Last updated: 2025-05-30

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Use Whole Blood GTEx gene expression counts for the analysis.

Correlation between PRS & expression PCs

# load prs & pcs
metadata_file <- "analysis/metadata_asthma.txt"
metadata <- read.csv(metadata_file, header = T, sep = "\t", stringsAsFactors = T)
metadata$sex <- as.factor(metadata$sex)

traits <- metadata$asthma
pc <- metadata[, c(1:5)]

# Calculate the correlation between each trait and each PC
correlation_matrix <- cor(traits, pc)
range(correlation_matrix)

[1] -0.1106564  0.1638967

correlation_matrix <- t(correlation_matrix)
correlation_matrix

            [,1]
PC1  0.163896657
PC2  0.002707918
PC3 -0.110656360
PC4  0.069712091
PC5 -0.078495540

Model 1: expression ~ PRS + sex + expression PCs

Continuous PRS

DESeq2 Differential Expression

# Load the gene expression data
gene_expr_file <- "data/gene_reads_2017-06-05_v8_whole_blood.gct"
raw_count_df <- fread(gene_expr_file, header = TRUE, sep = "\t", drop = "id")

# load protein_coding list
protein_coding <- fread("data/protein-coding_gene.txt", sep = "\t")
protein_coding <- protein_coding[, c("symbol", "ensembl_gene_id")]

# keep only protein-coding genes
raw_count_df <- raw_count_df[raw_count_df$Description %in% protein_coding$symbol, ]

id <- raw_count_df$Name
raw_count <- raw_count_df[, -c(1:2)]

# modify GTEx sample names matching names used in PRS data
colnames(raw_count) <- sub("^(GTEX-[^-.]+).*", "\\1", colnames(raw_count))

matching_samples <- intersect(rownames(metadata), colnames(raw_count))
final_count <- raw_count[ , ..matching_samples]

# prefilter: keep only rows that have a count of at least 10
keep_genes <- rowSums(final_count >= 10) > 0
final_count <- final_count[keep_genes, ]
id <- id[keep_genes]
dim(final_count)

prs_trait <- scale(traits) # Standardize PRS to mean = 0, sd = 1
    
# Add the standardized PRS to the metadata for continuous trait
metadata$asthma <- prs_trait
  
# Create the DESeqDataSet for the current trait
dds <- DESeqDataSetFromMatrix(
  countData = as.matrix(final_count),  # Raw counts
  colData = metadata,
  design = as.formula("~ PC1 + PC2 + PC3 + PC4 + PC5 + sex + asthma")
  )
rownames(dds) <- id
  
# Run DESeq2 analysis
dds <- DESeq(dds, parallel = TRUE, BPPARAM = MulticoreParam(4))
  
# Get the results for the current trait
res <- results(dds)
  
# Save the results to a file
write.csv(res, "differential_expression_asthma_results.csv")
  
# print a summary of the results
print(paste("Results for trait: asthma"))
print(summary(res))
  
# plot the MA-plot for the current trait
png(paste0("ma_plot_asthma.png"), width = 800, height = 600)
plotMA(res, main = paste("Continuous: MA Plot for Asthma"))
dev.off()
  
# volcano plot
res_tableOE <- as.data.frame(res)
res_tableOE$gene_name <- raw_count_df$Description[keep_genes]
res_tableOE <- mutate(res_tableOE, threshold_OE = padj < 0.1)
res_tableOE <- res_tableOE %>% arrange(padj) %>% mutate(genelabels = "")
res_tableOE$genelabels[1:10] <- res_tableOE$gene_name[1:10]
  
volcano_plot <- ggplot(res_tableOE, aes(x = log2FoldChange, y = -log10(padj))) +
    geom_point(aes(colour = threshold_OE)) +
    geom_text_repel(aes(label = genelabels)) +
    ggtitle(paste("Continuous: Volcano Plot for Asthma")) +
    xlab("log2 fold change") + 
    ylab("-log10 adjusted p-value") +
    theme(legend.position = "none",
          plot.title = element_text(size = rel(1.5), hjust = 0.5),
          axis.title = element_text(size = rel(1.25)))
  
# Save the volcano plot
png(paste0("volcano_plot_asthma.png"), width = 800, height = 600)
print(volcano_plot)
dev.off()

GO enrichment Analysis

file <- "analysis/asthma_wb/differential_expression_asthma_results.csv"

res_tableOE <- read.csv(file, header = T, row.names = 1)
  
deGenes <- res_tableOE[res_tableOE$padj < 0.1 & 
                         abs(res_tableOE$log2FoldChange) >= 0.5, ]
deGenes$gene_id <- gsub("\\.\\d+$", "", rownames(deGenes))

