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02_Kettunen16.R
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# Clear environment
rm(list = ls())
# Get libs and metadata ----
library(ieugwasr)
library(ggplot2)
library(plot3D)
# Set working directory
wd <- "/Users/fredriklandfors/projekt/lipase_genmimicry"
# Get data ----
cbind_df <- data.table::fread(file = paste0(wd, "/data/2_Kettunen20.txt"))
cbind_df <- as.data.frame(cbind_df)
# Remove scaling param
cbind_df <- subset(cbind_df, gwas_id.LPL != "met-c-934")
# Multiple comparisons correction ----
m_comp = 9
# Graphical parameters ----
## scatter_plot ----
scatter.width = 2.4
scatter.height = 2.4
scatter.hvline.color = "grey25"
scatter.hline.size = 0.3
scatter.vline.size = 0.3
scatter.abline.size = 0.3
scatter.smooth.size = 0.3
scatter.linerange.size = 0.3
scatter.axis.line = element_line(size = 0.4)
scatter.axis.ticks = element_line(size = 0.4)
scatter.plot.title = element_text(family = "Helvetica", size = 6, colour = "black", face = "bold", hjust = 0.5,
margin = margin(1,1,1,1))
scatter.plot.subtitle = element_text(family = "Helvetica", size = 5, colour = "black", face = "italic", hjust = 0.5,
margin = margin(1,1,2,1))
scatter.legend.text = element_text(family = "Helvetica", size = 4, colour = "black", face = "plain")
scatter.legend.key.size = unit(0, 'points')
scatter.legend.margin = margin(0, 0, 0, 0)
scatter.axis.title = element_text(family = "Helvetica", size = 5, face = "plain")
scatter.axis.text.x = element_text(family = "Helvetica", size = 5, colour = "black", face = "plain")
scatter.axis.text.y = element_text(family = "Helvetica", size = 5, colour = "black", face = "plain")
scatter.xlims = c(-3.5, 3.5)
scatter.ylims = c(-3.5, 3.5)
scatter.point.size = 1
scatter.stroke = 0.25
## av_plots ----
av.width = 2.4
av.height = 2.4
av.hvline.color = "grey25"
av.hline.size = 0.3
av.vline.size = 0.3
av.abline.size = 0.3
av.smooth.size = 0.3
av.linerange.size = 0.3
av.axis.line = element_line(size = 0.4)
av.axis.ticks = element_line(size = 0.4)
av.plot.title = element_text(family = "Helvetica", size = 6, colour = "black", face = "bold", hjust = 0.5,
margin = margin(1,1,1,1))
av.plot.subtitle = element_text(family = "Helvetica", size = 5, colour = "black", face = "italic", hjust = 0.5,
margin = margin(1,1,2,1))
av.legend.text = element_text(family = "Helvetica", size = 5, colour = "black", face = "plain")
av.legend.key.size = unit(5, 'points')
av.legend.margin = margin(0, 0, 0, 0)
av.axis.title = element_text(family = "Helvetica", size = 5, face = "plain")
av.axis.text.x = element_text(family = "Helvetica", size = 5, colour = "black", face = "plain")
av.axis.text.y = element_text(family = "Helvetica", size = 5, colour = "black", face = "plain")
av.xlims = c(-2, 2)
av.ylims = c(-2, 2)
av.point.size = 1
av.stroke = 0.25
## threed_plot ----
threed.width = 5*1.186296
threed.height = 5
threed.cex = 0.7
threed.lims = c(-2, 2)
# 2D plots ----
scatter_plot <- function(df,
title = "",
subtitle = "",
