Posted on 01/23/2004 7:18:12 AM PST by PatrickHenry
STANFORD, Calif. – Early humans migrating from Africa carried small genetic differences like so much flotsam in an ocean current. Today’s studies give only a snapshot of where that genetic baggage came to rest without revealing the tides that brought it there. Now researchers at the Stanford University School of Medicine have devised a model for pinpointing where mutations first appeared, providing a new way to trace the migratory path of our earliest ancestors.
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.
Sickle-cell - malaria
Cystic fibrosis - cholera
Tay-sachs - tuberculosis
Of course, when you think about it, there has to be some reason why these particular genes are comparatively concentrated, but before I read Diamond I only knew about sickle-cell.
Another book I recommend is by the author of the posted paper, Luigi Luca Cavalli-Sforza The Great Human Diasporas
I couldn't find an on-line copy to link to, but the book has the most interesting map showing the frequency of the rh-negative blood group. It's almost concentric circles centered on the Basque country.
And how is is this a limitation?
Then please do so. It would be publishable. You must use their inputs of mutation rates and their equations. You may not fudge the known inputs. I'll check this thread tomorrow for your results and a copy of your program to verify that it does what you claim.
I haven't read that, but I was impressed with an online essay of his I ran across while looking for the original paper: Luigi Luca Cavalli-Sforza: A Panoramic Synthesis of My Research .
ROFL!
Absolutely no reason to believe the above is true. And lots of reasons to believe it's false.
Yep. A good computer model is more than its output. And a bad computer model is just sh!t.
A long time ago in town very far away, I had a boss that thought of computer printouts as "truth paper". Once or twice I had it say whatever-the-hell-I-wanted and got a budget and staff increase. Later I had him predicting the equivalent of unicorns appearing in public. He never did catch on but I knew if I stayed I'd have him announcing the end of the world as we know it (and I'd feel fine).
Tomorrow has arrived. Here's a link to a well-known alternative model.
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