| tags: [ Gaston Genomic Data GWAS Heritability ] categories: [Coding Experiments ]
Filtering GWAS (w/ covariates) results
Introduction
The addition of covariates to the heritability and GWAS models significantly changed my results. As such, I need to filter for robust peaks in my new GWAS results and obtaining beta-values for the signiicant peaks.
Methods and Results
Load packages
require(magicfor)## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'magicfor'require(magrittr)
require(dplyr)
require(gaston)
require(qqman)Load data
nies_heritable_pheno240918 <- read.csv('C:/Users/Martha/Documents/Honours/Project/honours.project/Data/nies_heritable_pheno240918.csv', header = TRUE)
nies_covar <- read.csv('C:/Users/Martha/Documents/Honours/Project/honours.project/Data/nies_covar.csv', header = T)
head(nies_covar)## UUID Sex Age
## 1 219960 1 53
## 2 313180 1 55
## 3 320511 2 60
## 4 400011 1 23
## 5 400013 1 50
## 6 316131 2 77merged_nies_210818 <- read.bed.matrix("C:/Users/Martha/Documents/Honours/Project/honours.project/Data/merged_nies/merged_nies_geno_210818.bed")## Reading C:/Users/Martha/Documents/Honours/Project/honours.project/Data/merged_nies/merged_nies_geno_210818.fam
## Reading C:/Users/Martha/Documents/Honours/Project/honours.project/Data/merged_nies/merged_nies_geno_210818.bim
## Reading C:/Users/Martha/Documents/Honours/Project/honours.project/Data/merged_nies/merged_nies_geno_210818.bed
## ped stats and snps stats have been set.
## 'p' has been set.
## 'mu' and 'sigma' have been set.merged_nies_GRM <- GRM(merged_nies_210818)
merged_nies_eiK <- eigen(merged_nies_GRM)
merged_nies_eiK$values[ merged_nies_eiK$values < 0] <- 0
merged_nies_PC <- sweep(merged_nies_eiK$vectors, 2, sqrt(merged_nies_eiK$values), "*")1. Filter L K-value H
l_KvalH_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$L.K.value.H, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xl_KvalH_gwas <- na.omit(l_KvalH_gwas)
l_kvalH_filtered <- l_KvalH_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
l_kvalH_meff <- l_kvalH_filtered %>% filter(p < 1.84e-7)
l_kvalH_sig <- do.call(rbind, lapply(split(l_kvalH_meff,l_kvalH_meff$chr), function(x) {return(x[which.min(x$p),])}))
l_kvalH_res <- NULL
for (i in l_kvalH_sig$id) {
snpID <- i
snpCHR <- l_kvalH_filtered[l_kvalH_filtered$id == snpID,]$chr
snpPOS <- l_kvalH_filtered[l_kvalH_filtered$id ==snpID,]$pos
sig.peak <- l_kvalH_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
l_kvalH_res <- rbind(l_kvalH_res, sig.peak)
}
write.csv(l_kvalH_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/l_kvalH_res.csv')
l_kvalH_res## chr pos id A1 A2 freqA2 h2 LRT
## 1 2 176830012 rs72940750 C T 0.9898551 0.01237732 35.81496
## 2 2 176831387 rs7576095 A G 0.9898551 0.01237732 35.81496
## 3 2 176832679 rs6758571 G A 0.9898551 0.01237732 35.81496
## 4 2 176833429 rs139727961 G A 0.9898551 0.01237732 35.81496
## 5 2 176839936 rs72940766 A T 0.9898551 0.01237732 35.81496
## 6 2 176840625 rs72940776 T C 0.9898551 0.01237732 35.81496
## 7 2 176840683 rs72940781 C G 0.9898551 0.01237732 35.81496
## 8 2 176841189 rs6715723 T A 0.9898551 0.01237732 35.81496
## 9 3 77739700 rs1163757 A G 0.9869565 0.01781775 33.16277
## 10 6 82425908 rs73481987 C T 0.9753623 0.00000000 20.24474
## 11 6 82442490 rs114928787 T C 0.9855072 0.00000000 45.19722
## 12 6 82442833 rs116172930 C A 0.9855072 0.00000000 45.19722
## 13 6 82443802 rs74436554 C T 0.9855072 0.00000000 45.19722
## 14 6 82444791 rs78056249 C G 0.9855072 0.00000000 45.19722
## 15 6 82445716 rs77941466 C T 0.9855072 0.00000000 45.19722
## 16 6 82447004 rs116461493 T C 0.9855072 0.00000000 45.19722
## 17 6 82447732 rs77666578 G A 0.9855072 0.00000000 45.19722
## 18 6 82451563 rs75189229 A G 0.9855072 0.00000000 45.19722
## 19 6 82453938 rs117178778 A G 0.9855072 0.00000000 45.19722
## 20 6 82454058 rs116246536 T G 0.9855072 0.00000000 45.19722
## 21 6 82454140 rs78427866 A G 0.9855072 0.00000000 45.19722
## 22 6 82454380 rs147257041 A G 0.9855072 0.00000000 45.19722
## 23 6 82454449 rs140711073 T C 0.9855072 0.00000000 45.19722
## 24 6 82454505 rs142248448 T C 0.9855072 0.00000000 45.19722
## 25 6 82454620 rs114442506 A G 0.9855072 0.00000000 45.19722
## 26 6 82456537 rs145558293 A G 0.9855072 0.00000000 45.19722
## 27 6 82458393 rs77391407 C T 0.9855072 0.00000000 45.19722
## 28 6 82458546 rs77103853 G C 0.9855072 0.00000000 45.19722
## 29 6 82459011 rs115096710 T C 0.9855072 0.00000000 45.19722
## 30 6 82459713 rs77245672 G C 0.9855072 0.00000000 45.19722
## 31 6 82459810 rs77599496 T A 0.9855072 0.00000000 45.19722
## 32 6 82459890 rs77598967 G A 0.9855072 0.00000000 45.19722
## 33 6 82460167 rs115847999 C G 0.9855072 0.00000000 45.19722
## 34 6 82460379 rs116730626 G A 0.9855072 0.00000000 45.19722
## 35 6 82460650 rs149355051 G T 0.9855072 0.00000000 45.19722
## 36 6 82462126 rs76856243 T C 0.9855072 0.00000000 45.19722
## 37 6 82462603 rs6933239 C T 0.9855072 0.00000000 45.19722
## 38 6 82462992 rs6911216 A C 0.9855072 0.00000000 45.19722
## 39 6 82465617 rs6928806 A C 0.9855072 0.00000000 45.19722
## 40 6 82466574 rs74404091 C T 0.9855072 0.00000000 45.19722
## 41 6 82466715 rs79358384 G A 0.9855072 0.00000000 45.19722
## 42 6 82467200 rs77721025 A C 0.9855072 0.00000000 45.19722
## 43 6 82467706 rs116423281 A G 0.9855072 0.00000000 45.19722
## 44 6 82467953 rs142216660 T C 0.9855072 0.00000000 45.19722
## 45 6 82467988 rs140099919 A G 0.9855072 0.00000000 45.19722
## 46 6 82468668 rs78941585 G A 0.9855072 0.00000000 45.19722
## 47 6 82468774 rs75228268 A T 0.9855072 0.00000000 45.19722
## 48 6 82469116 rs77552654 T A 0.9855072 0.00000000 45.19722
## 49 6 82470649 rs149706908 G T 0.9855072 0.00000000 45.19722
## 50 6 82470651 rs144623191 T C 0.9855072 0.00000000 45.19722
## 51 6 82470859 rs192614396 C A 0.9855072 0.00000000 45.19722
## 52 6 82471119 rs151156468 C T 0.9855072 0.00000000 45.19722
## 53 6 82471338 rs140554533 A G 0.9855072 0.00000000 45.19722
## 54 6 82471381 rs150446760 C G 0.9855072 0.00000000 45.19722
## 55 6 82471679 rs138339145 T C 0.9855072 0.00000000 45.19722
## 56 6 82472140 rs142089466 T C 0.9855072 0.00000000 45.19722
## 57 6 82473676 rs147151928 G C 0.9855072 0.00000000 45.19722
## 58 6 82473679 rs140298049 C T 0.9855072 0.00000000 45.19722
## 59 6 82473688 rs149655171 A G 0.9855072 0.00000000 45.19722
## 60 6 82474468 rs146864360 T C 0.9855072 0.00000000 45.19722
## 61 6 82474648 rs140520501 C T 0.9855072 0.00000000 45.19722
## 62 6 82474867 rs150394443 T C 0.9855072 0.00000000 45.19722
## 63 6 82475320 rs143579868 A T 0.9855072 0.00000000 45.19722
## 64 6 82475696 rs115741750 C T 0.9855072 0.00000000 45.19722
## 65 6 82477810 rs77406497 T C 0.9855072 0.00000000 45.19722
## 66 6 82478378 rs115813741 T G 0.9855072 0.00000000 45.19722
## 67 6 82478650 rs78900243 G A 0.9855072 0.00000000 45.19722
## 68 6 82478870 rs76557363 T G 0.9855072 0.00000000 45.19722
## 69 6 82479264 rs77943130 T G 0.9855072 0.00000000 45.19722
## 70 6 82479546 rs115245280 A G 0.9855072 0.00000000 45.19722
## 71 6 82479664 rs75910615 A G 0.9855072 0.00000000 45.19722
## 72 6 82480370 rs76438934 A G 0.9855072 0.00000000 45.19722
## 73 6 82481986 rs115034961 A C 0.9855072 