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[OBG] Opposite selective pressures on stature and intelligence across populations

#1
Novel findings about recent selection on stature and the universality of genetic effects on phenotype.


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#2
Line 52 needs the link fixed.

Table 4 needs some rearranging so that numbers are not unnecessarily split over lines.

Table 5 refers to "top 10", but there are only 4 listed (?).

The negative loading of the Chinese study may be because the decreasing alleles instead of the increasing ones are mentioned. A data error not a sampling problem. Have you contacted the authors to make sure that they are correct?

Interesting combination of Allen's rule and Lynn's theory (rule?). The correlation inside populations might be due to assortative mating for height given its usefulness in physical combat. Or it the association of height and g is due to mutational load decreasing both hence creating the genetic correlation.
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#3
The mutational load-height link you mentioned is interesting and would provide a good explanation of the "paradox", as mutational load does not vary substantially by race. I double checked on SNPedia and it seems like their results are correct. However, their significant hits are only 5, compared to 10 of the other 5 studies. So the weight should be half. I suppose the polygenic scores should consist of the same number of alleles (10 for each of the 6 studies) in order to avoid a few "bad" alleles to skew the results. However, I tried to run PCA without the Hao et al's polygenic score and the results are practically identical

(2014-Mar-22, 23:51:13)Emil Wrote: The negative loading of the Chinese study may be because the decreasing alleles instead of the increasing ones are mentioned. A data error not a sampling problem. Have you contacted the authors to make sure that they are correct?

Interesting combination of Allen's rule and Lynn's theory (rule?). The correlation inside populations might be due to assortative mating for height given its usefulness in physical combat. Or it the association of height and g is due to mutational load decreasing both hence creating the genetic correlation.

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#4
Good data, sloppy presentation.

1. Almost half the references that are called out in the text are missing from the reference list.
2. The last page seems to be missing, so that Table 5 is truncated and Table 6 is missing entirely.
3. Line 14-15: Do the frequencies of all 46 alleles differ across populations? With so many SNPs, my guess is that only a certain proportion of them load on the first principal component. This is inevitable because it is extremely unlikely that all 46 SNPs replicate. The GWAS literature is full of associations that nobody can replicate. Suggestion: Expand Table 5 to present all 46 SNPs, with their PC loadings and the p value reported in the original study. What is the correlation between PC loading and p value? If SNPs with higher PC loadings tend to have lower p values, this would support the suspicion that many of those that don't load on the PC are false positives. If there is no such correlation, perhaps we need not one PC but 2 or 3. Also, somewhere you should mention that these 46 SNPs are not strongly linked and therefore can be considered independent in a statistical sense.
4. Line 30: Better allele frequencies instead of gene frequencies.
5. Line 30-31: Statistically significant in what comparison? Did you relate average height in the population to the PC for height-increasing alleles?
6. Line 33: The "focusing on a single study" comes out of the blue. Better: 2 sentences earlier, include the information that the earlier study used results from only one study. What study was this?
7. Line 51: The "et al" is missing from some of the references cited here, and the Hao reference is not in the list.
8. Spell out PCA.
9. Line 60-61: Was this a PCA without rotation? Was the unrotated first component the only component worth studying, or were there other components as well? Best: Present the scree plot, so readers can see whether a single component fits best or if there is something else that needs to be explored either in this study or in subsequent studies. Generally: Don't simplify too much and don't gloss over those findings that don't fit your theory or anybody else's theories. Data that don't fit into the theoretical mold are exactly what we need to guide future research.
10. For the same reason, include in the discussion the seeming anomaly of high prevalence of height-increasing alleles in African hunter-gatherers. These people (bushmen, pygmies) are not exactly known for their tall stature. This means there must be genetic determinants of height that are different from those that have been picked up in the association studies, or at least from those that form your first principal component. For future research you could propose, for example, that association studies for height should be performed in admixed populations such as Bantu-Pygmy. Such admixed populations are found in Africa. Perhaps some minor component that you find in PCA of all 46 SNPs with varimax rotation proves to be predictive of height differences between African hunter-gatherers and other Africans. Another kind of research that you can propose for the future: look at the functions of the genes in which the height SNPs are located. As far as I know there is a gene ontology data base somewhere, and perhaps you can group the genes that seem to be involved in height into different categories such as connective tissue proteins and growth factor receptors or signaling.
10. You discuss cold winters theory and Allen's rule. Another factor that might be important is selection for lower energy expenditure. Reducing body size is a brute-force approach through which natural selection can reduce energy expenditure. Perhaps East Asians are short because of cold winters, and pygmies and bushmen are short because of undernutrition in marginal habitats. In that case you cannot expect that the same shortness alleles were selected in the different populations.
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#5
I had computed the correlation between p value and PC loading, but was unsure on whether to report this or not because this was not significant (r= 0.224, p=0.111). However, it's in the expected direction (lower p values associated with higher PC loadings), which suggests there are some false positives.
All the PCAs are WITHOUT rotation because they produced only 1 PC which accounted for the vast majority of the variance.
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#6
Which correlation type was tried on the vectors of p-values and PC loading? Perhaps try Spearman's rho as well.
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#7
(2014-Apr-06, 12:06:42)Emil Wrote: Which correlation type was tried on the vectors of p-values and PC loading? Perhaps try Spearman's rho as well.


Tried it too but got a very similar result
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#8
The author also needs to attach the dataset before it can be published.
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#9
I will, but so far only 1 reviewer has posted his comments.
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#10
Perhaps more would comment if the datasets were attached. For instance, then I could have run the Spearman correlations myself and reported them (although I'm not a reviewer for this journal). Attaching datasets is a requirement per the rules for submission. Not just a requirement for 'just before publication'.
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