Category Archives: evolution

Nothing in Biology Makes Sense: The evolution of lizards on islands. No, not those lizards.

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Hemidactylus granti. Photo via Nothing in Biology Makes Sense!

Over at the collaborative science blog Nothing in Biology Makes Sense!, Noah Reid describes a new study dissecting the evolutionary history of island-dwelling lizards—not the field model Caribbean Anolis, but geckoes in the genus Hemidactylus, living on islands in the Indian Ocean.

The Socotra archipelago is a particularly interesting, but poorly studied island system. Socotra consists of four islands in the Indian Ocean. It is extremely isolated (150 miles from the horn of Africa, 240 miles from the Arabian Peninsula) yet it has a continental origin. That means it was once part of the supercontinent Gondwana and suggests that some species may have lived there since it first became an island (~17.6 million years ago). Socotra has a very high level of endemism, with 37% of its plant species and 90% of its reptiles occurring nowhere else.

To find out how some of those endemic reptiles got to Socotra, go read the whole thing.◼

Unfamiliar sperm, Tibetans, and cheese: Why evolutionary biology doesn’t excuse Todd Akin

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Be advised: rape, Republicans, and evolutionary psychology will be discussed herein. Also there is math.

So I didn’t think I’d be posting anything about that idiot who’s running for Senator from Missouri, Todd Akin, who said that it’s not necessary to ensure that abortion remain a safe and legal option for victims of rape because women’s bodies somehow kill or eject sperm from people they didn’t want to have sex with, or something. Which is just so obviously, stupidly, dangerously wrong that what could I possibly have to say about it that hasn’t already been said better elsewhere?

Then, on Monday, this happened:


Original tweet here.

That’s the immediate follow-up to a tweet linking to an article Bering wrote a couple weeks ago, in which he explains the reasoning behind an evolutionary psychology hypothesis that women’s bodies can, in some cases, do something very like what Todd Aikin thinks they can. In 2006, Jennifer Davis and Gordon Gallup (yes, that’s Gordon “adaptive homophobia” Gallup) noted in a book chapter [PDF] that there’s an interesting pattern associated with preeclampsia, a common, generally late-term complication of preganancy: pregnant women seem to be less likely to experience preeclampsia if they’ve been living with the father of the fetus for a longer time before becoming pregnant [$a]. The idea being, that if a woman is inseminated with “unfamiliar” sperm, her body is more likely to reject the resulting pregnancy.

If this sounds ridiculous to you, well, it did to me, too. I reacted right off the bat with Tweeted snark, and had some back-and-forth with Bering in which, I have to say, I didn’t acquit myself especially well. I try to do better than engaging in scientific debate while steam is coming out of my collar, and, once I’d cooled down a bit I attempted an apology (which recieved a perfectly polite response), and I then resolved to sit down and actually figure out the merit of Davis and Gallup’s hypothesis.

Spoiler alert: Further study did not make D&G’s hypothesis any more plausible. But my reasons for disbelieving it after doing the background reading aren’t what you might expect.

Preeclampsia and semen familiarity

First, a little more detail about preeclampsia. It’s a condition linked to pregnancy-induced hypertension—not so much a single complication as a cluster of symptoms that seem to be connected to an immune reaction against the “foreign” tissue of a fetus. As the name implies, it can escalate to eclampsia, which involves seizures and a coma, and can seriously endanger both the woman and her fetus; but preeclampsia can create dangerous complications [$a] even if it doesn’t get to that stage, causing premature delivery, restricted fetal growth, and loss of the pregnancy.

One of the first things I found out, following up on papers cited in Bering’s post and D&G’s original description of the hypothesis, is that the “semen familiarity” idea is actually quite widespread in the medical literature and, as far as I can tell, reasonably well supported. Various studies have found that preeclampsia is more common in first pregnancies; more common in women who have switch sexual partners between pregnancies [PDF]; and more common in women who become pregnant via artificial insemination from an unknown donor [$a] than in those who are artificially inseminated with their current partner’s sperm. That last study has a rather small sample size, but it’s about as close as you could get to an outright experimental test of the “semen familiarity” effect, I think.

However, if sperm familiarity is one factor contributing to the risk of preeclampsia, it’s not the only thing, and its role isn’t quite universally accepted. It’s a risk factor in the epidemiological sense, not a direct, clear-cut cause. Women who are overweight, who are diabetic, who smoke, or who have hypertension when they become pregnant have an elevated risk of preeclampsia independent of any effect of sperm familiarity. A relatively recent review article notes that there’s some support for an alternative hypothesis that longer waits between pregnancies is a stronger determinant; women who change partners often also tend to wait longer between pregnancies because, well, it can take a while to make that kind of switch. Studies that account for time between pregnancies have found that, in fact, switching partners can be associated with a somewhat reduced risk of preeclampsia.

