Prostate Cancer Polygenic Risk Score on Metabolites

Included here are the methods and results for the regression of 228 metabolites on a prostate cancer predisposition score (PCa_Poly) of 63 SNPs in a subset of subjects (940 cases and 654 controls) within ProtecT. Weights for the score were obtained from seven prostate cancer GWAS in Europeans. MR-Base was used to import the selected GWAS files into R, where the files were merged and SNPs in LD removed. SNPs without effect estimates were also removed, and the remaining SNPs available for weights were harmonized with the SNPs from the genotype data, such that the effect alleles were the same. Genotype data and generated principal components were extracted from the Haplotype Reference Consortium imputation files using Plink and merged in R with metabolomic data. Linear regression models adusting for age and principal components were clustered by study centre and estimated with robust standard errors. Three models were ran: 1) the effects of the PCa_Poly on metabolites for all 1594 subjects, 2) the effects of PCa_Poly on metabolites among cases, and 3) the effects of PCa_Poly on metabolites among controls. A multiple-testing corrected p-value of 0.0014 was derived by dividing 0.05 by the number of principal components explaining 99% of the variance in the metabolite data. Whilst none of the results for the three models had p-values less than 0.0014, a handful of results had p-values <0.05.

Preparing the ProtecT genetic files

#!/bin/bash
#PBS -l walltime=00:05:00,nodes=1:ppn=1
#PBS -o top_snps.txt
#PBS -j oe
# Set the name of the job
#PBS -N ProtecT_Principal_Components_and_Polygenic_PCa_SNP_Extraction_
echo Running on host `hostname`
echo Time is `date`
echo Directory is `pwd`
echo PBS job ID is $PBS_JOBID
echo This jobs runs on the following machines:
echo `cat $PBS_NODEFILE | uniq`

# Script to extract the instruments for 1) metabolites associated with case-control status in ProtecT 
# and 2) for the prostate cancer predisposition risk score

# use WinSCP to copy the hrc imputed files to a folder in my home directory:
# /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr

# qsub hrc_vcf_to_plink.sh -I -l walltime=05:00:00

# set working directory 
cd /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr

# Open Plink and read-in the vcf files 

module add apps/plink-1.90

plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr01.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr1
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr02.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr2
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr03.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr3
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr04.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr4
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr05.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr5
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr06.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr6
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr07.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr7
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr08.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr8
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr09.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr9
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr10.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr10
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr11.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr11
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr12.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr12
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr13.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr13
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr14.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr14
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr15.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr15
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr16.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr16
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr17.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr17
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr18.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr18
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr19.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr19
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr20.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr20
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr21.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr21
plink --vcf /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/data_chr22.vcf.gz --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr22

# Make binary files for Plink 

plink --bfile chr1 --exclude mymerged-merge.missnp --make-bed --out chr1
plink --bfile chr2 --exclude mymerged-merge.missnp --make-bed --out chr2
plink --bfile chr3 --exclude mymerged-merge.missnp --make-bed --out chr3
plink --bfile chr4 --exclude mymerged-merge.missnp --make-bed --out chr4
plink --bfile chr5 --exclude mymerged-merge.missnp --make-bed --out chr5
plink --bfile chr6 --exclude mymerged-merge.missnp --make-bed --out chr6
plink --bfile chr7 --exclude mymerged-merge.missnp --make-bed --out chr7
plink --bfile chr8 --exclude mymerged-merge.missnp --make-bed --out chr8
plink --bfile chr9 --exclude mymerged-merge.missnp --make-bed --out chr9
plink --bfile chr10 --exclude mymerged-merge.missnp --make-bed --out chr10
plink --bfile chr11 --exclude mymerged-merge.missnp --make-bed --out chr11
plink --bfile chr12 --exclude mymerged-merge.missnp --make-bed --out chr12
plink --bfile chr13 --exclude mymerged-merge.missnp --make-bed --out chr13
plink --bfile chr14 --exclude mymerged-merge.missnp --make-bed --out chr14
plink --bfile chr15 --exclude mymerged-merge.missnp --make-bed --out chr15
plink --bfile chr16 --exclude mymerged-merge.missnp --make-bed --out chr16
plink --bfile chr17 --exclude mymerged-merge.missnp --make-bed --out chr17
plink --bfile chr18 --exclude mymerged-merge.missnp --make-bed --out chr18
plink --bfile chr19 --exclude mymerged-merge.missnp --make-bed --out chr19
plink --bfile chr20 --exclude mymerged-merge.missnp --make-bed --out chr20
plink --bfile chr21 --exclude mymerged-merge.missnp --make-bed --out chr21
plink --bfile chr22 --exclude mymerged-merge.missnp --make-bed --out chr22

# Merge all the files 
# First create a snp list .txt file and then merge files with the .bim .bed and .fam chr01 files

plink --bfile /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/chr1 --merge-list filelist --make-bed --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/mymerged


# LD prune mymerged to obtain a merged dataset usable for the principal components
# create a file called "ld.awk" that has this: 

($1 == 1) && ($4 >= 48227413) && ($4 <= 52227412) {print $2}
($1 == 2) && ($4 >= 86000000) && ($4 <= 100500000) {print $2}
($1 == 2) && ($4 >= 183291755) && ($4 <= 190291755) {print $2}
($1 == 3) && ($4 >= 47524996) && ($4 <= 50024996) {print $2}
($1 == 3) && ($4 >= 83417310) && ($4 <= 86917310) {print $2}
($1 == 5) && ($4 >= 128972101) && ($4 <= 131972101) {print $2}
($1 == 5) && ($4 >= 44500000) && ($4 <= 50500000) {print $2}
($1 == 6) && ($4 >= 57000000) && ($4 <= 64000000) {print $2}
($1 == 6) && ($4 >= 25392021) && ($4 <= 33392022) {print $2}
($1 == 6) && ($4 >= 139958307) && ($4 <= 142458307) {print $2}
($1 == 7) && ($4 >= 55000000) && ($4 <= 66000000) {print $2}
($1 == 8) && ($4 >= 7962590) && ($4 <= 11962591) {print $2}
($1 == 8) && ($4 >= 111930824) && ($4 <= 114930824) {print $2}
($1 == 8) && ($4 >= 43000000) && ($4 <= 50000000) {print $2}
($1 == 10) && ($4 >= 37000000) && ($4 <= 43000000) {print $2}
($1 == 11) && ($4 >= 87860352) && ($4 <= 90860352) {print $2}
($1 == 12) && ($4 >= 33000000) && ($4 <= 40000000) {print $2}
($1 == 20) && ($4 >= 32536339) && ($4 <= 35066586) {print $2}
($1 == 1) && ($4 >= 48287981) && ($4 <= 52287979) {print $2}
($1 == 2) && ($4 >= 86088343) && ($4 <= 101041482) {print $2}
($1 == 2) && ($4 >= 134666269) && ($4 <= 138166268) {print $2}
($1 == 2) && ($4 >= 183174495) && ($4 <= 190174494) {print $2}
($1 == 3) && ($4 >= 47524997) && ($4 <= 50024996) {print $2}
($1 == 3) && ($4 >= 83417311) && ($4 <= 86917310) {print $2}
($1 == 3) && ($4 >= 88917311) && ($4 <= 96017310) {print $2}
($1 == 5) && ($4 >= 44464244) && ($4 <= 50464243) {print $2}
($1 == 5) && ($4 >= 97972101) && ($4 <= 100472101) {print $2}
($1 == 5) && ($4 >= 128972102) && ($4 <= 131972101) {print $2}
($1 == 5) && ($4 >= 135472102) && ($4 <= 138472101) {print $2}
($1 == 6) && ($4 >= 25392022) && ($4 <= 33392022) {print $2}
($1 == 6) && ($4 >= 56892042) && ($4 <= 63942041) {print $2}
($1 == 6) && ($4 >= 139958308) && ($4 <= 142458307) {print $2}
($1 == 7) && ($4 >= 55225792) && ($4 <= 66555850) {print $2}
($1 == 8) && ($4 >= 7962591) && ($4 <= 11962591) {print $2}
($1 == 8) && ($4 >= 42880844) && ($4 <= 49837447) {print $2}
($1 == 8) && ($4 >= 111930825) && ($4 <= 114930824) {print $2}
($1 == 10) && ($4 >= 36959995) && ($4 <= 43679994) {print $2}    
($1 == 11) && ($4 >= 46043425) && ($4 <= 57243424) {print $2}
($1 == 11) && ($4 >= 87860353) && ($4 <= 90860352) {print $2}
($1 == 12) && ($4 >= 33108734) && ($4 <= 41713733) {print $2}
($1 == 12) && ($4 >= 111037281) && ($4 <= 113537280) {print $2}
($1 == 20) && ($4 >= 32536340) && ($4 <= 35066586) {print $2}


# data=<YOUR PLINK ROOT FILE NAME>
# Get snp list with no long range LD regions
awk -f ld.awk mymerged.bim > nold.txt

# Get independent SNPs excluding any long range LD regions
plink --bfile mymerged --exclude nold.txt --indep 100 5 1.01 --out indep

# Calculate PCs for unrelateds
plink --bfile mymerged --extract indep.prune.in --pca 20 --out mymerged

# Extract the snps for the polygenic score

module add apps/plink-1.90
#rs10009409,rs1016343,rs10187424,rs1041449,rs10486567,rs10774740,rs10875943,rs10896449,rs10934853,rs10936632,rs10993994,rs11135910,rs11214775,rs11228565,rs11568818,rs11650494,rs11902236,rs12051443,rs12155172,rs1218582,rs12198220,rs12480328,rs12597458,rs12621278,rs1270884,rs130067,rs1447295,rs1465618,rs1512268,rs16901979,rs16902094,rs17021918,rs17023900,rs17599629,rs17694493,rs17765344,rs1894292,rs1933488,rs2121875,rs2238776,rs2242652,rs2273669,rs2292884,rs2427345,rs2430386,rs2660753,rs2735839,rs3096702,rs3771570,rs3850699,rs4242382,rs4245739,rs4430796,rs445114,rs4713266,rs4962416,rs56232506,rs6062509,rs6763931,rs684232,rs6869841,rs6983267,rs71277158,rs7127900,rs7141529,rs721048,rs7241993,rs7584330,rs7611694,rs7679673,rs76934034,rs7725218,rs7758229,rs7929962,rs8008270,rs80130819,rs8014671,rs8064454,rs8102476,rs902774,rs9287719,rs9364554,rs9443189

plink --bfile /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/mymerged --snps rs10009409,rs1016343,rs10187424,rs1041449,rs10486567,rs10774740,rs10875943,rs10896449,rs10934853,rs10936632,rs10993994,rs11135910,rs11214775,rs11228565,rs11568818,rs11650494,rs11902236,rs12051443,rs12155172,rs1218582,rs12198220,rs12480328,rs12597458,rs12621278,rs1270884,rs130067,rs1447295,rs1465618,rs1512268,rs16901979,rs16902094,rs17021918,rs17023900,rs17599629,rs17694493,rs17765344,rs1894292,rs1933488,rs2121875,rs2238776,rs2242652,rs2273669,rs2292884,rs2427345,rs2430386,rs2660753,rs2735839,rs3096702,rs3771570,rs3850699,rs4242382,rs4245739,rs4430796,rs445114,rs4713266,rs4962416,rs56232506,rs6062509,rs6763931,rs684232,rs6869841,rs6983267,rs71277158,rs7127900,rs7141529,rs721048,rs7241993,rs7584330,rs7611694,rs7679673,rs76934034,rs7725218,rs7758229,rs7929962,rs8008270,rs80130819,rs8014671,rs8064454,rs8102476,rs902774,rs9287719,rs9364554,rs9443189 --recode A --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/poly8

# Command to count the number of columns in allSNPs.raw
head -1 allSNPs.raw | tr '|' ' ' | wc -w

# Command to test which allele is reported by Plink 

grep -n  "rs10009409" data_chr04.info | awk -F  ":" '{print $1}'
cat -n data_chr15.info | grep '^ *1116092'

less -S data_chr04.info

---

Preparing the score

install.packages("devtools")
library('devtools')
devtools::install_github("MRCIEU/MRInstruments")
library(MRInstruments) 
install_github("MRCIEU/TwoSampleMR")
library('TwoSampleMR')
library('plyr')
library(lattice)
library(readr)
library(readxl)
library(stringr)
library('lmtest')
library('sandwich')

setwd("C:/Users/charl/Dropbox/Bristol")

# Read in the exposure data

data(gwas_catalog) 

# Read-in the prostate cancer hits from various GWASes of prostate cancer in Europeans

# Study=="Al Olama AA"

pca_gwas <- subset(gwas_catalog, grepl("Al Olama AA", Author) & Phenotype == "Prostate cancer")
pca_exp_dat <- format_data(pca_gwas)

# LD clumping (will do all at the end)
#pca_exp_dat <- clump_data(pca_exp_dat)
#head(pca_exp_dat)
#pca_exp_dat$SNP

# Study=="Kote-Jarai Z"

Kote_gwas <- subset(gwas_catalog, grepl("Kote-Jarai Z", Author) & Phenotype == "Prostate cancer")
Kote_exp_dat <- format_data(Kote_gwas)

total <- rbind(pca_exp_dat,Kote_exp_dat,by="SNP")

