Category: population genetics

More Genetic Structure and DifferInt Analysis

An important topic.

I have been looking a bit more at the DifferInt program (currently unable to find anything better), testing some model genotypes to better understand the relationship between different levels of integration with respect to the amount of differentiation.  One finding which is clear that it is when genetic differentiation – at the lowest genepool level – between groups is shallow is when the program is scalable at the level of the highest level of integration.

A test model was devised with two populations of eleven individuals each.  Six loci were considered.  Initially, the two populations were constructed to be genetically identical. Four individuals of the second population had alleles at one lock rearranged so that four heterozygotes were made into four homozygotes (two of each type), without changing the total number of each allele type for that locus in that population.  After this change, the genepool differentiation was 0.0303, but the multilocus genotype neglecting elementary genic differences (MGNEGD) was 0.3636 – a twelve-fold increase in differentiation.  In this simple model of shallow genetic difference, a discrete representation of genetic structure (MGNEGD) is seen to exhibit sharply increased (and quantitatively scalable) differentiation with even a small change in allele structuring in genetically similar (model) populations.

However, when differentiation at the genepool level is already fairly high, then MGNEGD rises to complete differentiation quickly, and the ability to evaluate genetic structure becomes non-scalable using this program.  It could be that the SNP database I utilized for my initial human study was enriched in SNPs that sharply differentiate between ethnies and so all levels of differentiation were high in the analysis; perhaps completely random SNPs would be better? On the other hand, we are most concerned about the distinctive genome (with respect to EGI).  

In a more realistic model of human genetic differentiation, two populations were set up, each consisting of ten individuals, each assayed over 100 loci.  90 of these loci were absolutely identical between the two populations and 10 loci differed between the populations with respect to the frequencies of alleles at the loci.  In some cases, it was 100%  of one allele pair compared to 100% of another; in other cases it was more subtle – for example one population having 20% AA, 60% AT, and 20% TT while the other population was 20% AA, 50% AT, and 30% TT for the same locus.  The genepool differentiation between the two populations was 0.0370; the MGNEGD was 1.000 – complete differentiation.  This again shows that with enough loci studied and differentiated populations, analysis of discrete sets of multilocus genotypes (see my definition of genetic structure below) will reach complete differentiation.  The implications for genetic interests should be obvious.

It might be a good idea to review my idea of genetic structure again here.

Genetic structure as per my definition can be viewed as a form of linkage disequilibrium of alleles over all the loci in the genome, or this distinctive genome, of at least whatever number of loci that were assayed.  Each specific permutation of multilocus genotypes is a discrete entity, so that one would expect, of course, district genetic structures between any set of individuals who are not identical twins; there would be differences in genetic structure within families, never mind within ethnies.

However – and this is the key point that separates my idea from the run-of-the mill evaluations of genetic structure – I envision genetic structure to be defined by specific ranges of multilocus genotypes.  Therefore, while there is going to be, naturally, individual variation of discrete multilocus genotypes within families, there will be a family-specific range of multilocus genotypes, a range within which all the individual genotypes, of that family will fall within.  Likewise, there will be ethny-specific ranges of multilocus genotypes, so that members of an ethny will exhibit genotypes that – while they differ on an individual level – will fall within a range, a set, of genotypes characteristic of that ethny.  

It then follows, that while multilocus genotypes will be differentiated from each other, the extent of that differentiation will differ.  Different families will exhibit different ranges, or sets, of possible multilocus genotypes, but families belonging to the same ethny will exhibit ranges that are more similar to each other than that of families of different ethnies (the same goes for individuals of course, across families or across ethnies).  Ethnies belonging to the same continental population group (i.e., intra-racial) will exhibit more similar ranges of possibilities of multilocus genotypes than that of inter-racial comparisons.  One could think of it also as frequency distributions of multilocus genotypes, of all the alleles possibilities at all the relevant loci considered together as a discrete entity, and one can compare how similar the frequency distributions are, with more overlap from those more similar.  