# Separate upregulated and downregulated genes
upregulated_genes <- deGenes[deGenes$log2FoldChange > 0, ]$gene_id
downregulated_genes <- deGenes[deGenes$log2FoldChange < 0, ]$gene_id
  
# Run GO enrichment for upregulated genes
gse_up <- enrichGO(gene = upregulated_genes, ont = "BP", 
                   OrgDb = "org.Hs.eg.db", keyType = "ENSEMBL", readable = T)

# Run GO enrichment for downregulated genes
gse_down <- enrichGO(gene = downregulated_genes, ont = "BP", 
                     OrgDb = "org.Hs.eg.db", keyType = "ENSEMBL", readable = T)
  
# Convert enrichment results to data frames and calculate additional ratios
gse_up <- as.data.frame(gse_up)
gse_down <- as.data.frame(gse_down)

gse_up$GeneRatio_num <- as.numeric(sapply(strsplit(gse_up$GeneRatio, "/"), function(x) x[1])) / 
                        as.numeric(sapply(strsplit(gse_up$GeneRatio, "/"), function(x) x[2]))
gse_up$BgRatio_num <- as.numeric(sapply(strsplit(gse_up$BgRatio, "/"), function(x) x[1])) / 
                        as.numeric(sapply(strsplit(gse_up$BgRatio, "/"), function(x) x[2]))
gse_up <- cbind(gse_up, FoldEnrich = gse_up$GeneRatio_num / gse_up$BgRatio_num)

gse_down$GeneRatio_num <- as.numeric(sapply(strsplit(gse_down$GeneRatio, "/"), function(x) x[1])) / 
                          as.numeric(sapply(strsplit(gse_down$GeneRatio, "/"), function(x) x[2]))
gse_down$BgRatio_num <- as.numeric(sapply(strsplit(gse_down$BgRatio, "/"), function(x) x[1])) / 
                          as.numeric(sapply(strsplit(gse_down$BgRatio, "/"), function(x) x[2]))
gse_down <- cbind(gse_down, FoldEnrich = gse_down$GeneRatio_num / gse_down$BgRatio_num)

if (nrow(gse_up) >= 20) {
    enrich_plot_up <- plotEnrich(gse_up[1:20,], plot_type = "dot", scale_ratio = 0.5) +
    labs(title = "Continuous: Upregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 10)) 
}else{
    enrich_plot_up <- plotEnrich(gse_up, plot_type = "dot", scale_ratio = 0.5) +
    labs(title = "Continuous: Upregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 10)) 
}

if (nrow(gse_down) >= 20) {
    enrich_plot_down <- plotEnrich(gse_down[1:20,], plot_type = "dot", scale_ratio = 0.5) +
    labs(title = "Continuous: Downregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 10)) 
}else{
    enrich_plot_down <- plotEnrich(gse_down, plot_type = "dot", scale_ratio = 0.5) +
    labs(title = "Continuous: Downregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 10)) 
}

# Arrange the two plots side by side
combined_plot <- grid.arrange(enrich_plot_up, enrich_plot_down, ncol = 2)

# Save the combined plot
ggsave("enrichment_plot_asthma.png", plot = combined_plot, width = 12, height = 6)

# Save the GO enrichment results to CSV
write.csv(gse_up, "GO_enrichment_asthma_upregulated.csv")
write.csv(gse_down, "GO_enrichment_asthma_downregulated.csv")

Quantile PRS

DESeq2 Differential Expression

metadata_file <- "analysis/metadata_asthma_quantile.txt"
metadata <- read.csv(metadata_file, header = T, sep = "\t", stringsAsFactors = T)
metadata$sex <- as.factor(metadata$sex)

# Create the DESeqDataSet for the current trait
dds <- DESeqDataSetFromMatrix(
  countData = as.matrix(final_count),
  colData = metadata,
  design = as.formula("~ PC1 + PC2 + PC3 + PC4 + PC5 + sex + asthma")
  )
rownames(dds) <- id
  
# Run DESeq2 analysis
dds <- DESeq(dds, parallel = TRUE, BPPARAM = MulticoreParam(4))
  
# Get the results for the current trait
res <- results(dds)
  
# Save the results to a file
write.csv(res, "differential_expression_asthma_quantile_results.csv")
  
# print a summary of the results
print(paste("Results for trait: asthma"))
print(summary(res))
  
# plot the MA-plot for the current trait
png(paste0("ma_plot_quantile_asthma.png"), width = 800, height = 600)
plotMA(res, main = paste("Quantile: MA Plot for Asthma"))
dev.off()
  
