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Citation: Corona, Erik, et al. “Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration.” PLoS Genetics 9.5 (2013): e1003447.

The above figure is from a paper in PLoS GENETICS, Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration. The authors synthesize two diverse domains of human genomics. First, there are biomedically focused genome-wide association studies and their like which attempt to identify risk alleles for particular diseases. In some cases these risk alleles are very penetrant, in that a particular state predicts with high likelihood a disease phenotype. But in most cases the yield is elevated or decreased risks for highly complex traits such as type 2 diabetes. Second, there is the domain of evolutionary genomics which attempts to reconstruct a phylogenetic and population genetic history so as to frame contemporary patterns of variation in their proper context. How this might be important or of interest is obvious in the case of malaria resistance genes. Alleles conferring resistance have arisen in multiple populations due to parallel environmental pressures. Phylogenetic relationships between these populations should inform your predictions as to the likely similarities of the mutations between the populations. Meanwhile, population genetic theory can give you clues as to the likelihood of multiple adaptations.

The goal here is to increase understanding of the nature of the emergence of disease, and perhaps target individual risk more effectively. Above in the figure you see two interesting patterns: risk for type 2 diabetes alleles as a function of descent, and risk as a function of de novo mutation or independent selective event. The phylogenetic tree represents real relationships as inferred from the >600,0000 SNPs in the HGDP data set. The risk alleles were culled from the literature, and were computed for individuals and populations. The real population risks were then compared to a model of risks which might occur in a scenario with this particular phylogenetic tree and the normal process of random genetic drift (see methods for the gory details!). What you see are phylogenetic relationships (African populations shifted toward higher risk) and independent events (Pima Indians shifted toward higher risk) where there is a higher risk toward diabetes (red shifted).

There are all sorts of shortcomings to this analysis. The authors are limited by the risk alleles in their study, which is certainly far less than thorough or exhaustive. Additionally, their population coverage was thin in some regions, resulting in reduced ability to even squeeze power from their model in particular cases. But one thing that jumps out at you is that the patterns here inferred from risk alleles in a highly polygenic disease like type 2 diabetes don’t even track what you see in the real world. Many South Asian groups have very high risks of type 2 diabetes. It just so happens that these groups are not in the HGDP sample. There is actually a rather informative critique from two epidemiologists in the comments of this paper. They make many points that came to mind in the specifics. But they ended in a fashion which raised my eyebrows:

Finally, the need to avoid stigmatizing populations based on genetic risk has been much discussed.It is not difficult to imagine a media announcement based on this publication – “Genetic risk of diabetes found in African populations”. Similar claims were made for intelligence not very long ago. Not all speculation is neutral.

As it happens I come from a population with very high risk for metabolic disease. I have no idea if I’m stigmatized by this fact, but I am very glad that medical professionals are becoming aware of differential risks, and moving beyond coarse one-size-fits-all understandings of human health. The BMI values developed for European Americans are probably rather inappropriate to South Asians because of the way we distribute fat (in short, we need to be thinner to exhibit the same risk profile all things equal). Again , I have no idea if this is stigmatizing, but it is real.

So despite all the real concerns I have with the methodology in the paper above, I believe that these sorts of analyses are essential parts of the broader answer. We now live in the age of the antiobiotic revolution and an understanding of germ theory. Those were the big returns on investment for public health. For the short term gains in human well being and life expectancy are going to be on the margin, through increments. Despite all the skepticism I have with initial attempts to work out the relationship between population history and disease, one must begin somewhere.

Citation: Corona, Erik, et al. “Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration.” PLoS Genetics 9.5 (2013): e1003447

(Republished from Discover/GNXP by permission of author or representative)
 
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I recently listened to Paul Ewald talk about how a lot of cancer is due to infection on the radio show To the Best of Our Knowledge. That wasn’t too surprising, Ewald has been making the case for a connection between infection and lots of diseases for a while. What jumped out at me is his claim that kissing can spread some of the viruses. Here’s something he told Discover a few years back:

D: How do we get infected with these dangerous pathogens?

PE: Two of the most powerful examples are sexual transmission and kissing transmission, and by that I mean juicy kissing, not just a peck on the cheek. If you think about these modes of transmission, in which it might be a decade before a person has another partner, you realize that rapidly replicating is not very valuable—the winning strategy for the microbe would be to keep a low profile, requiring persistent infections for years. So we would expect that disproportionately, the sexually transmitted pathogens would be involved in causing cancer, or chronic diseases in general. You can test this. Just look at the pathogens that are accepted as causing cancer—Epstein-Barr virus, Kaposi’s sarcoma–associated herpesvirus, human T lymphotropic virus 1—and find out whether they’re transmitted this way. They almost all are. A random sample would yield maybe 15 to 20 percent of pathogens associated with cancer being sexually transmitted, yet the figure is almost 100 percent. When you look at viruses alone, it is 100 percent.

If a lot of kissing and number of sexual partners is predictive of risk of cancer, my immediate thought is that this naturally explains a lot of the cancer that runs in families. Families can pass on genes and cultural norms which would favor or disfavor certain behaviors.

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Cancer, Disease, Health 
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ResearchBlogging.org Last week I reviewed ideas about the effect of “exogenous shocks” to an ecosystem of creatures, and how it might reshape their evolutionary trajectory. These sorts of issues are well known in their generality. They have implications from the broadest macroscale systematics to microevolutionary process. The shocks point to changes over time which have a general effect, but what about exogenous parameters which shift spatially and regularly? I’m talking latitudes here. The further you get from the equator the more the climate varies over the season, and the lower the mean temperature, and, the less the aggregate radiation the biosphere catches. Allen’s rule and Bergmann’s rule are two observational trends which biologists have long observed in relation to many organisms. The equatorial variants are slimmer in their physique, while the polar ones are stockier. Additionally, there tends to be an increase in mean mass as one moves away from the equator.

But these rules are just general observations. What process underlies these observations? The likely culprit would be natural selection of course. But the specific manner in which this process shakes out, on both the organismic and genetic level, still needs to be elucidated in further detail. A new paper in PLoS Genetics attempts to do this more rigorously and deeply than has been done before for one particular world wide mammalian species, H. sapiens sapiens. We have spanned the latitudes and longitudes, and so we’re a perfect test case for an exploration of the broader microevolutionary forces which shape variation.

The paper is Adaptations to Climate-Mediated Selective Pressures in Humans. Its technical guts can be intimidating, but its initial questions and final answers are less daunting. So let’s jump straight to the last paragraph of the discussion:

The results of this genome scan not only increase our understanding of the genetic landscape of adaptation across the human genome, but they may also have a more practical value. For example, they can be used to select candidate genes for common disease risk and to generate specific testable hypotheses regarding the functions of specific genes and variants. While the results of genome-wide scans for association with diseases and other traits are accumulating at a rapid pace, interpretation of these results is often ambiguous because the power to detect all common variants that are important in the etiology of the phenotype is incomplete. This is especially true in the case of complex traits, where variants at many loci may contribute to the phenotype, each with a small effect. By combining the evidence from GWAS with evidence of selection, it may be possible to separate true causative regions from the background noise inherent in genome-wide screens for association. To facilitate this, all of our empirical rank statistics are publically available. Moreover, results of selection scans that detect evidence for spatially-varying selection may be especially relevant to diseases that show substantial differences in prevalence across ethnic groups (e.g., sodium-sensitive hypertension, type 2 diabetes, prostate cancer, osteoporosis). In the future, this approach could be extended by including additional populations and aspects of the environment to gain a more complete understanding of how natural selection has shaped variation across the genome in worldwide populations. Furthermore, whereas we relied on linkage disequilibrium between (potentially un-genotyped) adaptive variants and genotyped SNPs, whole genome re-sequencing data should give a more complete picture of the variation that underlies adaptation.

How’d they infer this? First, they had a pretty wide coverage of populations from across the world. They pooled the HGDP and HapMap, as well as a few other populations of interest, Ethiopians, some Siberian groups, and Australian Aboriginals. I do wish that the Aboriginal data set was public, but it doesn’t seem to be! The Ethiopians are I assume the ones you can find in Behar et al. The authors had a null model which was predicated on the fact that variation in the frequencies of given genetic morphs, single nucleotide polymorphisms, should be bested predicted by population history and relationships. That is, two populations will differ on a given locus in proportion to their genetic divergence, due to random forces such as genetic drift. Perturbations from this null model are possible targets of natural selection, which reshapes regions of the genome in a deterministic manner aiming at particular ends. Two 21st century classic examples of this phenomenon seem to be skin pigmentation and lactase persistence. Different populations with the same phenotype, in particular, light skin and the ability to digest lactose sugar as an adult, exhibit divergent genetic architectures.

