How We Evolve

Feature / by Benjamin Phelan /

A growing number of scientists argue that human culture itself has become the foremost agent of biological change.

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By invoking ancient demography via the anthropological record, Hawks believes he has identified what has been driving all the adaptive evolution he detected: an explosion in the global human population roughly coincident with the agricultural revolution of some 10,000 years ago. We invented agriculture, started eating different food, and began dwelling in cities. Our numbers swelled, our world changed, and our DNA is still catching up.

Spencer Wells, director of the Genographic Project, an attempt to reconstruct human migration patterns by sampling DNA from the world’s populations, has studied humanity’s transition to agriculture extensively. Hawks’s result was no surprise to him.

“The biggest change in our lifestyle as a species has happened in the past 10,000 years,” Wells says. “We spent most of the past million or so years of evolution living as hunter-gatherers, hunting game on the African savannas, or gathering shellfish on the coast, gradually moving out to Eurasia. Then, suddenly, in the past 10,000 years, we become a species that settles down. The diversity of food sources drops precipitously from over 100 in the hunter-gatherer diet to fewer than 10 in the average agricultural diet. And then, of course, you build up the population densities and disease takes off.”

Such changes to environment, diet, and disease load are classic agents of natural selection. The three acting in concert could certainly accelerate evolution. But it might seem odd that a larger population is required to produce a faster rate of evolution, especially if you happen to be American.

Scientists have finally been granted access to the particles of evolution.

The early and mid-20th century witnessed a tension between two interpretations of evolutionary theory. Sewall Wright, an American, argued that for rapid evolution to occur, what was required was a small, semi-isolated population through which a mutation could spread quickly, even by genetic drift. Thereafter, that population could migrate and spread the allele in other populations. R.A. Fisher, a Brit, argued that, in fact, a large population was required, because only a large population can produce large numbers of mutations. Because most mutations are neutral, he reasoned, it takes a large number of mutations to produce one beneficial allele. American biologists were most influenced by Wright, but Fisher’s work is where Hawks and Harpending find their support.

Fisher developed a mathematical model of how beneficial mutations should move through a population toward fixation, the point at which all members of a species have the allele. The shape of the curve is characterized by slow dispersion at first, because the mutation initially exists in only one member of the species. It takes a long time for a new allele to reach an appreciable frequency in a population, but at a certain point the growth rate becomes much steeper; many carriers bear many offspring, and the gene becomes widespread. But during the last leg of the push toward fixation, the rate decreases and begins to resemble a curve approaching an asymptote.

When anthropologists analyzed caches of ancient Eurasian skeletons, they found evidence that Fisher’s model was correct. In the DNA of a group of 5,000-year-old skeletons from Germany, they discovered no trace of the lactase allele, even though it had originated a good 3,000 years beforehand. Similar tests done on 3,000-year-old skeletons from Ukraine showed a 30 percent frequency of the allele. In the modern populations of both locales, the frequency is around 90 percent.

“This is the curve that Fisher predicted,” says Hawks. “The frequency [of the lactase allele] that we have at different times fits this curve. This means that the maximum rate of change in frequency of this gene was within the past 3,000 years, even though the gene originated 8,000 years ago.”

Seeing the mathematical model he was using borne out in data other than his own was encouraging to Hawks: Many of the alleles he’d identified as being under selection seem to show a similar trajectory toward fixation.

“My attitude about recent human evolution comes straight out of mathematics,” he says. “I can say, this is population growth, and these are the effects it should have. And as long as I keep observing data that’s consistent with that idea, I think it’s a strong model…. Once you can connect history with genes, you can build up knowledge from the standpoint of anthropology, then let the biochemists work out what each gene does.”

Being able to understand the purpose of a given gene, however, is perhaps the main challenge facing the current generation. Hawks doesn’t know what function the genes he identified as evolving perform, but such information isn’t important for his purposes. He is content with linking demographic history with mathematics and gene surveys and hypothesizing natural selection based on the confluence of those streams of evidence. A biochemist, though, might balk at saying that a gene is under selection without knowing what the gene actually does.

“Human genetics made a major leap forward at the turn of the millennium,” says Pardis Sabeti, an evolutionary geneticist at MIT’s Broad Institute who has done a great deal of work on methods for assessing genomic surveys like HapMap, the first draft of which was published in 2005. HapMap is a leaner and in some ways more powerful version of the Human Genome Project, as it compiles only those regions of the human genome — less than 1 percent — that have the potential to differ from person to person. In comparing different populations’ genetic information, it’s possible to tease out patterns of gene inheritance, how certain genes correlate with certain diseases, and even the likely geographic origin of some mutations.

One of the methods that Sabeti has developed to identify selection is to search for rare alleles on long haplotypes, which is useful for identifying selection in the past 30,000 years or so. Using the long haplotype test on HapMap data, Sabeti was able to find what appears to be a signature for recent natural selection on genes that are associated with resistance to lassa, a hemorrhagic fever that’s endemic to parts of central and western Africa. She is perhaps more cautious than Hawks in her conclusions, though; they are in different fields and have different standards of proof.

“I’m a little guarded on the findings for lassa, because the question is, is the finding real?” she says. “The strongest signal of selection we’ve detected in a West African population is on a gene called Large, which has been biologically linked to lassa.” Lassa is a poorly understood and infrequently studied pathogen, she says, so there was not much literature to consult about genes possibly associated with it. However, a microbiologist named Stefan Kunz had demonstrated that if Large is deleted from a mouse’s DNA, lassa is unable to infect it.

“That was exciting, because otherwise we’d look at the gene and say ‘I don’t know what it does,’ and that would have been the end of it. But now we could see a link,” she says. “But when you look at selection you never believe your results completely because it’s circumstantial. We have basic evidence that it seems to be evolving and we can link it to this disease, but we don’t have a real biological link.”

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