diff --git a/Data/Phenotypic data.xlsx b/Data/Phenotypic data.xlsx
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diff --git a/Data/allPheno_ccfounders_NASH.csv b/Data/allPheno_ccfounders_NASH.csv
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+++ b/Data/allPheno_ccfounders_NASH.csv
@@ -0,0 +1,97 @@
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+"Aim2a_Step1","WSB/EiJ","WSB","CD","#FF2600","HDP-006070",13.61,12.26,13,13.59,13.62,14,14.23,14.57,14.67,14.54,14.04,14.32,13.77,13.54,13.48,12.7,13.38,13.4,0,-9.91917707567965,-4.48199853049228,-0.146950771491547,0.0734753857457736,2.86554004408524,4.55547391623807,7.05363703159442,7.78839088905217,6.83321087435709,3.15944158706833,5.21675238795004,1.1756061719324,-0.514327700220428,-0.95518001469507,-6.68626010286554,-1.68993387215282,-1.54298310066127,0.3911,2.91865671641791,0.0758,0.565671641791045,0.1182,0.882089552238806,0.0608,0.453731343283582,0.0159,0.11865671641791,0.1614,1.2044776119403,0.1813,1.35298507462687,0.0386,0.288059701492537,0.230000000000018,3,2261.84127442314,1617.26172220318,0.729,10554.5509751213,1722,3816.45348102979,2765.62088437461,17981.3378015118,0.738,22496,14.32,1.319,10.822,9.208,75.574,12.45,99,311,294,177,155,66,39,45,44,4545,12.3,74,26,29,40,46,-1905,1582.36,1290.01,981.95,311.82,-22397.55,25.14,15.49,21.34,NA,NA,9.7130478602521,425,77,2.6,2.487,0.18,1.04,10,7,0.181176470588235,700,5.51073991725459,2.803761721,0,0,0,0,NA,NA,NA,NA,50.7249639425859,1287.12640806748,188.093610354035,2.36318174416517,12.9785116198868,0.105972275523102,0.581996036766225,5.06447256539789,2.30540231572393,13413.9382911685
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+"Aim2a_Step1","WSB/EiJ","WSB","WD","#FF2600","HDP-006072",15.67,16.03,16.24,17.55,18.25,19.04,19.45,19.7,20.44,21.28,20.7,20.91,20.09,21.04,20.91,19.66,21.64,22.26,0,2.297383535418,3.63752393107848,11.9974473516273,16.464582003829,21.5060625398851,24.122527121889,25.7179323548181,30.4403318442885,35.8008934269305,32.0995532865348,33.4396936821953,28.2067645181876,34.2693044033184,33.4396936821953,25.4626675175495,38.0982769623484,42.0548819400128,0.7975,3.58265947888589,0.1174,0.527403414195867,0.6168,2.77088948787062,0.4606,2.06918238993711,0.0291,0.130727762803235,0.2839,1.27538185085355,0.2443,1.09748427672956,0.1287,0.578167115902965,1.40000000000001,3.61,3424.44408441006,2509.75333144315,0.736,16211.9196200737,2630,4145.75114423427,3120.25294665641,19683.4420470655,0.732,8100,20.91,4.312,13.951,20.622,66.718,20.31,171,373,425,486,446,310,175,153,128,23565,19.5,103,106,70,83,99,-750,3637.57,6998.48,4478.57,3977.62,75643.65,32.85,87.7,18.56,40.46,394.05,38.5675905606659,78,93,3.8,3.545,0.3,0.36,3,6,1.19230769230769,2000,6.04235717099564,8.332373619,0.5,0,0.5,1,NA,NA,NA,NA,72.971245180336,1230.19342642428,223.755820043749,5.57924845455526,27.8423197861209,0.244703879585757,1.22115437658425,6.81508527984795,2.52748642818259,13885.2170787742
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..3713e0d
--- /dev/null
+++ b/README.md
@@ -0,0 +1,2 @@
+# Metanolic_health_Cellsystems
+Scripts and data used to analyze data for "Systems genetics of metabolic health in the BXD mouse genetic reference population"
diff --git a/Scripts/Script_Fig1.R b/Scripts/Script_Fig1.R
new file mode 100644
index 0000000..956aba0
--- /dev/null
+++ b/Scripts/Script_Fig1.R
@@ -0,0 +1,273 @@
+########################################################################
+#clean.workplace
+########################################################################
+rm(list = ls())
+dev.off()
+cat("\014")
+########################################################################
+#Library and Function
+########################################################################
+
+library(ggplot2)
+library(dplyr)
+library(data.table)
+library(ggrepel)
+library(parallel)
+library(Hmisc)
+library(RColorBrewer)
+library(scales)
+library(ggpubr)
+library(cowplot)
+library(FactoMineR)
+
+`%ni%` <- Negate(`%in%`)
+
+########################################################################
+#Obtained health score and its related phenotype 
+########################################################################
+phenotypic_combine           <- openxlsx::read.xlsx("./Data_upload/Phenotypic data.xlsx")
+rownames(phenotypic_combine) <- phenotypic_combine$Strain_id
+
+which.col               <- c("MHS", "body_fat", "Blood_Glucose_.mmol.L.", "Resting_insulin", "Blood_Triglycerides_.mmol.L.", "Blood_TotalCholesterol_.mmol.L.")
+
+result                  <- phenotypic_combine[, which.col]
+result                  <- result[!is.na(result$MHS),]
+result                  <- result[!is.na(result$Resting_insulin),]
+result                  <- result[!is.na(result$Blood_Glucose_.mmol.L.),]
+result                  <- result[!is.na(result$Blood_Triglycerides_.mmol.L.),]
+result                  <- result[!is.na(result$body_fat),]
+result                  <- result[!is.na(result$Blood_TotalCholesterol_.mmol.L.),]
+result$Strain_id        <- rownames(result)
+result_melt             <- reshape2::melt(result, id.var = "Strain_id")
+result_melt             <- result_melt[order(result_melt$value, decreasing = T), ]
+result_melt             <- merge(Strain_id, result_melt, by = "Strain_id")
+result_melt$split       <- paste0(result_melt$strain, "_", result_melt$diet, "_", result_melt$variable)
+
+data_lt                 <- split(result_melt, result_melt$split, drop = T)
+
+data_ave                <- do.call(rbind, 
+                                   lapply(data_lt, function(y){ #y= data_lt[["BXD39_HFD_Resting_insulin"]]
+                                     ave      <- as.data.frame(mean(y$value))
+                                     ave$sd   <- sd(y$value)
+                                     colnames(ave) <- c("ave", "sd")
+                                     ave$strain    <- unique(y$strain)
+                                     ave$pheno     <- unique(y$variable)
+                                     ave$diet      <- unique(y$diet)
+                                     return(ave)
+                                   }))
+
+
+data     <- data_ave
+data$id  <- paste0(data$strain, "_",data$diet)
+data     <- reshape2::dcast(data, id ~ pheno, value.var = "ave"  )
+data$diet <- gsub(".*_", "", data$id)
+data$strain <- gsub("_.*", "", data$id)
+
+####Figure1 B
+#Z score
+Zscore_for      <- function(x){
+  z <- (x - mean(x)) / sd(x)
+  return(z)
+}
+#need to do Z-score
+data_ave$merge          <- paste0(data_ave$strain, "_", data_ave$diet)
+data_ave                <- data_ave[order(data_ave$ave, decreasing = F),]
+tt                      <- split(data_ave, data_ave$pheno)
+data_ave                <- do.call(rbind, lapply(tt, function(lt){ #lt = tt[[1]]
+  lt$zscore             <- Zscore_for(lt$ave)
+  lt
+}))
+
+data_ave$label          <- paste0(gsub("_", " (", data_ave$merge), ")")
+
+# Hierarchical clustering on phenotypes1
+data_dcast <- reshape2::dcast(data_ave, merge~pheno, value.var="zscore")
+data_dcast[is.na(data_dcast)] <- 0 # For the purpose of clustering, we consider "NA" equivalent to no correlation
+rownames(data_dcast) <- data_dcast$merge
+hc <- hclust(dist(t(data_dcast[,-1])))
+rowInd <- hclust(dist(data_dcast[,-1]))$order
+colInd <- hclust(dist(t(data_dcast[,-1])))$order
+
+orders                  <- data_ave[data_ave$pheno == "MHS", ]
+orders                  <- orders$label[order(orders$zscore, decreasing = T)]
+data_ave$label          <- factor(data_ave$label, levels = orders)
+data_ave$pheno          <- factor(data_ave$pheno, levels = c( "Blood_Triglycerides_.mmol.L.", "Blood_Glucose_.mmol.L.", "Blood_TotalCholesterol_.mmol.L.", "Resting_insulin", "body_fat", "MHS" ), 
+                                  labels = c("Triglycerides", "Glucose", "Total Cholesterol", "Insulin", "Body fat", "Metabolic health score"))
+
+Heatmap_palette         <- c(rev(brewer.pal(7,"Blues")), "white", brewer.pal(7,"Reds"))
+g <- ggplot(data_ave, aes(x = label, y = pheno, col = zscore, fill = zscore)) + 
+  geom_tile() +
+  #facet_grid(cols = vars(diet) , space="free", scales="free") +
+  scale_fill_gradientn(colours= Heatmap_palette , limits=c(-2,2) ,na.value="gray87", oob = squish) +
+  scale_color_gradientn(colours= Heatmap_palette , limits=c(-2,2) ,na.value="gray87", oob = squish) +
+  scale_y_discrete(expand=c(0, 0))+
+  #coord_fixed(2) +
+  theme_bw(base_size = 8) +
+  ylab(NULL) +
+  xlab(NULL) +
+  theme(
+    plot.title = element_text(hjust = 0.5, size = 8, face = "bold"),
+    axis.title = element_text(size = 8),
+    #axis.text.x = element_blank(),
+    #axis.ticks.x = element_blank(),
+    axis.text.x = element_text(angle = 90, size = 10, color = "black", vjust = 0.5, hjust = 1),
