Similar selection pressures on fluid g and educational attainment-related SNPs

Author: Davide Piffer. Email: pifferdavide@gmail.com

A recent GWAS has examined the additive genetic variance accounting for variation in general cognitive function or fluid g (Davies et al., 2015)These were assessed using a battery of information-processing tests including memory, block design, matrix reasoning, reaction time, letter-number sequencing (Davies et al. , 2015).

Since my use of an educational attainment GWAS has been criticized for being affected by environmental variables and for not being strictly an intelligence measure, I decided to see if I could replicate this result on an independent sample and using different measures, hopefully tapping into a more “culture-free” construct, such as fluid g. The typical reaction to using educational attainment is that it could be influenced by environmental variables correlated to genetic variation (see for example the comments by this reviewer: http://openpsych.net/forum/showthread.php?tid=262&pid=3737#pid3737).

13 SNPs with genome-wide significance (p<5*10-8) were identified (Davies et al., 2015).  10 hits (i.e. the allele with a positive effect on the phenotype) were derived and 3 were ancestral alleles. Table 1 reports the average frequency of the 13 SNPs for the 26 populations in 1000 Genomes  and the frequency of the top 10 SNPs with an effect on years on education from Rietveld et al. (2013) . The correlation between the two polygenic scores (e.g. average population frequency of GWAS hits) is very high: r= 0.964. Their correlation to population IQ is also substantial: r= 0.817 and 0.715 for Davies et al, 2015 and Rietveld et al, 2013, respectively.

Table 1. Average frequency of intelligence (fluid g) and education (years of education)-increasing alleles from two independent GWAS.

Population  Davies et al, 2015. Top 13 SNPs Rietveld et al., 2013. Top 10 SNPs IQ
Afr.Car.Barbados 0.262 0.317 83
US Blacks 0.320 0.360 85
Bengali Bangladesh 0.371 0.368 81
Chinese Dai 0.498 0.463
Utah Whites 0.521 0.534 99
Chinese, Bejing 0.509 0.468 105
Chinese, South 0.477 0.448 105
Colombian 0.471 0.476 83.5
Esan, Nigeria 0.286 0.341 71
Finland 0.556 0.573 101
British, GB 0.529 0.548 100
Gujarati Indian, Tx 0.391 0.403
Gambian 0.262 0.325 62
Iberian, Spain 0.533 0.566 97
Indian Telegu, UK 0.280 0.293
Japan 0.425 0.399 105
Vietnam 0.511 0.491 99.4
Luhya, Kenya 0.228 0.292 74
Mende, Sierra Leone 0.311 0.355 64
Mexican in L.A. 0.358 0.370 88
Peruvian, Lima 0.300 0.288 85
Punjabi, Pakistan 0.324 0.357 84
Puerto Rican 0.476 0.483 83.5
Sri Lankan, UK 0.308 0.323 79
Toscani, Italy 0.553 0.562 99
Yoruba, Nigeria 0.270 0.340 71

As overrepresentation of derived alleles among GWAS hits is a potential counfound (due to different frequencies of derived alleles among population caused by drift and bottlenecks or GWAS artifacts: see my previous posts for an explanation), a baseline frequency of derived alleles (DAF) was estimated using the 693 SNPs significant for human stature in the largest GWAS to date (Wood et al, 2014).

A multiple regression was ran with population IQ and the two variables (baseline DAF and polygenic score) was ran for the two GWAS hits.

Table 2. Standardized beta coefficients. DAF= derived allele frequency. DP (derived alleles with positive effect on the trait).

  Baseline DAF Davies DP
Rietveld et al., 2013 0.406 0.464
Davies et al, 2015 0.307 0.587

Both polygenic scores emerged as better predictors than baseline DAF. A DAF-calibrated score was calculated by subtracting baseline DAF from the frequency of derived hits. This likely represents selection signal on derived alleles as it controls for evolutionary dynamics such as random drift and population bottlenecks. Since the two population-level polygenic scores were highly correlated (r= 0.953), an average score was computed and is reported in table 3, ranked in descending order. This score is highly correlated to the average of the two polygenic scores obtained using all the SNPs (table 1), r= 0.971. However, the correlation with population IQ is slightly lower, at r= 0.687.

Table 3. DAF-calibrated polygenic scores for derived alleles and average polygenic score. Ranked in descending order. DAF= derived allele frequency.

Population DAF-free Derived hits. Rietveld et al, 2013 DAF-free Derived hits. Davies et al, 2013 Average
Toscani, Italy 0.186 0.178 0.182
Finland 0.188 0.173 0.180
Iberian, Spain 0.188 0.155 0.171
British, GB 0.171 0.149 0.160
Utah Whites 0.160 0.140 0.150
Vietnam 0.114 0.160 0.137
Chinese, Bejing 0.092 0.155 0.123
Chinese Dai 0.087 0.145 0.116
Puerto Rican 0.104 0.116 0.110
Colombian 0.103 0.107 0.105
Chinese, South 0.064 0.127 0.096
Japan 0.025 0.072 0.049
Gujarati Indian, Tx 0.033 0.038 0.036
Mende, Sierra Leone 0.021 0.038 0.029
US Blacks 0.014 0.026 0.020
Esan, Nigeria 0.007 0.013 0.010
Bengali Bangladesh -0.011 0.022 0.006
Yoruba, Nigeria 0.005 0.002 0.004
Mexican in L.A. -0.007 0.000 -0.003
Gambian -0.022 -0.015 -0.018
Afr.Car.Barbados -0.036 -0.022 -0.029
Punjabi, Pakistan -0.036 -0.028 -0.032
Luhya, Kenya -0.046 -0.050 -0.048
Sri Lankan, UK -0.057 -0.042 -0.050
Peruvian, Lima -0.087 -0.045 -0.066
Indian Telegu, UK -0.090 -0.064 -0.077

Conclusion

We can see that genetic variants increasing fluid intelligence and educational attainment are highly correlated at the population-level, suggesting two things: 1) there are common selection pressures on the two phenotypes or 2) educational attainment is a good proxy for g and the SNPs found by Rietveld et al., 2013 are actually g-related (as was suggested by their replication on g in a sub-sample). The findings in the present study debunk two criticisms of my work: 1) That the observed allele frequency differences were “specific” to educational attainment and not really about intelligence and 2) that derived allele differences caused by GWAS artifacts or random drift could mediate the effects. I showed that the observed effects are not due to different baseline derived allele frequencies, thus ruling this out as a possible confound. A discrepancy with IQ estimates is that East Asians lag behind Europeans and that South Asians and Hispanics don’t perform better than sub-Saharan Africans, a finding that is difficult to explain at present.

Again, we observe a tendency for derived alleles (human-specific mutations or not shared with non-human primates) to be overrepresented among the most significant intelligence GWAS hits, confirming the prediction stemming  from the evolutionary fact that intelligence has dramatically increased during human evolution.

References:

Davies, G., Armstrong, N., Bis, J. C., et al. (2015). Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53949).Molecular Psychiatry, 20:183-192. doi: 10.1038/mp.2014.188

Rietveld, C.A., Medland, S.E., Derringer, J., Yang, J., Esko, T., Martin, N.W., et al. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340, 1467-1471. doi: http://doi.org/10.1126/science.1235488

Wood AR, Esko T, Yang J,et al.: Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014; 46(11): 1173–86.

 

 

 

 

 

 

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