Gblup and GWA of lession scores
Juan P. Steibel
February 17, 2017
Load data
First we load the data and perform a few data object checks
rm(list=ls())
setwd("C:\\Users\\steibelj\\OneDrive\\Documents\\kaitlin")
library(plyr)
#--------------------
load("lesions_wide_format.Rdata")
all_lesions<-lesions_wide_format
library(xlsx)## Loading required package: rJava## Loading required package: xlsxjarshead(all_lesions)## ids Finisher_Post_Front_ls Finisher_Post_Middle_ls Finisher_Post_Rear_ls
## 1 1 63 74 28
## 2 2 44 62 19
## 3 3 58 76 41
## 4 4 79 85 27
## 5 5 101 83 26
## 6 6 NaN NaN NaN
## Finisher_Post_Total_ls Finisher_Pre_Front_ls Finisher_Pre_Middle_ls
## 1 165 11 17
## 2 125 10 27
## 3 175 17 27
## 4 191 15 20
## 5 210 6 15
## 6 NaN NaN NaN
## Finisher_Pre_Rear_ls Finisher_Pre_Total_ls Finisher_Stable_Front_ls
## 1 9 37 19
## 2 10 47 5
## 3 6 50 7
## 4 11 46 4
## 5 9 30 25
## 6 NaN NaN NaN
## Finisher_Stable_Middle_ls Finisher_Stable_Rear_ls
## 1 7 10
## 2 5 0
## 3 15 5
## 4 1 1
## 5 16 6
## 6 NaN NaN
## Finisher_Stable_Total_ls Nursery_Post_Front_ls Nursery_Post_Middle_ls
## 1 36 6 2
## 2 10 1 0
## 3 27 3 3
## 4 6 3 5
## 5 47 10 3
## 6 NaN 8 0
## Nursery_Post_Rear_ls Nursery_Post_Total_ls Nursery_Pre_Front_ls
## 1 2 10 1
## 2 0 1 4
## 3 0 6 0
## 4 4 12 2
## 5 0 13 3
## 6 1 9 1
## Nursery_Pre_Middle_ls Nursery_Pre_Rear_ls Nursery_Pre_Total_ls
## 1 0 0 1
## 2 2 0 6
## 3 0 0 0
## 4 0 0 2
## 5 3 0 6
## 6 0 0 1
## Nursery_Stable_Front_ls Nursery_Stable_Middle_ls Nursery_Stable_Rear_ls
## 1 6 11 7
## 2 0 0 0
## 3 0 1 0
## 4 1 0 0
## 5 1 1 0
## 6 0 0 0
## Nursery_Stable_Total_ls Sow_Post_Front_ls Sow_Post_Middle_ls
## 1 24 95 99
## 2 0 NaN NaN
## 3 1 18 53
## 4 1 NaN NaN
## 5 2 65 27
## 6 0 NaN NaN
## Sow_Post_Rear_ls Sow_Post_Total_ls Sow_Pre_Front_ls Sow_Pre_Middle_ls
## 1 32 226 6 8
## 2 NaN NaN NaN NaN
## 3 30 101 4 8
## 4 NaN NaN NaN NaN
## 5 9 101 12 13
## 6 NaN NaN NaN NaN
## Sow_Pre_Rear_ls Sow_Pre_Total_ls Sow_Stable_Front_ls
## 1 4 18 9
## 2 NaN NaN NaN
## 3 7 19 5
## 4 NaN NaN NaN
## 5 3 28 5
## 6 NaN NaN NaN
## Sow_Stable_Middle_ls Sow_Stable_Rear_ls Sow_Stable_Total_ls
## 1 6 8 23
## 2 NaN NaN NaN
## 3 9 4 18
## 4 NaN NaN NaN
## 5 5 6 16
## 6 NaN NaN NaNall_lesions[is.na(all_lesions)]<-NA
head(all_lesions,0)## [1] ids Finisher_Post_Front_ls
## [3] Finisher_Post_Middle_ls Finisher_Post_Rear_ls
## [5] Finisher_Post_Total_ls Finisher_Pre_Front_ls
## [7] Finisher_Pre_Middle_ls Finisher_Pre_Rear_ls
## [9] Finisher_Pre_Total_ls Finisher_Stable_Front_ls
## [11] Finisher_Stable_Middle_ls Finisher_Stable_Rear_ls
## [13] Finisher_Stable_Total_ls Nursery_Post_Front_ls
## [15] Nursery_Post_Middle_ls Nursery_Post_Rear_ls
## [17] Nursery_Post_Total_ls Nursery_Pre_Front_ls
## [19] Nursery_Pre_Middle_ls Nursery_Pre_Rear_ls
## [21] Nursery_Pre_Total_ls Nursery_Stable_Front_ls
## [23] Nursery_Stable_Middle_ls Nursery_Stable_Rear_ls
## [25] Nursery_Stable_Total_ls Sow_Post_Front_ls
## [27] Sow_Post_Middle_ls Sow_Post_Rear_ls
## [29] Sow_Post_Total_ls Sow_Pre_Front_ls
## [31] Sow_Pre_Middle_ls Sow_Pre_Rear_ls
## [33] Sow_Pre_Total_ls Sow_Stable_Front_ls
## [35] Sow_Stable_Middle_ls Sow_Stable_Rear_ls
## [37] Sow_Stable_Total_ls
## <0 rows> (or 0-length row.names)#Create data
ls(all_lesions)## [1] "Finisher_Post_Front_ls" "Finisher_Post_Middle_ls"
## [3] "Finisher_Post_Rear_ls" "Finisher_Post_Total_ls"
## [5] "Finisher_Pre_Front_ls" "Finisher_Pre_Middle_ls"
## [7] "Finisher_Pre_Rear_ls" "Finisher_Pre_Total_ls"
## [9] "Finisher_Stable_Front_ls" "Finisher_Stable_Middle_ls"
## [11] "Finisher_Stable_Rear_ls" "Finisher_Stable_Total_ls"
## [13] "ids" "Nursery_Post_Front_ls"
## [15] "Nursery_Post_Middle_ls" "Nursery_Post_Rear_ls"
## [17] "Nursery_Post_Total_ls" "Nursery_Pre_Front_ls"
## [19] "Nursery_Pre_Middle_ls" "Nursery_Pre_Rear_ls"
## [21] "Nursery_Pre_Total_ls" "Nursery_Stable_Front_ls"
## [23] "Nursery_Stable_Middle_ls" "Nursery_Stable_Rear_ls"
## [25] "Nursery_Stable_Total_ls" "Sow_Post_Front_ls"
## [27] "Sow_Post_Middle_ls" "Sow_Post_Rear_ls"
## [29] "Sow_Post_Total_ls" "Sow_Pre_Front_ls"
## [31] "Sow_Pre_Middle_ls" "Sow_Pre_Rear_ls"
## [33] "Sow_Pre_Total_ls" "Sow_Stable_Front_ls"
## [35] "Sow_Stable_Middle_ls" "Sow_Stable_Rear_ls"
## [37] "Sow_Stable_Total_ls"lesion_index<-grep("_ls",colnames(all_lesions))
all_lesions[lesion_index]<-log(all_lesions[lesion_index]+1)
load("covar_wide_format.Rdata")
covar_w<-covar_wide_format
ls(covar_w)## [1] "Animal" "BF" "Birthdate"
## [4] "Exit_Wt" "Exit_Wt_Date" "Fin_Wt"
## [7] "Fin_Wt_Date" "Finisher_Pen" "Finisher_Post_Obs"
## [10] "Finisher_Pre_Obs" "Finisher_Stable_Obs" "Litter"
## [13] "LMA" "Nurs_Wt" "Nurs_Wt_Date"
## [16] "Nursery_Pen" "Nursery_Post_Obs" "Nursery_Pre_Obs"
## [19] "Nursery_Stable_Obs" "Rep" "Sex"
## [22] "Sow_Pen" "Sow_Post_Obs" "Sow_Pre_Obs"
## [25] "Sow_Stable_Obs" "Sow_Wt" "Sow_Wt_Date"dim(all_lesions)## [1] 1093 37dim(covar_w)## [1] 1093 27sum(all_lesions$ids!=covar_w$Animal)## [1] 0mp<-read.table("SNP_Map.txt",header=T,sep="\t")
head(mp)## Index Name Chromosome Position GenTrain.Score SNP ILMN.Strand
