10.19 Region-specific \(f_4\) ratio

Now we can re-calculate the \(f_4\)-ratio only within promoter regions.

# f4-ratio with only promoters
f4_filtered <- f4ratio(data = new_snps_keep,
                       X = pops, A = "Altai", B = "Vindija", C = "Yoruba", O = "Chimp") %>%
  # convert z score to pvalue
  mutate(p = 2 * pnorm(-abs(Zscore)))

f4_filtered

##       A       B           X      C     O     alpha   stderr Zscore          p
## 1 Altai Vindija      French Yoruba Chimp -0.005541 0.028515 -0.194 0.84617588
## 2 Altai Vindija   Sardinian Yoruba Chimp  0.002263 0.031027  0.073 0.94180612
## 3 Altai Vindija         Han Yoruba Chimp  0.066668 0.029767  2.240 0.02509092
## 4 Altai Vindija      Papuan Yoruba Chimp  0.010940 0.030057  0.364 0.71585801
## 5 Altai Vindija Khomani_San Yoruba Chimp  0.026367 0.031975  0.825 0.40937159
## 6 Altai Vindija       Mbuti Yoruba Chimp  0.005176 0.030194  0.171 0.86422377
## 7 Altai Vindija       Dinka Yoruba Chimp  0.008542 0.026768  0.319 0.74972651

Plot the region-excluded \(f_4\)-ratios

ggplot(f4_filtered,
       aes(x = X, y = alpha, color = p < 0.05)) +
  geom_point() +
  geom_errorbar(aes(ymin = alpha - 2 * stderr, ymax = alpha + 2 * stderr), width = 0.5) +
  geom_hline(yintercept = 0, linetype = 2) +
  labs(y = "Neanderthal ancestry proportion", x = "Present-day individual")

Except for the Han population, we see almost no Neanderthal ancestry when we calculate the \(f_4\)-ratio within promoters – supporting the idea that functionally important genomic regions are depleted for Neanderthal introgression.