They naturally looked to see how these deviations tracked environmental parameters you see above. Keep in mind that they did take into account correlations between these variables. Additionally, correlation does not equal causation, so there could be other variables which are correlated with the ones which they explored which might be responsible for the systematic perturbations.

Their method yielded a Bayes factor (BF) which measures the deviation from the null model for a given SNP. To judge off the bat whether these SNPs are plausibly the targets of adaptation you want to check to see if they’re enriched for certain classes of SNPs. They found that the SNPs which rejected the null model, where population history and demographics predicts genetic variation, tended to be much more likely to be genic or nonsynonymous. This means that the base pair is embedded in a coding gene, as opposed to much of the genome which isn’t translated into proteins. A nonsynonymous base pair is one at a location which changes the protein coded. Normally these sorts of changes are selected against because you don’t want to change the protein function, but when a population is adapting to a new environment this is obviously not so.

There are a host of results in the paper, but one pattern which seemed of interest was that different sets of SNPs can be selected in different population pools. Below are two panels which show the SNPs with significant BF, and how they vary as a function of the climatic variable depending upon the populations which are sampled. To the left you see the cluster which varied in western Eurasia, while in the left you see those which varied in eastern Eurasia. In a broad sense the target of selection was the same, but the specific SNPs which were pulled out the set of potential targets still exhibits stochasticity:

Natural selection is deterministic in the broadest scale, but in its instantiations it can exhibit a great deal of randomnes. Same phenotype. Different genotype. Similarly, the heat death of the universe may be determined, but there’s a lot of contingency of epiphemenonal detail between now and then. Modulating the range of populations analyzed often shifted the value of the statistic for a given SNP. Remember, averaging over the aggregate can remove important local information. That being said, the Venn Diagram below shows that there was a disproportionate tendency for the signals detected to be world wide. This indicates that the wheel isn’t reinvented as much as we might think. I wonder if it points to the limitations baked into the human genome in terms of the plasticity and flexibility of all its various pathways. There’s a structural engineer vetoing the elegant fancies of the architect?

The leftmost panel highlights the West Eurasian signals and the middle panel the East Eurasians.

As noted above these sorts of studies have both evolutionary and biomedical relevance. Perhaps the most intriguing result, albeit expected from other areas of research, is the role of antagonistic pleiotropy in many diseases. Concretely, it may be that a change in a particular location may increase reproductive fitness in a novel environment at the cost of later morbidity in life. The authors suggest that pathogenic resistance and inflammatory response may have the side effect of increasing susceptibility to a range of diseases of old age. Why is this important? I think that the authors are implying in part that a plausible evolutionary mechanism of adaptation should change our prior expectation that a given genome wide association is a false positive. At least I think that. If a SNP was the target of natural selection and shows up on GWAS, keep an eye on it! All the better if you have a good functional understanding of what’s going on there.

But more long-term, it might change our perception of the basal risk for classes of morbidity as they vary by population. Human populations have had different evolutionary histories. Their disease risks then might vary a great deal. Between population differences may be a lot less paradoxical than we think….

Citation: Hancock AM, Witonsky DB, Alkorta-Aranburu G, Beall CM, & Gebremedhin A (2011). Adaptations to Climate-Mediated Selective Pressures in Humans PLoS Genetics, : 10.1371/journal.pgen.1001375

(Republished from Discover/GNXP by permission of author or representative)
 
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ResearchBlogging.org Over the past day I’ve seen reports in the media of a new paper which claims that long-term urbanization in a region is strongly correlated with genetic variants for disease resistance. I managed to find the paper on Evolution‘s website as an accepted manuscript, ANCIENT URBANISATION PREDICTS GENETIC RESISTANCE TO TUBERCULOSIS:

A link between urban living and disease is seen in recent and historical records, but the presence of this association in prehistory has been difficult to assess. If the transition to urbanisation does result in an increase in disease-based mortality, we might expect to see evidence of increased disease resistance in longer-term urbanised populations, as the result of natural selection. To test this, we determined the frequency of an allele (SLC11A1 1729 + 55del4) associated with natural resistance to intra-cellular pathogens such as tuberculosis and leprosy. We found a highly significantly correlation with duration of urban settlement – populations with a long history of living in towns are better adapted to resisting these infections. This correlation remains strong when we correct for auto-correlation in allele frequencies due to shared population history. Our results therefore support the interpretation that infectious disease loads became an increasingly important cause of human mortality after the advent of urbanisation, highlighting the importance of population density in determining human health and the genetic structure of human populations.

298px-Pericles_Pio-Clementino_Inv269In some ways this seems plausible. There are a priori reasons why we’d expect to see a great deal of evolutionary change in regions of the genome correlated with variations in immune response. Diseases are one of the most likely reasons for why sex exists in complex multicellular species; sex allows a slow-reproducing population to bend with the rapid-fire punches of their pathogens by shuffling their defenses constantly. The results from recent work mapping patterns of variation in relation to natural selection generally indicate that immune related regions show plenty of signs of adaptation. No surprise, a “Red Queen” model whereby pathogens and their hosts constantly co-evolve would imply that immunologically relevant genes would never be at equilibrium frequencies for long, so we’d have a good shot at catching “selective sweeps” on some of the immune loci.

So how do cities play into this picture? I suspect that the picture is more complicated than the presentation in the paper, though I believe that the authors were constrained by considerations of space from evaluating all possibilities in full depth. There are two facts which I think are critical to understanding the pattern of variation here:


- All pre-modern societies were predominantly rural demographically. The difference between an “urban civilization” like Rome and a non-urban one such as Dark Age Ireland was that ~25% of the residents of the Roman Empire lived in urban areas (generously defined) while ~0% of Dark Age Irish lived in urban areas. Rome is generally considered to be a very urban pre-modern society, perhaps the most urban large-scale society before the 17th century.

- I also believe that ancient cities were population sinks. People simply did not replace themselves and cities only perpetuated their massive scale by serving as magnets for excess population from the rural hinterlands. Without appropriate political structures to maintain the population and generate incentives for an inflow migration ancient cities withered away very fast (Rome’s population went from hundreds of thousands to tens to of thousands in the 100 years from the early 6th to early 7th century because of political instability).

Before I go on much, let’s address the results presented in the paper. Below you see the frequencies of the allele which is more protective against tuberculosis in tabular form and on a map, as well as the logistic regressions which show the relationship between time since urbanization and the allele frequencies. Please note that they corrected for genetic relatedness in their regression, so the correlation isn’t just due to population stratification on a world wide scale.

[nggallery id=15]

Since the allele which confers resistance is at a high frequency everywhere the difference is between those populations where the genotypes are predominantly in a homozygote state (e.g., Iranians), and those where only around half are resistant homozygotes (e.g., Sami). The authors note that because of the high frequency everywhere, including populations with no history of agriculture such as the Sami, one can’t posit a model where positive selection drove the disease resistant alleles from 0 to fixation. Rather, it perturbed the equilibrium frequency. Using the Tajima’s D statistic they do find evidence of balancing selection in both East Asians and Europeans. This would be in keeping with frequency-dependent models of pathogen-host co-evolution.

As I said before there are strong reasons to assume that natural selection reshaped the genomes of populations over the past 10,000 years. It really isn’t if, it’s how and what. The authors present some evidence for a particular variant of the gene SLC11A1 being the target of natural selection. To really accept this specific case I think we’ll need some follow up research. Rather, I want to focus on the narrative which is being pushed in the media that cities were the adaptive environments which really drove the shift in allele frequencies. I don’t think this was the case, I think the cities were essential, but I don’t think ancient urbanites left many descendants. Instead, I think cities, or urbanization, is first and foremost a critical gauge of population density and social complexity. Second, I believe that cities serve as facilitators and incubators for plague. In other words both urbanization and disease adaptation are derived from greater population density, while urbanization also serves a catalytic role in the spread of disease. This could explain the strong correlation we see.

I believe that the Eurasians who may have been subject to natural selection due to the rise of infectious disease are almost all the descendants by and large of ancient rural peasants, or, their rentier elites. These peasants were subject to much greater disease stress even without living in urban areas than hunter-gatherers and pastoralists because their population densities were higher, and quite often they were living a greater proportion of their lives snuggly against the Malthusian lid. Hunter-gatherers may have been healthier on average because of a more diversified diet as well as lower population densities due to endemic warfare. In contrast, agriculturalists lived closely packed together and were far more numerous than hunter-gatherers, and, their immune systems were probably less robust because of the shift away from a mix of meat, nuts and vegetables, to mostly grains.

