Height, IQ,polygenes: selection signal or noise?

Okbay et al. (2016) reported 162 independent SNPs that reached genome-wide significance (P < 5*10-8) in the pooled-sex EduYears meta-analysis of the discovery and replication samples (N =405,072). 161 SNPs were found in 1000 Genomes.  These were divided into 32 subsets of 5 SNPs and factor analyzed. The correlations of factor loadings and corr x pop IQ with p value were r= -0.273 and -0.008, respectively. Moreover, the two vectors (factor loadings and corr x pop IQ) were intercorrelated (r= 0.223), implying that the internal coherence of the factors is correlated to their predictive validity.

The scatterplot is shown in figure 1.

mcvokbay

The top 4 significant SNPs sets (N=20) were used to compute a polygenic score and the 4 factor scores were averaged. These were chosen because they had the highest loadings, highest correlation to population IQ and lowest p value (respectively, 0.383 and 0.83, compared to an average of 0.22 and 0.11 for the entire dataset), hence suggesting more signal in the data.

The largest GWAS to date (Wood et al., 2016) identified 697 SNPs which reached statistical significance for their association with human height. Factor analysis was carried out on 69 sets of 10 SNPs.

The top 10 significant SNPs for height were chosen because they had a higher average factor loading (0.419) than the entire set (0.166), actually the third highest among 69 sets of 10 SNPs. Polygenic and factor scores are reported in table 1. The latter are also reported in table 2 and 3, in descending order.

Table 1. Factor and polygenic scores. Top significant SNPs for height and educational attainment (IQ) GWAS.

Population PS_IQ IQ_Top_4_Fs_Mean Height_PS F_Height
Afr.Car.Barbados 0.339 -1.124 0.636 1.342
US Blacks 0.358 -0.904 0.612 0.662
Bengali Bangladesh 0.368 -0.051 0.503 -0.349
Chinese Dai 0.43 0.736 0.417 -1.381
Utah Whites 0.412 0.838 0.569 0.483
Chinese, Bejing 0.471 1.175 0.419 -1.456
Chinese, South 0.45 1.058 0.418 -1.504
Colombian 0.374 0.201 0.515 -0.103
Esan, Nigeria 0.345 -1.307 0.653 1.629
Finland 0.43 0.76 0.417 0.524
British, GB 0.421 0.832 0.551 0.299
Gujarati Indian, Tx 0.386 -0.059 0.524 -0.333
Gambian 0.342 -1.196 0.61 1.33
Iberian, Spain 0.419 0.728 0.552 0.245
Indian Telegu, UK 0.372 -0.127 0.521 -0.475
Japan 0.459 1.235 0.419 -1.568
Vietnam 0.435 0.845 0.417 -1.321
Luhya, Kenya 0.338 -1.306 0.618 1.263
Mende, Sierra Leone 0.332 -1.475 0.624 1.278
Mexican in L.A. 0.36 0.143 0.502 -0.561
Peruvian, Lima 0.304 -0.28 0.496 -0.803
Punjabi, Pakistan 0.39 0.091 0.519 -0.402
Puerto Rican 0.374 -0.012 0.525 0.254
Sri Lankan, UK 0.373 0.025 0.5 -0.576
Toscani, Italy 0.415 0.511 0.562 0.238
Yoruba, Nigeria 0.343 -1.338 0.638 1.285

Table 2. IQ factor scores sorted in descending order.

Population IQ_Top_4_factors_Mean
Japan 1.235
Chinese, Bejing 1.175
Chinese, South 1.058
Vietnam 0.845
Utah Whites 0.838
British, GB 0.832
Finland 0.76
Chinese Dai 0.736
Iberian, Spain 0.728
Toscani, Italy 0.511
Colombian 0.201
Mexican in L.A. 0.143
Punjabi, Pakistan 0.091
Sri Lankan, UK 0.025
Puerto Rican -0.012
Bengali Bangladesh -0.051
Gujarati Indian, Tx -0.059
Indian Telegu, UK -0.127
Peruvian, Lima -0.28
US Blacks -0.904
Afr.Car.Barbados -1.124
Gambian -1.196
Luhya, Kenya -1.306
Esan, Nigeria -1.307
Yoruba, Nigeria -1.338
Mende, Sierra Leone -1.475

 

Table 3. Height factor scores in descending order

Population Factor_Height_10SNPs
Esan, Nigeria 1.629
Afr.Car.Barbados 1.342
Gambian 1.33
Yoruba, Nigeria 1.285
Mende, Sierra Leone 1.278
Luhya, Kenya 1.263
US Blacks 0.662
Finland 0.524
Utah Whites 0.483
British, GB 0.299
Puerto Rican 0.254
Iberian, Spain 0.245
Toscani, Italy 0.238
Colombian -0.103
Gujarati Indian, Tx -0.333
Bengali Bangladesh -0.349
Punjabi, Pakistan -0.402
Indian Telegu, UK -0.475
Mexican in L.A. -0.561
Sri Lankan, UK -0.576
Peruvian, Lima -0.803
Vietnam -1.321
Chinese Dai -1.381
Chinese, Bejing -1.456
Chinese, South -1.504
Japan -1.568

 

There is a strong negative correlation between height and intelligence factor scores (r=-0.778).

The correlation between population IQ estimates (Piffer, 2015) with the average factor score and the polygenic score were r=0.923 and  0.867. The very high correlation of the factor score exceeds the 99% C.I. produced with a simulation using 200 iterations on random SNPs.

East Asians top the IQ rankings but are at the bottom of the height rankings. The opposite is true of African populations. Europeans have mid-high scores for both IQ and height, whereas South Asians and Hispanics/Latinos have mid to low scores on both traits.

The higher internal (i.e. factor loadings) and external (i.e. corr x IQ) coherence of factors extracted from more significant SNPs and the different patterns observed for height and IQ suggest that these SNPs represent signal of polygenic selection and not merely phylogenetic autocorrelation. Another important finding is that the signal is restricted to the most significant hits of each GWAS.

The individual scores are dependent on the choice of SNPs and the computational method (e.g. polygenic vs factor scores) but the overall pattern isn’t affected, since it is pretty consistent across GWAS samples and publications.

 

 

References

Okbay, A., Beauchamp, J.P., Fontana, M.A., Lee, J., Pers, T.H., et al. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, doi:10.1038/nature17671

Piffer, D. (2015). A review of intelligence GWAS hits: Their relationship to country IQ and the issue of spatial autocorrelation. Intelligence, 53, 43-50.

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

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s