## 1 1 1_10573221 1 10573221 0.8320 [T/C] BOT
## 2 2 1_10673082 1 10673082 0.8076 [T/C] BOT
## 3 3 1_10723065 1 10723065 0.8107 [A/G] TOP
## 4 4 1_11337555 1 11337555 0.7925 [A/G] TOP
## 5 5 1_11407894 1 11407894 0.8670 [A/G] TOP
## 6 6 1_11426075 1 11426075 0.8675 [T/C] BOT
## Customer.Strand NormID
## 1 BOT 0
## 2 BOT 0
## 3 BOT 0
## 4 BOT 0
## 5 TOP 0
## 6 BOT 0map<-mp[,c(3,4)]
rownames(map)<-mp$Name
colnames(map)<-c("chr","pos")
head(map)## chr pos
## 1_10573221 1 10573221
## 1_10673082 1 10673082
## 1_10723065 1 10723065
## 1_11337555 1 11337555
## 1_11407894 1 11407894
## 1_11426075 1 11426075Create a new dataframe and prepare all columns
dts<-cbind(all_lesions,covar_w)
class(dts)## [1] "data.frame"ls(dts)## [1] "Animal" "BF"
## [3] "Birthdate" "Exit_Wt"
## [5] "Exit_Wt_Date" "Fin_Wt"
## [7] "Fin_Wt_Date" "Finisher_Pen"
## [9] "Finisher_Post_Front_ls" "Finisher_Post_Middle_ls"
## [11] "Finisher_Post_Obs" "Finisher_Post_Rear_ls"
## [13] "Finisher_Post_Total_ls" "Finisher_Pre_Front_ls"
## [15] "Finisher_Pre_Middle_ls" "Finisher_Pre_Obs"
## [17] "Finisher_Pre_Rear_ls" "Finisher_Pre_Total_ls"
## [19] "Finisher_Stable_Front_ls" "Finisher_Stable_Middle_ls"
## [21] "Finisher_Stable_Obs" "Finisher_Stable_Rear_ls"
## [23] "Finisher_Stable_Total_ls" "ids"
## [25] "Litter" "LMA"
## [27] "Nurs_Wt" "Nurs_Wt_Date"
## [29] "Nursery_Pen" "Nursery_Post_Front_ls"
## [31] "Nursery_Post_Middle_ls" "Nursery_Post_Obs"
## [33] "Nursery_Post_Rear_ls" "Nursery_Post_Total_ls"
## [35] "Nursery_Pre_Front_ls" "Nursery_Pre_Middle_ls"
## [37] "Nursery_Pre_Obs" "Nursery_Pre_Rear_ls"
## [39] "Nursery_Pre_Total_ls" "Nursery_Stable_Front_ls"
## [41] "Nursery_Stable_Middle_ls" "Nursery_Stable_Obs"
## [43] "Nursery_Stable_Rear_ls" "Nursery_Stable_Total_ls"
## [45] "Rep" "Sex"
## [47] "Sow_Pen" "Sow_Post_Front_ls"
## [49] "Sow_Post_Middle_ls" "Sow_Post_Obs"
## [51] "Sow_Post_Rear_ls" "Sow_Post_Total_ls"
## [53] "Sow_Pre_Front_ls" "Sow_Pre_Middle_ls"
## [55] "Sow_Pre_Obs" "Sow_Pre_Rear_ls"
## [57] "Sow_Pre_Total_ls" "Sow_Stable_Front_ls"
## [59] "Sow_Stable_Middle_ls" "Sow_Stable_Obs"
## [61] "Sow_Stable_Rear_ls" "Sow_Stable_Total_ls"
## [63] "Sow_Wt" "Sow_Wt_Date"sum(dts$ids!=dts$Animal)## [1] 0rownames(dts)<-dts$id
sum(rownames(dts)!=dts$Animal)## [1] 0pen_idx<-grep("Pen",colnames(dts))
penf<-as.data.frame(lapply(dts[,pen_idx],as.factor))
ob_idx<-grep("Obs",colnames(dts))
obsf<-as.data.frame(lapply(dts[,ob_idx],as.factor))
pastef<-function(v1,v2,sep="_"){
re<-paste(v1,v2,sep=sep)
re[is.na(v1)|is.na(v2)]<-NA
re<-as.factor(re)
return(re)
}
dts[,pen_idx]<-as.data.frame(lapply(penf,pastef,v2=dts$Rep))
sapply(dts[,pen_idx],levels)## $Nursery_Pen
## [1] "ML1_2" "ML1_3" "ML1_4" "ML1_5" "ML1_6" "ML1_7" "ML1_8" "ML1_9"
## [9] "ML2_2" "ML2_3" "ML2_4" "ML2_5" "ML2_6" "ML2_7" "ML2_8" "ML2_9"
## [17] "ML3_2" "ML3_3" "ML3_4" "ML3_5" "ML3_6" "ML3_7" "ML3_8" "ML3_9"
## [25] "ML4_2" "ML4_3" "ML4_4" "ML4_5" "ML4_6" "ML4_7" "ML4_8" "ML4_9"
## [33] "ML5_2" "ML5_3" "ML5_4" "ML5_5" "ML5_6" "ML5_7" "ML5_8" "ML5_9"
## [41] "ML6_2" "ML6_3" "ML6_4" "ML6_5" "ML6_6" "ML6_7" "ML6_8" "ML6_9"
## [49] "ML7_3" "ML7_4" "ML7_5" "ML7_6" "ML7_7" "ML7_8" "ML7_9" "MR1_2"
## [57] "MR1_3" "MR1_4" "MR1_5" "MR1_6" "MR1_7" "MR1_8" "MR1_9" "MR2_2"
## [65] "MR2_3" "MR2_4" "MR2_5" "MR2_6" "MR2_7" "MR2_8" "MR2_9" "MR3_2"
## [73] "MR3_3" "MR3_4" "MR3_5" "MR3_6" "MR3_7" "MR3_8" "MR3_9" "MR4_2"
## [81] "MR4_3" "MR4_4" "MR4_5" "MR4_6" "MR4_7" "MR4_8" "MR4_9" "MR5_2"
## [89] "MR5_3" "MR5_4" "MR5_5" "MR5_6" "MR5_7" "MR5_8" "MR5_9" "MR6_2"
## [97] "MR6_3" "MR6_4" "MR6_5" "MR6_6" "MR6_7" "MR6_8" "MR6_9" "MR7_3"
## [105] "MR7_4" "MR7_5" "MR7_6" "MR7_7" "MR7_8" "MR7_9"
##
## $Finisher_Pen
## [1] "10_2" "10_3" "10_4" "10_5" "10_6" "10_7" "10_8" "10_9" "11_2" "11_3"
## [11] "11_4" "11_5" "11_6" "11_7" "11_8" "11_9" "12_2" "12_3" "12_4" "12_5"
## [21] "12_6" "12_7" "12_8" "12_9" "13_2" "13_3" "13_4" "13_5" "13_6" "13_7"
## [31] "13_8" "13_9" "2_2" "2_3" "2_4" "2_5" "2_6" "2_7" "2_8" "2_9"
## [41] "3_2" "3_3" "3_4" "3_5" "3_6" "3_7" "3_8" "3_9" "4_2" "4_3"
## [51] "4_4" "4_5" "4_6" "4_7" "4_8" "4_9" "5_2" "5_3" "5_4" "5_5"
## [61] "5_6" "5_7" "5_8" "5_9" "6_3" "6_4" "6_5" "6_6" "6_7" "6_8"
## [71] "6_9" "9_3" "9_4" "9_5" "9_6" "9_7" "9_8" "9_9"
##
## $Sow_Pen
## [1] "10_2" "10_6" "10_7" "10_8" "11_3" "11_4" "11_5" "11_9" "12_2" "12_6"
## [11] "12_7" "12_8" "13_3" "13_4" "13_5" "13_9" "2_2" "2_6" "2_7" "2_8"
## [21] "3_3" "3_4" "3_5" "3_7" "3_9" "4_2" "4_3" "4_6" "4_7" "4_8"
## [31] "5_3" "5_4" "5_5" "5_9" "6_6" "6_7" "6_8" "9_3" "9_4" "9_5"
## [41] "9_9"dts[,ob_idx]<-obsf
sapply(dts[,ob_idx],levels)## $Finisher_Post_Obs
## [1] "CO" "JS" "KW"
##
## $Finisher_Pre_Obs
## [1] "CO" "JS" "KW"
##
## $Finisher_Stable_Obs
## [1] "CO" "JS" "KW"
##
## $Nursery_Post_Obs
## [1] "CO" "JS" "KW"
##
## $Nursery_Pre_Obs
## [1] "#N/A" "CO" "JS" "KW"
##
## $Nursery_Stable_Obs
## [1] "CO" "JS" "KW"
##
## $Sow_Post_Obs
## [1] "CO" "JS" "KW"
##
## $Sow_Pre_Obs
## [1] "CO" "JS" "KW"
##
## $Sow_Stable_Obs
## [1] "CO" "JS" "KW"dts[is.na(dts)]<-NA
dts$Rep<-as.factor(dts$Rep)
na<-rowSums(is.na(dts))#
#dts<-dts[na==0,]