800px-Republik_Venedig_Handelswege01A downstream consequence of agriculture was the rise of cities through the intermediate result of much higher population densities. I accept the literary depiction of ancient cities as filthy and unhealthful. There’s almost certainly a reason that pre-modern elites idealized rustic life, and had country villas. Additionally, though I assume that both the rural peasantry and urban proletarian led miserable lives, I believe that in terms of reproductive fitness the former were superior to the latter. From what I have read city life only became healthier than rural life in the United States in 1900, in large part due to a massive public health campaign triggered by fear of immigrant contagion. The high mortality rates and low reproductive fitness of urbanites implies that evolutionarily the more important role of cities were as nexus points for trade and the spread of disease. The book Justinian’s Flea chronicles the pandemic in the Roman Empire in the 6th century, in particular its origin in Constantinople from points east. We’re well aware today that a globalized world means that there’s an interconnectedness which can bring us strength through comparative advantage, but also catastrophe through contagion. This is a general dynamic, not simply one applicable to disease, but in the world before modern medicine the utility of trade networks for pathogens would have been of great importance.

One can imagine societies through the organismic lens as if they were cyclical wind-up toys. In the initial stage of expansion and integration political stability and concentration of power results in a peace which allows for the increase in population as more land inputs are thrown into primary production. Eventually diminishing returns kicks in and there’s no more land, so the labor squeezes itself more tightly on fixed land endowments. Their median physiological fitness declines as the pie gets cut into more and more pieces. All the while these massive numbers of peasants serve as the source of revenue for extractive elites, who found and patronize cities where they can signal their status and concentrate their wealth. Most pre-modern cities, like Rome and Constantinople, would have been economic parasites, depending on rents and plunder. As a sidelight cities such as Constantinople which were placed at transportation hubs would also become the focal points for trade, especially if they could be termini themselves for the luxury good trade which was dependent on the demand from rentier elites in residence in the metropolis. Finally, these cities would also be magnets for masses of armies because of the inevitably of sieges.

400px-Plato_Silanion_Musei_Capitolini_MC1377Eventually the combination of factors would result in the outbreak of plague. Social order would collapse, people would flee the cities, and populations would drop as the tightly run ship on the Malthusian margin ran aground. As the population dropped median health and wealth would return, and susceptibility to plague would decrease. And then the cycle of expansion and integration would start anew.

This is I believe the story of the rise and fall of urban societies which reshaped the genomes of people who lived across much of Eurasia. It isn’t a tale of urbanites, rather, urbanites for most of history have almost certainly been epiphenomena in a genetic sense. They’re the excess rural population which finds its way to the polis. Because of the squalor and lack of public health the lot of the urbanite was to consign their genes to oblivion. But for this deal with the devil the urban man had an opportunity to become immortal, and live on in human memory. It is their names which echo down through history, and roll off the tongues of the descendants of the peasants who have long ago forgotten their own genetic forebears.

Citation: Barnes, I., Duda, A., Pybus, O., & Thomas, M. G. (2010). ANCIENT URBANISATION PREDICTS GENETIC RESISTANCE TO TUBERCULOSI Evolution : 10.1111/j.1558-5646.2010.01132.x

Image Credit: Marie-Lan Nguyen, Nikater

(Republished from Discover/GNXP by permission of author or representative)
 
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behcetprev1Nature has two papers out about something called “Behçet’s disease.” It has apparently also been termed the “Silk Road Disease”, because of its associations with populations connected to the Central Eurasian trade networks.Though described by Hippocrates 2,500 years ago, apparently it was “discovered” only in the 20th century by a Turkish physician. The reason that that might be is obvious; the prevalence of Behçet’s disease is far higher in Turkey than any other nation. Two orders of magnitude difference between Northwest Europeans and Turks. East Asian populations are somewhere between Europeans and Turks, while the coverage of Inner Asia itself is thin (the first case diagnosed in Mongolia was in 2003). Additionally, the relatively similar frequency in Morocco and Iran, despite the latter nation being strong influenced by Turkic migration (25-30% of Iranian citizens are ethnically Turk), and the former not at all, leads to me wonder if there may be convergence or parallelism, rather than common ancestry, at work (or, more likely, a combination of both). The relationship between Morocco and Japan to the Silk Road in a direct fashion is tenuous at best. These were two polities which managed to be just outside the maximum expanse of Turanian empires. The Japanese famously repulsed the Mongol invasion ordered by Kublai Khan, while the Arab rulers of Morocco never fell under Ottoman control.And the early documentation by Hippocrates makes me wonder at the frequency of the disease in Greece itself. Greeks presumably contributed to the ancestry of modern Anatolian Turks, but it is far less likely because of the nature of the Ottoman system that Turks would have contributed to the ancestry of Greeks. I can’t find prevalence data for Greece, but it may be an open question in what direction the disease spread along the Silk Road.

ResearchBlogging.org But studies like these are nice because they are steps to overcoming one of the main issues with genome-wide associations: they use a narrow population sample, and so are not of necessary world wide relevance. Remember that even if a risk allele is not the direct cause of the disease, if it is closely associated with that alleles which are, it is of diagnostic utility. At least within that particular population. This study used groups from western and eastern Eurasia to check the power of particular single nucleotide polymorphisms (SNPs) to predict disease risk. First, Genome-wide association studies identify IL23R-IL12RB2 and IL10 as Behçet’s disease susceptibility loci:

Behçet’s disease is a chronic systemic inflammatory disorder characterized by four major manifestations: recurrent ocular symptoms, oral and genital ulcers and skin lesions1. We conducted a genome-wide association study in a Japanese cohort including 612 individuals with Behçet’s disease and 740 unaffected individuals (controls). We identified two suggestive associations on chromosomes 1p31.3 (IL23R-IL12RB2, rs12119179, P = 2.7 × 10−8) and 1q32.1 (IL10, rs1554286, P = 8.0 × 10−8). A meta-analysis of these two loci with results from additional Turkish and Korean cohorts showed genome-wide significant associations (rs1495965 in IL23R-IL12RB2, P = 1.9 × 10−11, odds ratio = 1.35; rs1800871 in IL10, P = 1.0 × 10−14, odds ratio = 1.45).

And, Genome-wide association study identifies variants in the MHC class I, IL10, and IL23R-IL12RB2 regions associated with Behçet’s disease:

Behçet’s disease is a genetically complex disease of unknown etiology characterized by recurrent inflammatory attacks affecting the orogenital mucosa, eyes and skin. We performed a genome-wide association study with 311,459 SNPs in 1,215 individuals with Behçet’s disease (cases) and 1,278 healthy controls from Turkey. We confirmed the known association of Behçet’s disease with HLA-B*51 and identified a second, independent association within the MHC Class I region. We also identified an association at IL10 (rs1518111, P = 1.88 × 10−8). Using a meta-analysis with an additional five cohorts from Turkey, the Middle East, Europe and Asia, comprising a total of 2,430 cases and 2,660 controls, we identified associations at IL10 (rs1518111, P = 3.54 × 10−18, odds ratio = 1.45, 95% CI 1.34–1.58) and the IL23R-IL12RB2 locus (rs924080, P = 6.69 × 10−9, OR = 1.28, 95% CI 1.18–1.39). The disease-associated IL10 variant (the rs1518111 A allele) was associated with diminished mRNA expression and low protein production.

Observe that the SNPs differ between the two studies. Here are the tables which show the SNPs, their odds ratios and statistical significance for the first and second paper respectively.

bechet1

behcet2

In the second paper they actually did an analysis of the effect of the disease associated allele at one of the SNPs, rs1518111. The A allele is disease associated.

behcet3

Finally, the last paragraphs to the two papers:

We report here a GWAS identifying two new susceptibility loci for Behçet’s disease; these loci include interleukin and interleukin receptor genes, which are central in immune response. The quantitative alteration of these cytokines (and others in the same cascade) could help explain in part the complex pathophysiology of Behçet’s disease and suggest new therapeutic avenues.

And:

In summary, we report a GWAS and meta-analysis identifying common variants in IL10 and at the IL23R-IL12RB2 locus that predispose to Behçet’s disease. Our study also supports the association of HLA-B*51 as the primary association to Behçet’s disease within the MHC region and reveals another independent MHC Class I association telomeric to HLA-B. Expression studies indicate that the disease-associated IL10 variants are associated with decreased expression of this anti-inflammatory cytokine. This may suggest a mechanism, possibly in concert with commensal microorganisms…that results in an inflammation-prone state that increases susceptibility to Behçet’s disease.

The relationship to commensal microorganisms may be pointing to a major reason why the frequency of the illness seems to decrease as one moves north. This could be a case where genetically susceptibilities toward expression of the illness interact with environmental factors. One could imagine, for example, that the harsh cold and light population of Inner Asia may have incubated particular susceptibilities which never manifested themselves because of the environment. But with the shift toward the denser and moister climes of western and eastern Eurasia the combination of genes and environment resulted in the emergence of the disease.

With that said, again, I’m curious as to the nature of the SNPs, and the phylogenetics of the disease causing mutations. Do they derive from common mutants? Implying then that common ancestry via the Silk Road was critical. If the genetic variation around the mutants implies common descent then the Silk Road may have been critical in the spread of the risk alleles, but it would still be an open question whether they flowed from east to west or west to east, contingent on patterns of genetic variation. Or, are they independent mutations? Perhaps they’re side effects of adaptations?

