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Paper rejected by Intelligence with comments

Here is my much awaited and forbidden response to this review!

Overall, this is a good review, although not devoid of some generic comments that are irritating because they leave the questions that they raise unanswered and they do not provide any references to back up their claims (e.g. “one research group with control of an extremely large family cohort is currently working on a manuscript documenting that years of education is subject to a very peculiar form of confounding”). Really? Which “peculiar form of confounding”? Which large family cohort and which manuscript? Which research group?
Isn’t it funny how a reviewer can afford to be generic and not provide any justification or references to back up their claims, but the authors have got to take extreme pains to make sure that everything is backed by sound evidence? Why this double standard?Perhaps because reviewers work for free, and nobody likes to do unpaid work.
There are a few serious comments that deserve consideration. For example: “A GWAS of Europeans is more likely to detect SNPs with high minor allele frequencies.The minor allele is usually the derived allele, and thus the use of SNPs ascertained to have low p-values in a GWAS of Europeans will lead to an overrepresentation of SNPs with high derived allele frequencies specifically in Europeans. If the derived allele tends to have a positive effect (as the authors claim), this is certainly an issue that needs to be carefully addressed.”
This argument is not explained very clearly. It’s another example of how the reviewers expect crystal-clear clarity from the authors, but they can get away with making rather obscure comments that leave room for different interpretations.
This could mean two things. 1) That the GWAS tends to select trait increasing alleles that are derived.However, upon closer inspection it turns out to be fallacious. There is the wrong assumption that the GWAS hits always have a positive beta, which is not the case. Positive and negative betas are randomly distributed across GWAS hits. Thus, when the GWAS selects the hit with a negative beta, which should be more likely to be the minor allele and hence derived, the allele with a positive beta (in this case, IQ enhancing) is going to be more likely to be the major allele and hence ancestral.
Nonetheless, I counted the number of derived alleles among the alleles increasing height in the latest and biggest GWAS meta-analysis of variation human stature. This would give us an estimate of the GWAS bias towards picking derived alleles
The derived to total allele count ratio is 370/691 or 53.5%. Assessing the statistical significance this result is problematic because many SNPs are in linkage disequilibrium and violate the assumption that they represent independent observations. It’s likely that this is just a statistical fluke but nonetheless, we can give the reviewer’s fallacious reasoning the benefit of the doubt.
The derived to total allele count ratio for intelligence enhancing alleles is 42/66 or 63.6%. Good news is that here we can apply binomial probability to calculate statistical significance because the SNPs were pruned for LD by the authors (Rietveld et al., 2014). We can see that the probability p that X(the number of derived alleles)>=42 is 0.0179.
However, to be fair we have got to include the knowledge acquired by the height GWAS and assume that there is a bias for derived alleles in the GWAS results. The best estimate of this bias is equal to the percentage of derived alleles in excess of 50% in the height meta-analysis, that is 3.5%.
A binomial calculation assuming a background frequency of 53.5% will yield a p value of 0.062, which is not extremely strong but not too shabby either.
However, the more likely interpretation of the reviewer’s comment is that the minor alleles picked by the GWAS tend to have higher frequencies among the GWAS reference population (i.e. Europeans) than the average genome-wide frequencies of minor alleles. Minor alleles are more likely to be derived alleles, hence these derived alleles will have higher frequencies among Europeans compared to other populations. Since derived alleles tend to have a positive effect, the frequency of alleles with positive effect will tend to be higher among Europeans than other populations. It was hard work translating the reviewers’ obscure words into an understandable sentence.
We can again give the reviewer benefit of the doubt and see if derived alleles with a positive effect have higher frequencies among Europeans compared to ancestral alleles with a positive effect and if their average frequencies are still correlated to population IQs.
As we can see from the table, it is indeed the case, as the reviewer had predicted, that derived alleles have a higher frequency among Europeans, whether they have a positive effect or not. But the question is: Are derived alleles with a positive effect better predictors of population IQ than derived alleles with a negative effect? If the alleles contain signal that goes above and beyond that produced by being derived, the correlation between derived positive and country IQ should be stronger than that between derived negative and country IQ. In other words, this would tell us that the GWAS found signal above and beyond that provided simply by (ancestral vs derived) allele status.
The correlation between DP (derived alleles with positive effect) and country IQ is r= 0.83. The correlation between country IQ and AP (ancestral alleles with positive effect) is r=-0.65.
This implies that the signal in the total polygenic score (average frequency of all derived and ancestral alleles together) is partly driven by the derived alleles. However, a closer inspection of the matrix will tell us that the correlation between derived alleles with negative effect and IQ is r=0.65, which is lower than that between derived alleles with positive effect and population IQ (r=0.83).
Clearly, more SNPs are required to validate this picture. I am currently checking the frequencies of the Rietveld et al. 2013 alleles and will try to update this post by next week.
