The reason this approach won’t work is that genes aren’t linear factors that can added up together in that way. Even in something as simple as milk production, you need to do selection over multiple generations and evaluate each generation separately, building up small genetic changes over time.
If you could construct an actual model relating various genes to intelligence, in a way that took into account genetic interactions, then you could do what you propose in a single generation, but we are very very far from being able to construct such a model at present.
As it stands today, if you just carried out that naive approach you would end up with a non-viable embryo or, in the best-case scenario, a slightly-higher-than-average intelligence person. Not a super-genius.
When researching my book I was told by experts that the intelligence genes which vary throughout the human population probably are linear. Consider President Obama who has a very high IQ but who also has parents who are genetically very different from each other. If intelligence genes worked in a non-additive complex way people with such genetically diverse parents would almost always be very unintelligent. We don’t observe this.
HLS students of any skin color have high IQs as measured by standardized tests. The school’s 25th percentile LSAT score is 170, which is 97.5th percentile for the subset of college graduates who take the LSAT. 44% of HLS students are people of color.
There are decades of studies of the heritability of IQ. Some of them measure H², which is full heritability and some of them measure h², “narrow sense heritability”; and some measure both. Narrow sense heritability is the linear part, a lower bound for the full broad sense heritability. A typical estimate of the nonlinear contribution is H²-h²=10%. In neither case do they make any assumptions about the genetic structure. Often they make assumptions about the relation between genes and environment, but they never assume linear genetics. Measuring h² is not assuming linearity, but measuring linearity.
This paper finds a lower bound for h² of 0.4 and 0.5 for crystallized and fluid intelligence, respectively, in childhood. I say lower bound because it only uses SNP data, not full genomes. It mentions earlier work giving a narrow sense heritability of 0.6 at that age. That earlier work probably has more problems disentangling genes from environment, but is unbiased given its assumptions.
We fitted a linear mixed model y = µ + g + e, where y is the phenotype, m is the mean term, g is the aggregate additive genetic effect of all the SNPs and e is the residual effect.
If you have 3511 individuals and 549692 SNPs you won’t find any nonlinear effects.
3511 observations of 549692 SNPs is already overfitted 3511 observations of 549692 * 549691 gene interactions is even more overfitted and I wouldn’t expect that the four four principal components they calculate to find an existing needle in that haystack.
Apart from that it’s worth noting that IQ is g fitted to a bell curve. You wouldn’t expect a variable that you fit to a bell curve to behave fully linearly.
No, they didn’t try to measure non-linear effects. Nor did they try to measure environment. That is all irrelevant to measuring linear effects, which was the main thing I wanted to convey. If you want to understand this, the key phrase is “narrow sense heritability.” Try a textbook. Hell, try wikipedia.
That it did well on held-back data should convince you that you don’t understand overfitting.
Actually, I would expect a bell curve transformation to be the most linear.
That it did well on held-back data should convince you that you don’t understand overfitting.
They didn’t do well on the gene level: Analyses of individual SNPs and genes did not result in any replicable genome-wide significant association
No, they didn’t try to measure non-linear effects. Nor did they try to measure environment. That is all irrelevant to measuring linear effects, which was the main thing I wanted to convey.
No, the fact that you can calculate a linear model that predicts h_2 in a way that fits 0.4 or 0.5 of the variance doesn’t mean that the underlying reality is structured in a way that gene’s have linear effects.
To make a causal statement that genes work in a linear way the summarize statistic of is not enough.
I would not recommend making confident pronouncements which make it evident you have no clue what you are talking about.
While I haven’t worked with the underlying subjects in the last few years I did take bioinformatics courses by people who had a clue what they were talking about and the confident pronouncement I make are what I learned there.
No, it was assumed that genes controlling milk production were linear, because it was much easier to study them that way, and unfortunately over time many people came to simply accept that fact as true, when it has never been proven (in fact it’s been proven conclusively otherwise).
The reason this approach won’t work is that genes aren’t linear factors that can added up together in that way. Even in something as simple as milk production, you need to do selection over multiple generations and evaluate each generation separately, building up small genetic changes over time.