+    axis.text.y = element_text(size = 8, color = "black"),
+    legend.title = element_text(size = 8),
+    legend.text = element_text(size = 8),
+    strip.background = element_rect( fill = "white"),
+    strip.text = element_text(size = 8),
+    legend.position = "right"
+    #panel.border = element_blank()
+  )
+
+g2 <- ggplot(data_ave, aes(x = label, y = "diet", col = diet, fill = diet)) + 
+  geom_tile() +
+  scale_color_manual(values = c("CD" = "#FF9300", "HFD" = "#107F40")) + 
+  scale_fill_manual(values = c("CD" = "#FF9300", "HFD" = "#107F40")) + 
+  scale_y_discrete(expand=c(0, 0))+
+  #coord_fixed(2) +
+  theme_bw(base_size = 8) +
+  ylab(NULL) +
+  xlab(NULL) +
+  theme(
+    plot.title = element_text(hjust = 0.5, size = 8, face = "bold"),
+    axis.title = element_text(size = 8),
+    #axis.text.x = element_blank(),
+    #axis.ticks.x = element_blank(),
+    axis.text.x = element_text(angle = 90, size = 10, color = "black", vjust = 0.5, hjust = 1),
+    axis.text.y = element_text(size = 8, color = "black"),
+    legend.title = element_text(size = 8),
+    legend.text = element_text(size = 8),
+    strip.background = element_rect( fill = "white"),
+    strip.text = element_text(size = 8),
+    legend.position = "right"
+    #panel.border = element_blank()
+  )
+
+g_all   <- plot_grid(g,g2, align = "hv", nrow = 2, ncol =1, rel_heights = c(2.8,1.2))
+
+########################################################################
+#Use scatter plot to show Metabolic health score (Figure 1C)
+########################################################################
+
+Strain_id               <- phenotypic_combine[,c("Strain_id", "strain", "diet")]
+which.col               <- c("Strain_id","strain","diet","MHS")
+result                  <- phenotypic_combine[, which.col]
+result                  <- result[!is.na(result$MHS),]
+result$split            <- paste0(result$strain, "_", result$diet)
+
+data_lt                 <- split(result, result$split, drop = T)
+
+data_ave                <- do.call(rbind, 
+                                   lapply(data_lt, function(y){ #y= data_lt[[1]]
+                                     ave      <- as.data.frame(mean(y$MHS))
+                                     ave$sd   <- sd(y$MHS)
+                                     colnames(ave) <- c("ave", "sd")
+                                     ave$strain    <- unique(y$strain)
+                                     ave$diet      <- unique(y$diet)
+                                     return(ave)
+                                   }))
+
+data_df                  <- reshape2::dcast(data_ave, strain~diet, value.var = "ave")
+
+data.plot               <- phenotypic_combine[, c("Strain_id", "strain", "diet", "MHS")]
+data.plot               <- data.plot[!is.na(data.plot$MHS), ]
+data.plot.tt            <- split(data.plot, data.plot$strain)
+
+Pvalue                  <- do.call(rbind, lapply(data.plot.tt, function(tt){ #tt= data.plot.tt[[3]]
+  print(unique(tt$strain))
+  if (length(unique(tt$diet)) > 1) {
+    
+    HFD                  <- tt[tt$diet == "HFD", ]
+    CD                   <- tt[tt$diet == "CD", ]
+    
+    if(nrow(HFD) > 1 & nrow(CD) >1){
+      P <- t.test(HFD$MHS, CD$MHS)
+      out <- data.table(strain = unique(tt$strain),
+                        P      = P$p.value)
+    }else{
+      out <- NULL
+    }
+  }else{
+    out <- NULL
+  }
+  out
+}))
+tmp          <- Pvalue[Pvalue$P > 0.05, ]
+#data_df$is_highlight     <- ifelse(abs(data_df$CD - data_df$HFD) < 1, "Yes", "No")
+data_df$is_highlight     <- ifelse((data_df$strain %in% tmp$strain) & abs(data_df$CD - data_df$HFD) < 1, "Yes", "No")
+data_df$label            <- data_df$strain
+#data_df$label[data_df$is_highlight == "No"] <- NA
+
+g <- ggplot(data = data_df, aes(x= CD, y = HFD, label = label, color = is_highlight)) + 
+  geom_point() +
+  scale_color_manual(values = c("Yes" = "orange", "No" = "#969696")) + 
+  geom_text_repel(size=3, parse = F) +
+  geom_hline(yintercept = 0)+
+  geom_vline(xintercept = 0) +
+  theme_bw() + 
+  theme(axis.text.x = element_text(size =10, color = "black"),
+        plot.title = element_text(hjust = 0.5, size = 22, face = "bold"),
+        axis.title = element_text(size = 13),
+        axis.text.y = element_text(size = 10, color = "black"),
+        axis.ticks = element_blank(),
+        legend.title = element_text(size = 13, color = "black"),
+        legend.text = element_text(size = 11),
+        strip.background = element_rect( fill = "white"),
+        panel.grid = element_blank(),
+        #panel.border = element_blank()
+        plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"),
+        legend.position = "right"
+  )
+
+
+########################################################################
+#the correlation with other phenotype (Figure 1D)
+########################################################################
+
+which.col  <- c("MHS", "HOMA_IR", "OGTT_AUC_Glucose", "BW_gained", "scWAT.", "Liver.", "Blood_ALAT_.u.L.", "diet")
+#conditions
+Diets      <- c("HFD", "CD")
+all(which.col %in% colnames(phenotypic_combine) )
+data       <- phenotypic_combine[, colnames(phenotypic_combine) %in% which.col]
+data       <- data[!is.na(data$MHS),]
+
+
+lowerFn <- function(data, mapping, method = "lm", ...) {
+  p <- ggplot(data = data, mapping = mapping) +
+    geom_point(alpha = 0.5, size = 0.8) +
+    geom_smooth(method = method,  se=FALSE) +
+    stat_cor(
+      aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
+      method = "pearson"
+    )
+  p
+}
+
+g <- ggpairs(data, 
+             mapping = aes(color = diet, group = diet),
+             lower = list(continuous = wrap(lowerFn, method = "lm")),
+             upper  = list(continuous = "blankDiag"),
+             diag  = list(continuous = "blankDiag"),
+             #insulin resistance
+             columns = c("health_score_real","HOMA_IR", "OGTT_AUC_Glucose"),
+             columnLabels = c("Metabolic health score", "HOMA (IR)", "Glucose (AUC)")) +
+  
+  # columns = c("health_score_real", "BW_gained", "scWAT."),
+  # columnLabels = c("Metabolic health score", "BW (gained)", "scWAT (%)")) +
+  # columns = c("health_score_real", "Liver.", "Blood_ALAT_.u.L."),
+  # columnLabels = c("Metabolic health score", "Liver weight (%)", "ALT")) +
+  scale_colour_manual("",values = c("CD" = "#FF9300", "HFD" = "#107F40")) +
+  theme_bw() +
+  theme(
+    plot.title = element_text(hjust = 0.5, size = 10, face = "bold"),
+    axis.title = element_text(size = 8),
+    axis.text.x = element_text(size = 8, color = "black"),
+    axis.text.y = element_text(size = 8, color = "black"),
+    legend.title = element_text(size = 8, face = "bold"),
+    legend.text = element_text(size = 8),
+    strip.background = element_rect( fill = "white"),
+    strip.text = element_text(size = 8),
+    legend.position = "bottom"
+    #panel.border = element_blank()
+  ) 
diff --git a/Scripts/Script_Fig2.R b/Scripts/Script_Fig2.R
new file mode 100644
index 0000000..64b978c
--- /dev/null
+++ b/Scripts/Script_Fig2.R
@@ -0,0 +1,143 @@
+
+########################################################################
+#clean.workplace
+########################################################################
+rm(list = ls())
+dev.off()
+cat("\014")
+########################################################################
+#Library and Function
+########################################################################
+
+library(ggplot2)
+library(dplyr)
+library(data.table)
+library(ggrepel)
+library(parallel)
+library(Hmisc)
+library(RColorBrewer)
+library(scales)
+library(ggpubr)
+library(cowplot)
+library(FactoMineR)
+
+`%ni%` <- Negate(`%in%`)
+
+
+########################################################################
+#Figure 2B
+########################################################################
+
+data.plot #the phenotypic data in UKBB
+data.plot$age_stage              <- ifelse(data.plot$age_first_visit <= 50, 1, ifelse(data.plot$age_first_visit > 60, 3, 2))
+
+lt        <- list(
+  
+  Age50   =  data.plot[data.plot$age_stage ==1,],
+  Age60   =  data.plot[data.plot$age_stage ==2,],
+  Age70   =  data.plot[data.plot$age_stage ==3,]
+  
+)
+
+p_MS     <- mapply(data = lt, age = names(lt), function(data, age){ #data = lt[[3]] age = names(lt)[1]
+
+  data$match                 <- paste0(data$metabolic_syndrome_i0, "_", data$p31)
+  data$match                 <- as.factor(data$match)
+  tmp_male                   <- lm(MHS ~ metabolic_syndrome_i0  + body_mass_index_BMI + age_first_visit + initial_50k_release + pc_1 + pc_2 + pc_3 + pc_4 + pc_5 + pc_6 + pc_7 + pc_8 + pc_9 + pc_10, data = data[data$p31 == "Male",])
+  summary(tmp_male)