x_var, y_var,
scale_var = "TG",
x_gene, y_gene,
x_mut, y_mut,
x_rsid = "rsid.x",
y_rsid = "rsid.y",
x_lim = c(-1, 1),
y_lim = c(-1, 1),
point_size = 2) {
out <- ggplot(data = df, mapping = aes_string(x = x_var, y = y_var)) +
ggtitle(label = paste0(title),
subtitle = paste0(subtitle)) +
xlab(paste0(x_gene," (", x_rsid,"-", df[1, paste0("ea.", x_gene)],
" ", x_mut, ")\n[1-SD effect on parameter per 1-SD TG change]")) +
ylab(paste0(y_gene," (", y_rsid,"-", df[1, paste0("ea.", y_gene)],
" ", y_mut, ")\n[1-SD effect on parameter per 1-SD TG change]")) +
scale_x_continuous(limits = x_lim, breaks = seq(-3, 3, 1)) +
scale_y_continuous(limits = y_lim, breaks = seq(-3, 3, 1)) +
geom_hline(yintercept = 0, size = scatter.hline.size, color = scatter.hvline.color, linetype = "solid") +
geom_vline(xintercept = 0, size = scatter.vline.size, color = scatter.hvline.color, linetype = "solid") +
geom_abline(intercept = 0, slope = 1, color = "grey50", linetype = "dashed",
size = scatter.abline.size) +
geom_linerange(aes_string(
xmin = paste0("se_min.", scale_var, x_gene),
xmax = paste0("se_max.", scale_var, x_gene)
), color = "grey75", size = scatter.linerange.size
) +
geom_linerange(aes_string(
ymin = paste0("se_min.", scale_var, y_gene),
ymax = paste0("se_max.", scale_var, y_gene)
), color = "grey75", size = scatter.linerange.size
) +
geom_smooth(method = "lm", formula = y ~ x, color = "black",
linetype = "dashed", size = scatter.smooth.size, fullrange = TRUE,
level = 1 - 0.05 / m_comp) +
geom_point(aes_string(fill = paste0("class.", x_gene)),
size = point_size, stroke = scatter.stroke,
alpha = 1, shape = 21, color = "black") +
theme_classic() +
theme(
plot.title = scatter.plot.title,
plot.subtitle = scatter.plot.subtitle,
legend.title = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1, 0.05),
legend.key.size = scatter.legend.key.size,
legend.text = scatter.legend.text,
legend.margin = scatter.legend.margin,
axis.title = scatter.axis.title,
axis.text.x = scatter.axis.text.x,
axis.text.y = scatter.axis.text.y,
axis.line = scatter.axis.line,
axis.ticks = scatter.axis.ticks
) +
scale_fill_manual(breaks = c("ApoA-I", "ApoB", "VLDL", "IDL", "LDL",
"HDL", "Fatty acids", "Other lipids", "Other metabolites"),
values = c("white", "black", "#00A08A", "#F98400", "#F2AD00",
"#5BBCD6", "#FD6467", "#046C9A", "grey50")
) +
coord_fixed(ratio = 1)
return(out)
}
## angptl4 ----
angptl4_plot <- scatter_plot(
cbind_df,
title = "HUMAN PLASMA: LPL vs. ANGPTL4",
subtitle = "Systemic effects on metabolic parameters per 1-SD TG change",
y_var = "beta.tg.LPL",
x_var = "beta.tg.ANGPTL4",
scale_var = "tg.",
y_gene = "LPL",
x_gene = "ANGPTL4",
y_rsid = "rs115849089",
x_rsid = "rs116843064",
y_mut = "[eQTL]",
x_mut = "[E40K]",
y_lim = scatter.xlims,
x_lim = scatter.ylims,
point_size = scatter.point.size
)
## angptl3 ----
angptl3_plot <- scatter_plot(
cbind_df,
title = "HUMAN PLASMA: LPL vs. ANGPTL3",
subtitle = "Systemic effects on metabolic parameters per 1-SD TG change",
y_var = "beta.tg.LPL",