Citation: Remmers EF, Cosan F, Kirino Y, Ombrello MJ, Abaci N, Satorius C, Le JM, Yang B, Korman BD, Cakiris A, Aglar O, Emrence Z, Azakli H, Ustek D, Tugal-Tutkun I, Akman-Demir G, Chen W, Amos CI, Dizon MB, Kose AA, Azizlerli G, Erer B, Brand OJ, Kaklamani VG, Kaklamanis P, Ben-Chetrit E, Stanford M, Fortune F, Ghabra M, Ollier WE, Cho YH, Bang D, O’Shea J, Wallace GR, Gadina M, Kastner DL, & Gül A (2010). Genome-wide association study identifies variants in the MHC class I, IL10, and IL23R-IL12RB2 regions associated with Behçet’s disease. Nature genetics PMID: 20622878

Citation: Mizuki N, Meguro A, Ota M, Ohno S, Shiota T, Kawagoe T, Ito N, Kera J, Okada E, Yatsu K, Song YW, Lee EB, Kitaichi N, Namba K, Horie Y, Takeno M, Sugita S, Mochizuki M, Bahram S, Ishigatsubo Y, & Inoko H (2010). Genome-wide association studies identify IL23R-IL12RB2 and IL10 as Behçet’s disease susceptibility loci. Nature genetics PMID: 20622879

(Republished from Discover/GNXP by permission of author or representative)
 
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How we perceive nature and describe its shape are a matter of values and preferences. Nature does not take notice of our distinctions; they exist only as instruments which aid in our comprehension. I’ve brought this up in relation to issues such as categorization of recessive vs. dominant traits. The offspring of people of Sub-Saharan African and non-African ancestry where the non-African parent has straight or wavy hair tend to have very curly hair. Therefore, one may say that the tightly curled hair form is dominant to straight or wavy hair. But, it is also the case that there is some modification in relation to the African parent in the offspring, so the dominance is not complete. When examining the morphology of the follicle, which determines the extent of the hair’s curl, the offspring may in fact exhibit some differences from both parents. In other words our perception of the outcomes of inheritance are contingent to some extent on our categorization of the traits as well as our specific focus along the developmental pathway.

Or consider the division between “traits” and “diseases.” The quotations are necessary. Lactose intolerance is probably one of the best cases to illustrate the gnarly normative obstructions which warp our perceptions. As a point of fact lactose intolerance is the ancestral human state, and numerically predominant. It is the “wild type.” Lactose tolerance is a relatively recent adaptation, found among a variety of West Eurasian and African populations. A more politically correct term, lactase persistence, probably better encapsulates the evolutionary history of the trait, which has shifted from the class of disease to that of genetic trait when we evaluate the bigger picture (obviously diseases are simply “bad” traits”).


Sometimes though the issues are more cut & dried. No one would doubt that sickle-cell anemia is a disease. It has a major fitness impact in a colloquial sense, as well as evolutionarily. It kills you, and it kills your potential genetic lineage. But, it is also a byproduct of adaptation to endemic malaria. Sickle-cell disease one of the classical illustrations of heterozygote advantage, whereby those who carry one copy of the mutation on the gene have increased fitness vis-a-vis those who carry two normal copies of the gene. The increase in frequency of the mutant gene though is balanced by the fact that mutant homozygotes have decreased fitness.

We can then construct a narrative of the long term evolutionary dynamics from this initial condition. When a new exogenous stress hits a population mean fitness drops immediately (take a look at the biographies of the Popes, and observe how many died of malaria in the Dark Ages when that disease was new to Italy). Natural selection quickly increases in frequency any alleles which confer protection against the exogenous stress. But, baked into the cake of how genetics in complex organisms usually works, one allele may often have multiple downstream consequences. This is pleiotropy. This means that if a change at a locus increases aggregate fitness, it may nevertheless destabilize long established biochemical pathways. In the short term evolution simply takes the net fitness impact into account. Over the long term one assumes that “better solutions” will emerge which do not have so high a fitness drag, perhaps through the evolution of modifier genes which mask the deleterious outcomes of the initial mutant. This sort of ad hoc trial and error and “duct-taping” of kludges is part and parcel of how adaption works in situations where shocks out of equilibrium states are common.

In many cases the byproducts of a genetic change may be benign. To my knowledge no one knows major negative consequences of carrying the alleles which confer lactase persistence (excepting some studies indicating higher obesity, but this seems a marginal fitness impact which has only come to the fore in the past century in all likelihood). But in other cases the outcomes may not be as serious as that of sickle-cell anemia, but may rise above the level of significance where one must note the existence of a disease which is a secondary consequence of adaptation to meet a new challenge.

Yesterday I pointed to a paper which illustrates just this phenomenon, Association of Trypanolytic ApoL1 Variants with Kidney Disease in African-Americans:

African-Americans have higher rates of kidney disease than European-Americans. Here, we show that in African-Americans, focal segmental glomerulosclerosis (FSGS) and hypertension-attributed end-stage kidney disease (H-ESKD) are associated with two independent sequence variants in the APOL1 gene on chromosome 22 {FSGS odds ratio = 10.5 [95% confidence interval (CI) 6.0 to 18.4]; H-ESKD odds ratio = 7.3 (95% CI 5.6 to 9.5)}. The two APOL1 variants are common in African chromosomes but absent from European chromosomes, and both reside within haplotypes that harbor signatures of positive selection. Apolipoprotein L-1 (ApoL1) is a serum factor that lyses trypanosomes. In vitro assays revealed that only the kidney disease-associated ApoL1 variants lysed Trypanosoma brucei rhodesiense. We speculate that evolution of a critical survival factor in Africa may have contributed to the high rates of renal disease in African-Americans.

In its implementation the paper has a lot of moving parts, but the outcome is straightforward. If you haven’t, you might read Genomes Unzipped and its post How to read a genome-wide association study. This is a case where the original association studies were not reporting false results, but, it seems that one had to take a further step to really understand the likely molecular genetic and evolutionary underpinnings of what was going on. These results suggest that the original signals of association for variants within the MYH9 gene were actually signals from within APOL1, which happened to be next to MYH9. The region around MYH9 had already showed up in tests to detect natural selection through patterns of linkage disequilibrium (non-random associations of alleles at different loci within the genome, in this case the relevant consideration are adjacent loci across continuous regions of the genome which come together to form haplotype blocks). Since the footprint of natural selection on the genome is often wide that did not imply that MYH9 was the target of natural selection per se, opening the likely possibility for other causal associations. A convenience in light of the difficulty of establishing a plausible functional relationship between renal failure and MYH9.

To explore the possibility of nearby functional candidates the researchers focused on a number of alleles within this genomic region which exhibited maximal European-African frequency differences in the 1000 Genomes Project. Once they ascertained the between population differences they then looked at differences in allele frequencies in cases and controls within the African American population for the two diseases in question (those with the trait/disease vs. those without). Table 1 has the top line raw results:

apo1

WT = “Wild Type,” the ancestral allelic variant found in most populations. G1 and G2 are two haplotypes, associated alleles across the locus of the APOL1 gene. G1 consists of the two derived non-synonymous coding variants rs73885319 (S342G) and rs60910145 (I384M) within an exonic region of APOL1. Non-synonymous simply means that a change at that base pair alters the amino acid coded, and exons are the genomics regions whose information is eventually translated into proteins. In other words, these are non-neutral functionally significant genomic regions which do something. G2 is a 6 base pair deletion, rs71785313, close to G1 in APOL1.

apo12To more formally model the relationship between the alleles which are found to differ between cases and controls they performed a logistic regression. The alleles serve as independent variables which can predict the probable outcome of the dependent variable, the probability of FSGS or H-ESKD in this case (renal failure). Figure 1 to the left has a summary of some of the results of the regression in graphical form for FSGS. I’ve rotated it so it can fit on the screen. Basically the strong signals are to the right of the chart (from your perspective). The y-axis displays (horizontal from your perspective) negative-log of p-values for a signal at a particular marker, which is defied by the x-axis (vertical for you). The labels show the particular gene at that genomic position. The smaller the p-value, the more probable that the signal is real and not random. This produces huge spikes in the negative-log values (in the body of the paper they present p-values on the order of 10-35).