# Study=="Eeles RA"

Eeles_gwas <- subset(gwas_catalog, grepl("Eeles RA", Author) & Phenotype == "Prostate cancer")
Eeles_exp_dat <- format_data(Eeles_gwas)

total <- rbind(total,Eeles_exp_dat,by="SNP")

# Study==Thomas G

Thomas_gwas <- subset(gwas_catalog, grepl("Thomas G", Author) & Phenotype == "Prostate cancer")
Thomas_exp_dat <- format_data(Thomas_gwas)

total <- rbind(total,Thomas_exp_dat,by="SNP")

# Study==Berndt SI

Berndt_gwas <- subset(gwas_catalog, grepl("Berndt SI", Author) & Phenotype == "Prostate cancer")
Berndt_exp_dat <- format_data(Berndt_gwas)

total <- rbind(total,Berndt_exp_dat,by="SNP")

# Study==Schumacher FR
Schumacher_gwas <- subset(gwas_catalog, grepl("Schumacher FR", Author) & Phenotype == "Prostate cancer")
Schumacher_exp_dat <- format_data(Schumacher_gwas)

total <- rbind(total,Schumacher_exp_dat,by="SNP")

# Study==Gudmundsson J
Gudmundsson_gwas <- subset(gwas_catalog, grepl("Gudmundsson J", Author) & Phenotype == "Prostate cancer")
Gudmundsson_exp_dat <- format_data(Gudmundsson_gwas)

total <- rbind(total,Gudmundsson_exp_dat,by="SNP")

## LD clump all the snps

total <- clump_data(total)

# Order the SNPs

total_removed=total[order(total$'SNP'),]

# Multiple SNP entries. Just keep the first instance

total_removed2 <- do.call(rbind, lapply(split(total_removed, total_removed$SNP), `[`, 1, ))
dim(total_removed2)
rownames(total_removed2) <- NULL
total_removed2=total_removed2[order(total_removed2$'SNP'),]
total_removed2$SNP

# Remove SNPs with missing betas

which(total_removed2$SNP=="rs4242384")
total_removed2<- total_removed2[-c(54), ]
which(total_removed2$SNP=="rs1775148")
total_removed2<- total_removed2[-c(36), ]
which(total_removed2$SNP=="rs7153648")
total_removed2<- total_removed2[-c(70), ]
which(total_removed2$SNP=="rs1859962")
total_removed2<- total_removed2[-c(37), ]
which(total_removed2$SNP=="rs6465657")
total_removed2<- total_removed2[-c(59), ]
which(total_removed2$SNP=="rs7130881")
total_removed2<- total_removed2[-c(66), ]
which(total_removed2$SNP=="rs7501939")
total_removed2<- total_removed2[-c(69), ]
which(total_removed2$SNP=="rs6545977")
total_removed2<- total_removed2[-c(59), ]

write.csv(total_removed2$SNP,"C:/Users/ca16591/Dropbox/Bristol/snplist_poly7.csv")

#module add apps/plink-1.90
#plink --bfile /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/mymerged --snps rs10009409,
#rs1016343,rs10187424,rs1041449,rs10486567,rs10774740,rs10875943,rs10896449,rs10934853,rs10936632,
#rs10993994,rs11135910,rs11214775,rs11228565,rs11568818,rs11650494,rs11902236,rs12051443,rs12155172,
#rs1218582,rs12198220,rs12480328,rs12597458,rs12621278,rs1270884,rs130067,rs1447295,rs1465618,rs1512268,
#rs16901979,rs16902094,rs17021918,rs17023900,rs17599629,rs17694493,rs17765344,rs1894292,rs1933488,rs2121875,
#rs2238776,rs2242652,rs2273669,rs2292884,rs2427345,rs2430386,rs2660753,rs2735839,rs3096702,rs3771570,rs3850699,
#rs4242382,rs4245739,rs4430796,rs445114,rs4713266,rs4962416,rs56232506,rs6062509,rs6763931,rs684232,rs6869841,
#rs6983267,rs71277158,rs7127900,rs7141529,rs721048,rs7241993,rs7584330,rs7611694,rs7679673,rs76934034,rs7725218,
#rs7758229,rs7929962,rs8008270,rs80130819,rs8014671,rs8064454,rs8102476,rs902774,rs9287719,rs9364554,rs9443189 
#--recode A --out /panfs/panasas01/sscm/ca16591/hrc_protect_1sampleMr/poly8

# Read in the metabolite data

setwd("C:/Users/ca16591/Dropbox/Bristol")
#library(lattice)
#library(readr)
#library(readxl)

cleaned_met3 <- read.csv("C:/Users/ca16591/Dropbox/Bristol/metabol.data.clean.demo3.20.02.17.csv")
data <- cleaned_met3

completedHistoryData <- read.csv("C:/Users/ca16591/Dropbox/Bristol/completedHistoryData.csv")
data$Glol_imputed <- completedHistoryData$Glol
mname=colnames(data)[-c(1:93)]  

smname=mname[-c(229:231)]
smname[[length(smname)+1]] <- "Glol_imputed"
data$casecontrol_n=ifelse(data$casecontrol=="Control", 0,1)
data$pounds = (data$p1dhl_wght_stones)*14
data$weight = data$pounds + data$p1dhl_wght_pounds
data$feet_to_inches = (data$p1dhl_tall_ft)*12
data$height_inches = data$p1dhl_tall_in + data$feet_to_inches
data$inches = (data$height_inches^2)
data$inches_squared = (data$inches)
data$bmi=(data$weight*703)/data$inches_squared 

data$fac_centre <- as.factor(data$centre)
data$fhhistpca <- ifelse(data$fhhistpca=="yes", 1, 0)
data$fhhistpca2 <- completedHistoryData$family_history
data$fhhistpca2 <- as.factor(data$fhhistpca2)
data[, 94:321][data[, 94:321] == 0] <- NA
head(data$XXL.VLDL.P)
which(data$sampleid==5511753)
data <- data[-c(569), ]
dim(data)
head(data$Glol)
smname=mname[-c(229:231)]
head(data)

# Log transforming function 

logtransform<-function(dataset,smname)  
{   
  tx=dataset[,smname]   
  tx=apply(tx,2,function(x){if (min(x,na.rm=T)==0) x=x+1 else x })  
  tx=log(tx)    
  dataset[,smname]=tx   
  dataset   
}   

data=data
logtransform(data[smname])
head(data$subjectid)

# Read in the ProtecT snps and merge with the metabolite dataset

setwd("M:/data/protect/_devs/PROTECT_Clinical/data/hrc_protect_1sampleMR")

polySNPs <- read_excel("M:/data/protect/_devs/PROTECT_Clinical/data/hrc_protect_1sampleMR/poly8.xlsx")

snps_data <- merge(data, polySNPs, id=subjectid, quote="")

# Merge in the principal components obtained in Plink

eigenvec <- read_excel("M:/data/protect/_devs/PROTECT_Clinical/data/hrc_protect_1sampleMR/eigenvec.xlsx")
eigen_snps_data <- merge(eigenvec, snps_data, id=subjectid, quote="")

# Change working directory back to my home drive 

setwd("C:/Users/ca16591/Dropbox/Bristol")

# Order the snps and check that there is the same number pulled in from ProtecT as we have with weights

colnames(eigen_snps_data) 
eigen_snps_data=eigen_snps_data[ , order(names(eigen_snps_data))]

difftest=setdiff(colnames(eigen_snps_data[c(229:311)]), total_removed2$SNP)

# Save the prostate instrument weight data as "weights"

weights=total_removed2
dim(weights)
rownames(weights)=NULL

write.csv(weights, "C:/Users/ca16591/Dropbox/Bristol/total_removed2.csv")

# Save the master dataset as "eigen_snps_data"

write.csv(eigen_snps_data, "C:/Users/ca16591/Dropbox/Bristol/polydataset.csv")

snpname=colnames(eigen_snps_data[c(229:311)])

# Script to harmonize the alleles 

protect_snps=read.csv("C:/Users/ca16591/Dropbox/Bristol/t2_snps_with_alleles.csv")
weights2 <- merge(protect_snps, weights, id=SNP, quote="")
head(weights2)
dim(weights2)

#Merge into weights the columns from poly8 with the effect allele in a separate column

which(weights2$Protect_eaf!=weights2$effect_allele.exposure)
myvars=c("SNP", "Protect_eaf", "gene.exposure", "effect_allele.exposure", "other_allele.exposure","beta.exposure","se.exposure", "pval.exposure")
weights2=weights2[myvars]
weights2$beta.exposure=as.numeric(weights2$beta.exposure)

# To harmonize the alleles, multiple the beta estimate by -1 for those snps that don't match

ifelse(weights2$Protect_eaf==weights2$effect_allele.exposure,weights2$beta.exposure, (weights2$beta.exposure*(-1)))

# check rs10486567 to see if beta is reversed (was 0.113328685307003)

# To identify which snps of these not include in the score
newdata <-  subset(weights2, Protect_eaf==effect_allele.exposure | Protect_eaf==other_allele.exposure)
dim(newdata)
newdata$columns=rownames(newdata)
newdata$snp="snp"

# check newdata to see if rs10187424 is dropped

# Get the list of the truncated snps 

pastename=paste("snp", newdata$columns, "+")
pastename=str_replace_all(string=pastename, pattern=" ", repl="")
pastename=noquote(pastename)
pastename

# Create the score by multiplying the weights2 by the genotype data for each snp and summing 

colnames(eigen_snps_data)

#rs10009409 
for (i in eigen_snps_data[c(229)]){
  eigen_snps_data$snp1=i*weights2$beta.exposure[1]
}

#rs1016343
for (i in eigen_snps_data[c(230)]){
  eigen_snps_data$snp2=i*weights2$beta.exposure[2]
}

#rs10187424
#for (i in eigen_snps_data[c(231)]){
#  eigen_snps_data$snp3=i*weights2$beta.exposure[3]
#}

#rs1041449
for (i in eigen_snps_data[c(232)]){
  eigen_snps_data$snp4=i*weights2$beta.exposure[4]
}

#rs10486567 
#recoded
for (i in eigen_snps_data[c(233)]){
  eigen_snps_data$snp5=i*weights2$beta.exposure[5]
}

#rs10774740
#recoded
for (i in eigen_snps_data[c(234)]){
  eigen_snps_data$snp6=i*weights2$beta.exposure[6]
}

#rs10875943
for (i in eigen_snps_data[c(235)]){
  eigen_snps_data$snp7=i*weights2$beta.exposure[7]
}

#rs10896449
#recoded
for (i in eigen_snps_data[c(236)]){
  eigen_snps_data$snp8=i*weights2$beta.exposure[8]
}

#rs10934853
for (i in eigen_snps_data[c(237)]){
  eigen_snps_data$snp9=i*weights2$beta.exposure[9]
}

#rs10936632
#recoded
for (i in eigen_snps_data[c(238)]){
  eigen_snps_data$snp10=i*weights2$beta.exposure[10]
}

#rs10993994
for (i in eigen_snps_data[c(239)]){
eigen_snps_data$snp11=i*weights2$beta.exposure[11]
}

#for (i in eigen_snps_data[c(240)]){
#  eigen_snps_data$snp12=i*weights2$beta.exposure[12]
#}

#rs11214775
#recoded
for (i in eigen_snps_data[c(241)]){
  eigen_snps_data$snp13=i*weights2$beta.exposure[13]
}

#rs11228565
for (i in eigen_snps_data[c(242)]){
  eigen_snps_data$snp14=i*weights2$beta.exposure[14]
}

#rs11568818
#for (i in eigen_snps_data[c(243)]){
#  eigen_snps_data$snp15=i*weights2$beta.exposure[15]
#}

#rs11650494
for (i in eigen_snps_data[c(244)]){
  eigen_snps_data$snp16=i*weights2$beta.exposure[16]
}

#rs11902236
#for (i in eigen_snps_data[c(245)]){
#  eigen_snps_data$snp17=i*weights2$beta.exposure[17]
#}

#rs12051443
for (i in eigen_snps_data[c(246)]){
  eigen_snps_data$snp18=i*weights2$beta.exposure[18]
}

#rs12155172
for (i in eigen_snps_data[c(247)]){
  eigen_snps_data$snp19=i*weights2$beta.exposure[19]
}

#rs1218582
for (i in eigen_snps_data[c(248)]){
  eigen_snps_data$snp20=i*weights2$beta.exposure[20]
}

#rs12198220
#recoded
for (i in eigen_snps_data[c(249)]){
  eigen_snps_data$snp21=i*weights2$beta.exposure[21]
}

#rs12480328
#recoded
for (i in eigen_snps_data[c(250)]){
  eigen_snps_data$snp22=i*weights2$beta.exposure[22]
}

#rs12597458
#recoded

for (i in eigen_snps_data[c(251)]){
  eigen_snps_data$snp23=i*weights2$beta.exposure[23]
}

for (i in eigen_snps_data[c(252)]){
  eigen_snps_data$snp24=i*weights2$beta.exposure[24]
}

for (i in eigen_snps_data[c(253)]){
  eigen_snps_data$snp25=i*weights2$beta.exposure[25]
}

for (i in eigen_snps_data[c(254)]){
  eigen_snps_data$snp26=i*weights2$beta.exposure[26]
}

for (i in eigen_snps_data[c(255)]){
  eigen_snps_data$snp27=i*weights2$beta.exposure[27]
}

for (i in eigen_snps_data[c(256)]){
  eigen_snps_data$snp28=i*weights2$beta.exposure[28]
}

