How does intelligence even work at a genetic level?
Our best estimate based on the last decade of data is that the genetic component of intelligence is controlled by somewhere between
10,000 and
24,000 variants. We also know that each one, on average, contributes about +-0.2 IQ points.
Genetically altering IQ is more or less about flipping a sufficient number of IQ-decreasing variants to their IQ-increasing counterparts. This sounds overly simplified, but it’s surprisingly accurate; most of the variance in the genome is linear in nature, by which I mean the effect of a gene doesn’t usually depend on which other genes are present.
So modeling a continuous trait like intelligence is actually extremely straightforward: you simply add the effects of the IQ-increasing alleles to to those of the IQ-decreasing alleles and then normalize the score relative to some reference group.
To simulate the effects of editing on intelligence, we’ve built a model based on summary statistics from UK Biobank, the assumptions behind which you can find
in the appendix
Based on the model, we can come to a surprising conclusion: there is enough genetic variance in the human population to create a genome with a predicted IQ of about 900. I don’t expect such an IQ to actually result from flipping all IQ-decreasing alleles to their IQ-increasing variants for the same reason I don’t expect to reach the moon by climbing a very tall ladder; at some point, the simple linear model will break down.
But we have strong evidence that such models function quite well within the current human range, and likely somewhat beyond it. So we should actually be able to genetically engineer people with greater cognitive abilities than anyone who’s ever lived, and do so without necessarily making any great trade-offs.
Even if a majority of iq-increasing genetic variants had some tradeoff such as increasing disease risk (which current evidence suggests they mostly don’t), we could always select the subset that doesn’t produce such effects. After all, we have 800 IQ points worth of variants to choose from!
Total maximum gain
Given the current set of publicly available intelligence-associated variants available in UK biobank, here’s a graph showing the expected effect on IQ from editing genes in adults.
Not very impressive! There are several factors at play deflating the benefits shown by this graph.
- As a rough estimate, we expect about 50% of the targeted edits to be successfully made in an average brain cell. The actual amount could be more or less depending on editor and delivery efficiency.
- Some genes we know are involved in intelligence will have zero effect if edited in adults because they primarily influence brain development (see here for more details). Though there is substantial uncertainty here, we are assuming that, on average, an intelligence-affecting variant will have only 50% of the effect size as it would if edited in an embryo.
- We can only target about 90% of genetic variants with prime editors.
- About 98% of intelligence-affecting alleles are in non-protein-coding regions. The remaining 2% of variants are in protein-coding regions and are probably not safe to target with current editing tools due to the risk of frameshift mutations.
- Off-target edits, insertions and deletions will probably have some negative effect. As a very rough guess, we can probably maintain 95% of the benefit after the negative effects of these are taken into account.
- Most importantly, the current intelligence predictors only identify a small subset of intelligence-affecting genetic variants. With more data we can dramatically increase the benefit from editing for intelligence. The same goes for many other traits such as Alzheimer’s risk or depression.
The limitations in 1-5 together reduce the estimated effect size by 79% compared to making perfect edits in an embryo. If you could make those same edits in an embryo, the gains would top out around 30 IQ points.
But the bigger limitation originates from the size of the data set used to train our predictor. The more data used to train an intelligence predictor, the more of those 20,000 IQ-affecting variants we can identify, and the more certain we can be about exactly which variant among a cluster is actually causing the effect.
And the more edits you can make, the better you can take advantage of that additional data. You can see this demonstrated pretty clearly in the graph below, where each line represents a differently sized training set.
Our current predictors are trained using about 135,000 samples, which would place it just above the lowest line on the graph. There are existing databases right now such as the million veterans project with sample sizes of (you guessed it) one million. A predictor trained with that data would fall between the red and purple lines in the graph above.
Companies like 23&Me genotyped their
12 millionth customer two years ago and could probably get at perhaps 3 million customers to take an IQ test or submit SAT scores. A predictor trained with that amount of data would perform about as well as the brown line on the graph above.
So larger datasets could increase the effect of editing by as much as 13x! If we hold the number of edits performed constant at 2500, here’s how the expected gain would vary as a function of training set size:
Now we’re talking! If someone made a training set with 3 million data points (a realistic possibility for companies like 23&Me), the IQ gain could plausibly be over 100 points (though keep in mind the uncertainties listed in the numbered list above). Even if we can only make 10% of that many edits, the expected effect size would still be over one standard deviation.
, but nonetheless, if I were evaluating the intelligence of a person based on a self-reported IQ score and an SAT score, I'd put far more weight in the SAT score since the number is more reliable.