You can see that it is in APOL1 that the biggest signals reside. The first panel, A, throws all the SNPs into the mix. On MYH9 they highlight a few SNPs which combine to form the E-1 haplotype, which is strongly associated with cases (this is where the association between disease and genetic variants on MYH9 are coming from). This haplotype is found in conjunction with G1 and G2 on APOL1. E-1 is present in 89% of haplotypes carrying G1 and in 76% of haplotypes carrying G2. A classic illustration of likely correlation but not causation. The second panel controls for the effect of G1. In other words, this is showing you the variation in the dependent variable that remains after you take the largest independent variable, G1, into account. The G2 haplotype is the largest effect independent variable after G1 is taken into account; in other words, it explains most of the residual variation in FSGS probability. Finally, the last panel controls for both G1 and G2. As you can see there aren’t any major signals left; the distribution is relatively flat. Logically once you account for the variables which produce change in an outcome you shouldn’t see any impact of other variables. And that’s what happens here. They also performed controls where MYH9 was held constant, and that does not eliminate the signals in APOL1. MYH9 is conditional on its correlation with APOL1. This was the correlation which showed up on the original association studies. The exact same pattern of signals within the logistic regression model was replicated for H-ESKD. G1 had the strongest signal, then G2. The markers within MYH9 was not significant once one controlled for the variants in G1 and G2.

It is important to remember though that these markers are segregating within a human population where individuals have three potential genotypes. Ancestral homozygote, homozygote for the mutants, and heterozygote. They found that a recessive model of expression of disease is most appropriate in the case of these risk alleles. That is, most of the increased risk is accounted for by the change from one risk allele, the heterozygote state, to two risk alleles, the homozygote state. One risk allele increased odds of renal failure by 1.26, but two by 7.3. The odds ratio of two risk alleles compared to a base rate of one risk allele was 5.8. They report that the results for FSGS were broadly similar. This matters because the frequency of the trait/disease in a random mating population is conditional on the homozygotes if it has a recessive expression pattern. G1 was present in 40% of Yoruba HapMap data set, but in none of the two Eurasian groups, Europeans and East Asians. G2 was found in three Yoruba, but in none of the Eurasian groups. Assuming Hardy-Weinberg equilibrium the Yoruba should have 16% of the population at sharply elevated risk for FSGS and H-ESKD because they’d be homozygotes for the G1 allele.

Once they established which markers seem to implicated in this phenotypic variation, they wanted to focus on how the frequencies of those markers came to be. Specifically, G1 and G2 seem to be derived haplotypes which arose out of the ancestral background. In plain English 20,000 years ago Africans should have looked like all non-Africans genomically, at least on the functionally relevant segments, but within the last 10,000 years it looks like new variants rose in frequency driven by natural selection to new environmental stresses. The region has already broadly been surveyed by linkage disequilibrium based tests, which basically look for regions of long haplotypes, homogenized zones of the genome where many individuals have the variation removed because one gene rose so rapidly in frequency that huge adjacent sections hitchhiked up in frequency. Presumably this may have happened with the MYH9 haplotype correlated with the traits under consideration here; G1 and G2 dragged up the E-1 haplotype as a secondary consequence of their own rise to prominence among some Sub-Saharan African populations.

So next authors turned to tried & tested techniques and focused on the risk markers which they had discovered earlier in their research, G1 and G2. Specifically, EHH, which is best at detecting selection where sweeps have nearly completed (e.g., the derived variant is at frequency 0.95 within the population), iHS, which is best at detecting sweeps which have not completed (e.g., the derived variant is at frequency 0.6), as well as ΔiHH, which I am less familiar with but is reputedly similar to iHS but uses absolute haplotype length as opposed to relative haplotype length. Figure 2 show the results of these tests:

apol13

The resolution isn’t the best, but G1 and G2 seem to be outliers on all three tests to detect natural selection by using patterns of linkage disequilibrium. The first panel is EHH, the second and third show iHS and ΔiHH respectively, with the position of the markers being outliers among the distribution of values for the genome within the Yoruba. This is not proof of adaptation, but it changes our weights of possibilities. Additionally, they note that Europeans exhibit no such patterns on these markers. Visually the position of the markers in the latter two panels would be closer to the mode of the distribution in Europeans.

To review, first they confirmed a causal relationship between a particular set of markers, haplotypes, and the traits of interest. Second, they confirmed that said markers seem to bear the hallmarks of genomic regions subject to natural selection. We know that focal segmental glomerulosclerosis (FSGS) end-stage kidney disease (H-ESKD), the traits whose relationship to the G1 and G2 haplotypes seem confirmed, are unlikely to be targets of positive natural selection. To get a better sense of that we need to look at Apol1, the protein product of APOL1, and what it does. At this point I’ll quote the paper:

ApoL1 is the trypanolytic factor of human serum that confers resistance to the Trypanosoma brucei brucei (T. brucei brucei) parasite…T. brucei brucei has evolved into two additional subspecies, Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense, which have both acquired the ability to infect humans…T. brucei rhodesiense is predominantly found in Eastern and Southeastern Africa, while T. brucei gambiense is typically found in Western Africa, though some overlap exists…Since these parasites exist only in sub-Saharan Africa, we hypothesized that the APOL1 gene may have undergone natural selective pressure to counteract these trypanosoma adaptations. As an initial test of this hypothesis, we performed in vitro assays to compare the trypanolytic potential of the variant, disease-associated forms of ApoL1 proteins with that of the “wild-type” form of ApoL1 protein that is not associated with renal disease.

We’re talking about sleeping sickness. Here’s a description:

It starts with a headache, joint pains and fever. It is the kind you would expect to get over quickly. But after a while, things get worse. You fall asleep most of the time, are confused and get intense pains and convulsions.

If you do not get treatment, your body begins to waste away. Eventually, you slip into coma and die. This is human African trypanosommiasis, better known as sleeping sickness. If untreated, it kills 100% of its victims in a very short time.

Cheery. I think we have a plausible reason for natural selection to kick into overdrive! Or more specifically, we have a plausible external selection pressure which will drive fitness differentials which correlate with genetic variation. Increased probability of kidney disease seems preferable to this. In terms of the molecular genetics it looks like a factor, serum resistance-associated protein (SRA), produced by T. brucei rhodesiense binds to a specific location of Apol1, and that mutations at G1 and G2 change exactly that location within the protein. So these mutants may block the ability of T. brucei rhodesiense to turn off the body’s defenses against trypanosomes.

To test this they examined the in vitro lytic potential of serum produced by individuals carrying the G1 and G2 haplotypes against the three subspecies of of Trypanosoma. T. brucei brucei, which normal Apol1 can lyse, and T. brucei rhodesiense and T. brucei gambiense which can infect humans (endemic to eastern and western Africa respectively, though the former extends into west Africa as well).

- All 75 samples lysed brucie brucie

- None lysed brucie gambiense

- 46 samples lysed SRA-positive brucie rhodesiense, all 46 samples were from G1 or G2 carrying individuals

- The potency of G2 seemed higher than G1 against SRA-positive samples of brucie rhodesiense, though not SRA-negative samples, where G1 seemed as potent

- Recombinants of Apol1 which had only one of the two SNPs of the G1 haplotype were less effective against brucie rhodesiense than those which had both (G1 haplotype)

- Recombinants with G1 and G2 were not more effective against brucie rhodesiense than those with G2 alone

- Recombinants with G1 alone were more potent against SRA-negative brucie rhodesiense than those with G2 alone

- G2 was necessary and sufficient to block SRA binding to Apol1 and allow lysing of brucie rhodesiense. G1 did not block SRA binding to Apol1, but was still sufficient to lyse brucie rhodesiense, but far less potent against SRA-positive brucie rhodesiense than G2

It seems that the G1 and G2 haplotypes utilize different mechanisms to enable the lysing of invasive pathogens, and so prevent the development of sleeping sickness. Their means differ, but the ends are the same. The authors note that even minimal amounts of plasma serum produced by G2 individuals seems potent enough to block the binding of SRA to Apol1 and so enable lysis. And introduction of such plasma into the bloodstreams of individuals who do not have resistance may then be highly efficacious as a preventative treatment against sleeping sickness. They do note that they did not explore in detail the mechanism by which the G1 and G2 variants result in suscepbility to kidney failure, but that’s presumably for the future.

Finally, the second to last paragraph where they bring it all together:

It will be interesting to determine the distribution of these mutations throughout sub-Saharan Africa. In present-day Africa, T. brucei rhodesiense is found in the Eastern part of the continent, while we noted high frequency of the trypanolytic variants and the signal of positive selection in a West African population. Changes in trypanosome biology and distribution and/or human migration may explain this discrepancy, or resistance to T. brucei rhodesiense could have favored the spreading of T. brucei gambiense in West Africa. Alternatively, ApoL1 variants may provide immunity to a broader array of pathogens beyond just T. brucei rhodesiense, as a recent report linking ApoL1 with anti-Leishmania activity may suggest…Thus, resistance to T. brucei rhodesiense may not be the only factor causing these variants to be selected.