One would also expect a solid correlation, or association, between the differentiation as measured by an allele-by-allele genepool/beanbag approach, single locus genotypes, and multilocus genotypes. The relative extent of differences should correlate in at least a qualitative sense between these levels of “genetic integration.”  Hence, as previously noted at this blog, “complete differentiation” at the multilocus genotype level should differ in extent dependent upon how similar or different the genotypes are from each other.  One should in theory be able to quantitate this in a continuous fashion, rather than just having a binary yes/no undifferentiated/completely differentiated choice.

This is obviously an important topic.  If we are to make decisions based on genetic interests, don’t we need to have a better understanding about what those interests actually are, quantitatively speaking?

It’s true that we know enough right now to justify taking action in defense of genetic interests; even at the lowest levels of genetic integration, and even with estimates of child equivalents based on Fst, we already know that mass migration of alien peoples is genocide.

So, yes, I’m sympathetic to the argument that in general, qualitatively speaking, it is more important to actualize a defense of the interests we already know about than to fine-tune our understanding of these interests. But why not both?  Nothing stops us from both organizing on a political and metapolitical level while at the same time continuing to refine our understanding of this topic.  While most of my work now concerns the political and metapolitical implications of defending EGI and of actualizing a High Culture, surely there is also a place for a better understanding of EGI and for a better understanding of Spenglerian cycles and how to control them foe civilizational benefit.

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Meet Shah Jorjani

Madness.

Let’s take a look at some “movement” stupidity from several weeks ago.  I’ve been busy with the DifferInt analyses over the past few weeks, but will get around to evaluating Der Movement’ s more recent antics when I have the time.

We can start with the following.

Since Greg Johnson quoted excerpts from Jorjani to, I presume, take another swipe at Spencer, let’s take a closer look at some other points Jorjani has to make.  When you read this, please remember that Jorjani has been taken seriously by BOTH Johnson and Spencer; he’s spoken at Counter-Currents events, and he was high in the councils of the Alt Right corporation (never mind Arktos).

….Croatia is a part of Iranian civilization. During Tito’s rule, which imposed a Slavic identity on all of Yugoslavia, scholars were actually prosecuted, imprisoned, and even tortured for researching and writing about the Iranian origin of the Croatian people. Specifically, they are part of the Scythian branch of Iranians – cousins of the Persians who rode deep into Europe.

Yes sir, Croatians are actually Iranians with a dastardly fake Slavic identity.  No doubt – no doubt whatsoever! – population genetics studies will place Croatians right there with Iranians and distant from those alien Balkan Slavs!  And if such studies don’t yield that result, then, by golly, it’s the ghost of Tito imposing an anti-Promethean contamination, falsifying the genetics!

Steve Bannon was known to be a reader of Arktos books and Michael’s plan was to send me into the White House to cultivate a relationship with Bannon, and through him, to influence President Trump. My main reason for wanting to have such influence was to help determine Iran policy.

As the Iron Sheik used to say, “Iran number one!”

…the funding for a capital investment that would have established me as the majority shareholder of the Alt-Right Corporation…

Very, very, very carefully consider the ramifications of this delusional Persian supremacist intimately involved with the Alt Right, a “movement” ostensibly representing the interests of indigenous Europeans.

This is about the reorientation of the trajectory of geopolitics in the Middle East, the Caucasus, and Central Asia. It is about aborting a Renaissance of the Persian Empire…

And at the heart of this world historical mission is…Jorjani.

Except that we are not Germans. 

No, you’re not.

Through the Scythians (i.e. the Saxons) and the Alans, we lent the Germans and Goths our Faustian (i.e. Zoroastrian) genius and chivalric spirit but those northern Barbarians never understood the essence of our cosmopolitan humanism.

Err…isn’t “cosmopolitan humanism” the very thing all the grand “traditionalists” of the Alt Right allegedly oppose?  Cosmopolitan humanism?  What would Evola think? Savitri Devi?  Oh, the Kali Yuga of it all!  The men who can’t tell time!  The Age of Iron!  Oh Guenon, where are thou?

