Piffer’s results replicated (again) by latest GWAS (N=147,194)

The results are new, but the game is getting old. However, given the replicability crisis in the social sciences (which I had the misfortune of trying on my own skin at my PhD lab), any replicate (does this word exist?) should be welcome with open arms.

In a recent paper, I published my estimates of genotypic intelligence/EA (Educational Attainment) or more appropriately, the coefficient of polygenic selection, as cognitive ability is not due only to common variants (those that add up to create a polygenic score) but also rare variants (those missed by GWAS arrays) and de-novo mutations (those that uniquely arise in each individual).

My latest estimates  were published in June, one month before the Hill et al. paper came out on Biorxiv. My 9 SNPs heavyweight was published in January 2017  (although the publication was delayed by a particularly tough-passive-aggressive, weak and slow Frontiers editor). This genetic heavyweight also predicted evolution of intelligence within Europe since the Bronze Age.

In short, these guys used wealth (household income) and educational attainment to power the search for intelligence genes. In vulgar terms, these traits are genotypically correlated, hence pooling them together should increase the power to detect signal. They call this approach MTAG (Multi-trait analysis of genome-wide association studies). Actually it was invented by Turley et al. but it does not really matter because every week a new tool to power GWAS is invented, each one as fancy as the other, and all the names sound like GATTACA. The method is not very original, but it is very common-sensical and is very brute-force driven (something very common in this field). However, the authors were very generous because they provided the full list of SNPs in the Biorxiv preprint, something that is not to be taken for granted.

It might seem strange that household income was thrown in together with educational attainment and intelligence. The authors defend their position by citing the finding that “household Income shows a genetic correlation of rg = 0.82 with education and rg = 0.65 with the GWAS meta-analysis of Sniekers et al.”.

To many of us, even educational attainment seemed a not too good proxy for cognitive abilities, and we are right to question whether adding an even less perfect proxy will increase power or just muddle the waters.

However, the authors validated their polygenic scores on childhood IQ and verbal-numerical reasoning, finding strong correlations: childhood IQ, rg = 0.84, SE = 0.06;
years of education, rg = 0.90, SE = 0.0005; and verbal numerical reasoning, rg = 0.85, SE = 0.0.

As usual, I computed the frequencies of the alleles with a positive beta in Hill et al. This is a large sample (N=107) of loci that independently reached GWAS significance.  Then I computed a polygenic score (PS or PGS) as a weighted mean (using Beta coefficients for each SNP as the weight).

What was the outcome? As can be seen in table 1, these are roughly similar to my previous estimates, giving top scores to East Asians, followed by Finns, and then other Europeans. Then Latin Americans and Africans again get the lowest scores.

The correlations with my previous estimates are moderately high (0.65 for the Sniekers et al. Intelligence factor, 0.79 for the 9 replicated EA SNPs and 0.8 for the Sniekers et al. Intelligence/EA replicated SNPs.  The correlation with population IQ is 0.64, not very high,  because the South Asians (Pakistani, Indians) appear to have large positive residuals. There is also a very odd result because Mende from Sierra Leone get a much higher score than all the other African populations, and this did not happen with the scores obtained from the other GWAS. Maybe there is a typo or an error in the frequency file, or some genuine statistical anomaly.

It’s possible that this discrepancy is due to chance, or due to some genetic variants involved in wealth but not in educational attainment/intelligence.

A much larger GWAS will come out later this year or next year from the James Lee group, so I will update you then.

Factor scores of “successful” alleles ( intelligence, EA and household income alleles).

Population G factor (Sniekers et al.) EA factor (Piffer, 2017 from Okbay et al, Davies et al and Rietveld et al.) Int-EA factor (Sniekers et al.) PGS. Hill et al. 2017
Afr.Car.Barbados -1.276 -1.351 -1.063 -0.926
US Blacks -0.961 -1.177 -0.997 -0.884
Bengali Bangladesh -0.075 -0.209 -0.66 0.249
Chinese Dai 1.35 1.017 1.251 1.197
Utah Whites 0.844 0.471 0.754 -0.025
Chinese, Bejing 1.109 1.511 1.374 1.717
Chinese, South 1.208 1.382 1.635 1.727
Colombian 0.357 0.01 -0.113 -0.727
Esan, Nigeria -1.66 -1.453 -1.255 -1.014
Finland 0.771 0.702 0.581 0.574
British, GB 0.797 0.745 0.782 -0.341
Gujarati Indian, Tx -0.049 0.271 -0.001 0.857
Gambian -1.358 -1.397 -1.186 -0.846
Iberian, Spain 0.631 0.35 0.476 -0.574
Indian Telegu, UK -0.074 0.049 -0.212 0.249
Japan 0.878 1.342 1.321 1.768
Vietnam 1.267 1.346 1.925 1.888
Luhya, Kenya -1.599 -1.488 -1.255 -1.017
Mende, Sierra Leone -1.444 -1.403 -1.165 -0.367
Mexican in L.A. 0.215 0.056 -0.259 -0.895
Peruvian, Lima -0.06 0.05 -0.762 -1.021
Punjabi, Pakistan 0.066 0.24 0.035 0.336
Puerto Rican 0.375 -0.004 -0.208 -1.154
Sri Lankan, UK -0.391 0.134 -0.432 0.401
Toscani, Italy 0.764 0.248 0.677 -0.371
Yoruba, Nigeria -1.684 -1.443 -1.243 -0.803

 

 

 

 

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