Category: genetic integration

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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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

Sallis Agrees With the Alt Right on Something

Some good sense.

I essentially agree with and endorse this article, with some caveats, and it should be read together with this piece I wrote several years ago.

The AltRight.com article is reasonably sound, although one caveat is that if one approaches these tests with a sense of realism with respect to their limitations – limitations spelled out in my Counter-Currents piece – then getting tested may not be a bad idea.  Having the raw data could be useful if you can find someone who can do a genetic kinship analysis with it. But taking the details of the data literally – thinking that there’s a real difference between 100% A, 0% B vs. 99.3% A, 0.7% B, for example – is ludicrous. I would take even the 90% confidence readings with a large grain of salt, and the 50% confidence readings are so absurd that the salt grain needs to be the size of the iceberg that sunk the Titanic.

The other caveat to the article is that the comments section is mixed; some comments are useful, some are asinine, so caveat emptor.

There are two basic questions here.

1. Is 23andMe a good test?

2. Assuming an ancestry test is good, is it worthwhile?

To which I answer: 1) No and 2) Maybe, depending on context.

In an absolute sense, 23andMe is superior to DNAPrint’s tests from ~15 years ago; in a relative sense – comparing each test to the “state of  the art” available at the time – it really isn’t better at all.  With the level of understanding and methodology we have today, coupled with a prudent interpretation of the data, one could do much better.

What if a test was sound?  Well, sure, it can be interesting, but I’ll repeat something I’ve been hammering home here over the past few years – the only biopolitically relevant genetic metric is genetic kinship (at all levels of genetic integration).  If one can measure that, then it is useful. All else can be interesting, but not directly important from an EGI standpoint.

And if people are going to hysterically obsess over sub-fractional admixture percentages then this is missing the forest for the trees.

A Brief Popgen Comment

Unpicked fruit.

I have previously discussed reasons why I rarely discuss population genetics anymore, the most important of which is that much of it is biopolitically irrelevant – only genetic kinship really matters. A secondary reason is that there isn’t much going on these days of great interest, particularly in fields of study such as European and Jewish population genetics. Virtually all of the “low hanging fruit” has been picked with respect to the kinds of things (politically motivated) population geneticists want to study.  Some interesting new papers may come out, but we can all notice that those sorts of things are much less frequent now than they were, say, 10 years or so ago.

Of course there are important things – higher hanging fruit to be picked – that can be studied; for example, global assays of genetic kinship or the application of genetic integration analysis to human data, to ascertain how genetic distance is changed when genetic structure is taken into account.

But population geneticists show no inclination to study that, likely because it is virtually certain the findings would support and reinforce “racist” White nationalist narratives. And as we know, leftist academics are allergic to the truth.   They won’t make the effort to reach up and pick that higher hanging fruit.

Epistatic EGIs

Amplifying the importance of EGI.

How do the papers on social epistasis and social genetic effects affect our understanding of EGI?

This would greatly increase the importance of EGI.  Not only do we need to be concerned with differences in gene frequencies and with genetic structure/integration (this latter concern a significant increase in genetic interest compared to the original formulation) between individuals and groups, but now we need to understand and, if possible, quantify the interests inherent in how these genetic difference interact epistatically in a social genetic fashion.  Thus, not only do we have to compare and contrast distinctive genetic information between, say, groups A,B, and C but we need to understand how the genepools of A,B, and C actually dynamically interact with each other – as described in the social epistasis and social genetic effects papers – to affect the fitness (and hence genetic interests) of these groups. This represents an enormous increase in the importance and impact of genetic interests, and one can speculate that these interactive networks of genes would represent genetic interests that would increase exponentially, and not merely linearly, with increasing genetic distance, given that each unit of distance affects a wide array of overlapping epistatic interactions.  Not only is the original formulation a tip of the iceberg compared to considerations of genetic structure/integration, but even this latter concern is a tip of the iceberg compared to the possible full ramifications of how genetically distinct populations can interact, influencing fitness and hence ultimate interests.


Thus, three levels of genetic interests:


1. The original version involving gene frequencies in isolation (“beanbag genetics”).


2. Genetic integration/structure.


3. Social genetic effects including social epistasis.

More analysis to come in future posts.

More on the Ethnotype

More thoughts.

A correspondent has shown interest in my ethnotype idea and has made two major suggestions, one I mostly agree with and the other I have some reservations about but partially agree with.
First, the suggestion was made that the ethnotype is best conceived as a normal distribution. Thus, while all the possible (and, of course, existing) genotypes of an ethny contribute to the ethnotype, some are more possible, or more frequent, than others.  Therefore, one will observe a cluster of more common genotypes defining the central or median part of the normal distribution curve, with outliers (the y axis is of course frequency, the x axis may be defined in various ways; perhaps a 3-D rather than 2-D distribution is best; in any case the genotypes making up the ethnotype can be distributed both relative to each other and relative to those of other ethnies).
This has certain advantages.  One can observe how the central tendency varies with time.  If one wanted, one could break up the genotypes to look at various traits (see second point below, but this in my opinion deviates from what I’m considering here, which is the entire genotype as an integrated genetic unit.  Another important advantage is how it handles the question of miscegenation and assimilation, including the assimilation of hybrids (this assumes that hybrids would be assimilated and not ejected from the population, which could be favored).  Consider mixing across wide racial lines.  Assume small-scale mixing that affects only a small fraction of the population.  This would increase the range of possible, and actual, genotypes, but would not really alter the mass of more central genotypes that make up the median ethnotype.
On the other hand, more massive miscegenation, assimilation, etc., particularly with widely divergent populations, would indeed shift the entire normal distribution and alter the central/median types, indicative of more serious effects on genetic interests.
In general, this may not be a bad idea.
The second idea, of which I am less enthusiastic, is to tie the ethnotypes to phenotypes, stressing functional genes (and, as above, possibly dividing the ethnotypes, if desired, into more specific traits).  Now, this confuses my use of the ethnotype concept – that is genetic – with the more anthropological phenotypic view.  I’m not defining ethnotype to describe a racial phenotype or set of phenotypes.  I’m using it to express the reality that while individual phenotypes are ephemeral, the range of possible genotypes of an ethny can be reasonably stable over long periods of evolutionary time.  And by genotype, I consider the entire genetic integration of individuals of a population, not individual alleles in isolation.  Further, while I am willing to grant (true) functional genes a higher per-allele value than (true) non-functional genes (since the functional ones influence their own replication, I do not – for reasons I have discussed many times – relegate non-functional genes to irrelevance.  It is the entire distinctive genome that contributes to genetic interests.  One must be careful that a sole focus on form, function, and phenotype does not lead to a John Ray-like memetic attitude that large scale miscegenation and genetic replacement is acceptable as long as certain phenotypic traits are maintained (e.g., “White-looking” heavily admixed mestizos of Latin America).
Again, a focus on form, function, and phenotype (while it has its relevance in particular contexts) deviates from the objective of my ethnotype definition: to capture the reality of a relatively stable set of (genetically integrated) genotypes (genetic structures) that define an ethny and its genetic interests, and to distinguish the ethnotype from an individual and unique “one-shot” genotype.

