= 27d567bc2d48d9f40e9ccc20ea370b15)
gwern comments on SRG 4: Biological Cognition, BCIs, Organizations - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (139)
One problem is that for that approach, you would need, say, standardized IQ tests and genomes for a large number of people, and then to identify genome properties correlated with high IQ.
First, all biologists everywhere are still obsessed with "one gene" answers. Even when they use big-data tools, they use them to come up with lists of genes, each of which they say has a measurable independent contribution to whatever it is they're studying. This is looking for your keys under the lamppost. The effect of one gene allele depends on what alleles of other genes are present. But try to find anything in the literature acknowledging that. (Admittedly we have probably evolved for high independence of genes, so that we can reproduce thru sex.)
Second, as soon as you start identifying genome properties associated with IQ, you'll get accused of racism.
? I see mentions of stuff like dominance and interaction all the time; the reason people tend to ignore it in practice seems to be that the techniques which assume additive/independence work pretty well and explain a lot of the heritability. For example, height the other day: "Defining the role of common variation in the genomic and biological architecture of adult human height"
Seems like an excellent start to me.
It would be better than nothing. I am grinding one of my favorite axes more than I probably should. But those numbers make my case. My intuition says it would be hard to mine a few million SNPs, pick the most strongly associated 9500, and have them account for less than .29 of the variance, even if there were no relationship at all. And height is probably a very simple property, which may depend mainly on the intensity and duration of expression of a single growth program, minus interference from deficiencies or programs competing for resources.
"My intuition says it would be hard to mine a few million SNPs, pick the most strongly associated 9500, and have them account for less than .29 of the variance, even if there were no relationship at all."
With sample sizes of thousands or low tens of thousands you'd get almost nothing. Going from 130k to 250k subjects took it from 0.13 to 0.29 (where the total contribution of all common additive effects is around 0.5).
Most of the top 9500 are false positives (the top 697 are genome-wide significant and contribute most of the variance explained). Larger sample sizes let you overcome noise and correctly weight the alleles with actual effects. The approach looks set to explain everything you can get (and the bulk of heritability for height and IQ) without whole genome sequencing for rare variants just by scaling up another order of magnitude.