#for (i in eigen_snps_data[c(257)]){
#  eigen_snps_data$snp29=i*weights2$beta.exposure[29]
#}

for (i in eigen_snps_data[c(258)]){
  eigen_snps_data$snp30=i*weights2$beta.exposure[30]
}

for (i in eigen_snps_data[c(259)]){
  eigen_snps_data$snp31=i*weights2$beta.exposure[31]
}

for (i in eigen_snps_data[c(260)]){
  eigen_snps_data$snp32=i*weights2$beta.exposure[32]
}

for (i in eigen_snps_data[c(261)]){
  eigen_snps_data$snp33=i*weights2$beta.exposure[33]
}

for (i in eigen_snps_data[c(262)]){
  eigen_snps_data$snp34=i*weights2$beta.exposure[34]
}

for (i in eigen_snps_data[c(263)]){
  eigen_snps_data$snp35=i*weights2$beta.exposure[35]
}

for (i in eigen_snps_data[c(264)]){
  eigen_snps_data$snp36=i*weights2$beta.exposure[36]
}

for (i in eigen_snps_data[c(265)]){
  eigen_snps_data$snp37=i*weights2$beta.exposure[37]
}

for (i in eigen_snps_data[c(266)]){
  eigen_snps_data$snp38=i*weights2$beta.exposure[38]
}

#for (i in eigen_snps_data[c(267)]){
#  eigen_snps_data$snp39=i*weights2$beta.exposure[39]
#}

#for (i in eigen_snps_data[c(268)]){
#  eigen_snps_data$snp40=i*weights2$beta.exposure[40]
#}

for (i in eigen_snps_data[c(269)]){
  eigen_snps_data$snp41=i*weights2$beta.exposure[41]
}

for (i in eigen_snps_data[c(270)]){
  eigen_snps_data$snp42=i*weights2$beta.exposure[42]
}

for (i in eigen_snps_data[c(271)]){
  eigen_snps_data$snp43=i*weights2$beta.exposure[43]
}

for (i in eigen_snps_data[c(272)]){
  eigen_snps_data$snp44=i*weights2$beta.exposure[44]
}

for (i in eigen_snps_data[c(273)]){
  eigen_snps_data$snp45=i*weights2$beta.exposure[45]
}

for (i in eigen_snps_data[c(274)]){
  eigen_snps_data$snp46=i*weights2$beta.exposure[46]
}

for (i in eigen_snps_data[c(275)]){
  eigen_snps_data$snp47=i*weights2$beta.exposure[47]
}

for (i in eigen_snps_data[c(276)]){
  eigen_snps_data$snp48=i*weights2$beta.exposure[48]
}

#for (i in eigen_snps_data[c(277)]){
#  eigen_snps_data$snp49=i*weights2$beta.exposure[49]
#}

for (i in eigen_snps_data[c(278)]){
  eigen_snps_data$snp50=i*weights2$beta.exposure[50]
}

for (i in eigen_snps_data[c(279)]){
  eigen_snps_data$snp51=i*weights2$beta.exposure[51]
}

for (i in eigen_snps_data[c(280)]){
  eigen_snps_data$snp52=i*weights2$beta.exposure[52]
}

for (i in eigen_snps_data[c(281)]){
  eigen_snps_data$snp53=i*weights2$beta.exposure[53]
}

for (i in eigen_snps_data[c(282)]){
  eigen_snps_data$snp54=i*weights2$beta.exposure[54]
}

for (i in eigen_snps_data[c(283)]){
  eigen_snps_data$snp55=i*weights2$beta.exposure[55]
}

for (i in eigen_snps_data[c(284)]){
  eigen_snps_data$snp56=i*weights2$beta.exposure[56]
}

for (i in eigen_snps_data[c(285)]){
  eigen_snps_data$snp57=i*weights2$beta.exposure[57]
}

#for (i in eigen_snps_data[c(286)]){
#  eigen_snps_data$snp58=i*weights2$beta.exposure[58]
#}

#for (i in eigen_snps_data[c(287)]){
#  eigen_snps_data$snp59=i*weights2$beta.exposure[59]
#}

#for (i in eigen_snps_data[c(288)]){
#  eigen_snps_data$snp60=i*weights2$beta.exposure[60]
#}

#for (i in eigen_snps_data[c(289)]){
#  eigen_snps_data$snp61=i*weights2$beta.exposure[61]
#}

for (i in eigen_snps_data[c(290)]){
  eigen_snps_data$snp62=i*weights2$beta.exposure[62]
}

for (i in eigen_snps_data[c(291)]){
  eigen_snps_data$snp63=i*weights2$beta.exposure[63]
}

#for (i in eigen_snps_data[c(292)]){
#  eigen_snps_data$snp64=i*weights2$beta.exposure[64]
#}

#for (i in eigen_snps_data[c(293)]){
#  eigen_snps_data$snp65=i*weights2$beta.exposure[65]
#}

for (i in eigen_snps_data[c(294)]){
  eigen_snps_data$snp66=i*weights2$beta.exposure[66]
}

#for (i in eigen_snps_data[c(295)]){
#  eigen_snps_data$snp67=i*weights2$beta.exposure[67]
#}

#for (i in eigen_snps_data[c(296)]){
#  eigen_snps_data$snp68=i*weights2$beta.exposure[68]
#}

#for (i in eigen_snps_data[c(297)]){  eigen_snps_data$snp69=i*weights2$beta.exposure[69]
#}

#for (i in eigen_snps_data[c(298)]){
#  eigen_snps_data$snp70=i*weights2$beta.exposure[70]
#}

for (i in eigen_snps_data[c(299)]){
  eigen_snps_data$snp71=i*weights2$beta.exposure[71]
}

for (i in eigen_snps_data[c(300)]){
  eigen_snps_data$snp72=i*weights2$beta.exposure[72]
}

for (i in eigen_snps_data[c(301)]){
  eigen_snps_data$snp73=i*weights2$beta.exposure[73]
}

for (i in eigen_snps_data[c(302)]){
  eigen_snps_data$snp74=i*weights2$beta.exposure[74]
}

#for (i in eigen_snps_data[c(303)]){
#  eigen_snps_data$snp75=i*weights2$beta.exposure[75]
#}

for (i in eigen_snps_data[c(304)]){
  eigen_snps_data$snp76=i*weights2$beta.exposure[76]
}

for (i in eigen_snps_data[c(305)]){
  eigen_snps_data$snp77=i*weights2$beta.exposure[77]
}

for (i in eigen_snps_data[c(306)]){
  eigen_snps_data$snp78=i*weights2$beta.exposure[78]
}

for (i in eigen_snps_data[c(307)]){
  eigen_snps_data$snp79=i*weights2$beta.exposure[79]
}

for (i in eigen_snps_data[c(308)]){
  eigen_snps_data$snp80=i*weights2$beta.exposure[80]
}

for (i in eigen_snps_data[c(309)]){
  eigen_snps_data$snp81=i*weights2$beta.exposure[81]
}

for (i in eigen_snps_data[c(310)]){
  eigen_snps_data$snp82=i*weights2$beta.exposure[82]
}

for (i in eigen_snps_data[c(311)]){
  eigen_snps_data$snp83=i*weights2$beta.exposure[83]
}

# Create the score 

attach(eigen_snps_data)
colnames(eigen_snps_data)
eigen_snps_data$poly_score2 <- apply(eigen_snps_data[,437:500],1,sum)
summary(eigen_snps_data$poly_score2)

# Principal components for multiple-testing correction

ncomp<-function(data,metabolite.name)   
{   
  tx=data;      
  ptx=scale(tx[,metabolite.name])   
  pca=princomp(~.,data=data.frame(ptx),na.action=na.exclude)    
  var=pca$sdev^2 / sum(pca$sdev^2)  
  cumvar=cumsum(var)    
  n=which(cumvar>=0.99)[1]  
  n 
}   
ncomp(data,smname)
0.05/37 

# Regress metabolites on the poly_score

confint.robust <- function(object, parm, level = 0.95, ...)
{
  cf <- coef(object)
  pnames <- names(cf)
  if (missing(parm))
    parm <- pnames
  else if (is.numeric(parm))
    parm <- pnames[parm]
  a <- (1 - level)/2
  a <- c(a, 1 - a)
  pct <- stats:::format.perc(a, 3)
  fac <- qnorm(a)
  ci <- array(NA, dim = c(length(parm), 2L), dimnames = list(parm,
                                                             pct))
  ses <- sqrt(diag(sandwich::vcovHC(object)))[parm]
  ci[] <- cf[parm] + ses %o% fac
  ci
}

linearRegress_t<-function(metabolite.name,exposure.name,dataset,covariate.name='T',metabolite.log='T',robustSE='T', subset=NULL)    
{   
  ##subseting   
  tx=dataset    
  if(!is.null(subset)) {ind=with(tx,eval(parse(text=subset))); tx=tx[ind,]} 
  
  ##log-transform and scaling the metabolites   
  logname=metabolite.name
  scalename=metabolite.name
  if (metabolite.log=='T') {tx=logtransform(tx,logname)}
  tx[,scalename]=scale(tx[,scalename])
  
  ##linear regression of exposure with metabolites      
  add=numeric() 
  fom=formula(paste('met~',paste(c('poly_score2', covariate.name=c('age', 'one', 'two', 'three', 'four', 'five', 'six',cluster = "centre", adjust =T)),collapse='+')))
  for (j in 1:length(metabolite.name))  
  { 
    met=tx[,metabolite.name[j]];    
    fit=lm(fom,data=tx) 
    if(robustSE=='T'){
      temp=c(nobs(fit),coeftest(fit, vcov =vcovHC(fit,"HC1"))[exposure.name,], confint.robust(fit,exposure.name,level=0.95))
      names(temp)=c('N',names(coeftest(fit, vcov = vcovHC(fit, "HC1"))[exposure.name,]),colnames(confint.robust(fit,exposure.name,level=0.95)))
      
      
    }else{
      temp=c(summary(fit)$coef['poly_score2',],confint(fit)['poly_score2',]);   
    }
    
    add=rbind(add,temp);    
  }     
  rownames(add)=metabolite.name 
  add=data.frame(add,check.names = F)   
  add   
}   
poly_result=linearRegress_t(smname,'poly_score2', eigen_snps_data, covariate.name=c('age','one', 'two', 'three', 'four', 'five', 'six',cluster = "centre", adjust =T),robustSE='T') 

head(poly_result)
poly_result$Metabolite=rownames(poly_result)


myvars=c("Metabolite", "Estimate","Std. Error",  "2.5 %",  "97.5 %", "Pr(>|t|)", "N" )
poly_result=poly_result[myvars]

poly_result <- poly_result[order(poly_result$'Pr(>|t|)'),]

head(poly_result)

write.csv(poly_result, "C:/Users/ca16591/Dropbox/Bristol/polygenic_prostate_predis_total.csv")

# Among just cases
as.factor(eigen_snps_data$casecontrol)

eigen_snps_data_cases <- eigen_snps_data[ which(casecontrol=='Case'),]
dim(eigen_snps_data_cases)

eigen_snps_data_cases

linearRegress_c<-function(metabolite.name,exposure.name,dataset,covariate.name='T',metabolite.log='T',robustSE='T', subset=NULL)    
{   
  ##subseting   
  tx=dataset    
  if(!is.null(subset)) {ind=with(tx,eval(parse(text=subset))); tx=tx[ind,]} 
  
  ##log-transform and scaling the metabolites   
  logname=metabolite.name
  scalename=metabolite.name
  if (metabolite.log=='T') {tx=logtransform(tx,logname)}
  tx[,scalename]=scale(tx[,scalename])
  
  ##linear regression of exposure with metabolites      
  add=numeric() 
  fom=formula(paste('met~',paste(c('poly_score2', covariate.name=c('age', 'one', 'two', 'three', 'four', 'five', 'six',cluster = "centre", adjust =T)),collapse='+')))
  for (j in 1:length(metabolite.name))  
  { 
    met=tx[,metabolite.name[j]];    
    fit=lm(fom,data=tx) 
    if(robustSE=='T'){
      temp=c(nobs(fit),coeftest(fit, vcov =vcovHC(fit,"HC1"))[exposure.name,], confint.robust(fit,exposure.name,level=0.95))
      names(temp)=c('N',names(coeftest(fit, vcov = vcovHC(fit, "HC1"))[exposure.name,]),colnames(confint.robust(fit,exposure.name,level=0.95)))
      
      
    }else{
      temp=c(summary(fit)$coef['poly_score2',],confint(fit)['poly_score2',]);   
    }
    
    add=rbind(add,temp);    
  }     
  rownames(add)=metabolite.name 
  add=data.frame(add,check.names = F)   
  add   
}   
poly_result_cases=linearRegress_c(smname,'poly_score2', eigen_snps_data_cases, covariate.name=c('age','one', 'two', 'three', 'four', 'five', 'six',cluster = "centre", adjust =T),robustSE='T') 

head(poly_result_cases)
poly_result_cases$Metabolite=rownames(poly_result_cases)


myvars=c("Metabolite", "Estimate","Std. Error",  "2.5 %",  "97.5 %", "Pr(>|t|)", "N" )
poly_result_cases=poly_result_cases[myvars]

poly_result_cases <- poly_result_cases[order(poly_result_cases$'Pr(>|t|)'),]

head(poly_result_cases)

write.csv(poly_result_cases, "C:/Users/charl/Dropbox/Bristol/polygenic_prostate_predis_cases.csv")

