from a supervised admixture analysis of my family and myself. The fact that there are three replicates of me is because I converted my 23andMe, Ancestry, and Family Tree DNA raw data into plink files three times. Notice that the results are broadly consistent. This emphasizes that discrepancies between DTC companies in their results are due to their analytic pipeline, not because of data quality.

The results are not surprising. I’m about ~14% “Dai”, reflecting East Asian admixture into Bengalis. My wife is ~0% “Dai”. My children are somewhere in between. At a low fraction, you expect some variance in the F1.

Now below are results for three Swedes with the same reference panel:

Group ID Dai Gujrati Lithuanians Sardinian Tamil
Sweden Sweden17 0.00 0.09 0.63 0.28 0.00
Sweden Sweden18 0.00 0.08 0.62 0.31 0.00
Sweden Sweden20 0.00 0.05 0.72 0.23 0.00

All these were run on supervised admixture frameworks where I used Dai, Gujrati, Lithuanians, Sardinians, and Tamils, as the reference “ancestral” populations. Another way to think about it is: taking the genetic variation of these input groups, what fractions does a given test focal individual shake out at?

The commands are rather simple. For my family:
bash TestScript

For the Swedes:
bash Sweden TestScript

The commands need to be run in a folder: ancestry_supervised/.

You can download the zip file. Decompress and put it somewhere you can find it.

Here is what the scripts do. First, imagine you have raw genotype files downloaded fromy 23andMe, Ancestry, and Family Tree DNA.

Download the files as usual. Rename them in an intelligible way, because the file names are going to be used for generating IDs. So above, I renamed them “razib_23andMe.txt” and such because I wanted to recognize the downstream files produced from each raw genotype. Leave the extensions as they are. You need to make sure they are not compressed obviously. Then place them all in  RAWINPUT/.

The script looks for the files in there. You don’t need to specify names, it will find them. In plink the family ID and individual ID will be taken from the text before the extension in the file name. Output files will also have the file name.

Aside from the raw genotype files, you need to determine a reference file. In REFERENCEFILES/ you see the binary pedigree/plink file Est1000HGDP. The same file from the earlier post. It would be crazy to run supervised admixture on the dozens of populations in this file. You need to create a subset.

For the above I did this:
grep "Dai\|Guj\|Lithua\|Sardi\|Tamil" Est1000HGDP.fam > ../keep.keep

./plink --bfile REFERENCEFILES/Est1000HGDP --keep keep.keep --make-bed --out REFERENCEFILES/TestScript

When the script runs, it converts the raw genotype files into plink files, and puts them in INDIVPLINKFILES/. Then it takes each plink file and uses it as a test against the reference population file. That file has a preprend on group/family IDs of the form AA_Ref_. This is essential for the script to understand that this is a reference population. The .pop files are automatically generated, and the script inputs in the correct K by looking for unique population numbers.

The admixture is going to be slow. I recommend you modify by adding the number of cores parameters so it can go multi-threaded.

When the script is done it will put the results in RESULTFILES/. They will be .csv files with strange names (they will have the original file name you provided, but there are timestamps in there so that if you run the files with a different reference and such it won’t overwrite everything). Each individual is run separately and has a separate output file (a .csv).

But this is not always convenient. Sometimes you want to test a larger batch of individuals. Perhaps you want to use the reference file I provided as a source for a population to test? For the Swedes I did this:
grep "Swede" REFERENCEFILES/Est1000HGDP.fam > ../keep.keep

./plink --bfile REFERENCEFILES/Est1000HGDP --keep keep.keep --make-bed --out INDIVPLINKFILES/Sweden

Please note the folder. There are modifications you can make, but the script assumes that the test files are inINDIVPLINKFILES/. The next part is important. The Swedish individuals will have AA_Ref_ prepended on each row since you got them out of Est1000HGDP. You need to remove this. If you don’t remove it, it won’t work. In my case, I modified using the vim editor:
vim Sweden.fam

You can do it with a text editor too. It doesn’t matter. Though it has to be the .fam file.

After the script is done, it will put the .csv file in RESULTFILES/. It will be a single .csv with multiple rows. Each individual is tested separately though, so what the script does is append each result to the file (the individuals are output to different plink files and merged in; you don’ t need to know the details). If you have 100 individuals, it will take a long time. You may want to look in the .csv file as the individuals are being added to make sure it looks right.