Rolling the die against rape

But so if preeclampsia is indeed made more likely by unfamiliar semen, how much of a selective advantage could this tendency incur? (Assume, for the moment, that there’s a genetic underpinning to the tendency to respond to unfamiliar sperm by developing preeclampsia; natural selection can’t act on any trait that isn’t passed from parent to offspring with reasonable reliability, no matter how useful or detrimental that trait might be.) As I’ve noted in other contexts, a very basic result in population genetics is that, for natural selection to over come the effects of genetic drift and mutation [$a], it has to have some minimumn strength; any selective advantage at all isn’t enough for a gene variant to spread via natural selection.

To determine whether selection favoring preeclampsia as a response to unfamiliar sperm might be strong enough to overcome drift and mutation, we’ll have to do some back-of-the envelope calculations. Here, I can draw on some data from the medical literature, but this is all pretty crude, so grab your salt-cellar.

First, how much more likely does unfamiliar semen make preeclampsia? In that above-mentioned comparison of artificial insemination by unknown donors versus familiar partners, which was published in 1997, women recieiving donor sperm were about 1.85 times more likely to develop preeclampsia than those who recieved sperm from their partners; a much larger study from 1999 [PDF] reports that preganancies resulting from sperm donation had about 1.4 times the risk of preeclampsia seen in comparable natural pregnancies.

That baseline risk is about anywhere from 2 to 7 percent of pregnancies. So if we take the higher end of both estimates (the baseline probability of preeclampsia, and the factor by which unfamiliar sperm increases it), we’re talking about an effect that elevates the probability of developing preeclampsia up to about 13 percent. That’s not zero, but (to take Trisha Greenhalgh’s advice to heart) let’s try to think about that in concrete terms: it’s less than the probability of rolling a six with one toss of a die.

Then, consider that not all preeclampsia cases result in loss of the pregnancy. The current risk of fetal death associated with preeclampsia is about 1 to 2 percent of cases; so now figure that you have to toss that die more than enough times to roll six 100 times—more than six hundred tosses—to be reasonably sure of ending just two rape-related pregnancies this way. Put it another way: a (purely hypothetical) gene variant responsible for making a woman likely to develop preeclampsia when she encounters unfamiliar sperm would help her avoid carrying a rapist’s baby to term with something less than one chance in 300.

(One caveat: of course, I’m working from present-day risks of pregnancy loss due to preeclampsia, and of course preeclampsia would’ve been more likely to result in pregnancy loss—and also maternal death—before the advent of modern medicine. But I wasn’t able to find similarly precise estimates of those risks predating modern medicine, and in any event Davis and Gallup, and Bering, discuss the hypothesis in terms of its implications for modern society.)

Strong enough for selection?

Now, let’s compare that educated guess to the estimated strength of natural selection acting on two adaptations biologists have studied much more closely in humans: the capacity to survive in high-altitude, low-oxygen conditions, and the ability to digest milk sugars as adults. These are each cases where a useful genetic variant has spread through a population, which means selection overcame drift and mutation; although I don’t believe that either adaptive variant has “fixed,” or spread to the entire population.

In the first case, a gene variant found in people living on the high Tibetan plateau is associated with reduced risk of death for the children of women carrying the variant. A 2004 study of Tibetan women found that those without the variant gave birth to about 4.5 children, and an average of 2.5 of those children died before the age of 15; women carrying the beneficial gene variant had about the same number of live births, but only an average of 0.5 children who died. In other words, carrying the high-altitude gene variant meant they had twice as many children surivive to age 15.

In the second case, a 2009 study used population genetic data to estimate the strength of selection on the gene variant responsible for lactase persistance, the ability to digest milk sugars as an adult, in European populations that have historically raised cattle for milk. The estimated selective benefit of being able to digest milk was about 1.8 percent. That is, people in those European populations who couldn’t digest milk had about 98.2 children for every 100 children born to people who could digest milk.

Stack those selective effects alongside that proposed for preeclampsia as a response to rape: less than a one-in-six chance for a two percent chance of losing an unwanted pregnancy, or somewhat less than three out of a thousand rape-related pregnancies ended prematurely. And, as Kate Clancy notes in her excellent discussion of the Akins fiasco, preeclampsia characteristically occurs late in pregnancy—so, in the rare cases when it does end an unwanted pregnancy, it does so after a mother has already invested months of resources in supporting the fetus.