+  tmp_female                 <- lm(MHS ~ metabolic_syndrome_i0  + body_mass_index_BMI + age_first_visit + initial_50k_release + pc_1 + pc_2 + pc_3 + pc_4 + pc_5 + pc_6 + pc_7 + pc_8 + pc_9 + pc_10, data = data[data$p31 == "Female",])
+  summary(tmp_female)
+  #data$metabolic_syndrome_i0 <- as.factor(data$metabolic_syndrome_i0)
+  pdensity <- ggdensity(
+    data, x = "MHS", 
+    color= "match", palette = c("#a1d99b", "#1b7837", "#bcbddc","#af8dc3"),
+    size = 1.5,
+    alpha = 0
+  ) + ggtitle(age) +
+    annotate("text", x=-6, y=0.3, label= paste0("Male: ", nrow(data[data$p31 == "Male",]) , "\n", "Female: ",  nrow(data[data$p31 == "Female",]) )) +
+    annotate("text", x=4, y=0.3, label= paste0("Coefficient:\nMetS (Male): ", round(tmp_male$coefficients[2], digits = 4), "***\n", "MetS (Female): ", round(tmp_female$coefficients[2], digits = 4), "***")) +
+    scale_y_continuous(expand = expansion(mult = c(0, 0.05)), position = "left")  +
+    scale_x_continuous(expand = expansion(mult = c(0.01, 0.01)), limits = c(-8, 8), breaks = c(-8, -4, 0, 4, 8)) +
+    theme_bw(base_size = 12) +
+    theme(
+      plot.title = element_text(hjust = 0.5, size = 10, face = "bold"),
+      axis.title = element_text(size = 8),
+      axis.text.x = element_text(size = 8, color = "black"),
+      axis.text.y = element_text(size = 8, color = "black"),
+      legend.title = element_text(size = 8, face = "bold"),
+      legend.text = element_text(size = 8),
+      strip.background = element_rect( fill = "white"),
+      strip.text = element_text(size = 8),
+      legend.position = "bottom"
+      #panel.border = element_blank()
+    ) 
+}, SIMPLIFY = F)
+
+p_MS_all  <- plot_grid(plotlist = p_MS, align = "hv", ncol = 1)
+
+
+########################################################################
+#Figure 2C
+#check the correlation between metabolic health score and 
+#other general traits
+########################################################################
+
+library(jtools)
+library(broom)
+library(cli)
+library(forestmangr)
+
+general_traits_data <- lapply(general_traits, function(x){
+  ols               <- lm(healthscore_v2 ~ x + body_mass_index_BMI + genetic_sex_male + age + age_squared + age_x_sex_male + age_squared_x_sex_male + initial_50k_release + pc_1 + pc_2 + pc_3 + pc_4 + pc_5 + pc_6 + pc_7 + pc_8 + pc_9 + pc_10, data = data.plot)
+  model_output      <- tidy(ols)
+  out_conf          <- tidy(ols, conf.int = TRUE)
+  lm_model_out      <- round_df(out_conf, digits=2)
+  lm_model_out      <- lm_model_out[2,] #remove the intercept 
+  lm_model_out$phenotype <- x
+  lm_model_out
+})
+
+general_traits_data            <- do.call(rbind, general_traits_data)
+general_traits_data            <- general_traits_data[!is.na(general_traits_data$estimate), ]
+general_traits_data$adjP       <- p.adjust(general_traits_data$p.value, method = "BH")
+general_traits_data_select     <- general_traits_data[general_traits_data$adjP < 0.05,]
+general_traits_data_select     <- general_traits_data_select[general_traits_data_select$estimate !=0,]
+
+
+phenotype_choose               <- c("whole_body_fat_percentage (n =217547)", "a_body_shape_index_ABSI (n =216648)", "waist_hip_ratio (n =217522)", "albumin_creatinine_ratio_in_urine (n =57089)",
+                                    "Apolipoprotein_B (n =217057)", "Triglycerides (n =217548)", "FSI (n =215954)", "Glucose (n =217548)", "ALAT_ASAT_ratio (n =216796)", 
+                                    "Cystatin_C (n =217425)", "LDL_direct (n =217259)", "Cholesterol (n =217525)","eGFR (n =217214)", "HDL_cholesterol (n =217548)",
+                                    "Arm_fat_free_mass_right (n =217480)", "Arm_fat_free_mass_left (n =217435)", "Apolipoprotein_A (n =217312)", "Trunk_fat_mass (n =217395)", "Haemoglobin_concentration (n =212318)"
+)
+phenotype_choose_v2           <- paste0(gsub(" \\(.*$", "", phenotype_choose), collapse = "|")
+general_traits_data_select    <- general_traits_data_select[grepl(phenotype_choose_v2, general_traits_data_select$phenotype), ]
+p <- ggplot(result.plot, aes(x=reorder(phenotype, estimate), y= estimate)) +
+  geom_hline(yintercept = 0, color = "red") +
+  geom_point(size = 4, alpha =1) + coord_flip() +
+  theme_bw(base_size = 12) +
+  theme(
+    plot.title = element_text(hjust = 0.5, size = 10, face = "bold"),
+    axis.title = element_text(size = 8),
+    axis.text.x = element_text(size = 8, color = "black"),
+    axis.text.y = element_text(size = 8, color = "black"),
+    legend.title = element_text(size = 8, face = "bold"),
+    legend.text = element_text(size = 8),
+    strip.background = element_rect( fill = "white"),
+    strip.text = element_text(size = 8),
+    legend.position = "bottom"
+
+  ) 
+
+########################################################################
+#Figure 2D
+#Cox model
+########################################################################
+cox_model     <- coxph(Surv(disease_time, 
+                                disease_event) ~ MHS_H + body_mass_index_BMI + genetic_sex_male + age + age_squared + age_x_sex_male + age_squared_x_sex_male + initial_50k_release + pc_1 + pc_2 + pc_3 + pc_4 + pc_5 + pc_6 + pc_7 + pc_8 + pc_9 + pc_10, 
+                           data = data_use, 
+                           singular.ok=T) 
+    
+
+
+
+
diff --git a/Scripts/Script_Fig3&S3.R b/Scripts/Script_Fig3&S3.R
new file mode 100644
index 0000000..ba45300
--- /dev/null
+++ b/Scripts/Script_Fig3&S3.R
@@ -0,0 +1,180 @@
+
+########################################################################
+#clean.workplace
+########################################################################
+rm(list = ls())
+dev.off()
+cat("\014")
+########################################################################
+#Library and Function
+########################################################################
+
+library(ggplot2)
+library(dplyr)
+library(data.table)
+library(ggrepel)
+library(parallel)
+library(Hmisc)
+library(RColorBrewer)
+library(scales)
+library(ggpubr)
+library(cowplot)
+library(WGCNA)
+library(FactoMineR)
+
+`%ni%` <- Negate(`%in%`)
+
+
+########################################################################
+#WGCNA
+########################################################################
+
+clusterWGCNA <- function(dataset, powers, analysis, organ){ #dataset = datExpr analysis = diet
+  print("running pickSoftThreshold")
+  set.seed(20000001)
+  sft <- pickSoftThreshold(t(dataset), 
+                           powerVector = powers, 
+                           verbose = 5, 
+                           networkType = "signed hybrid", 
+                           blockSize = 25000, 
+                           corFnc = "bicor",corOptions = list(quick = 0.5, use = 'pairwise.complete.obs'),
+                           moreNetworkConcepts = T)
+  
+  pdf(paste0(file_out, organ, "_", analysis, "_09_module_generation_pickSoftThreshold.pdf"), width = 10, height = 10)
+  cex1 = 0.9;
+  
+  par(mfrow = c(1,2));
+  plot(sft$fitIndices[,1], sft$fitIndices[,2],
+       xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,unsigned R^2",type="n",
+       main = paste("Scale independence"));
+  text(sft$fitIndices[,1], sft$fitIndices[,2],
+       labels=powers,cex=cex1,col="red");
+  # this line corresponds to using an R^2 cut-off of h
+  abline(h=0.85,col="red")
+  # Mean connectivity as a function of the soft-thresholding power
+  plot(sft$fitIndices[,1], sft$fitIndices[,5],
+       xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
+       main = paste("Mean connectivity"))
+  text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
+  
+  dev.off()    
+  
+  fit.sequence <- sft$fitIndices[,"SFT.R.sq"]
+  names(fit.sequence) <- sft$fitIndices[,"Power"]
+  fit.sequence[1] <- 0 #never take the first power
+  bestpower <-  as.numeric(ifelse(max(fit.sequence)> 0.85, names(fit.sequence)[min(which(fit.sequence > 0.85))], names(fit.sequence)[which.max(fit.sequence)]))
+  minmodsize <- 30
+  
+  set.seed(20000001)
+  net <- blockwiseModules(t(dataset), power = bestpower, networkType = "signed hybrid",
+                          TOMType = "signed", minModuleSize = minmodsize,
+                          reassignThreshold = 1e-6, mergeCutHeight = 0.15,
+                          numericLabels = TRUE, pamStage = TRUE, pamRespectsDendro = FALSE,
+                          saveTOMs = FALSE, minKMEtoStay = 0.2, minCoreKME = 0.3, minCoreKMESize = 5,
+                          saveTOMFileBase = paste0(file_out), useBranchEigennodeDissim = FALSE,
+                          verbose = 1, maxBlockSize = 25000, corType = "bicor", quickCor = 1,
+                          nThreads = 0)
+  
+  mergedColors = labels2colors(net$colors)
+  
+  