# volcano plot
res_tableOE <- as.data.frame(res)
res_tableOE$gene_name <- raw_count_df$Description[keep_genes]
res_tableOE <- mutate(res_tableOE, threshold_OE = padj < 0.1)
res_tableOE <- res_tableOE %>% arrange(padj) %>% mutate(genelabels = "")
res_tableOE$genelabels[1:10] <- res_tableOE$gene_name[1:10]
  
volcano_plot <- ggplot(res_tableOE, aes(x = log2FoldChange, y = -log10(padj))) +
    geom_point(aes(colour = threshold_OE)) +
    geom_text_repel(aes(label = genelabels)) +
    ggtitle(paste("Quantile: Volcano Plot for Asthma")) +
    xlab("log2 fold change") + 
    ylab("-log10 adjusted p-value") +
    theme(legend.position = "none",
          plot.title = element_text(size = rel(1.5), hjust = 0.5),
          axis.title = element_text(size = rel(1.25)))
  
# Save the volcano plot
png(paste0("volcano_plot_quantile_asthma.png"), width = 800, height = 600)
print(volcano_plot)
dev.off()

GO enrichment Analysis

file <- "analysis/asthma_wb/differential_expression_asthma_quantile_results.csv"

res_tableOE <- read.csv(file, header = T, row.names = 1)
  
deGenes <- res_tableOE[res_tableOE$padj < 0.1 & 
                         abs(res_tableOE$log2FoldChange) >= 0.5, ]
deGenes$gene_id <- gsub("\\.\\d+$", "", rownames(deGenes))

# Separate upregulated and downregulated genes
upregulated_genes <- deGenes[deGenes$log2FoldChange > 0, ]$gene_id
downregulated_genes <- deGenes[deGenes$log2FoldChange < 0, ]$gene_id
  
# Run GO enrichment for upregulated genes
gse_up <- enrichGO(gene = upregulated_genes, ont = "BP", 
                   OrgDb = "org.Hs.eg.db", keyType = "ENSEMBL", readable = T)

# Run GO enrichment for downregulated genes
gse_down <- enrichGO(gene = downregulated_genes, ont = "BP", 
                     OrgDb = "org.Hs.eg.db", keyType = "ENSEMBL", readable = T)
  
# Convert enrichment results to data frames and calculate additional ratios
gse_up <- as.data.frame(gse_up)
gse_down <- as.data.frame(gse_down)

gse_up$GeneRatio_num <- as.numeric(sapply(strsplit(gse_up$GeneRatio, "/"), function(x) x[1])) / 
                        as.numeric(sapply(strsplit(gse_up$GeneRatio, "/"), function(x) x[2]))
gse_up$BgRatio_num <- as.numeric(sapply(strsplit(gse_up$BgRatio, "/"), function(x) x[1])) / 
                        as.numeric(sapply(strsplit(gse_up$BgRatio, "/"), function(x) x[2]))
gse_up <- cbind(gse_up, FoldEnrich = gse_up$GeneRatio_num / gse_up$BgRatio_num)

gse_down$GeneRatio_num <- as.numeric(sapply(strsplit(gse_down$GeneRatio, "/"), function(x) x[1])) / 
                          as.numeric(sapply(strsplit(gse_down$GeneRatio, "/"), function(x) x[2]))
gse_down$BgRatio_num <- as.numeric(sapply(strsplit(gse_down$BgRatio, "/"), function(x) x[1])) / 
                          as.numeric(sapply(strsplit(gse_down$BgRatio, "/"), function(x) x[2]))
gse_down <- cbind(gse_down, FoldEnrich = gse_down$GeneRatio_num / gse_down$BgRatio_num)

if (nrow(gse_up) >= 20) {
    enrich_plot_up <- plotEnrich(gse_up[1:20,], plot_type = "dot", scale_ratio = 0.4) +
    labs(title = "Quantile: Upregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}else{
    enrich_plot_up <- plotEnrich(gse_up, plot_type = "dot", scale_ratio = 0.4) +
    labs(title = "Quantile: Upregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}

if (nrow(gse_down) >= 20) {
    enrich_plot_down <- plotEnrich(gse_down[1:20,], plot_type = "dot", scale_ratio = 0.4) +
    labs(title = "Quantile: Downregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}else{
    enrich_plot_down <- plotEnrich(gse_down, plot_type = "dot", scale_ratio = 0.4) +
    labs(title = "Quantile: Downregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}