As Clancy points out, an adaptation to prevent pregnancy by rape would be much more effective if it caused miscarriage well before preeclampsia could even come into play—and, indeed, Davis and Gallup proposed, at the end of their book chapter, that earlier miscarriages might also be related to semen familiarity. They cite no data to test that hypothesis, and I haven’t found any published since their book chapter. But as Clancy describes quite clearly, we have reaonable evidence that rates of pregnancy from rape are similar to rates of pregnancy from consensual sex, and that would seem to close the book on the question of anti-rape defenses in early pregnancy.

In other words, if women have evolved some sort of physiological adaptation to avoid getting pregnant as a result of rape—whether via elevated risk of preeclampsia or another means—the actual benefits conferred by such an adaptation are so miniscule as to stretch the definition of “adaptive” to meaninglessness. But I can think of another well-known adaptation that does allow women to end unwanted pregnancies with a high degree of reliability: human intelligence. Women have been using abortifacients and other means to end pregnancies, sometimes well before preeclampsia typically occurs, since the dawn of recorded history, and modern medical technology from hormonal birth control to emergency contraception to, yes, abortion itself makes this simpler and safer than it’s ever been.

Contrary to Jesse Bering’s quippy title, Darwin’s morning after pill isn’t some mysterious power of a woman’s reproductive tract; it’s the big brain that millions of generations of evolutionary history gave her.◼

As noted in the main text, all calculations herein are back-of-the-envelope estimates, and subject to the foibles of my limited numerical skills; if you see something wrong with them, let me know in the comments!

References

Beall, C. M., K. Song, R. C. Elston, and M. C. Goldstein. 2004. “Higher offspring survival among Tibetan women with high oxygen saturation genotypes residing at 4,000 m.” Proc. Nat. Academy Sci. U.S.A. 101:14300. DOI: 10.1073/pnas.0405949101.

Davis J.A., and G.G. Gallup Jr. 2006. “Preeclampsia and other pregnancy complications as an adaptive response to unfamiliar semen.” in Female Infidelity and Paternal Uncertainty: Evolutionary Perspectives on Male Anti-Cuckolding Tactics. SM Platek and TK Shackleford, eds. Pages 191-204. Full text PDF.

Dekker, G., and P.-Y. Robillard. 2007. “Pre-eclampsia: Is the immune maladaptation hypothesis still standing?: An epidemiological update.” Journal of Reproductive Immunology 76:8-16. DOI: 10.1016/j.jri.2007.03.015.

Gerbault, P., C. l. Moret, M. Currat, and A. Sanchez-Mazas. 2009. “Impact of selection and demography on the diffusion of lactase persistence.” PLoS ONE 4:e6369. DOI: 10.1371/journal.pone.0006369.

Haldane, J. B. S. 1927. “A mathematical theory of natural and artificial selection. Part V: Selection and mutation.” Proceedings of the Cambridge Philosophical Society 23:838-844. DOI: 10.1017/S0305004100015644.

Hoy, J., A. Venn, J. Halliday, G. Kovacs, and K. Waalwyk. 1999. “Perinatal and obstetric outcomes of donor insemination using cryopreserved semen in Victoria, Australia.” Human Reproduction 14:1760-1764. DOI: 10.1093/humrep/14.7.1760.

Li, D.-K., and S. Wi. 2000. “Changing paternity and the risk of preeclampsia/eclampsia in the subsequent pregnancy.” American Journal of Epidemiology 151:57-62. Full text PDF.

MacKay, A. P., C. J. Berg, and H. K. Atrash. 2001. “Pregnancy-related mortality from preeclampsia and eclampsia.” Obstetrics & Gynecology 97:533. Full text PDF.

Robillard, P.-Y., and T. Hulsey. 1996. “Association of pregnancy-induced-hypertension, pre-eclampsia, and eclampsia with duration of sexual cohabitation before conception.” The Lancet 347:619 DOI: 10.1016/S0140-6736(94)91638-1.

Sibai, B., G. Dekker, and M. Kupferminc. 2005. “Pre-eclampsia.” The Lancet 365:785-799. DOI: 10.1016/S0140-6736(05)17987-2.

Skjaerven, R., A. J. Wilcox, and R. T. Lie. 2002. “The interval between pregnancies and the risk of preeclampsia.” New England Journal of Medicine 346:33-38. DOI: 10.1056/NEJMoa011379.

Smith, G. N., M. Walker, J. L. Tessier, and K. G. Millar. 1997. “Increased incidence of preeclampsia in women conceiving by intrauterine insemination with donor versus partner sperm for treatment of primary infertility.” American Journal of Obstetrics and Gynecology 177:455-458. DOI: 10.1016/S0002-9378(97)70215-1.

Is corn the new milk? Evolutionarily speaking, that is.

