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A few weeks ago people were arguing about the utility of the model based clustering packages which produce intuitive bar plots which break down individual and population percentages. To understand the fundamental basis of these packages I’ll refer you to the original Pritchard et al. paper. As you probably know at this point one of the major parameters of the packages is the K value, which refers to the number of populations which are going to be assumed as the constituents of the genetic variation. A key point is that those who use the packages are forcing the variation to fit a particular model. You can take the data for Icelanders, to pick an example, and find K = 100. It will be produce results, but I suspect you’ll intuit that this really isn’t the best model in terms of fitting reality. Similarly, you can take a population of Northern Europeans, West Africans, and East Asians, and set K = 2. This will likely separate the Eurasians from the Africans, as that’s the natural phylogenetic affinity. But K = 3 is probably a better fit to the data. By this, I mean that Northern Europeans and East Asians are not, and have not been for a long time, random mating populations. K = 3 reflects this reality.

So far this is intuitive. Is there a formal way to check this? Yes. A variety. Structure outputs log likelihoods for each K. Admixture gives you cross-validation errors. For a full treatment of how Admixture estimates cross-validation error see Alexander et al. An intuitive way to think about how you should interpret these values is that they are giving you a sense of where you are trying to squeeze too many K’s out of the data set. Admixture’s cross-validation value has a simple interpretation, look for the lowest point on the graph.

Going to back to the HGDP data set I wanted to know where that point on the scale of K’s was. Looking over the populations I assumed more than 5, but likely less than 20. That wide range tells you that I don’t honestly have a good intuition (some distinct populations are going to be hard to separate in pooled data sets because there hasn’t been much time since divergence, or they are not really genetically separate populations).


The first thing I did was prep the HGDP data bit in terms of quality with Plink. I filtered to SNPs with minor allele frequencies greater than 0.05, to get variants which might be informative on the interpopulation scale. Then I removed SNPs which were missing in more than 1% of individuals. Finally, I also LD pruned the SNPs (basically thinning the markers so that I got rid of variants which weren’t adding more information because they were near other SNPs). Additionally I also removed individuals which were very closely related to others in the data set. This resulted in a data set of 1,024 individuals and 116,840 SNPs.

Then I ran Admixture 20 times with default five-fold cross-validation from K = 2 to K = 20. Here’s the result in a scatterplot:

cverrorbig

You can’t see some of the points because the variation in error was so small at the lower K’s. It is clear that a few K’s do not accurately capture the variation in the HGDP data set. To put it different there aren’t four distinct randomly mating populations in the HGDP data set (K = 4).

Here’s a zoom in.

cverrorZoom

These results make it clear there’s a ‘valley’ across the interval K = 11 to K = 16, with the lowest mean cross-validation error at K = 16. Not only does K = 16 have the lowest cross-validation error, but below K = 4 it has the lowest variation in cross-validation error as well. This does not mean that there are 16 natural populations which best defines the world’s genetic variation. For why this is not so I’ll point you to Daniel Falush’s post What did we learn from Rosenberg et al. 2002, actually?, which highlights some other major dependencies of Structure-like model based clustering.

But, a complementary point is that the number of K’s within the data are not arbitrary and subjective. And that’s because human genetic variation exhibits geographic structure consistently across many forms of vizualization and inference. A second more tendentious point I would also like to add is that the new generation of population structure inference methodologies are pointing to the likelihood that human genetic variation did not emerge through isolation by distance dynamics across clinal gradients.

Addendum: I’m merging my 20 runs, starting with K = 16. But that’s going to take time. I’m also running K = 2 to K = 20 with a different data set, which expands beyond the HGDP, with 20 replicates.

 
• Category: Science • Tags: Genomics, HGDP, Race, Structure 
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Back in the 1990s there was a lot of controversy around the Human Genome Diversity Project. In fact there were whole books devoted to the sociology of the project. Though on some of the details critics of the project may have had a point, their overall aim of stalling scientific inquiry in this area failed in totality. A few years ago a team out of the University of Chicago even produced a web browser so you can explore the data yourself. To my knowledge this hasn’t resulted in massive genocidal action against indigenous peoples; the human race doesn’t seem to need any scientific backing for that, alas.

