Simple Statistical Tools to Detect Signals of Recent Polygenic Selection

Abstract

A growing body of evidence shows that most psychological traits are polygenic, that is they involve the action of many genes with small effects. However, the study of selection has disproportionately been on one or a few genes and their associated sweep signals (rapid and large changes in frequency). If our goal is to study the evolution of psychological variables, such as intelligence, we need a model that explains the evolution of phenotypes governed by many common genetic variants. This study illustrates simple statistical tools to detect signals of recent polygenic selection: a) ANOVA can be used to reveal significant deviation from random distribution of allele frequencies across racial groups. b) Principal component analysis can be used as a tool for finding a factor that represents the strength of recent selection on a phenotype and the underlying genetic variation. c) Method of correlated vectors: the correlation between genetic frequencies and the average phenotypes of different populations is computed; then, the resulting correlation coefficients are correlated with the corresponding alleles' genome-wide significance. This provides a measure of how selection acted on genes with higher signal to noise ratio. Another related test is that alleles with large frequency differences between populations should have a higher genome-wide significance value than alleles with small frequency differences. This paper fruitfully employs these tools and shows that common genetic variants exhibit subtle frequency shifts and that these shifts predict phenotypic differences across populations.Key Words: polygenic adaptation; evolution; intelligence; height; selective sweep ABSTRACTA growing body of evidence shows that most psychological traits are polygenic, that is they involve the action of many genes with small effects. However, the study of selection has disproportionately been on one or a few genes and their associated sweep signals (rapid and large changes in frequency). If our goal is to study the evolution of psychological variables, such as intelligence, we need a model that explains the evolution of phenotypes governed by many common genetic variants. This study illustrates simple statistical tools to detect signals of recent polygenic selection: a) ANOVA can be used to reveal significant deviation from random distribution of allele frequencies across racial groups. b) Principal component analysis can be used as a tool for finding a factor that represents the strength of recent selection on a phenotype and the underlying genetic variation. c) Method of correlated vectors: the correlation between genetic frequencies and the average phenotypes of different populations is computed; then, the resulting correlation coefficients are correlated with the corresponding alleles' genome-wide significance. A significant difference between the allele frequencies for the three races was found. Post-hoc test revealed that East Asians had significantly higher frequencies of IQ increasing alleles than Africans. In contrast, th...

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

“…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.…”

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

“…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)…”

“…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' genomewide 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.…”

“…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' genomewide significance (p value).…”