+  rownames(net$MEs) <- colnames(dataset)
+  ME <- net$MEs
+  ME.colors <- labels2colors(as.numeric(gsub("ME", "", colnames(ME))))
+  METree <- hclust(dist(t(ME)), method = "average");
+  
+  net$METree <- METree
+  net$ME.colors <- ME.colors
+  net$sft <- sft
+  clusteringResult <- data.frame(cluster = as.numeric(net$colors), gene = rownames(dataset))
+  net$clusteringResult <- clusteringResult
+  net$analysis <- analysis
+  net$dataset <- dataset
+  net$powers  <- powers
+  net$bestpower <- bestpower
+  saveRDS(net,paste0(file_out, organ, "_", analysis, "_net.RDS"))
+}
+
+#dataset is the gene expression data
+#analysis is an indication
+#organ is liver
+powers <- c(c(1:10), seq(from = 12, to=20, by=2))
+clusterWGCNA(dataset = dataset, powers = powers, analysis = "all", organ = "liver")
+
+########################################################################
+#Figure S3
+########################################################################
+
+# Convert labels to colors for plotting
+mergedColors = labels2colors(net$colors)
+# Plot the dendrogram and the module colors underneath
+plotDendroAndColors(
+  net$dendrograms[[1]],
+  mergedColors[net$blockGenes[[1]]],
+  "Module colors",
+  dendroLabels = FALSE,
+  hang = 0.03,
+  addGuide = TRUE,
+  guideHang = 0.05 )
+
+########################################################################
+#check the MHS-associated modules
+########################################################################
+
+MEs2       <- as.data.frame(net$MEs)
+#module 0 is not a meaningful module
+res_lt <- do.call(rbind, mclapply(colnames(MEs2)[colnames(MEs2) %ni% c("id", "ME0")], function(probe){ #probe= colnames(expression)[2]
+  expression_tmp <- as.data.frame(MEs2[, probe])
+  rownames(expression_tmp) <- rownames(MEs2)
+  colnames(expression_tmp) <- "module"
+  expression_tmp$sample    <- rownames(expression_tmp)
+    phenotype_tmp           <- as.data.frame(pheno[,"MHS"])
+    rownames(phenotype_tmp) <- rownames(pheno)
+    colnames(phenotype_tmp) <- "phenotype"
+    phenotype_tmp$sample    <- rownames(phenotype_tmp)
+    tmp                     <- merge(expression_tmp, phenotype_tmp, by = "sample")
+    tmp$diet                <- gsub(".*_", "", tmp$sample)
+    tmp$strain              <- gsub("_.*", "", tmp$sample)
+    tmp                     <- tmp[!is.na(tmp$phenotype), ]
+    set.seed(12072021)
+    lm1                     <- lm(formula =  phenotype ~ module + diet, data = tmp)
+    coe                     <- summary(lm1)[["coefficients"]]
+    cor                     <- data.table(
+      slope   = coe[2,1],
+      p.slope = coe[2,4],
+      module  = probe
+    )
+}, mc.cores = 20))
+
+res_lt$adjp.slope <- p.adjust(res_lt$p.slope, method = "BH")
+
+
+########################################################################
+#get the enriched genesets of the MHS-related modules
+########################################################################
+
+res             <- clusterProfiler::enricher(gene = genes.names.tmp,
+                                             TERM2GENE = Genesets[[geneset]][, c("gs_name", "gene_symbol")],
+                                             universe = universe,
+                                             minGSSize = 10,
+                                             maxGSSize = 1500)
+
+
+
+################Correlation analyses##################################
+
+data_cor <- do.call(rbind, lapply(colnames(lipid_ave)[colnames(lipid_ave) %ni% c("id")], function(lipid){ #lipid = colnames(lipid_ave)[colnames(lipid_ave) %ni% c("id")][1]
+  #print(lipid)
+  phenotype_tmp           <- as.data.frame(lipid_ave[,c(lipid, "id")])
+  colnames(phenotype_tmp) <- c("lipid", "id")
+  tmp                     <- merge(pheno_data, phenotype_tmp, by = "id")
+  tmp                     <- tmp[!is.na(tmp$MHS), ]
+  tmp                     <- tmp[!is.na(tmp$lipid), ]
+  cor_test                <- cor.test(tmp$MHS, tmp$lipid, method = "pearson")
+  cor                     <- data.table(r = cor_test$estimate, p = cor_test$p.value, pheno = "MHS", lipid = lipid)
+  return(cor)
+}))
+
+
+
+
diff --git a/Scripts/Script_Fig4.R b/Scripts/Script_Fig4.R
new file mode 100644
index 0000000..787e966
--- /dev/null
+++ b/Scripts/Script_Fig4.R
@@ -0,0 +1,607 @@
+########################################################################
+#clean.workplace
+########################################################################
+
+rm(list = ls())
+
+########################################################################
+#library
+########################################################################
+
+library(qtl2)
+library(ggplot2)
+require(plyr)
+library(dplyr)
+library(data.table)
+library(ggrepel)
+library(qtl2convert)
+library(readxl)
+require(emma)
+require(mlmm)
+library(MASS)
+library(parallel)
+library(ggpubr)
+library(effsize)
+library(RColorBrewer)
+library(scales)
+
+########################################################################
+#QTL mapping
+#same code for BXD and CC mice
+########################################################################
+
+# Operator for "Not in"
+`%ni%` = Negate(`%in%`)
+
+#pheno.norm#
+pheno.norm <- function(pheno.data){ # pheno.data <- data_tmp[, 20]
+
+  # test if there is any zero or negative values in the phenotype data
+  if(length(which(pheno.data <= 0)) != 0){
+    # if non-positive value exists, use quantile transformation
+    message(paste0("Phenotype has non-positive values, quantile transformation will be applied!"))
+    quantNorm = function(x){
+      qnorm(rank(x, na.last = "keep",ties.method = "average")/(length(x)+1))
+    }
+    pheno.data.norm <- quantNorm(pheno.data)
+  }else{
+    # if all values are positive, boxcox transformation could be used
+    #message(paste0("Phenotype has only positive values, Boxcox transformation will be applied!"))
+    # scale all data into data that centers around 1, by dividing the average
+    if (length(which(pheno.data >= 0)) != 0) {
+      pheno.center <- pheno.data / mean(pheno.data, na.rm = TRUE)
+      # run the box-cox transformation
+      bc <- boxcox(pheno.center ~ 1, lambda = seq(-2, 2, 0.25), plotit=FALSE)
+      trans <- bc$x[which.max(bc$y)]
+      if(trans == 0){
+        pheno.data.norm <- log(pheno.center)
+      }else{
+        pheno.data.norm <- (pheno.center^trans - 1)/trans
+      }
+    }else{
+      pheno.data.norm <- NA
+    }
+  }
+  return(pheno.data.norm)
+}
+
+
+########################################################################
+#Data input and output paths
+########################################################################
+
+
+if (!dir.exists(file_out)) {
+  dir.create(file_out, recursive = T)
+}
+
+
+#obtained from genenetwork
+geno
+colnames(geno)               <- gsub("C57BL.6J", "C57BL/6J", gsub("DBA.2J", "DBA/2J", colnames(geno)))
+#because the order of these two locus in the physical and genetic map does not match, remove them
+geno                         <- geno[geno$Locus %ni% c("UNC5348732", "rs6377403"),]
+
+phenotypic_combine           <- openxlsx::read.xlsx("./Data_upload/Phenotypic data.xlsx")
+rownames(phenotypic_combine) <- phenotypic_combine$Strain_id
+
+Diets                        <- c("CD","HFD")
+
+lapply(Diets, function(diet){ #diet = Diets[1]
+  print(diet)
+  
+  if (!dir.exists(paste0(file_out, diet, "/rawdata"))) {
+    dir.create(paste0(file_out, diet, "/rawdata"), recursive = T)
+  }
+  
+  print("read phenotypic data")
+  
+  pheno                      <- phenotypic_combine[phenotypic_combine$diet == diet,]
+  pheno                      <- pheno[pheno$strain %in% colnames(geno),]
+  
+  #Perform QTL mapping for the MHS and its components
+  phenotype_names            <- colnames(phenotypic_combine)[colnames(phenotypic_combine) %ni% c("Strain_id", "strain", "diet", "id")]
+  
+  tmp_res                    <- do.call(rbind, lapply(phenotype_names, function(phenotype_name){ #phenotype_name = phenotype_names[9]
+    
+    print(phenotype_name)
+    pheno_tmp                <- pheno[, c("Strain_id", phenotype_name)]
+    pheno_tmp                <- pheno_tmp[!is.na(pheno_tmp[,2]), ]
+    pheno_norm               <- as.data.frame(pheno.norm(pheno_tmp[,2]))
+    rownames(pheno_norm)     <- pheno_tmp$Strain_id
+    colnames(pheno_norm)     <- phenotype_name
+  
+  write2csv(pheno_norm, paste0(file_out, diet, "/rawdata/bxd_pheno_", phenotype_name, ".csv"), overwrite=TRUE, row.names = "Strain",
+            "BXD phenotype data")
+  
+  # read the genotypes (and maps)
+  geno_id <- lapply(pheno_tmp$Strain_id, function(x){ # x <- pheno$id[1]
+    #print(x)
+    y <- unique(pheno[pheno$Strain_id == x,]$strain)
+    z <- data.frame(geno[, which(colnames(geno) == y)])