The convenience of these scripts is that it does some merging/flipping/cleaning for you. And, it formats the output so you don’t have to.

I originally developed these scripts on a Mac, but to get it to work on Ubuntu I made a few small modifications. I don’t know if it still works on Mac, but you should be able to make the modifications if not. Remember for a Mac you will need the make versions of plink and admixture.

For supervised analysis, the reference populations need to make sense and be coherent. Please check the earlier tutorial and use the PCA functions to remove outliers.

Again, here is the download to the zip files. And, remember, this only works on Ubuntu for sure (though now I hear it’s easy to run Ubuntu in Windows).

The fault in our parameters

Of the books I own, Elements of Evolutionary Genetics is one I consult frequently because of its range and comprehensiveness. The authors, Brian Charlesworth and Deborah Charlesworth’s encyclopedic knowledge of the literature. To truly understand the evolutionary process in all its texture and nuance it is important to absorb a fair amount of theory, and Elements of Evolutionary Genetics does do that (though it’s not as abstruse as something like An Introduction to Population Genetics Theory).

When I see a paper by one of the Charlesworths, I try to read it. Not because I have a love of Drosophila or Daphnia, but because to develop strong population-genetics intuitions it always helps to stand on the shoulders of giants. So with that, I pass on this preprint, Mutational load, inbreeding depression and heterosis in subdivided populations:

This paper examines the extent to which empirical estimates of inbreeding depression and inter-population heterosis in subdivided populations, as well as the effects of local population size on mean fitness, can be explained in terms of estimates of mutation rates, and the distribution of selection coefficients against deleterious mutations provided by population genomics data. Using results from population genetics models, numerical predictions of the genetic load, inbreeding depression and heterosis were obtained for a broad range of selection coefficients and mutation rates. The models allowed for the possibility of very high mutation rates per nucleotide site, as is sometimes observed for epiallelic mutations. There was fairly good quantitative agreement between the theoretical predictions and empirical estimates of heterosis and the effects of population size on genetic load, on the assumption that the deleterious mutation rate per individual per generation is approximately one, but there was less good agreement for inbreeding depression. Weak selection, of the order of magnitude suggested by population genomic analyses, is required to explain the observed patterns. Possible caveats concerning the applicability of the models are discussed.

Burmese are a bit Bengali

About ten years ago I read the book The River of Lost Footsteps: Histories of Burma. Though I have read books where Burma figures prominently (e.g., Strange Parallels), this is the only history of Burma I have read. The author is Burmese, and provide something much more than a travelogue, as might have been the case if he was of Western background. By chance over the past month or so I’ve been in contact with the author, who made a few inquiries as to the genetics of his own family (he came with genotypes in hand). But this brought us to the issue of the genetics of the Burmese people, and their position in the historical-genetic landscape.

The author of The River of Lost Footsteps reminded me of something that’s curious about Southeast Asia: its Indic influences tend to be from the south of the subcontinent. In particular, the native scripts derive from a South Indian parent. Could genetics confirm this connection as well? Also, could genetics give some insights as to the timing of admixture/gene-flow?

In theory, yes.

I had a lot of Southeast Asian datasets to play with, and did a lot of pruning to remove outliers (e.g., people with obvious recent Chinese ancestry). First, comparing them to Bangladeshis it seems that even without local ancestry tract analysis that Burmese and Malays have more varied, and so likely recent, exogenous ancestry than Bangladeshis. At least this is evidence on the PCA plot, where these two groups exhibit strong admixture clines toward South Asians.

But what about the question of Southeast Asian affinities? This needs deeper analysis. Three-population tests, which measure admixture with outgroups when compared to a dyad of populations which are modeled as a clade, can be informative.