dim(dts)## [1] 1093 64colnames(dts)## [1] "ids" "Finisher_Post_Front_ls"
## [3] "Finisher_Post_Middle_ls" "Finisher_Post_Rear_ls"
## [5] "Finisher_Post_Total_ls" "Finisher_Pre_Front_ls"
## [7] "Finisher_Pre_Middle_ls" "Finisher_Pre_Rear_ls"
## [9] "Finisher_Pre_Total_ls" "Finisher_Stable_Front_ls"
## [11] "Finisher_Stable_Middle_ls" "Finisher_Stable_Rear_ls"
## [13] "Finisher_Stable_Total_ls" "Nursery_Post_Front_ls"
## [15] "Nursery_Post_Middle_ls" "Nursery_Post_Rear_ls"
## [17] "Nursery_Post_Total_ls" "Nursery_Pre_Front_ls"
## [19] "Nursery_Pre_Middle_ls" "Nursery_Pre_Rear_ls"
## [21] "Nursery_Pre_Total_ls" "Nursery_Stable_Front_ls"
## [23] "Nursery_Stable_Middle_ls" "Nursery_Stable_Rear_ls"
## [25] "Nursery_Stable_Total_ls" "Sow_Post_Front_ls"
## [27] "Sow_Post_Middle_ls" "Sow_Post_Rear_ls"
## [29] "Sow_Post_Total_ls" "Sow_Pre_Front_ls"
## [31] "Sow_Pre_Middle_ls" "Sow_Pre_Rear_ls"
## [33] "Sow_Pre_Total_ls" "Sow_Stable_Front_ls"
## [35] "Sow_Stable_Middle_ls" "Sow_Stable_Rear_ls"
## [37] "Sow_Stable_Total_ls" "Animal"
## [39] "Rep" "Litter"
## [41] "Sex" "Nursery_Pen"
## [43] "Finisher_Pen" "Sow_Pen"
## [45] "Finisher_Post_Obs" "Finisher_Pre_Obs"
## [47] "Finisher_Stable_Obs" "Nursery_Post_Obs"
## [49] "Nursery_Pre_Obs" "Nursery_Stable_Obs"
## [51] "Sow_Post_Obs" "Sow_Pre_Obs"
## [53] "Sow_Stable_Obs" "Birthdate"
## [55] "Nurs_Wt_Date" "Nurs_Wt"
## [57] "Fin_Wt_Date" "Fin_Wt"
## [59] "Sow_Wt_Date" "Sow_Wt"
## [61] "Exit_Wt_Date" "Exit_Wt"
## [63] "BF" "LMA"lsdata<-dts
save(lsdata,file="lession_plus.Rdata")Start to prepare data for gwa and gblup
library(regress)
library(gwaR)
load("Z_and_G_matrices.Rdata")
dim(G)## [1] 1079 1079dim(Z)## [1] 1079 52925#----------------------------
dim(map)## [1] 68516 2sum(!colnames(Z)%in%rownames(map))## [1] 0map<-map[colnames(Z),]
dim(map)## [1] 52925 2to_include<-as.character(map$chr)%in%(1:18)
map<-map[to_include,]
Z<-Z[,to_include]
map$chr<-droplevels(map$chr)
#Notice an issue with the SNP: many unpositioned SNP and even a Y-chr SNP!
#unmapped SNP are OK for computing G but they should be dropped from GWA
#Y-snp and X-SNP COULD be included in GWA. not in G.now create a dataframe with analysis details and run gwa
cont_struc<-rbind(
c("Finisher_Post_Front_ls",y~Sex+Rep+Finisher_Pre_Front_ls+Finisher_Pre_Obs:Finisher_Post_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Post_Middle_ls",y~Sex+Rep+Finisher_Pre_Middle_ls+Finisher_Pre_Obs:Finisher_Post_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Post_Rear_ls",y~Sex+Rep+Finisher_Pre_Rear_ls+Finisher_Pre_Obs:Finisher_Post_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Post_Total_ls",y~Sex+Rep+Finisher_Pre_Total_ls+Finisher_Pre_Obs:Finisher_Post_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Stable_Front_ls",y~Sex+Rep+Finisher_Stable_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Stable_Middle_ls",y~Sex+Rep+Finisher_Stable_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Stable_Rear_ls",y~Sex+Rep+Finisher_Stable_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Stable_Total_ls",y~Sex+Rep+Finisher_Stable_Obs+Fin_Wt,~Finisher_Pen),
c("Nursery_Post_Front_ls",y~Sex+Rep+Nursery_Pre_Front_ls+Nursery_Pre_Obs:Nursery_Post_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Post_Middle_ls",y~Sex+Rep+Nursery_Pre_Middle_ls+Nursery_Pre_Obs:Nursery_Post_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Post_Rear_ls",y~Sex+Rep+Nursery_Pre_Rear_ls+Nursery_Pre_Obs:Nursery_Post_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Post_Total_ls",y~Sex+Rep+Nursery_Pre_Total_ls+Nursery_Pre_Obs:Nursery_Post_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Stable_Front_ls",y~Sex+Rep+Nursery_Stable_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Stable_Middle_ls",y~Sex+Rep+Nursery_Stable_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Stable_Rear_ls",y~Sex+Rep+Nursery_Stable_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Stable_Total_ls",y~Sex+Rep+Nursery_Stable_Obs+Nurs_Wt,~Nursery_Pen),
c("Sow_Post_Front_ls",y~Rep+Sow_Pre_Front_ls+Sow_Pre_Obs:Sow_Post_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Post_Middle_ls",y~Rep+Sow_Pre_Middle_ls+Sow_Pre_Obs:Sow_Post_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Post_Rear_ls",y~Rep+Sow_Pre_Rear_ls+Sow_Pre_Obs:Sow_Post_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Post_Total_ls",y~Rep+Sow_Pre_Total_ls+Sow_Pre_Obs:Sow_Post_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Stable_Front_ls",y~Rep+Sow_Stable_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Stable_Middle_ls",y~Rep+Sow_Stable_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Stable_Rear_ls",y~Rep+Sow_Stable_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Stable_Total_ls",y~Rep+Sow_Stable_Obs+Sow_Wt,~Sow_Pen)
)
for(i in 1:nrow(cont_struc)){
#obtain GBLUP
system.time({
gb<-gblup(cont_struc[i,][[1]],dts,
c(cont_struc[i,][[2]],cont_struc[i,][[3]]),
G,pos=c(T,T,T))
})
#check VCs
print(cont_struc[i,][[1]])
print(gb)
print(varcomp(gb))
#perform GWA
system.time({
GV<-gwas(gb,t(Z))
})
#summarize it
print(summary(GV)) #this is not working OK, I guess. Check and compare to getpvalue.