0.00000000 45.19722
## 74 6 82482183 rs79967481 G A 0.9855072 0.00000000 45.19722
## 75 6 82482254 rs116699765 T C 0.9855072 0.00000000 45.19722
## 76 6 82483125 rs78639250 T C 0.9855072 0.00000000 45.19722
## 77 6 82483522 rs75763492 A G 0.9855072 0.00000000 45.19722
## 78 6 82483898 rs76617768 T C 0.9855072 0.00000000 45.19722
## 79 6 82484287 rs76652063 C A 0.9855072 0.00000000 45.19722
## 80 6 82484758 rs114024754 A G 0.9855072 0.00000000 45.19722
## 81 6 82485370 rs79031996 C G 0.9855072 0.00000000 45.19722
## 82 6 82485916 rs77517398 C T 0.9855072 0.00000000 45.19722
## 83 6 82486863 rs80252551 A T 0.9855072 0.00000000 45.19722
## 84 6 82488997 rs114963095 G T 0.9855072 0.00000000 45.19722
## 85 6 82489053 rs77561386 C T 0.9855072 0.00000000 45.19722
## 86 6 82489164 rs76602861 A G 0.9855072 0.00000000 45.19722
## 87 6 82490178 rs79435750 A G 0.9855072 0.00000000 45.19722
## 88 6 82490367 rs80347071 C G 0.9855072 0.00000000 45.19722
## 89 6 82492170 rs74785531 C T 0.9855072 0.00000000 45.19722
## 90 6 82492233 rs74361967 C T 0.9855072 0.00000000 45.19722
## 91 6 82492321 rs75373608 A T 0.9855072 0.00000000 45.19722
## 92 10 64099069 rs74804993 G A 0.9826087 0.27039317 31.70206
## 93 16 13935176 rs1800067 A G 0.9550725 0.27378980 33.23207
## 94 18 48853167 rs151115377 A G 0.9840580 0.28905661 28.59857
## p
## 1 2.169754e-09
## 2 2.169754e-09
## 3 2.169754e-09
## 4 2.169754e-09
## 5 2.169754e-09
## 6 2.169754e-09
## 7 2.169754e-09
## 8 2.169754e-09
## 9 8.475773e-09
## 10 6.814066e-06
## 11 1.781574e-11
## 12 1.781574e-11
## 13 1.781574e-11
## 14 1.781574e-11
## 15 1.781574e-11
## 16 1.781574e-11
## 17 1.781574e-11
## 18 1.781574e-11
## 19 1.781574e-11
## 20 1.781574e-11
## 21 1.781574e-11
## 22 1.781574e-11
## 23 1.781574e-11
## 24 1.781574e-11
## 25 1.781574e-11
## 26 1.781574e-11
## 27 1.781574e-11
## 28 1.781574e-11
## 29 1.781574e-11
## 30 1.781574e-11
## 31 1.781574e-11
## 32 1.781574e-11
## 33 1.781574e-11
## 34 1.781574e-11
## 35 1.781574e-11
## 36 1.781574e-11
## 37 1.781574e-11
## 38 1.781574e-11
## 39 1.781574e-11
## 40 1.781574e-11
## 41 1.781574e-11
## 42 1.781574e-11
## 43 1.781574e-11
## 44 1.781574e-11
## 45 1.781574e-11
## 46 1.781574e-11
## 47 1.781574e-11
## 48 1.781574e-11
## 49 1.781574e-11
## 50 1.781574e-11
## 51 1.781574e-11
## 52 1.781574e-11
## 53 1.781574e-11
## 54 1.781574e-11
## 55 1.781574e-11
## 56 1.781574e-11
## 57 1.781574e-11
## 58 1.781574e-11
## 59 1.781574e-11
## 60 1.781574e-11
## 61 1.781574e-11
## 62 1.781574e-11
## 63 1.781574e-11
## 64 1.781574e-11
## 65 1.781574e-11
## 66 1.781574e-11
## 67 1.781574e-11
## 68 1.781574e-11
## 69 1.781574e-11
## 70 1.781574e-11
## 71 1.781574e-11
## 72 1.781574e-11
## 73 1.781574e-11
## 74 1.781574e-11
## 75 1.781574e-11
## 76 1.781574e-11
## 77 1.781574e-11
## 78 1.781574e-11
## 79 1.781574e-11
## 80 1.781574e-11
## 81 1.781574e-11
## 82 1.781574e-11
## 83 1.781574e-11
## 84 1.781574e-11
## 85 1.781574e-11
## 86 1.781574e-11
## 87 1.781574e-11
## 88 1.781574e-11
## 89 1.781574e-11
## 90 1.781574e-11
## 91 1.781574e-11
## 92 1.797316e-08
## 93 8.179025e-09
## 94 8.904774e-08manhattan(x = l_kvalH_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,10), genomewideline = -log10(1.84e-7), main = "L K-value H", annotatePval = 1.84e-7)
2. Filter L ACD
l_acd_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$L.AC.Depth, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xl_acd_gwas <- na.omit(l_acd_gwas)
l_acd_filtered <- l_acd_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
l_acd_meff <- l_acd_filtered %>% filter(p < 1.84e-7)
l_acd_sig <- do.call(rbind, lapply(split(l_acd_meff,l_acd_meff$chr), function(x) {return(x[which.min(x$p),])}))
l_acd_res <- NULL
for (i in l_acd_sig$id) {
snpID <- i
snpCHR <- l_acd_filtered[l_acd_filtered$id == snpID,]$chr
snpPOS <- l_acd_filtered[l_acd_filtered$id ==snpID,]$pos
sig.peak <- l_acd_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
l_acd_res <- rbind(l_acd_res, sig.peak)
}
write.csv(l_acd_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/l_acd.csv')
l_acd_res## chr pos id A1 A2 freqA2 h2 LRT p
## 1 11 87921800 rs17151049 C T 0.9362319 0.04134164 34.86515 3.533494e-09manhattan(x = l_acd_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,10), genomewideline = -log10(1.84e-7), main = "Left Anterior Chamber Depth", annotatePval = 1.84e-7)
3. L CDR
l_cdr_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$L.CDR, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xl_cdr_gwas <- na.omit(l_cdr_gwas)
l_cdr_filtered <- l_cdr_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
l_cdr_meff <- l_cdr_filtered %>% filter(p < 1.84e-7)
l_cdr_sig <- do.call(rbind, lapply(split(l_cdr_meff,l_cdr_meff$chr), function(x) {return(x[which.min(x$p),])}))
l_cdr_res <- NULL
for (i in l_cdr_sig$id) {
snpID <- i
snpCHR <- l_cdr_filtered[l_cdr_filtered$id == snpID,]$chr
snpPOS <- l_cdr_filtered[l_cdr_filtered$id ==snpID,]$pos
sig.peak <- l_cdr_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
l_cdr_res <- rbind(l_cdr_res, sig.peak)
}
write.csv(l_cdr_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/l_cdr_res.csv')
l_cdr_res## chr pos id A1 A2 freqA2 h2 LRT p
## 1 4 52923518 rs115266839 A G 0.9884058 0.7137481 28.51051 9.319103e-08
## 2 17 67425699 rs191190284 T A 0.9869565 0.7285248 31.57717 1.916699e-08manhattan(x = l_cdr_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,10), genomewideline = -log10(1.84e-7), main = "Left Cup-to-Disc Ratio", annotatePval = 1.84e-7)
4. L IOP
l_iop_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$L.IOP.mmHg, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xl_iop_gwas <- na.omit(l_iop_gwas)
l_iop_filtered <- l_iop_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
l_iop_meff <- l_iop_filtered %>% filter(p < 1.84e-7)
l_iop_sig <- do.call(rbind, lapply(split(l_iop_meff,l_iop_meff$chr), function(x) {return(x[which.min(x$p),])}))
l_iop_res <- NULL
for (i in l_iop_sig$id) {
snpID <- i
snpCHR <- l_iop_filtered[l_iop_filtered$id == snpID,]$chr
snpPOS <- l_iop_filtered[l_iop_filtered$id ==snpID,]$pos
sig.peak <- l_iop_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
l_iop_res <- rbind(l_iop_res, sig.peak)
}
write.csv(l_iop_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/l_iop_res.csv')
l_iop_res## NULLmanhattan(x = l_iop_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,10), genomewideline = -log10(1.84e-7), main = "Left Intraocular Pressure")
5. R ACD
r_acd_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$R.AC.Depth, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xr_acd_gwas <- na.omit(r_acd_gwas)
r_acd_filtered <- r_acd_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
r_acd_meff <- r_acd_filtered %>% filter(p < 1.84e-7)
r_acd_sig <- do.call(rbind, lapply(split(r_acd_meff,r_acd_meff$chr), function(x) {return(x[which.min(x$p),])}))
r_acd_res <- NULL
for (i in r_acd_sig$id) {
snpID <- i
snpCHR <- r_acd_filtered[r_acd_filtered$id == snpID,]$chr
snpPOS <- r_acd_filtered[r_acd_filtered$id ==snpID,]$pos
sig.peak <- r_acd_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
r_acd_res <- rbind(r_acd_res, sig.peak)
}
write.csv(r_acd_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/r_acd_res.csv')