Al-Ahwaz and a Kurdish nation have nothing but Sunni fundamentalism and barbaric tribalism to offer the world, whereas our Persian civilizational heritage has not only held Iran together for centuries it has, repeatedly, offered all of humanity the best chance at forming a world order based on innovation, compassion, and social justice.

Social justice?  I guess that goes along with all of the “cosmopolitan humanism.”  How about multiculturalism?  The wonderfully cosmopolitan Persian Empire was multicultural, no?  Let’s have the Alt Right fight for multicultural cosmopolitan humanist social justice!  

We know that you do not really have a government “of the people, by the people, and for the people.” You are oppressed by a rogue dictatorship. Rest assured that after we liberate ourselves and secure our future, we will bring the ever-living fire of true freedom to your bountiful continent as we once brought it to Greece. Far be it from us to leave your resistance movement in the hands of the Alt-Right or comparable culturally impoverished and regressive reactionaries. We are coming to save you, America. So speaks the living spirit of Xerxes, King of Kings, Light of the Aryans…

Once again, both Johnson and Spencer think/have thought that this Jorjani is someone that needs to be seriously listened to, someone who needs to be high in WN councils.


The essential problem here, I believe, is the strong need, the strong desire, the strong craving, of the Far Right for affirmation and approval.  And when such affirmation and approval comes from someone with some sort of credentials, such as Jorjani’s academic status, then the craving becomes too intense to resist.  Caution is thrown to the winds, which is one major reason that the Far Right is so easily infiltrated, betrayed, hobbled by defectives, and infested with all sorts of bizarre flotsam and jetsam.  Further, since most in the Far Right lack any sort of formal scientific training whatsoever, they are unable to distinguish between what’s valuable and what’s not (hint: the HBD cult falls into the latter category).  Therefore, someone with a solid scientific background would have been less apt to become impressed with Jorjani’s esoteric techno-babble, and would have looked with prudent skepticism on taking anything else the fellow had to say seriously.

When oh when will the Alt Right darkness be dispelled from the land so that sane racial nationalism can come into the light?

Genetic Detection of Immigrants

Multilocus genotypes.

Detecting immigrants from the analysis of multilocus genotypes: paper here.  An old paper; of course, methodology has gone past this since; nevertheless, it deserves to be noted, for the idea that looking at multilocus genotypes allows for distinguishing genetic types even when “bean bag genetics” differentiation is low.  The basic premise; emphasis added:

Immigration is an important force shaping the social structure, evolution, and genetics of populations. A statistical method is presented that uses multilocus genotypes to identify individuals who are immigrants, or have recent immigrant ancestry. The method is appropriate for use with allozymes, microsatellites, or restriction fragment length polymorphisms (RFLPs) and assumes linkage equilibrium among loci. Potential applications include studies of dispersal among natural populations of animals and plants, human evolutionary studies, and typing zoo animals of unknown origin (for use in captive breeding programs). The method is illustrated by analyzing RFLP genotypes in samples of humans from Australian, Japanese, New Guinean, and Senegalese populations. The test has power to detect immigrant ancestors, for these data, up to two generations in the past even though the overall differentiation of allele frequencies among populations is low.

Classical theory in population genetics has focused on the long term effects of immigration on allele frequency distributions in semi-isolated populations, concentrating on the stationary distribution resulting from a balance between forces of immigration, genetic drift, and mutation (1–4). Less theory exists addressing the effect of recent immigration among populations with low levels of genetic differentiation. A theory describing the effects of immigration on the genetic composition of individuals in populations that are not at genetic equilibrium is needed to interpret much of the data being generated using current genetic techniques.

