Category: genetic variation

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.

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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

West Eurasian PCA

Genetic data.

Following up on this take a look at Fig. S6 here.

Major take-home points:

1. It is remarkable how European genetic variation mirrors geography (a la Novembre); one can discern the crude outlines of the European continent from European samples of the PCA plot. This (again) suggests that intra-European genetic variation is most clinal and heavily shaped by geographic factors and those population processes affected by geography.  If Der Movement’s fantasies were instead correct, one would see a sharp discordance between the plot locations of (some of) the samples and the geographical association

2. Jews and some Turks (presumably the more “Europid” of these) are located in between the southernmost extreme of Europe’s genetic variation and samples from the Middle/Near East. This is consistent with Jews being a mix of Middle/Near Eastern and predominantly (Southern European) European ancestry, and with a common Neolithic major component between Southern Europe and Anatolia, as well as the fact that some Turks are heavily admixed with European ancestry (e.g., Greek/Balkan).

3. The wonderfully “Aryan” Iranians are genetically distinct from all types of European stocks.

Of course, in the end, regarding biopolitics, what matters is genetic kinship, which should be assayed directly; nevertheless, these data can be of some racial-historical interest.

Genetic Variation and Environmental Interactions

Genetic variation and environment.

Of interest, re: genetics, culture, and race, I note this methodology paper:

Identifying interactions between genetics and the environment (GxE) remains challenging. We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on associations between environmental variables and allele-specific expression. Combining whole-blood RNA-seq with extensive environmental annotations collected from 922 human individuals, we identified 35 GxE interactions, compared with only four using standard GxE interaction testing. EAGLE provides new opportunities for researchers to identify GxE interactions using functional genomic data.                    

Basic findings were that environmental risk factors (e.g., substance abuse, exercise, BMI) can interact with genetic variation and affect gene expression. But the effects were modest, these were not large influences compared to other possible (e.g., additive) effects, and may have been affected by confounding factors (a possible problem when probing interactions for which there can be many variables).  In addition, some of the observed effects may have been in part epigenetic, presumably modifications due to environmental factors, rather than interactions between those factors and gene sequence variation itself.

On the one hand, the effects, being modest, cannot plausibly be invoked by anti-genetic determinists to prop up environment as the primary factor affecting gene expression (and, hence, eventual phenotype).  On the other hand, effects were observed, and these cannot be dismissed.  Of interest would be effects and interactions due to environmental factors other than those cited above.

Can culture, through its many manifestations, shaping the environment, interact with genetic variation to affect gene expression and, thus, phenotypic outcomes?  Would different ethnic and racial groups, characterized by group-specific genetic variation, exhibit variable gene expression when immersed in the same cultural environment?  Conversely, would genetically similar individuals and groups exhibit altered gene expression when placed in radically different cultural environments?  

And this goes beyond the more fundamental observation that genes affect culture (through the different phenotypes of culture creators, maintainers, or destroyers) and, conversely, culture can actually affect genetic variation itself (rather than just interact with it) by exerting selective pressure favoring one genotype over another.  Gene-culture cross-talk, if you will. See this old TOQ paper I wrote some time ago for more on that topic. Also, epigenetic effects, mentioned above, are another way in which culture can affect gene expression, but not to the extent, or in the manner, than the anti-determinists fervently hope.  The basic foundation for all of this is genetic variation; there is no evading that inconvenient (for some people) truth.

In summary, all of this bolsters the importance of genetic variation and, hence, genetic interests.  It also shows how reckless the globalists are in their indiscriminate mixing of genes and cultures (in Western nations).

Failure of Fst/Gst

Population genetics.

Both “movement” fetishists as well as anti-racist liars like to misuse Fst/Gst in genetic distance discussions (*) to promote their respective agendas.  Unfortunately for them, Fst/Gst is not really a (direct) measure of genetic distance, and particularly fails even as an indirect proxy when comparing populations that exhibit different levels of heterozygosity (e.g., human ethnies) and/or when considering loci with more than two allele variants.  To have the ill-informed trying to parse differences of, say, Fst/Gst = 0.0060 vs. 0.0065 and trying to make relevant conclusions from that is laughable.  The following are a small sampling of links to cite the next time some idiot tries to play such games (emphasis added):

See here.