However, this brings us to a deeper issue lying at the core of the reviewer’s argument. That is, even if the derived alleles with GWAS negative and positive effect were equally correlated to population IQ, this tells us only about the GWAS’ ability to pick up signal. One could simply assume based on evolutionary theory that derived alleles are necessarily enriched for intelligence increasing mutations and that the GWAS is not very good at discriminating between neutral alleles and those with a positive effect. Why should there be any ancestral alleles that increase intelligence? Any alleles accounting for individual differences in intelligence are necessarily human specific, hence derived. Thus, it is sensible to expect that ancestral alleles have more false positives, which might explain the negative correlation between the frequencies of ancestral alleles with positive effects and population IQ. When studying a trait such as intelligence, which distinguishes humans from other animals, it would be sensible to focus on derived alleles. The fact that even derived alleles with a GWAS negative effect on IQ are still positively correlated to country differences in IQ is probably due to their higher likelihood of containing false negatives.
It is not too unreasonable to assume that the population with a higher genome-wide frequency of derived alleles will be smarter than a population with a lower frequency of derived alleles, because it’s genetically more different from primates. The reviewer’s explanation for the observed higher frequency of derived alleles among Europeans is that it is an artifact of GWAS, because low p values alleles will have an overrepresentation of high frequency minor alleles. I cannot find a reference for this in the reviewer’s comments, it’d have been helpful. We do not know how much it can explain this phenomenon. One would have to compute the average genome-wide frequency of derived alleles and see if there are similar differences. If there were, then it’d not be an artifact of GWAS. Then we would have to figure out why Africans have lower frequencies of derived alleles. Is that a product of random drift or selection?
. The reviewer thinks that this is not necessarily an indicator of positive selection, and stated: “it is not necessarily the case that an association between derived status and a positive effect points toward selection increasing the mean of the trait. Such selection can actually lead to the opposite association (between derived status and a negative effect) at certain allele frequencies.”
I must confess that I do not understand this argument. Surely if a mutation unique to the human lineage (arisen after the most recent common ancestor of all living humans) had been detrimental, making humans less intelligent than primates, this would have been selected against, hence disappearing from the genome? Purifying selection is much more common than positive selection because random mutations are usually deleterious.
Selection increasing the mean of the trait does actually produce an increase in derived alleles when there has been positive directional selection for the trait in a species. We know that this is the case for humans, as cranial capacity and behavioral complexity has dramatically increased in the last 4 million years and modern humans are much more intelligent than non-human primates. Selection must necessarily have increased the intelligence-enhancing mutations, hence the derived alleles.
The reviewer’s argument would apply to height, as there has not really been increase in stature, at least from Homo Erectus to Homo Sapiens Sapiens. And that’s indeed what we found: height increasing alleles are only marginally enriched for derived alleles (53%), a finding that is likely a fluke.
Another comment worthy of consideration is this: “the extrapolation to non-European populations is still problematic because the accuracy of the polygenic score declines in such populations as a result of differing LD patterns (Scutari et al., 2015). “
Differences in LD should simply reduce the frequency differences at the tag SNPs between populations, compared to the real causal SNPs. This is due to a phenomenon called “attenuation”. Indeed, correction for attenuation is used “to rid a correlation coefficient from te weakening effect of measurement error (Jensen, 1998). This scenario works in the case that the frequency differences between tag and causal SNPs are due to random error, so that the mean frequency of the cognitive ability alleles is equal to the (genome-wide) background frequency (which for a mathematical reasons, is 50%). If instead there is a systematic bias, so that the mean frequency of the causal alleles is lower than the background frequency, then attenuation will reduce observed population-level frequency differences at tag alleles. As the reviewer says,” A GWAS of Europeans is more likely to detect SNPs with high minor allele frequencies”. Hence, the average frequency of at the causal alleles identified by the GWAS tends to be lower than 50%. That this is true, can be seen from the tables displaying the average frequencies of educational attainment increasing alleles, which tend to be much lower than 50%, especially at the lowest p values.
For example, let the average frequency of causal alleles be 40 % in the reference European population. We also know that the average genome-wide frequency of alleles is 50 % in all populations (the sum of two alleles is always 100). If LD breaks down at some loci so that the tag SNP is uncorrelated to the causal SNP, the tag SNP in the non-European population will have a bias towards higher frequency compared to that of the European population. Hence, differences in LD should cause non-European populations to have higher frequencies at the tag SNPs (that is, the “GWAS hits”) than European populations and to reduce frequency differences among these populations, as all of them tend to be closer to 50%.
So this is the opposite than what the reviewer said:“ Now suppose that in a different population the SNPs are uncorrelated, the reference allele at the causal SNP has a somewhat higher frequency, and the reference allele at the tag SNP has a much lower frequency. Then the inference made from comparing the polygenic scores of the two populations is exactly the opposite of the truth. “
The reviewer’s argument can perhaps apply to a single SNPs but there is no reason why there should be a systematic bias in the direction predicted by that argument (actually, as we have seen, the bias direction is opposite to that predicted by the reviewer) and sadly the reviewer just assumes that this is so, without providing any justification.