# Among controls 

as.factor(eigen_snps_data$casecontrol)

eigen_snps_data_controls <- eigen_snps_data[ which(casecontrol=='Control'),]
dim(eigen_snps_data_controls)

poly_result_controls=linearRegress_c(smname,'poly_score2', eigen_snps_data_controls, covariate.name=c('age','one', 'two', 'three', 'four', 'five', 'six',cluster = "centre", adjust =T),robustSE='T')   

head(poly_result_controls)
poly_result_controls$Metabolite=rownames(poly_result_controls)


myvars=c("Metabolite", "Estimate","Std. Error",  "2.5 %",  "97.5 %", "Pr(>|t|)", "N" )
poly_result_controls=poly_result_controls[myvars]

poly_result_controls <- poly_result_controls[order(poly_result_controls$'Pr(>|t|)'),]

head(poly_result_controls)

write.csv(poly_result_controls, "C:/Users/charl/Dropbox/Bristol/polygenic_prostate_predis_controls.csv")

---

Characteristics of the 63 SNPs

SNP	effect_allele.exposure	beta.exposure	se.exposure
rs10009409	T	0.0769610	0.0127551
rs1016343	T	0.2700271	0.0561224
rs1041449	G	0.0582689	0.0102041
rs10486567	G	0.1133287	0.0586735
rs10774740	G	0.1310283	0.0229592
rs10875943	C	0.0676586	0.0153061
rs10896449	G	0.0953102	0.0637755
rs10934853	A	0.1133287	0.0204082
rs10936632	A	0.1043600	0.0153061
rs10993994	T	0.1484200	0.0637755
rs11214775	G	0.0676586	0.0127551
rs11228565	A	0.2070142	0.0382653
rs11650494	A	0.1397619	0.0331633
rs12051443	A	0.0582689	0.0102041
rs12155172	A	0.0487902	0.0255102
rs1218582	G	0.0582689	0.0153061
rs12198220	T	0.1133287	0.0255102
rs12480328	T	0.1222176	0.0178571
rs12597458	G	0.1043600	0.0229592
rs12621278	A	0.2851789	0.0459184
rs1270884	A	0.0676586	0.0153061
rs130067	G	0.0487902	0.0178571
rs1447295	A	0.4700036	0.0867347
rs1465618	T	0.0769610	0.0229592
rs16901979	A	0.5007753	0.1428571
rs16902094	G	0.1906204	0.0280612
rs17021918	C	0.1043600	0.0178571
rs17023900	G	0.2311117	0.0408163
rs17599629	G	0.0769610	0.0127551
rs17694493	G	0.0769610	0.0153061
rs17765344	A	0.1739533	0.0204082
rs1894292	G	0.0953102	0.0153061
rs1933488	A	0.1133287	0.0153061
rs2242652	G	0.1397619	0.0204082
rs2273669	G	0.0676586	0.0204082
rs2292884	G	0.1310283	0.0255102
rs2430386	T	0.1310283	0.0204082
rs2660753	T	0.1655144	0.0637755
rs2735839	G	0.1823216	0.0586735
rs3096702	A	0.0676586	0.0153061
rs3850699	A	0.0953102	0.0153061
rs4242382	A	0.5068176	0.1020408
rs4245739	A	0.0953102	0.0229592
rs4430796	A	0.1655144	0.0714286
rs445114	T	0.1988509	0.0510204
rs4713266	C	0.0582689	0.0102041
rs4962416	C	0.1570037	0.0637755
rs56232506	A	0.0582689	0.0102041
rs6983267	G	0.2468601	0.0765306
rs71277158	T	0.1988509	0.0331633
rs721048	A	0.1397619	0.0280612
rs7679673	C	0.1133287	0.0204082
rs76934034	T	0.1222176	0.0204082
rs7725218	G	0.1397619	0.0229592
rs7758229	T	0.1397619	0.0255102
rs7929962	T	0.1397619	0.0204082
rs80130819	A	0.1310283	0.0229592
rs8014671	G	0.0582689	0.0102041
rs8064454	C	0.2151114	0.0255102
rs8102476	C	0.1133287	0.0178571
rs902774	A	0.1570037	0.0331633
rs9287719	C	0.0582689	0.0102041
rs9364554	T	0.1570037	0.0459184
rs9443189	G	0.0769610	0.0153061