In this paper we consider the multilocus genotypes that result when individuals are immigrants, or have recent immigrant ancestry. We propose a test that allows recent immigrants to be identified on the basis of their multilocus genotypes; the test has considerable power for detecting immigrant individuals even when the overall level of genetic differentiation among populations is low. Molecular genetic techniques that allow multilocus genotypes to be described from single individuals are relatively new, and much of the information contained in these types of data is not fully exploited by estimators of long term gene flow that are currently available (5–7). We provide an example of an application of the method to restriction fragment length polymorphism (RFLP) genotypes from human populations; the method may also be applied to analyze multilocus allozyme and microsatellite data.

Also:

 At least three potentially misleading results may arise when applying the method considered here. First, the failure to reject the hypothesis that an individual was an immigrant, or descended from immigrants, may simply reflect the fact that the appropriate populations for comparison were not included in the analysis. Second, an individual might incorrectly appear to have originated in a particular population other than the one from which it was sampled. This might be due to similarities in allele frequencies, due to long-term gene flow, between that population and a third population from which the individual actually originated, but which was not included in the sample of populations. Third, the fact that many pairwise comparisons between populations are performed for each of a large number of individuals means that some individuals will appear to be immigrants purely by chance.

See this as well.  And also this.

In the late 1990s and early 2000s, there was some work going on in population genetics concerning multilocus genotypes.  A lot of good could have come from that if it was continued.  By an interesting coincidence, work on this subject essentially ended around the same time Der Movement and the HBDers went online talking about, and dissecting, population genetics studies.  It could be a coincidence, but given how most population geneticists are hysterical SJWs, maybe some of them decided not to investigate areas of their field that would focus attention on the great degree of actual ethnoracial differentiation that exists when genetic structure is taken into account.

More Problems With Fst

More Fst follies.

Commonly used measures such as FST and its derivatives based on gene identity probabilities do not reliably reflect difference, as they can be maximal when almost all populations are identical and very small when populations are completely distinct.

I’ve been saying this for years, citing other papers, noting the stupidity of people like J Richards of Majority Rights, who was breathlessly pontificating about minute differences in Fst values – a metric that cannot reliably determine genetic differentiation, since it is dependent upon the genetic variation within groups; it essentially is more about the apportioning of genetic variation within and between groups.

But all of this won’t stop Der Movement “activists” from using discredited metrics when it serves their purposes.

Preliminary Quantitation of Genetic Structure

Genetic differentiation increases with higher levels of genetic integration.

Ted Sallis

Introduction

I have finally performed some preliminary analyses of genetic structure – which I (predominantly) define as the association of alleles at different loci, an association that differs between individuals, between families, and between ethnies. The lack of genetic structure calculations is one of the two major genetics-based weaknesses of On Genetic Interests, the other being the reliance on Fst – which is not a real measure of genetic differentiation – rather than on genetic kinship data.  I’m not going to directly get into genetic kinship here (but note that the “genepool” level of analysis of DifferInt does give sort of a measure of genetic kinship, if the numbers are “crunched” appropriately), but since I’ve been discussing genetic structure for so long, here I present a minimal proof-of-principle of genetic structure quantitation with some human SNP data. This is not an optimal study, which needs to be performed by those with the time, expertise, databases, and computational resources do it well and efficiently (the same goes for global genetic kinship assays). Also, the methodology itself is not optimal and doesn’t cover the entirety of the genetic structure concept, but it does at least cover the underlying core principle.  

Methods

The DifferInt program dealing with genetic integration (1-3) – based on the work of Gillet and Gregorius on “genetic integration” (2) – was utilized, as well as some lists of human SNPs and publicly available HapMap population SNP frequency data. Thus, HapMap populations were analyzed. Europeans (EURO) included CEU (Utah residents of Northern and Western European ancestry) and TSI Tuscans, East Asians (EASIA) included CHB and CHD Chinese and JPT Japanese as well as a separate set of Chinese samples previously named HCB (instead of CHB), South Asians (SOUTH ASIAN) included GIH Gujarati Indians, Negroes (AFRICA) includes YRI Nigerians and ASW SE USA African ancestry and LKK and MKK Kenyans, and there also was Mex (MEXICAN: Mexican ancestry). I also produced a CEU-YRI hybrid by taking ~ ½ the alleles from CEU and ~ ½ from YRI – obviously, this is NOT how real admixture would take place (there would be mixing of both alleles at single loci as well as multiple loci, as well as other important differences consequent to sexual reproduction) – this is merely a very crude proof-of-principle.