But, if I was a Lefty the-man-is-racist type I think I might assert that the chips which were used to generate the 600,000 markers for the HGDP public data set are racist! I’m not one of those types, so what I really am concerned about is ascertainment bias. From what I have heard many of the SNP chips floating around today are looking for variants found in Europeans most often. That’s because so many study populations in medical genetics are of European descent. This is not a total deal breaker, a lot of European variation is useful in understanding world wide patterns of variation. But ultimately it’s not optimal.


Today we take a major step in changing this. Nick Patterson sent me a nice heads up on a project out of David Reich’s lab. Using the full genomes of disparate human populations, as well as other primates, and archaic humans, the group has collaborated with Affymetrix to produce a panel which is much more finely tuned toward the concerns of those interested in the demographic and adaptive history of human populations.

You can find the files here, at ftp://ftp.cephb.fr/hgdp_supp10/. In particular see the technical document. When I get some time I’ll be playing with this, rest assured.

Finally, Nick adds an important caution:

We hope that this array, and the HGDP data we have produced will be a major resource for population genetic studies. The data are undoubtedly complicated, (13 different ascertainment schemes (!)) and users should read the technical documentation, and especially the short readme file. In particular note that the ancient DNA alleles are not high quality (especially the Neandertal) and there are numerous potential traps in analysis

(Republished from Discover/GNXP by permission of author or representative)
 
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I have noted a few times that one thing you have to be careful about in two dimensional plots which show genetic variance is that the dimensions in which the data are projected upon are often generated from the data itself. So adding more data can change the spatial relationships of previous data points. Additionally, in 23andMe’s global similarity advanced plot you are projected onto the dimensions generated from the HGDP data set. There are some practical reasons for this. First, it’s computationally intensive to recalculate components of variance every time someone is added to the data set. Second, it isn’t as if the ethnic identity of any given individual is validated. What would you do if an alien sent in a kit and spuriously put “French” as their ancestry?

So, in reply to this comment: “Let me rephrase: is there any difference when you switch to the world-wide plot? I imagine not, or you would’ve mentioned it.” Actually, there is a slight difference. Below on the right you have a “world view,” with my position being marked with green, and on the left a “zoom in” for Central/South Asia in the HGDP data set.


Because of the “business” of the plot it is hard to see the difference. But when I wasn’t “sharing” genes with people this is what you saw:

1) There is a definite gap between a Central Asian Hazara/Uyghur cluster and a South Asian one which consists of the Pakistani groups.

2) In the Central/South Asia zoom I’m in the gap between the two clusters, about 1/3 of the way toward the Central Asian cluster away from the South Asian cluster (the next closest individual shifted in that direction who isn’t a family member is Bangladeshi).

3) In contrast, in the world view I’m on the edge of the Central Asian cluster, toward the South Asian one, but definitely separated by a clean gap from it.

You can see some generalized differences between the two plots. The Central/South Asia view has a major linear cluster, with the Kalash a distinctive outgroup. In the world view this is not so, rather, you have a group of Pakistanis with non-trivial African admixture shifted in that direction (mostly Makrani, but one of the Sindhis in the HGDP data set seems to be a brownlatto!). Since there isn’t much African variance in the South Asian zoom aside from what the admixed individuals bring to the table naturally it doesn’t shake out as one of the two top dimensions. So what’s going on with me? I don’t have a good hypothesis, but I suspect that my likely Southeast Asian ancestry shifted me further toward the Asian cluster in the world view. There are some groups very closely related to the Burmese in the HGDP (e.g., Naxi) which are in the world view, and, naturally not in the Central/South Asia zoom. When you break ancestry into “European” and “Asian” components then the Hazara/Uyghur cluster is an OK substitute (both are hybrids, with “European” and “Asian” ancestry in about equal proportions), but this is actually a first approximation. These two groups have more “northern” Asian ancestry, while mine is more “southern.” Because of their inclusion in the Central/South Asia cluster the west-east dimension in Eurasia is constructed from more northern East Asian populations, which might underestimate my East Asian element.