x_var = "beta.tg.ANGPTL3",
scale_var = "tg.",
y_gene = "LPL",
x_gene = "ANGPTL3",
y_rsid = "rs115849089",
x_rsid = "rs11207977",
y_mut = "[eQTL]",
x_mut = "[eQTL]",
y_lim = scatter.xlims,
x_lim = scatter.ylims,
point_size = scatter.point.size
)
## angptl8 ----
angptl8_plot <- scatter_plot(
cbind_df,
title = "HUMAN PLASMA: LPL vs. ANGPTL8 R59W",
subtitle = "Systemic effects on metabolic parameters per 1-SD TG change",
y_var = "beta.tg.LPL",
x_var = "beta.tg.ANGPTL8",
scale_var = "tg.",
y_gene = "LPL",
x_gene = "ANGPTL8",
y_rsid = "rs115849089",
x_rsid = "rs2278426",
y_mut = "[eQTL]",
x_mut = "[R59W]",
y_lim = scatter.xlims,
x_lim = scatter.ylims,
point_size = scatter.point.size
) + theme(legend.position = c(0.4, 0.7),)
## write to file ----
ggsave(plot = angptl4_plot,
path = paste0(wd, "/plots/02_Kettunen16"),
filename = "angptl4_Kettunen16.pdf",
width = scatter.width, height = scatter.height, units = "in", dpi = 1100)
ggsave(plot = angptl3_plot,
path = paste0(wd, "/plots/02_Kettunen16"),
filename = "angptl3_Kettunen16.pdf",
width = scatter.width, height = scatter.height, units = "in", dpi = 1100)
ggsave(plot = angptl8_plot,
path = paste0(wd, "/plots/02_Kettunen16"),
filename = "angptl8_Kettunen16.pdf",
width = scatter.width, height = scatter.height, units = "in", dpi = 1100)
# AV plots ----
av_plot_1 <- function(df,
title = "",
subtitle = "",
x_var, y_var,
scale_var = "TG",
x_gene, y_gene, s_gene,
x_mut, y_mut, s_mut,
x_rsid = "rsid.x",
y_rsid = "rsid.y",
s_rsid = "rsid.s",
x_lim = c(-1, 1),
y_lim = c(-1, 1),
point_size = 2) {
out <- ggplot(data = df, mapping = aes_string(x = x_var, y = y_var)) +
ggtitle(label = paste0(title),
subtitle = paste0(subtitle)) +
xlab(paste0(x_gene,
#" (", x_rsid,"-", df[1, paste0("ea.", x_gene)],
#" ", x_mut, ")",
" | ",
s_gene,
#" (", s_rsid,"-", df[1, paste0("ea.", s_gene)],
#" ", s_mut, ")",
"\n[1-SD effect on parameter per 1-SD TG change]")) +
ylab(paste0(y_gene,
#" (", y_rsid,"-", df[1, paste0("ea.", y_gene)],
#" ", y_mut, ")",
" | ",
s_gene,
#" (", s_rsid,"-", df[1, paste0("ea.", s_gene)],
#" ", s_mut, ")",
"\n[1-SD effect on parameter per 1-SD TG change]")) +
scale_x_continuous(limits = x_lim, breaks = seq(x_lim[1], x_lim[2], 0.5)) +
scale_y_continuous(limits = y_lim, breaks = seq(y_lim[1], y_lim[2], 0.5)) +
geom_hline(yintercept = 0, size = av.hline.size, color = "black", linetype = "solid") +
geom_vline(xintercept = 0, size = av.vline.size, color = "black", linetype = "solid") +
geom_abline(intercept = 0, slope = 1, color = "grey50", linetype = "dashed",
size = av.abline.size) +
geom_smooth(method = "lm", formula = y ~ x, color = "black",
linetype = "dashed", size = av.smooth.size, fullrange = TRUE,
level = 1 - 0.05 / m_comp) +
geom_point(aes_string(fill = paste0("class.", x_gene)),
size = point_size, stroke = av.stroke,
alpha = 1, shape = 21, color = "black") +
theme_classic() +
theme(
plot.title = av.plot.title,
plot.subtitle = av.plot.subtitle,
legend.title = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1, 0.05),