+    colnames(z) <- x
+    return(z)
+  })
+  geno_id <- do.call(cbind, geno_id)
+  g <- cbind(geno[, c("Chr", "Locus", "Mb_mm9", "Mb_mm10", "cM_BXD")], geno_id)
+  #g <- cbind(geno[, c("Chr", "Locus", "cM", "Mb")], geno_id)
+  g <- g[-which(g$Chr=="Y"),]
+  g <- g[-which(g$Chr=="M"),]
+  
+  # unique(g$Chr)
+  # grab genetic and physical maps
+  gmap <- data.frame(g[,c("Locus","Chr","cM_BXD")])
+  colnames(gmap) <- c("marker", "chr", "pos")
+  rownames(gmap) <- gmap[,"marker"]
+  
+  pmap <- data.frame(g[,c("Locus","Chr","Mb_mm10")])
+  colnames(pmap) <- c("marker", "chr", "pos")
+  rownames(pmap) <- pmap[,"marker"]
+  
+  # reorder genetic and physical map by physical order
+  pmap <- pmap[order(factor(pmap$chr, c(1:19,"X")), as.numeric(pmap$pos)),]
+  gmap <- gmap[rownames(pmap),]
+  # write genetic and physical maps
+  write2csv(gmap, paste0(file_out, diet, "/rawdata/bxd_gmap_", phenotype_name, ".csv"), comment="Genetic map for BXD data", overwrite=TRUE)
+  write2csv(pmap, paste0(file_out, diet, "/rawdata/bxd_pmap_", phenotype_name, ".csv"), comment="Physical map (mm10 Mbp) for BXD data", overwrite=TRUE)
+  # grab genotypes
+  g <- g[, !(colnames(g) %in% c("Chr", "Mb_mm9", "Mb_mm10", "cM_BXD"))]
+  colnames(g)[colnames(g)=="Locus"] <- "marker"
+  rownames(g) <- g[,"marker"]
+  # reorder markers as in the maps
+  g <- g[rownames(pmap),]
+  # write the genotypes
+  write2csv(g, paste0(file_out, diet, "/rawdata/bxd_geno_", phenotype_name, ".csv"), comment="Genotypes for BXD data", overwrite=TRUE)
+  # write cross info (BxD for all strains)
+  crossinfo <- data.frame(id=names(g)[-1], cross_direction=rep("BxD", ncol(g)-1))
+  write2csv(crossinfo, paste0(file_out, diet, "/rawdata/bxd_crossinfo_", phenotype_name, ".csv"), overwrite=TRUE,
+            comment=paste0("Cross info for BXD data\n#",
+                           "(all lines formed from cross between female B and male D)"))
+  
+  # write json file
+  write_control_file(paste0(file_out, diet,"/rawdata/bxd_", phenotype_name, ".json"),
+                     description="BXD mouse data from the fasted study",
+                     crosstype="risib",
+                     geno_file=paste0("bxd_geno_", phenotype_name, ".csv"),
+                     geno_transposed=TRUE,
+                     geno_codes=list(B=1, D=2),
+                     xchr="X",
+                     gmap_file=paste0("bxd_gmap_", phenotype_name, ".csv"),
+                     pmap_file=paste0("bxd_pmap_", phenotype_name, ".csv"),
+                     pheno_file=paste0("bxd_pheno_", phenotype_name, ".csv"),
+                     crossinfo_file = paste0("bxd_crossinfo_", phenotype_name, ".csv"),
+                     crossinfo_codes = c("BxD"=0),
+                     alleles=c("B", "D"),
+                     na.strings=c("-", "H","NA"),
+                     overwrite=TRUE)
+  ###########read.data################
+  print("read.data")
+  BXD <- read_cross2(paste0(file_out, diet,"/rawdata/bxd_", phenotype_name, ".json"))
+  summary(BXD)
+  ###########Calculating genotype probabilities#######               
+  map <- insert_pseudomarkers(BXD$gmap, step=1)
+  ############calculating the QTL genotype probabilities
+  pr <- calc_genoprob(BXD, map, error_prob=0.002)
+  apr <- genoprob_to_alleleprob(pr)
+  ###########Calculating a kinship matrix##########
+  print("Calculating a kinship matrix")
+  grid <- calc_grid(map = map, step=1)
+  pr_grid <- probs_to_grid(pr, grid)
+  kinship_grid <- calc_kinship(pr_grid, "loco")
+  ##########Performing a genome scan########
+  print("Performing a genome scan")
+  set.seed(20210609)
+  out_kinship <- scan1(pr_grid, BXD$pheno, kinship_grid, cores = 45)
+  dir.create(paste0(file_out, diet,"/result"))
+  save(out_kinship, file = paste0(file_out, diet,"/result/out_kinship_", phenotype_name, ".RData"))
+  ##############Performing a permutation test########
+  print("Performing a permutation test")
+  set.seed(20210609)
+  operm <- scan1perm(pr_grid, BXD$pheno, kinship_grid, n_perm=10000, cores = 35)
+  save(operm, file = paste0(file_out, diet,"/result/permutation_test_", phenotype_name, ".RData"))
+  # load(paste0(file_out, diet,"/result/permutation_test.RData"))
+  # load(paste0(file_out, diet,"/result/out_kinship.RData"))
+  #use P value = 0.05 and P value = 0.1 to find QTL peaks
+  res3        <- find_peaks(out_kinship, map, threshold= summary(operm, 0.1), prob=0.95, peakdrop=5)
+  
+  res3
+  
+  }))
+  write.table(tmp_res, paste0(file_out, diet,"/result/significant_qtl2_gene_position_0.1.txt"), quote=F, sep='\t', row.names=F)
+  
+})
+
+
+########################################################################
+#Manhattan plot (Fig 4A)
+########################################################################
+
+theme_graphs <- theme_bw(base_size = 12) + 
+  theme(axis.text=element_text(size=8,color="black"),
+        plot.title=element_text(face = "bold",size=7,hjust = 0.5,vjust = 0.5),
+        plot.subtitle = element_text(size=7,hjust = 0.5, vjust = 0.5),
+        axis.title=element_text(size=12,hjust = 0.5),
+        legend.title = element_blank(),
+        legend.text=element_text(size=8),
+        strip.text = element_text(size=10),
+        strip.background = element_blank(),
+        legend.position = "right",
+        #strip.background = element_rect(fill = "gray88" ,colour="black", size = 0.3),
+        element_line(color = "gray25", size = 0.5),
+        plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"),
+        panel.spacing = unit(0, "lines"))
+
+
+#get the physical and genetic position of each marker
+#diet = "HFD"
+BXD                     <- read_cross2(paste0(file_out, diet,"/rawdata/bxd_MHS.json"))
+map                     <- insert_pseudomarkers(BXD$gmap, step=1)
+map_mb                  <- interp_map(map, BXD$gmap, BXD$pmap)
+
+position                <- do.call(rbind, lapply(c(1:19, "X"), function(x){ #x= "X"
+  position_cm           <- as.data.frame(map[[x]])
+  colnames(position_cm) <- "position_cm"
+  position_cm$locus     <- rownames(position_cm)
+  position_mb           <- as.data.frame(map_mb[[x]])
+  colnames(position_mb) <- "position_mb"
+  position_mb$locus     <- rownames(position_mb)
+  position_mer          <- merge(position_mb, position_cm, by = "locus")
+  position_mer$chr      <- x
+  position_mer
+}))
+
+
+#geno preparation
+position$chr            <- as.numeric(gsub("X", "20", position$chr))
+Geno                    <- as.data.frame(position[,c("chr","locus","position_mb")])
+colnames(Geno)          <- c("Chr","Locus","Mb_mm10")
+Geno$BP                 <- Geno$Mb_mm10*10^6
+
+
+Diets_lt                <- lapply(Diets, function(diet){ #diet = Diets[1]
+      print(diet)
+      #Find gene region of the peak
+      print("read out_kinship")
+      list_result_file      <- list.files(path = paste0(file_out, "/", diet,"/result"), full.names = T)
+      list_out_kinship      <- list_result_file[grepl("_kinship_MHS",list_result_file)]
+      load(list_out_kinship, verbose = T)
+      out_kinship           <- as.data.frame(out_kinship)
+      out_kinship$locus     <- rownames(out_kinship)
+      out_kinship           <- reshape2::melt(out_kinship[, c("locus", "MHS")], id.vars = "locus")
+      out_kinship$diets     <- diet
+
+     colnames(out_kinship)  <- c("Locus", "phenotypes", "LOD", "diets")
+     data_1                 <- merge(out_kinship, Geno, by = "Locus")
+     data_1                 <- data_1[,c("Locus", "phenotypes", "LOD", "Chr","BP")]
+     colnames(data_1)       <-c("SNP", "phenotype", "LOD", "CHR", "BP")
+     data_1$CHR              <- factor(data_1$CHR, levels = c(1:20), labels = c(1:19, "X"))
+
+     # Compute chromosome size
+     a <- data_1 %>% group_by(CHR) %>% 
+     summarise(chr_len=max(BP)) %>%
+     # Calculate cumulative position of each chromosome
+     mutate(tot=as.numeric(cumsum(chr_len))- as.numeric(chr_len)) %>%
+     #select(-chr_len) %>%
+     # Add this info to the initial dataset
+     left_join(data_1, ., by=c("CHR"="CHR")) %>%
+     # Add a cumulative position of each SNP
+     arrange(CHR, BP) %>%
+     mutate(BPcum=as.numeric(BP)+as.numeric(tot))
+
+     axisdf = a %>% group_by(CHR) %>% dplyr::summarize(center=(max(BPcum) + min(BPcum) ) / 2 )
+
+     if(diet == "HFD"){
+        color = "#1b9e77"
+    }else{
+        color = "#d95f02"
+    }
+     
+     load(paste0(file_out, diet,"/result/permutation_test_MHS.RData"), verbose = T)
+     thres    <- summary(operm, 0.05)[1]
+     thres_s  <- summary(operm, 0.1)[1]
+  p1          <- ggplot(a, aes(x=BPcum, y=LOD)) +
+    geom_line(size = 0.2, color = color) +
+    geom_hline(yintercept = thres, color = color, size = 0.2, linetype = c("solid")) +