Results for the effects of PCa predisposition score on metabolites

Metabolite	Estimate	2.5%	97.5%	Pval	N
M.LDL.TG..	0.0703696	0.0148762	0.1258631	0.0127910	1592
His	-0.0669785	-0.1237354	-0.0102216	0.0206961	1590
L.LDL.TG..	0.0626723	0.0068230	0.1185216	0.0275231	1594
S.LDL.TG..	0.0581847	0.0023200	0.1140495	0.0408485	1591
Tyr	-0.0554159	-0.1135386	0.0027067	0.0610521	1590
IDL.TG..	0.0540765	-0.0034563	0.1116093	0.0648820	1593
M.LDL.C..	-0.0462568	-0.0969226	0.0044090	0.0732789	1592
M.HDL.CE..	-0.0482979	-0.1025503	0.0059545	0.0803174	1594
M.HDL.PL..	0.0480024	-0.0063793	0.1023842	0.0830342	1594
M.HDL.C..	-0.0476557	-0.1029753	0.0076639	0.0908610	1594
S.LDL.C..	-0.0447814	-0.0973260	0.0077632	0.0945571	1591
M.LDL.CE..	-0.0384309	-0.0838484	0.0069865	0.0969699	1592
L.VLDL.FC..	0.0476500	-0.0094028	0.1047029	0.1011201	1580
L.LDL.C..	-0.0432441	-0.0955871	0.0090990	0.1050519	1594
S.LDL.CE..	-0.0406603	-0.0898802	0.0085595	0.1051861	1591
IDL.C..	-0.0465245	-0.1031170	0.0100681	0.1067829	1593
Alb	-0.0459912	-0.1022035	0.0102211	0.1083249	1594
Val	-0.0474486	-0.1058698	0.0109726	0.1100528	1593
L.HDL.TG..	0.0459410	-0.0106457	0.1025277	0.1112367	1590
Glol	0.1229248	-0.0315016	0.2773513	0.1121471	281
L.HDL.TG	0.0447662	-0.0108790	0.1004113	0.1141614	1590
L.LDL.CE..	-0.0428962	-0.0968840	0.0110917	0.1191215	1594
S.LDL.CE	-0.0419889	-0.0966318	0.0126540	0.1316549	1591
M.LDL.PL..	0.0412435	-0.0135741	0.0960610	0.1398797	1592
M.LDL.CE	-0.0396135	-0.0934895	0.0142624	0.1490906	1592
S.LDL.C	-0.0416610	-0.0984207	0.0150987	0.1497966	1591
S.LDL.PL..	0.0409241	-0.0150584	0.0969067	0.1514028	1591
HDL.TG	0.0409651	-0.0154171	0.0973472	0.1535326	1594
L.VLDL.FC	0.0416349	-0.0169814	0.1002512	0.1628795	1580
M.LDL.C	-0.0401600	-0.0967804	0.0164604	0.1639552	1592
S.HDL.TG..	0.0405310	-0.0169807	0.0980427	0.1663645	1594
S.LDL.FC	-0.0405157	-0.0985588	0.0175274	0.1707241	1591
XS.VLDL.TG..	0.0390836	-0.0182145	0.0963816	0.1805149	1594
S.VLDL.C..	-0.0376308	-0.0944723	0.0192107	0.1938413	1594
L.VLDL.C	0.0384687	-0.0204472	0.0973847	0.1994149	1580
M.HDL.TG..	0.0380451	-0.0203150	0.0964052	0.2007183	1594
L.VLDL.PL	0.0376704	-0.0209862	0.0963270	0.2069550	1580
S.VLDL.CE..	-0.0358790	-0.0917980	0.0200400	0.2079792	1594
L.VLDL.L	0.0375085	-0.0212633	0.0962804	0.2098337	1580
S.LDL.L	-0.0369697	-0.0949329	0.0209936	0.2106567	1591
L.VLDL.P	0.0372951	-0.0214855	0.0960756	0.2125098	1580
IDL.CE..	-0.0356672	-0.0918976	0.0205632	0.2134255	1593
L.VLDL.CE	0.0369370	-0.0219659	0.0958399	0.2177294	1580
S.VLDL.TG..	0.0362985	-0.0215291	0.0941261	0.2178635	1594
L.VLDL.TG	0.0367375	-0.0219936	0.0954685	0.2190760	1580
M.LDL.FC	-0.0361573	-0.0942855	0.0219709	0.2221486	1592
M.LDL.L	-0.0359521	-0.0937653	0.0218612	0.2222784	1592
XXL.VLDL.FC..	0.0367765	-0.0228861	0.0964390	0.2253122	1532
M.VLDL.PL..	-0.0373996	-0.0980130	0.0232138	0.2258811	1594
S.LDL.P	-0.0355414	-0.0936254	0.0225426	0.2297721	1591
S.HDL.C	-0.0325232	-0.0864547	0.0214084	0.2366505	1594
M.LDL.P	-0.0347410	-0.0926385	0.0231565	0.2389230	1592
TotCho	-0.0464416	-0.1245495	0.0316662	0.2426456	1591
LDL.C	-0.0336773	-0.0903134	0.0229587	0.2432448	1594
FAw3.FA	-0.0342072	-0.0921855	0.0237710	0.2449264	1590
L.HDL.PL..	-0.0321253	-0.0877125	0.0234619	0.2566463	1590
L.LDL.CE	-0.0327512	-0.0899905	0.0244880	0.2615010	1594
S.HDL.TG	0.0332406	-0.0251283	0.0916094	0.2630529	1594
M.LDL.FC..	0.0323394	-0.0246342	0.0893129	0.2652554	1592
L.LDL.C	-0.0323646	-0.0895749	0.0248457	0.2669188	1594
MUFA.FA	0.0332420	-0.0255855	0.0920695	0.2671005	1590
PUFA.FA	-0.0319827	-0.0887054	0.0247400	0.2677234	1590
Gly	0.0322751	-0.0250010	0.0895512	0.2688483	1591
LDL.D	0.0294551	-0.0240995	0.0830097	0.2804495	1594
L.LDL.FC	-0.0311883	-0.0879708	0.0255941	0.2810879	1594
M.HDL.FC..	-0.0304749	-0.0871008	0.0261509	0.2909168	1594
XL.HDL.C..	-0.0310099	-0.0887805	0.0267608	0.2915462	1594
EstC	-0.0309219	-0.0891432	0.0272995	0.2972286	1591
M.HDL.TG	0.0310636	-0.0276707	0.0897978	0.2988275	1594
L.LDL.L	-0.0301333	-0.0878973	0.0276308	0.3059320	1594
FAw3	-0.0309143	-0.0904818	0.0286533	0.3061027	1590
L.LDL.PL..	0.0291932	-0.0268260	0.0852124	0.3065456	1594
M.HDL.CE	-0.0297266	-0.0872579	0.0278047	0.3105941	1594
XS.VLDL.C..	-0.0293287	-0.0862804	0.0276231	0.3121246	1594
S.HDL.CE	-0.0264462	-0.0781344	0.0252421	0.3153689	1594
L.LDL.PL	-0.0292444	-0.0874144	0.0289255	0.3237603	1594
PC	-0.0309646	-0.0932433	0.0313142	0.3287136	1591
L.LDL.P	-0.0288082	-0.0867117	0.0290953	0.3288428	1594
M.HDL.C	-0.0286092	-0.0863488	0.0291304	0.3308323	1594
UnSat	-0.0280872	-0.0850954	0.0289209	0.3332025	1590
S.LDL.PL	-0.0281983	-0.0868144	0.0304178	0.3449380	1591
XL.HDL.TG	0.0271160	-0.0297550	0.0839870	0.3493629	1594
Leu	-0.0270310	-0.0843784	0.0303164	0.3534876	1594
TotPG	-0.0282099	-0.0880501	0.0316302	0.3541618	1591
IDL.C	-0.0268786	-0.0846638	0.0309066	0.3613156	1593
M.LDL.PL	-0.0272598	-0.0860506	0.0315310	0.3627082	1592
IDL.CE	-0.0266807	-0.0846767	0.0313152	0.3666162	1593
XL.HDL.CE..	-0.0260917	-0.0833104	0.0311269	0.3701611	1594
Serum.TG	0.0262884	-0.0316941	0.0842709	0.3730715	1594
XXL.VLDL.C	0.0248388	-0.0302109	0.0798884	0.3754027	1532
Serum.C	-0.0262063	-0.0843422	0.0319297	0.3762592	1594
VLDL.D	0.0253812	-0.0322415	0.0830038	0.3871156	1594
XXL.VLDL.FC	0.0249475	-0.0317614	0.0816565	0.3874355	1532
XS.VLDL.PL..	-0.0228220	-0.0748187	0.0291748	0.3889056	1594
XL.VLDL.P	0.0262018	-0.0336315	0.0860351	0.3895841	1555
M.VLDL.CE..	-0.0251352	-0.0824886	0.0322182	0.3896857	1594
DHA	-0.0260493	-0.0859623	0.0338637	0.3906524	1590
IDL.PL	-0.0252808	-0.0831036	0.0325419	0.3907884	1593
XL.VLDL.TG	0.0260992	-0.0336551	0.0858535	0.3907907	1555
XL.HDL.FC..	-0.0233402	-0.0769964	0.0303160	0.3935225	1594
IDL.TG	0.0256294	-0.0334699	0.0847287	0.3940500	1593
XS.VLDL.CE..	-0.0245886	-0.0814462	0.0322690	0.3960363	1594
XL.VLDL.L	0.0258346	-0.0340031	0.0856724	0.3963063	1555
M.HDL.FC	-0.0249227	-0.0828791	0.0330337	0.3985131	1594
IDL.L	-0.0248943	-0.0828483	0.0330597	0.3992048	1594
PUFA	-0.0253358	-0.0844393	0.0337678	0.3998496	1590
S.HDL.C..	-0.0224152	-0.0747149	0.0298845	0.4000557	1594
FAw6.FA	-0.0241067	-0.0813386	0.0331252	0.4057813	1590
L.VLDL.PL..	0.0239058	-0.0328803	0.0806919	0.4083744	1580
XXL.VLDL.C..	0.0234249	-0.0327648	0.0796147	0.4124055	1532
bOHBut	0.0240081	-0.0347927	0.0828089	0.4224754	1577
IDL.FC	-0.0225542	-0.0784582	0.0333498	0.4284457	1593
S.HDL.L	-0.0237860	-0.0835350	0.0359631	0.4342943	1594