Ideally, DifferInt populations would be ethnic groups and within each population there would be the individuals of that population, each with their distinct genotypes.  Due to the limitations of this study, the design was somewhat different and at a broader level of analysis. Here, the populations are continental population groups (races) and the “individuals’ within the populations are the ethnic groups themselves – actually the consensus genotypes at each locus for that ethnic group.  Therefore, the entire set of consensus genotypes for an ethnic group is what is being called a single “individual” here.  The consensus genotypes are such that for each gene locus, the most frequent genotype at that locus for the ethnic group was chosen.  So, for example, if a locus has AA – 0.2, AG – 0.3, GG – 0.5, then GG was the genotype chosen in this case.  This results in a “model” individual of a consensus ethnic genotype set.  This is sub-optimal for three related reasons: it doesn’t cover the intra-ethnic group variation; it doesn’t cover the frequency distributions of genotype per locus that are, of course, very important; and there are cases where the most frequent genotype is only slightly more frequent than the second most frequent genotype.  SNPs used are those for which I found genotype data for all twelve ethnic groups evaluated; the SNPs were taken from publicly available information sources.  51 SNPs of my initial list fit the requirements.

Whenever there were two genotypes listed as being of equal frequency at a given locus for any group, I chose the genotype that was the same as to the majority of the other groups.  In other words, I was conservative, and when there was a choice, I always chose the option that minimized differences between the greatest number of groups. That serves two purposes: first, to ensure that whatever differences that are observed are definitive, and not merely in part the result of cherry picking of genotypes; second, to obviate claims of a “racist agenda” in attempting to maximize group differences.  

The three levels of analysis are the genepool (i.e., individual allele “bean-bag” genetics), single locus genotypes (association of alleles at one gene locus – i.e., from the two homologous chromosomes), and, most importantly and consistent with my general basic idea about genetic structure, the multilocus genotypes (the association of all the different single locus genotypes together, how genetic variants at multiple loci are associated with each other).  

Each of these levels can be analyzed with “elementary genic differences” or “neglecting elementary genic differences.”  Considering elementary genic differences is an analysis of the number of individual genes that differ in the types of alleles; from the DifferInt manual: “The genic difference between genetic types at the same level of integration is basically determined by the number of their individual genes that differ in allelic type.”

Neglecting elementary genic differences is a discrete differentiation in which 0 is identity of all alleles of all loci and 1.0 being if the types “differ by at least one allele at one locus” – also from the manual: “By replacing the elementary genic difference between genetic types by the discrete difference, the measures…are based only on relative frequencies of the genetic types of the individuals in the population.”  Differentiation is higher when measured with the second, discrete analysis as compared to the first one. Keep in mind that in my crude model the “individuals” are consensus genotypes based on SNP frequency data from ethnic data sets; thus it would make sense that measuring the “discrete difference” would work best for such coarse-grained, “single-point” distinct and discrete pooled samples. Just measuring the numbers of individual genes that differ by allelic type (elementary genic differences) is not measuring (in my opinion) genetic structure (as I define it) per se; measuring the relative frequencies (neglecting elementary genic differences) is somewhat closer to my conception, so I used that for my analysis.

Differentiation is at a scale of 0 (exactly alike, no differentiation) to 1.0 (completely differentiated).

A major flaw in my study is using consensus genotypes, as opposed to actually listing all the individual samples or being able to use allele frequency data (which DifferInt does not do) since, ultimately, we want a range of ethny-specific genotypes characteristic of each group; it would not be a single, fixed consensus genotype.  Using fixed consensus genotypes also makes it even more imperative to look at the discrete DifferInt metrics that neglect the “elementary genic differences.”

