Luke Muehlhauser: I’d like to start by familiarizing our readers with some of the basic facts relevant to the genetic architecture of cognitive ability, which I’ve drawn from the first half of apresentation you gave in February 2013:
The human genome consists of about 3 billion base pairs, but humans are very similar to each other, so we only differ from each other on about 3 million of these base pairs.
Because there’s so much repetition, we could easily store the entire genome of every human on earth (~3mb per genome, compressed).
Rather than scanning an entire genome, we can just scan the roughly 10 million locations where humans are likely to differ (these are called SNPs).
Scanning someone’s SNPs costs about $200; scanning their entire genome costs $1000 or more.
But, genotyping costs are falling so quickly that SNPs may be irrelevant soon, as it’ll be simpler and cheaper to just sequence entire genomes.
To begin to understand the genetic architecture of cognitive ability, we can compare it to the genetic architecture of height, since the genetic architectures of height and cognitive ability are qualitatively the same.
For example, (1) height and cognitive ability are relatively stable and reliable traits (in adulthood), meaning that if you measure a person’s height or cognitive ability at multiple times you’ll get roughly the same result each time, (2) height and cognitive ability arevalid traits, in that they “measure something real” that is predictive of various life outcome measures like income, (3) both height and cognitive ability are highly heritable, and (4) both height and cognitive ability are highly polygenic, meaning that many different genes contribute to height and cognitive ability.
All cognitive observables — e.g. vocabulary, digit recall (short term memory), ability to solve math puzzles, spatial rotation ability, cognitive reaction time — appear to be positively correlated. Because of this, we can (lossily) compress the data for how a person scores on different cognitive tests to a single number, which we call IQ, and this single number is predictive of their scores on all cognitive tests, and also life outcome measures like income, educational attainment, job performance, and mortality.
This contradicts some folk wisdom. E.g. parents often believe that “Johnny’s good at math, so he’s probably not going to be good with words.” But in fact, the data show that math skill is quite predictive of verbal skill, because (roughly) all cognitive abilities are positively correlated.
By convention, IQ is normally distributed in the population with a mean at 100 and a standard deviation of 15.