This is a very long review already. But, while I have your attention, I think I need to point to another paper on the same topic which has a slightly different twist. I won’t dig into the details with the same thoroughness as above, but rather I’ll highlight the value-add of this group’s contribution. It’s an Open Access paper, unlike the one above, so you can review it in depth yourself. Missense mutations in the APOL1 gene are highly associated with end stage kidney disease risk previously attributed to the MYH9 gene:

MYH9 has been proposed as a major genetic risk locus for a spectrum of nondiabetic end stage kidney disease (ESKD). We use recently released sequences from the 1000 Genomes Project to identify two western African-specific missense mutations (S342G and I384M) in the neighboring APOL1 gene, and demonstrate that these are more strongly associated with ESKD than previously reported MYH9 variants. The APOL1 gene product, apolipoprotein L-1, has been studied for its roles in trypanosomal lysis, autophagic cell death, lipid metabolism, as well as vascular and other biological activities. We also show that the distribution of these newly identified APOL1 risk variants in African populations is consistent with the pattern of African ancestry ESKD risk previously attributed to MYH9. Mapping by admixture linkage disequilibrium (MALD) localized an interval on chromosome 22, in a region that includes the MYH9 gene, which was shown to contain African ancestry risk variants associated with certain forms of ESKD…MYH9 encodes nonmuscle myosin heavy chain IIa, a major cytoskeletal nanomotor protein expressed in many cell types, including podocyte cells of the renal glomerulus. Moreover, 39 different coding region mutations in MYH9 have been identified in patients with a group of rare syndromes, collectively termed the Giant Platelet Syndromes, with clear autosomal dominant inheritance, and various clinical manifestations, sometimes also including glomerular pathology and chronic kidney disease…Accordingly, MYH9 was further explored in these studies as the leading candidate gene responsible for the MALD signal. Dense mapping of MYH9 identified individual single nucleotide polymorphisms (SNPs) and sets of such SNPs grouped as haplotypes that were found to be highly associated with a large and important group of ESKD risk phenotypes, which as a consequence were designated as MYH9-associated nephropathies…These included HIV-associated nephropathy (HIVAN), primary nonmonogenic forms of focal segmental glomerulosclerosis, and hypertension affiliated chronic kidney disease not attributed to other etiologies…The MYH9 SNP and haplotype associations observed with these forms of ESKD yielded the largest odds ratios (OR) reported to date for the association of common variants with common disease risk…Two specific MYH9 variants (rs5750250 of S-haplotype and rs11912763 of F-haplotype) were designated as most strongly predictive on the basis of Receiver Operating Characteristic analysis…These MYH9 association studies were then also extended to earlier stage and related kidney disease phenotypes and to population groups with varying degrees of recent African ancestry admixture…and led to the expectation of finding a functional African ancestry causative variant within MYH9. However, despite intensive efforts including re-sequencing of the MYH9 gene no suggested functional mutation has been identified…This led us to re-examine the interval surrounding MYH9 and to the detection of novel missense mutations with predicted functional effects in the neighboring APOL1 gene, which are significantly more associated with ESKD than all previously reported SNPs in MYH9.

Table one has the top line results. Focus on the first two rows, they’re “G1″ from the earlier study (that is, the two SNPs which combine to form the G1 haplotype).

apo14

Here’s a difference between the previous paper and this one: the table above uses cases and controls from African Americans and Hispanic Americans. The original paper which the genomic data on this sample is drawn from calculates the average ancestry of African, European and Native American in the two groups is as follows (I did some rounding to keep the values round):

African American – 85%, 10%, 5%
Hispanic American – 30%, 55%, 15%

Not surprisingly the Hispanic American sample here is mostly Puerto Rican and Dominican, explaining the greater African than Native American ancestry. Nevertheless, it is a sufficiently different genetic background to test the effects of the same marker against different genes. They confirmed the association of the markers of large effect in African Americans within the Hispanic cohort. The risk allele frequency in the African American control group is 21% vs. 37% in the cases. For Hispanic Americans are 6% and 23% for the same categories.

OK, now to the most interesting point in this short paper:

HIVAN has been considered as the most prominent of the nondiabetic forms of kidney disease within what has been termed the MYH9-associated nephropathies…We have reported absence of HIVAN in HIV infected Ethiopians, and attributed this to host genomic factors (Behar et al. 2006). Therefore, we examined the allele frequencies of the APOL1 missense mutations in a sample set of 676 individuals from 12 African populations, including 304 individuals from four Ethiopian populations…We coupled this with the corresponding distributions for the African ancestry leading MYH9 S-1 and F-1 risk alleles. A pattern of reduced frequency of the APOL1 missense mutations and also of the MYH9 risk variants was noted in northeastern African in contrast to most central, western, and southern African populations examined…Especially striking was the complete absence of the APOL1 missense mutations in Ethiopia. This combination of the reported lack of HIVAN and observed absence of the APOL1 missense mutations is consistent with APOL1 being the functionally relevant gene for HIVAN risk and likely the other forms of kidney disease previously associated with MYH9.

apo16Bingo. The previous paper focused on African Americans (along with the HapMap Yoruba). But the pattern of variation within Africa is interesting as well. Ethiopians are not quite like other Africans, having a great deal of admixture with populations from Arabia (many of the languages of highland Ethiopia are Semitic). But the majority of their ancestry remains similar to that of other Sub-Saharan Africans. As a point of contrast the ecology of Ethiopia differs a great deal from the rest of Sub-Saharan Africa because of its elevation, and concomitant frigidity. The mean monthly low in Addis Ababa is around 10 (50 for Americans) degrees and mean high 20-25 (high 60s to mid 70s for Americans). There isn’t much variation from month to month because of the low latitude, but the high elevation keeps the temperatures relatively moderate. Different environments result in different selection pressures, and Ethiopia has a very unique environment within Africa. The tsetse fly which serves as a vector forTtrypanosomes does not seem to be present in the Ethiopian highlands. The map above shows the distribution within Africa of one the markers which defines the G1 haplotype in the previous paper. Note that the modal frequency is in the west of Africa, and the frequency drops off to the east (though the geographic coverage leaves a bit to be desired if you look at the raw data which went into generating this map, which smooths over huge discontinuities).

One of the points I want to reemphasize from the tests of natural selection in the first paper is that these genetic adaptations are likely to be new, otherwise recombination would have broken up the long haplotypes and reduced linkage disequilibrium. New as in the last 10,000 years. It is interesting that a particular subspecies of Trypanosome which is immune to these genetic adaptations is endemic to west Africa. We may be seeing evolution in action here, or at least the arms race between man and pathogen where man is always one step behind. In contrast, the subspecies which is effectively diffused by the genetic adaptations reviewed here is present in higher numbers precisely in the regions where the resistance mutations are extant at lower proportions. Perhaps there are different mutations in these regions of Africa, not yet properly identified. Or perhaps the we’re seeing humans in this region at an earlier stage of the dance, so to speak.

Citation: Giulio Genovese, David J. Friedman, Michael D. Ross, Laurence Lecordier, Pierrick Uzureau, Barry I. Freedman, Donald W. Bowden, Carl D. Langefeld, Taras K. Oleksyk, Andrea Uscinski Knob, Andrea J. Bernhardy, Pamela J. Hicks, George W. Nelson, Benoit Vanhollebeke, Cheryl A. Winkler, Jeffrey B. Kopp, Etienne Pays, & Martin R. Pollak (2010). Association of Trypanolytic ApoL1 Variants with Kidney Disease in African-Americans Science : 10.1126/science.1193032

Citation: Tzur S, Rosset S, Shemer R, Yudkovsky G, Selig S, Tarekegn A, Bekele E, Bradman N, Wasser WG, Behar DM, & Skorecki K (2010). Missense mutations in the APOL1 gene are highly associated with end stage kidney disease risk previously attributed to the MYH9 gene. Human genetics PMID: 20635188

(Republished from Discover/GNXP by permission of author or representative)
 
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ResearchBlogging.orgBelow I note that sex matters when it comes to evolution, specifically in the case of how sexual reproduction forces the bits of the genome to be passed back and forth across sexes. In fact, the origin of sex is arguably the most important evolutionary question after the origin of species, and it remains one of the most active areas of research in evolutionary genetics. More specifically the existence of males, who do not bear offspring themselves but seem to be transient gene carriers is a major conundrum. But that’s not the main issue in this post. Let’s take the existence of males as a given. How do sex differences play out in evolutionary terms shaping other phenotypes? Consider Bateman’s principle:

Bateman’s principle is the theory that females almost always invest more energy into producing offspring than males, and therefore in most species females are a limiting resource over which the other sex will compete.

Female ova are energetically more expensive, and scarcer, than male sperm. Additionally, in mammals and other live-bearing species the female invests more time and energy after the point of fertilization but before the young exhibit any modicum of organismic independence (the seahorse being the exception). And, often the female is the “primary caregiver” in the case of species where the offspring require more care after birth. The logic of Bateman’s principle is so obvious when its premises are stated that it easily leads to a proliferation of numerous inferences, and many data are “explained” by its operation (in Mother Nature: Maternal Instincts and How They Shape the Human Species the biological anthroplogist Sarah Hrdy moots the complaint that the principle is applied rather too generously in the context of an important operationally monogamous primate, humans).