+    #geom_point(data=subset(x, signif=="Yes"), color = pals::brewer.dark2(3)[3], shape = 17, size =2, alpha= 0.8) +
+    geom_hline(yintercept = thres_s, color = color, size = 0.2, linetype = c("twodash")) +
+    scale_color_manual("",values = color) +
+    scale_x_continuous(label = axisdf$CHR, breaks= axisdf$center, expand = c(0.01, 0.01)) +
+    scale_y_continuous(expand = c(0, 0.2)) + 
+    theme_graphs + 
+    xlab("Chromosomes") + ylab("LOD score")
+  
+  ggsave(filename = paste0("./Figures/11-manhattan_",diet, ".png"), p1, width = 5.5, height = 2.5)
+  ggsave(filename = paste0("./Figures/11-manhattan_",diet, ".pdf"), p1, width = 5.5, height = 2.5, useDingbats = FALSE)
+  
+  return(p1)
+})
+
+########################################################################
+#Boxplot plot (Fig 4B, 4C)
+########################################################################
+#get locus with the highest LOD under the significant QTL peak
+locus                <- c("rs32906553", "rs31874398")
+names(locus)         <- c("CD_Chr8", "HFD_Chr7")
+
+mapply(locu = locus, name = names(locus), function(locu, name){
+  print(name)
+  #name = names(locus)[2]
+  #locu = locus[2]
+  pheno            <- phenotypic_combine
+  geno             <- read.csv(paste0(file_out, gsub("_.*", "", name),"/rawdata/bxd_geno.csv"),comment.char = "#")
+  geno.tmp         <- geno[geno$marker == locu, ]
+  geno.tmp         <- reshape2::melt(geno.tmp, id.vars = "marker")
+  geno.tmp$Strain  <- gsub("^X", "", gsub("\\.", "-", geno.tmp$variable))
+  pheno.tmp        <- pheno[, c("Strain_id", "MHS")]
+  
+  tmp              <- merge(pheno.tmp, geno.tmp, by.x = "Strain_id", by.y = "Strain")
+  tmp              <- tmp[!is.na(tmp$MHS), ]
+  
+  p <- ggplot(tmp, aes(x=value, y= MHS, color= value, fill = value)) +
+    geom_boxplot(alpha = 0.5, outlier.color = "white") +
+    geom_jitter(aes(colour = value)) +
+    stat_boxplot(geom= 'errorbar' , width = 0.3, position = position_dodge(width = 0.75) ) +
+    stat_compare_means(comparisons = list(c("B", "D")), label = "p.signif",position = "identity",method = "t.test") +
+    theme_graphs + theme(panel.grid =element_blank(),legend.position = "right") +
+    scale_x_discrete(expand = expansion(mult = c(0.5, 0.5))) +
+    scale_colour_manual(values = c("B" = "#CB181D", "D" = "#2171B5")) +
+    scale_fill_manual(values = c("B" = "#CB181D", "D" = "#2171B5")) +
+    labs(x="",
+         y="") +
+    scale_y_continuous(expand = expansion(mult = c(0.2, 0.2)))
+  
+})
+
+########################################################################
+#Dot plot (Fig 4D) 
+#Obtained QTL peaks of the components of MHS
+#then plot the QTL peaks
+########################################################################
+
+phenotype_name             <- data.frame(phenotype = colnames(phenotypic_combine)[colnames(phenotypic_combine) %ni% c("Strain_id", "strain", "diet")],
+                                         name = c("MHS", "BW (gained)", "Fat (%)", "Glucose (AUC)", "Insulin (AUC)", "Glucose (OGTT)", "Insulin (OGTT)", "HOMA (IR)", "eWAT (%)",
+                                                  "Liver (%)", "pWAT (%)", "scWAT (%)", "ALAT", "Glucose", "HDL", "LDL", "Total Cholesterol", "Triglycerides"))
+
+lapply(Diets, function(diet){
+  
+list_result_file         <- list.files(path = paste0(file_out, diet,"/result"), full.names = T)
+list_out_kinship         <- list_result_file[grepl("_kinship",list_result_file)]
+
+res  <- do.call(cbind, lapply(list_out_kinship, function(i){ # i= list_out_kinship[1]
+  
+  load(i, verbose = T)
+  
+  return(out_kinship)
+  
+}))
+
+
+res                       <- as.data.frame(res[, phenotype_name$phenotype])
+
+res$Locus                  <- rownames(res)
+
+data_melt                  <- reshape2::melt(res, id.vars = "Locus")
+colnames(data_melt)        <- c("Locus", "phenotypes", "LOD")
+
+data_melt_table            <- as.data.table(data_melt)
+data_1                     <- merge(data_melt_table, position, by.x = "Locus", by.y = "locus")
+colnames(data_1)           <- c("SNP", "phenotype", "LOD","BP","CM", "CHR")
+data_1$CHR                 <- factor(data_1$CHR, levels = c(1:20), labels = c(1:19, "X"))
+data_1$BP                  <- data_1$BP * 10^6
+# Compute chromosome size
+a <- data_1 %>% group_by(CHR) %>% 
+  summarise(chr_len=max(BP)) %>%
+  # Calculate cumulative position of each chromosome
+  mutate(tot=as.numeric(cumsum(chr_len))- as.numeric(chr_len)) %>%
+  #select(-chr_len) %>%
+  # Add this info to the initial dataset
+  left_join(data_1, ., by=c("CHR"="CHR")) %>%
+  # Add a cumulative position of each SNP
+  arrange(CHR, BP) %>%
+  mutate(BPcum=as.numeric(BP)+as.numeric(tot))
+axisdf = a %>% group_by(CHR) %>% dplyr::summarize(center=( max(BPcum) + min(BPcum) ) / 2 )
+
+accm  <- unique(a[, c("CHR", "tot")])
+
+
+#Find significant QTLs
+print("read significant QTLs")
+list_significant_QTLs    <- list_result_file[grepl("gene_position_0.1.txt",list_result_file)]
+significant_QTLs         <- read.table(list_significant_QTLs, header = T)
+
+QTLs                     <- do.call(rbind, lapply(1:nrow(significant_QTLs), function(i){ #i =1
+  x                          <- significant_QTLs[i,]
+  
+  start                      <- position[position$position_cm == x$ci_lo & position$chr == x$chr, "position_mb"][1] * 10^6
+  end                        <- position[position$position_cm == x$ci_hi & position$chr == x$chr, "position_mb"][1] * 10^6
+  pos_mb                     <- position[position$position_cm == x$pos & position$chr == x$chr, "position_mb"][1] * 10^6
+  
+  start_accm                 <- start + accm$tot[accm$CHR == x$chr ]
+  end_accm                   <- end   + accm$tot[accm$CHR == x$chr ]
+  pos_accm                   <- pos_mb + accm$tot[accm$CHR == x$chr ]
+  tmp                        <- data.frame(phenotype = x$lodcolumn, chr = x$chr, pos = x$pos, pos_mb = pos_mb, LOD = x$lod, 
+                                           start_cm = x$ci_lo, end_cm = x$ci_hi, start_mb = start, end_mb = end, 
+                                           start_accm = start_accm, end_accm = end_accm, pos_accm = pos_accm)
+  tmp
+}))
+
+
+QTLs                    <- merge(QTLs, phenotype_name, by = "phenotype")
+
+if(diet == "HFD"){
+  color = "#1b9e77"
+  #QTLs$name            <- factor(QTLs$name, levels = rev(c("MHS", "Glucose", "HOMA (B)", "BW (gained)", "Fat (%)", "Lean (%)", "Blood pressure (Systolic)","Liver (%)", "eWAT (%)","Gastrocnemius (%)","Kidney (%)", "ALPL")))
+  
+}else{
+  color = "#d95f02"
+  #QTLs$name            <- factor(QTLs$name, levels = rev(c("MHS", "Glucose", "Glucose (AUC)", "Insulin (AUC)", "BW (gained)", "Lean (%)", "Heart (%)", "Blood pressure (Diastolic)", "ASAT", "ALAT", "ALPL", "RER (Night)")))
+}
+
+
+p1 <- ggplot(a, aes(x=BPcum)) +
+  geom_segment(data = QTLs, aes(x=start_accm, xend= end_accm, y=name, yend=name), color="black") +
+  geom_point(data = QTLs, aes(x=pos_accm, y=name), color=color, size = 3) +
+  geom_vline(xintercept = accm$tot[-1], size = 0.2, linetype = c("twodash")) +
+  scale_x_continuous(label = axisdf$CHR, breaks= axisdf$center, expand = c(0.01, 0.01), limits= c(0, max(a$BPcum))) +
+  theme_bw(base_size = 14) + 
+  theme(axis.text=element_text(size=8,color="black"),
+        plot.title=element_text(size=8,hjust = 0.5,vjust = 0.5),
+        plot.subtitle = element_text(size=8,hjust = 0.5, vjust = 0.5),
+        axis.title=element_text(size=8,hjust = 0.5),
+        legend.title = element_blank(),
+        legend.text=element_text(size=8),
+        legend.position = "none",
+        strip.text = element_text(size=8),
+        strip.background = element_blank(),
+        #strip.background = element_rect(fill = "gray88" ,colour="black", size = 0.3),
+        element_line(color = "gray25", size = 0.5),
+        plot.margin = unit(c(0.1,0.1,0.1,0.1), "cm"),
+        panel.spacing = unit(0, "lines"),
+        panel.grid.major = element_blank(),
+        panel.grid.minor = element_blank()) + 
+  xlab("Chromosomes") + ylab("LOD score")
+
+})
+
+########################################################################
+#Dot plot (Fig 4E) 
+#Effect size
+########################################################################
+
+Diets_lt <- do.call(rbind, lapply(Diets, function(diet){ #diet = Diets[1]
+  print(diet)
+  print("read phenotypic data")
+  
+  phenotypic_data       <- phenotypic_combine[phenotypic_combine$diet == diet, ]
+  phenotypic_data       <- phenotypic_data[phenotypic_data$strain %in% colnames(geno), ]
+  phenotypic_data
+  # read the genotypes (and maps)
+  geno_id <- lapply(phenotypic_data$Strain_id, function(x){ #
+    #print(x)
+    y <- phenotypic_data[phenotypic_data$Strain_id==x,]$strain
+    z <- data.frame(geno[, which(colnames(geno) == y)])
