of ancient human migrations tracked by computer simulation
Researchers can pinpoint ‘we were here’
signs from our human ancestors
By AMY ADAMS
Early humans migrating from Africa carried
small genetic differences like so much flotsam in an ocean current.
Today’s studies have given only a snapshot of where that
genetic baggage came to rest without revealing where the changes
arose. Until now.
Researchers at the School of Medicine have devised a model for
pinpointing where mutations – most of which cause no physical
change – first appeared. The work provides a new way to trace
the migratory path of early humans.
The project was led by Luca Cavalli-Sforza, PhD, professor of
genetics, emeritus, who spent most of his career tracking the
evolution of modern humans. Much of this 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 over the past 50,000 years.
Based on his Y chromosome studies, Cavalli-Sforza traced the point
in time any given mutations appeared in a population. 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. Knowing both the date and
location of the mutation’s origin enables researchers to
place a dated “we were here” sign on the route of human
With the help of senior application software developer Christopher
Edmonds and statistician Anita Lillie, both of Stanford,
Cavalli-Sforza built a computer model to simulate how mutations
spread in a migrating population.
The results of this work were published online in the Jan. 20 issue
of Proceedings of the National Academies of Science.
Over the course of 64,000 simulations the group noticed two trends.
If a mutation appeared within a settled computer
“village,” it usually disappeared due to chance. If it
did persist in the population, it remained rare.
If, on the other hand, the mutation appeared in a migrating
population, it became common in that group. That’s because
the population was smaller, so the mutated individual had a higher
chance of passing along the genetic trait. The mutation remained
most common at the leading edge of the migration, a situation
Cavalli-Sforza refers to as “surfing” the migratory
wave. Eventually, mutations become most common at boundaries, such
as the edge of continents, where migration screeched to a halt.
From these simulations the group came up with a model for
pinpointing 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 location 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.
By following the migratory route backward from the centroid
researchers can flag the spot where the mutation arose. Combining
the mutation’s birthplace with the evolutionary date of the
mutation’s origin, geneticists can map the progress of early
humans traveling across continents.
How the model works
For their human migration simulation, Professor
Luca Cavalli-Sforza and his team reduced the world’s
continents to a simple rectangular grid. They populated left-hand
squares with computerized human populations. These electronic
villages had realistic rates for population growth, migration and
The simulated inhabitants reproduced and those offspring could
migrate to any neighboring square as long as it wasn’t filled
to a pre-set capacity. This population growth filled the initial
squares, then pushed the computerized people to migrate at a
constant rate across their rectangular territory until all the
spaces were filled.
When a mutation appeared, descendants reproduced and migrated at
the same rate as other individuals.
The simulation showed the mutation’s actual origin (a star in
the figure below) and shaded the squares to indicate a
mutation’s prevalence. An “X” indicates the
On the left, a mutation arose in a
migrating population and surfed the migratory wave to its boundary,
where the mutation became common. On the right, a mutation appeared
in an inhabited area but did not travel, remaining rare in the