# Arrange the two plots side by side
combined_plot <- grid.arrange(enrich_plot_up, enrich_plot_down, ncol = 2)

# Save the combined plot
ggsave("enrichment_plot_quantile_asthma.png", plot = combined_plot, width = 12, height = 6)

# Save the GO enrichment results to CSV
write.csv(gse_up, "GO_enrichment_quantile_asthma_upregulated.csv")
write.csv(gse_down, "GO_enrichment_quantile_asthma_downregulated.csv")

Model 2: expression ~ PRS + sex + expression PCs + 2 genotype PCs

Obtain genotype PCs

# Load the gene expression data
gene_expr_file <- "data/gene_reads_2017-06-05_v8_whole_blood.gct"
raw_count_df <- fread(gene_expr_file, header = TRUE, sep = "\t", drop = "id")

# load protein_coding list
protein_coding <- fread("data/protein-coding_gene.txt", 
                        sep = "\t")
protein_coding <- protein_coding[, c("symbol", "ensembl_gene_id")]

# keep only protein-coding genes
raw_count_df <- raw_count_df[raw_count_df$Description %in% protein_coding$symbol, ]

id <- raw_count_df$Name
raw_count <- raw_count_df[, -c(1:2)]

# modify GTEx sample names matching names used in PRS data
colnames(raw_count) <- sub("^(GTEX-[^-.]+).*", "\\1", colnames(raw_count))

matching_samples <- intersect(rownames(metadata), colnames(raw_count))
final_count <- raw_count[ , ..matching_samples]

# prefilter: keep only rows that have a count of at least 10
keep_genes <- rowSums(final_count >= 10) > 0
final_count <- final_count[keep_genes, ]
id <- id[keep_genes]
dim(final_count)

[1] 16893   670

# obtain genotype PCs
geno_pc <- read.table("data/pca.eigenvec")
names(geno_pc) = c("FID","IID",paste0("geno_PC", c(1:(ncol(geno_pc)-2))))
geno_pc <- geno_pc[geno_pc$FID %in% matching_samples, ]

eigenval <- scan("data/pca.eigenval")
pve <- data.frame(PC = 1:20, pve = eigenval/sum(eigenval)*100)

ggplot(geno_pc) + geom_point(aes(x = geno_PC1, y = geno_PC2)) + 
  labs(x = paste0("PC1 (", signif(pve$pve[1], 3), "%)"), 
       y = paste0("PC2 (", signif(pve$pve[2], 3), "%)")) + theme_classic()

Version	Author	Date
36ca2c8	ElisaChen	2025-05-30

ggplot(geno_pc) + geom_point(aes(x = geno_PC1, y = geno_PC3)) + 
    labs(x = paste0("PC1 (", signif(pve$pve[1], 3), "%)"), 
         y = paste0("PC3 (", signif(pve$pve[3], 3), "%)")) + theme_classic()

Version	Author	Date
36ca2c8	ElisaChen	2025-05-30

ggplot(pve, aes(PC, pve)) + geom_point() + geom_line() +
  labs(x = "Genotype PC", y = "Percentage variance explained") + theme_classic()

Version	Author	Date
36ca2c8	ElisaChen	2025-05-30

Continuous PRS

DESeq2 Differential Expression

metadata_file <- "analysis/metadata_asthma.txt"
metadata <- read.csv(metadata_file, header = T, sep = "\t", stringsAsFactors = T)
metadata$sex <- as.factor(metadata$sex)

metadata <- cbind(metadata, geno_pc[,3:ncol(geno_pc)])

# Standardize PRS for the current trait
prs_trait <- scale(metadata$asthma)

# Add the standardized PRS to the metadata for continuous trait
metadata$asthma <- prs_trait

# Create the DESeqDataSet for the current trait
dds <- DESeqDataSetFromMatrix(
  countData = as.matrix(final_count),  # Raw counts
  colData = metadata[, 1:9],
  design = as.formula("~ PC1 + PC2 + PC3 + PC4 + PC5 + sex + geno_PC1 + geno_PC2 + asthma")
)
rownames(dds) <- id

# Run DESeq2 analysis
dds <- DESeq(dds, parallel = TRUE, BPPARAM = MulticoreParam(4))

# Get the results for the current trait
res <- results(dds)
  
# Save the results to a file
write.csv(res, "differential_expression_asthma_results_M2.csv")
  
# print a summary of the results
print("Results for trait: Asthma")
print(summary(res))
  
# plot the MA-plot for the current trait
png(paste0("ma_plot_asthma_M2.png"), width = 800, height = 600)
plotMA(res, main = "Continuous (M2): MA Plot for Asthma")
dev.off()
  