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colorful fall corn

Corn. (Flickr: srqpix)

ResearchBlogging.orgIt is a widespread misconception that, as we developed the technology to reshape our environment to our preferences, human beings neutralized the power of natural selection. Quite the opposite is true: some of the best-known examples of recent evolutionary change in humans are attributable to technology. People who colonized high-altitude environments were selected for tolerance of low-oxygen conditions in the high Himalayas and Andes; populations that have historically raised cattle for milk evolved the ability to digest milk sugars as adults.

A recent study of population genetics in Native American groups suggests that another example is ripening in the experimental fields just a few blocks away from my office at the University of Minnesota: Corn, or maize, may have exerted natural selection on the human populations that first cultivated it.

The target of this new study is an allele called 230Cys, a variant of a gene involved in transporting cholesterol. 230Cys is known only in Native American populations, and it’s associated with abnormally low production of HDL cholesterol (that’s the “good” kind of cholesterol) and thereby increased risk for obesity, diabetes, and heart disease. In Native American populations, the genetic code near 230Cys shows the reduced diversity associated with a selective sweep, which suggests that, although it’s not particuarly helpful now, this variant may have been favored by selection in the past.

One of the biggest dietary changes in the history of Native American humans was the domestication of corn, which provided a staple crop to support settlements across North and South America long before Europeans arrived. However, a staple crop is something of a double-edged sword: it can provide a more predictable food source than hunting and gathering—but if the crop fails, it means famine. It’s been proposed that the 230Cys variant makes people who carry it better at storing food as fat, which might come in handy for ancient farmers who had to weather bad harvests every few years.

2011.08.27 - Corn!

Corn on display at the 2011 Minnesota State Fair. (Flickr: jby)

So the new study looks for an association between frequencies of 230Cys and corn-based agriculture in Native populations from Central and South America. The study’s authors—a big international team from universities in Brazil, Argentina, Mexico, Chile, Costa Rica, France, and Great Britain—first show that there’s a strong correlation between the frequency of Cys230 in Native populations and the length of time that domestic corn has been grown by those populations, as determined by the radiocarbon date of maize pollen found in archeological sites. That is, 230Cys is more common in Native populations that have a longer history of growing corn.

The team also used genetic data from the vicinity of Cys230 to estimate the age of the allele, and found that it probably originated between 19,000 and 7,000 years ago—which is to say, all the copies of Cys230 in the population genetic sample are descended from a single mutation that occurred after humans colonized the Americas. The lower age estimate is also pretty close to how long ago native populations are thought to have first begun farming maize.

That data makes a pretty good case for 230Cys having arisen as an adaptation to the diet created by Native American corn-based agriculture. But it’s not the whole story, by a long shot. Although 230Cys is strongly associated with metabolic disease in today’s modern, mostly famine-free, lifestyle, it only explains about four percent of variation in blood cholesterol levels. Moreover, it’s not clear to me that agriculture based on maize should be more prone to famine than agriculture based on wheat or rice—so why didn’t European and Asian populations evolve their own versions of 230Cys? It seems much more probable that there are a lot of other genes involved in determining how human bodies respond to modern-day feasting or prehistoric famine.

And, in fact, a 2010 study of world-wide human population genetics found evidence of selection associated with both climate and with diet type across the genome. That study found genetic markers with strong associations to climate and diet in close proximity to genes connected to blood glucose levels, diabetes risk, cancer risk, and, yes, blood cholesterol levels. The climate and dietary categories examined in that study are very broad, however, so it’s hard to know what, specifically, helped create the natural selection suggested by the observed associations between gene variants and evironments.

Corn and 230Cys may be the most recently described specific case of recent human evolution in response to agricultural technology—but we can expect to find a lot more stories like this one as we dig deeper into human population genetics.◼

References

Acuña-Alonzo, V., T. Flores-Dorantes, J. K. Kruit, T. Villarreal-Molina, O. Arellano-Campos, T. Hünemeier, A. Moreno-Estrada, M. G. Ortiz-López, H. Villamil-Ramírez, P. León-Mimila, & et al. (2010). A functional ABCA1 gene variant is associated with low HDL-cholesterol levels and shows evidence of positive selection in Native Americans. Human Molecular Genetics, 19, 2877-85 : 10.1093/hmg/ddq173

Hancock, A. M., D. B. Witonsky, E. Ehler, G. Alkorta-Aranburu, C. Beall, A. Gebremedhin, R. Sukernik, G. Utermann, J. Pritchard, & G. Coop (2010). Human adaptations to diet, subsistence, and ecoregion are due to subtle shifts in allele frequency. Proc. Nat. Acad. Sci. USA., 107, 8924-8930 : 10.1073/pnas.0914625107

Hünemeier, T., C. E. G. Amorim, S. Azevedo, V. Contini, V. Acuña-Alonzo, F. Rothhammer, J.-M. Dugoujon, S. Mazières, R. Barrantes, M. T. Villarreal-Molina, & et al. (2012). Evolutionary responses to a constructed niche: Ancient Mesoamericans as a model of gene-culture coevolution. PLoS ONE, 7 : 10.1371/journal.pone.0038862

Nothing in Biology Makes Sense: Music, evolved?