+    colnames(z) <- x
+    return(z)
+  })
+  geno_id <- do.call(cbind, geno_id)
+  g <- cbind(geno[, c("Chr", "Locus", "Mb_mm9", "Mb_mm10", "cM_BXD")], geno_id)
+  g <- g[!grepl("Y|M", g$Chr),]
+
+
+
+  pheno_selected     <- data.table(
+
+    phenotype = c( "scWAT.", "pWAT.", "body_fat", "Blood_Glucose_.mmol.L.","Resting_insulin", "OGTT_AUC_Insulin_.AUC.", "HOMA_IR","Liver.", "Blood_ALAT_.u.L.", "Blood_TotalCholesterol_.mmol.L."),
+    labels    = c( "scWAT (%)", "pWAT (%)","Body fat (%)", "Glucose", "Insulin","insulin (AUC)", "HOMA (IR)","Liver weight (%)", "ALT", "Total cholesterol"),
+    category  = c( "Obesity","Obesity","Obesity", "Insulin resistance", "Insulin resistance", "Insulin resistance", "Insulin resistance","Liver damage", "Liver damage", "Liver damage")
+
+  )
+  
+  result       <- do.call(rbind, lapply(locus, function(x){ #x = locus[1]
+    geno_data  <- as.data.frame(g[g$Locus == x,])
+    geno_data  <- geno_data[, colnames(geno_data)[!grepl("Chr|Mb_mm9|Mb_mm10|cM_BXD", colnames(geno_data))]]
+    geno_data  <- reshape2::melt(geno_data, id.vars = "Locus")
+    colnames(geno_data) <- c("Locus", "id", "genotype")
+    
+    pheno.data  <- phenotypic_data[, pheno_selected$phenotype]
+    
+    pheno.data_norm <- do.call(cbind, lapply(pheno_selected$phenotype, function(i){ # i = phenotype_name$phenotype[1]
+      
+      
+      tmp       <- pheno.data[, i]
+      names(tmp)<- rownames(pheno.data)
+      tmp       <- tmp[!is.na(tmp)]
+      tmp       <- pheno.norm(tmp)
+      tmp_return<- as.data.frame(tmp[match(rownames(pheno.data), names(tmp))])
+      colnames(tmp_return) <- i
+      rownames(tmp_return) <- rownames(pheno.data)
+      tmp_return
+      
+    }))
+    
+    pheno.data_norm$Strain_id <- rownames(pheno.data_norm)
+    
+    pheno.data_norm           <- reshape2::melt(pheno.data_norm, id.vars = "Strain_id")
+    colnames(pheno.data_norm) <- c("id", "phenotype", "value")
+    pheno.geno.data <- merge(pheno.data_norm, geno_data, by = "id")
+    pheno.geno_lt   <- split(pheno.geno.data, pheno.geno.data$phenotype)
+    
+    result_2        <- do.call(rbind, lapply(pheno.geno_lt, function(y){ #y = pheno.geno_lt[[1]]
+      
+      print(as.character(unique(y$phenotype)))
+      y          <- y[y$value < 1e80, ]
+      y          <- y[!is.na(y$value), ]
+      if (length(y$value) >20) {
+        bb            <- y[y$genotype == "B", "value"]
+        dd            <- y[y$genotype == "D", "value"]
+        p             <- t.test(bb,dd)$p.value
+        effect_size   <- cohen.d(bb[!is.na(bb)], dd[!is.na(dd)])$estimate
+        effect_factor <- as.character(cohen.d(bb[!is.na(bb)], dd[!is.na(dd)])$magnitude)
+        
+        p             <- data.frame(phenotype = as.character(unique(y$phenotype)),
+                                    locus     = x,
+                                    P_value   = p,
+                                    effect_size = effect_size,
+                                    effect_factor = effect_factor,
+                                    stringsAsFactors = F)
+      }else{
+        p <- NULL
+      }
+      return(p)
+    }))
+  }))
+  
+  result$adjustP <- p.adjust(result$P_value, method = "BH")
+  result$log10P  <- -log10(result$adjustP)
+  
+  result$diet    <- diet
+  return(result)
+}))
+
+result_data        <- Diets_lt[(Diets_lt$locus == "rs32906553" & Diets_lt$diet == "CD")|(Diets_lt$locus == "rs31874398" & Diets_lt$diet == "HFD"), ]
+result_data        <- merge(pheno_selected, result_data, by = "phenotype")
+
+result_data       <- result_data[order(result_data$effect_size, decreasing = T),]
+
+# Hierarchical clustering on phenotypes1
+data_dcast <- reshape2::dcast(result_data, labels~locus, value.var="effect_size")
+data_dcast[is.na(data_dcast)] <- 0 # For the purpose of clustering, we consider "NA" equivalent to no correlation
+rownames(data_dcast) <- data_dcast$labels
+hc <- hclust(dist(t(data_dcast[,-1])))
+rowInd <- hclust(dist(data_dcast[,-1]))$order
+colInd <- hclust(dist(t(data_dcast[,-1])))$order
+
+result_data$locus <- factor(result_data$locus, levels = rev(colnames(data_dcast)[-1][colInd]))
+result_data$labels <- factor(result_data$labels, levels = data_dcast$labels[rowInd])
+
+Heatmap_palette <- c(rev(brewer.pal(7,"Blues")),"white",brewer.pal(7,"Reds"))
+
+result_data$stars <- cut(result_data$adjustP, breaks=c(-Inf, 0.001, 0.01, 0.05, Inf), label=c("***", "**", "*", ""))
+
+result_data$locus <- factor(result_data$locus, levels = c("rs32906553", "rs31874398"))
+
+g <- ggplot(result_data, aes(x = locus, y = labels, fill = effect_size)) +
+  geom_point(aes(size = log10P, fill = effect_size, color = effect_size)) +
+  scale_size(range = c(3,8)) +
+  scale_fill_gradientn(colours= Heatmap_palette , limits=c(-1,1) ,na.value="gray87", oob=squish) +
+  scale_color_gradientn(colours= Heatmap_palette , limits=c(-1,1) ,na.value="gray87", oob=squish) +
+  facet_grid(rows = vars(category), space="free", scales="free") +
+  geom_text(aes(label=stars), color="black", size=4, vjust = 0.8, hjust = 0.5) +
+  theme_bw(base_size = 14) +
+  theme(
+    plot.title = element_text(hjust = 0.5, size = 13, face = "bold"),
+    #axis.title = element_text(size = 13),
+    axis.text.x = element_text(angle= 90, hjust= 1, vjust=0.5,size = 11, color = "black"),
+    #axis.text.x = element_text( hjust= 0.5, vjust=0.5,size = 11, color = "black"),
+    axis.text.y = element_text(size = 11, color = "black"),
+    axis.title = element_blank(),
+    #axis.ticks = element_blank(),
+    legend.title = element_text(size = 10, face = "bold"),
+    legend.text = element_text(size = 8),
+    strip.background = element_rect( fill = "white"),
+    strip.text = element_text(size = 11)
+    #panel.border = element_blank()
+  ) 
+
+
+
diff --git a/Scripts/Script_Fig5.R b/Scripts/Script_Fig5.R
new file mode 100644
index 0000000..dd83759
--- /dev/null
+++ b/Scripts/Script_Fig5.R
@@ -0,0 +1,117 @@
+########################################################################
+#clean.workplace
+########################################################################
+
+rm(list = ls())
+
+########################################################################
+#library
+########################################################################
+
+library(qtl2)
+library(ggplot2)
+require(plyr)
+library(dplyr)
+library(data.table)
+library(ggrepel)
+library(qtl2convert)
+library(readxl)
+require(emma)
+require(mlmm)
+library(MASS)
+library(parallel)
+library(ggpubr)
+library(effsize)
+library(RColorBrewer)
+library(scales)
+
+########################################################################
+#GWAS analyses for 43 inbred strains
+########################################################################
+
+# Operator for "Not in"
+`%ni%` = Negate(`%in%`)
+
+#pheno.norm#
+pheno.norm <- function(pheno.data){ # pheno.data <- data_tmp[, 20]
+
+  # test if there is any zero or negative values in the phenotype data
+  if(length(which(pheno.data <= 0)) != 0){
+    # if non-positive value exists, use quantile transformation
+    message(paste0("Phenotype has non-positive values, quantile transformation will be applied!"))
+    quantNorm = function(x){
+      qnorm(rank(x, na.last = "keep",ties.method = "average")/(length(x)+1))
+    }
+    pheno.data.norm <- quantNorm(pheno.data)
+  }else{
+    # if all values are positive, boxcox transformation could be used
+    #message(paste0("Phenotype has only positive values, Boxcox transformation will be applied!"))
+    # scale all data into data that centers around 1, by dividing the average
+    if (length(which(pheno.data >= 0)) != 0) {
+      pheno.center <- pheno.data / mean(pheno.data, na.rm = TRUE)
+      # run the box-cox transformation
+      bc <- boxcox(pheno.center ~ 1, lambda = seq(-2, 2, 0.25), plotit=FALSE)
+      trans <- bc$x[which.max(bc$y)]
+      if(trans == 0){
+        pheno.data.norm <- log(pheno.center)
+      }else{
+        pheno.data.norm <- (pheno.center^trans - 1)/trans
+      }
+    }else{
+      pheno.data.norm <- NA
+    }
+  }
+  return(pheno.data.norm)
+}
+
+####phewad.clac######
+phewas.calc <- function(pheno, geno, kmatrix, pmatrix=NULL){
+  pheno.num <- ncol(pheno)
+  for(i in 1:pheno.num){ #i=1
+    print(i)
+    pheno.i <- pheno[, i]
+    names(pheno.i) <- rownames(pheno)
+    pheno.i.not_na <- !is.na(pheno.i)
+    pheno.i <- pheno.i[pheno.i.not_na]
+    pheno.length <- length(pheno.i)
+    geno.at_index  <- t(geno)
+    geno.at_index <- geno.at_index[rownames(geno.at_index)%in%names(pheno.i),]
+    if(pheno.length < 15){
+      pheno.name <- colnames(pheno)[i]
+      message(paste0("phenotype ", pheno.name, " has ", pheno.length, " strains, not enough for analysis!"))