There’s actually a much better example than me though who I’m sharing genes with. This individual is an ethnic Persian. Note that in the world view they seem to be on the margins of the European cluster, verging toward the Central/South Asia group. But when you do the Central/South Asia zoom view, they’re in that cluster! Note the very different positions. Their “neighbor” in the zoom view is totally different from their neighbor in the world view:

My argument for why I’m more “Asian” in the world view is that the world view has Asian groups to which I am closer, which are excluded in my zoom view. A much more extreme case seems to be happening with this Persian individual, whose family is from northern Iran and has an oral history of Russian ancestry on one of his lineages.

This is the sort of reason why I assume any reader who points to a paper and a plot and asserts that “this proves X” is somewhat cognitively challenged. The patterns in PCA aren’t necessarily arbitrary. But, they do need to be interpreted with care. One set of results isn’t dispositive of any given position in a debate, at least least until you get to the ridiculous boundary conditions (in some ways, I think of a lot of genetic data visualization like I think of regression. It’s how people use/interpret it that is problematic, not the method itself).

Finally, doesn’t it seem ridiculous to you that South Asians are being projected onto a plot where the dimensions are generated from liminal populations! Imagine, if you will, that Europeans were projected onto a plot generated from the variance of Finnic and Slavic groups only. That’s a good analogy. The Pakistani groups in the HGDP data set are not good representatives of South Asian genetic variation, because they’re shifted to the margins of the distribution. That’s one reason that the Harappa Ancestry Project is so needful (and why if you just got your v3 results and are Iranian, Tibetan, Burmese, or South Asian, you should send it in. And v2 folks as well!).

(Republished from Discover/GNXP by permission of author or representative)
 
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Two of the main avenues of research which I track rather closely in this space are genome-wide association studies (GWAS), which attempt to establish a connection between a trait/disease and particular genetic markers, and inquiries into the evolutionary parameters which shape the structure of variation within the human genome. Often with specific relation to a particular trait/disease. By evolutionary parameters I mean stochastic and deterministic forces; mutation, migration, random drift, and natural selection. These two angles are obviously connected. Both focus on phenomena which are proximate in relation to the broader evolutionary principle: the ultimate raison d’être, replication. Stochastic forces such as random genetic drift reflect the error of sampling of genes from generation to generation during the process of reproduction, while adaptation through natural selection is an outcome of the variation of reproductive fitness as a function of variation of heritable traits. Both of these forces have been implicated in diseases and traits which come under the purview of GWAS (and linkage mapping).

GWAS are regularly in the news because of their relevance in identifying the causal genetic factors for specific diseases. For example, schizophrenia. But they can be useful in a non-disease context as well. Human pigmentation is a character whose genetic architecture has been well elucidated thanks to a host of recent association studies. The common disease-common variant has yielded spectacular results for pigmentation; it does seem a few common variants are responsible for most of the variation on this trait. But this has been the exception rather than the rule.

One reason for this disjunction between the promise of GWAS and the concrete tangible outcomes is that many traits/diseases of interest may be polygenic and quantitative. This implies that variation in phenotype is controlled by variation across many genes, and, that the variation itself exhibits gradual continuity (a continuity which can be modeled as a normal distribution of values). The power of GWAS to detect correlated variation across genes and traits of small marginal effect is obviously limited. In contrast, it seems that about half a dozen genes can explain most of the between population variation in pigmentation. One SNP is able to account for 25-40% of the difference in shade between Europeans and Africans. This SNP is fixed in Europeans, nearly absent in Africans and East Asians, and segregating in both ancestral and derived variants in groups such as South Asians and African Americans. In contrast, though traits such as schizophrenia and height are substantially heritable, much of the variation at the population level of the trait is explainable by variation in genes. The effect size at any given locus may be small, or the variation may be accumulated through the sum of larger effect variants of low frequency. In other words, many common variants of small effect, or numerous distinctive rare variants of large effect.