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SATB Choral Music Music. Photo by Andy Buscemi.

Over at the collaborative science blog Nothing in Biology Makes Sense!, guest contributor James Gaines writes about the evolutionary context of music-making.

Music is one of the few social constructs that truly permeates human culture, and reasons for this have fascinated scientists and philosophers for centuries. Even Darwin himself wrote on the subject, speculating about whether and how natural selection could explain it. Today, there seem to be three major ideas behind why music evolved.

For a breakdown of those three evolutionary hypotheses, go read the whole thing.◼

Nothing in Biology Makes Sense: Searching for Ronald Fisher

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Geneticist Ronald A. Fisher. Photo via WikiMedia Commons.

This week at the collaborative science blog Nothing in Biology Makes Sense!, my lab-mate John Stanton-Geddes writes about the current state of evolutionary genetics, as presented at the recent Evolution meetings in Ottawa:

One theme that emerged through the meeting was “The genetic basis for [insert trait here]. While this goal of mapping phenotype to genotype has been a primary goal of many evolutionary ecologists since the first QTL mapping studies, it has recently come under strong criticism, notably in a fantastic paper by Matthew Rockman in the journal Evolution last year, but also by Pritchard and Di Rienzo 2010 and in a forthcoming article by Ruth Shaw (full disclosure: Ruth was my PhD advisor) and Mike Travisano.

Readers of Denim and Tweed will recognize that John’s complaint about our ongoing fixation (ha!) on individual genes of large effect mirrors some of my own recent thinking. So naturally, I think you should go read the whole thing.◼

They also serve: Adaptation from standing variation

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Standing and waiting. Photo by Image Zen.

ResearchBlogging.orgEver since Charles Darwin and Alfred Russell Wallace first described the workings of natural selection, one popular way to summarize about selective change has gone something like this: A population of critters is well-adapted to its environment until that environment changes—maybe the critters move to a new climate, maybe the climate changes on them, maybe some new competitors or predators move in. Life gets harder for our critters, until one of them is born … different. That lucky mutant has a never-before-seen trait that lets it cope in the new conditions, and in a few generations, every critter in the population is a descendent of that original mutant.

That narrative isn’t wrong. But it does miss one of the key insights that led to the discovery of natural selection—natural populations are variable.

That population of critters encountering new conditions of life may very well not need to wait around for the lucky mutant before it can begin adapting to new conditions. Mutations happen at random, and continuously—and, particularly if they don’t leave the mutant much less fit, can hang around in a population for generations. And this “standing” variation is raw material waiting for natural selection to act.

High-octane fuel for adaptation

There’s good reason to think that natural selection is more efficient when it has standing variation to work with. Joachim Hermisson and Pleuni Pennings demonstrated this principle rather neatly in a 2005 theory paper, in which they modeled the fate of new genetic mutations that had a weak negative effect when they first appeared in a population, but then became beneficial after the population’s environment changed.

Normally, when a new mutation appears in a population, it’s almost immediately lost to the random effects of genetic drift, even if it confers a benefit. This means that a new mutation needs to be quite strongly favored by selection to have a high probability of “fixing,” or spreading through an entire population.

However, under Hermisson and Pennings’s model, the mutations considered are only those that survive the initial effects of drift. The flip-side of the randomness that can make a weakly beneficial mutation disappear can also help a weakly deleterious mutation spread, achieving an equilibrium between drift, selection, and new mutation events that create new copies of the same variant to replace the ones lost to selection or drift. So, when conditions changed, and the mutation became even weakly beneficial, it was ready to start spreading.

Natural selection is more effective when it works with standing variants. Figure 1 from Hermisson and Pennings (2005).

This graph, the key figure from Hermisson and Pennings’s paper, shows the probability that a mutation will “fix,” or spread to dominate the population over the course of several generations, given the power of natural selection (alpha, the term on the horizontal axis). The dotted line tracks the probability of fixation for a brand-new mutation; the solid line tracks probability of fixation for a mutation that existed before selection began to act, and had achieved mutation-selection-drift equilibrium. No matter how strong selection is, the pre-existing mutation is more likely to “fix” than the new mutation—and that difference is most pronounced when selection favoring the mutation is weakest.

In other words, if mutations provide the variation that fuels evolution by natural selection, standing variation is fuel with a substantially higher octane rating.