LA.FA	-0.0235991	-0.0836875	0.0364893	0.4359183	1590
XS.VLDL.TG	0.0233090	-0.0357404	0.0823585	0.4377408	1594
VLDL.TG	0.0229421	-0.0352091	0.0810934	0.4383276	1594
M.VLDL.FC..	0.0229837	-0.0352784	0.0812457	0.4383898	1594
IDL.P	-0.0228717	-0.0809536	0.0352101	0.4395921	1594
XXL.VLDL.CE	0.0209040	-0.0323381	0.0741461	0.4405942	1532
L.VLDL.TG..	-0.0219164	-0.0790067	0.0351738	0.4504391	1580
DHA.FA	-0.0217418	-0.0793973	0.0359136	0.4574811	1590
S.HDL.P	-0.0224880	-0.0824377	0.0374617	0.4612362	1594
SFA.FA	0.0221511	-0.0374842	0.0817865	0.4648249	1590
FAw6	-0.0217346	-0.0805662	0.0370970	0.4681617	1590
L.VLDL.C..	0.0211976	-0.0363224	0.0787177	0.4690186	1580
S.VLDL.TG	0.0215614	-0.0370572	0.0801800	0.4698176	1594
XL.VLDL.CE	0.0225872	-0.0390591	0.0842335	0.4716543	1555
M.VLDL.TG..	0.0207443	-0.0359120	0.0774006	0.4722868	1594
XL.VLDL.PL	0.0216329	-0.0378066	0.0810725	0.4746159	1555
AcAce	0.0212497	-0.0373106	0.0798099	0.4758175	1594
S.HDL.PL..	0.0197010	-0.0348522	0.0742541	0.4781895	1594
S.HDL.FC	-0.0211447	-0.0798298	0.0375404	0.4788761	1594
S.HDL.CE..	-0.0181769	-0.0689369	0.0325830	0.4820556	1594
LA	-0.0212019	-0.0805678	0.0381640	0.4826474	1590
M.VLDL.TG	0.0207182	-0.0373802	0.0788166	0.4836326	1594
S.LDL.FC..	0.0202247	-0.0371823	0.0776318	0.4889799	1591
ApoA1	-0.0199688	-0.0775573	0.0376196	0.4958991	1594
XL.VLDL.C..	-0.0254682	-0.0990438	0.0481074	0.4960244	1555
M.VLDL.C..	-0.0198268	-0.0771578	0.0375041	0.4972311	1594
L.LDL.TG	0.0201781	-0.0384064	0.0787625	0.4985534	1594
LDL.TG	0.0200655	-0.0384538	0.0785848	0.5004762	1594
XL.HDL.PL..	0.0197415	-0.0380135	0.0774964	0.5021209	1594
M.VLDL.FC	0.0198337	-0.0383640	0.0780313	0.5031830	1594
XL.VLDL.C	0.0207218	-0.0409963	0.0824400	0.5095383	1555
M.HDL.L	-0.0195458	-0.0777768	0.0386853	0.5098331	1594
M.VLDL.P	0.0192940	-0.0388635	0.0774515	0.5145994	1594
M.VLDL.L	0.0191473	-0.0390415	0.0773360	0.5180202	1594
Cit	-0.0190495	-0.0773645	0.0392655	0.5211553	1593
XS.VLDL.FC..	-0.0169552	-0.0689179	0.0350075	0.5221903	1594
S.LDL.TG	0.0187690	-0.0394086	0.0769467	0.5261324	1591
FreeC	-0.0184689	-0.0766467	0.0397089	0.5331860	1591
SM	-0.0187193	-0.0783598	0.0409212	0.5376182	1590
M.LDL.TG	0.0183592	-0.0401994	0.0769178	0.5379596	1592
XL.VLDL.CE..	-0.0198086	-0.0833329	0.0437157	0.5394757	1555
XXL.VLDL.L	0.0174466	-0.0387951	0.0736883	0.5422042	1532
L.HDL.FC..	-0.0171265	-0.0728232	0.0385702	0.5462285	1590
M.VLDL.PL	0.0177987	-0.0402952	0.0758926	0.5472489	1594
M.HDL.P	-0.0177575	-0.0760351	0.0405201	0.5495895	1594
Ile	-0.0173991	-0.0750018	0.0402036	0.5524687	1594
TG.PG	0.0174196	-0.0404273	0.0752666	0.5542676	1591
HDL2.C	-0.0173114	-0.0749378	0.0403150	0.5553599	1594
HDL.C	-0.0172429	-0.0747694	0.0402835	0.5561728	1594
XL.HDL.TG..	0.0170114	-0.0398471	0.0738700	0.5569562	1594
XXL.VLDL.P	0.0167385	-0.0395239	0.0730008	0.5588563	1532
XXL.VLDL.TG..	-0.0155933	-0.0691403	0.0379537	0.5670774	1532
Gp	0.0162658	-0.0398789	0.0724104	0.5695404	1594
XL.VLDL.FC	0.0174663	-0.0434369	0.0783696	0.5732011	1555
XL.HDL.PL	0.0159513	-0.0404352	0.0723378	0.5785602	1594
XS.VLDL.CE	-0.0160175	-0.0739194	0.0418844	0.5870088	1594
XS.VLDL.C	-0.0159576	-0.0739284	0.0420132	0.5888117	1594
XXL.VLDL.PL..	-0.0111525	-0.0529977	0.0306927	0.6003911	1532
XXL.VLDL.TG	0.0147563	-0.0414555	0.0709681	0.6060002	1532
M.VLDL.C	0.0152000	-0.0430640	0.0734640	0.6082898	1594
Pyr	0.0148866	-0.0423649	0.0721380	0.6094556	1593
S.VLDL.FC..	-0.0144791	-0.0715189	0.0425607	0.6180987	1594
Phe	-0.0137205	-0.0683143	0.0408734	0.6220226	1594
IDL.FC..	-0.0125022	-0.0630601	0.0380557	0.6272322	1593
XS.VLDL.PL	-0.0141040	-0.0713243	0.0431163	0.6284895	1594
XS.VLDL.FC	-0.0140191	-0.0712885	0.0432503	0.6307116	1594
M.HDL.PL	-0.0140711	-0.0722949	0.0441526	0.6349668	1594
L.VLDL.CE..	-0.0136343	-0.0702075	0.0429389	0.6360970	1580
IDL.PL..	-0.0129261	-0.0675166	0.0416644	0.6409260	1593
S.VLDL.PL	0.0138262	-0.0453811	0.0730336	0.6461044	1594
Crea	-0.0133863	-0.0711478	0.0443751	0.6489129	1589
S.VLDL.CE	-0.0132937	-0.0717914	0.0452041	0.6553425	1594
S.VLDL.P	0.0132686	-0.0456796	0.0722167	0.6581819	1594
XXL.VLDL.PL	0.0118792	-0.0420191	0.0657775	0.6649373	1532
XL.HDL.P	0.0120611	-0.0437373	0.0678594	0.6712730	1594
HDL3.C	-0.0115099	-0.0684726	0.0454528	0.6911880	1594
S.VLDL.L	0.0117089	-0.0472772	0.0706949	0.6964004	1594
XL.HDL.L	0.0108024	-0.0449102	0.0665149	0.7034318	1594
Remnant.C	-0.0108727	-0.0692336	0.0474882	0.7144786	1594
XL.VLDL.FC..	-0.0114490	-0.0773063	0.0544082	0.7328458	1555
M.VLDL.CE	0.0101086	-0.0481732	0.0683904	0.7333188	1594
S.VLDL.FC	0.0102070	-0.0490219	0.0694360	0.7347145	1594
Ace	0.0108311	-0.0530686	0.0747308	0.7368344	1594
Gln	0.0095288	-0.0479938	0.0670514	0.7437143	1594
ApoB	-0.0097365	-0.0683314	0.0488585	0.7442134	1594
L.LDL.FC..	-0.0087436	-0.0632917	0.0458044	0.7529391	1594
Ala	-0.0084842	-0.0656308	0.0486624	0.7707999	1594
TotFA	-0.0081167	-0.0662898	0.0500564	0.7839437	1590
XS.VLDL.L	-0.0080520	-0.0667303	0.0506263	0.7875014	1594
XXL.VLDL.CE..	0.0068574	-0.0484381	0.0621529	0.8075052	1532
S.HDL.PL	-0.0063844	-0.0646174	0.0518486	0.8294603	1594
L.HDL.FC	-0.0058406	-0.0629396	0.0512584	0.8408406	1590
S.VLDL.C	-0.0058729	-0.0650374	0.0532917	0.8453350	1594
VLDL.C	0.0055813	-0.0526980	0.0638605	0.8507190	1594
HDL.D	0.0052755	-0.0518163	0.0623672	0.8560351	1594
L.HDL.C..	-0.0051930	-0.0626093	0.0522233	0.8591059	1590
XS.VLDL.P	-0.0053062	-0.0641343	0.0535220	0.8593505	1594
XL.HDL.FC	0.0045171	-0.0501520	0.0591861	0.8711460	1594
L.HDL.PL	-0.0046451	-0.0623431	0.0530529	0.8743853	1590
MUFA	0.0045969	-0.0534832	0.0626770	0.8763847	1590
SFA	-0.0044571	-0.0621068	0.0531925	0.8791944	1590
S.VLDL.PL..	0.0040566	-0.0532658	0.0613789	0.8894822	1594
Lac	-0.0032654	-0.0572988	0.0507681	0.9055688	1594
XL.VLDL.PL..	-0.0030554	-0.0583716	0.0522608	0.9136519	1555
XL.HDL.C	0.0029256	-0.0522969	0.0581481	0.9171687	1594
XL.HDL.CE	0.0025053	-0.0528153	0.0578259	0.9291581	1594
L.HDL.CE..	0.0023915	-0.0539404	0.0587234	0.9336055	1590
Glc	0.0020966	-0.0547812	0.0589745	0.9423423	1589
S.HDL.FC..	0.0015774	-0.0539256	0.0570804	0.9554563	1594
XL.VLDL.TG..	0.0016203	-0.0571629	0.0604034	0.9568501	1555
L.HDL.C	-0.0009837	-0.0584154	0.0564480	0.9731711	1590
ApoB.ApoA1	-0.0007674	-0.0589024	0.0573676	0.9793219	1594
L.HDL.P	0.0007222	-0.0568225	0.0582668	0.9803383	1590
L.HDL.CE	0.0002180	-0.0572031	0.0576391	0.9940520	1590
L.HDL.L	-0.0002067	-0.0577351	0.0573217	0.9943704	1590

