Human Migration Tracked In Stanford Computer Simulation

The study was led by Luca Cavalli-Sforza, PhD, emeritus professor of genetics, who has spent most of his career tracking the evolution of modern humans. Much of his current work involves following mutations in the Y chromosome, which is passed exclusively from father to son, as humans migrated from Africa and spread to the rest of the world during the past 50,000 years.

These mutations, most of which cause no physical change, tend to appear at a constant rate, providing a genetic timer. For example, if a population has 10 mutations after 50,000 years of evolution from the common ancestor in Africa, then the fifth mutation probably arose 25,000 years ago. But where was the population located at that time? Until now genetics hasn't had an answer.

"If we know the time when a mutation arose we know something. If we also knew the place we'd know almost everything," Cavalli-Sforza said.

With the help of senior application software developer Christopher Edmonds and statistician Anita Lillie, both researchers at Stanford, Cavalli-Sforza built a computer model to simulate how mutations spread in a migrating population. The results of this work are published in this week's online issue of Proceedings of the National Academies of Science.

The group reduced the world's continents to a simple rectangular grid. They populated the first few squares with computerized human populations and gave those electronic villages realistic rates for population growth, migration and mutations. The inhabitants had more than one child, on average, and those offspring could migrate to any neighboring square as long as it wasn't filled to capacity. This population growth filled the initial squares to capacity and pushed the computerized people to migrate at a constant rate across their rectangular territory until the next space was filled.

When a mutation appeared within a population, descendants reproduced and migrated at the same rate as other individuals. Most of the mutations, however, simply disappeared due to chance.

Those mutations that stayed in the population until the simulation ended showed one of two patterns. If the mutation appeared in a heavily populated area, it had a lower chance of surviving for many generations or reaching high numbers. In these cases, the mutation remained extremely rare in the local population.

If a mutation appeared in a person at the edge of the migration front where the population was scarce, the mutation was more likely to spread through the population. The mutation-carrying person multiplied and the offspring migrated, taking the mutation to neighboring squares. If these neighboring squares were previously unoccupied, the mutated person had a high probability of reproducing and passing along the mutation. The mutation itself remained most common in the migratory wave front, a situation Cavalli-Sforza refers to as "surfing" the migratory wave.

Over the course of 64,000 simulations, the group came up with a model for finding a mutation's origin. First they identified the mutation's farthest edge - corresponding with a boundary such as the ocean or mountain range in human populations. Then they calculated the average area of where the mutation is distributed - called the mutation's centroid. According to the models, the centroid is about half the distance between where the mutation arose and where it ended up.

In at least some simulations, the mutation no longer existed in the population where it first arose. Without the group's way of estimating distance, there might be no trace of the mutation's place of origin. Now they can generate a dated "we were here" sign to place on the route of human migration.

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