ResearchBlogging.org These nuances of genetic architecture are not irrelevant to the possible evolutionary arc of the traits in question. One model of the adaptation leading to the high frequency of a trait or disease is that a novel mutation rapidly “sweeps” to fixation, or nearly to fixation. In other words, it shifts from nearly ~0% to nearly ~100% frequency in the population of alleles at that locus, driven by positive selection. This sort of rapid “hard sweep” would also result in “hitchhiking” of associated variants in the genomic regions adjacent to the originally favored mutant, producing regions of high linkage disequilibrium in the genome and haplotype blocks of associated alleles across loci. Such a model does seem possible in the case of some of the variants which are responsible for diversity of pigmentation. But this neat dovetailing between the strong association of a few variants with trait variance, and signatures of positive selection being driven by adaptation, is not so easy to come by in many instances.

There are other evolutionary possibilities in terms of what could drive a high frequency of particular alleles. Population bottlenecks and inbreeding can crank up the frequency of a variant simply through chance. This may be the origin of many traits and diseases expressed recessively or in quasi-Mendelian form which run in specific populations. Let’s set such stochastic possibilities to the side for now. The well of natural selection is not quite tapped out simply by models of positive selection drawing upon singular new mutations. Another model is that of “soft sweeps” operating upon standing genetic variation. Consider for example a trait which has a heritability of 0.50. 50% of the variance in trait value can be explained by variance in genes. Selection correlated with trait value can rapidly change the distribution of the trait within the population, as modeled by the breeder’s equation. But no new mutations are necessary in this model, rather, the frequencies of extant alleles changes over time. In fact, as the proportions shift novel combinations of alleles which were once too rare to be found together in the same individual will emerge, and so offer up the possibility that the mean trait value in generation t + n generations may be outside of the range of trait values at t = 0.

Over time such selection on a quantitative trait theoretically exhausts its own fuel, genetic variation. But quite often this is not practically operative, because such traits are subject to a background level of novel mutation and balancing selection. Stabilizing selection around a median phenotype, as well as frequency dependence and shifting environmental pressures, may produce a circumstance where adaptation never moves beyond the transient flux toward a new equilibrium. The element of the eternal race is at the heart of the Red Queen’s Hypothesis, where pathogen and host engage in an evolutionary war, and host immune responses are subject to negative frequency dependence. As the frequency of an allele rises, its relative fitness declines. As its frequency declines, its fitness rises.

Naturally such complex evolutionary models, subject to contingency and less non-trivially powerful in their generality, only become appealing when simple hard sweep models no longer suffice. But it seems highly plausible that the genetic architecture of some traits, those which seem plagued by ‘missing heritability,’ are going to necessitate somewhat more baroque evolutionary models to explain their ultimate emergence & persistence. A new paper in PLoS Genetics tackles this complexity by looking at the patterns of variation of SNPs implicated in GWAS in the HGDP data set. Genome-Wide Association Study SNPs in the Human Genome Diversity Project Populations: Does Selection Affect Unlinked SNPs with Shared Trait Associations? First, the abstract:

Genome-wide association studies (GWAS) have identified more than 2,000 trait-SNP associations, and the number continues to increase. GWAS have focused on traits with potential consequences for human fitness, including many immunological, metabolic, cardiovascular, and behavioral phenotypes. Given the polygenic nature of complex traits, selection may exert its influence on them by altering allele frequencies at many associated loci, a possibility which has yet to be explored empirically. Here we use 38 different measures of allele frequency variation and 8 iHS scores to characterize over 1,300 GWAS SNPs in 53 globally distributed human populations. We apply these same techniques to evaluate SNPs grouped by trait association. We find that groups of SNPs associated with pigmentation, blood pressure, infectious disease, and autoimmune disease traits exhibit unusual allele frequency patterns and elevated iHS scores in certain geographical locations. We also find that GWAS SNPs have generally elevated scores for measures of allele frequency variation and for iHS in Eurasia and East Asia. Overall, we believe that our results provide evidence for selection on several complex traits that has caused changes in allele frequencies and/or elevated iHS scores at a number of associated loci. Since GWAS SNPs collectively exhibit elevated allele frequency measures and iHS scores, selection on complex traits may be quite widespread. Our findings are most consistent with this selection being either positive or negative, although the relative contributions of the two are difficult to discern. Our results also suggest that trait-SNP associations identified in Eurasian samples may not be present in Africa, Oceania, and the Americas, possibly due to differences in linkage disequilibrium patterns. This observation suggests that non-Eurasian and non-East Asian sample populations should be included in future GWAS