r_acd_res## chr pos id A1 A2 freqA2 h2 LRT p
## 1 11 87921800 rs17151049 C T 0.9362319 0 36.69173 1.383648e-09manhattan(x = r_acd_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,10), genomewideline = -log10(1.84e-7), main = "Right Anterior Chamber Depth", annotatePval = 1.84e-7)
6. R Axial length
r_axL_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$R.Axial.Length, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xr_axL_gwas <- na.omit(r_axL_gwas)
r_axL_filtered <- r_axL_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
r_axL_meff <- r_axL_filtered %>% filter(p < 1.84e-7)
r_axL_sig <- do.call(rbind, lapply(split(r_axL_meff,r_axL_meff$chr), function(x) {return(x[which.min(x$p),])}))
r_axL_res <- NULL
for (i in r_axL_sig$id) {
snpID <- i
snpCHR <- r_axL_filtered[r_axL_filtered$id == snpID,]$chr
snpPOS <- r_axL_filtered[r_axL_filtered$id ==snpID,]$pos
sig.peak <- r_axL_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
r_axL_res <- rbind(r_axL_res, sig.peak)
}
write.csv(r_axL_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/r_axL_res.csv')
r_axL_res## chr pos id A1 A2 freqA2 h2 LRT p
## 1 3 19256649 rs151052496 T C 0.9666667 0.3916311 28.21833 1.083735e-07manhattan(x = r_axL_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,10), genomewideline = -log10(1.84e-7), main = "Right Axial Length", annotatePval = 1.84e-7)
7. R CDR
r_cdr_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$R.CDR, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xr_cdr_gwas <- na.omit(r_cdr_gwas)
r_cdr_filtered <- r_cdr_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
r_cdr_meff <- r_cdr_filtered %>% filter(p < 1.84e-7)
r_cdr_sig <- do.call(rbind, lapply(split(r_cdr_meff,r_cdr_meff$chr), function(x) {return(x[which.min(x$p),])}))
r_cdr_res <- NULL
for (i in r_cdr_sig$id) {
snpID <- i
snpCHR <- r_cdr_filtered[r_cdr_filtered$id == snpID,]$chr
snpPOS <- r_cdr_filtered[r_cdr_filtered$id ==snpID,]$pos
sig.peak <- r_cdr_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
r_cdr_res <- rbind(r_cdr_res, sig.peak)
}
write.csv(r_cdr_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/r_cdr_res.csv')
r_cdr_res## NULLmanhattan(x = r_cdr_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,10), genomewideline = -log10(1.84e-7), main = "Right Cup-to-Disc Ratio")
8.R cyl
r_cyl_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$R.Cyl..pre.dilate, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)
r_cyl_gwas <- na.omit(r_cyl_gwas)
r_cyl_filtered <- r_cyl_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
r_cyl_meff <- r_cyl_filtered %>% filter(p < 1.84e-7)
r_cyl_sig <- do.call(rbind, lapply(split(r_cyl_meff,r_cyl_meff$chr), function(x) {return(x[which.min(x$p),])}))
r_cyl_res <- NULL
for (i in r_cyl_sig$id) {
snpID <- i
snpCHR <- r_cyl_filtered[r_cyl_filtered$id == snpID,]$chr
snpPOS <- r_cyl_filtered[r_cyl_filtered$id ==snpID,]$pos
sig.peak <- r_cyl_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
r_cyl_res <- rbind(r_cyl_res, sig.peak)
}
write.csv(r_cyl_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/r_cyl_res.csv')
r_cyl_res## chr pos id A1 A2 freqA2 h2 LRT p
## 1 9 78643317 rs12377322 T A 0.8942029 0.5227802 31.15654 2.380362e-08
## 2 9 78643821 rs11137782 T C 0.8942029 0.5227802 31.15654 2.380362e-08
## 3 9 78644279 rs10867228 C T 0.8942029 0.5227802 31.15654 2.380362e-08
## 4 9 78645867 rs9644983 A T 0.8942029 0.5227802 31.15654 2.380362e-08
## 5 9 78650464 rs7870042 G T 0.8869565 0.5180705 30.20807 3.880911e-08manhattan(x = r_cyl_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,12), genomewideline = -log10(1.84e-7), main = "Right Cyl Pre-dilate", annotatePval = 1.84e-7)
9. R IOP
r_iop_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$R.IOP.mmHg, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xr_iop_gwas <- na.omit(r_iop_gwas)
r_iop_filtered <- r_iop_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
r_iop_meff <- r_iop_filtered %>% filter(p < 1.84e-7)
r_iop_sig <- do.call(rbind, lapply(split(r_iop_meff,r_iop_meff$chr), function(x) {return(x[which.min(x$p),])}))
r_iop_res <- NULL
for (i in r_iop_sig$id) {
snpID <- i
snpCHR <- r_iop_filtered[r_iop_filtered$id == snpID,]$chr
snpPOS <- r_iop_filtered[r_iop_filtered$id ==snpID,]$pos
sig.peak <- r_iop_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
r_iop_res <- rbind(r_iop_res, sig.peak)
}
write.csv(r_iop_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/r_iop_res.csv')
r_iop_res## NULLmanhattan(x = r_iop_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,12), genomewideline = -log10(1.84e-7), main = "Right Intraocular Pressure")
10. R Kval V
r_kvalV_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$R.K.value.V, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xr_kvalV_gwas <- na.omit(r_kvalV_gwas)
r_kvalV_filtered <- r_kvalV_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
r_kvalV_meff <- r_kvalV_filtered %>% filter(p < 1.84e-7)
r_kvalV_sig <- do.call(rbind, lapply(split(r_kvalV_meff,r_kvalV_meff$chr), function(x) {return(x[which.min(x$p),])}))
r_kvalV_res <- NULL
for (i in r_kvalV_sig$id) {
snpID <- i
snpCHR <- r_kvalV_filtered[r_kvalV_filtered$id == snpID,]$chr
snpPOS <- r_kvalV_filtered[r_kvalV_filtered$id ==snpID,]$pos
sig.peak <- r_kvalV_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
r_kvalV_res <- rbind(r_kvalV_res, sig.peak)
}
write.csv(r_kvalV_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/r_kvalV_res.csv')
r_kvalV_res## chr pos id A1 A2 freqA2 h2 LRT
## 1 1 205916566 rs72752923 C A 0.9782609 0.6629444 24.87971
## 2 1 205916729 rs16830364 G A 0.9782609 0.6629444 24.87971
## 3 1 205916931 rs60255052 C T 0.9811594 0.5937446 36.80908
## 4 1 205917041 rs60716530 C T 0.9782609 0.6629444 24.87971
## 5 1 205917115 rs60644810 G A 0.9782609 0.6629444 24.87971
## 6 1 205917248 rs35694044 A T 0.9782609 0.6629444 24.87971
## 7 1 205917652 rs16830370 T C 0.9782609 0.6629444 24.87971
## 8 1 205918853 rs16856462 C T 0.9782609 0.6629444 24.87971
## 9 1 205919195 rs16856468 G A 0.9782609 0.6629444 24.87971
## 10 1 205919303 rs60001177 T C 0.9782609 0.6629444 24.87971
## 11 1 205919401 rs12723666 A G 0.9782609 0.6629444 24.87971
## 12 1 205920201 rs16856470 T C 0.9811594 0.5937446 36.80908
## 13 1 205920404 rs16856473 A G 0.9811594 0.5937446 36.80908
## 14 1 205922403 rs68189466 G A 0.9797101 0.5975327 28.63845
## 15 1 205922720 rs35648260 G T 0.9797101 0.5975327 28.63845
## 16 1 205922775 rs35360452 G T 0.9797101 0.5975327 28.63845
## 17 1 205925474 rs12727528 A G 0.9797101 0.5975327 28.63845
## 18 1 205935183 rs1891309 G A 0.9782609 0.5942699 26.60640
## 19 1 205935330 rs1891310 T G 0.9782609 0.5942699 26.60640
## 20 1 205936240 rs1473537 T C 0.9739130 0.5975376 31.17921
## 21 1 205942026 rs72752928 T C 0.9782609 0.5942699 26.60640
## 22 1 205943691 rs66593238 T C 0.9782609 0.5942699 26.60640
## 23 1 205944627 rs9438407 T G 0.9782609 0.5942699 26.60640
## 24 1 205945388 rs12741299 T C 0.9782609 0.5942699 26.60640
## 25 1 205946647 rs34265780 T C 0.9782609 0.5942699 26.60640
## 26 4 119321195 rs77203710 A G 0.9855072 0.6483435 27.86200
## 27 4 119322197 rs1397614 T C 0.9855072 