Genetic architecture of intelligence from SNP distance measures

From a presentation by Steve Hsu (pdf slides):

Quantitative traits: many alleles, each of small effect. GWAS discovery of individual loci is hard.

But, phenotype differences must be associated with LARGE number of genetic differences.

Investigate pairwise genetic distance as g score (or height) are varied. Extract underlying genetic architecture:
1. Distribution of associated alleles dominated by small MAF (Minor Allele Frequency)
2. More (−) than (+) minor alleles (MAF < 0.5)
3. Rough estimate of 10k causal alleles in total [. . .]

Select outlier groups H and L. Averaging over pairs eliminates fluctuations in distance which are uncorrelated to phenotype.

Average pairwise genetic distance changes with mean IQ and IQ difference: ∼ 39 SNPs per population SD [. . .]

Low IQ = more rare (−) variants. Larger genetic distances between individuals. Similar results for height. [. . .]

Geniuses and Giants: Fewer deleterious alleles.

(A) 39 SNPs per SD of IQ suggests roughly 10k causal variants.

(B) Exceptional cognitive ability = of order 100’s fewer rare (−) variants than an average person.

Apropos of the last post, here's Hsu's comment when linking to these slides:
In the context of human genetics, it's clear there's plenty of room at the top -- possibly as much as +30 SDs based on existing variance in the human population! (Compare to the result of selection in maize.)
Nor does Hsu shrink from the practical implications:

Imagine what a couple might pay to ensure that they get the best out of 10 or 50 possible offspring, optimizing over their choice of heritable attributes. Compare this with the cost of a Harvard education or K-12 private school tuition. The cost of an IVF cycle is down to a few thousand dollars and could go even lower.

Genetic prediction at high accuracy will probably be possible once of order millions of genotype-phenotype data pairs are available for analysis. I predict about 5-10 years. The advance in the Nature article makes me confident that the necessary reproductive technologies will also be available.

I hope that progressive governments will make this procedure free for everyone. The benefits from increased economic output, decreased welfare and criminality rates, etc. far outweigh the cost of what I have described above ( = few cycles of IVF + running my algorithms provided at dirt cheap licensing rates ;-) [. . .]

You can't use US numbers for real medical costs -- our system has huge distortions. Few $K is the cost in Taiwan or Korea and success rates are if anything higher there. That's not even factoring the economies of scale that would arise if a large fraction of couples wanted it.

Who says the US is the first market for this?

No doubt Hsu is correct that Asia is unlikely to hesitate in applying the lessons of quantitative genetics to humans -- regardless of what Alexis Madrigal-types would have for the US. The Chinese government funds the genetics of intelligence study Hsu is involved in. Also see Hsu's slides from a previous talk (pdf):


• Suppose that we can non- destructively sequence gametes (sperm and egg cells).

• We can imagine parents choosing which gametes to unite in order to constitute their offspring.

• In particular, they might choose to unite gametes bearing many g-enhancing alleles. [. . .]

“Suppose we knew, for instance, twenty [loci affecting] mental characters. These would combine in over a million [homozygous] mental types. In practice each of these would naturally occur rather less frequently than one in a billion, or in a country like England, about once in 20,000 generations.

“It will give some idea as to the excellence of the best of these types when we consider that the Englishmen from Shakespeare to Darwin ... have occurred within ten generations; the thought of a race of men combining the illustrious qualities of these giants, and breeding true to them, is almost too overwhelming ...

“... but such a race will inevitably arise in whatever country first sees the inheritance of mental characters elucidated.”— RONALD A. FISHER, “MENDELISM AND BIOMETRY”

The Atlantic: How Eugenic Breeding Transformed the Dairy Industry

The Perfect Milk Machine: How Big Data Transformed the Dairy Industry:
There is a reason, of course, that the semen that Badger-Bluff Fanny Freddie produces has become such a hot commodity in what one artificial-insemination company calls "today's fast paced cattle semen market." In January of 2009, before he had a single daughter producing milk, the United States Department of Agriculture took a look at his lineage and more than 50,000 markers on his genome and declared him the best bull in the land. And, three years and 346 milk- and data-providing daughters later, it turns out that they were right. [. . .]

No matter how you apportion the praise or blame, the net effect is the same. Thousands of years of qualitative breeding on family-run farms begat cows producing a few thousand pounds of milk in their lifetimes; a mere 70 years of quantitative breeding optimized to suit corporate imperatives quadrupled what all previous civilization had accomplished. And the crazy thing is, we're at the cusp of a new era in which genomic data starts to compress the cycle of trait improvement, accelerating our path towards the perfect milk-production machine, also known as the Holstein dairy cow. [. . .]

"Animal breeders for many decades have used models that assume most traits are influenced by thousands of genes with very small effects. Some [individual] genes do have detectable effects, but many studies of plant and animal traits conclude that most of the genetic variation is from many little effects."

