David Reich

And at different times in the past, those groups are more or less represented.

So the whole strength of the methodology Ali Akbari developed is it corrects for that changing ancestry over time.

And as I mentioned before, really what's being asked here is we've divided up our whole data set into an archipelago of little populations in different places in space and time.

And we're asking in each place in space and time a little pocket of people in Britain from 4,000 years ago to 3,500 years ago, a little pocket of people in Hungary, a little pocket of people...

in Italy from 2,000 years ago to 1,500 years ago.

And each of these places where the ancestry is relatively similar without being too disrupted in that short period by migrations, we watch to see if the genetic changes blow in the same direction.

And what we're doing here is we're measuring the strength of selection at each point in time after correcting for the big population changes that have occurred.

One thing you can see in the data is the migration impact is huge.

So, for example, if you look at the trajectory for measures of cognitive performance like scores on intelligence tests in white British people today, but you look at the predictor of that in people in ancient times, the estimate for the hunter-gatherers of Europe is like...

three standard deviations below the modern mean.

So that's hugely different.

And then you see a huge jump from them to the farmers who are like at the mean, at zero, and that's migration.

So what you're seeing is those two groups had different set points for those trades.

And then the step path store lists have a lower set value of this.

And so you see huge fluctuations in the predictor of this trait over time.

That doesn't prove selection.

What that is just telling you is migration.

But what our test is telling you is in addition to those fluctuations due to migration, is there a consistent effect of natural selection blowing the trait in the same direction over all places and times.

And that's what we're detecting.

Right.