---

Among cases, results for the effects of PCa predisposition score on metabolites

Metabolite	Estimate	2.5%	97.5%	Pval	N
His	-0.0798869	-0.1571298	-0.0026439	0.0423154	938
Alb	-0.0656477	-0.1368580	0.0055627	0.0704714	940
FAw3.FA	-0.0542994	-0.1271083	0.0185094	0.1394054	938
Val	-0.0547475	-0.1308975	0.0214025	0.1567463	939
Pyr	0.0528507	-0.0218764	0.1275778	0.1639532	940
Leu	-0.0514415	-0.1254367	0.0225537	0.1714033	940
L.VLDL.FC..	0.0527775	-0.0260472	0.1316022	0.1879452	929
M.HDL.PL..	0.0424164	-0.0289728	0.1138056	0.2427621	940
S.HDL.FC	-0.0446311	-0.1205042	0.0312419	0.2467184	940
Ile	-0.0429705	-0.1169666	0.0310256	0.2530036	940
FAw3	-0.0423282	-0.1185668	0.0339105	0.2708006	938
Tyr	-0.0412977	-0.1151160	0.0325205	0.2711492	938
S.HDL.P	-0.0402187	-0.1187783	0.0383410	0.3133887	940
S.HDL.L	-0.0401044	-0.1184700	0.0382611	0.3136356	940
DHA.FA	-0.0369329	-0.1095948	0.0357289	0.3136775	938
L.VLDL.FC	0.0384147	-0.0385659	0.1153952	0.3258290	929
LDL.D	0.0334094	-0.0341169	0.1009356	0.3310632	940
Cit	-0.0351116	-0.1071766	0.0369533	0.3369745	939
Glol	0.0919520	-0.1066941	0.2905980	0.3463224	176
DHA	-0.0355600	-0.1107662	0.0396462	0.3469911	938
L.HDL.TG	0.0316754	-0.0381294	0.1014803	0.3720829	939
L.VLDL.C	0.0338254	-0.0427458	0.1103965	0.3842731	929
M.HDL.FC..	-0.0321991	-0.1070782	0.0426800	0.3975188	940
XL.HDL.TG	0.0313631	-0.0417924	0.1045186	0.3993787	940
L.VLDL.PL	0.0322971	-0.0438789	0.1084732	0.4037024	929
L.VLDL.CE	0.0323216	-0.0441405	0.1087838	0.4050582	929
M.LDL.TG..	0.0284732	-0.0388852	0.0958316	0.4051325	938
L.VLDL.L	0.0320747	-0.0442232	0.1083726	0.4077692	929
XL.HDL.P	0.0283847	-0.0394445	0.0962139	0.4105244	940
L.VLDL.P	0.0317441	-0.0445458	0.1080339	0.4125779	929
M.HDL.C..	-0.0296504	-0.1015576	0.0422568	0.4173002	940
XL.HDL.L	0.0276524	-0.0400058	0.0953105	0.4215457	940
XL.VLDL.CE..	-0.0347197	-0.1201753	0.0507358	0.4230712	912
L.VLDL.TG	0.0309566	-0.0452570	0.1071702	0.4238575	929
M.HDL.CE	-0.0298459	-0.1034855	0.0437938	0.4257297	940
M.HDL.C	-0.0298611	-0.1037582	0.0440361	0.4269778	940
S.HDL.C	-0.0280467	-0.0975800	0.0414865	0.4275983	940
M.HDL.FC	-0.0300067	-0.1045634	0.0445500	0.4283469	940
UnSat	-0.0282307	-0.0990855	0.0426241	0.4325091	938
M.LDL.CE..	-0.0243310	-0.0862856	0.0376236	0.4405648	938
L.HDL.TG..	0.0275301	-0.0443544	0.0994146	0.4516246	939
Phe	-0.0264729	-0.0956265	0.0426808	0.4522564	940
XL.HDL.CE	0.0253018	-0.0415595	0.0921632	0.4569591	940
M.HDL.CE..	-0.0265063	-0.0971995	0.0441869	0.4601547	940
S.HDL.PL	-0.0277287	-0.1019323	0.0464750	0.4621510	940
XL.HDL.C	0.0248334	-0.0419185	0.0915854	0.4645955	940
L.VLDL.TG..	-0.0270267	-0.1008760	0.0468225	0.4712156	929
XL.HDL.PL	0.0254204	-0.0442102	0.0950511	0.4724944	940
M.HDL.L	-0.0265771	-0.1004372	0.0472830	0.4789234	940
XS.VLDL.CE..	0.0250898	-0.0447520	0.0949316	0.4796217	940
L.VLDL.C..	0.0265014	-0.0484107	0.1014134	0.4865458	929
XL.HDL.FC	0.0232016	-0.0430716	0.0894748	0.4913641	940
HDL.TG	0.0246883	-0.0465023	0.0958788	0.4943081	940
M.HDL.P	-0.0255748	-0.0994273	0.0482777	0.4955415	940
XL.VLDL.TG	0.0266312	-0.0509714	0.1042339	0.4985506	912
L.HDL.PL..	-0.0243748	-0.0952184	0.0464689	0.4991901	939
XL.VLDL.P	0.0262140	-0.0513578	0.1037858	0.5051733	912
M.HDL.TG..	0.0250736	-0.0494787	0.0996260	0.5081591	940
XL.VLDL.L	0.0259579	-0.0515973	0.1035131	0.5092688	912
XS.VLDL.FC..	-0.0218304	-0.0879204	0.0442596	0.5164659	940
S.LDL.TG..	0.0222156	-0.0454665	0.0898977	0.5176707	938
bOHBut	0.0243008	-0.0499246	0.0985263	0.5196779	936
S.HDL.TG..	0.0237310	-0.0491801	0.0966421	0.5213439	940
XL.VLDL.C..	-0.0335425	-0.1371231	0.0700382	0.5238450	912
XL.VLDL.PL	0.0247395	-0.0533536	0.1028326	0.5322890	912
L.VLDL.PL..	0.0225472	-0.0495282	0.0946227	0.5379187	929
M.HDL.PL	-0.0226484	-0.0961461	0.0508492	0.5439361	940
S.LDL.CE..	-0.0178271	-0.0783242	0.0426700	0.5626571	938
S.HDL.CE	-0.0194307	-0.0859348	0.0470733	0.5655861	940
Crea	0.0208613	-0.0516705	0.0933930	0.5714970	937
M.LDL.C..	-0.0177021	-0.0798877	0.0444834	0.5757675	938
M.VLDL.PL..	-0.0213847	-0.0969443	0.0541749	0.5780839	940
XL.VLDL.FC	0.0220276	-0.0572715	0.1013266	0.5842242	912
XXL.VLDL.CE	0.0182999	-0.0486631	0.0852630	0.5901120	899
S.HDL.FC..	-0.0192814	-0.0897412	0.0511783	0.5905116	940
XXL.VLDL.C	0.0185977	-0.0496134	0.0868088	0.5907546	899
XL.HDL.C..	-0.0197437	-0.0937422	0.0542548	0.5993990	940
MUFA.FA	0.0197233	-0.0549415	0.0943881	0.6028175	938
XXL.VLDL.FC	0.0181719	-0.0519443	0.0882882	0.6091869	899
XXL.VLDL.TG	0.0180459	-0.0524532	0.0885450	0.6140443	899
XXL.VLDL.L	0.0177940	-0.0527812	0.0883693	0.6192294	899
XL.HDL.CE..	-0.0184936	-0.0918002	0.0548131	0.6194244	940
XXL.VLDL.P	0.0177517	-0.0528863	0.0883898	0.6203924	899
XL.VLDL.C	0.0189999	-0.0617732	0.0997730	0.6429935	912
PUFA.FA	-0.0161836	-0.0858681	0.0535010	0.6471710	938
XL.VLDL.CE	0.0178815	-0.0625297	0.0982927	0.6612405	912
S.LDL.C..	-0.0138270	-0.0760558	0.0484018	0.6623017	938
HDL.D	0.0150888	-0.0566769	0.0868545	0.6791163	940
M.LDL.CE	-0.0143197	-0.0824335	0.0537941	0.6796647	938
S.LDL.CE	-0.0134954	-0.0804234	0.0534327	0.6920753	938
XXL.VLDL.PL	0.0127105	-0.0537822	0.0792031	0.7062717	899
XXL.VLDL.PL..	-0.0086770	-0.0552094	0.0378555	0.7133417	899
L.LDL.TG..	0.0124027	-0.0545156	0.0793210	0.7149788	940
L.LDL.FC..	0.0128063	-0.0564314	0.0820439	0.7152231	940
M.LDL.PL..	0.0121373	-0.0535306	0.0778052	0.7165044	938
ApoA1	-0.0129730	-0.0838075	0.0578616	0.7184051	940
AcAce	0.0136687	-0.0612093	0.0885468	0.7194201	940
M.LDL.TG	0.0132125	-0.0595254	0.0859505	0.7205566	938
XS.VLDL.C..	0.0125002	-0.0582295	0.0832299	0.7277539	940
LDL.TG	0.0127254	-0.0603745	0.0858254	0.7315812	940
L.LDL.TG	0.0125981	-0.0602056	0.0854018	0.7331459	940
M.LDL.FC..	0.0120662	-0.0584854	0.0826177	0.7368956	938
S.LDL.FC	-0.0120911	-0.0834409	0.0592587	0.7390928	938
M.HDL.TG	0.0121385	-0.0627330	0.0870100	0.7493216	940
PUFA	-0.0122203	-0.0877430	0.0633024	0.7500960	938
XS.VLDL.CE	0.0114365	-0.0595808	0.0824538	0.7513262	940
S.LDL.C	-0.0108468	-0.0795944	0.0579007	0.7566143	938
L.HDL.FC..	0.0107209	-0.0579299	0.0793716	0.7585768	939
Gly	-0.0114872	-0.0852342	0.0622598	0.7592184	938
M.LDL.C	-0.0106597	-0.0800139	0.0586946	0.7627124	938
HDL.C	-0.0109418	-0.0823486	0.0604649	0.7628912	940
S.HDL.TG	0.0113444	-0.0629958	0.0856846	0.7633913	940
XL.HDL.PL..	0.0112613	-0.0631937	0.0857162	0.7658338	940
SFA.FA	0.0105485	-0.0618089	0.0829059	0.7742803	938
S.LDL.TG	0.0107872	-0.0637315	0.0853060	0.7754035	938
S.VLDL.FC..	-0.0102282	-0.0824091	0.0619527	0.7803827	940
Ala	-0.0098104	-0.0807740	0.0611532	0.7859961	940
HDL3.C	-0.0097031	-0.0803174	0.0609111	0.7860674	940
Serum.TG	0.0102985	-0.0645599	0.0851568	0.7862945	940
S.LDL.PL..	0.0091303	-0.0576174	0.0758780	0.7881259	938
XL.VLDL.PL..	0.0107587	-0.0680992	0.0896166	0.7883962	912
Lac	-0.0096582	-0.0806170	0.0613005	0.7885844	940
XXL.VLDL.FC..	0.0095485	-0.0619854	0.0810824	0.7915782	899
HDL2.C	-0.0095160	-0.0811883	0.0621564	0.7938702	940
S.LDL.L	-0.0091507	-0.0802389	0.0619375	0.8003299	938
XXL.VLDL.TG..	0.0084747	-0.0583022	0.0752516	0.8028138	899
IDL.TG	0.0092104	-0.0644049	0.0828258	0.8051445	940
XL.HDL.TG..	0.0089403	-0.0631022	0.0809828	0.8070900	940
S.LDL.P	-0.0086693	-0.0800638	0.0627251	0.8114156	938
L.HDL.C..	0.0085625	-0.0624962	0.0796212	0.8127755	939
L.HDL.CE..	0.0083223	-0.0614083	0.0780530	0.8146699	939
ApoB.ApoA1	0.0087139	-0.0650397	0.0824676	0.8162505	940
M.LDL.L	-0.0083158	-0.0792927	0.0626611	0.8179487	938
M.LDL.P	-0.0082210	-0.0793464	0.0629045	0.8203546	938
M.VLDL.CE	0.0085682	-0.0667876	0.0839239	0.8228378	940
L.VLDL.CE..	-0.0079252	-0.0802738	0.0644234	0.8296421	929
S.LDL.PL	-0.0080395	-0.0818481	0.0657690	0.8303630	938
VLDL.D	0.0077415	-0.0652597	0.0807427	0.8347199	940
IDL.C..	0.0067848	-0.0585244	0.0720940	0.8383113	940
VLDL.TG	0.0071246	-0.0679451	0.0821944	0.8516934	940
TotPG	-0.0067765	-0.0789193	0.0653663	0.8531068	938
S.VLDL.TG..	0.0067871	-0.0660296	0.0796039	0.8544436	940
L.HDL.FC	0.0066316	-0.0645613	0.0778244	0.8545785	939
M.VLDL.CE..	0.0065820	-0.0643406	0.0775046	0.8552568	940
M.LDL.FC	-0.0065955	-0.0778995	0.0647086	0.8557786	938
IDL.FC	0.0058761	-0.0601855	0.0719377	0.8612898	940
XS.VLDL.PL..	-0.0054745	-0.0689653	0.0580164	0.8652125	940
M.VLDL.C	0.0064095	-0.0690741	0.0818930	0.8671770	940
EstC	-0.0059836	-0.0774327	0.0654655	0.8692504	938
IDL.C	0.0058270	-0.0641497	0.0758037	0.8700786	940
XS.VLDL.C	0.0059065	-0.0653318	0.0771449	0.8703794	940
IDL.PL	0.0057402	-0.0642854	0.0757658	0.8720776	940
S.HDL.C..	-0.0052954	-0.0706629	0.0600721	0.8733842	940
Ace	-0.0052221	-0.0717803	0.0613362	0.8740702	940
IDL.CE	0.0056951	-0.0651295	0.0765196	0.8744944	940
IDL.P	0.0056810	-0.0654540	0.0768159	0.8753108	940
L.HDL.C	0.0057082	-0.0659567	0.0773731	0.8754785	939
IDL.L	0.0055656	-0.0651891	0.0763203	0.8771902	940
L.LDL.C..	0.0047773	-0.0560733	0.0656278	0.8775026	940
L.HDL.CE	0.0055953	-0.0660298	0.0772204	0.8778582	939
VLDL.C	0.0057838	-0.0693584	0.0809259	0.8794276	940
S.HDL.PL..	0.0052128	-0.0625619	0.0729875	0.8798451	940
XS.VLDL.FC	-0.0053281	-0.0762118	0.0655556	0.8824095	940
L.HDL.P	0.0052307	-0.0666650	0.0771265	0.8861452	939
Remnant.C	0.0053224	-0.0685854	0.0792303	0.8873198	940
M.VLDL.C..	0.0050170	-0.0663709	0.0764049	0.8900979	940
M.LDL.PL	-0.0051401	-0.0787366	0.0684563	0.8907989	938
M.VLDL.FC..	-0.0050382	-0.0783420	0.0682655	0.8922998	940
L.HDL.L	0.0048538	-0.0670277	0.0767354	0.8942829	939
L.LDL.FC	0.0046172	-0.0636339	0.0728684	0.8942872	940
XL.HDL.FC..	-0.0043905	-0.0692802	0.0604992	0.8943094	940
FreeC	0.0047743	-0.0665654	0.0761140	0.8953525	938
M.VLDL.L	0.0050041	-0.0702424	0.0802505	0.8957918	940
M.VLDL.TG	0.0049312	-0.0700118	0.0798741	0.8968994	940
M.VLDL.P	0.0049449	-0.0702429	0.0801328	0.8969388	940
LA	-0.0050225	-0.0816557	0.0716107	0.8973914	938
IDL.CE..	0.0044163	-0.0653047	0.0741372	0.9010518	940
SM	0.0047479	-0.0708508	0.0803466	0.9015405	938
XL.VLDL.TG..	0.0050618	-0.0775806	0.0877042	0.9041608	912
L.LDL.PL..	-0.0039244	-0.0707173	0.0628684	0.9081851	940
IDL.TG..	0.0040163	-0.0648673	0.0728998	0.9085493	940
M.VLDL.PL	0.0040652	-0.0711448	0.0792751	0.9152077	940
M.VLDL.FC	0.0038877	-0.0712762	0.0790517	0.9188487	940
ApoB	0.0038529	-0.0708861	0.0785918	0.9192296	940
PC	-0.0037205	-0.0762711	0.0688301	0.9195796	938
Glc	0.0036059	-0.0674932	0.0747050	0.9206899	938
MUFA	0.0037155	-0.0714491	0.0788801	0.9223494	938
XL.VLDL.FC..	0.0042619	-0.0820039	0.0905278	0.9225977	912
Gln	0.0034677	-0.0679117	0.0748471	0.9234774	940
S.VLDL.C..	-0.0033997	-0.0736808	0.0668814	0.9241936	940
LA.FA	-0.0035016	-0.0786547	0.0716515	0.9260639	938
TotFA	-0.0032321	-0.0786229	0.0721588	0.9326569	938
XS.VLDL.P	0.0027774	-0.0707542	0.0763091	0.9407055	940
XS.VLDL.L	0.0027403	-0.0703185	0.0757992	0.9411315	940
IDL.FC..	0.0021425	-0.0555984	0.0598835	0.9417212	940
S.VLDL.FC	-0.0028179	-0.0791849	0.0735491	0.9419504	940
S.HDL.CE..	-0.0023584	-0.0664279	0.0617111	0.9422760	940
S.VLDL.C	-0.0026974	-0.0778615	0.0724667	0.9436224	940
FAw6	-0.0026668	-0.0776019	0.0722683	0.9442172	938
XS.VLDL.TG	0.0023887	-0.0730102	0.0777877	0.9501486	940
L.LDL.C	0.0021809	-0.0671192	0.0714811	0.9507171	940
FAw6.FA	0.0021907	-0.0681194	0.0725008	0.9508374	938
TotCho	-0.0021425	-0.0743178	0.0700327	0.9533855	938
L.HDL.PL	0.0018936	-0.0702705	0.0740576	0.9588108	939
SFA	-0.0018825	-0.0764412	0.0726762	0.9602894	938
S.VLDL.CE..	0.0016601	-0.0669655	0.0702857	0.9620647	940
XXL.VLDL.C..	0.0015749	-0.0672773	0.0704271	0.9640140	899
S.VLDL.TG	0.0015925	-0.0742362	0.0774211	0.9669795	940
Serum.C	-0.0013670	-0.0727267	0.0699927	0.9699564	940
L.LDL.CE	0.0012901	-0.0683030	0.0708831	0.9709602	940
L.LDL.P	0.0012609	-0.0695283	0.0720501	0.9720884	940
XS.VLDL.PL	0.0012532	-0.0694240	0.0719304	0.9721923	940
L.LDL.PL	0.0011977	-0.0701743	0.0725697	0.9736995	940
L.LDL.L	0.0011014	-0.0693305	0.0715332	0.9754960	940
TG.PG	0.0009772	-0.0734311	0.0753855	0.9793729	938
S.VLDL.PL..	0.0009485	-0.0724216	0.0743185	0.9797199	940
S.VLDL.L	-0.0009818	-0.0772446	0.0752809	0.9797447	940
M.VLDL.TG..	0.0008658	-0.0697226	0.0714543	0.9807528	940
S.VLDL.PL	-0.0008566	-0.0774552	0.0757421	0.9823915	940
S.LDL.FC..	-0.0007891	-0.0719715	0.0703934	0.9826146	938
S.VLDL.P	-0.0007526	-0.0770305	0.0755253	0.9844770	940
Gp	0.0006997	-0.0708737	0.0722730	0.9846507	940
S.VLDL.CE	0.0006845	-0.0725437	0.0739126	0.9853236	940
L.LDL.CE..	-0.0005477	-0.0658111	0.0647156	0.9868642	940
XXL.VLDL.CE..	0.0004061	-0.0699509	0.0707630	0.9909362	899
IDL.PL..	-0.0002511	-0.0683544	0.0678522	0.9941794	940
XS.VLDL.TG..	0.0000709	-0.0711729	0.0713147	0.9984346	940
LDL.C	0.0000436	-0.0685050	0.0685922	0.9990041	940