Harder to spot

But the same features that make adaptation from standing variation so much more efficient also act as a sort of population genetic stealthing. This is because adaptation from standing variation has very different effects on the genetics of an adapting population than the spread of a single new mutation.

The key to this difference is that gene variants, or alleles, aren’t transmitted from one generation to another one at a time. Instead, they come as part of chromosome regions, physically linked to genetic code that may have nothing to do with the function of the focal gene. And population geneticists use that fact to zero in on genetic regions that might have been recently affected by selection.

It’s a little bit like buying LEGO bricks—or, at least, how it used to be when I was still buying a lot of LEGOs, back before you could custom-build your own sets online. Say you want a hundred copies of a particularly special type of LEGO brick, one that’s only available in a single kit. To get those hundred bricks, you need to buy a hundred copies of that one kit. So you end up with a selection of bricks—the ones you wanted, and the ones that came with the ones you wanted—that probably doesn’t have a very wide diversity of brick types.

But suppose you want a hundred copies of a more common LEGO brick, one that’s included in dozens of different kits—kits for pirate ships and castles, race cars and railroads. You might still need to buy a hundred kits, but you can buy many different kinds of kits, and so in addition to the hundred copies of the brick you want, you also have bricks to build anything from a starship to a dragon.

The Dawn Of Man LEGOs, evolving. Photo by Kaptain Kobold.

Selection on a single beneficial mutation is like that first LEGO shopping case, where there’s only one kit containing the brick you want. The one lucky mutation exists with only one “genetic background” of other, associated genetic code, and so when the mutation spreads through the population, a chunk of that background code spreads with it. (At least, until recombination can separate the favored mutation from its background; that takes time, sometimes a lot of time.)

Just as purchasing a hundred copies of the same LEGO kit would leave an obvious mark on the makeup of your brick collection, a selective sweep that starts with a single mutation—what’s called a “hard sweep”—results in a region of genetic code with noticeably lower variation across the population, because everyone is carrying the original lucky mutation plus its associated background.

Figure 4 from Linnen et al. (2009), demonstrates the reduced diversity in a gene region associated with fur color in deer mice. Image from Linnen et al. (2009).

In practice, biologists use this principle in two major ways. First, if a biologist has a particular gene in mind that might have recently experienced selection, she can collect DNA sequence data in the vicinity of that gene for many individuals in a popualtion, and see whether it’s less diverse than it ought to be. This is how Catherine Linnen and her collaborators demonstrated that a population of deer mice living on light-colored soils in the Sand Hills of Nebraska had experienced natural selection for lighter color. In a study [PDF] I’ve discussed previously, the team identified a genetic region that was associated with coat color in the mice, then collected sequence data from that region in mice collected from the light-soil population. Compared to the same genetic region in mice from nearby sites with dark soil, the light-soil mice had markedly less variation in the coat-color region.

Alternatively, biologists who don’t know which genes might have been targeted by natural selection can collect sequence data from a whole lot of gene regions—or even “scan” the whole genome—and compare the diversity at each region. Any region that has lower diversity than most of the other sampled regions may have experienced selection recently, and is probably a good candidate for follow-up study.

But selection froms standing variation doesn’t leave such a clear mark on the genome. It’s more like that second LEGO shopping spree, for a brick found in many different kits. If a useful variant is located on many different genetic backgrounds, than selection can make the variant more common in the population without necessarily reducing the diversity of gene regions near the focal variant. This is called a “soft sweep.” Soft sweeps present a problem for those of us who want to find genes that have recently been affected by natural selection—without the loss of diversity, genetic regions that have undergone soft sweeps may not stand out in the genome as a whole.

Searching for soft sweeps

As we collect and analyze more genome-scale population genetic datasets, biologists are coming around to the idea that easy-to-detect hard sweeps may be the exception [$a], rather than the rule, for evolution in natural populations—in no small part because the evidence of hard sweeps just isn’t there [PDF].

But the absence of hard sweeps doesn’t mean that soft sweeps are going on all over the place instead. For instance, in an (ongoing) analysis I presented [PDF] at the recent Evolution meetings in Ottawa, I examined patterns of diversity in genetic regions close to genetic markers that are very strongly associated with differing climate conditions in the small but awesome wildflower Medicago truncatula—and I found little evidence of recent hard sweeps. Does that mean all those strongly associated gene variants are strongly associated as a result of adaptation from standing variation? Maybe; but some portion of the associations could also be due to population genetic processes like drift and isolation-by-distance—I’m still thinking about ways to kill the soft sweep hypothesis.