But the general behavioral point is rooted in realities of anatomy and life-history; in many dioecious species males and females exhibit a great deal of biological and behavioral dimorphism. But the direction and nature of dimorphism varies. Male gorillas and elephant seals are far larger than females of their kind, but among raptors females are larger. If evolution operated like Newtonian mechanics I assume we wouldn’t be theorizing about why species or sex existed at all, we’d all long ago have evolved toward perfectly adapted spherical cows floating in our own effluvium, a species which is a biosphere.

Going beyond what is skin deep, in humans it is often stated that males are less immunologically robust than females. Some argue that this is due to higher testosterone levels, which produce a weakened immune system. Amtoz Zahavi might argue that this is an illustration of the ‘handicap principle’. Only very robust males who are genetically superior can ‘afford’ the weakened immune system which high testosterone produces, in addition to the various secondary sexual characteristics beloved of film goers. Others would naturally suggest that male behavior is to blame. For example, perhaps males forage or wander about more, all the better to catch bugs, and they pay less attention to cleanliness.

But could there be a deeper evolutionary dynamic rooted in the differential behaviors implied from Bateman’s principle? A new paper in The Proceedings of the Royal Society explores this question with a mathematical model, The evolution of sex-specific immune defences:

Why do males and females often differ in their ability to cope with infection? Beyond physiological mechanisms, it has recently been proposed that life-history theory could explain immune differences from an adaptive point of view in relation to sex-specific reproductive strategies. However, a point often overlooked is that the benefits of immunity, and possibly the costs, depend not only on the host genotype but also on the presence and the phenotype of pathogens. To address this issue we developed an adaptive dynamic model that includes host–pathogen population dynamics and host sexual reproduction. Our model predicts that, although different reproductive strategies, following Bateman’s principle, are not enough to select for different levels of immunity, males and females respond differently to further changes in the characteristics of either sex. For example, if males are more exposed to infection than females (e.g. for behavioural reasons), it is possible to see them evolve lower immunocompetence than females. This and other counterintuitive results highlight the importance of ecological feedbacks in the evolution of immune defences. While this study focuses on sex-specific natural selection, it could easily be extended to include sexual selection and thus help to understand the interplay between the two processes.

The paper is Open Access, so you can read it for yourself. The formalism is heavy going, and the text makes it clear that they stuffed a lot of it into the supplements. You can basically “hum” through the formalism, but I thought I’d lay it out real quick, or at least major aspects.

This shows the birth rate of a given genotype contingent upon population density & proportions of males & females infected with a pathogen

graphic-1

These equations takes the first and nests them into an epidemiological framework which illustrates pathogen transmission (look at the first right hand term in the first two)

graphic-3

And these are the three models that they ran computations with

graph4

There are many symbols in those equations which aren’t obvious, and very difficult to keep track of. Here’s the table which shows what the symbols mean….

symboltable

If you really want to understand the methods and derivations, as well how the details of how they computa e evolutionarily stable strategies, you’ll have to go into the supplements. Let’s just assume that their findings are valid based on their premises.

Note:

- They assume no sexual selection
- They assume unlimited male gametes, so total reproductive skew where one male fertilizes all females is possible
- Fecundity is inversely correlated with population density
- Total population growth is ultimately dependent on females, they are the “rate limiting” sex
- Total population growth is proportional to density
- There is no acquired immunity
- There is no evolution of the pathogen in this model

Basically the model is exploring a quantitative trait which exhibits characteristics in relation to resistance of acquiring the pathogen and tolerance of it once the pathogen is acquired. In terms of the “three models,” the first is one where there is resistance to the pathogen, individuals recover from infection and decrease pathogen fitness. The second is one of tolerance, individuals are infected, but may still reproduce while infected. Note that the ability to resist or tolerate infection has a trade off, reduced lifespan (consider some forms of malaria resistance). The third model shows the trade off of tolerance and resistance.

The “pay off” of the paper is that they show that the male evolutionarily stable strategy (ESS), that is, a morph which can not be “invaded” by a mutation, may be one of reduced immune resistance in certain circumstances of high rates of infection. There is an exploration of varying rates of virulence, but there was no counterintuitive finding so I won’t cover that. In any case, here’s the figure:

graphresistence

The text is small, so to clarify:

1) The two panels on the top left are for model 1, and show variation in male and female recovery from infection left to right (resistance)

2) The two panels on the bottom left are for model 2, and show variation in male and female fecundity when infected left to right (tolerance)

3) The four panels on the right are for model 3, and show variation in recovery in the top two panels and fecundity in the bottom two, with male parameters varied on the left and female on the right

The vertical axis on all of the panels are male infection rate, the horizontal the female infection rate. Circled crosses (⊕) indicate regions (delimited by solid lines) where females evolve higher immunocompetence than males. The lighter shading indicates a higher value of the trait at ESS (recovery or fecundity). Note that the two top left panels show a peculiar pattern for males, the sort of counterintuitive finding which the model promises: when infection rates among males are very high their resistance levels drop. Why? The model is constructed so that resistance has a cost, and if they keep getting infected the cost is constant and there’s no benefit as they keep getting sick. In short it is better to breed actively for a short time and die than attempt to fight a losing battle against infection (I can think of possible explanations of behavior and biological resistance in high disease human societies right now). It is at medium levels of infection rates that males develop strong immune systems so that they recover. The bottom right portion of panel which shows variation in male resistance illustrates a trend where high female infection results in reduced immune state in males. Why? The argument is simple; female population drops due to disease result in a massive overall population drop and the epidemiological model is such that lower densities hinder pathogen transmission. So the cost for resistance becomes higher than the upside toward short-term promiscuous breeding in hopes of not catching the disease. Another point that is notable from the panels is that males seem to be more sensitive to variation in infection rates. This makes sense insofar as males exhibit a higher potential variance in reproductive outcomes because of the difference in behavior baked into the model (males have higher intrasexual competition).

One can say much more, as is said in the paper. Since you can read it yourself, I commend you to do so if you are curious. Rather, I would like a step back and ask: what does this “prove?” It does not prove anything, rather, this is a model with many assumptions which still manages to be quite gnarly on a first run through. It is though suggestive in joint consideration with empirical trends which have long been observed. Those empirical trends emerge out of particular dynamics and background parameters, and models can help us formalize and project abstractly around real concrete biological problems. The authors admit their model is simple, but they also assert that they’ve added layers of complexity which is necessary to understand the dynamics in the real world with any level of clarity. In the future they promise to add sexual selection, which I suspect will make a much bigger splash than this.

I’ll let them finish. From their conclusion:

We assessed the selective pressures on a subset of sex-specific traits (recovery rate, reproductive success during infection and lifespan) caused by arbitrary differences between males and females in infection rate or virulence (i.e. disease-induced death rate). In so doing, we covered a range of scenarios whereby sex-specific reproductive traits such as hormones and behaviour could plausibly affect the exposure to infection…r the severity of disease…First, we showed that changes in the traits of either sex affect the selective pressures on both sexes, either in the same or in opposite directions, depending on the ecological feedbacks. For example, an increase in male susceptibility (or exposure) to infection favours the spread of the pathogen in the whole population and therefore tends to select for higher resistance or tolerance in both sexes if the cost of immunity is constitutive. However, above a certain level of exposure, the benefit of rapid recovery in males decreases owing to constant reinfection (we assume no acquired immunity). This selects for lower resistance in males, ultimately leading to the counterintuitive situation where males with higher susceptibility or exposure to infection than females evolve lower immunocompetence…A similar pattern arises if the cost of immunity is facultative, in the form of a trade-off between rate of recovery and relative fecundity during infection (model (iii)): if males happen to be more susceptible (or exposed) to infection than females, they are predicted to evolve a longer infectious period balanced by higher sexual activity during infection than females.

Restif, O., & Amos, W. (2010). The evolution of sex-specific immune defences Proceedings of the Royal Society B: Biological Sciences DOI: 10.1098/rspb.2010.0188

(Republished from Discover/GNXP by permission of author or representative)
 
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Doubling Of Sexually Transmitted Infections Among Over-45s In Under A Decade. Dare we say an “epidemic???” If you want to push the envelope of course, She was 82. He was 95. They had dementia. They fell in love. And then they started having sex. In any case:

While the numbers of infections identified in younger age groups rose 97% during the period of the study, those identified in the over 45s rose 127%.

“Indeed, it may be argued that older people are more susceptible [to sexually transmitted infections] as they are less likely to use condoms than younger people,” they say, adding that as successive waves of people with more liberal sexual attitudes and behaviours age, the problem is likely to worsen.

