4 ms·

If this study has captured anything beyond systematically correlated measurement errors (a problem which plagues high-throughput sequencing assays), it is almos

If this study has captured anything beyond systematically correlated measurement errors (a problem which plagues high-throughput sequencing assays), it is almost certainly ancillary population substructure which correlates with socio-economic status.

gwern 9y ago
Alex, why do you say these hurtful things? You know perfectly well that they use principal components to successfully control away population structure, that the genetic correlations between cohorts are high, the hits replicate across populations, the enrichment sets and tissue expression are predominantly in the nervous system as expected, the polygenic scores are predictive of success even looking at just kids in poor families, and the within-family polygenic scores perform fine. No, it's not some measurement error on the several different SNP chips they used in the ~12 different cohorts or the entirely separate GWASes whose results they replicated, and it's definitely not 'residual population structure'. It was silly 4 years ago to write off GWAS results with that sort of logic, and it has only gotten sillier.
- theWatcher37 9y ago
  Man, that burn was orbital. Comments like these are why I come to HN
- Houshalter 9y ago
  >they use principal components to successfully control away population structure This sounds really interesting, how does that work exactly?
  - gwern 9y ago
    Nothing that special. You take the SNPs, extract the first 7 or whatever principal components, and include them in the regression. To the extent that there's some population structure and subgroups are smarter/richer, it'll get controlled away. No guarantee that it'll work, but it apparently does (probably because most of the structure is in the first few components and including PCs beyond that is harmless).
    - johndevor 9y ago
      How much of IQ do you think is correlated to genetics?
      - sillysaurus3 9y ago
        It's a politically-charged question, so people tend to be very careful to ensure that whatever they say is correct. It's probably difficult to give a convincing argument in the space of an average HN comment.
        berberous 9y ago
        Is linking genetics to intelligence politically-charged, or just linking race to intelligence?
        sillysaurus3 9y ago
        Why not take a guess and see what happens?
      - andrewprock 9y ago
        Given that the definition of IQ is built upon constantly shifting sands, it's really hard to say.
      - maroonblazer 9y ago
        According to this study[0] - and if experts are to be believed - it's not zero (in the case of B-W differences). "Around 90% of experts believed that genes had at least some influence on cross-national differences in cognitive ability." [0]http://journal.frontiersin.org/article/10.3389/fpsyg.2016.00399/full http://journal.frontiersin.org/article/10.3389/fpsyg.2016.00...
      - lacker 9y ago
        According to the NIH: Researchers have conducted many studies to look for genes that influence intelligence. Many of these studies have focused on similarities and differences in IQ within families, particularly looking at adopted children and twins. These studies suggest that genetic factors underlie about 50 percent of the difference in intelligence among individuals. https://ghr.nlm.nih.gov/primer/traits/intelligence https://ghr.nlm.nih.gov/primer/traits/intelligence So, going with the NIH, I'd say about half.
- AlexCoventry 9y ago
  > ...even looking at just kids in poor families... At these sample sizes, that is an interesting idea. Has anyone checked?