Results

(w/o EGD = without [neglecting] elementary genic differences – see above)

Genepool:

EURO/EASIA: 0.3603, EURO/AFRICA: 0.4779, EURO/SOUTH ASIAN: 0.1765, EURO/MEXICAN: 0.1863, EASIAN/AFRICAN: 0.4240, EASIA:/SOUTH ASIAN: 0.2868, EASIA/MEXICAN: 0.2475, AFRICA/SOUTH ASIAN: 0.3922, AFRICA/MEXICAN: 0.4265, SOUTH ASIAN/MEXICAN: 0.2157

Note that the relative differentiation between groups at the genepool level is consistent with what is expected from standard population genetics studies.

Single-locus (w/o EGD):

EURO/EASIA: 0.5784, EURO/AFRICA: 0.8235, EURO/SOUTH ASIAN: 0.3039, EURO/MEXICAN: 0.3529, EASIAN/AFRICAN: 0.7026, EASIA:/SOUTH ASIAN: 0.4951, EASIA/MEXICAN: 0.4461, AFRICA/SOUTH ASIAN: 0.6765, AFRICA/MEXICAN: 0.7108, SOUTH ASIAN/MEXICAN: 0.4118

There is a considerable increase in differentiation considering association of alleles at single loci.  This makes sense, particularly since in many cases differences between ethnies are at the level of whether alleles at the relevant loci are homozygous or heterozygous (which would also have obvious implications for traits affected in a dominant/recessive fashion by the SNP differences, or by gene sequences linked to such differences).

Multiple-locus (w/o EGD):

Was 1.0000 for all comparisons: complete differentiation.

That is not surprising, as combinations of alleles are going to be relatively specific in an ethny-dependent fashion, and the more loci looked at the greater the proneness to distinctiveness.  Of course, with the relatively blunt instrument of combining DifferInt with consensus genotype data, one would expect complete differentiation (with enough loci looked at) at almost any level of genetic difference. The problem here is that while this is informative in a qualitative sense, it doesn’t help gauge relative differences in the degree of “complete differentiation.”  For example, the “complete differentiation” comparing Europeans and South Asians when considering multiple loci is expected to be less than that between, say, Europeans and Africans.  The closer two groups are at the genepool level, the less “complete differentiation” should be expected at the multiple-locus level.  Note that single-locus differences (above) track well with the genepool differences, so the same should be expected at the multiple-locus level if a more scalable metric could be designed.

This lack of scalability at the multiple-locus level may be due to DifferInt itself and/or the type of crude, consensus, discrete SNP data I am using  If it were possible to include allele frequency data – which could be done with this program by actually separately listing each individual with their own genotype rather than a consensus – that would likely help.  Or, if the program itself was changed so that one could just directly include the frequency data for each allelic type rather than having to actually enter each individual as such (although with the proper computational resources and programs I presume listing the individuals would be easy, but I formatted everything by hand, which was tedious).  Alternatively, one could look at relative genetic structure by looking at SNP permutations (not the same type of permutation analysis that DifferInt can do).  One could ask, to what degree are different permutations of allelic types more similar or different? That would be very informative for EGI purposes, if properly designed.

In any case, at least for the data used here, DifferInt was reasonably quantitatively scalable for genepool and single-locus analyses, while multiple-locus analyses were more qualitative.

Also let us look at the CEU/TSI intra-EURO comparison:

Genepool: 0.0392, Single-locus (w/o EGD): 0.0784, Multiple-locus (w/o EGD): 1.0000