# volcano plot
res_tableOE <- as.data.frame(res)
res_tableOE$gene_name <- raw_count_df$Description[keep_genes]
res_tableOE <- mutate(res_tableOE, threshold_OE = padj < 0.1)
res_tableOE <- res_tableOE %>% arrange(padj) %>% mutate(genelabels = "")
res_tableOE$genelabels[1:10] <- res_tableOE$gene_name[1:10]
  
volcano_plot <- ggplot(res_tableOE, aes(x = log2FoldChange, y = -log10(padj))) +
  geom_point(aes(colour = threshold_OE)) +
  geom_text_repel(aes(label = genelabels)) +
  ggtitle("Continuous (M2): Volcano Plot for Asthma") +
    xlab("log2 fold change") + 
    ylab("-log10 adjusted p-value") +
    theme(legend.position = "none",
          plot.title = element_text(size = rel(1.5), hjust = 0.5),
          axis.title = element_text(size = rel(1.25)))
  
# Save the volcano plot
png(paste0("volcano_plot_asthma_M2.png"), width = 800, height = 600)
print(volcano_plot)
dev.off()

GO enrichment Analysis

file <- "analysis/asthma_wb/differential_expression_asthma_results_M2.csv"

res_tableOE <- read.csv(file, header = T, row.names = 1)
  
deGenes <- res_tableOE[res_tableOE$padj < 0.1 & 
                         abs(res_tableOE$log2FoldChange) >= 0.5, ]
deGenes$gene_id <- gsub("\\.\\d+$", "", rownames(deGenes))

# Separate upregulated and downregulated genes
upregulated_genes <- deGenes[deGenes$log2FoldChange > 0, ]$gene_id
downregulated_genes <- deGenes[deGenes$log2FoldChange < 0, ]$gene_id
  
# Run GO enrichment for upregulated genes
gse_up <- enrichGO(gene = upregulated_genes, ont = "BP", 
                   OrgDb = "org.Hs.eg.db", keyType = "ENSEMBL", readable = T)

# Run GO enrichment for downregulated genes
gse_down <- enrichGO(gene = downregulated_genes, ont = "BP", 
                     OrgDb = "org.Hs.eg.db", keyType = "ENSEMBL", readable = T)
  
# Convert enrichment results to data frames and calculate additional ratios
gse_up <- as.data.frame(gse_up)
gse_down <- as.data.frame(gse_down)

gse_up$GeneRatio_num <- as.numeric(sapply(strsplit(gse_up$GeneRatio, "/"), function(x) x[1])) / 
                        as.numeric(sapply(strsplit(gse_up$GeneRatio, "/"), function(x) x[2]))
gse_up$BgRatio_num <- as.numeric(sapply(strsplit(gse_up$BgRatio, "/"), function(x) x[1])) / 
                        as.numeric(sapply(strsplit(gse_up$BgRatio, "/"), function(x) x[2]))
gse_up <- cbind(gse_up, FoldEnrich = gse_up$GeneRatio_num / gse_up$BgRatio_num)

gse_down$GeneRatio_num <- as.numeric(sapply(strsplit(gse_down$GeneRatio, "/"), function(x) x[1])) / 
                          as.numeric(sapply(strsplit(gse_down$GeneRatio, "/"), function(x) x[2]))
gse_down$BgRatio_num <- as.numeric(sapply(strsplit(gse_down$BgRatio, "/"), function(x) x[1])) / 
                          as.numeric(sapply(strsplit(gse_down$BgRatio, "/"), function(x) x[2]))
gse_down <- cbind(gse_down, FoldEnrich = gse_down$GeneRatio_num / gse_down$BgRatio_num)

if (nrow(gse_up) >= 20) {
    enrich_plot_up <- plotEnrich(gse_up[1:20,], plot_type = "dot", scale_ratio = 0.5) +
    labs(title = "Continuous (M2): Upregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}else{
    enrich_plot_up <- plotEnrich(gse_up, plot_type = "dot", scale_ratio = 0.5) +
    labs(title = "Continuous (M2): Upregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}

if (nrow(gse_down) >= 20) {
    enrich_plot_down <- plotEnrich(gse_down[1:20,], plot_type = "dot", scale_ratio = 0.5) +
    labs(title = "Continuous (M2): Downregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}else{
    enrich_plot_down <- plotEnrich(gse_down, plot_type = "dot", scale_ratio = 0.5) +
    labs(title = "Continuous (M2): Downregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}