#pvalues
pv<-getpvalue(GV,log.p = F)
#manhattan plot and qqplots
manhattan_plot(pv,map, threshold = 0.000001,main=cont_struc[i,][[1]])
qqgplot(pv,main=cont_struc[i,][[1]])
}## [1] "Finisher_Post_Front_ls"
## gblup analysis of trait: Finisher_Post_Front_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Finisher_Pre_Front_ls + Fin_Wt + Finisher_Pre_Obs:Finisher_Post_Obs
##
## random effects equation:
## ~G + Finisher_Pen + In
##
## log-likelihood: -2.503498 converged in: 8 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) 2.45441804 0.198025917 12.3944283
## Sexg -0.01783826 0.051382887 -0.3471634
## Rep3 0.04289838 0.163795312 0.2619024
## Rep4 -0.36002593 0.151459251 -2.3770481
## Rep5 -0.33090323 0.160178587 -2.0658394
## Rep6 -0.41562600 0.155842438 -2.6669629
## Rep7 -0.32645411 0.198425760 -1.6452204
## Rep8 -0.29033199 0.160220606 -1.8120765
## Rep9 -0.33990354 0.163433370 -2.0797683
## Finisher_Pre_Front_ls 0.18028680 0.038504871 4.6821817
## Fin_Wt 0.01457692 0.002890343 5.0433170
## Finisher_Pre_ObsCO:Finisher_Post_ObsCO 0.53471291 0.114539112 4.6683871
## Finisher_Pre_ObsJS:Finisher_Post_ObsCO 0.64664254 0.163561409 3.9535154
## Finisher_Pre_ObsKW:Finisher_Post_ObsCO 0.63791913 0.111889370 5.7013381
## Finisher_Pre_ObsCO:Finisher_Post_ObsJS 0.23158274 0.171093562 1.3535444
## Finisher_Pre_ObsJS:Finisher_Post_ObsJS 0.37143598 0.074350365 4.9957520
## Finisher_Pre_ObsKW:Finisher_Post_ObsJS 0.42474683 0.063984091 6.6383194
## Finisher_Pre_ObsCO:Finisher_Post_ObsKW 0.09640274 0.137078319 0.7032676
## Finisher_Pre_ObsJS:Finisher_Post_ObsKW 0.14199435 0.073132075 1.9416153
## p.value
## (Intercept) 0.000000e+00
## Sexg 7.284686e-01
## Rep3 7.933967e-01
## Rep4 1.745181e-02
## Rep5 3.884365e-02
## Rep6 7.654013e-03
## Rep7 9.992437e-02
## Rep8 6.997438e-02
## Rep9 3.754679e-02
## Finisher_Pre_Front_ls 2.838377e-06
## Fin_Wt 4.575304e-07
## Finisher_Pre_ObsCO:Finisher_Post_ObsCO 3.035736e-06
## Finisher_Pre_ObsJS:Finisher_Post_ObsCO 7.701132e-05
## Finisher_Pre_ObsKW:Finisher_Post_ObsCO 1.188706e-08
## Finisher_Pre_ObsCO:Finisher_Post_ObsJS 1.758818e-01
## Finisher_Pre_ObsJS:Finisher_Post_ObsJS 5.860694e-07
## Finisher_Pre_ObsKW:Finisher_Post_ObsJS 3.172795e-11
## Finisher_Pre_ObsCO:Finisher_Post_ObsKW 4.818890e-01
## Finisher_Pre_ObsJS:Finisher_Post_ObsKW 5.218369e-02
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.13495722 0.02603022 0.31949855
## Finisher_Pen 0.02495741 0.00892020 0.05908433
## In 0.26248858 0.01862037 0.62141711
## Estimate StdError prop.var se
## G 0.13495722 0.02603022 0.31949855 0.05514736
## Finisher_Pen 0.02495741 0.00892020 0.05908433 0.02068122
## In 0.26248858 0.01862037 0.62141711 0.06361812
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 1 2 3 3
## attr(,"class")
## [1] "summary.gwas"
## [1] "Finisher_Post_Middle_ls"
## gblup analysis of trait: Finisher_Post_Middle_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Finisher_Pre_Middle_ls + Fin_Wt + Finisher_Pre_Obs:Finisher_Post_Obs
##
## random effects equation:
## ~G + Finisher_Pen + In
##
## log-likelihood: 73.5974 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) 2.472075723 0.203727253 12.1342416
## Sexg -0.026556804 0.062130029 -0.4274391
## Rep3 -0.037641871 0.156151477 -0.2410600
## Rep4 -0.704142316 0.151277635 -4.6546359
## Rep5 -0.439603838 0.161065154 -2.7293541
## Rep6 -0.429542184 0.152009975 -2.8257500
## Rep7 -0.728477065 0.191246145 -3.8091072
## Rep8 -0.586810856 0.153608411 -3.8201740
## Rep9 -0.578050081 0.164472478 -3.5145703
## Finisher_Pre_Middle_ls 0.298567790 0.034804054 8.5785348
## Fin_Wt 0.007613344 0.002945693 2.5845679
## Finisher_Pre_ObsCO:Finisher_Post_ObsCO 0.774296945 0.112857824 6.8608176
## Finisher_Pre_ObsJS:Finisher_Post_ObsCO 0.852159973 0.160887192 5.2966303
## Finisher_Pre_ObsKW:Finisher_Post_ObsCO 0.942975981 0.107606853 8.7631592
## Finisher_Pre_ObsCO:Finisher_Post_ObsJS 0.271038318 0.164960703 1.6430478
## Finisher_Pre_ObsJS:Finisher_Post_ObsJS 0.374385893 0.074590433 5.0192213
## Finisher_Pre_ObsKW:Finisher_Post_ObsJS 0.591883362 0.063039635 9.3890671
## Finisher_Pre_ObsCO:Finisher_Post_ObsKW -0.095251599 0.130434368 -0.7302646
## Finisher_Pre_ObsJS:Finisher_Post_ObsKW -0.036831711 0.070967529 -0.5189939
## p.value
## (Intercept) 0.000000e+00
## Sexg 6.690595e-01
## Rep3 8.095086e-01
## Rep4 3.245536e-06
## Rep5 6.345852e-03
## Rep6 4.717007e-03
## Rep7 1.394695e-04
## Rep8 1.333576e-04
## Rep9 4.404661e-04
## Finisher_Pre_Middle_ls 0.000000e+00
## Fin_Wt 9.750111e-03
## Finisher_Pre_ObsCO:Finisher_Post_ObsCO 6.846745e-12
## Finisher_Pre_ObsJS:Finisher_Post_ObsCO 1.179592e-07
## Finisher_Pre_ObsKW:Finisher_Post_ObsCO 0.000000e+00
## Finisher_Pre_ObsCO:Finisher_Post_ObsJS 1.003730e-01
## Finisher_Pre_ObsJS:Finisher_Post_ObsJS 5.188135e-07
## Finisher_Pre_ObsKW:Finisher_Post_ObsJS 0.000000e+00
## Finisher_Pre_ObsCO:Finisher_Post_ObsKW 4.652285e-01
## Finisher_Pre_ObsJS:Finisher_Post_ObsKW 6.037650e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.05529746 0.01601668 0.1531418
## Finisher_Pen 0.05283027 0.01298733 0.1463091
## In 0.25295895 0.01554128 0.7005491
## Estimate StdError prop.var se
## G 0.05529746 0.01601668 0.1531418 0.04252140
## Finisher_Pen 0.05283027 0.01298733 0.1463091 0.03250257
## In 0.25295895 0.01554128 0.7005491 0.05804723
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 1 5