legend.key.size = av.legend.key.size,
legend.text = av.legend.text,
legend.margin = av.legend.margin,
axis.title = av.axis.title,
axis.text.x = av.axis.text.x,
axis.text.y = av.axis.text.y,
axis.line = av.axis.line,
axis.ticks = av.axis.ticks
) +
scale_fill_manual(breaks = c("ApoA-I", "ApoB", "VLDL", "IDL", "LDL",
"HDL", "Fatty acids", "Other lipids", "Other metabolites"),
values = c("white", "black", "#00A08A", "#F98400", "#F2AD00",
"#5BBCD6", "#FD6467", "#046C9A", "grey50")
)
return(out)
}
## regressions ----
# y = LPL
# x = ANGPTL8
# s = ANGPTL3
res_ys <- lm(formula = beta.tg.LPL ~ beta.tg.ANGPTL8, data = cbind_df)$residuals
res_xs <- lm(formula = beta.tg.ANGPTL3 ~ beta.tg.ANGPTL8, data = cbind_df)$residuals
res_yx <- lm(formula = beta.tg.LPL ~ beta.tg.ANGPTL3, data = cbind_df)$residuals
res_sx <- lm(formula = beta.tg.ANGPTL8 ~ beta.tg.ANGPTL3, data = cbind_df)$residuals
cbind_df <- cbind(cbind_df, res_yx, res_sx, res_ys, res_xs)
## angptl3 + angptl8 ----
av_plot_a3_a8 <- av_plot_1(
cbind_df,
title = "HUMAN PLASMA: LPL vs. ANGPTL3 | ANGPTL8",
subtitle = "Conditional systemic effects on metabolic parameters",
y_var = "res_ys",
x_var = "res_xs",
scale_var = "tg.",
y_gene = "LPL",
x_gene = "ANGPTL3",
s_gene = "ANGPTL8 R59W",
y_rsid = "rs115849089",
x_rsid = "rs11207977",
s_rsid = "rs2278426",
y_mut = "[eQTL]",
x_mut = "[eQTL]",
s_mut = "[R59W]",
y_lim = av.xlims,
x_lim = av.ylims,
point_size = av.point.size
)
## write to file ----
ggsave(plot = av_plot_a3_a8,
path = paste0(wd, "/plots/02_Kettunen16"),
filename = "avplot1_Kettunen16.pdf",
width = av.width, height = av.height, units = "in", dpi = 1100)
# 3D plots ----
threed_plot <- function(x, y, z, col_grp,
theta, phi,
bty) {
# Compute the linear regression
fit <- lm(z ~ x + y)
# create a grid from the x and y values (min to max) and predict values for every point
# this will become the regression plane
grid.lines = 30
x.pred <- seq(min(x), max(x), length.out = grid.lines)
y.pred <- seq(min(y), max(y), length.out = grid.lines)
xy <- expand.grid(x = x.pred, y = y.pred)
z.pred <- matrix(predict(fit, newdata = xy),
nrow = grid.lines,
ncol = grid.lines)
# create the fitted points for droplines to the surface
fitpoints <- predict(fit)
# scatter plot with regression plane
plot3D::scatter3D(
x, y, z,
xlim = threed.lims,
ylim = threed.lims,
zlim = threed.lims,
pch = 19, cex = threed.cex, cex.lab = threed.cex, cex.axis = threed.cex, tck = -.02,
nticks = 7,
ticktype = "detailed",
colvar = col_grp,
col = rev(c("white", "black", "#00A08A", "#F98400", "#F2AD00",
"#5BBCD6", "#FD6467", "#046C9A", "grey50")),
colkey = FALSE,
theta = theta,
phi = phi,
bty = bty,
xlab = "\nANGPTL3\n[1-SD effect per 1-SD TG change]",
ylab = "\n\n\nANGPTL8 R59W\n[1-SD effect per 1-SD TG change]",
zlab = "\n\nLPL\n[1-SD effect per 1-SD TG change]",
surf = list(x = x.pred, y = y.pred, z = z.pred,
facets = TRUE,
fit = fitpoints,
col = ramp.col(
col = c("black","black"),
n = 300,
alpha = 0.1
),
border = "black"),
main = "")