  - gwern 9y ago
    Yes.
    - virgilp 9y ago
      With what results? No predictive value, in poor families? (also, do include some marker of sarcasm in your comments, it can get confusing)
      - gwern 9y ago
        No, of course they have predictive value. If they didn't, that would not support my point that the GWAS results are valid. The specific study I had in mind was "The Genetics of Success: How Single-Nucleotide Polymorphisms Associated With Educational Attainment Relate to Life-Course Development" https://www.gwern.net/docs/genetics/2016-belsky.pdf https://www.gwern.net/docs/genetics/2016-belsky.pdf , Belsky et al 2016.
        AlexCoventry 9y ago
        I apologize for misinterpreting your response as sarcastic. Why do you believe that PCA successfully captures all the relevant population structure? Probablistically, it's a Gaussian linear model... no reason to expect allele frequencies to follow that. If that were the case, methods like coalescent theory would be unnecessary. Why do you believe that high genetic correlations between cohorts is sufficient to ensure safe inference from GWAS meta analysis? These are tiny effect sizes, and the argument is essentially that the correlations with intelligence measurements are statistically significant. Unless you believe that your null model is an otherwise entirely accurate reflection of how the data is generated, of course you're going to see spuriously significant results in huge samples. I'll respond to your other points when/if I catch up on the literature. I'd be grateful if you could link the papers about the enrichment sets and tissue expression results.
- a_bonobo 9y ago
  >use principal components to successfully control away population structure PCA doesn't perfectly control away population structure, especially if you have cryptic tiny subpopulations in your dataset that only few individuals are in. In that case you don't have enough signal to establish the subpopulations. It's also a function of the way you call your SNPs - if you don't have enough SNPs, or focus on a different method, PCA won't magically know about those unobserved populations.
  - theparanoid 9y ago
    The most famous recent study used Iceland [0], which is about as homogeneous as you can imagine. [0] http://www.pnas.org/content/114/5/E727.full http://www.pnas.org/content/114/5/E727.full
  - AlexCoventry 9y ago
    I'm pretty sure he was being sarcastic.
    - nathan_f77 9y ago
      Wow, I actually have no idea if gwern was being sarcastic or not. I didn't consider it until I read your comment. There's a few clues in there that do suggest sarcasm, but I really can't tell, because I know nothing about the field. I don't think they were being sarcastic though, because the rest of their replies are pretty serious.
      - AlexCoventry 9y ago
        He says some things with perfect confidence which are unknowable or false, but you do need a background in the field to see that. Also, he starts out with what seems to be an outrageous appeal to emotion, but I suppose "hurtful" could mean "harmful" rather than "mean."
  - gwern 9y ago
    They may not perfectly control, but they do the job well enough that the error from that is minimal and of less importance than other issues like limited SNP panel coverage, sample error, or measurement error (a _huge_ limit on current GWASes and very underappreciated, given how few papers attempt to correct for it or even mention what sort of psychometric properties the available data has). It is certainly not true, as Alex claims, that the results are nothing but systematic measurement error or population structure.