0.6483435 27.86200
## 28 4 119362510 rs112314510 A G 0.9855072 0.6483435 27.86200
## 29 4 119362552 rs111968273 T A 0.9855072 0.6483435 27.86200
## 30 4 119367677 rs113059419 A G 0.9869565 0.6382046 29.84634
## 31 4 119372303 rs112760591 G A 0.9855072 0.6483435 27.86200
## 32 4 119372480 rs111609258 C G 0.9855072 0.6483435 27.86200
## 33 4 119378224 rs147602981 G A 0.9855072 0.6483435 27.86200
## 34 4 119381167 rs143847948 A C 0.9855072 0.6483435 27.86200
## 35 4 119390257 rs113224532 T C 0.9855072 0.6483435 27.86200
## 36 4 119392279 rs117856633 C A 0.9855072 0.6483435 27.86200
## 37 4 119398681 rs113825627 C G 0.9855072 0.6483435 27.86200
## 38 4 119403429 rs112414671 A G 0.9869565 0.6382046 29.84634
## 39 4 119404439 rs183181597 A G 0.9855072 0.6483435 27.86200
## 40 4 119404927 rs113317707 C A 0.9855072 0.6483435 27.86200
## 41 4 119408454 rs184703141 T C 0.9855072 0.6483435 27.86200
## p
## 1 6.102138e-07
## 2 6.102138e-07
## 3 1.302822e-09
## 4 6.102138e-07
## 5 6.102138e-07
## 6 6.102138e-07
## 7 6.102138e-07
## 8 6.102138e-07
## 9 6.102138e-07
## 10 6.102138e-07
## 11 6.102138e-07
## 12 1.302822e-09
## 13 1.302822e-09
## 14 8.723252e-08
## 15 8.723252e-08
## 16 8.723253e-08
## 17 8.723252e-08
## 18 2.494165e-07
## 19 2.494165e-07
## 20 2.352724e-08
## 21 2.494165e-07
## 22 2.494165e-07
## 23 2.494165e-07
## 24 2.494165e-07
## 25 2.494165e-07
## 26 1.302832e-07
## 27 1.302832e-07
## 28 1.302832e-07
## 29 1.302832e-07
## 30 4.676781e-08
## 31 1.302832e-07
## 32 1.302832e-07
## 33 1.302832e-07
## 34 1.302832e-07
## 35 1.302832e-07
## 36 1.302832e-07
## 37 1.302832e-07
## 38 4.676781e-08
## 39 1.302832e-07
## 40 1.302832e-07
## 41 1.302832e-07manhattan(x = r_kvalV_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,12), genomewideline = -log10(1.84e-7), main = "Right K-value V", annotatePval = 1.84e-7)
11. UVAF
uvaf_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$totaluvafmm, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xuvaf_gwas <- na.omit(uvaf_gwas)
uvaf_filtered <- uvaf_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
uvaf_meff <- uvaf_filtered %>% filter(p < 1.84e-7)
uvaf_sig <- do.call(rbind, lapply(split(uvaf_meff,uvaf_meff$chr), function(x) {return(x[which.min(x$p),])}))
uvaf_res <- NULL
for (i in uvaf_sig$id) {
snpID <- i
snpCHR <- uvaf_filtered[uvaf_filtered$id == snpID,]$chr
snpPOS <- uvaf_filtered[uvaf_filtered$id ==snpID,]$pos
sig.peak <- uvaf_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
uvaf_res <- rbind(uvaf_res, sig.peak)
}
write.csv(uvaf_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/uvaf_res.csv')
uvaf_res## chr pos id A1 A2 freqA2 h2 LRT
## 1 4 22963447 rs75555621 T C 0.9753623 0.4146371 28.40614
## 2 4 22968049 rs961611 G C 0.9753623 0.4146371 28.40614
## 3 4 22994702 rs77135796 C A 0.9768116 0.4088979 26.77749
## 4 7 107578936 rs191522213 G C 0.9884058 0.4941277 33.16299
## 5 8 34731289 rs116937259 C T 0.9884058 0.4585009 28.19133
## 6 8 34738629 rs141115618 C G 0.9898551 0.4695116 21.02868
## 7 8 34743083 rs192922472 T C 0.9884058 0.4585009 28.19133
## 8 9 68575580 rs34054758 A G 0.9753623 0.4111507 24.90233
## 9 9 68576337 rs35666199 G C 0.9753623 0.4111507 24.90233
## 10 9 68580538 rs6560237 C T 0.9753623 0.4111507 24.90233
## 11 9 68581265 rs7048633 T C 0.9753623 0.4111507 24.90233
## 12 9 68583308 rs71501771 T A 0.9753623 0.4111507 24.90233
## 13 9 68586048 rs35551873 T C 0.9753623 0.4111507 24.90233
## 14 9 68586254 rs7045561 G A 0.9753623 0.4111507 24.90233
## 15 9 68587181 rs13288798 G T 0.9753623 0.4111507 24.90233
## 16 9 68587313 rs13284137 G A 0.9753623 0.4111507 24.90233
## 17 9 68588522 rs139047286 C T 0.9753623 0.4111507 24.90233
## 18 9 68588999 rs7859065 C T 0.9753623 0.4111507 24.90233
## 19 9 68594362 rs13290124 A G 0.9753623 0.4111507 24.90233
## 20 9 68595978 rs10511959 A G 0.9753623 0.4111507 24.90233
## 21 9 68597806 rs34412731 T C 0.9753623 0.4111507 24.90233
## 22 9 68599834 rs12004993 G A 0.9753623 0.4111507 24.90233
## 23 9 68605388 rs76930250 G C 0.9753623 0.4111507 24.90233
## 24 9 68605806 rs13287094 A G 0.9753623 0.4111507 24.90233
## 25 9 68608085 rs34921768 G A 0.9753623 0.4111507 24.90233
## 26 9 68608420 rs13299492 A G 0.9753623 0.4111507 24.90233
## 27 9 68612655 rs34344868 A G 0.9753623 0.4111507 24.90233
## 28 9 68615281 rs34805084 A G 0.9739130 0.4125906 22.36735
## 29 9 68618011 rs71501772 G C 0.9739130 0.4125906 22.36735
## 30 9 68618330 rs34763246 T C 0.9739130 0.4125906 22.36735
## 31 9 68619315 rs34436601 T C 0.9739130 0.4125906 22.36735
## 32 9 68621728 rs13289515 C G 0.9739130 0.4125906 22.36735
## 33 9 68621879 rs13289852 A G 0.9753623 0.4245203 19.55458
## 34 9 68624185 rs117741244 T C 0.9811594 0.3772754 32.16159
## 35 9 68629924 rs117987088 T C 0.9811594 0.3772754 32.16159
## p
## 1 9.835299e-08
## 2 9.835299e-08
## 3 2.282816e-07
## 4 8.474810e-09
## 5 1.098960e-07
## 6 4.524601e-06
## 7 1.098960e-07
## 8 6.030939e-07
## 9 6.030939e-07
## 10 6.030939e-07
## 11 6.030939e-07
## 12 6.030939e-07
## 13 6.030939e-07
## 14 6.030939e-07
## 15 6.030939e-07
## 16 6.030939e-07
## 17 6.030939e-07
## 18 6.030939e-07
## 19 6.030939e-07
## 20 6.030939e-07
## 21 6.030939e-07
## 22 6.030939e-07
## 23 6.030939e-07
## 24 6.030939e-07
## 25 6.030939e-07
## 26 6.030939e-07
## 27 6.030939e-07
## 28 2.251700e-06
## 29 2.251700e-06
## 30 2.251700e-06
## 31 2.251700e-06
## 32 2.251700e-06
## 33 9.776623e-06
## 34 1.418682e-08
## 35 1.418682e-08manhattan(x = uvaf_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,12), genomewideline = -log10(1.84e-7), main = "Ultraviolet Autofluorescence", annotatePval = 1.84e-7)
12. Component 1
pc1_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$coord.Dim.1, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)
head(pc1_gwas)## chr pos id A1 A2 freqA2 h2 LRT p
## 1 1 590318 rs2808353 G A 0.9855072 0.6054434 9.920181 0.0016347630
## 2 1 778897 rs138660747 A C 0.9956522 0.5790574 13.392422 0.0002526429
## 3 1 783175 rs10751453 T C 1.0000000 0.5793392 13.578441 0.0002287982
## 4 1 804046 rs3131973 A G 1.0000000 0.5793392 13.578441 0.0002287982
## 5 1 817186 rs3094315 G A 0.8333333 0.5978608 1.781937 0.1819116129
## 6 1 833068 rs12562034 A G 0.8695652 0.6057170 3.573507 0.0587082129chr7_pc1 <- pc1_gwas[pc1_gwas$chr == 7,]
write.csv(chr7_pc1, 'C:/Users/Martha/Documents/Honours/Project/honours.project/chr7_pc1.csv')
pc1_gwas <- na.omit(pc1_gwas)
pc1_filtered <- pc1_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
pc1_meff <- pc1_filtered %>% filter(p < 1.84e-7)
pc1_sig <- do.call(rbind, lapply(split(pc1_meff,pc1_meff$chr), function(x) {return(x[which.min(x$p),])}))
pc1_res <- NULL
for (i in pc1_sig$id) {
snpID <- i
snpCHR <- pc1_filtered[pc1_filtered$id == snpID,]$chr
snpPOS <- pc1_filtered[pc1_filtered$id ==snpID,]$pos
sig.peak <- pc1_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
pc1_res <- rbind(pc1_res, sig.peak)
}
write.csv(pc1_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/pc1_res.csv')
pc1_res## chr pos id A1 A2 freqA2 h2 LRT