# Arrange the two plots side by side
combined_plot <- grid.arrange(enrich_plot_up, enrich_plot_down, ncol = 2)

# Save the combined plot
ggsave("enrichment_plot_asthma_M2.png", plot = combined_plot, width = 12, height = 6)

# Save the GO enrichment results to CSV
write.csv(gse_up, "GO_enrichment_asthma_upregulated_M2.csv")
write.csv(gse_down, "GO_enrichment_asthma_downregulated_M2.csv")

Quantile PRS

DESeq2 Differential Expression

metadata_file <- "analysis/metadata_asthma_quantile.txt"
metadata <- read.csv(metadata_file, header = T, sep = "\t", stringsAsFactors = T)
metadata$sex <- as.factor(metadata$sex)

metadata <- cbind(metadata, geno_pc[,3:ncol(geno_pc)])

# Create the DESeqDataSet for the current trait
dds <- DESeqDataSetFromMatrix(
  countData = as.matrix(final_count),  # Raw counts
  colData = metadata[, 1:9],
  design = as.formula("~ PC1 + PC2 + PC3 + PC4 + PC5 + sex + geno_PC1 + geno_PC2 + asthma")
)
rownames(dds) <- id

# Run DESeq2 analysis
dds <- DESeq(dds, parallel = TRUE, BPPARAM = MulticoreParam(4))

# Get the results for the current trait
res <- results(dds)
  
# Save the results to a file
write.csv(res, "differential_expression_asthma_quantile_results_M2.csv")
  
# print a summary of the results
print("Results for trait: Asthma")
print(summary(res))
  
# plot the MA-plot for the current trait
png(paste0("ma_plot_quantile_asthma_M2.png"), width = 800, height = 600)
plotMA(res, main = "Quantile (M2): MA Plot for Asthma")
dev.off()
  
# volcano plot
res_tableOE <- as.data.frame(res)
res_tableOE$gene_name <- raw_count_df$Description[keep_genes]
res_tableOE <- mutate(res_tableOE, threshold_OE = padj < 0.1)
res_tableOE <- res_tableOE %>% arrange(padj) %>% mutate(genelabels = "")
res_tableOE$genelabels[1:10] <- res_tableOE$gene_name[1:10]
  
volcano_plot <- ggplot(res_tableOE, aes(x = log2FoldChange, y = -log10(padj))) +
  geom_point(aes(colour = threshold_OE)) +
  geom_text_repel(aes(label = genelabels)) +
  ggtitle("Quantile (M2): Volcano Plot for Asthma") +
    xlab("log2 fold change") + 
    ylab("-log10 adjusted p-value") +
    theme(legend.position = "none",
          plot.title = element_text(size = rel(1.5), hjust = 0.5),
          axis.title = element_text(size = rel(1.25)))
  
# Save the volcano plot
png(paste0("volcano_plot_quantile_asthma_M2.png"), width = 800, height = 600)
print(volcano_plot)
dev.off()

GO enrichment Analysis

file <- "analysis/asthma_wb/differential_expression_asthma_quantile_results_M2.csv"

res_tableOE <- read.csv(file, header = T, row.names = 1)
  
deGenes <- res_tableOE[res_tableOE$padj < 0.1 & 
                         abs(res_tableOE$log2FoldChange) >= 0.5, ]
deGenes$gene_id <- gsub("\\.\\d+$", "", rownames(deGenes))

# Separate upregulated and downregulated genes
upregulated_genes <- deGenes[deGenes$log2FoldChange > 0, ]$gene_id
downregulated_genes <- deGenes[deGenes$log2FoldChange < 0, ]$gene_id
  
# Run GO enrichment for upregulated genes
gse_up <- enrichGO(gene = upregulated_genes, ont = "BP", 
                   OrgDb = "org.Hs.eg.db", keyType = "ENSEMBL", readable = T)

# Run GO enrichment for downregulated genes
gse_down <- enrichGO(gene = downregulated_genes, ont = "BP", 
                     OrgDb = "org.Hs.eg.db", keyType = "ENSEMBL", readable = T)
  