## attr(,"class")
## [1] "summary.gwas"
## [1] "Finisher_Post_Rear_ls"
## gblup analysis of trait: Finisher_Post_Rear_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Finisher_Pre_Rear_ls + Fin_Wt + Finisher_Pre_Obs:Finisher_Post_Obs
##
## random effects equation:
## ~G + Finisher_Pen + In
##
## log-likelihood: -1.453691 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) 2.037957402 0.231001720 8.8222607
## Sexg -0.173197136 0.077576196 -2.2326067
## Rep3 0.085693918 0.188181199 0.4553798
## Rep4 -0.586260174 0.184276680 -3.1814127
## Rep5 -0.289348655 0.193292661 -1.4969459
## Rep6 -0.334100901 0.184344994 -1.8123676
## Rep7 -0.708072277 0.223069500 -3.1742227
## Rep8 -0.411214533 0.186256157 -2.2077903
## Rep9 -0.414346801 0.196517182 -2.1084508
## Finisher_Pre_Rear_ls 0.254389730 0.035289600 7.2086318
## Fin_Wt 0.008879167 0.003352343 2.6486453
## Finisher_Pre_ObsCO:Finisher_Post_ObsCO 0.595956397 0.119009652 5.0076308
## Finisher_Pre_ObsJS:Finisher_Post_ObsCO 0.841101347 0.172356128 4.8800200
## Finisher_Pre_ObsKW:Finisher_Post_ObsCO 0.624064838 0.116541093 5.3548909
## Finisher_Pre_ObsCO:Finisher_Post_ObsJS 0.160546817 0.176207532 0.9111235
## Finisher_Pre_ObsJS:Finisher_Post_ObsJS 0.256474981 0.078799921 3.2547619
## Finisher_Pre_ObsKW:Finisher_Post_ObsJS 0.343767026 0.069128604 4.9728623
## Finisher_Pre_ObsCO:Finisher_Post_ObsKW 0.214036772 0.138702155 1.5431395
## Finisher_Pre_ObsJS:Finisher_Post_ObsKW -0.023558858 0.073650842 -0.3198722
## p.value
## (Intercept) 0.000000e+00
## Sexg 2.557489e-02
## Rep3 6.488360e-01
## Rep4 1.465587e-03
## Rep5 1.344073e-01
## Rep6 6.992941e-02
## Rep7 1.502384e-03
## Rep8 2.725890e-02
## Rep9 3.499202e-02
## Finisher_Pre_Rear_ls 5.651035e-13
## Fin_Wt 8.081511e-03
## Finisher_Pre_ObsCO:Finisher_Post_ObsCO 5.510411e-07
## Finisher_Pre_ObsJS:Finisher_Post_ObsCO 1.060751e-06
## Finisher_Pre_ObsKW:Finisher_Post_ObsCO 8.560805e-08
## Finisher_Pre_ObsCO:Finisher_Post_ObsJS 3.622303e-01
## Finisher_Pre_ObsJS:Finisher_Post_ObsJS 1.134875e-03
## Finisher_Pre_ObsKW:Finisher_Post_ObsJS 6.597153e-07
## Finisher_Pre_ObsCO:Finisher_Post_ObsKW 1.227969e-01
## Finisher_Pre_ObsJS:Finisher_Post_ObsKW 7.490652e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.06884347 0.01893582 0.1553651
## Finisher_Pen 0.09203077 0.02020550 0.2076939
## In 0.28223347 0.01764812 0.6369410
## Estimate StdError prop.var se
## G 0.06884347 0.01893582 0.1553651 0.04108823
## Finisher_Pen 0.09203077 0.02020550 0.2076939 0.03861665
## In 0.28223347 0.01764812 0.6369410 0.05574232
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Finisher_Post_Total_ls"
## gblup analysis of trait: Finisher_Post_Total_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Finisher_Pre_Total_ls + Fin_Wt + Finisher_Pre_Obs:Finisher_Post_Obs
##
## random effects equation:
## ~G + Finisher_Pen + In
##
## log-likelihood: 157.4845 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) 3.092702216 0.196991198 15.6996975
## Sexg -0.058589427 0.054263746 -1.0797159
## Rep3 -0.049477369 0.151414917 -0.3267668
## Rep4 -0.560677891 0.143069271 -3.9189260
## Rep5 -0.398181492 0.150712502 -2.6419938
## Rep6 -0.402578372 0.145409280 -2.7685879
## Rep7 -0.543521617 0.181161683 -3.0002018
## Rep8 -0.440875516 0.148252870 -2.9738076
## Rep9 -0.451153068 0.153600306 -2.9371886
## Finisher_Pre_Total_ls 0.305531383 0.036879200 8.2846533
## Fin_Wt 0.009491378 0.002713596 3.4977123
## Finisher_Pre_ObsCO:Finisher_Post_ObsCO 0.614205225 0.101830225 6.0316593
## Finisher_Pre_ObsJS:Finisher_Post_ObsCO 0.746805228 0.145977792 5.1158825
## Finisher_Pre_ObsKW:Finisher_Post_ObsCO 0.773509640 0.097914723 7.8998297
## Finisher_Pre_ObsCO:Finisher_Post_ObsJS 0.193379284 0.150317735 1.2864702
## Finisher_Pre_ObsJS:Finisher_Post_ObsJS 0.312862263 0.067632540 4.6259133
## Finisher_Pre_ObsKW:Finisher_Post_ObsJS 0.472988255 0.057316885 8.2521626
## Finisher_Pre_ObsCO:Finisher_Post_ObsKW 0.053948019 0.118475617 0.4553512
## Finisher_Pre_ObsJS:Finisher_Post_ObsKW 0.008951760 0.064567071 0.1386428
## p.value
## (Intercept) 0.000000e+00
## Sexg 2.802687e-01
## Rep3 7.438443e-01
## Rep4 8.894440e-05
## Rep5 8.241957e-03
## Rep6 5.629980e-03
## Rep7 2.698007e-03
## Rep8 2.941294e-03
## Rep9 3.312026e-03
## Finisher_Pre_Total_ls 2.220446e-16
## Fin_Wt 4.692671e-04
## Finisher_Pre_ObsCO:Finisher_Post_ObsCO 1.622847e-09
## Finisher_Pre_ObsJS:Finisher_Post_ObsCO 3.122775e-07
## Finisher_Pre_ObsKW:Finisher_Post_ObsCO 2.886580e-15
## Finisher_Pre_ObsCO:Finisher_Post_ObsJS 1.982790e-01
## Finisher_Pre_ObsJS:Finisher_Post_ObsJS 3.729514e-06
## Finisher_Pre_ObsKW:Finisher_Post_ObsJS 2.220446e-16
## Finisher_Pre_ObsCO:Finisher_Post_ObsKW 6.488566e-01
## Finisher_Pre_ObsJS:Finisher_Post_ObsKW 8.897324e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.08386041 0.017570578 0.2650744
## Finisher_Pen 0.03826893 0.009887334 0.1209642
## In 0.19423625 0.013356934 0.6139614
## Estimate StdError prop.var se
## G 0.08386041 0.017570578 0.2650744 0.05095720
## Finisher_Pen 0.03826893 0.009887334 0.1209642 0.02916262
## In 0.19423625 0.013356934 0.6139614 0.06002648
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 2 3 3 4
## attr(,"class")
## [1] "summary.gwas"
## [1] "Finisher_Stable_Front_ls"
## gblup analysis of trait: Finisher_Stable_Front_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Finisher_Stable_Obs + Fin_Wt
##
## random effects equation:
## ~G + Finisher_Pen + In
##
## log-likelihood: -134.5507 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 1.918656e+00 0.240441732 7.979712922 1.554312e-15
## Sexg 1.892485e-01 0.071051012 2.663558489 7.731897e-03
## Rep3 4.769760e-01 0.182128575 2.618897305 8.821450e-03
## Rep4 1.001010e-01 0.179087864 0.558949170 5.761964e-01
## Rep5 1.683441e-01 0.175626171 0.958536435 3.377923e-01
## Rep6 -4.539540e-01 0.185753770 -2.443848096 1.453154e-02
## Rep7 -4.216113e-01 0.189544246 -2.224342585 2.612540e-02
## Rep8 -1.409232e-01 0.188531603 -0.747478046 4.547751e-01
## Rep9 -4.129692e-01 0.178240647 -2.316919437 2.050812e-02
## Finisher_Stable_ObsJS 2.920675e-01 0.102558710 2.847807725 4.402151e-03
## Finisher_Stable_ObsKW -2.639171e-01 0.069635382 -3.789985271 1.506562e-04
## Fin_Wt -2.778651e-05 0.003484568 -0.007974164 9.936376e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.09455502 0.02539940 0.1776062
## Finisher_Pen 0.06278573 0.01667261 0.1179327
## In 0.37504510 0.02349282 0.7044611
## Estimate StdError prop.var se
## G 0.09455502 0.02539940 0.1776062 0.04527629
## Finisher_Pen 0.06278573 0.01667261 0.1179327 0.02912678
## In 0.37504510 0.02349282 0.7044611 0.05978193
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Finisher_Stable_Middle_ls"