## 1 3 55984018 rs6797314 G A 0.7434783 0.6586685 27.95227
## 2 3 55987608 rs1458061 G A 0.7434783 0.6586685 27.95227
## 3 3 55993888 rs73086924 C T 0.7434783 0.6586685 27.95227
## 4 3 55999668 rs57141558 T C 0.7434783 0.6586685 27.95227
## 5 3 56001963 rs12631548 T C 0.7434783 0.6586685 27.95227
## 6 3 56008809 rs6779691 G A 0.7434783 0.6586685 27.95227
## 7 3 56012614 rs1458060 A G 0.7434783 0.6586685 27.95227
## 8 3 56013361 rs17216524 A G 0.7434783 0.6586685 27.95227
## 9 4 108787323 rs2880902 A G 0.9478261 0.6113166 27.40749
## 10 4 108790372 rs2123367 T C 0.9478261 0.6113166 27.40749
## 11 4 108801259 rs17039415 A T 0.9492754 0.6103985 27.72720
## 12 4 108803644 rs58390864 A G 0.9521739 0.6122931 27.21106
## 13 4 108805308 rs76953844 C T 0.9521739 0.6122931 27.21106
## 14 5 110018330 rs1563324 G A 0.9637681 0.5676447 19.94451
## 15 5 110027474 rs1455554 G A 0.8782609 0.5647180 29.10011
## 16 7 7976971 rs73049252 A G 0.9376812 0.5770405 31.19643
## 17 7 7978549 rs7804306 A G 0.9304348 0.5752165 31.43087
## 18 7 7978858 rs73049256 T A 0.9376812 0.5770405 31.19643
## 19 7 7979532 rs67343471 T G 0.9275362 0.5747390 28.67310
## 20 7 7981025 rs56073406 G A 0.9304348 0.5752165 31.43087
## 21 7 7981281 rs112270482 C T 0.9376812 0.5770405 31.19643
## 22 7 7981982 rs6975218 G A 0.9304348 0.5752165 31.43087
## 23 7 7982015 rs73049270 T C 0.9376812 0.5770405 31.19643
## 24 7 7982385 rs73049276 A G 0.9376812 0.5770405 31.19643
## 25 7 7985192 rs113537684 T G 0.9376812 0.5770405 31.19643
## 26 7 7985209 rs60466842 A C 0.9188406 0.5777130 24.95373
## 27 7 7992584 rs73049290 C T 0.9376812 0.5770405 31.19643
## 28 7 7993887 rs17566854 C A 0.9376812 0.5770405 31.19643
## 29 7 7995282 rs56017154 A G 0.9376812 0.5770405 31.19643
## 30 7 7997876 rs142269087 A G 0.9376812 0.5770405 31.19643
## 31 7 7999017 rs73049293 C T 0.9376812 0.5770405 31.19643
## 32 7 7999429 rs73049297 A G 0.9376812 0.5770405 31.19643
## 33 7 8001135 rs138912252 A G 0.9376812 0.5770405 31.19643
## 34 7 8001658 rs147420655 A G 0.9376812 0.5770405 31.19643
## 35 7 8002315 rs73049300 G C 0.9376812 0.5770405 31.19643
## 36 7 8010614 rs73050915 T G 0.9376812 0.5770405 31.19643
## 37 7 8011977 rs147455256 G A 0.9101449 0.5998346 19.90503
## 38 7 8012093 rs149303885 T G 0.9376812 0.5770405 31.19643
## 39 7 8012957 rs34746652 A G 0.9101449 0.5998346 19.90503
## 40 7 8016318 rs35509690 C A 0.9101449 0.5998346 19.90503
## 41 7 8016462 rs145954895 A C 0.9376812 0.5770405 31.19643
## 42 7 8017141 rs35143180 A C 0.9101449 0.5998346 19.90503
## 43 7 8019638 rs73050929 A C 0.9376812 0.5770405 31.19643
## 44 7 8027509 rs13225545 C T 0.9101449 0.5998346 19.90503
## 45 7 8027882 rs73050940 G A 0.9376812 0.5770405 31.19643
## 46 7 8028562 rs56142807 T C 0.9376812 0.5770405 31.19643
## 47 9 80083293 rs56681765 T G 0.9753623 0.5989193 24.82235
## 48 9 80083941 rs73465194 G A 0.9753623 0.5989193 24.82235
## 49 9 80085229 rs73465197 G T 0.9753623 0.5989193 24.82235
## 50 9 80087626 rs60135507 C G 0.9753623 0.5989193 24.82235
## 51 9 80089053 rs12340079 T A 0.9753623 0.5989193 24.82235
## 52 9 80090326 rs12005290 T C 0.9275362 0.5992298 27.75820
## 53 9 80090686 rs113228275 C A 0.9753623 0.5989193 24.82235
## 54 12 131726697 rs71470343 T C 0.7565217 0.5488483 22.67821
## 55 12 131727978 rs11612305 G A 0.7565217 0.5488483 22.67821
## 56 12 131732148 rs34459128 A G 0.7565217 0.5488483 22.67821
## 57 12 131733497 rs11246782 C A 0.7565217 0.5488483 22.67821
## 58 12 131735840 rs12426142 G T 0.7565217 0.5488483 22.67821
## 59 12 131740230 rs35811529 A T 0.7565217 0.5488483 22.67821
## 60 12 131741218 rs77720672 A G 0.7565217 0.5488483 22.67821
## 61 12 131750373 rs11613616 A G 0.7565217 0.5488483 22.67821
## 62 12 131751961 rs12814215 A G 0.7565217 0.5488483 22.67821
## 63 12 131753205 rs1051219 T C 0.7550725 0.5423594 24.58083
## 64 12 131755796 rs3741526 T C 0.5246377 0.5707837 29.04004
## 65 12 131757714 rs10902445 A G 0.5246377 0.5707837 29.04004
## 66 12 131759383 rs10751692 C T 0.5231884 0.5683912 29.00795
## 67 12 131780621 rs35769880 A G 0.7565217 0.5499785 21.93496
## 68 12 131782218 rs12824836 C T 0.7579710 0.5474037 21.97864
## 69 12 131788771 rs10751693 A G 0.5260870 0.5692210 28.57329
## 70 12 131789723 rs78792048 C A 0.7579710 0.5474037 21.97864
## 71 12 131790985 rs7302570 A C 0.7579710 0.5474037 21.97864
## 72 12 131792417 rs7972213 C T 0.5362319 0.5669502 28.19673
## 73 12 131793860 rs35198651 A G 0.7579710 0.5474037 21.97864
## 74 12 131794164 rs34705844 G A 0.7579710 0.5474037 21.97864
## 75 12 131805033 rs11615253 T C 0.7550725 0.5490112 22.59556
## 76 12 131805444 rs12319878 C T 0.5188406 0.5715208 28.23844
## 77 12 131805478 rs7963727 C G 0.7550725 0.5490112 22.59556
## 78 16 27016081 rs17705663 C G 0.6507246 0.5446572 22.92850
## 79 16 27016338 rs4787900 A G 0.6507246 0.5446572 22.92850
## 80 16 27016563 rs4787399 T C 0.6507246 0.5446572 22.92850
## 81 16 27016645 rs4787901 G T 0.6507246 0.5446572 22.92850
## 82 16 27016827 rs4787902 A G 0.6405797 0.5346800 20.73884
## 83 16 27016873 rs17638409 T C 0.6507246 0.5446572 22.92850
## 84 16 27016944 rs34531249 G A 0.6507246 0.5446572 22.92850
## 85 16 27016958 rs62031995 A T 0.6507246 0.5446572 22.92850
## 86 16 27017238 rs13335990 C T 0.6507246 0.5446572 22.92850
## 87 16 27020618 rs8052041 A C 0.7478261 0.5491575 28.81302
## 88 16 27021732 rs4787400 T C 0.7478261 0.5491575 28.81302
## p
## 1 1.243452e-07
## 2 1.243452e-07
## 3 1.243452e-07
## 4 1.243452e-07
## 5 1.243452e-07
## 6 1.243452e-07
## 7 1.243452e-07
## 8 1.243452e-07
## 9 1.647920e-07
## 10 1.647920e-07
## 11 1.396851e-07
## 12 1.824121e-07
## 13 1.824121e-07
## 14 7.972263e-06
## 15 6.873315e-08
## 16 2.331949e-08
## 17 2.066693e-08
## 18 2.331949e-08
## 19 8.568577e-08
## 20 2.066693e-08
## 21 2.331949e-08
## 22 2.066693e-08
## 23 2.331949e-08
## 24 2.331949e-08
## 25 2.331949e-08
## 26 5.872267e-07
## 27 2.331949e-08
## 28 2.331949e-08
## 29 2.331949e-08
## 30 2.331949e-08
## 31 2.331949e-08
## 32 2.331949e-08
## 33 2.331949e-08
## 34 2.331949e-08
## 35 2.331949e-08
## 36 2.331949e-08
## 37 8.138577e-06
## 38 2.331949e-08
## 39 8.138577e-06
## 40 8.138577e-06
## 41 2.331949e-08
## 42 8.138577e-06
## 43 2.331949e-08
## 44 8.138577e-06
## 45 2.331949e-08
## 46 2.331949e-08
## 47 6.286430e-07
## 48 6.286430e-07
## 49 6.286430e-07
## 50 6.286430e-07
## 51 6.286430e-07
## 52 1.374645e-07
## 53 6.286430e-07
## 54 1.915270e-06
## 55 1.915270e-06
## 56 1.915270e-06
## 57 1.915270e-06
## 58 1.915270e-06
## 59 1.915270e-06
## 60 1.915270e-06
## 61 1.915270e-06
## 62 1.915270e-06
## 63 7.125695e-07
## 64 7.089778e-08
## 65 7.089778e-08
## 66 7.208182e-08
## 67 2.820481e-06
## 68 2.757022e-06
## 69 9.021813e-08
## 70 2.757022e-06
## 71 2.757022e-06
## 72 1.095896e-07
## 73 2.757022e-06
## 74 2.757022e-06
## 75 1.999466e-06
## 76 1.072535e-07
## 77 1.999466e-06
## 78 1.681401e-06
## 79 1.681401e-06
## 80 1.681401e-06
## 81 1.681401e-06
## 82 5.263742e-06
## 83 1.681401e-06
## 84 1.681401e-06
## 85 1.681401e-06
## 86 1.681401e-06
## 87 7.971348e-08
## 88 7.971348e-08manhattan(x = pc1_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,12), genomewideline = -log10(1.84e-7), main = "Component 1", annotatePval = 1.84e-7)
13. Component 3
pc3_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$coord.Dim.3, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xpc3_gwas <- na.omit(pc3_gwas)
