The purpose of this study is to find inter-correlations between polygenic scores and their correlations with environmental variables. This helps shed light on selective pressure on these phenotypes. The 3 main hypotheses are: 1) Positive correlation of eveningness with latitude and distance from East Africa. According to this theory, a longer circadian period was adaptive for entrainment to the photoperiod that changed with seasons farther away from the equator(Pittendrigh, 1993). Latitudinal clines in circadian period have been found in insects and plants (Hut et al., 2013) 2)Positive correlation between latitude and height PGS, due to Bergmann’s rule. 3)Positive correlation between latitude and PGS EDU (a proxy for general cognitive ability and long term planning), as latitude is correlated with harsher climate and survival challenges of building shelter, storing food and hunting large game. Another research question is whether the positive correlation between eveningness and cognitive ability within populations holds also at the between-population level.

File to upload: “HGDP_PGS.csv” (download from: https://osf.io/ewxqj/)

Create bar charts for PGS_Height, PGS_EDU, PGS_Chron

HGDP_PGS=read.csv(file.choose(),header=TRUE)
#Create bar charts
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.4.4
df_PGS=HGDP_PGS

#Height
ggplot(df_PGS, aes(PGS_Height_Z, Population)) +
  geom_segment(aes(x = 0, y = Population, xend = PGS_Height_Z, yend = Population), color = "grey50") +
  geom_point()+
  labs(title = "Height PGS for 52 HGDP populations",
       caption = "Wood et al., 2018 GWAS") 

#EDU
ggplot(df_PGS, aes(PGS_EDU_Z, Population)) +
  geom_segment(aes(x = 0, y = Population, xend = PGS_EDU_Z, yend = Population), color = "grey50") +
  geom_point()+
  labs(title = "EDU PGS for 52 HGDP populations",
       caption = "Lee et al., 2018 GWAS") 

#Chronotype
ggplot(df_PGS, aes(PGS_Chron_Z, Population)) +
  geom_segment(aes(x = 0, y = Population, xend = PGS_Chron_Z, yend = Population), color = "grey50") +
  geom_point()+
  labs(title = "Chronotype (eveningness) PGS for 52 HGDP populations",
       caption = "Lee et al., 2018 GWAS")

Compute correlation matrix

##              PGS_Chron_Z PGS_Height_Z PGS_EDU_Z Distance.AA Latitude
## PGS_Chron_Z         1.00         0.47      0.38        0.40     0.50
## PGS_Height_Z        0.47         1.00      0.05        0.06     0.37
## PGS_EDU_Z           0.38         0.05      1.00       -0.29     0.57
## Distance.AA         0.40         0.06     -0.29        1.00    -0.23
## Latitude            0.50         0.37      0.57       -0.23     1.00
## B.C_Skin.PGS       -0.26        -0.77     -0.09        0.45    -0.49
##              B.C_Skin.PGS B.C_Height_PGS Biasutti
## PGS_Chron_Z         -0.26           0.75     0.73
## PGS_Height_Z        -0.77           0.86     0.39
## PGS_EDU_Z           -0.09           0.22     0.60
## Distance.AA          0.45           0.30     0.02
## Latitude            -0.49           0.67     0.80
## B.C_Skin.PGS         1.00          -0.76    -0.37
##                PGS_Chron_Z PGS_Height_Z PGS_EDU_Z Distance.AA Latitude
## PGS_Chron_Z              1         0.47      0.38        0.40     0.50
## PGS_Height_Z            NA         1.00      0.05        0.06     0.37
## PGS_EDU_Z               NA           NA      1.00       -0.29     0.57
## Distance.AA             NA           NA        NA        1.00    -0.23
## Latitude                NA           NA        NA          NA     1.00
## B.C_Skin.PGS            NA           NA        NA          NA       NA
## B.C_Height_PGS          NA           NA        NA          NA       NA
## Biasutti                NA           NA        NA          NA       NA
##                B.C_Skin.PGS B.C_Height_PGS Biasutti
## PGS_Chron_Z           -0.26           0.75     0.73
## PGS_Height_Z          -0.77           0.86     0.39
## PGS_EDU_Z             -0.09           0.22     0.60
## Distance.AA            0.45           0.30     0.02
## Latitude              -0.49           0.67     0.80
## B.C_Skin.PGS           1.00          -0.76    -0.37
## B.C_Height_PGS           NA           1.00     0.79
## Biasutti                 NA             NA     1.00

reorder_cormat <- function(cormat){
  # Use correlation between variables as distance
  dd <- as.dist((1-cormat)/2)
  hc <- hclust(dd)
  cormat <-cormat[hc$order, hc$order]
}

# Reorder the correlation matrix
cormat <- reorder_cormat(cormat)
upper_tri <- get_upper_tri(cormat)
# Melt the correlation matrix
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Create a ggheatmap
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
  geom_tile(color = "white")+
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1,1), space = "Lab", 
                       name="Pearson\nCorrelation") +
  theme_minimal()+ # minimal theme
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 12, hjust = 1))+
  coord_fixed()


ggheatmap + 
  geom_text(aes(Var2, Var1, label = value), color = "black", size = 4) +
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.major = element_blank(),
    panel.border = element_blank(),
    panel.background = element_blank(),
    axis.ticks = element_blank(),
    legend.justification = c(1, 0),
    legend.position = c(0.6, 0.7),
    legend.direction = "horizontal")+
  guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
                               title.position = "top", title.hjust = 0.5))

The results are in line with the predictions: 1) The correlation between eveningness (PGS_Chron_Z) and latitude, distance from AA are r= +0.50, + 0.40. 2)There is a positive correlation (r= 0.37) between latitude and height. 3)Positive correlation between latitude and polygenic score for Educational attainment (r=0.57). The Height PGS is also highly correlated (r=0.86) to the PGS computed by Berg and Coop (2014), suggesting that the results are robust to different GWAS and PGS computation methods (e.g. different significance thresholds, LD clumping, etc.). The skin pigmentation index (Biasutti) is correlated to PGS EDU, PGS Chronotype and PGS Height (r=0.6, 0.73, 0.39 respectively). There was also a weak positive correlation (0.38) between eveningness and PGS EDU, matching the within-population pattern.

In a follow up study I will take into account the potential confounding factors of linkage disequilibrium and population structure.