gwern 9y ago

Alex, why do you say these hurtful things? You know perfectly well that they use principal components to successfully control away population structure, that the genetic correlations between cohorts are high, the hits replicate across populations, the enrichment sets and tissue expression are predominantly in the nervous system as expected, the polygenic scores are predictive of success even looking at just kids in poor families, and the within-family polygenic scores perform fine. No, it's not some measurement error on the several different SNP chips they used in the ~12 different cohorts or the entirely separate GWASes whose results they replicated, and it's definitely not 'residual population structure'. It was silly 4 years ago to write off GWAS results with that sort of logic, and it has only gotten sillier.

theWatcher37 9y ago
Man, that burn was orbital. Comments like these are why I come to HN
Houshalter 9y ago
>they use principal components to successfully control away population structure This sounds really interesting, how does that work exactly?
- gwern 9y ago
  Nothing that special. You take the SNPs, extract the first 7 or whatever principal components, and include them in the regression. To the extent that there's some population structure and subgroups are smarter/richer, it'll get controlled away. No guarantee that it'll work, but it apparently does (probably because most of the structure is in the first few components and including PCs beyond that is harmless).
  - johndevor 9y ago
    How much of IQ do you think is correlated to genetics?
    - sillysaurus3 9y ago
      It's a politically-charged question, so people tend to be very careful to ensure that whatever they say is correct. It's probably difficult to give a convincing argument in the space of an average HN comment.
      - berberous 9y ago
        Is linking genetics to intelligence politically-charged, or just linking race to intelligence?
        sillysaurus3 9y ago
        Why not take a guess and see what happens?
    - andrewprock 9y ago
      Given that the definition of IQ is built upon constantly shifting sands, it's really hard to say.
    - maroonblazer 9y ago
      According to this study[0] - and if experts are to be believed - it's not zero (in the case of B-W differences). "Around 90% of experts believed that genes had at least some influence on cross-national differences in cognitive ability." [0]http://journal.frontiersin.org/article/10.3389/fpsyg.2016.00399/full http://journal.frontiersin.org/article/10.3389/fpsyg.2016.00...
    - lacker 9y ago
      According to the NIH: Researchers have conducted many studies to look for genes that influence intelligence. Many of these studies have focused on similarities and differences in IQ within families, particularly looking at adopted children and twins. These studies suggest that genetic factors underlie about 50 percent of the difference in intelligence among individuals. https://ghr.nlm.nih.gov/primer/traits/intelligence https://ghr.nlm.nih.gov/primer/traits/intelligence So, going with the NIH, I'd say about half.
AlexCoventry 9y ago
> ...even looking at just kids in poor families... At these sample sizes, that is an interesting idea. Has anyone checked?
- gwern 9y ago
  Yes.
  - virgilp 9y ago
    With what results? No predictive value, in poor families? (also, do include some marker of sarcasm in your comments, it can get confusing)
    - gwern 9y ago
      No, of course they have predictive value. If they didn't, that would not support my point that the GWAS results are valid. The specific study I had in mind was "The Genetics of Success: How Single-Nucleotide Polymorphisms Associated With Educational Attainment Relate to Life-Course Development" https://www.gwern.net/docs/genetics/2016-belsky.pdf https://www.gwern.net/docs/genetics/2016-belsky.pdf , Belsky et al 2016.
      - AlexCoventry 9y ago
        I apologize for misinterpreting your response as sarcastic. Why do you believe that PCA successfully captures all the relevant population structure? Probablistically, it's a Gaussian linear model... no reason to expect allele frequencies to follow that. If that were the case, methods like coalescent theory would be unnecessary. Why do you believe that high genetic correlations between cohorts is sufficient to ensure safe inference from GWAS meta analysis? These are tiny effect sizes, and the argument is essentially that the correlations with intelligence measurements are statistically significant. Unless you believe that your null model is an otherwise entirely accurate reflection of how the data is generated, of course you're going to see spuriously significant results in huge samples. I'll respond to your other points when/if I catch up on the literature. I'd be grateful if you could link the papers about the enrichment sets and tissue expression results.
a_bonobo 9y ago
>use principal components to successfully control away population structure PCA doesn't perfectly control away population structure, especially if you have cryptic tiny subpopulations in your dataset that only few individuals are in. In that case you don't have enough signal to establish the subpopulations. It's also a function of the way you call your SNPs - if you don't have enough SNPs, or focus on a different method, PCA won't magically know about those unobserved populations.
- theparanoid 9y ago
  The most famous recent study used Iceland [0], which is about as homogeneous as you can imagine. [0] http://www.pnas.org/content/114/5/E727.full http://www.pnas.org/content/114/5/E727.full
- AlexCoventry 9y ago
  I'm pretty sure he was being sarcastic.
  - nathan_f77 9y ago
    Wow, I actually have no idea if gwern was being sarcastic or not. I didn't consider it until I read your comment. There's a few clues in there that do suggest sarcasm, but I really can't tell, because I know nothing about the field. I don't think they were being sarcastic though, because the rest of their replies are pretty serious.
    - AlexCoventry 9y ago
      He says some things with perfect confidence which are unknowable or false, but you do need a background in the field to see that. Also, he starts out with what seems to be an outrageous appeal to emotion, but I suppose "hurtful" could mean "harmful" rather than "mean."
- gwern 9y ago
  They may not perfectly control, but they do the job well enough that the error from that is minimal and of less importance than other issues like limited SNP panel coverage, sample error, or measurement error (a _huge_ limit on current GWASes and very underappreciated, given how few papers attempt to correct for it or even mention what sort of psychometric properties the available data has). It is certainly not true, as Alex claims, that the results are nothing but systematic measurement error or population structure.