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May 21, 2011

Kissing and cancer

Filed under: Cancer,Disease,Health — Razib Khan @ 10:39 pm

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 ...

April 24, 2011

The evolutionary effect of the sky gods

ResearchBlogging.orgLast 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 ...

September 23, 2010

The city that kills you makes you strong!

ResearchBlogging.orgOver 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.

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

July 26, 2010

Diseases of the Silk Road

Filed under: Disease,Genetics,genome-wide association,Genomics,GWA,Health — Razib Khan @ 7:42 am

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.orgBut 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 microorganismsthat 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

July 21, 2010

Disease as a byproduct of adaptation

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

April 23, 2010

Differences in swine flu response by population

Filed under: anthropology,Disease,Genetics,Population Structure — Razib @ 1:24 am

Remember when there was talk about how SARS might disproportionately hit Chinese in comparison to other populations? Here’s a new paper on how Swine Flu may progress in different populations, Clinical Findings and Demographic Factors Associated With ICU Admission in Utah Due to Novel 2009 Influenza A(H1N1) Infection:

The ICU cohort of 47 influenza patients had a median age of 34 years, Acute Physiology and Chronic Health Evaluation II score of 21, and BMI of 35 kg/m2. Mortality was 17% (8/47). All eight deaths occurred among the 64% of patients (n = 30) with ARDS, 26 (87%) of whom also developed multiorgan failure. Compared with the Salt Lake County population, patients with novel A(H1N1) were more likely to be obese (22% vs 74%; P < .001), medically uninsured (14% vs 45%; P < .001), and Hispanic (13% vs 23%; P < .01) or Pacific Islander (1% vs 26%; P < .001). Observed ICU admissions were 15-fold greater than expected for those with BMI ≥ 40 kg/m2 (standardized morbidity ratio 15.8, 95% CI, 8.3-23.4) and 1.5-fold greater than expected among those with BMI of 30 to 39 kg/m2 for age-adjusted and sex-adjusted rates for Salt Lake County.

Remember that these are 47 intensive care patients, the most extreme cases. Here’s a table with N’s & odds ratios:

I’m struck by the Pacific Islander results. Obviously there are some confounds here, Pacific Islanders are heavier and have lower socioeconomic status from what I know. But the odds ratio is so high. Unfortunately I don’t see the obesity levels of the Pacific Islanders broken out, rather, they controlled for ethnicity by looking only at the white population (and obesity, smoking, etc., still mattered). I wonder how much more susceptible groups from low density or isolated societies, like Pacific Islanders, are to endemic infectious diseases.

Citation: doi: 10.1378/chest.09-2517, CHEST April 2010 vol. 137 no. 4 752-758

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March 31, 2010

When sickliness is manliness

Filed under: Behavior,Disease,Evolution,Evolutionary Genetics,Genetics,Immunity,Sex — Razib Khan @ 2:25 am

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 computae 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

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