Outgroup Pop1 Pop2 f3 z
Bangladeshi Telugu Cambodians -0.00183999 -46.3322
Bangladeshi Telugu Han -0.00220121 -46.046
Burma Telugu Han -0.00406071 -51.0018
Burma Han Bangladeshi -0.00348186 -49.1398
Burma Han Punjabi_ANI_2 -0.00418193 -47.2351
Cambodians Telugu Viet -0.00126923 -16.91
Cambodians Punjabi_ANI_2 Viet -0.00129881 -15.6039
Cambodians Bangladeshi Viet -0.000970022 -14.5642
Malay Igorot Telugu -0.00249795 -18.758
Malay Igorot Bangladeshi -0.00223454 -18.5212
Malay Igorot Punjabi_ANI_2 -0.00250732 -18.3027
Malay Igorot Cambodians -0.00107817 -16.6214
Viet Han Cambodians -0.000569337 -13.1139

Bangladeshis show strong signatures with both Cambodians and Han. This is in accordance with earlier analysis which suggests Austro-Asiatic and Tibeto-Burman contributions to the “East Asian” element of Bengali ancestry. The Burmese always have Han ancestry, with a South Asian donor as well. This aligns with other PCA analysis which shows the Burmese samples skewed toward Han Chinese. Burma is a compound of different ethnic groups. Some are Austro-Asiatic. The Bamar, the core “Burman” group, have some affinities to Tibetans. And the Shan are a Thai people who are relatively late arrivals.

Cambodians have a weaker admixture signature and are paired with a South Asian group and their geographic neighbors the Vietnamese. The Malays are similar to Cambodians but have the Igorot  people from the Philippines as one of their donors. And finally, not surprisingly the Vietnamese show some mixture between Han-like and Cambodian-like ancestors.

Further PCA analysis shows that while Cambodians and Malays tend to skew somewhat neutrally to South Asians (the recent Indian migration to Malaysia is mostly Tamil), the Burmese are shifted  toward Bangladeshis:

Click to enlarge

Finally, I ran some admixture analyses.

First, I partitioned the samples with an unsupervised set of runs (K = 4 and K = 5). In this way I obtained reified reference groups as follows:

“Austronesians” (Igorot tribesmen from the Philippines)
“Austro-Asiatic” (a subset of Cambodians with the least exogeneous admixture)
“North Indians” (Punjabis)
“South Indians” (A subset of middle-caste Telugus highest on the modal element in South Indians)
“Han” (a proxy for “northern” East Asian)

The results are mostly as you’d expect. In line with three-population tests, the Vietnamese are Han and Austro-Asiatic. More of the former than latter. There is a minor Austronesian component. Notice there is no South Asian ancestry in this group.

In contrast, Cambodians have low levels of both North and South Indian. These out sample Cambodians are still highly modal for Austro-Asiatic though.

Malays are more Austro-Asiatic than Austronesian, which might surprise. But the Igorot samples are highly drifted and distinct. I think these runs are underestimating Austronesian in the Malays. Notice that some of the Malays have South Asian ancestry, but a substantial number do not. This large range in admixture is what you see in PCA as well. I think this strongly points to the fact that Malays have been receiving gene-flow from India recently, as it is not a well mixed into the population.

The Bangladeshi outgroup is mostly a mix of North and South Indian, with a slight bias toward the latter. No surprise. As I suggested earlier you can see that the Bangladeshi samples are hard to model as just a mix of Burmese with South Asians. The Austro-Asiatic component is higher in them than the Burmese. This could be because Burma had recent waves of northern migration (true), and, eastern India prior to the Indo-Aryan expansion was mostly inhabited by Austro-Asiatic Munda (probably true). That being said, the earlier analysis suggested that the Munda cannot be the sole source of East Asian ancestry in Bengalis.

Finally, every single Burmese sample has South Asian ancestry. Much higher than Cambodians. And, there is variance.  I think that leads us to the likely conclusion that Burma has been subject to continuous gene-flow as well as recent pulses of admixture from South Asia. The variation in South Asian ancestry in the Burmese is greater than East Asian ancestry in Bengalis. I believe this is due to more recent admixture in Burmese due to British colonial Indian settlement in that country.

The cultural and historical context of this discussion is the nature of South Asian, Indic, influence, on Southeast Asia. One can not deny that there has been some gene-flow between Southeast Asia and South Asia. In prehistoric times it seems that Austro-Asiatic languages moved from mainland Southeast Asia to India. More recently there is historically attested, and genetically confirmed, instances of colonial Indian migration. But, the evidence from Cambodia suggests that this is likely also ancient, as unlike Malaysia or Burma, Cambodia did not have any major flow of Indian migrants during the colonial period. One could posit that perhaps the Cambodian Indian affinity is a function of “Ancestral South Indian.” But the Cambodians are not skewed toward ASI-enriched groups in particular. And, I know for a fact that appreciable frequencies of R1a1a exist within the male Khmer population (this lineage is common in South Asia, especially the north and upper castes).