## gblup analysis of trait: Finisher_Stable_Middle_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Finisher_Stable_Obs + Fin_Wt
##
## random effects equation:
## ~G + Finisher_Pen + In
##
## log-likelihood: -186.2137 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 2.027132665 0.226047647 8.9677229 0.000000e+00
## Sexg 0.138702453 0.061765797 2.2456191 2.472841e-02
## Rep3 0.347397484 0.161152738 2.1557033 3.110684e-02
## Rep4 0.030442006 0.158601674 0.1919400 8.477892e-01
## Rep5 0.271206177 0.155250876 1.7468898 8.065645e-02
## Rep6 -0.400671341 0.165664578 -2.4185698 1.558166e-02
## Rep7 -0.067096845 0.168279524 -0.3987226 6.900976e-01
## Rep8 -0.231351467 0.168054176 -1.3766481 1.686211e-01
## Rep9 -0.465167913 0.158374427 -2.9371403 3.312542e-03
## Finisher_Stable_ObsJS 0.088193911 0.100463823 0.8778674 3.800157e-01
## Finisher_Stable_ObsKW -0.456371565 0.071051300 -6.4231276 1.335025e-10
## Fin_Wt -0.003664094 0.003337107 -1.0979849 2.722111e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.07544427 0.02473285 0.13549504
## Finisher_Pen 0.03441747 0.01263057 0.06181247
## In 0.44694290 0.02643619 0.80269249
## Estimate StdError prop.var se
## G 0.07544427 0.02473285 0.13549504 0.04276909
## Finisher_Pen 0.03441747 0.01263057 0.06181247 0.02203087
## In 0.44694290 0.02643619 0.80269249 0.06053509
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Finisher_Stable_Rear_ls"
## gblup analysis of trait: Finisher_Stable_Rear_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Finisher_Stable_Obs + Fin_Wt
##
## random effects equation:
## ~G + Finisher_Pen + In
##
## log-likelihood: -100.5876 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 1.2068413136 0.23149568 5.2132346 1.855759e-07
## Sexg 0.2038775407 0.07098974 2.8719298 4.079736e-03
## Rep3 0.2802196318 0.16819811 1.6660094 9.571155e-02
## Rep4 -0.0253075960 0.16873048 -0.1499883 8.807739e-01
## Rep5 0.1474591930 0.16536772 0.8917048 3.725512e-01
## Rep6 -0.3507369247 0.17382373 -2.0177736 4.361485e-02
## Rep7 -0.2528402154 0.17571630 -1.4389116 1.501756e-01
## Rep8 -0.2619216162 0.17548253 -1.4925794 1.355473e-01
## Rep9 -0.3784231370 0.16908212 -2.2381026 2.521437e-02
## Finisher_Stable_ObsJS 0.0499060492 0.10064075 0.4958831 6.199769e-01
## Finisher_Stable_ObsKW -0.1600536097 0.06795232 -2.3553812 1.850371e-02
## Fin_Wt 0.0008132142 0.00339638 0.2394356 8.107679e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.05034022 0.01899732 0.1026772
## Finisher_Pen 0.06470916 0.01663071 0.1319850
## In 0.37522720 0.02174951 0.7653378
## Estimate StdError prop.var se
## G 0.05034022 0.01899732 0.1026772 0.03778298
## Finisher_Pen 0.06470916 0.01663071 0.1319850 0.03095487
## In 0.37522720 0.02174951 0.7653378 0.05650681
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Finisher_Stable_Total_ls"
## gblup analysis of trait: Finisher_Stable_Total_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Finisher_Stable_Obs + Fin_Wt
##
## random effects equation:
## ~G + Finisher_Pen + In
##
## log-likelihood: -96.13393 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 2.837924661 0.234685930 12.0924363 0.000000e+00
## Sexg 0.216473205 0.070518294 3.0697453 2.142414e-03
## Rep3 0.417006382 0.178044011 2.3421534 1.917283e-02
## Rep4 0.071087251 0.175584060 0.4048616 6.855792e-01
## Rep5 0.196354530 0.172236311 1.1400298 2.542739e-01
## Rep6 -0.505469287 0.181782626 -2.7806249 5.425439e-03
## Rep7 -0.308942204 0.185297787 -1.6672741 9.545991e-02
## Rep8 -0.229759541 0.184340235 -1.2463885 2.126218e-01
## Rep9 -0.501371163 0.174871261 -2.8670873 4.142688e-03
## Finisher_Stable_ObsJS 0.185690046 0.099871969 1.8592809 6.298733e-02
## Finisher_Stable_ObsKW -0.355069495 0.067396693 -5.2683519 1.376540e-07
## Fin_Wt -0.001850918 0.003397355 -0.5448114 5.858832e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.08449161 0.02317520 0.1699988
## Finisher_Pen 0.06389535 0.01641962 0.1285587
## In 0.34862610 0.02172225 0.7014425
## Estimate StdError prop.var se
## G 0.08449161 0.02317520 0.1699988 0.04439483
## Finisher_Pen 0.06389535 0.01641962 0.1285587 0.03041113
## In 0.34862610 0.02172225 0.7014425 0.05913705
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 1 1