The Ethnotype

Introducing a new genetic concept.

The following I see as extremely important.
This paper discusses the “beanbag” approach to population genetics.

In a sexual population, each genotype is unique, never to recur. The life expectancy of a genotype is a single generation. In contrast, the population of genes endures. The quantities that are followed, in mathematical theories or in observations, are allele frequencies. The geneticist knows that at any desired time, the genotype frequencies can be obtained by the simple binomial rule.


Now, herein lies a problem I see with mainstream population geneticists (and other, related scientists) blinded perhaps by anti-racist political correctness.  It’s true than an exact, specific genotype is unique (except for identical twins) and does not recur.  The error – the fundamentalerror – these people make is not admitting that some genotypes are more similar than to others.  It’s not just a comparison between a genepool and a genotype, at opposite ends of the genetic integration scales.  There are levels in between the general population of genes at one end and the unique, never-to-be-reproduced genotype at the other end.
I therefore name one such level, which is of importance to the preservationist viewpoint: the ethnotype. 
An ethnotype is a range of possible genotypes that characterizes populations that have specific genepools.  An ethnotype is not as specific as a genotype, and ethnotype can be found in the many millions, and is stable across evolutionary time.  Otzi the Iceman and contemporary Europeans can be said to belong to the same broad ethnotype.  Ethnotypes can be considered to total set of possible genotypes produced by a genepool, the total set of possible allele combinations, and that will be different from that produced by another genepool.
Ethnotypes can be broader or narrower.  Europeans vs. East Asians are examples of two ethnotypes, each consisting of specific combinations of alleles from their respective genepools (ethnotypes, like genotypes, are emergent properties of genepools, and the frequencies of ethnotypes should be calculable from genepool allele frequencies as are genotypes).  One can go narrower: different types of Europeans (North, South, East, West, Central, etc.) can be thought of as being represented by a specific ethnotype or set of ethnotypes, the same for East Asians or any other population group.
Thus, while the forces of independent assortment and recombination at meiosis, combined with genetic drift and various forms of selection, insure that exact genotypes will never again be reproduced, ethnotypes will continue to be reproduced.  The European genepool may produce Isaac Newton or Michelangelo or Tesla only once, but can produce allele combinations reasonably similar to those individuals and similar to Europeans worldwide over and over again, as long as the genepool says intact.  Of course, over time, with drift and selection, the genepool changes, so that the possible ranges of ethnotypes and genotypes produced from the genepool will be altered, but these ranges will be more similar than to alien peoples. 
Therefore, the European genepool of 1016 AD had the potential to produce a different set of ethnotypes and genotypes than the European genepool of 2016; nevertheless, both are much more similar to each other than to, say, East Asian genepools of any date picked.  Again, genotypes are one-shot affairs, while ethnotypes are more stable over time, since they are a less specific, and more generalized, arrangement of genepool alleles.
The advantages of considering comparisons at the ethnotype level are that (a) this is the level that has the most practical significance (including selection) at the population level, as populations are collections of genotypes, not a soup of randomized alleles floating around; (b) given that genetic distance increases with increasing genetic integration and that the ethnotype is at a higher level than at the genepool, then considering the genetic structure inherent in the ethnotype will increase the level of genetic interests; and (c) while not as unique as the genotype, the ethnotype is unique in cross-population comparisons AND has the advantage of being preservable.  Thus, while genotype can be preserved only by cloning, ethnotypes can be preserved, to a reasonable degree over time, by following the precepts of Salterian Universal Nationalism.  Preserving the ethnotype can be done today, via acts of political will and social convention, no new technology needs be implemented.  Further, while “beanbag” genetics will tell you that miscegenation in some cases (at least at the parental level) can be compensated by increased reproduction and replication of the individual alleles, ethnotypes are specific to particular ethny genepool – no number of hybrids could reproduce the genetic structure of ethnotypes; hence, the ethnotype concept better represents the preservationist imperative.  I may add that ethnotypes better represent an ethny’s phenotypes as well, since phenotype is produced not by individual alleles working alone, but by the interaction of the whole genome with the environment.

Further, the ethnotype concept is compatible with eugenics, since, unlike the genotype, we are not talking about a fixed, perfectly unique set of genetics, but a more flexible range of genetic types that can still exhibit similarity over time even with some degree of substitution if alleles (again, consider the similarity of Otzi to today’s Europeans).