And now the author summary:

Natural selection exerts its influence by changing allele frequencies at genomic polymorphisms. Alleles associated with harmful traits decrease in frequency while those associated with beneficial traits become more common. In a simple case, selection acts on a trait controlled by a single polymorphism; a large change in allele frequency at this polymorphism can eliminate a deleterious phenotype from a population or fix a beneficial one. However, many phenotypes, including diseases like Type 2 Diabetes, Crohn’s disease, and prostate cancer, and physiological traits like height, weight, and hair color, are controlled by multiple genomic loci. Selection may act on such traits by influencing allele frequencies at a single associated polymorphism or by altering allele frequencies at many associated polymorphisms. To search for cases of the latter, we assembled groups of genomic polymorphisms sharing a common trait association and examined their allele frequencies across 53 globally distributed populations looking for commonalities in allelic behavior across geographical space. We find that variants associated with blood pressure tend to correlate with latitude, while those associated with HIV/AIDS progression correlate well with longitude. We also find evidence that selection may be acting worldwide to increase the frequencies of alleles that elevate autoimmune disease risk.

This is a paper where jumping to the methods might be useful. Though I’m sure that the authors did not intend it, sometimes it felt as if you were following the marble being manipulated by the carnival tender. Since I was not familiar with some of the terms for the statistics, a simple allusion to the methods without elaborating in detail did not suffice. In any case, the key here is that they focused on the set of SNPs which have been associated with trait variance in GWAS, and compared those to the total SNPs found in the HGDP data set of 53 populations. Note that not all SNPs in GWAS were in the HGDP SNP panel. But for the general questions being asked the intersection of SNPs sufficed. Additionally, they generated a further subset of SNPs which were highly likely to be associated with trait variance. These were SNPs where other SNPs of related function were within 1 MB, or, SNPs which were found in more than one GWAS.

There were four primary statistics within the paper: Delta, Fst, LLC, and iHS. Fst and iHS are familiar. Fst measures the extent of between population variance across a set of populations. High Fst means a great deal of population structure, while Fst ~ 0 means basically no population structure. iHS is a test to detect the probability of natural selection based on patterns of linkage disequilibrium in the genome. Basically the important thing for the purposes of this paper is that iHS tends to be good at detecting alleles at moderate frequencies still presumably going through sweeps. This is in contrast to the older EHH test, which only detects sweeps which are nearly complete. If the authors are focusing on polygenic traits and soft sweeps the likelihood of that showing up on EHH is low since that is predicated on hard, nearly complete, sweeps. LLC measures the correlation between genetic variant of a trait as a function of latitude and longitude. Presumably this would be useful for smoking out those traits driven by ecological pressures (an obvious example in a general sense are consistent changes in area-to-volume ratio across taxa as organisms proceed from warmer to colder climes). Finally, Delta measures the allele frequency difference across the set of populations. The sign of Delta is simply a function of whether the allele frequency in question is higher in the first or second population in the comparison.

In doing their comparisons the authors did not simply compare across all 53 populations in a pairwise fashion. Rather, they often pooled continental or regional groups. To the left is a slice of table 1. It shows the populations used to generate the Delta values, and how they were pooled. The HGDP populations are broken down by region in a rather straightforward manner. But also note that some of the comparisons are between populations within regions, and those with different lifestyles. I assume that the comparisons highlighted within the paper were performed with the aim of squeezing maximal informative juice in such an exploratory endeavor. There are no obligate hunter-gatherers within the Eurasian populations in the HGDP data set to my knowledge, so a comparison between agriculturalists and hunter-gatherers would not be possible. There is such a comparison available in the African data set. The authors generated p-values by comparing the GWAS SNPs to random SNPs within the HGDP data set. In particular, they were looking for signatures of distinctiveness among the HGDP data set.

