Evidence of polygenic selection on human stature inferred from spatial distribution of allele frequencies

Introduction

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.

Methods

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.

Results