# Convert enrichment results to data frames and calculate additional ratios
gse_up <- as.data.frame(gse_up)
gse_down <- as.data.frame(gse_down)

gse_up$GeneRatio_num <- as.numeric(sapply(strsplit(gse_up$GeneRatio, "/"), function(x) x[1])) / 
                        as.numeric(sapply(strsplit(gse_up$GeneRatio, "/"), function(x) x[2]))
gse_up$BgRatio_num <- as.numeric(sapply(strsplit(gse_up$BgRatio, "/"), function(x) x[1])) / 
                        as.numeric(sapply(strsplit(gse_up$BgRatio, "/"), function(x) x[2]))
gse_up <- cbind(gse_up, FoldEnrich = gse_up$GeneRatio_num / gse_up$BgRatio_num)

gse_down$GeneRatio_num <- as.numeric(sapply(strsplit(gse_down$GeneRatio, "/"), function(x) x[1])) / 
                          as.numeric(sapply(strsplit(gse_down$GeneRatio, "/"), function(x) x[2]))
gse_down$BgRatio_num <- as.numeric(sapply(strsplit(gse_down$BgRatio, "/"), function(x) x[1])) / 
                          as.numeric(sapply(strsplit(gse_down$BgRatio, "/"), function(x) x[2]))
gse_down <- cbind(gse_down, FoldEnrich = gse_down$GeneRatio_num / gse_down$BgRatio_num)

if (nrow(gse_up) >= 20) {
    enrich_plot_up <- plotEnrich(gse_up[1:20,], plot_type = "dot", scale_ratio = 0.4) +
    labs(title = "Quantile (M2): Upregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}else{
    enrich_plot_up <- plotEnrich(gse_up, plot_type = "dot", scale_ratio = 0.4) +
    labs(title = "Quantile (M2): Upregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}

if (nrow(gse_down) >= 20) {
    enrich_plot_down <- plotEnrich(gse_down[1:20,], plot_type = "dot", scale_ratio = 0.4) +
    labs(title = "Quantile (M2): Downregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}else{
    enrich_plot_down <- plotEnrich(gse_down, plot_type = "dot", scale_ratio = 0.4) +
    labs(title = "Quantile (M2): Downregulated Enrichment Pathways for Asthma") + 
    theme(plot.title = element_text(size = 6)) 
}

# Arrange the two plots side by side
combined_plot <- grid.arrange(enrich_plot_up, enrich_plot_down, ncol = 2)

# Save the combined plot
ggsave("enrichment_plot_quantile_asthma_M2.png", plot = combined_plot, width = 12, height = 6)

# Save the GO enrichment results to CSV
write.csv(gse_up, "GO_enrichment_quantile_asthma_upregulated_M2.csv")
write.csv(gse_down, "GO_enrichment_quantil_asthma_downregulated_M2.csv")

Results

Summary table

Summary of Differential Expression and Enriched Pathways for Asthma PRS Models
Model	Significant DE genes	Up-regulated genes	Down-regulated genes	Up-regulated GO pathways	Down-regulated GO pathways
continuous	575	525	50	43	8
continuous_M2	587	536	51	44	8
quantile	1028	987	41	251	64
quantile_M2	1037	993	44	275	61

