Did Climate Cooling Select for Cognitive Abilities?
In my previous posts, I found that ancient climate cooling predicted higher height polygenic scores and more introverted, less neurotic personality profiles.
Educational attainment (EA) was the next obvious trait to examine.
Ancient people did not attend universities, take standardized tests, or spend years in formal schooling. Yet the modern educational attainment polygenic score captures much more than education itself. It reflects thousands of genetic variants associated with cognition, persistence, long-term planning, and the ability to navigate complex social systems.
That raises an intriguing question:
When a region became colder than it had been a millennium earlier, did the genetic profile associated with educational attainment also change?
The answer depends on how the question is asked.
Using my primary climate-history model, the effect is essentially zero. But when cooling and warming are allowed to operate differently, a more interesting pattern appears: cooling predicts higher EA polygenic scores, while warming shows little evidence of producing the reverse effect.
Unlike height, where the signal is straightforward, EA turns out to be a more complicated and perhaps more revealing case.
I will not repeat the full methodological details here because they are identical to those used in the previous height and personality analyses. This post uses the same AADR sample, climate reconstructions, Mundlak within/between-location framework, ancestry controls, and robustness procedures described earlier.
The only thing that changes is the outcome variable: educational attainment polygenic score (EA PGS).
The first question is simple: if a location became colder or warmer than it had been a millennium earlier, did its EA polygenic scores change as well?
To test that, I added 1,000-year temperature change to the baseline model and asked whether it improved prediction.
Table 1. Linear 1,000-Year Temperature Change and EA PGS
The result is straightforward: there is no detectable linear relationship.
Adding 1,000-year temperature change does not improve prediction of EA polygenic scores in either the pooled or Mundlak specifications.
If this were the only model examined, the conclusion would be simple:
There is no evidence that millennium-scale climate change influenced ancient EA polygenic scores.
Goodbye cold-winters theory!
The Split-Slope Diagnostic Changes the Picture
The problem with the linear model is that it assumes cooling and warming are mirror images of one another.
Nature does not necessarily behave that way.
To test this possibility, I replaced the single temperature-change term with separate cooling and warming variables:
Cooling = max(−Δ1000, 0)
Warming = max(Δ1000, 0)
This allows cooling and warming to have independent effects.
Table 2. Split-Slope Cooling and Warming Terms for EA PGS
Now a different pattern emerges.
Across both winter temperature and annual mean temperature models, stronger cooling predicts higher EA polygenic scores.
Warming, however, shows little evidence of producing the opposite effect.
The improvement in model fit is substantial. The split-slope specification outperforms both the current-temperature model and the standard linear temperature-change model.
In other words, the signal is not a smooth climate gradient. It appears to be driven specifically by cooling episodes.
The coldest-quarter cooling coefficient is directly interpretable: within the same rounded one-degree location bin, each additional 1 C of cooling over the previous millennium predicts +0.1405 SD in EA PGS (SE = 0.0591, p = 0.017). A 2 C cooling episode corresponds to about +0.281 EA_z; a 3 C cooling episode corresponds to about +0.422 EA_z. These are model-implied shifts, not raw before-after changes in the same population.
To make that scale less abstract, Figure 3 shows the strongest local cooling windows in the sample and translates each cooling episode into the EA_z shift implied by the split-slope model.
Figure 1. Largest Local Cooling Windows and Predicted EA-PGS Shift
A Possible Threshold Effect
One way to think about this result is as a threshold rather than a gradient.
For height, cooling and warming behave approximately as opposite ends of the same continuum. Cooler conditions predict higher height scores; warmer conditions predict lower ones.
EA appears different.
The data suggest that cooling events may increase the prevalence of EA-associated alleles, but warming events do not necessarily reverse the process.
If true, this would imply an asymmetric evolutionary response: environmental deterioration imposes selection pressures that environmental improvement does not automatically undo.
That interpretation remains speculative, but it fits the observed pattern better than a simple linear model.
"If true, this would imply an asymmetric evolutionary response: environmental deterioration imposes selection pressures that environmental improvement does not automatically undo."
Once you have it, you don't lose it.
I imagine any long-term adverse event tends to increase a subsequent rise in intelligence. The degree depends on how long and how adverse.
The problem is whether modern EA polygenic scores can be projected back in time. They might not be timeless and universal measures of “educational ability.” They are built from associations between genetic variants and years of schooling in modern populations with particular social institutions. In other words, If the GWAS is mostly modern populations, then applying the score to ancient populations from very different times, environments, and ancestry backgrounds may produce a signal that is not really about educational attainment.