Not surprisingly, the intra-European comparison exhibits little differentiation at the genepool level, which is doubled for single-locus integration.  Multiple-locus again shows complete differentiation.  On the one hand, this multiple-locus finding is expected, and makes sense, since the overall genetic structures of CEU and TSI are different.  However, we once again observe the problem of scalability.  EURO/AFRICAN and CEU/TSI both exhibit complete differentiation at the multiple-locus level, but the two are not obviously equivalent. The combinations of alleles at multiple loci for CEU vs TSI are going to be more similar than that for EURO vs. AFRICAN, even if both cases exhibit complete differentiation.  Again, this is a problem with the type of data I used as input, but I suspect as well it is a feature of the program itself. Consider that EURO/AFRICAN differentiation at the genepool level was already at the level of 0.4779 and the maximum possible is 1.0000.  So, it is obvious that the differences are not properly scalable, and likely would not be even with optimal data.  In a properly scalable analytical system, the maximal possible differentiation with multiple-locus analysis should be many-fold greater than that of genepool (and associated with the number of loci examined).  It is at the multiple-locus level that I find this program weakest, which is unfortunate since that is the most important level of analysis.


What the program considers is not perfectly aligned with my conception of genetic structure, but it is not orthogonal either.  There is considerable conceptual overlap; thus utilizing the program at least for a proof-of-principle demonstration is useful.  

The hybrid model (26 loci from CEU, 25 from YRI) is below.  This is, admittedly, highly artificial and not biologically realistic, but makes the general point (real admixture actually would be expected to cause even more differentiation than shown here):

Genepool: 

CEU/YRI: 0.5090, CEU/Hybrid: 0.2640, YRI/Hybrid: 0.2450

As CEU would be expected to be a bit more differentiated from YRI (and other Africans) as are TSI, the CEU/YRI genepool differentiation is slightly higher than the more general EURO/AFRICA, although another possibility is that the non-YRI Africans are closer to Europeans than are YRI. Hybrid values are in between CEU and YRI.

Single-locus (w/o EGD): 

CEU/YRI: 0.8341, CEU/Hybrid: 0.4510, YRI/Hybrid: 0.3922

This increases as expected.

Multiple-locus (w/o EGD): 1.0000 for all comparisons.

Complete differentiation, as expected, but again flawed by lack of scale.  The “complete differentiation”: between CEU/YRI would be expected to be larger than that between CEU/Hybrid, bit that cannot be distinguished in this analysis.  Nevertheless, this shows that merely increasing production of hybrid offspring cannot compensate for foregone parental kinship at the multiple-locus level.

Discussion

The findings (even with the limitations of the analysis) strongly support and extend the EGI concept; ethnies are more genetically differentiated at the level of higher genetic integration than at the mere “beanbag” genepool approach of individual alleles.

However, the gulf between family and ethny is also likely to be increased when genetic structure is taken into account, so one must be prudent in balancing investments.  However, keep in mind two things.  First, the typical ethny is larger than the typical extended family by five to eight orders of magnitude, so the ethny-ethny differences are of greater relative import than the family-ethny differences.  Second, differences will be expected to increase with genetic integration at every level of genetic interest – not only ethny-ethny and family-ethny, but also, for example, between self and family. But the family is needed for the self to have genetic continuity (although one can argue that the larger extended family could be dispensed with as long as the nuclear family is intact, or even that a human male just “spreads his seed” sans family structures), and one can argue that the family needs some sort of ethny, some sort of national culture, for secure familial genetic continuity.  Families mixed beyond wide racial lines are characterized by a deficit of genetic interests for the divergent members of such families, so the fact that those families are less dependent on national ethnies need not concern us, in any reasonable quest to maximize net genetic interests. So, in summary, when all is said and done, the findings here actually INCREASE the validity of ethnic genetic interests (with “ethnic” meaning ethny, which can include race). 

In the future, I may perform some additional analyses with this program and with these (and other) data; but the main point has already been established. Or, better yet, if I can think of other methods of analyzing the data to yield more useful results that would be more optimal.  It would be helpful if others, with more time and computational resources (including better data sets, can generate additional DifferInt data as well as designing better methods for assaying genetic structure (or finding other existing programs; I will search for such as well).

This was a crude analysis, yet very useful I think to “break the ice” on the topic, especially since I can’t help but notice that no one else has been doing it (insofar as I know).  Do you have the time and resources to do better?  Great: Go to it.


Final Conclusions


1. Although the analysis has limitations, it demonstrates that human genetic differentiation increases as genetic structure is considered.