As far as Burma goes, I think an older period of South Indian cultural influence, and some gene-flow seems likely. But, with the expansion of Bengali settlement to the east over the past 2,000 years, more recent South Asian ancestry is probably enriched for that ethnolinguistic group.

I’m going to try and follow-up with some ancestry tract analysis….

Soft & hard selection vs. soft & hard sweeps

When I was talking to Matt Hahn I made a pretty stupid semantic flub, confusing “soft selection” with “soft sweeps.” Matt pointed out that soft/hard selection were terms more appropriate to quantitative genetics rather than population genomics. His viewpoint is defensible, though going back into the literature on soft/selection, e.g., Soft and hard selection revisited, the main thinkers pushing the idea were population geneticists who were also considering ecological questions.*

The strange thing is that I had already known the definitions of hard and soft selection on some level because I had read about them as I was getting confused with hard and soft sweeps! But this was more than ten years ago now, and since then I haven’t given the matter enough thought obviously, as I defaulted back to confusing the two classes of terms, just as I used to.

Matt pointed out that truncation selection is a form of hard selection. All individuals below (or above) a certain phenotype value have a fitness of zero, as they don’t reproduce. In a single locus context, hard selection would involve deleterious lethal alleles, whose impact on the genotype was the same irrespective of ecological context. So in a hard selection, it operates by reducing the fitness of individuals/genotypes to zero.

For soft selection, context matters much more, and you would focus more on relative fitness differences across individuals/genotypes. Some definitions of soft vs. hard selection emphasize that in the former case fitness is defined relative to the local ecological patch, while the latter is a universal estimate. Soft selection does not necessarily operate through the zero fitness value for a genotype, but rather differential fitness. Hard selection can crash your population size. Soft selection does not necessarily do that.

Though I won’t outline the details, one of the originators of the soft/hard selection concept analogized them to density-dependent/independent dynamics in ecology. If you know the ecological models, the correspondence probably is obvious to you.

As for hard and soft sweeps, these are particular terms of relevance to genomics, because genome-wide data has allowed for their detection through the impact they have on the variation in the genome. A “sweep” is a strong selective event that tends to sweep away variation around the focus of selection. A hard sweep begins with a single mutant, and positive selection tends to drive it toward fixation.

A classical example is lactase persistence in Northern Europeans and Northwest South Asians (e.g., Punjabis). The mutation in the LCT gene is the same across a huge swath of Eurasia. And, the region around the genome is also the same, because regions of the genome adjacent to that single mutation increased in frequency as well (they “hitchhiked”). This produces a genetic block of highly reduced diversity since the hard selective sweep increases the frequency of so many variants which are associated with the advantageous one, and may drive to extinction most other competitive variants.

Someone is free to correct me in the comments, but it strikes me that many hard selective sweeps are driven by soft selection. Fitness differentials between those with the advantageous alleles and those without it are not so extreme, and obviously context dependent, even in cases of hard sweeps on a single locus.

The key to understanding soft sweeps is that there isn’t a focus on a singular mutation. Rather, selection can target multiple mutations, which may have the same genetic position, but be embedded within different original gene copies. In fact, soft selection often operates on standing variation, preexistent alleles which were segregating in the population at low frequencies or were totally neutral. Genetic signatures of these events are less striking than those for hard sweeps because there is far less diminishment of diversity, since it’s not the increase in the frequency of a singular mutation and the hitchhiking of its associated flanking genomic region.

Soft sweeps can clearly occur with soft selection. But truncation selection can occur on polygenic traits, so depending on the architecture of the trait (i.e., effect size distribution across the loci) one can imagine them associated with hard selection as well.

Going back to the conversation I had with Matt the reason semantics is important is that terms in population genetics are informationally rich, and lead you down a rabbit-hole of inferences. If population genetics is a toolkit for decomposing reality, then you need to have your tools well categorized and organized. On occasion it is important to rectify the names.

* There are two somewhat related definitions of soft/hard selection. I’ll follow Wallace’s original line here, though I’m not sure they differ that much.