+      next
+    }else{
+      pheno.name <- colnames(pheno)[i]
+      geno_imp.at_index <- as.matrix(geno.at_index)
+      kmatrix.i <- kmatrix[rownames(kmatrix)%in%names(pheno.i), colnames(kmatrix)%in%names(pheno.i)]
+      names(pheno.i)
+      mygwas <- mlmm(Y = pheno.i, X = geno_imp.at_index, K = kmatrix.i, nbchunks = 2, maxsteps = 3)
+      pval <- mygwas$pval_step[[1]]$out
+      colnames(pval)[2] <- pheno.name
+      if (length(pmatrix) == 0){
+        pmatrix <- pval
+      }else{
+        pmatrix <- join(pmatrix, pval, by = "SNP", type = "left", match = "all")
+      }
+    }
+  }
+  return(pmatrix)
+}
+
+
+########################################################################
+# function to calculate kinship matrix from genotype data
+# input:
+# geno: a p by m numeric genotype matrix containing p markers in rows and m samples in columns
+#     colnames(genotype) should be the sample IDs
+#     NOTE: the input genotype matrix should be coded in a minor allele fashion,
+#     i.e. minor allel = 0, major allele = 1, heterozygous = 0.5
+########################################################################
+kinship.emma <- function(geno) { #geno = geno_ori[, 1:2]
+  K_mat <- emma.kinship(geno)            # Calculate kinship using the emma package
+  colnames(K_mat) <- colnames(geno)      # emma does not set the rownames and column names, so we should do it
+  rownames(K_mat) <- colnames(geno)
+  return(K_mat)
+}
+
+
diff --git a/Scripts/Script_FigS1.R b/Scripts/Script_FigS1.R
new file mode 100644
index 0000000..1ae4ef1
--- /dev/null
+++ b/Scripts/Script_FigS1.R
@@ -0,0 +1,79 @@
+########################################################################
+#clean.workplace
+########################################################################
+rm(list = ls())
+dev.off()
+cat("\014")
+########################################################################
+#Library and Function
+########################################################################
+
+library(ggplot2)
+library(dplyr)
+library(data.table)
+library(ggrepel)
+library(readxl)
+library(parallel)
+library(forcats) 
+library(Hmisc)
+library(RColorBrewer)
+library(scales)
+library(ggpubr)
+library(cowplot)
+
+`%ni%` <- Negate(`%in%`)
+set.seed(1234567)
+
+########################################################################
+#Obtained health score and its related phenotype 
+#from /data_preparation/03-health-score
+#use the result directly
+########################################################################
+phenotypic_combine      <- openxlsx::read.xlsx("./Data_upload/Phenotypic data.xlsx")
+
+rownames(phenotypic_combine) <- phenotypic_combine$Strain_id
+Strain_id               <- phenotypic_combine[,c("Strain_id", "strain", "diet")]
+
+data.plot               <- phenotypic_combine[, c("Strain_id", "strain", "diet", "MHS")]
+data.plot               <- data.plot[!is.na(data.plot$MHS), ]
+data.plot.tt            <- split(data.plot, data.plot$strain)
+
+strain_orders           <- names(data.plot.tt)
+BXD_strains             <- strain_orders[grepl("BXD", strain_orders)]
+BXD_strains             <- BXD_strains[order(as.numeric(gsub("BXD|a|b", "", BXD_strains)), decreasing = F)]
+BXD_strains             <- c(BXD_strains, strain_orders[!grepl("BXD", strain_orders)])
+
+P.all                   <- lapply(BXD_strains, function(BXD_strain){ #tt= data.plot.tt[[3]]
+  
+  tt                    <- data.plot.tt[[BXD_strain]]
+  print(BXD_strain)
+  
+  p <- ggplot(tt, aes(x=diet, y=MHS, color=diet, fill = diet)) +
+    geom_boxplot(alpha = 0.5, outlier.color = "white") +
+    #facet_grid(cols = vars(name), scales = "free", space = "free") +
+    geom_jitter(aes(colour = diet)) +
+    stat_boxplot(geom= 'errorbar' , width = 0.3, position = position_dodge(width = 0.75) ) +
+    stat_compare_means(comparisons = list(c("CD", "HFD")), label = "p.signif",position = "identity", method = "t.test") +
+    theme_bw()+
+    theme(axis.text=element_text(size=8,color="black"),
+          plot.title=element_text(face = "bold",size=11,hjust = 0.5,vjust = 0, margin=margin(0,0,5,0)),
+          plot.subtitle = element_text(size=10,hjust = 0.5, vjust = 0, margin=margin(0,0,5,0)),
+          axis.title=element_text(size=5,hjust = 0.5),
+          legend.title = element_blank(),
+          legend.text=element_text(size=11),
+          strip.text = element_text(face = "bold",size=10),
+          strip.background = element_blank(),
+          #strip.background = element_rect(fill = "gray88" ,colour="black", size = 0.3),
+          element_line(color = "gray25", size = 0.5),
+          plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"),
+          panel.spacing = unit(0, "lines"),
+          panel.grid =element_blank(),legend.position = "none") +
+    scale_x_discrete(expand = expansion(mult = c(0.5, 0.5))) +
+    scale_colour_manual(values = c("CD" = "#FF9300", "HFD" = "#107F40")) +
+    scale_fill_manual(values = c("CD" = "#FF9300", "HFD" = "#107F40")) +
+    labs(x="",
+         y="") + ggtitle(unique(tt$strain)) +
+    scale_y_continuous(expand = expansion(mult = c(0.2, 0.2)))
+})
+
+p.all.plot             <- plot_grid(plotlist = P.all, align = "hv", nrow = 7)
diff --git a/Scripts/Script_FigS2.R b/Scripts/Script_FigS2.R
new file mode 100644
index 0000000..bb9fc54
--- /dev/null
+++ b/Scripts/Script_FigS2.R
@@ -0,0 +1,125 @@
+####################################
+#clean.workplace
+####################################
+
+rm(list = ls())
+
+####################################
+#Library
+####################################
+
+library(tidyverse)
+library(data.table)
+library(ggpubr)
+
+`%ni%` <- Negate(`%in%`)
+
+
+######Read phenotypic data from a CC founders######
+
+phenotypic_combine
+
+#select the components of MHS
+risk_factors            <- c("Fat_mass_perc", "ogtt_glycemia_t0", "ogtt_insulin_t0", "plasma_Cholesterol", "plasma_TGL")
+pheno                   <- phenotypic_combine[, risk_factors]
+rownames(pheno)         <- phenotypic_combine$ID
+#only use the mouse with all components of MHS
+pheno.filter            <- pheno[rowSums(is.na(pheno)) < 1, ]
+
+#calculate MHS
+Zscore_for              <- function(x){ 
+  z <- (x - mean(x)) / sd(x)
+  return(z)
+}
+counttemp2              <- data.frame(apply(pheno.filter, 2,Zscore_for))
+counttemp               <- data.frame(apply(counttemp2, 1, sum))
+colnames(counttemp)     <- "MHS"
+counttemp$MHS           <- counttemp$MHS * (-1)
+counttemp$Strain_id     <- rownames(counttemp)
+
+#merge MHS into the previous phenotype data table
+phenotypic_combine      <- merge(phenotypic_combine,counttemp, by.x = "ID", by.y = "Strain_id")
+phenotypic_combine$strain_diet <- paste0(phenotypic_combine$StrainShort, "_", phenotypic_combine$Diet)
+
+#use the mean value of MHS in WD condition to order strains
+
+WD_phenotype           <- phenotypic_combine[phenotypic_combine$Diet == "WD", c("ID", "Strain", "MHS")]
+
+mean_value             <- do.call(rbind, lapply(split(WD_phenotype, WD_phenotype$Strain), function(x){
+  tmp                  <- data.table(Strain = unique(x$Strain), mean = mean(x$MHS, na.rm = T))
+}))
+
+
+order                   <- mean_value$Strain[order(mean_value$mean, decreasing = T)]
+phenotypic_combine$Strain <- factor(phenotypic_combine$Strain, levels = order)
+
+###FigS2A
+p <- ggplot(phenotypic_combine, aes(x=Diet, y=MHS, color= Diet)) +
+  geom_boxplot(outlier.color = "white", fill = NA) +
+  geom_jitter() +
+  scale_colour_manual(values = c("CD" = "#FF9300", "WD" = "#107F40")) +
+  stat_boxplot(geom= 'errorbar' , width = 0.3, position = position_dodge(width = 0.75) ) +
+  facet_grid(cols = vars(Strain), scales = "free", space = "free") +
+  stat_compare_means(comparisons = list(c("CD", "WD")), label = "p.signif",position = "identity",method = "t.test") +
+  theme_bw()+
+  theme(axis.text.y =element_text(size=8,color="black"),
+        axis.text.x = element_blank(),
+        axis.ticks.x = element_blank(),
+        plot.title=element_text(face = "bold",size=11,hjust = 0.5,vjust = 0, margin=margin(0,0,5,0)),
+        plot.subtitle = element_text(size=10,hjust = 0.5, vjust = 0, margin=margin(0,0,5,0)),
+        axis.title=element_text(size=5,hjust = 0.5),
+        legend.title = element_blank(),
+        legend.text=element_text(size=11),
+        strip.text = element_text(face = "bold",size=10),
+        strip.background = element_blank(),
+        #strip.background = element_rect(fill = "gray88" ,colour="black", size = 0.3),
+        element_line(color = "gray25", size = 0.5),
+        plot.margin = unit(c(0.5,0.5,0.5,0.5), "cm"),
+        panel.spacing = unit(0, "lines")) +
+  labs(x="",
+       y="MHS") +
+  scale_y_continuous(expand = expansion(mult = c(0.2, 0.2)))
+
+
+
+###FigS2B
+#select the NASH-related parameters
+traits <- c("Steatosis_score", "Lobular_inflammation_score", "NAS_score")
+
+p_all_traits <- lapply(traits, function(trait){ #trait = traits[1]
+  print(trait)
+  plot.tmp   <- phenotypic_combine[, c("Diet", trait, "Strain", "StrainColors")]
+  colnames(plot.tmp) <- c("Diet", "trait", "Strain", "StrainColors")
+  #keep the same order as the MHS
+  plot.tmp$Strain <- factor(plot.tmp$Strain, levels = order)
+  
+p <- ggplot(plot.tmp, aes(x=Diet, y=trait, color= Diet)) +
+  geom_boxplot(outlier.color = "white", fill = NA) +
+  geom_jitter() +
+  scale_colour_manual(values = c("CD" = "#FF9300", "WD" = "#107F40")) +
+  stat_boxplot(geom= 'errorbar' , width = 0.3, position = position_dodge(width = 0.75) ) +
+  facet_grid(cols = vars(Strain), scales = "free", space = "free") +
+  stat_compare_means(comparisons = list(c("CD", "WD")), label = "p.signif",position = "identity",method = "t.test") +
+  theme_bw()+
+  theme(axis.text.y =element_text(size=8,color="black"),
+        axis.text.x = element_blank(),
+        axis.ticks.x = element_blank(),
+        plot.title=element_text(face = "bold",size=11,hjust = 0.5,vjust = 0, margin=margin(0,0,5,0)),
+        plot.subtitle = element_text(size=10,hjust = 0.5, vjust = 0, margin=margin(0,0,5,0)),
+        axis.title=element_text(size=5,hjust = 0.5),
+        legend.title = element_blank(),
+        legend.text=element_text(size=11),
+        strip.text = element_text(face = "bold",size=10),
+        strip.background = element_blank(),
+        #strip.background = element_rect(fill = "gray88" ,colour="black", size = 0.3),
+        element_line(color = "gray25", size = 0.5),
+        plot.margin = unit(c(0.5,0.5,0.5,0.5), "cm"),
+        panel.spacing = unit(0, "lines")) +
+  labs(x="",
+       y= trait) +
+  scale_y_continuous(expand = expansion(mult = c(0.2, 0.2)))
+
+
+})
+
+