See also here:

Likewise, when diversity is equated with heterozygosity, standard similarity measures formed by taking the ratio of mean within-subpopulation diversity to total diversity necessarily approach unity when diversity is high, even if the subpopulations are completely dissimilar (no shared alleles). None of these measures can be interpreted as measures of differentiation or similarity. 

At Wikipedia:

Also, strictly speaking FST is not a genetic distance, as it does not satisfy the triangle inequality. As a consequence new tools for measuring genetic differentiation continue being developed.

And this article here:

One underutilized approach is the coupling of indirect metrics of gene flow (e.g. F-statistics, Dest_Chao) with more direct measures such as kinship or parentage analyses (e.g. Loiselle et al. 1995; Selkoe et al. 2006; Buston et al. 2009; Christie et al. 2010; Palsbøll et al. 2010). Broadly speaking, kinship analyses provide an index of the relative relatedness of all genotyped individuals in a data set, and parentage is a distinct case of kinship whereby the most likely parents of individual juveniles are identified (Vekemans & Hardy 2004; Jones & Arden 2003; reviewed in Blouin 2003; Jones et al. 2010). Kinship coefficients (also known as coefficients of coancestry) are widely interpreted as the probability of identity by descent of the genes, but they are more properly defined as ‘ratios of differences of probabilities of identity in state’ (Hardy & Vekemans 2002, p. 23) from homologous genes sampled randomly from each pair of individuals (Hardy & Vekemans 2002; Rousset 2002; Blouin 2003; Vekemans & Hardy 2004).

By comparison, F-statistics and Dest_Chao are often blind to the relatedness of individuals; different population samples with the same kinship structure can have very different levels of genetic differentiation among them and vice versa.

*True, Salter used Fst in On Genetic Interests, but only because there was no other data available for that purpose at that time.  And Salter makes clear in the book that the proper approach would be to use data from global assays of genetic kinship which did not (and still do not) exist for human ethnies.  It is interesting that population geneticists and ecologists will calculate genetic kinship for plant and non-human animal species, but are either too lazy or politically-motivated to do so for human population groups. However, anecdotal evidence from the genetic kinship data that companies such as 23andme and DeCode used to present to their customers suggest that human genetic kinship findings would not be to the liking of either the fetishists or the anti-racists. 

Behold the Parasite

Jews and net EGI.

Of course, Jews are neither wasps nor fungi, nor do they stand in the same relationship to us as do the parasitic wasps and fungi to their hosts just mentioned. Jews are either a closely related species to us, or are a subspecies of the same species. In either case, as repulsive as are parasites, and as loathe as we may be to admit it, Jews are genetically quite similar to us and are in fact extensively cross-bred with us. Doesn’t this effectively rule out their being biological parasites upon us? 

No, not at all. In fact, it makes it even more likely. In 1909, an Italian entomologist named Carlo Emery discovered what is now known as Emery’s Rule. The rule states that that social parasites (that is, parasites of social species — and Homo sapiens is certainly a social species) tend to be parasites of species or genera to which they are closely related. Matt Johnston of the University of Arizona states that, “One explanation for the apparently close relationship between social parasites and their hosts is that in order to get past the hosts’ defenses, the parasite needs to have evolved communication systems similar to the host. This may be more likely if the two share a close evolutionary history.”

This is why I talk about the importance of net genetic interests (not that anyone listens). If all you care about are gross genetic interests, then you would simply measure the genetic distances involved, calculate the child equivalents, and conclude that since Jews are genetically quite similar to, and cross-bred with, Europeans, then their presence in Western societies does not exert much of an EGI cost at all. However, Jews are a highly specialized, evolved parasitic ethny with interests that are incompatible with that of Europeans, and as such Jewish behavior exerts a significant fitness cost on Europeans, so that the net effect on European EGI is enormous. Therefore, net EGI takes into account all factors that affect the genetic interests of an ethny, and provides a final tally of the outcome. If Jews promote mass alien immigration, desegregation, miscegenation, and overall societal degeneration (that imposes severe costs on, among other things, family stability and reproductive success), then their presence is extremely destructive to host EGI regardless of what the relative genetic distances are between Jews and White Gentiles. Further, if Jews consider themselves a different group than are White Gentiles, and pursue a group evolutionary strategy of their own, they would not care that their behavior damages the interests of an ethny relatively genetically similar to their own. Of course, Identity is based upon more than just genetic distances, and issues of Identity, by influencing behavior, directly affect genetic interests.