Such distinctiveness is expected. The set of SNPs associated with diseases and traits of note are not likely to be a representative subset of the SNPs across the whole genome. Remember that a neutral model of molecular evolution means that we should expect most genetic variation within the genome is going to be due to stochastic forces. Panel A of figure 1 shows that in fact the SNPs derived from GWAS did exhibit a different pattern from the total set of SNPs in the HGDP panel. Observe that the distribution of minor allele frequency (MAF) is somewhat skewed toward higher values for the GWAS SNPs. If the logic of GWAS is geared toward “common variants” which will be frequent enough within the population to generate an effect which is powerful enough to be picked up by the studies given their sample sizes, the bias toward more common variants (higher MAF) is understandable.

To the left are some SNPs and traits which had low p-values (i.e., they were deviated from expectation beyond what you’d expect from random noise). Not very surprisingly they found that pigmentation related SNPs tended to show up strongly in all the measures of population differentiation and variation. rs28777 is found in SLC45A2, a locus which differentiates Europeans from non-Europeans. rs1834640 is in SLC24A5, which differentiates Europeans + Middle Easterners + Central/South Asians from other populations. rs12913832 is a “blue eye” related variant. That is, it’s one of the markers associated with blue vs. non-blue eye color differences in Europeans.

Seeing that pigmentation has been one of the few traits which has been well elucidated by the current techniques, it should be expected that more subtle and thorough methods aimed at detecting genetic variation across and within populations should stumble upon those markers first. The authors note that “SNPs and study groups associated with pigmentation and immunological traits made up a majority of those that reached significance in our analysis.” There has long been a tendency toward finding signatures of selection around pigmentation and disease related loci.

One pattern which was also evident in terms of geography in the patterns of low p-values was the tendency for Eurasian groups to be enriched. This is illustrated in figure 2. Most of the SNPs from the GWAS studies were derived from study populations which were European. Because of this there is probably a bias in the set of SNPs being evaluated which are particular informative for Europeans and related populations. Additionally, it may also be that Eurasians were subject to different selective pressures as they left the ancestral African environment ~150-50,000 years B.P. In any case, for purposes of medical analysis the authors did find that using SNPs from East Asian populations produced somewhat different results than using those from European populations. Though some studies have shown a broad applicability of SNPs across populations, there are no doubt many variants in non-European populations which have simply not been detected because GWAS studies are not particularly focused on non-European populations. Consider:

… However, our results indicate that SNPs associated with pigmentation in GWAS display unusual allele frequency patterns almost exclusively in Europe, the Middle East, and Central Asia. This suggests to us that there may be SNPs, perhaps in or near genes other than SLC45A2, IRF4, TYR, SLC24A4, HERC2, MC1R, and ASIP, which are associated with pigmentation in non-Eurasian populations, but which have yet to be identified by GWAS. GWAS for pigmentation traits carried out using non-European subjects are needed to explore this possibility further.

There are two major other classes of trait/disease which were found to vary systematically across the HGDP populations:

- High blood pressure associated variants seemed to decrease with latitude

- Infectious and autoimmune disease SNPs had elevated scores. Specifically, there were some HIV related SNPs associated with Europeans which seem to confer resistance

The first set of traits would naturally come out of GWAS derived SNPs, since so much medical research goes into identifying risk and treating high blood pressure and other circulatory ailments. A consistent pattern where geography and not ancestry predict variation is an excellent tell for exogenous selective pressures. The physical nature of the earth is such that as mammals spread away from the equators their physiques will be reshaped by different sets of ecological parameters. Siberian populations have developed adaptations to cold stress, and there seem to be consistent cross-taxa shifts in body form to maximize or minimize heat radiation among mammals.