Pennings and Hermisson followed up their original theory paper with a study comparing the power of several different statistical tests to detect soft sweeps, and they found some promising results with an approach based on linkage between genetic variants in the vicinity of a favored variant. More recently, Pennings has approached the question of adaptation from standing variation from a somewhat different angle, by studying selective sweeps in human immunodeficiency virus, HIV. The evolution of HIV after it infects a patient, and as it adapts to antiviral drugs, is quite well understood—to the point that virologists know to expect particular mutations to sweep the viral population within a patient who starts taking a particular drug.

In an analysis recently published in PLoS Computational Biology, Pennings found that the virus’s evolution of drug resistance could be based on standing variation in about 6% of patients on a standard anti-viral drug cocktail—which is to say, about 6% of all patients carry viral populations that are primed to evolve drug resistance the moment therapy begins. (Pennings’s lab website has a good explanation of the clinical implications of this result, with video, even.)

Then, at the Ottawa Evolution meetings, Pennings presented [PDF] an examination of HIV genetic samples taken from multiple patients undergoing antiviral treatment. She identified cases when the virus’s adaptation to the drugs was fueled by standing variation or based on a mutation that occurred after the drug treatment started; one resistance mutation evolved to fixation via a soft sweep in eight out of 23 patients. [Correction, 6 Aug 2012: See Pennings’s comment below for a correction on this point; it’s not known whether this particular soft sweep started from standing variation, or whether it’s simply the case that two different mutations with the same effect managed to sweep the population together.]

If evolutionary biologists want to understand how natural selection helped make the living world we see around us today, it looks like we’re going to have to learn to love soft sweeps. We’re still learning how to differentiate the aftermath of soft sweeps from the results of other, non-selective processes. But fortunately, we live in an era when the genome-scale data that may let us untangle this question are increasingly easy to collect.◼

I started working on this post quite a while before the Ottawa Evolution meetings, when I was pleased to meet Pleuni Pennings for the first time. If there are mistakes in what I’ve written above, they’re my own; but I hope she’ll let me know if I’ve made any!

References

Flintoft, L. (2011). Human evolution: Sweep model is swept away. Nature Reviews Genetics, 12, 228-9 DOI: 10.1038/nrg2978

Hermisson, J., & Pennings, P.S. (2005). Soft sweeps: Molecular population genetics of adaptation from standing genetic variation. Genetics, 169 (4), 2335-52 DOI: 10.1534/genetics.104.036947

Hernandez, R. D., J. L. Kelley, E. Elyashiv, S. Melton, A. Auton, G. McVean, G. Sella, & M. Przeworski (2011). Classic selective sweeps were rare in recent human evolution. Science, 331, 920-4 DOI: 10.1126/science.1198878

Linnen, C. R., E. P. Kingsley, J. D. Jensen, & H. E. Hoekstra (2009). On the origin and spread of an adaptive allele in deer mice. Science, 325, 1095-8 DOI: 10.1126/science.1175826

Oleksyk, T. K., M. W. Smith, & S. J. O’Brien (2010). Genome-wide scans for footprints of natural selection. Phil. Trans. Royal Soc. B, 365, 185-205 DOI: 10.1098/rstb.2009.0219

Pennings, P.S. (2012). Standing genetic variation and the evolution of drug resistance in HIV. PLoS Computational Biology, 8 : 10.1371/journal.pcbi.1002527

Pennings, P.S., & J. Hermisson (2006). Soft sweeps III: The signature of positive selection from recurrent mutation. PLoS Genetics, 2 DOI: 10.1371/journal.pgen.0020186.eor

Pritchard, J. K., & A. Di Rienzo (2010). Adaptation—not by sweeps alone Nature Reviews Genetics, 11, 665-7 DOI: 10.1038/nrg2880

#Evol2012: Ottawa in retrospect

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2012.07.09 - Parliament Parliament, viewed across the Rideau Canal. Photo by jby.

I’m now back from Evolution 2012 and in the process of getting back up to speed with non-conference life—i.e., a daily routine that isn’t eight hours (less a lunch break) of listening to people talking about science in fifteen-minute chunks, then going out to drink and talk about the science until midnight. Returning to a schedule in which I can think about the same scientific topic for hours on end is a bit disorienting.

For in-the-moment (more or less) writing about the meetings and everything discussed there, I suggest, of course, contributions by myself and the rest of the crew at Nothing in Biology Makes Sense, plus notes from Jeremy Fox at the Oikos blog (with posts for days one, two, and three). And, of course, there’s the formidable feed of updates via the conference’s Twitter hashtag, about which more below.

All in all, I had a great time, and saw a lot of really cool science. This was the first Evolution meeting I’ve been to where I was never at a loose end—every moment I was in the Convention Centre, I had someone to go see, or a talk to go hear. And, honestly, I finished the meeting without having checked in with everyone I’d have liked to.