pc3_filtered <- pc3_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
pc3_meff <- pc3_filtered %>% filter(p < 1.84e-7)
pc3_sig <- do.call(rbind, lapply(split(pc3_meff,pc3_meff$chr), function(x) {return(x[which.min(x$p),])}))
pc3_res <- NULL
for (i in pc3_sig$id) {
snpID <- i
snpCHR <- pc3_filtered[pc3_filtered$id == snpID,]$chr
snpPOS <- pc3_filtered[pc3_filtered$id ==snpID,]$pos
sig.peak <- pc3_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
pc3_res <- rbind(pc3_res, sig.peak)
}
write.csv(pc3_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/pc3_res.csv')
pc3_res## chr pos id A1 A2 freqA2 h2 LRT
## 1 1 205916566 rs72752923 C A 0.9782609 0.143088794 35.24216
## 2 1 205916729 rs16830364 G A 0.9782609 0.143088794 35.24216
## 3 1 205916931 rs60255052 C T 0.9811594 0.108712498 42.57820
## 4 1 205917041 rs60716530 C T 0.9782609 0.143088794 35.24216
## 5 1 205917115 rs60644810 G A 0.9782609 0.143088794 35.24216
## 6 1 205917248 rs35694044 A T 0.9782609 0.143088794 35.24216
## 7 1 205917652 rs16830370 T C 0.9782609 0.143088794 35.24216
## 8 1 205918853 rs16856462 C T 0.9782609 0.143088794 35.24216
## 9 1 205919195 rs16856468 G A 0.9782609 0.143088794 35.24216
## 10 1 205919303 rs60001177 T C 0.9782609 0.143088794 35.24216
## 11 1 205919401 rs12723666 A G 0.9782609 0.143088794 35.24216
## 12 1 205920201 rs16856470 T C 0.9811594 0.108712498 42.57820
## 13 1 205920404 rs16856473 A G 0.9811594 0.108712498 42.57820
## 14 1 205922403 rs68189466 G A 0.9797101 0.114606518 40.95377
## 15 1 205922720 rs35648260 G T 0.9797101 0.114606518 40.95377
## 16 1 205922775 rs35360452 G T 0.9797101 0.114606518 40.95377
## 17 1 205925474 rs12727528 A G 0.9797101 0.114606518 40.95377
## 18 1 205935183 rs1891309 G A 0.9782609 0.111957179 36.55094
## 19 1 205935330 rs1891310 T G 0.9782609 0.111957179 36.55094
## 20 1 205936240 rs1473537 T C 0.9739130 0.146103347 28.74682
## 21 1 205942026 rs72752928 T C 0.9782609 0.111957179 36.55094
## 22 1 205943691 rs66593238 T C 0.9782609 0.111957179 36.55094
## 23 1 205944627 rs9438407 T G 0.9782609 0.111957179 36.55094
## 24 1 205945388 rs12741299 T C 0.9782609 0.111957179 36.55094
## 25 1 205946647 rs34265780 T C 0.9782609 0.111957179 36.55094
## 26 2 85686354 rs6759832 C T 0.9884058 0.188036952 33.52783
## 27 3 42006157 rs71315517 G C 0.9869565 0.115130201 31.26507
## 28 4 119362510 rs112314510 A G 0.9855072 0.093109543 44.61575
## 29 4 119362552 rs111968273 T A 0.9855072 0.093109543 44.61575
## 30 4 119367677 rs113059419 A G 0.9869565 0.058720068 51.72960
## 31 4 119372303 rs112760591 G A 0.9855072 0.093109543 44.61575
## 32 4 119372480 rs111609258 C G 0.9855072 0.093109543 44.61575
## 33 4 119378224 rs147602981 G A 0.9855072 0.093109543 44.61575
## 34 4 119381167 rs143847948 A C 0.9855072 0.093109543 44.61575
## 35 4 119390257 rs113224532 T C 0.9855072 0.093109543 44.61575
## 36 4 119392279 rs117856633 C A 0.9855072 0.093109543 44.61575
## 37 4 119398681 rs113825627 C G 0.9855072 0.093109543 44.61575
## 38 4 119403429 rs112414671 A G 0.9869565 0.058720068 51.72960
## 39 4 119403485 rs185357281 A G 0.9869565 0.080424209 30.74033
## 40 4 119404439 rs183181597 A G 0.9855072 0.093109543 44.61575
## 41 4 119404927 rs113317707 C A 0.9855072 0.093109543 44.61575
## 42 4 119408454 rs184703141 T C 0.9855072 0.093109543 44.61575
## 43 5 25293191 rs138955515 A G 0.9855072 0.123321257 35.15979
## 44 5 25300269 rs140158697 C T 0.9855072 0.123321257 35.15979
## 45 5 25331088 rs114133913 T A 0.9855072 0.123321257 35.15979
## 46 6 121261079 rs116936384 G C 0.9898551 0.135566367 30.33235
## 47 8 80306741 rs111864836 G T 0.9884058 0.133289843 46.82630
## 48 8 80306859 rs113627660 G C 0.9884058 0.133289843 46.82630
## 49 8 80306990 rs7825643 G T 0.9724638 0.288636205 20.82125
## 50 8 80307293 rs7845792 A G 0.9884058 0.133289843 46.82630
## 51 8 80307388 rs7845575 G A 0.9869565 0.154786090 36.21982
## 52 8 80307700 rs114654811 C T 0.9884058 0.133289843 46.82630
## 53 8 80307701 rs115278078 A T 0.9884058 0.133289843 46.82630
## 54 8 80307837 rs79018076 T C 0.9884058 0.133289843 46.82630
## 55 8 80308098 rs112501384 G C 0.9884058 0.133289843 46.82630
## 56 8 80309142 rs60539023 G A 0.9724638 0.288636205 20.82125
## 57 8 80309305 rs11996376 A G 0.9884058 0.133289843 46.82630
## 58 8 80309598 rs79431653 C G 0.9884058 0.133289843 46.82630
## 59 8 80309626 rs11996450 T C 0.9884058 0.133289843 46.82630
## 60 8 80310193 rs60741023 C G 0.9884058 0.133289843 46.82630
## 61 8 80310485 rs7844286 C T 0.9884058 0.133289843 46.82630
## 62 8 80310716 rs7844696 A T 0.9884058 0.133289843 46.82630
## 63 8 80310720 rs7826434 G A 0.9884058 0.133289843 46.82630
## 64 8 80311242 rs80031189 C T 0.9884058 0.133289843 46.82630
## 65 8 80311252 rs113823684 A G 0.9884058 0.133289843 46.82630
## 66 8 80311311 rs61300312 A C 0.9724638 0.288636205 20.82125
## 67 8 80311329 rs142907856 A G 0.9884058 0.133289843 46.82630
## 68 8 80311457 rs112582272 A G 0.9884058 0.133289843 46.82630
## 69 8 80311580 rs116773209 G A 0.9884058 0.133289843 46.82630
## 70 8 80311736 rs113314270 G T 0.9884058 0.133289843 46.82630
## 71 8 80311746 rs73261635 G A 0.9724638 0.288636205 20.82125
## 72 8 80311838 rs117693425 A G 0.9884058 0.133289843 46.82630
## 73 8 80312291 rs11988873 C T 0.9884058 0.133289843 46.82630
## 74 8 80312345 rs11988904 A T 0.9884058 0.133289843 46.82630
## 75 8 80312623 rs36051398 A C 0.9884058 0.133289843 46.82630
## 76 8 80313237 rs73261640 G A 0.9724638 0.288636205 20.82125
## 77 8 80313751 rs114426401 T C 0.9884058 0.133289843 46.82630
## 78 8 80314228 rs74981396 G A 0.9884058 0.133289843 46.82630
## 79 8 80317028 rs11998344 T G 0.9884058 0.133289843 46.82630
## 80 8 80317208 rs113244900 C T 0.9884058 0.133289843 46.82630
## 81 8 80318583 rs57246091 A G 0.9884058 0.133289843 46.82630
## 82 8 80318643 rs113901603 G A 0.9884058 0.133289843 46.82630
## 83 8 80319691 rs113718590 A G 0.9884058 0.133289843 46.82630
## 84 8 80319794 rs111833373 C T 0.9724638 0.288636205 20.82125
## 85 8 80320713 rs11987997 A G 0.9884058 0.133289843 46.82630
## 86 8 80321002 rs111527437 T A 0.9884058 0.133289843 46.82630
## 87 8 80321694 rs112815920 G C 0.9884058 0.133289843 46.82630
## 88 8 80323890 rs11998156 C A 0.9884058 0.133289843 46.82630
## 89 8 80323964 rs16907573 G A 0.9884058 0.133289843 46.82630
## 90 8 80324156 rs111257240 C T 0.9884058 0.133289843 46.82630
## 91 8 80325047 rs113437388 T C 0.9884058 0.133289843 46.82630
## 92 8 80325311 rs73261674 G C 0.9724638 0.288636205 20.82125
## 93 8 80326033 rs111370206 G A 0.9884058 0.133289843 46.82630
## 94 8 80326181 rs73261679 A C 0.9724638 0.288636205 20.82125
## 95 8 80326935 rs147955676 A G 0.9884058 0.133289843 46.82630
## 96 8 80326985 rs61039050 A C 0.9724638 0.288636205 20.82125
## 97 8 80327347 rs7840954 C T 0.9724638 0.288636205 20.82125
## 98 8 80327348 rs7822886 A G 0.9724638 0.288636205 20.82125
## 99 8 80327442 rs7841095 C T 0.9884058 0.133289843 46.82630
## 100 8 80327549 rs7822828 C A 0.9884058 0.133289843 46.82630
## 101 8 80327674 rs7841418 C T 0.9724638 0.288636205 20.82125
## 102 8 80327732 rs7823481 C G 0.9884058 0.133289843 46.82630
## 103 8 80328450 rs11990649 A G 