In the second case you have resistance to disease cropping up again, as well as pleiotropy, whereby genetic changes can have multiple downstream consequences. Often this is temporally simultaneous; consider the tame silver foxes. But sometimes you have a change in the past which has a subsequent consequence later in time due to different selective pressures. It is not that surprising that immunological responses can be multi-purpose, so even though Europeans did not develop resistance to HIV as a general selective pressure, similar pressures seem to have resulted in responses with general utility and now a specific use in relation to HIV. Selection can often be a blunt instrument, interposing itself into a network of interactions with multiple consequences, reshaping many traits simultaneously in the process of maximizing local fitness. This is most clear when you have a trait such as sicke-cell disease, which emerges only because the fitness benefit of heterozygosity is so great. But no doubt when it comes to many traits the byproducts are more subtle, or may seem cryptic to us. We still do not know why EDAR was driven to higher frequency in East Asians (less body odor and thick straight hair seem implausible targets for selection).

And just as natural selection can be blunt and rude in its impact on the covariance of genes and traits, so its relaxation may remove a suffocating vice. Consider the possibilities with blood pressure: perhaps the reason that northern Eurasians have lower blood pressure is that selection for other correlated traits associated with higher values were relaxed, allowing for fitness to be maximized in this particular dimension. Similarly, African Americans have a lower frequency of the sickle-cell disease than their ~80% West African ancestry would entail, because without the pressure of endemic malaria selection for the heterozygote was removed, allowing for the purging of the allele from the gene pool.

Nevertheless, the authors do conclude::

Despite our broad-based approach, we found only a few examples of what may be a polygenic response to a single selective pressure.</b> We did use stringent significance criteria which might mean that additional examples can be found among the study groups that did not quite meet our threshold of significance. It may also be that there is something about “GWAS” traits and their underlying genetics that served to undermine our approach.

They have several suggestions for why this didn’t pan out:
- The GWAS variants aren’t the primary source of the variation. It could be copy number variants, rare large effect variants (“synthetic”)

- Epistasis. Gene-gene interaction, which would mask or confound linear associations between variants and traits

- Low impact of selection on GWAS SNPs, or, balancing or negative selection

They finish:

In summary, we have examined 1,336 trait-associated SNPs in the 53 CEPH-HGDP populations looking for individual SNPs and groups of SNPs with unusual allele frequency patterns and elevated iHS scores. We identified 13 different traits with an associated SNP or study group that produced a significantly elevated score for at least one delta, Fst, LLC, or iHS measure, a small percentage of the total number of traits analyzed. We believe that the limited number of positive results could be due to our stringent significance criteria or to features of the genetic architecture of the traits themselves. Specifically, the roles of rare variants, epistasis, and pleiotropy in human complex traits are, although areas of active inquiry, still generally not well understood. Our measures may also not be optimal for detecting all types of selection acting on GWAS traits. It has been speculated that variants underlying complex traits will be influenced primarily by negative or balancing selection, which may not produce extreme values for our measures, particularly if these forces are relatively uniform across populations or are acting on many regions in the genome.

If selective pressures on polygenic traits are so common perhaps genomicists are going to be thumbing through Introduction to Quantitative Genetics. These are traits and evolutionary processes which lack clear distinction. In many ways modeling positive selection and hard sweeps resembles the economics of equilibriums. When it comes to continuous and quantitative traits subject to the effect of many genes a different way of thinking has to come to the fore. The transient no longer becomes a punctuation between the stasis, but the thing in and of itself. There are for example HLA genes in humans which are found in chimpanzees, because the nature of the eternal race between host and pathogen means that all the old tricks are preserved, at least at low frequencies. Human variation in intelligence, height, and all sorts of other liabilities and characteristics, may have always been with us, being buffeted continuously by a swarm of selective pressures. The question is, can our crude statistical methods ever get a grip on this diffuse but all-powerful net?

Citation: Casto AM, & Feldman MW (2011). Genome-Wide Association Study SNPs in the Human Genome Diversity Project Populations: Does Selection Affect Unlinked SNPs with Shared Trait Associations? PLoS Genetics : 10.1371/journal.pgen.1001266

(Republished from Discover/GNXP by permission of author or representative)
 
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Razib Khan
About Razib Khan

"I have degrees in biology and biochemistry, a passion for genetics, history, and philosophy, and shrimp is my favorite food. If you want to know more, see the links at http://www.razib.com"