2. A considerable amount of this increase in genetic differentiation is at the single-locus level, which I had not previously considered as being that important.


3. Most importantly, the multiple-locus analysis shows complete differentiation.


4. A problem in this analysis is with the scalability of the multiple-locus determinations, and the program is unable to evaluate the entire genetic structure concept; better methods, analyzed with better data, are required.  In the meantime, it would be useful to even just have more in-depth analyses using DifferInt.


5. When all is said and done, this analysis, even with its limitations, extends the EGI concept.


References

2. Gillet, E.M., Gregorius, H.-R. (2008) Measuring differentiation among populations at different levels of genetic integration. BMC Genetics 9, 60. http://dx.doi.org/10.1186/1471-2156-9-60

3. Gillet, E.M. (2013) DifferInt: Compositional differentiation among populations at three levels of genetic integration. Molecular Ecology Resources 13, 953-964. http://dx.doi.org/10. 1111/1755-0998.12145

Siberian Gene Flow into Europe

Raciology alert!

Something to read, emphasis added:

Siberia and Northwestern Russia are home to over 40 culturally and linguistically diverse indigenous ethnic groups, yet genetic variation and histories of peoples from this region are largely uncharacterized. We present deep whole-genome sequencing data (∼38×) from 28 individuals belonging to 14 distinct indigenous populations from that region. We combined these data sets with additional 32 modern-day and 46 ancient human genomes to reconstruct genetic histories of several indigenous Northern Eurasian populations. We found that Siberian and East Asian populations shared 38% of their ancestry with a 45,000-yr-old Ust’-Ishim individual who was previously believed to have no modern-day descendants. Western Siberians trace 57% of their ancestry to ancient North Eurasians, represented by the 24,000-yr-old Siberian Mal’ta boy MA-1. Eastern Siberian populations formed a distinct sublineage that separated from other East Asian populations ∼10,000 yr ago. In addition, we uncovered admixtures between Siberians and Eastern European hunter-gatherers from Samara, Karelia, Hungary, and Sweden (from 8000–6600 yr ago); Yamnaya people (5300–4700 yr ago); and modern-day Northeastern Europeans. Our results provide new insights into genetic histories of Siberian and Northeastern European populations and evidence of ancient gene flow from Siberia into Europe.

Perhaps some of that leaked into the David Bromstad and Bjork family lines. Actually, Bromstad would seem to be a solid candidate to be recruited by HopeNotHate for their next Alt Right infiltration.  Infiltrate the Nutzis as well.  And if the Alt Wrong mistake him for a high-IQ Chinaman, so much the better, although I suspect the Alt Wrong and the Nutzis, with their yellow fever, would be more prone to accept Bjork as an infiltrator.


In any case, the idea of absolute racial purity takes another hit, and the unscientific “hypodescent” paradigm, as applied to Old World demographics, is again shown to be untenable, unless all of Northeastern Europe is now to be considered “Asian.”

Rasse und Der Movement in Der News

Odds and ends.

This podcast was a lot better than the usual Alt Right material, reasonably professional and lacking in juvenile jackassery.  That’s probably because it is a European (Swedish) production, cohosted by an American (newly minted) lawyer.  Hopefully, this quality will continue.

When reading this, keep in mind that the “federal government” these days means Donald J. Trump. Dem dere refugees keep on coming though, don’t they? Of, we’ll be told that his “travel ban” is getting blocked.  Well, how about keeping the damn “refugees” in some sort of prison or concentration camp, instead of dumping them onto the good people of Tennessee?

But, but, but…I thought we were all exactly the same?  Emphasis added:

Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes.

Objective, non-political, ethnically disinterested anthropological and genetic analysis at this website, no doubt.

As far as Catalonia goes, my opinion is if they want independence, give it to them, but it’s really an irrelevant issue in the last analysis.  What are they going to do with their vaunted independence and ethnonationalist sovereignty?  Import more alien immigrants and “refugees?”