0.9884058 0.133289843 46.82630
## 104 8 80329801 rs56960849 A G 0.9884058 0.133289843 46.82630
## 105 8 80330197 rs11995624 C T 0.9884058 0.133289843 46.82630
## 106 8 80330467 rs7837761 A G 0.9869565 0.154786090 36.21982
## 107 8 80332180 rs113042618 T C 0.9884058 0.133289843 46.82630
## 108 8 80332423 rs7001142 G T 0.9884058 0.133289843 46.82630
## 109 8 80332520 rs6980915 G A 0.9884058 0.133289843 46.82630
## 110 8 80332901 rs6473220 T C 0.9884058 0.133289843 46.82630
## 111 8 80332907 rs6473221 A G 0.9884058 0.133289843 46.82630
## 112 8 80333316 rs7835862 C T 0.9884058 0.133289843 46.82630
## 113 8 80333340 rs7817696 A G 0.9884058 0.133289843 46.82630
## 114 8 80333506 rs7817634 T C 0.9884058 0.133289843 46.82630
## 115 8 80333599 rs57419267 C T 0.9884058 0.133289843 46.82630
## 116 8 80333823 rs59130219 C T 0.9884058 0.133289843 46.82630
## 117 8 80334098 rs73691385 T A 0.9884058 0.133289843 46.82630
## 118 8 80334328 rs7840559 C T 0.9884058 0.133289843 46.82630
## 119 8 80334334 rs7822177 G A 0.9884058 0.133289843 46.82630
## 120 8 80334582 rs111631456 A G 0.9884058 0.133289843 46.82630
## 121 8 80334982 rs58109903 T C 0.9884058 0.133289843 46.82630
## 122 8 80335583 rs73691388 G A 0.9884058 0.133289843 46.82630
## 123 8 80336124 rs7837108 T C 0.9884058 0.133289843 46.82630
## 124 8 80336300 rs7840857 T G 0.9884058 0.133289843 46.82630
## 125 8 80336310 rs7840861 T G 0.9884058 0.133289843 46.82630
## 126 8 80337292 rs111888115 T C 0.9884058 0.133289843 46.82630
## 127 8 80337308 rs56871558 T C 0.9884058 0.133289843 46.82630
## 128 8 80337408 rs56116331 A G 0.9884058 0.133289843 46.82630
## 129 8 80337443 rs58592997 T C 0.9884058 0.133289843 46.82630
## 130 8 80337629 rs76783901 A G 0.9884058 0.133289843 46.82630
## 131 8 80337634 rs74468856 G A 0.9884058 0.133289843 46.82630
## 132 8 80337985 rs73691394 C A 0.9884058 0.133289843 46.82630
## 133 8 80338076 rs73691395 T C 0.9884058 0.133289843 46.82630
## 134 8 80338219 rs73691397 C G 0.9884058 0.133289843 46.82630
## 135 8 80338255 rs73691398 G A 0.9884058 0.133289843 46.82630
## 136 8 80338835 rs58958046 A G 0.9884058 0.133289843 46.82630
## 137 8 80338910 rs58786240 C T 0.9884058 0.133289843 46.82630
## 138 8 80338955 rs76867788 C T 0.9884058 0.133289843 46.82630
## 139 8 80339017 rs73691399 T C 0.9884058 0.133289843 46.82630
## 140 8 80339217 rs73691400 C G 0.9884058 0.133289843 46.82630
## 141 8 80339959 rs61233095 C A 0.9884058 0.133289843 46.82630
## 142 8 80340238 rs56408864 A T 0.9884058 0.133289843 46.82630
## 143 8 80340749 rs7826667 A G 0.9884058 0.133289843 46.82630
## 144 8 80341236 rs7000592 G C 0.9884058 0.133289843 46.82630
## 145 12 5987972 rs142100550 T G 0.9884058 0.005360959 38.16451
## 146 14 77833818 rs146124823 G A 0.9898551 0.166384925 29.95453
## 147 18 48847895 rs11665050 A G 0.9782609 0.103675022 19.65233
## 148 18 48849360 rs141608868 C G 0.9782609 0.103675022 19.65233
## 149 18 48850433 rs78852912 C G 0.9782609 0.103675022 19.65233
## 150 18 48853167 rs151115377 A G 0.9840580 0.033825553 28.68549
## 151 18 48855955 rs143153469 A G 0.9782609 0.103675022 19.65233
## 152 18 48858545 rs117947063 G A 0.9782609 0.103675022 19.65233
## 153 20 2977131 rs145336914 G C 0.9753623 0.181907176 30.97578
## 154 20 2979974 rs141744903 G A 0.9681159 0.254200455 19.82805
## 155 20 2985515 rs6138975 C T 0.9681159 0.254200455 19.82805
## 156 20 2987464 rs74893620 A G 0.9681159 0.254200455 19.82805
## 157 20 2994423 rs60040561 T G 0.9681159 0.254200455 19.82805
## 158 20 2998407 rs78221943 G A 0.9681159 0.254200455 19.82805
## 159 20 3004167 rs58156936 C A 0.9681159 0.254200455 19.82805
## 160 20 3004208 rs146526220 A G 0.9681159 0.254200455 19.82805
## 161 20 3004309 rs6132999 T C 0.9681159 0.254200455 19.82805
## 162 20 3004313 rs6133000 T C 0.9681159 0.254200455 19.82805
## 163 20 3004887 rs3746692 A C 0.9681159 0.254200455 19.82805
## 164 20 3006691 rs6138976 T G 0.9681159 0.254200455 19.82805
## 165 20 3009458 rs75990124 T G 0.9681159 0.254200455 19.82805
## 166 20 3009687 rs6138979 A G 0.9681159 0.254200455 19.82805
## 167 20 3011439 rs16988201 T C 0.9681159 0.254200455 19.82805
## 168 20 3013926 rs6133003 G A 0.9681159 0.254200455 19.82805
## 169 22 44078144 rs11705487 T C 0.9753623 0.150776423 26.95579
## 170 22 44088452 rs147207731 A G 0.9797101 0.082632633 35.44711
## p
## 1 2.911523e-09
## 2 2.911523e-09
## 3 6.791206e-11
## 4 2.911523e-09
## 5 2.911523e-09
## 6 2.911523e-09
## 7 2.911523e-09
## 8 2.911523e-09
## 9 2.911523e-09
## 10 2.911523e-09
## 11 2.911523e-09
## 12 6.791206e-11
## 13 6.791206e-11
## 14 1.558732e-10
## 15 1.558732e-10
## 16 1.558732e-10
## 17 1.558732e-10
## 18 1.487281e-09
## 19 1.487281e-09
## 20 8.248511e-08
## 21 1.487281e-09
## 22 1.487281e-09
## 23 1.487281e-09
## 24 1.487281e-09
## 25 1.487281e-09
## 26 7.025159e-09
## 27 2.250933e-08
## 28 2.397550e-11
## 29 2.397550e-11
## 30 6.369519e-13
## 31 2.397550e-11
## 32 2.397550e-11
## 33 2.397550e-11
## 34 2.397550e-11
## 35 2.397550e-11
## 36 2.397550e-11
## 37 2.397550e-11
## 38 6.369519e-13
## 39 2.949701e-08
## 40 2.397550e-11
## 41 2.397550e-11
## 42 2.397550e-11
## 43 3.037304e-09
## 44 3.037304e-09
## 45 3.037304e-09
## 46 3.640046e-08
## 47 7.756561e-12
## 48 7.756561e-12
## 49 5.042030e-06
## 50 7.756561e-12
## 51 1.762689e-09
## 52 7.756561e-12
## 53 7.756561e-12
## 54 7.756561e-12
## 55 7.756561e-12
## 56 5.042030e-06
## 57 7.756561e-12
## 58 7.756561e-12
## 59 7.756561e-12
## 60 7.756561e-12
## 61 7.756561e-12
## 62 7.756561e-12
## 63 7.756561e-12
## 64 7.756561e-12
## 65 7.756561e-12
## 66 5.042030e-06
## 67 7.756561e-12
## 68 7.756561e-12
## 69 7.756561e-12
## 70 7.756561e-12
## 71 5.042030e-06
## 72 7.756561e-12
## 73 7.756561e-12
## 74 7.756561e-12
## 75 7.756561e-12
## 76 5.042030e-06
## 77 7.756561e-12
## 78 7.756561e-12
## 79 7.756561e-12
## 80 7.756561e-12
## 81 7.756561e-12
## 82 7.756561e-12
## 83 7.756561e-12
## 84 5.042030e-06
## 85 7.756561e-12
## 86 7.756561e-12
## 87 7.756561e-12
## 88 7.756561e-12
## 89 7.756561e-12
## 90 7.756561e-12
## 91 7.756561e-12
## 92 5.042030e-06
## 93 7.756561e-12
## 94 5.042030e-06
## 95 7.756561e-12
## 96 5.042030e-06
## 97 5.042030e-06
## 98 5.042030e-06
## 99 7.756561e-12
## 100 7.756561e-12
## 101 5.042030e-06
## 102 7.756561e-12
## 103 7.756561e-12
## 104 7.756561e-12
## 105 7.756561e-12
## 106 1.762689e-09
## 107 7.756561e-12
## 108 7.756561e-12
## 109 7.756561e-12
## 110 7.756561e-12
## 111 7.756561e-12
## 112 7.756561e-12
## 113 7.756561e-12
## 114 7.756561e-12
## 115 7.756561e-12
## 116 7.756561e-12
## 117 7.756561e-12
## 118 7.756561e-12
## 119 7.756561e-12
## 120 7.756561e-12
## 121 7.756561e-12
## 122 7.756561e-12
## 123 7.756561e-12
## 124 7.756561e-12
## 125 7.756561e-12
## 126 7.756561e-12
## 127 7.756561e-12
## 128 7.756561e-12
## 129 7.756561e-12
## 130 7.756561e-12
## 131 7.756561e-12
## 132 7.756561e-12
## 133 7.756561e-12
## 134 7.756561e-12
## 135 7.756561e-12
## 136 7.756561e-12
## 137 7.756561e-12
## 138 7.756561e-12
## 139 7.756561e-12
## 140 7.756561e-12
## 141 7.756561e-12
## 142 7.756561e-12
## 143 7.756561e-12
## 144 7.756561e-12
## 145 6.502437e-10
## 146 4.422975e-08
## 147 9.289015e-06
## 148 9.289015e-06
## 149 9.289015e-06
## 150 8.513918e-08
## 151 9.289015e-06
## 152 9.289015e-06
## 153 2.612691e-08
## 154 8.473026e-06
## 155 8.473026e-06
## 156 8.473026e-06
## 157 8.473026e-06
## 158 8.473026e-06
## 159 8.473026e-06
## 160 8.473026e-06
## 161 8.473026e-06
## 162 8.473026e-06
## 163 8.473026e-06
## 164 8.473026e-06
## 165 8.473026e-06
## 166 8.473026e-06
## 167 8.473026e-06
## 168 8.473026e-06
## 169 2.081630e-07
## 170 2.620714e-09manhattan(x = pc3_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,12), genomewideline = -log10(1.84e-7), main = "Component 3", annotatePval = 1.84e-7)
14. Component 4