My talk went pretty well, if I do say so myself. I finished well before the buzzer, and the questions afterward generally suggested that the audience understood what I was presenting. Plus, Pleuni Pennings, one of the authors of a cool series of papers I’d read and thought about a lot in preparing the analysis, came to the talk, and she had both good things to say, and some interesting suggestions.

I’ve posted my slideshow (which, I should warn you, presents preliminary results, and has relatively little explanatory text) online as a PDF document, in case you’re curious.

2012.07.10 - Museum of Civilization The Canadian Museum of Civilization, site of the end-of-meeting banquet. Photo by jby.

The Ottawa Convention Centre was a great venue for a huge meeting, and the critical support—coffee, snacks, lunch—was good, if somewhat parsimoniously distributed. The final banquet at the Canadian Museum of Civilization was nice, but marred by badly distributed (and, ultimately, insufficient) food. The timer chimes—or, as Luke Harmon called them, the call of the Canadian Electro-Frog—weren’t as annoying as I’d thought they’d be, though I do think they made things a bit too regimented until folks got used to them.

This was also far and away the most-tweeted Evolution meeting I’ve been to; it was actually possible to sample what was going on in the other sessions thanks to other Twitterers at the meeting. We’ve come a long way since I first suggested people live-blog and tweet the 2009 meetings, and hardly anyone showed up to do it. It’s a pity, then, that the Convention Centre wi-fi was unprepared for the volume of traffic that inevitably resulted—and which was probably exacerbated by the fact that most of the U.S. residents in attendance were using wi-fi with their smartphones rather than rack up huge bills for using “foreign” cellular data services.

I was also happy to be involved in re-starting “Outgroup,” a meet-up of queer folks at the conference that hasn’t convened since the 90s, apparently. I’d heard about it from Chris Smith, who was at one of the last Outgroup gatherings, and we both agreed it’d be nice to do again. So we put up a handwritten notice on the conference bulletin board, and I put the word out on Twitter that LGBT folks should meet up for lunch Sunday—and people showed up! (Although, as Sarcozona noted, the group was an overwhelmingly male. C’mon, ladies!)

But so I made some nice new connections via Outgroup, and the lunch added an extra level of networking to the meeting: hearing a bit about being out in a professional context back when Don’t Ask, Don’t Tell counted as a compromise and marriage equality was a pipe dream; or what it’s like to negotiate a faculty contract in a state where state schools are forbidden by law to provide benefits for non-married partners. I think we’ll definitely be gathering Outgroup again next year, when Evolution meets in Snowbird, Utah.◼

Confidential to Dan Savage

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Are you sure you want to be getting all uncritically book-endorse-y with Jesse Bering? I mean, yes, Bering’s a snappy writer with a nose for edgy topics, and the bit you excerpted—concerning the good ol’ “plunger penis” hypothesis [$]—is intriguing.

But.

This is Jesse Bering we’re talking about. Jesse “gay-bashing is adaptive” Bering. Jesse “natural selection is the only misogynist here” Bering. Jesse “Deep-Thinking Hebephile” Bering.

I mean, I don’t want to be making an ad-hominem argument here, but I tend to think that the point of popular science writing is for the audience to benefit from a writer’s perspective and expert judgement. And Jesse Bering’s judgement is in pretty serious question. (Don’t just take my word for it!) He might very well be a great psychologist—that field is beyond my expertise to assess—but it’s pretty clear that Bering’s knowledge of evolution begins and ends with an exceptionally superficial understanding of natural selection, and, more often than not, he rallies that superficial understanding (but not much actual scientific evidence) for the defense of some pretty damn’ regressive ideas.

Plus which, “plunger penis” isn’t exactly news: the paper Bering seems to be citing is from 2003, and Jared Diamond discussed the ways in which the human penis stands out (heh) in comparison to those of other apes in The Third Chimpanzee, which was first published in 1992. Wasn’t this covered in Sex at Dawn?

All I’m saying is, read that new book with a saltshaker handy.◼

Reference

Gallup, G. G., R. L. Burch, M. L. Zappieri, R. A. Parvez, M. L. Stockwell, & J. A. Davis (2003). The human penis as a semen displacement device. Evolution and Human Behavior, 24, 277-89 DOI: 10.1016/S1090-5138(03)00016-3

Carnival of Evolution, July 2012

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The Mousetrap Photo by annavsculture.

A new Carnival of Evolution is online at the Mousetrap. This edition of the monthly collection of online writing about evolution sorts a long list of blog posts into mousetrap-related themes, and it includes more than enough to fill up your e-reader for, say, the long flight out to some sort of academic conference in the capital of Canada.◼