}
## write to file ----
cbind_df$col_grp <- rep(NA, nrow(cbind_df))
cbind_df$col_grp <- ifelse(cbind_df$class.LPL == "ApoA-I", 9, cbind_df$col_grp)
cbind_df$col_grp <- ifelse(cbind_df$class.LPL == "ApoB", 8, cbind_df$col_grp)
cbind_df$col_grp <- ifelse(cbind_df$class.LPL == "VLDL", 7, cbind_df$col_grp)
cbind_df$col_grp <- ifelse(cbind_df$class.LPL == "IDL", 6, cbind_df$col_grp)
cbind_df$col_grp <- ifelse(cbind_df$class.LPL == "LDL", 5, cbind_df$col_grp)
cbind_df$col_grp <- ifelse(cbind_df$class.LPL == "HDL", 4, cbind_df$col_grp)
cbind_df$col_grp <- ifelse(cbind_df$class.LPL == "Fatty acids", 3, cbind_df$col_grp)
cbind_df$col_grp <- ifelse(cbind_df$class.LPL == "Other lipids", 2, cbind_df$col_grp)
cbind_df$col_grp <- ifelse(cbind_df$class.LPL == "Other metabolites", 1, cbind_df$col_grp)
### -45 degrees rotation ----
pdf(
file = paste0(wd, "/plots/3dscatters/angptl3-angptl8_Kettunen16_rot45dgr.pdf"),
width = threed.width,
height = threed.height
)
threed_plot(x = cbind_df$beta.tg.ANGPTL3,
y = cbind_df$beta.tg.ANGPTL8,
z = cbind_df$beta.tg.LPL,
col_grp = cbind_df$col_grp,
theta = -45,
phi = 10,
bty = "g")
dev.off()
### -13 degrees rotation ----
pdf(
file = paste0(wd, "/plots/3dscatters/angptl3-angptl8_Kettunen16_rot13dgr.pdf"),
width = threed.width,
height = threed.height
)
threed_plot(x = cbind_df$beta.tg.ANGPTL3,
y = cbind_df$beta.tg.ANGPTL8,
z = cbind_df$beta.tg.LPL,
col_grp = cbind_df$col_grp,
theta = -13 + 180 - 12,
phi = 10,
bty = "g")
dev.off()
### +32 degrees rotation ----
pdf(
file = paste0(wd, "/plots/3dscatters/angptl3-angptl8_Kettunen16_rot32dgr.pdf"),
width = threed.width,
height = threed.height
)
threed_plot(x = cbind_df$beta.tg.ANGPTL3,
y = cbind_df$beta.tg.ANGPTL8,
z = cbind_df$beta.tg.LPL,
col_grp = cbind_df$col_grp,
theta = -13 + 45,
phi = 10,
bty = "g")
dev.off()
### +155 degrees rotation ----
pdf(
file = paste0(wd, "/plots/Kettunen16/main_angptl3-angptl8_Kettunen16_rot155dgr.pdf"),
width = threed.width,
height = threed.height
)
threed_plot(x = cbind_df$beta.tg.ANGPTL3,
y = cbind_df$beta.tg.ANGPTL8,
z = cbind_df$beta.tg.LPL,
col_grp = cbind_df$col_grp,
theta = 155,
phi = 10,
bty = "g")
dev.off()
# Summary stats ----
## Regression analyses ----
f1 <- lm(formula = beta.tg.LPL ~ beta.tg.ANGPTL3, data = cbind_df)
f2 <- lm(formula = beta.tg.LPL ~ beta.tg.ANGPTL4, data = cbind_df)
f3 <- lm(formula = beta.tg.LPL ~ beta.tg.ANGPTL8, data = cbind_df)
f4 <- lm(formula = beta.tg.LPL ~ beta.tg.ANGPTL3 + beta.tg.ANGPTL8, data = cbind_df)
f5 <- lm(formula = res_ys ~ res_xs, data = cbind_df)
f6 <- lm(formula = res_yx ~ res_sx, data = cbind_df)
s1 <- summary(f1)
s2 <- summary(f2)
s3 <- summary(f3)
s4 <- summary(f4)
s5 <- summary(f5)
s6 <- summary(f6)
## Get coef confidence intervals ----
c1 <- confint(f1, level = 1 - 0.05 / m_comp)
c2 <- confint(f2, level = 1 - 0.05 / m_comp)
c3 <- confint(f3, level = 1 - 0.05 / m_comp)
c4 <- confint(f4, level = 1 - 0.05 / m_comp)
c5 <- confint(f5, level = 1 - 0.05 / m_comp)
c6 <- confint(f6, level = 1 - 0.05 / m_comp)