## attr(,"class")
## [1] "summary.gwas"
## [1] "Nursery_Post_Front_ls"
## gblup analysis of trait: Nursery_Post_Front_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Nursery_Pre_Front_ls + Nurs_Wt + Nursery_Pre_Obs:Nursery_Post_Obs
##
## random effects equation:
## ~G + Nursery_Pen + In
##
## log-likelihood: -151.5822 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) 0.52028493 0.193269534 2.6920173
## Sexg 0.18166016 0.055312745 3.2842370
## Rep3 0.56287474 0.177875534 3.1644304
## Rep4 0.65932676 0.166316494 3.9642897
## Rep5 0.07729980 0.177544129 0.4353836
## Rep6 0.54421271 0.178296924 3.0522832
## Rep7 0.22374433 0.188882778 1.1845671
## Rep8 0.65958938 0.216811025 3.0422317
## Rep9 0.33321627 0.163951713 2.0324049
## Nursery_Pre_Front_ls 0.20573797 0.032105268 6.4082311
## Nurs_Wt 0.09268348 0.009905246 9.3570089
## Nursery_Pre_ObsCO:Nursery_Post_ObsCO 0.71009533 0.144156499 4.9258642
## Nursery_Pre_ObsJS:Nursery_Post_ObsCO 0.70680792 0.106588501 6.6311836
## Nursery_Pre_ObsKW:Nursery_Post_ObsCO 0.50178422 0.117109364 4.2847489
## Nursery_Pre_ObsJS:Nursery_Post_ObsJS 0.65701428 0.122878188 5.3468747
## Nursery_Pre_ObsKW:Nursery_Post_ObsJS 0.68577018 0.108959383 6.2938149
## Nursery_Pre_ObsCO:Nursery_Post_ObsKW 0.09526909 0.145251886 0.6558889
## Nursery_Pre_ObsJS:Nursery_Post_ObsKW 0.04788004 0.078173403 0.6124850
## p.value
## (Intercept) 7.102127e-03
## Sexg 1.022589e-03
## Rep3 1.553868e-03
## Rep4 7.361481e-05
## Rep5 6.632840e-01
## Rep6 2.271077e-03
## Rep7 2.361886e-01
## Rep8 2.348310e-03
## Rep9 4.211268e-02
## Nursery_Pre_Front_ls 1.472176e-10
## Nurs_Wt 0.000000e+00
## Nursery_Pre_ObsCO:Nursery_Post_ObsCO 8.398833e-07
## Nursery_Pre_ObsJS:Nursery_Post_ObsCO 3.330047e-11
## Nursery_Pre_ObsKW:Nursery_Post_ObsCO 1.829457e-05
## Nursery_Pre_ObsJS:Nursery_Post_ObsJS 8.948591e-08
## Nursery_Pre_ObsKW:Nursery_Post_ObsJS 3.097573e-10
## Nursery_Pre_ObsCO:Nursery_Post_ObsKW 5.118956e-01
## Nursery_Pre_ObsJS:Nursery_Post_ObsKW 5.402169e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.14012006 0.03081159 0.25772861
## Nursery_Pen 0.03575283 0.01233925 0.06576166
## In 0.36779999 0.02448852 0.67650973
## Estimate StdError prop.var se
## G 0.14012006 0.03081159 0.25772861 0.05200779
## Nursery_Pen 0.03575283 0.01233925 0.06576166 0.02223680
## In 0.36779999 0.02448852 0.67650973 0.06379739
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Nursery_Post_Middle_ls"
## gblup analysis of trait: Nursery_Post_Middle_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Nursery_Pre_Middle_ls + Nurs_Wt + Nursery_Pre_Obs:Nursery_Post_Obs
##
## random effects equation:
## ~G + Nursery_Pen + In
##
## log-likelihood: -145.7139 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) -0.04794030 0.20621751 -0.2324744
## Sexg 0.21998748 0.06343951 3.4676732
## Rep3 1.02170459 0.18017997 5.6704672
## Rep4 0.86545486 0.17201178 5.0313697
## Rep5 0.47288581 0.18729280 2.5248478
## Rep6 0.77606731 0.18638680 4.1637461
## Rep7 0.52539967 0.19655998 2.6729737
## Rep8 0.87023956 0.22184287 3.9227745
## Rep9 0.48243973 0.17318418 2.7857032
## Nursery_Pre_Middle_ls 0.32890191 0.03246949 10.1295689
## Nurs_Wt 0.06341890 0.01077067 5.8881121
## Nursery_Pre_ObsCO:Nursery_Post_ObsCO 1.05379686 0.14927779 7.0593010
## Nursery_Pre_ObsJS:Nursery_Post_ObsCO 1.07152902 0.11427430 9.3768152
## Nursery_Pre_ObsKW:Nursery_Post_ObsCO 1.00768420 0.12397587 8.1280670
## Nursery_Pre_ObsJS:Nursery_Post_ObsJS 0.69529978 0.13360995 5.2039519
## Nursery_Pre_ObsKW:Nursery_Post_ObsJS 0.96572946 0.12149547 7.9486871
## Nursery_Pre_ObsCO:Nursery_Post_ObsKW 0.01672413 0.14752784 0.1133625
## Nursery_Pre_ObsJS:Nursery_Post_ObsKW -0.05084433 0.07877597 -0.6454294
## p.value
## (Intercept) 8.161696e-01
## Sexg 5.249853e-04
## Rep3 1.424087e-08
## Rep4 4.869882e-07
## Rep5 1.157484e-02
## Rep6 3.130683e-05
## Rep7 7.518213e-03
## Rep8 8.753506e-05
## Rep9 5.341175e-03
## Nursery_Pre_Middle_ls 0.000000e+00
## Nurs_Wt 3.906322e-09
## Nursery_Pre_ObsCO:Nursery_Post_ObsCO 1.673328e-12
## Nursery_Pre_ObsJS:Nursery_Post_ObsCO 0.000000e+00
## Nursery_Pre_ObsKW:Nursery_Post_ObsCO 4.440892e-16
## Nursery_Pre_ObsJS:Nursery_Post_ObsJS 1.950945e-07
## Nursery_Pre_ObsKW:Nursery_Post_ObsJS 1.776357e-15
## Nursery_Pre_ObsCO:Nursery_Post_ObsKW 9.097432e-01
## Nursery_Pre_ObsJS:Nursery_Post_ObsKW 5.186490e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.11506607 0.02795631 0.2114905
## Nursery_Pen 0.06363332 0.01604849 0.1169575
## In 0.36537269 0.02367591 0.6715520
## Estimate StdError prop.var se
## G 0.11506607 0.02795631 0.2114905 0.04808309
## Nursery_Pen 0.06363332 0.01604849 0.1169575 0.02788374
## In 0.36537269 0.02367591 0.6715520 0.06044358
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Nursery_Post_Rear_ls"
## gblup analysis of trait: Nursery_Post_Rear_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Nursery_Pre_Rear_ls + Nurs_Wt + Nursery_Pre_Obs:Nursery_Post_Obs
##
## random effects equation:
## ~G + Nursery_Pen + In
##
## log-likelihood: -101.3942 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) -0.23562555 0.21470727 -1.097427
## Sexg 0.14106030 0.06931666 2.035013
## Rep3 1.11954813 0.18720866 5.980216
## Rep4 0.79286511 0.18007897 4.402874
## Rep5 0.42649695 0.19737548 2.160841
## Rep6 0.59762628 0.19585593 3.051357
## Rep7 0.42116792 0.20520638 2.052411
## Rep8 0.80138405 0.22757516 3.521404
## Rep9 0.72677353 0.18353875 3.959782
## Nursery_Pre_Rear_ls 0.27238936 0.03048305 8.935766
## Nurs_Wt 0.08149935 0.01113694 7.317928
## Nursery_Pre_ObsCO:Nursery_Post_ObsCO 0.68952292 0.14816375 4.653790
## Nursery_Pre_ObsJS:Nursery_Post_ObsCO 0.83380810 0.11787977 7.073378
## Nursery_Pre_ObsKW:Nursery_Post_ObsCO 0.69102553 0.12631535 5.470638
## Nursery_Pre_ObsJS:Nursery_Post_ObsJS 0.57552734 0.13902693 4.139682
## Nursery_Pre_ObsKW:Nursery_Post_ObsJS 0.69866632 0.12897821 5.416933
## Nursery_Pre_ObsCO:Nursery_Post_ObsKW -0.05949288 0.14211665 -0.418620
## Nursery_Pre_ObsJS:Nursery_Post_ObsKW -0.07797088 0.07543011 -1.033684
## p.value
## (Intercept) 2.724548e-01
## Sexg 4.184957e-02
## Rep3 2.228426e-09
## Rep4 1.068260e-05
## Rep5 3.070766e-02
## Rep6 2.278099e-03
## Rep7 4.012970e-02
## Rep8 4.292682e-04
## Rep9 7.501806e-05
## Nursery_Pre_Rear_ls 0.000000e+00
## Nurs_Wt 2.517986e-13
## Nursery_Pre_ObsCO:Nursery_Post_ObsCO 3.258892e-06
## Nursery_Pre_ObsJS:Nursery_Post_ObsCO 1.512124e-12
## Nursery_Pre_ObsKW:Nursery_Post_ObsCO 4.484188e-08
## Nursery_Pre_ObsJS:Nursery_Post_ObsJS 3.477871e-05
## Nursery_Pre_ObsKW:Nursery_Post_ObsJS 6.063007e-08
## Nursery_Pre_ObsCO:Nursery_Post_ObsKW 6.754938e-01
## Nursery_Pre_ObsJS:Nursery_Post_ObsKW 3.012840e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.11249825 0.02623556 0.2147921
## Nursery_Pen 0.08972414 0.01903602 0.1713097
## In 0.32153166 0.02131096 0.6138982
## Estimate StdError prop.var se
## G 0.11249825 0.02623556 0.2147921 0.04690876
## Nursery_Pen 0.08972414 0.01903602 0.1713097 0.03281490
## In 0.32153166 0.02131096 0.6138982 0.05715196
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Nursery_Post_Total_ls"