I guess the “safe sex” message just isn’t getting through to the less young.

Related: Your generation was sluttier.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Disease, Sex 
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Yann points to a new paper, new paper, Cystic Fibrosis: Cystic fibrosis and lactase persistence: a possible correlation (Open Access):

The simplest and most economical explanation is that a dairy-milk diet became established in a single area and remained restricted to that area for a period of time sufficient to allow the T and the F508del alleles to attain high values. Then, in a second phase, the population of that area exported to the rest of Europe its dairy-milk diet culture together with the two adaptive genes, that is, the adaptogen and the two genetic adaptations to it. These two alleles would have then been amplified in the recipient populations because of their adaptive value owing to the co-imported dairy milk diet.

The two models to explain the high frequency of the deleterious CF allele in Europeans are that it has a high mutational bias and heterozygote advantage for those with one copy. Most people would say that the latter is much more likely. The authors here propose that the derived CF allele was a really kludgey adaptive response to a new cultural regime predicated on raw milk consumption. Paul has some Ireland related thoughts (as usual!). I’ve never seen the term “adaptopgen” before. In any case, I need to think on this case more…but I do think that if human evolution has been on hyperdrive the last 10,000 years we should be many kludgey genetic responses laying around the adaptive landscape…..

Related: Lactase persistence posts. Another from Yann, Is there a fitness advantage to being a CFTR carrier?

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Disease, Genetics 
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Absence of contagious yawning in children with autism spectrum disorder:

This study is the first to report the disturbance of contagious yawning in individuals with autism spectrum disorder (ASD). Twenty-four children with ASD as well as 25 age-matched typically developing (TD) children observed video clips of either yawning or control mouth movements. Yawning video clips elicited more yawns in TD children than in children with ASD, but the frequency of yawns did not differ between groups when they observed control video clips. Moreover, TD children yawned more during or after the yawn video clips than the control video clips, but the type of video clips did not affect the amount of yawning in children with ASD. Current results suggest that contagious yawning is impaired in ASD, which may relate to their impairment in empathy. It supports the claim that contagious yawning is based on the capacity for empathy.

Someone should do behavioral economics studies on groups of autistic individuals. Would surely validate the mid-20th century microeconomic consensus.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Disease, Psychology 
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Are Some Men Predisposed To Pedophilia?:

A difference in average height is a trait found in other illnesses with biological links. The average difference in height was two centimeters, which is similar to the shorter height associated with schizophrenia or Alzheimer’s disease.

Further research is necessary, but this finding re-enforces evidence that pedophilia has a biological cause, possibly related to brain development before birth.

I’m really not that interested in the biological origins of pedophilia, instead my attention was drawn to the fact that such a height difference is known for a range of disorders. In The Mating Mind Geoffrey Miller hypothesized that variance in mutational load across individuals tracked beauty. This is basically a “good genes” model for why organisms exhibit sexual preferences. Miller was assuming a polygynous social system, but this makes me wonder as to the importance of “good health” due to provisioning in a monogamous species.

Though height is about 80% heritable in modern environments that still leaves an unaccounted for 20%; where does that come from? Possibly infection or developmental instability early on for whatever exogenous reason. In pre-modern contexts one assumes that heritability would be a bit lower because of the random stresses during pregnancy and during early childhood growth. In any case, adult height in males would surely be a good proxy for how healthy he is, and how productive a provider he might be. Additionally, good genes is still operative in a scenario where ability to resist and fight off infection is a proxy for fitness.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Disease, Height 
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David Dobbs has an interesting article in The New York Times Magazine about Williams syndrome; a disorder characterized by verbosity and hypersociality in concert with abstraction capacities so attenuated that most suffers are mentally retarded. The piece juggles many phenomena, from general to domain specific intelligences and the interaction between environment and genetic biases which shape the mind’s developmental arc.

Inverted: Hidden Smarts: Abstract thought trumps IQ scores in autism:

There’s more to the intelligence of autistic people than meets the IQ. Unlike most individuals, children and adults diagnosed as autistic often score much higher on a challenging, nonverbal test of abstract reasoning than they do on a standard IQ test, say psychologist Laurent Mottron of Hopital Riviere-des-Prairies in Montreal and his colleagues.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Disease, Psychology 
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A comment below about gluten intolerance (which is associated with problems digesting products with wheat) made me curious. In much of Eurasia this would be a serious problem since wheat is the staff of life. Hard numbers are difficult to come by. This is as good as anything else I’ve seen:

Celiac disease affects as many as 1 in 300 people in Italy and southwestern Ireland, but is extremely rare in Africa, Japan, and China…According to a multicenter study in 2003, there is a 1 in 133 chance that people with no risk factors or family history in the U.S. have celiac disease. Additionally, a person’s risk increases to a 1 in 22 chance if they have a first-degree relative with celiac disease and a 1 in 39 chance if they have a second-degree relative…Around 60,000 Americans are diagnosed with celiac disease annually and a total of over 2 million have the disease, making it perhaps the most common genetic disorder in the United States…Celiac disease can occur at any age, and females are more commonly affected than males. Of females presenting during their fertile years, the male to female ratio is almost 3 to 1….

From Wiki:

The vast majority of coeliac patients have one of two types of HLA DQ, a gene that is part of the MHC class II antigen-presenting receptor (also called the human leukocyte antigen) system and distinguishes cells between self and non-self for the purposes of the immune system. There are 7 HLA DQ variants (DQ2 and D4 through 9). Two of these variants-DQ2 and DQ8-are associated with coeliac disease. Every person inherits two copies, one from each parent. The gene is located on the short arm of the sixth chromosome, and as a result of the linkage this locus has been labeled CELIAC1.

Coeliac disease shows incomplete penetrance, as the gene alleles associated with the disease appear in most patients, but are neither present in all cases nor sufficient by themselves cause the disease. Over 95% of coeliac patients have an isoform of DQ2 (DQA1*0501:DQB1*0201 haplotype or more simply DQ2.5) and DQ8 (DQA1*0301:DQB1*0302), which is inherited in families.

Incomplete penetrance might be due to the fact that there are other genetic actors which haven’t been elucidated that are necessary for the emergence of this syndrome. Or, there might be environmental or pathogenic triggers which only affect a minority with the necessary genetic predisposition. But in any case, my first thought was gluten intolerance might be the result of an incomplete selection sweep as populations shifted from hunter-gatherer lifestyles to agricultural ones. I’m skeptical of this since populations in Africa and Australia which don’t have a history of wheat agriculture don’t exhibit this syndrome. Additionally, though wheat agriculture is practiced in north China this was originally a region of millet production. Finally, all the reports suggest massive underestimates of the extent of this condition within the population. Like lactose intolerance this isn’t a disease with a clean set of symptoms which are easy to assay quantitatively (is there a way a metric for stool firmness?). The implication of MHC loci as necessary preconditions makes me wonder if gluten intolerance is simply a low frequency condition which is a byproduct of a disease adaptation on the genes in question which was operant in western Eurasia.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Disease, Genetics 
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Mutated gene raises autism risk, US study finds:

People with two copies of the mutated gene [MET] have 2 to 2.5 times the normal risk of autism and people with one mutated copy have 1.7 times the risk, he said.

Levitt said the mutation does not change the function of the gene, but changes gene expression — how active the gene is.

The study is going to be published in PNAS.

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Study finds gene related to brain development and function plays causal role in schizophrenia:


The study showed that genetic variation in OLIG2 was strongly associated with schizophrenia. In addition, OLIG2 also showed a genetic association with schizophrenia when examined together with two other genes previously associated with schizophrenia–CNP and ERBB4–which are also active in the development of myelin. The expression of these three genes was also coordinated. Taken together the data support an etiological role for oligodendrocyte abnormalities in the development of schizophrenia.

The paper is in PNAS. Interestingly the authors seriously consider the possibility of epistatic (gene to gene interaction) as being part of the risk for schizophrenia.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Disease 
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The Geographic Spread of the CCR5 Δ32 HIV-Resistance Allele. A picture is worth a lot of words, so….

Note the appeal to the “wave of advance” model of R.A. Fisher, you’ve seen it before in reference to the possibility of advantageous alleles spreading throughout populations via selection without concomitant wholesale demographic dislocations and migrations. The authors of the above article, which is freely available to all via PLOS, suggest that one possible reason that the Δ32 mutation isn’t more common is that selection hasn’t had enough time to operate. For comparison, consider lactose tolerance, though in Eurasia the allele that confers the ability to adults to easily digest milk products as adults probably has its origins in Northern Europe, in places like Northern India the phenotype has reached ~70% levels of prevalence. Contrary to some Aryan fantasists this does not imply that an ancient influx of Swedes transformed the South Asian demographic landscape, rather, selection knows a good thing.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Disease 
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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"