#9 SNPs
#removing 507: 178, 270
#removing 508: 176,276
AncientG_imp <-
  matrix(c(177, 4017, 273, 5037),
         nrow = 2,
         dimnames =
           list(c("Ancient", "Modern"),
                c("Positive_all", "Negative_all")))
AncientG_imp

ci.1=fisher.test(AncientG_imp, conf.level = 0.95)
ci.1
#Fisher's test 162 (130) SNPs by Okbay et al. 2016
#removing 507: 3305,4001
#removing 508: 3292, 3994
#average   3298.5,3997.5
AncientG_162 <-
  matrix(c(3298.5, 61666, 3997.5, 69114),
         nrow = 2,
         dimnames =
           list(c("Ancient", "Modern"),
                c("Positive_all", "Negative_all")))
AncientG_162
ci.2=fisher.test(AncientG_162, conf.level = 0.95)
ci.2

#Davies et al 2016
#removing 507: 267 285
#removing 508: 270 280
AncientG_Davies <-
  matrix(c(268.5, 5960, 282.5, 5106),
         nrow = 2,
         dimnames =
           list(c("Ancient", "Modern"),
                c("Positive_all", "Negative_all")))
AncientG_Davies
fisher.test(AncientG_Davies, alternative = "less")
fisher.test(AncientG_Davies, conf.int = FALSE)
fisher.test(AncientG_Davies, conf.level = 0.95)

#Chi Square test


chisq.test(AncientG_162)
chisq.test(AncientG_7)
chisq.test(AncientG_imp)
chisq.test(AncientG_Davies)

#Barnard test
library(Exact)

exact.test(AncientG_imp, method="Boschloo", alternative="two.sided", model="Binomial",cond.row = TRUE)
exact.test(AncientG_imp, method="csm approximate", alternative="two.sided") #Barnard
#likelihood ratio (G2)
# G-test
library(DescTools)
GTest(AncientG_162)
GTest(AncientG_imp)
GTest(AncientG_Davies)

library(RVAideMemoire)
G.test(AncientG_162)


#genomic age and polygenic score                                                                                                                                                      0))
ageps <- read_csv("~/ageps.csv")
#compute correlation between age and PS
cor(ageps$PS,ageps$Age,method="pearson",use="complete.obs")
#C.I. of the corr. coefficient
CIr(r=0.217,n=66,level=.95)
#linear regression
fitAGEPS <- lm(PS~Age, data=ageps)
hist(residuals(fitAGEPS))#draw histogram
library(ggplot2)
library(ggrepel)

ageps$AgeCE=2017-ageps$Age#transform data from BP into CE format
#draw scatterplot with CI
ggplot(ageps, aes(x=AgeCE, y=PS)) +
  geom_point(shape=1) +  # Use hollow circles
  labs(x="Year", y="130 SNP POLYcog")+
  geom_smooth(method=lm)

#calculate skewness and kurtosis
library(moments)
skewness(ageps$PS)  
kurtosis(ageps$PS)