For dairy cows -- or humans, for that matter -- it's just not as simple as the dominant-recessive single-gene paradigm that Mendel created. In fact, Mendel picked his model organism well. Its simplicity allowed him to focus in on the simplest possible genetic model and figure it out. He could easily manipulate the plant breeding; he could observe key traits of the plant; and these traits happened to be controlled by a single gene, so the math lay within human computational range. Pea plants were perfect for studying the basics of genetics.

With that in mind, allow me to suggest, then, that the dairy farmers of America, and the geneticists who work with them, are the Mendels of the genomic age. That makes the dairy cow the pea plant of this exciting new time in biology. Last week in the Proceedings of the National Academy of Science, two of the most successful bulls of all time had their genomes published.

This is a landmark in dairy herd genomics, but it's most significant as a sign that while genomics remains mostly a curiosity for humans, it's already coming of age when it comes to cattle. It's telling that the cutting-edge genomics company Illumina has precisely one applied market: animal science. They make a chip that measures 50,000 markers on the cow genome for attributes that control the economically important functions of those animals. [. . .]

Mendel may have worked with plants, the rules he revealed turned out to be universal for all living things. The same could be true of the statistical rules that dairy scientists are learning about how to match up genomic data with the physical attributes they generate. The statistical rules that reflect the way dozens or hundreds of genes come together to make a cow likely to develop mastitis, say, may be formally similar to the rules that govern what makes people susceptible to schizophrenia or prone to living for a long time. Researchers like the University of Queensland's Peter Visscher are bringing the lessons of animal science to bear on our favorite animal, ourselves.

Want to live for a very long time? Well, we hope to discover the group of genes that are responsible for longevity. The problem is that you have genomic data over here and you have phenotypic data, i.e. how things actually are, over there. What you need, then, is some way of translating between these two realms. And it's that matrix, that series of transformations, that animal scientists have been working on for the past decade.

And this is as deep as the author of this piece, Alexis Madrigal, gets into the "lessons of animal science" for humans. Predict lifespan and disease risk. But don't allow yourself to imagine this sort of information might have any more direct uses or implications for humans. Breathlessly promoting genetic betterment of cows in one article; mouthing sentiments like this in another:
Ever since humans deduced the powerful nature of DNA and all the associated molecules that do work in our cells, people have wondered: how long before we can simply change our own genes? On the one hand, all kinds of genetic diseases could be cured. On the dark side, if genetics sets the limits of human action, how long before we create genetically enhanced humans? And, like many things in bioethics, these thoughts are never very far away from the long shadow of the Nazis' eugenics program.

Selection for height in Northern Europeans

Evidence of widespread selection on standing variation in Europe at height-associated SNPs:
Strong signatures of positive selection at newly arising genetic variants are well documented in humans1, 2, 3, 4, 5, 6, 7, 8, but this form of selection may not be widespread in recent human evolution9. Because many human traits are highly polygenic and partly determined by common, ancient genetic variation, an alternative model for rapid genetic adaptation has been proposed: weak selection acting on many pre-existing (standing) genetic variants, or polygenic adaptation10, 11, 12. By studying height, a classic polygenic trait, we demonstrate the first human signature of widespread selection on standing variation. We show that frequencies of alleles associated with increased height, both at known loci and genome wide, are systematically elevated in Northern Europeans compared with Southern Europeans (P < 4.3 × 10−4). This pattern mirrors intra-European height differences and is not confounded by ancestry or other ascertainment biases. The systematic frequency differences are consistent with the presence of widespread weak selection (selection coefficients ~10−3–10−5 per allele) rather than genetic drift alone (P < 10−15).
Luke Jostins described this research last year:
Europeans differ systematically in their height, and these differences correlate with latitude. The average Italian is 171cm, whereas the average Swede is a full 4cm taller. Are these differences genetic? Have they been under evolutionary selection in recent human history?

Michael Turchin gave some pretty convincing answers to these questions, using genetic data from the 129 thousand individuals in the GIANT consortium. He compared the frequencies of alleles that are known to increase height, and found that they are more common in Northern Europe. Interestingly, he found the same relationship for alleles that have weaker evidence for height association, showing that there are still a large number of common height variants hiding in the genome, which are also more frequent in Northern Europe.

Height differences are thus heritable, but have they been under evolutionary selection? Or are these differences merely down to genetic drift? This can also be tested using the GIANT data, which shows significant statistical evidence of selection on height variants in recent history. On top of that, the magnitude of the selection is correlated with the effect size of the height variant, providing strong evidence that these variants are being selected specifically for their impact on height.

This is a textbook example of how an evolutionary study should be done; you show a phenotypic difference exists, that it is heritable, and that it is under selection. This opens the question as to why height has been selected in Northern Europe (or shortness in Southern Europe). Could the same data be used to test specific hypotheses there?