A recent GWAS ( Wood et al., 2014) based on a very large sample (N=250K) identified common variants responsible for normal variation in human height within populations.

Over the last few years, researchers have started moving away from the study of genetic evolution using a single-gene, Mendelian approach towards models that examine many genes together (polygenic). The more genes are involved in a given phenotype, the more the signal of natural selection will be “diluted” across different genomic regions (because each gene accounts for a tiny effect) making it difficult to detect it using approaches focused on a single gene ( Piffer, 2014a; Pritchard et al., 2010). A first attempt at empirically identifying polygenic selection was made by Turchin et al. (2012) on two populations (Northern and Southern Europeans) and evidence for higher frequency of height increasing alleles (obtained from GWAS studies) among Northern Europeans was provided. A drawback of that study was the reliance on populations from a single continent and that crude pairwise comparisons (e.g. French vs. Italian) were used without correlating frequency differences to average population height. Moreover, the strength of selection was not determined.

Two different approaches to identify selection based on the correlation of allele frequencies across different populations have been recently developed by Piffer (2013) and Berg & Coop (2014).

Piffer’s method uses factor analysis of trait increasing alleles (found by GWA studies) as a tool for finding a factor that represent the strength of selection on a phenotype and the underlying genetic variation ( Piffer, 2014a). An additional methodology consists of computing the correlation between genetic frequencies and the average phenotypes of different populations; then, the resulting correlation coefficients are correlated with the corresponding alleles’ genome-wide significance (p value). If the alleles contain selection signals, a positive correlation will be found, as alleles with high p value (more likely to be false positives) have a weaker correlation to average population phenotype ( Piffer, 2014a).

Piffer’s method ( Piffer, 2013; Piffer, 2014a) to identify signals of polygenic selection was used in this study and applied to the top five GWAS hits (ranked according to p value). Piffer (2014b) carried out a study on height SNPs but it was based on a smaller GWAS sample and an older version (phase 1) of the 1000 Genomes data, containing data for only 14 populations. This paper uses the phase 3 1000 Genomes data and the GWAS meta-analysis was carried out on a much larger sample size, which produces more hits with better significance. The aim of this paper is to test the hypothesis that stature has undergone natural or sexual selection in populations after humans dispersed in different continents giving rise to distinct genetic clusters.

This study also exploits additional information provided by frequencies of derived and ancestral alleles. At a theoretical level, an ancestral allele is the allele that was carried by the last common ancestor between humans and other primates whereas an allele is derived when it arose in the human lineage after the split from other primates. In practice, this allele is usually ascertained via comparison with chimpanzees. One limitation of this procedure is that if a mutation arose in chimpanzees after the split from humans, then the ancestral allele is not the chimp allele. Thus, 1000 Genomes infers ancestral alleles via alignment with 6 primate species ( Ensembl, 2015).

Derived allele frequencies (DAF) are not the same for all populations. Substantial DAF differences across populations have been found, largely due to random drift and population bottlenecks but in part also by different selection pressures ( Henn et al., 2015). Non-African populations tend to have higher frequencies of derived alleles, and DAF is positively correlated to distance from Africa ( Henn et al., 2015). If among the GWAS hits there is an overrepresentation of an allele type (more ancestral than derived alleles or vice versa), this could bias the polygenic score towards the populations with a higher background frequency of such an allele type. Thus, this study will take into account baseline differences in DAF to create an “unbiased” or “DAF-corrected” polygenic score.

Frequencies of alleles with a positive effect (height increasing) were obtained from 1000 Genomes (phase 3): http://browser.1000genomes.org/index.html comprising 26 populations belonging to five racial groups.

Average population height was obtained from the references listed at: http://en.wikipedia.org/wiki/Human_height, considering only statistics published after 2000 and young age groups (18–40). Only 11 populations met these criteria (see references in Table 1).

First, the entire sample of SNPs meeting genome-wide significance (N=697) was downloaded and the population frequencies for the alleles with a positive Beta in the 250k GWAS Meta-analysis were calculated. 4 SNPs could not be included because they were absent from 1000 Genomes.