pc4_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$coord.Dim.4, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)
pc4_gwas <- na.omit(pc4_gwas)
pc4_filtered <- pc4_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
pc4_meff <- pc4_filtered %>% filter(p < 1.84e-7)
pc4_sig <- do.call(rbind, lapply(split(pc4_meff,pc4_meff$chr), function(x) {return(x[which.min(x$p),])}))
pc4_res <- NULL
for (i in pc4_sig$id) {
snpID <- i
snpCHR <- pc4_filtered[pc4_filtered$id == snpID,]$chr
snpPOS <- pc4_filtered[pc4_filtered$id ==snpID,]$pos
sig.peak <- pc4_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
pc4_res <- rbind(pc4_res, sig.peak)
}
write.csv(pc4_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/pc4_res.csv')
pc4_res## chr pos id A1 A2 freqA2 h2 LRT p
## 1 3 187929196 rs10937327 T A 0.6898551 0.4657147 25.38934 4.685004e-07
## 2 3 187931111 rs16862725 G A 0.6898551 0.4657147 25.38934 4.685004e-07
## 3 3 187931631 rs6773854 C T 0.6898551 0.4657147 25.38934 4.685004e-07
## 4 3 187932117 rs11718110 C T 0.6898551 0.4657147 25.38934 4.685004e-07
## 5 3 187937514 rs36188296 G T 0.6753623 0.4784631 28.18476 1.102692e-07
## 6 3 187945323 rs73889243 G A 0.6855072 0.4657770 25.37970 4.708460e-07manhattan(x = pc4_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,12), genomewideline = -log10(1.84e-7), main = "Component 4", annotatePval = 1.84e-7)
15. Component 8
pc8_gwas <- association.test(merged_nies_210818, nies_heritable_pheno240918$coord.Dim.8, X = nies_covar, method="lmm", test = "lrt", response = "quantitative", K = merged_nies_GRM, eigenK = merged_nies_eiK, p = 2)## Warning in trans.X(X, eigenK$vectors[, seq_len(p)], mean(Y)): An intercept
## column was added to the covariate matrix Xpc8_gwas <- na.omit(pc8_gwas)
pc8_filtered <- pc8_gwas %>% filter(-log10(p)>1) %>% filter(freqA2 < 0.99)
pc8_meff <- pc8_filtered %>% filter(p < 1.84e-7)
pc8_sig <- do.call(rbind, lapply(split(pc8_meff,pc8_meff$chr), function(x) {return(x[which.min(x$p),])}))
pc8_res <- NULL
for (i in pc8_sig$id) {
snpID <- i
snpCHR <- pc8_filtered[pc8_filtered$id == snpID,]$chr
snpPOS <- pc8_filtered[pc8_filtered$id ==snpID,]$pos
sig.peak <- pc8_filtered %>%
filter(chr == snpCHR) %>%
filter(pos > snpPOS - 50000) %>%
filter(pos < snpPOS + 50000) %>%
filter(p < 1e-5)
pc8_res <- rbind(pc8_res, sig.peak)
}
write.csv(pc8_res, 'C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gwas_filter_covar/pc8_res.csv')
pc8_res## chr pos id A1 A2 freqA2 h2 LRT
## 1 2 147529465 rs1438873 T C 0.5231884 0.05628563 24.99400
## 2 2 147529616 rs6710858 T A 0.5231884 0.05628563 24.99400
## 3 2 147530296 rs6753791 C G 0.5231884 0.05628563 24.99400
## 4 2 147530428 rs12469553 C T 0.5927536 0.10982706 20.32671
## 5 2 147531132 rs4145334 T C 0.5927536 0.10982706 20.32671
## 6 2 147531215 rs1347827 C G 0.5231884 0.05628563 24.99400
## 7 2 147532048 rs4662523 A T 0.5231884 0.05628563 24.99400
## 8 2 147532877 rs1438875 T C 0.5231884 0.05628563 24.99400
## 9 2 147534397 rs12691757 G A 0.5231884 0.05628563 24.99400
## 10 2 147534556 rs12691758 T G 0.5231884 0.05628563 24.99400
## 11 2 147539904 rs11680551 C T 0.5985507 0.10655070 21.07651
## 12 2 147541431 rs4508547 A T 0.5985507 0.10655070 21.07651
## 13 2 147545563 rs1550320 C A 0.5985507 0.10655070 21.07651
## 14 2 147545749 rs1550319 T C 0.5985507 0.10655070 21.07651
## 15 2 147546686 rs13390421 A G 0.5985507 0.10655070 21.07651
## 16 2 147547000 rs13390757 A G 0.5985507 0.10655070 21.07651
## 17 2 147548196 rs17724539 C T 0.5985507 0.10655070 21.07651
## 18 2 147548936 rs6430197 C T 0.5985507 0.10655070 21.07651
## 19 2 147549509 rs7588634 G A 0.5985507 0.10655070 21.07651
## 20 2 147550185 rs17724612 G A 0.5985507 0.10655070 21.07651
## 21 2 147550211 rs17673675 C T 0.5985507 0.10655070 21.07651
## 22 2 147550250 rs17673688 G A 0.5985507 0.10655070 21.07651
## 23 2 147550602 rs10208724 G A 0.5985507 0.10655070 21.07651
## 24 2 147550839 rs10208983 G A 0.5985507 0.10655070 21.07651
## 25 2 147551751 rs11691040 C T 0.5985507 0.10655070 21.07651
## 26 2 147551802 rs11674017 G A 0.5985507 0.10655070 21.07651
## 27 2 147552307 rs10201767 A T 0.5985507 0.10655070 21.07651
## 28 2 147552592 rs7590223 C T 0.5985507 0.10655070 21.07651
## 29 2 147571339 rs13413754 C A 0.5840580 0.10888136 22.95492
## 30 2 147571830 rs13404881 C T 0.5840580 0.10888136 22.95492
## 31 2 147572239 rs12691759 G A 0.5565217 0.09976462 27.51019
## 32 2 147573800 rs4361065 A G 0.5855072 0.10899861 22.91986
## 33 2 147579262 rs1550316 C T 0.5927536 0.10763420 24.92120
## 34 2 147579526 rs11681804 A C 0.5927536 0.10763420 24.92120
## 35 2 147579844 rs12477396 G A 0.5927536 0.10763420 24.92120
## 36 2 147579987 rs12473660 C T 0.5927536 0.10763420 24.92120
## 37 2 147580536 rs2033825 A G 0.5927536 0.10763420 24.92120
## 38 9 108296877 rs10979308 A T 0.8072464 0.10149070 23.32826
## 39 9 108299426 rs1885973 T A 0.7768116 0.07077503 28.83857
## 40 9 108300235 rs10816656 G A 0.7826087 0.07913373 25.92305
## 41 9 108302318 rs1407849 T A 0.8057971 0.10175965 23.56368
## 42 9 108305442 rs16912866 G A 0.7768116 0.07077503 28.83857
## 43 9 108305644 rs16912868 G A 0.7768116 0.07077503 28.83857
## 44 9 108306203 rs1323384 A C 0.8086957 0.09845463 22.82954
## 45 9 108307394 rs7030371 G A 0.7768116 0.07077503 28.83857
## p
## 1 5.750902e-07
## 2 5.750902e-07
## 3 5.750902e-07
## 4 6.528273e-06
## 5 6.528273e-06
## 6 5.750902e-07
## 7 5.750902e-07
## 8 5.750902e-07
## 9 5.750902e-07
## 10 5.750902e-07
## 11 4.413054e-06
## 12 4.413054e-06
## 13 4.413054e-06
## 14 4.413054e-06
## 15 4.413054e-06
## 16 4.413054e-06
## 17 4.413054e-06
## 18 4.413054e-06
## 19 4.413054e-06
## 20 4.413054e-06
## 21 4.413054e-06
## 22 4.413054e-06
## 23 4.413054e-06
## 24 4.413054e-06
## 25 4.413054e-06
## 26 4.413054e-06
## 27 4.413054e-06
## 28 4.413054e-06
## 29 1.658453e-06
## 30 1.658453e-06
## 31 1.562689e-07
## 32 1.688979e-06
## 33 5.972204e-07
## 34 5.972204e-07
## 35 5.972204e-07
## 36 5.972204e-07
## 37 5.972204e-07
## 38 1.365783e-06
## 39 7.866881e-08
## 40 3.553010e-07
## 41 1.208469e-06
## 42 7.866881e-08
## 43 7.866881e-08
## 44 1.770240e-06
## 45 7.866881e-08manhattan(x = pc8_filtered, chr = "chr", bp = "pos", p = "p", snp = "id", ylim = c(0,12), genomewideline = -log10(1.84e-7), main = "Component 8", annotatePval = 1.84e-7)
Evidently, performing this filtering has reduced the number of significant peaks identified. This is not to say that the isolated SNVs are not indicative of true associations, but the time constraint for this project is not permitting for further investigations.