---

Among controls, results for the effects of PCa predisposition score on metabolites

Metabolite	Estimate	2.5%	97.5%	Pval	N
L.LDL.TG..	0.1380187	0.0347338	0.2413035	0.0085039	654
M.LDL.TG..	0.1308995	0.0298373	0.2319617	0.0107821	654
L.LDL.C..	-0.1252209	-0.2256451	-0.0247966	0.0140205	654
IDL.TG..	0.1289438	0.0233134	0.2345743	0.0161349	653
L.LDL.CE..	-0.1204028	-0.2202131	-0.0205926	0.0172809	654
IDL.C..	-0.1266099	-0.2351094	-0.0181105	0.0213851	653
S.LDL.TG..	0.1169412	0.0163803	0.2175022	0.0220211	653
Gly	0.0965295	0.0026854	0.1903736	0.0424750	653
S.VLDL.CE..	-0.1004165	-0.1983957	-0.0024372	0.0429485	654
XS.VLDL.CE..	-0.1019247	-0.2016975	-0.0021519	0.0436251	654
XS.VLDL.TG..	0.1007169	0.0018673	0.1995664	0.0445061	654
M.LDL.C..	-0.0910522	-0.1811476	-0.0009568	0.0466409	654
S.LDL.C..	-0.0955258	-0.1910705	0.0000190	0.0489229	653
S.VLDL.C..	-0.0976470	-0.1959831	0.0006890	0.0498567	654
S.VLDL.TG..	0.0926438	-0.0038369	0.1891245	0.0581336	654
S.LDL.C	-0.0969793	-0.1982867	0.0043280	0.0587275	653
S.LDL.CE	-0.0921662	-0.1886914	0.0043589	0.0596205	653
S.LDL.PL..	0.0971260	-0.0048885	0.1991405	0.0601402	653
XS.VLDL.C..	-0.0933794	-0.1918800	0.0051212	0.0611328	654
M.LDL.PL..	0.0918936	-0.0072333	0.1910205	0.0673699	654
M.LDL.C	-0.0922035	-0.1920864	0.0076793	0.0682434	654
LDL.C	-0.0934624	-0.1949735	0.0080488	0.0688633	654
S.LDL.FC	-0.0927506	-0.1940076	0.0085064	0.0703423	653
L.LDL.CE	-0.0930074	-0.1951457	0.0091309	0.0718114	654
S.LDL.CE..	-0.0799484	-0.1674375	0.0075408	0.0721213	653
L.LDL.C	-0.0930123	-0.1954194	0.0093948	0.0725859	654
L.LDL.FC	-0.0922891	-0.1943643	0.0097862	0.0740946	654
M.LDL.CE	-0.0829522	-0.1755434	0.0096390	0.0770373	654
M.VLDL.FC..	0.0837251	-0.0097730	0.1772232	0.0776538	654
M.VLDL.CE..	-0.0876955	-0.1860216	0.0106306	0.0777341	654
IDL.CE..	-0.0878481	-0.1864176	0.0107215	0.0786198	653
M.LDL.FC	-0.0899817	-0.1919573	0.0119939	0.0811646	654
S.LDL.L	-0.0884806	-0.1900786	0.0131174	0.0851712	653
M.HDL.CE..	-0.0790776	-0.1701596	0.0120044	0.0871020	654
L.LDL.PL..	0.0889748	-0.0140027	0.1919524	0.0873656	654
M.LDL.L	-0.0863702	-0.1878178	0.0150774	0.0923270	654
S.LDL.P	-0.0855809	-0.1870226	0.0158608	0.0953118	653
L.LDL.L	-0.0857292	-0.1880200	0.0165617	0.0974343	654
M.LDL.CE..	-0.0651109	-0.1424468	0.0122249	0.0975746	654
M.LDL.P	-0.0835189	-0.1850221	0.0179844	0.1037114	654
M.HDL.C..	-0.0761104	-0.1687955	0.0165747	0.1053134	654
L.LDL.PL	-0.0833470	-0.1852914	0.0185973	0.1059442	654
M.VLDL.PL..	-0.0821646	-0.1826412	0.0183120	0.1061871	654
L.LDL.P	-0.0828027	-0.1849482	0.0193428	0.1088339	654
IDL.C	-0.0833239	-0.1866740	0.0200262	0.1108443	653
IDL.CE	-0.0821179	-0.1848399	0.0206041	0.1138180	653
FAw6.FA	-0.0793879	-0.1794496	0.0206738	0.1177194	652
IDL.PL	-0.0803588	-0.1834637	0.0227462	0.1232416	653
IDL.L	-0.0786542	-0.1809377	0.0236292	0.1282033	654
L.HDL.TG..	0.0727343	-0.0216228	0.1670913	0.1282819	651
EstC	-0.0764926	-0.1779431	0.0249578	0.1360403	653
M.VLDL.C..	-0.0719324	-0.1694557	0.0255908	0.1447656	654
S.HDL.TG..	0.0702752	-0.0264620	0.1670124	0.1518690	654
IDL.P	-0.0739236	-0.1760033	0.0281561	0.1518796	654
LA.FA	-0.0727971	-0.1732262	0.0276320	0.1527163	652
S.HDL.TG	0.0701301	-0.0266874	0.1669476	0.1528923	654
IDL.FC	-0.0724007	-0.1745676	0.0297661	0.1615417	653
Serum.C	-0.0715830	-0.1728462	0.0296802	0.1621599	654
M.LDL.FC..	0.0699957	-0.0293768	0.1693682	0.1634870	654
Tyr	-0.0683681	-0.1652958	0.0285596	0.1644051	652
Crea	-0.0679312	-0.1645413	0.0286790	0.1650547	652
HDL.TG	0.0667313	-0.0281749	0.1616375	0.1655253	654
M.VLDL.TG..	0.0656617	-0.0307878	0.1621112	0.1788249	654
PUFA.FA	-0.0671556	-0.1662775	0.0319663	0.1814701	652
M.LDL.PL	-0.0673542	-0.1671021	0.0323937	0.1814893	654
PC	-0.0785296	-0.1952940	0.0382349	0.1828192	653
TotCho	-0.1131342	-0.2816590	0.0553905	0.1829652	653
VLDL.D	0.0641573	-0.0310731	0.1593876	0.1832461	654
S.LDL.PL	-0.0649870	-0.1630024	0.0330283	0.1897599	653
XXL.VLDL.FC..	0.0714167	-0.0363518	0.1791852	0.1910107	633
SFA.FA	0.0669077	-0.0354970	0.1693125	0.1958195	652
FAw6	-0.0616331	-0.1569478	0.0336816	0.2009142	652
M.HDL.PL..	0.0585936	-0.0325822	0.1497693	0.2052335	654
L.HDL.FC..	-0.0621807	-0.1606180	0.0362567	0.2124580	651
LA	-0.0578837	-0.1504740	0.0347067	0.2165332	652
S.VLDL.TG	0.0583135	-0.0348550	0.1514821	0.2166023	654
Serum.TG	0.0579966	-0.0349926	0.1509858	0.2182241	654
Pyr	-0.0552855	-0.1444079	0.0338369	0.2206375	653
S.HDL.C..	-0.0562171	-0.1467040	0.0342699	0.2210098	654
M.HDL.TG	0.0605485	-0.0371642	0.1582612	0.2212461	654
M.HDL.TG..	0.0597507	-0.0371715	0.1566729	0.2236362	654
TotPG	-0.0658660	-0.1730526	0.0413206	0.2237703	653
XS.VLDL.CE	-0.0623076	-0.1638378	0.0392225	0.2243119	654
XXL.VLDL.TG..	-0.0541287	-0.1430794	0.0348221	0.2268946	633
VLDL.TG	0.0562121	-0.0364522	0.1488764	0.2310629	654
L.HDL.TG	0.0563151	-0.0371344	0.1497646	0.2337476	651
S.LDL.FC..	0.0599288	-0.0397218	0.1595794	0.2341518	653
FreeC	-0.0603595	-0.1620359	0.0413169	0.2401456	653
M.VLDL.TG	0.0547107	-0.0378624	0.1472838	0.2432394	654
XS.VLDL.TG	0.0569043	-0.0402147	0.1540232	0.2470247	654
M.VLDL.FC	0.0535527	-0.0384536	0.1455589	0.2505380	654
AcAce	0.0543256	-0.0400388	0.1486901	0.2562161	654
XS.VLDL.PL..	-0.0525060	-0.1447636	0.0397517	0.2624884	654
L.VLDL.FC	0.0519312	-0.0399253	0.1437876	0.2645720	651
L.VLDL.TG	0.0525823	-0.0409786	0.1461432	0.2670447	651
L.VLDL.P	0.0523483	-0.0412592	0.1459559	0.2694769	651
L.VLDL.L	0.0522617	-0.0413024	0.1458257	0.2700722	651
L.VLDL.PL	0.0521662	-0.0412536	0.1455859	0.2702366	651
MUFA.FA	0.0549010	-0.0435419	0.1533439	0.2709702	652
XXL.VLDL.C..	0.0548562	-0.0439792	0.1536916	0.2712247	633
S.HDL.CE..	-0.0485400	-0.1356164	0.0385364	0.2722814	654
S.HDL.C	-0.0502249	-0.1405067	0.0400569	0.2733661	654
S.HDL.PL..	0.0521121	-0.0427657	0.1469900	0.2785622	654
PUFA	-0.0521809	-0.1477557	0.0433939	0.2798551	652
L.VLDL.C	0.0508859	-0.0426136	0.1443855	0.2827958	651
M.VLDL.P	0.0501680	-0.0421411	0.1424770	0.2832089	654
XS.VLDL.C	-0.0547275	-0.1559451	0.0464900	0.2843066	654
M.VLDL.L	0.0495788	-0.0427129	0.1418705	0.2888123	654
S.HDL.CE	-0.0467970	-0.1338571	0.0402631	0.2900505	654
TG.PG	0.0489377	-0.0449156	0.1427910	0.3032827	653
XL.HDL.FC..	-0.0506434	-0.1477331	0.0464463	0.3034220	654
L.VLDL.CE	0.0480620	-0.0455605	0.1416846	0.3110569	651
M.VLDL.PL	0.0469266	-0.0451469	0.1390001	0.3142160	654
SM	-0.0505537	-0.1503060	0.0491985	0.3163656	652
Glol	0.1528850	-0.1681832	0.4739533	0.3213397	105
L.VLDL.FC..	0.0419295	-0.0427095	0.1265686	0.3293328	651
His	-0.0405381	-0.1245118	0.0434355	0.3412392	652
Ile	0.0455656	-0.0492255	0.1403567	0.3425931	654
L.LDL.FC..	-0.0442263	-0.1375441	0.0490915	0.3498443	654
IDL.TG	0.0471226	-0.0552281	0.1494732	0.3622781	653
XS.VLDL.PL	-0.0442674	-0.1433525	0.0548178	0.3767345	654
IDL.FC..	-0.0408431	-0.1353202	0.0536340	0.3946815	653
XXL.VLDL.C	0.0404896	-0.0539057	0.1348849	0.3971389	633
IDL.PL..	-0.0402383	-0.1343648	0.0538881	0.3989608	653
XL.VLDL.FC..	-0.0407207	-0.1365069	0.0550655	0.4015983	643
L.HDL.PL..	-0.0394894	-0.1326960	0.0537171	0.4025762	651
S.VLDL.CE	-0.0415483	-0.1410103	0.0579136	0.4078760	654
XXL.VLDL.FC	0.0406769	-0.0568182	0.1381720	0.4100910	633
XL.HDL.TG..	0.0388028	-0.0556432	0.1332487	0.4174415	654
S.VLDL.P	0.0382490	-0.0554668	0.1319647	0.4199659	654
M.HDL.FC..	-0.0377106	-0.1302123	0.0547910	0.4209654	654
S.HDL.FC..	0.0376973	-0.0566162	0.1320107	0.4302201	654
S.VLDL.PL	0.0374575	-0.0568473	0.1317623	0.4325257	654
Remnant.C	-0.0382919	-0.1356215	0.0590377	0.4361776	654
Gp	0.0359218	-0.0564464	0.1282899	0.4412911	654
S.VLDL.FC..	-0.0367518	-0.1312938	0.0577902	0.4425001	654
M.VLDL.C	0.0347930	-0.0571269	0.1267130	0.4547674	654
XL.HDL.C..	-0.0362376	-0.1322119	0.0597366	0.4559527	654
XL.HDL.CE	-0.0373465	-0.1371166	0.0624235	0.4588572	654
HDL2.C	-0.0372271	-0.1382727	0.0638186	0.4664192	654
L.VLDL.CE..	-0.0337030	-0.1259263	0.0585202	0.4693099	651
Leu	0.0343431	-0.0594424	0.1281287	0.4696443	654
XL.HDL.C	-0.0361202	-0.1357783	0.0635378	0.4731558	654
ApoA1	-0.0368416	-0.1386037	0.0649205	0.4737910	654
S.VLDL.L	0.0340284	-0.0599135	0.1279702	0.4740504	654
XL.VLDL.PL..	-0.0259883	-0.1005062	0.0485296	0.4894238	643
HDL.C	-0.0351751	-0.1361934	0.0658432	0.4912248	654
XS.VLDL.FC	-0.0342078	-0.1327067	0.0642912	0.4919396	654
XL.VLDL.CE	0.0330728	-0.0627148	0.1288605	0.4957127	643
S.LDL.TG	0.0313191	-0.0631565	0.1257947	0.5121452	653
L.HDL.FC	-0.0328662	-0.1325340	0.0668016	0.5143203	651
UnSat	-0.0320216	-0.1291552	0.0651119	0.5149137	652
L.HDL.C..	-0.0332523	-0.1345473	0.0680427	0.5165734	651
ApoB	-0.0315229	-0.1277779	0.0647322	0.5168091	654
S.VLDL.FC	0.0310342	-0.0636297	0.1256981	0.5169459	654
M.HDL.CE	-0.0322080	-0.1306297	0.0662137	0.5173033	654
XL.VLDL.P	0.0310740	-0.0647720	0.1269200	0.5218753	643
XXL.VLDL.CE	0.0289721	-0.0607429	0.1186871	0.5240531	633
Ace	0.0425838	-0.0928763	0.1780438	0.5288027	654
XL.VLDL.TG	0.0304307	-0.0653003	0.1261617	0.5300418	643
XL.VLDL.L	0.0304377	-0.0654140	0.1262894	0.5304679	643
XL.HDL.FC	-0.0308872	-0.1294369	0.0676624	0.5348502	654
bOHBut	0.0302718	-0.0681058	0.1286495	0.5435056	641
M.HDL.C	-0.0299073	-0.1285571	0.0687424	0.5485926	654
XXL.VLDL.PL..	-0.0256620	-0.1110780	0.0597540	0.5505579	633
XS.VLDL.L	-0.0302437	-0.1307888	0.0703015	0.5512769	654
S.HDL.PL	0.0288764	-0.0706302	0.1283829	0.5660209	654
Alb	-0.0269141	-0.1218568	0.0680286	0.5756807	654
XL.VLDL.C	0.0268660	-0.0688297	0.1225616	0.5794685	643
LDL.D	0.0253302	-0.0658402	0.1165006	0.5839009	654
XL.HDL.CE..	-0.0259931	-0.1210799	0.0690936	0.5890359	654
HDL3.C	-0.0266632	-0.1248601	0.0715338	0.5917689	654
LDL.TG	0.0259948	-0.0740274	0.1260171	0.6069128	654
XL.HDL.TG	0.0236251	-0.0683844	0.1156345	0.6123204	654
L.VLDL.PL..	0.0244028	-0.0709994	0.1198051	0.6141812	651
L.LDL.TG	0.0253934	-0.0758598	0.1266465	0.6195047	654
XXL.VLDL.L	0.0233852	-0.0704980	0.1172683	0.6220213	633
XXL.VLDL.P	0.0217544	-0.0720403	0.1155491	0.6461142	633
M.HDL.FC	-0.0227597	-0.1208199	0.0753005	0.6461892	654
XL.HDL.L	-0.0228785	-0.1227942	0.0770373	0.6501615	654
XS.VLDL.P	-0.0227877	-0.1229847	0.0774093	0.6523258	654
L.HDL.PL	-0.0226217	-0.1228360	0.0775926	0.6552018	651
XL.VLDL.PL	0.0204235	-0.0729702	0.1138173	0.6656209	643
XL.HDL.PL..	0.0202451	-0.0756377	0.1161279	0.6769896	654
XL.VLDL.C..	-0.0192103	-0.1104093	0.0719888	0.6775371	643
XL.HDL.P	-0.0209073	-0.1208238	0.0790092	0.6785307	654
L.HDL.C	-0.0192407	-0.1191491	0.0806678	0.7031840	651
XS.VLDL.FC..	-0.0165933	-0.1033333	0.0701467	0.7061718	654
M.LDL.TG	0.0185301	-0.0823235	0.1193836	0.7159920	654
XXL.VLDL.TG	0.0167807	-0.0768509	0.1104123	0.7225527	633
TotFA	-0.0159588	-0.1085036	0.0765859	0.7329330	652
M.VLDL.CE	0.0148921	-0.0776928	0.1074771	0.7506270	654
L.HDL.L	-0.0159817	-0.1158527	0.0838892	0.7515358	651
L.HDL.CE	-0.0158529	-0.1157771	0.0840712	0.7535948	651
HDL.D	-0.0144886	-0.1121076	0.0831303	0.7691193	654
XL.VLDL.FC	0.0142254	-0.0815132	0.1099641	0.7691641	643
Val	-0.0139502	-0.1080246	0.0801241	0.7697108	654
S.VLDL.C	-0.0142257	-0.1117631	0.0833117	0.7727317	654
XXL.VLDL.PL	0.0132733	-0.0794733	0.1060200	0.7767243	633
L.HDL.P	-0.0141983	-0.1140693	0.0856726	0.7784850	651
Lac	0.0121246	-0.0762225	0.1004717	0.7863086	654
ApoB.ApoA1	-0.0133887	-0.1113586	0.0845813	0.7867854	654
Gln	0.0130766	-0.0862606	0.1124139	0.7948998	654
S.HDL.FC	0.0127592	-0.0855699	0.1110883	0.7975216	654
DHA.FA	0.0124298	-0.0865326	0.1113922	0.8040431	652
XXL.VLDL.CE..	0.0113605	-0.0810244	0.1037454	0.8076221	633
M.HDL.L	-0.0112749	-0.1117984	0.0892485	0.8242861	654
Phe	0.0100445	-0.0831636	0.1032525	0.8314991	654
L.HDL.CE..	-0.0103027	-0.1099897	0.0893844	0.8383149	651
FAw3.FA	0.0098450	-0.0906192	0.1103093	0.8463607	652
XL.HDL.PL	-0.0081754	-0.1070867	0.0907359	0.8700360	654
M.HDL.P	-0.0081237	-0.1088275	0.0925800	0.8731212	654
VLDL.C	0.0074560	-0.0864159	0.1013280	0.8752313	654
Ala	-0.0075712	-0.1076549	0.0925124	0.8808518	654
L.VLDL.TG..	-0.0062301	-0.0979590	0.0854988	0.8934846	651
MUFA	0.0060528	-0.0870479	0.0991535	0.8976926	652
L.VLDL.C..	0.0059033	-0.0847303	0.0965369	0.8978320	651
XL.VLDL.CE..	-0.0047392	-0.0982153	0.0887369	0.9200641	643
FAw3	-0.0047938	-0.1038393	0.0942517	0.9235979	652
SFA	-0.0044354	-0.0968308	0.0879600	0.9243599	652
Glc	-0.0032043	-0.0962822	0.0898735	0.9456252	651
S.HDL.L	-0.0029933	-0.1005649	0.0945783	0.9516517	654
M.HDL.PL	-0.0029191	-0.1039322	0.0980941	0.9543857	654
DHA	-0.0018880	-0.1051473	0.1013712	0.9711173	652
S.VLDL.PL..	-0.0012708	-0.0941114	0.0915698	0.9784044	654
S.HDL.P	0.0011985	-0.0967767	0.0991736	0.9807067	654
Cit	0.0012075	-0.0988033	0.1012183	0.9809441	654
XL.VLDL.TG..	0.0008852	-0.0910010	0.0927715	0.9848889	643

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