For each chromosome, the three alleles with the highest p values were selected, and these were all unlinked (>500Kb apart from each other). Only unlinked alleles were used to avoid the confounding influence of linkage on cross-population allele frequency. Selection was restricted only to the alleles with the highest significance because these are less likely to be false positives. The same number of SNPs (3) from each chromosome was used to get a representative sample of the entire genome, to avoid bias due to chromosome location. The conventional nominal p-value <5×10 ^-8 was used as significance threshold ( Barsh et al., 2012).

A polygenic score was calculated as the mean frequency of height increasing alleles (defined as those with a positive Beta coefficient in the meta-analysis).

Analyses were carried out using R.

Polygenic scores, created by averaging frequencies from 26 populations of all SNPs having attained genome-wide significance (N=693; p<5*10 ^-8) in the largest and most recent human height GWAS, was positively correlated with the average height of 11 populations (r=0.79). Another polygenic score, obtained from 66 height increasing alleles pruned for linkage disequilibrium, was positively correlated with the average height of 11 populations (r=0.83). The method of correlated vectors revealed that alleles with lower p values had a higher correlation with phenotypic height and polygenic score, suggesting that they tend to be enriched with signal of natural selection. A factor analysis of the top five GWAS hits produced a factor (whose loadings are all in the expected direction) which is significantly and strongly correlated both to population average height and to the polygenic scores. This showed an improvement over the correlation of the five single alleles with population height ( Table 3, last row) which averaged 0.66, which in turn improved over the average correlation of the 66 alleles, which was near zero.

The rankings of polygenic and factor scores match with the folk perception on the stature of various racial groups: Africans> Europeans> South/Central Asians> Hispanics> East Asians ( Table 2). However, the ranking of Africans was lower in the polygenic score computed using all the GWAS hits, whereas the others were little altered with respect to each other.

South East Asians had the lowest scores, a result which matches with their anthropometric description.

Within Europe, northern Europeans (Finns and White Americans) had a higher genotypic stature than their southern counterparts (Italians and Spaniards), confirming the results from a previous study on GWAS loci which compared northern vs southern Europeans ( Turchin et al., 2012).

A correction for background derived allele frequency (DAF) was performed using the SNPs for which this information was available (N=691). Derived alleles were less common among African populations, confirming findings from previous studies ( Henn et al., 2015). Since there were slightly more derived alleles among the GWAS hits (e.g. the alleles with a positive effect on stature), this biased downward the polygenic scores of African populations. When a DAF-corrected score was created, the African populations moved up from the bottom scores, which were in turn “occupied” by the East Asian populations (e.g. Japanese, Chinese and Vietnamese), confirming the traditional anthropometric description. There was also a marginal improvement in the correlation with average population height (r=0.82 vs 0.79 for the corrected and uncorrected scores, respectively).

A limitation was the unavailability of sound statistics on the average height of many populations. Moreover, although human height is largely heritable, it is also heavily influenced by nutrition and living conditions. The importance of environment is suggested by the dramatic secular trend which took place in the 20th century in developed countries (e.g. Arcaleni, 2006; Webb et al., 2008); an association with dietary intakes (i.e. milk consumption) and socioeconomic status has also been observed ( Mamidi et al., 2011; Webb et al., 2008). Most of the missing data were for developing countries which likely have not reached their full growth potential or ethnic groups living in Western societies (Indian Telegu or Gujarati) for which anthropometric statistics are not easily available. If the allele frequency factor represents a genuine signal of natural selection, then the difference between it and current phenotypic height could be used as an indicator of the quality of diet and living conditions in general.

Factor analysis of allele frequencies is a promising method for detecting signals of recent selection on polygenic traits.

The data referenced by this article are under copyright with the following copyright statement: Copyright: � 2016 Piffer D

Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

F1000Research: Dataset 1. All Hits., 10.5256/f1000research.6002.d109935 ( Piffer, 2015a).

F1000Research: Dataset 2. Hits 1+2+3, 10.5256/f1000research.6002.d41833 ( Piffer, 2014c).

F1000Research: Dataset 3. Method of correlated vectors (MCV) for the top 66 SNPs., 10.5256/f1000research.6002.d109933 ( Piffer, 2015b).

F1000Research: Dataset 4. Method of correlated vectors (MCV) for all SNPs., 10.5256/f1000research.6002.d109934 ( Piffer, 2015c).