Jensen, A.R. (1998). The g Factor: The Science of Mental Ability Praeger, Connecticut, USA
If you have not read the previous post already, please do so as it will help understand the reasoning presented here. Before I proceed with my discussion of derived alleles and their frequencies, I need to point out another inaccuracy in the reviewer's report. The whole review assumes that all the SNPs are from an educational attainment GWAS. In reality, I also employed SNPs with an effect on cognitive performance and fluid intelligence (Rietveld et al, 2014; Davies et al, 2014). The fact that the reviewer ignored this suggests that she/he found it convenient to focus on what was perceived as a potential weakness, as apparently population stratification confounds the results of educational attainment GWAS (we've got to take the reviewer's obscure comment at face value on this).
Moving on to the next analysis. To recapitulate, the reviewer thinks that the correlation between polygenic scores (average population allele frequency) and population IQ is driven by a methodological artifact (the reasoning is rather convolute so I refer to my previous post for its exegesis), producing higher frequencies of derived alleles among Europeans. Since in the Rietveld et al. (2014) sample of 69 SNPs, the alleles with a positive effect on IQ tended to be derived, this would explain the correlation between population IQ and polygenic score. Thus, I devised a method to check for this potential confounding factor. I divided the SNPs into four groups: derived with positive and negative effect; ancestral with positive and negative effects. If Europeans have higher frequencies at the positive effect alleles than Africans because these tend to be more derived, then also derived alleles with a negative effect should have higher frequencies among Europeans than Africans. Hence, if the polygenic score's correlation with population IQ is driven by derived allele status, this correlation should not differ between derived alleles with positive and negative effect.
The same procedure applied to the Rietveld et al. (2014) SNPs to control for differential distribution of derived alleles due to GWAS artifact or bottleneck effects (Henn et al., 2015) will be employed here. Alleles with a positive effect are divided into two sub-groups: those that are derived and those that are ancestral. Reversing their frequencies (1-n) yields the frequencies of derived negative and ancestral negative alleles, respectively. The 4 polygenic scores are reported in this table. First, we can see that the reviewer’s claim that derived alleles have higher frequencies among Europeans is debunked, as this is true only for derived alleles with a positive effect , but not those with a negative effect, which actually reach higher frequencies among South Asians (e.g. Indian Telegu: 0.458) but are otherwise equally distributed across Africans (e.g. Esan Nigeria: 0.372) and Europeans (e.g. British: 0.372). What is their correlation with population IQ? If GWAS hits really had higher frequencies among Europeans than Africans simply because (according to the reviewer) of a methodological artifact, this should apply irrespective of the effect on educational attainment. In other words, positive and negative effect derived alleles should be found at higher frequencies among Europeans. What about the polygenic scores correlations to population IQ? Again, if the polygenic scores’ correlation to population IQ were driven only by derived allele status, alleles with a positive effect on educational attainment should not be more strongly correlated to population IQ than alleles with a negative effect.
We can see that the correlation between derived positive polygenic score and IQ is 0.89, much higher than that between derived negative and IQ (-0.25). This suggests that the alleles pick selection signal that goes above and beyond random drift or effects of GWAS artifact. Another interesting result is that ancestral alleles with a positive effect do not seem to predict population IQ (r=0.25) confirming my prediction that intelligence enhancing alleles should be overrepresented among human-specific mutations. If we assume that human-specific mutations with a positive effect on IQ at the individual level are the least likely to contain false positives, we can consider this as the best measure of selection pressure strength across populations. We can see that this index peaks among Europeans (highest scores for Italians and British= 49%) and East Asians (e.g. Chinese Bejing= 45.4%). South Asians have lower scores (Bangladesh= 33.9%), and even lower in sub-Saharan African populations (around 30%).
Perhaps another measure of selection would be the difference between derived positive and derived negative allele frequencies. This would take into account the DAF (derived allele frequencies) distributions due to population bottlenecks and drift. We can see that even this measure is substantially correlated to population IQ (r=0.85).
A somewhat puzzling finding is the dramatic drop in the percentage of derived alleles with a positive effect when value goes above the conventional GWAS significance threshold (p<5*10-8). 9/10 of the GWAS significant hits were derived. However, only about 50% of those belonging to the second group (p value between 5*10-7 and 5*10-8) were derived. The dramatic drop is perhaps an artifact of adopting a dichotomous approach, dividing the groups by a conventional threshold. One would have to correlate the p value to the derived vs ancestral allele status. This was done in my paper using the 67 alleles found by Rietveld et al. (2014) to increase cognitive performance, and a slightly positive effect was found. Using the 109 SNPs (top 10 + 99 making up the second group), yields a correlation r= -0.019. Since derived alleles are coded as 1 and ancestral ones as 0, this implies that there is a very weak association between derived status and low p value. However, this is driven entirely by the top 10 SNPs. A limitation of this analysis is that the SNPs are not independent in LD, hence if there are clusters of SNPs around a certain p value, this will bias the derived allele count giving undue weight to alleles in that p value range. Bigger samples of SNPs pruned for LD will be required to replicate the association between derived status and positive effect found in the Rietveld et al. (2014) data set.