Model 1: Adjust without genotype PCs

Continuous PRS

Quantile PRS

Model 2: Adjust with genotype PCs

Continuous PRS

Quantile PRS

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] gridExtra_2.3               clusterProfiler_4.6.2      
 [3] enrichplot_1.18.4           org.Hs.eg.db_3.16.0        
 [5] AnnotationDbi_1.60.2        genekitr_1.2.8             
 [7] ggrepel_0.9.6               BiocParallel_1.32.6        
 [9] DESeq2_1.38.3               SummarizedExperiment_1.28.0
[11] Biobase_2.58.0              MatrixGenerics_1.10.0      
[13] matrixStats_1.2.0           GenomicRanges_1.50.2       
[15] GenomeInfoDb_1.34.9         IRanges_2.32.0             
[17] S4Vectors_0.36.2            BiocGenerics_0.44.0        
[19] corrplot_0.95               lubridate_1.9.4            
[21] forcats_1.0.0               stringr_1.5.1              
[23] dplyr_1.1.4                 purrr_1.0.2                
[25] readr_2.1.5                 tidyr_1.3.1                
[27] tibble_3.2.1                ggplot2_3.5.1              
[29] tidyverse_2.0.0             data.table_1.16.4          
[31] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] shadowtext_0.1.4       fastmatch_1.1-6        plyr_1.8.9            
  [4] igraph_1.5.1           lazyeval_0.2.2         splines_4.2.2         
  [7] usethis_3.1.0          urltools_1.7.3         digest_0.6.37         
 [10] yulab.utils_0.2.0      htmltools_0.5.8.1      GOSemSim_2.24.0       
 [13] viridis_0.6.5          GO.db_3.16.0           magrittr_2.0.3        
 [16] memoise_2.0.1          remotes_2.5.0          openxlsx_4.2.5.2      
 [19] tzdb_0.4.0             Biostrings_2.66.0      annotate_1.76.0       
 [22] graphlayouts_1.0.1     vroom_1.6.5            timechange_0.3.0      
 [25] prettyunits_1.2.0      colorspace_2.1-1       blob_1.2.4            
 [28] xfun_0.50              callr_3.7.6            crayon_1.5.3          
 [31] RCurl_1.98-1.16        jsonlite_1.8.9         scatterpie_0.2.4      
 [34] ape_5.7-1              glue_1.8.0             polyclip_1.10-7       
 [37] gtable_0.3.6           zlibbioc_1.44.0        XVector_0.38.0        
 [40] DelayedArray_0.24.0    pkgbuild_1.4.6         scales_1.3.0          
 [43] DOSE_3.24.2            DBI_1.2.3              miniUI_0.1.1.1        
 [46] Rcpp_1.0.14            progress_1.2.3         viridisLite_0.4.2     
 [49] xtable_1.8-4           gridGraphics_0.5-1     tidytree_0.4.6        
 [52] europepmc_0.4.3        bit_4.5.0.1            profvis_0.4.0         
 [55] htmlwidgets_1.6.4      httr_1.4.7             fgsea_1.24.0          
 [58] RColorBrewer_1.1-3     ellipsis_0.3.2         urlchecker_1.0.1      
 [61] pkgconfig_2.0.3        XML_3.99-0.18          farver_2.1.2          
 [64] sass_0.4.9             locfit_1.5-9.8         labeling_0.4.3        
 [67] ggplotify_0.1.2        tidyselect_1.2.1       rlang_1.1.5           
 [70] reshape2_1.4.4         later_1.4.1            munsell_0.5.1         
 [73] tools_4.2.2            cachem_1.1.0           downloader_0.4        
 [76] cli_3.6.3              generics_0.1.3         RSQLite_2.3.9         
 [79] gson_0.1.0             devtools_2.4.5         evaluate_1.0.3        
 [82] fastmap_1.2.0          yaml_2.3.10            ggtree_3.6.2          
 [85] processx_3.8.5         knitr_1.49             bit64_4.6.0-1         
 [88] fs_1.6.5               tidygraph_1.3.0        zip_2.3.2             
 [91] KEGGREST_1.38.0        ggraph_2.1.0           nlme_3.1-160          
 [94] mime_0.12              whisker_0.4.1          aplot_0.2.4           
 [97] ggvenn_0.1.10          xml2_1.3.6             compiler_4.2.2        
[100] rstudioapi_0.17.1      png_0.1-8              treeio_1.22.0         
[103] tweenr_2.0.3           geneplotter_1.76.0     bslib_0.9.0           
[106] stringi_1.8.4          ps_1.8.1               lattice_0.22-6        
[109] Matrix_1.6-4           vctrs_0.6.5            pillar_1.10.1         
[112] lifecycle_1.0.4        triebeard_0.4.1        jquerylib_0.1.4       
[115] cowplot_1.1.3          bitops_1.0-9           httpuv_1.6.15         
[118] patchwork_1.3.0        qvalue_2.30.0          R6_2.5.1              
[121] promises_1.3.2         sessioninfo_1.2.2      codetools_0.2-20      
[124] pkgload_1.4.0          MASS_7.3-58.1          rprojroot_2.0.4       
[127] withr_3.0.2            GenomeInfoDbData_1.2.9 parallel_4.2.2        
[130] hms_1.1.3              grid_4.2.2             ggfun_0.1.8           
[133] HDO.db_0.99.1          rmarkdown_2.29         git2r_0.33.0          
[136] getPass_0.2-4          ggforce_0.4.1          shiny_1.10.0          
[139] geneset_0.2.7

Relation between whole blood gene expression with Asthma PRS

Correlation between PRS & expression PCs

Model 1: expression ~ PRS + sex + expression PCs

Continuous PRS

DESeq2 Differential Expression

GO enrichment Analysis

Quantile PRS

DESeq2 Differential Expression

GO enrichment Analysis

Model 2: expression ~ PRS + sex + expression PCs + 2 genotype PCs

Obtain genotype PCs

Continuous PRS

DESeq2 Differential Expression

GO enrichment Analysis

Quantile PRS

DESeq2 Differential Expression

GO enrichment Analysis

Results

Summary table

Model 1: Adjust without genotype PCs

Continuous PRS

Quantile PRS

Model 2: Adjust with genotype PCs

Continuous PRS

Quantile PRS