If you could construct an actual model relating various genes to intelligence, in a way that took into account genetic interactions, then you could do what you propose in a single generation, but we are very very far from being able to construct such a model at present.
As it stands today, if you just carried out that naive approach you would end up with a non-viable embryo or, in the best-case scenario, a slightly-higher-than-average intelligence person. Not a super-genius.
When researching my book I was told by experts that the intelligence genes which vary throughout the human population probably are linear. Consider President Obama who has a very high IQ but who also has parents who are genetically very different from each other. If intelligence genes worked in a non-additive complex way people with such genetically diverse parents would almost always be very unintelligent. We don’t observe this.
Evidence?
Harvard Law Review
Counter-evidence: affirmative action.
In any case, it’s interesting that Obama’s SAT (or ACT) scores are sealed as are his college grades, AFAIK.
HLS students of any skin color have high IQs as measured by standardized tests. The school’s 25th percentile LSAT score is 170, which is 97.5th percentile for the subset of college graduates who take the LSAT. 44% of HLS students are people of color.
When I see funny terms like “people of color” (or, say, “gun deaths”), I get suspicious. A little bit of digging, and...
Black students constitute 10-12% of HLS students. Most of the “people of color” are Asians.
No, actually, genetic studies of both milk production and IQ show them to be mainly linear.
That selective breeding has to be done slowly has nothing to do with genetic structure.
What kind of study do you think shows IQ to be mainly linear?
I would guess that you confuse assumptions that the researchers behind a study make to reduce the amount of factors with finding of the study.
There are decades of studies of the heritability of IQ. Some of them measure H², which is full heritability and some of them measure h², “narrow sense heritability”; and some measure both. Narrow sense heritability is the linear part, a lower bound for the full broad sense heritability. A typical estimate of the nonlinear contribution is H²-h²=10%. In neither case do they make any assumptions about the genetic structure. Often they make assumptions about the relation between genes and environment, but they never assume linear genetics. Measuring h² is not assuming linearity, but measuring linearity.
This paper finds a lower bound for h² of 0.4 and 0.5 for crystallized and fluid intelligence, respectively, in childhood. I say lower bound because it only uses SNP data, not full genomes. It mentions earlier work giving a narrow sense heritability of 0.6 at that age. That earlier work probably has more problems disentangling genes from environment, but is unbiased given its assumptions.
The linked paper says:
If you have 3511 individuals and 549692 SNPs you won’t find any nonlinear effects. 3511 observations of 549692 SNPs is already overfitted 3511 observations of 549692 * 549691 gene interactions is even more overfitted and I wouldn’t expect that the four four principal components they calculate to find an existing needle in that haystack.
Apart from that it’s worth noting that IQ is g fitted to a bell curve. You wouldn’t expect a variable that you fit to a bell curve to behave fully linearly.
No, they didn’t try to measure non-linear effects. Nor did they try to measure environment. That is all irrelevant to measuring linear effects, which was the main thing I wanted to convey. If you want to understand this, the key phrase is “narrow sense heritability.” Try a textbook. Hell, try wikipedia.
That it did well on held-back data should convince you that you don’t understand overfitting.
Actually, I would expect a bell curve transformation to be the most linear.
They didn’t do well on the gene level:
Analyses of individual SNPs and genes did not result in any replicable genome-wide significant associationNo, the fact that you can calculate a linear model that predicts h_2 in a way that fits 0.4 or 0.5 of the variance doesn’t mean that the underlying reality is structured in a way that gene’s have linear effects.
To make a causal statement that genes work in a linear way the summarize statistic of is not enough.
I would not recommend making confident pronouncements which make it evident you have no clue what you are talking about.
While I haven’t worked with the underlying subjects in the last few years I did take bioinformatics courses by people who had a clue what they were talking about and the confident pronouncement I make are what I learned there.
OK, let’s try a simpler piece of advice: first, stop digging.
No, it was assumed that genes controlling milk production were linear, because it was much easier to study them that way, and unfortunately over time many people came to simply accept that fact as true, when it has never been proven (in fact it’s been proven conclusively otherwise).