References: Henn et al. (2015). Distance from sub-Saharan Africa predicts mutational load in diverse human genomes. PNAS
These longer posts are more suited to a blogging format where you can have tables and plots.
(2016-Jan-09, 22:43:49)Emil Wrote: These longer posts are more suited to a blogging format where you can have tables and plots.

Perhaps. But I also want to encourage people to discuss reviews of rejected papers!
Now it's published on my blog: https://topseudoscience.wordpress.com/20...ics-of-iq/
I know I've said this before, but if you want your research to have any impact on academia at large, it's essential that it be published in mainstream journals. Doing that is a lot more work, but if you want to bring about a paradigm shift in the social sciences, it isn't possible any other way. I'm glad you made the extra effort to do that with your 2015 paper.

Have you tried submitting to Personality and Individual Differences? They've often published papers like this in the past.
(2016-Jan-11, 07:57:22)Tetrapteryx Wrote: I know I've said this before, but if you want your research to have any impact on academia at large, it's essential that it be published in mainstream journals. Doing that is a lot more work, but if you want to bring about a paradigm shift in the social sciences, it isn't possible any other way. I'm glad you made the extra effort to do that with your 2015 paper.

Have you tried submitting to Personality and Individual Differences? They've often published papers like this in the past.

I don't like PAID. Everyone seems to think that PAID is the only alternative to Intelligence. Apart from my personal antipathy for that journal, I do not think that my paper falls into the domain of individual differences in personality. Their reviewers or editors will not have the expertise to evaluate such work, so I'd rather avoid "Intelligence II". I am not even sure that publishing my latest paper in Intelligence I achieved a bigger impact. So far I have not received any emails from academics (except those I personally know) as a result of reading my Intelligence paper. The Wikipedia entry about race differences in Intelligence doesn't even cite that paper. All I got was a retarded comment by an American geneticist at a conference. For all these reasons, I decided I will just find some experts as external reviewers and ask them to review this paper for OP. This is a new field of genetics and there aren't many experts. It'll be hard to find people who have the time to review my paper too.
(2016-Jan-11, 07:57:22)Tetrapteryx Wrote: I know I've said this before, but if you want your research to have any impact on academia at large, it's essential that it be published in mainstream journals. Doing that is a lot more work, but if you want to bring about a paradigm shift in the social sciences, it isn't possible any other way.

If we knew for certain that there were substantial, socially important behavioral genetic differences between populations, and if it were just a matter of establishing this, then yes. Since we don't know this, doing the research and publishing it somewhere serves a purpose. Emil and I, for example, have been working on a year long paper to be published in Mankind quarterly. I am certain that it will make ~ nil impact. But, at present, we are focused on verifying the model.
The IQ data looks sketchy which is a problem because it is now central to your argument.

This national IQ score is most certainly off.

"Sri Lankan, UK"
"Mexican in L.A."
The scores above are from immigrant groups. National IQs (Big G)-- which involve both population and individual level effects -- can not be compared with individual level IQs (Little g), let alone with ones from unrepresentative migrant populations.

"Gujarati Indian, Tx"
"Indian Telegu, UK"

You include "Sri Lankan, UK" even though, unless I am mistaken, there are no good intra-national IQ estimates for that group, but yet not "Indian Telegu, UK" even though there are OK UK Indian scores.

"Chinese Dai"
The only data we have is from: http://openpsych.net/forum/attachment.php?aid=616

Maybe, at some point, make alternative IQ estimates e.g., (a) Piffer original, (b) national only, © based on all available sources. Removing the immigrant groups and correcting up the Barbados NIQ to 87 had practically no effect on the associations. But correcting all the South Asian immigrant scores up to 100 (plausible) did.
Have you considered submitting to a journal with a stronger focus on genetics than Intelligence? You could try the Journal of Biosocial Science--that's where Cochran & Harpending's original study on Ashkenazi intelligence was published.
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