## gblup analysis of trait: Nursery_Post_Total_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Nursery_Pre_Total_ls + Nurs_Wt + Nursery_Pre_Obs:Nursery_Post_Obs
##
## random effects equation:
## ~G + Nursery_Pen + In
##
## log-likelihood: -52.31974 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) 0.928863007 0.186894536 4.96998482
## Sexg 0.197365180 0.055665728 3.54554205
## Rep3 0.784012101 0.170085990 4.60950428
## Rep4 0.725132030 0.160160467 4.52753443
## Rep5 0.195944031 0.172788893 1.13400825
## Rep6 0.586694120 0.172641794 3.39833194
## Rep7 0.316219708 0.182427301 1.73340123
## Rep8 0.761435981 0.206210668 3.69251498
## Rep9 0.428209422 0.159672377 2.68180026
## Nursery_Pre_Total_ls 0.269489168 0.027874731 9.66786617
## Nurs_Wt 0.084588014 0.009682565 8.73611616
## Nursery_Pre_ObsCO:Nursery_Post_ObsCO 0.816521326 0.135377100 6.03145823
## Nursery_Pre_ObsJS:Nursery_Post_ObsCO 0.893055326 0.102296797 8.73004192
## Nursery_Pre_ObsKW:Nursery_Post_ObsCO 0.766867633 0.111470871 6.87953384
## Nursery_Pre_ObsJS:Nursery_Post_ObsJS 0.683794652 0.118741662 5.75867509
## Nursery_Pre_ObsKW:Nursery_Post_ObsJS 0.795854563 0.107446391 7.40699207
## Nursery_Pre_ObsCO:Nursery_Post_ObsKW 0.007813796 0.134592201 0.05805534
## Nursery_Pre_ObsJS:Nursery_Post_ObsKW 0.013384521 0.071681906 0.18672106
## p.value
## (Intercept) 6.695814e-07
## Sexg 3.918065e-04
## Rep3 4.036302e-06
## Rep4 5.967588e-06
## Rep5 2.567911e-01
## Rep6 6.779811e-04
## Rep7 8.302438e-02
## Rep8 2.220473e-04
## Rep9 7.322717e-03
## Nursery_Pre_Total_ls 0.000000e+00
## Nurs_Wt 0.000000e+00
## Nursery_Pre_ObsCO:Nursery_Post_ObsCO 1.624867e-09
## Nursery_Pre_ObsJS:Nursery_Post_ObsCO 0.000000e+00
## Nursery_Pre_ObsKW:Nursery_Post_ObsCO 6.004974e-12
## Nursery_Pre_ObsJS:Nursery_Post_ObsJS 8.477670e-09
## Nursery_Pre_ObsKW:Nursery_Post_ObsJS 1.292300e-13
## Nursery_Pre_ObsCO:Nursery_Post_ObsKW 9.537045e-01
## Nursery_Pre_ObsJS:Nursery_Post_ObsKW 8.518793e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.1205613 0.02590443 0.26144945
## Nursery_Pen 0.0457014 0.01239390 0.09910813
## In 0.2948640 0.01998953 0.63944242
## Estimate StdError prop.var se
## G 0.1205613 0.02590443 0.26144945 0.05150601
## Nursery_Pen 0.0457014 0.01239390 0.09910813 0.02582160
## In 0.2948640 0.01998953 0.63944242 0.06159193
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Nursery_Stable_Front_ls"
## gblup analysis of trait: Nursery_Stable_Front_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Nursery_Stable_Obs + Nurs_Wt
##
## random effects equation:
## ~G + Nursery_Pen + In
##
## log-likelihood: -125.1843 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 0.05191378 0.22993826 0.2257727 8.213782e-01
## Sexg 0.14248748 0.06148409 2.3174691 2.047819e-02
## Rep3 0.50502858 0.18047491 2.7983312 5.136741e-03
## Rep4 2.57578383 0.16575688 15.5395291 0.000000e+00
## Rep5 0.72055975 0.17231820 4.1815651 2.895093e-05
## Rep6 0.10420580 0.17086116 0.6098858 5.419375e-01
## Rep7 0.77662783 0.18063378 4.2994605 1.712144e-05
## Rep8 0.99564467 0.19216065 5.1813139 2.203283e-07
## Rep9 0.98549768 0.17637008 5.5876691 2.301376e-08
## Nursery_Stable_ObsJS 0.52450690 0.14174093 3.7004617 2.152076e-04
## Nursery_Stable_ObsKW 0.01425329 0.12181231 0.1170103 9.068519e-01
## Nurs_Wt 0.03613911 0.01047981 3.4484523 5.638091e-04
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.13342929 0.02892955 0.2516754
## Nursery_Pen 0.05957848 0.01502508 0.1123774
## In 0.33715646 0.02259866 0.6359472
## Estimate StdError prop.var se
## G 0.13342929 0.02892955 0.2516754 0.05023291
## Nursery_Pen 0.05957848 0.01502508 0.1123774 0.02696069
## In 0.33715646 0.02259866 0.6359472 0.06021670
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Nursery_Stable_Middle_ls"
## gblup analysis of trait: Nursery_Stable_Middle_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Nursery_Stable_Obs + Nurs_Wt
##
## random effects equation:
## ~G + Nursery_Pen + In
##
## log-likelihood: -250.0858 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 0.17755714 0.26523439 0.6694348 5.032181e-01
## Sexg 0.17672692 0.07161502 2.4677354 1.359708e-02
## Rep3 0.44989273 0.20685757 2.1748913 2.963826e-02
## Rep4 2.09743303 0.19042460 11.0145067 0.000000e+00
## Rep5 0.81940885 0.19807743 4.1368108 3.521663e-05
## Rep6 0.11844802 0.19608246 0.6040725 5.457954e-01
## Rep7 0.93937217 0.20701518 4.5376969 5.687191e-06
## Rep8 1.03939394 0.22091184 4.7050169 2.538450e-06
## Rep9 0.95736669 0.20328475 4.7094861 2.483421e-06
## Nursery_Stable_ObsJS 0.29977321 0.16513297 1.8153444 6.947100e-02
## Nursery_Stable_ObsKW -0.40757782 0.14191760 -2.8719330 4.079695e-03
## Nurs_Wt 0.03344677 0.01203475 2.7791818 5.449602e-03
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.1694685 0.03658176 0.2504168
## Nursery_Pen 0.0852717 0.02034489 0.1260026
## In 0.4220054 0.02840634 0.6235806
## Estimate StdError prop.var se
## G 0.1694685 0.03658176 0.2504168 0.04981390
## Nursery_Pen 0.0852717 0.02034489 0.1260026 0.02830447
## In 0.4220054 0.02840634 0.6235806 0.05931397
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Nursery_Stable_Rear_ls"
## gblup analysis of trait: Nursery_Stable_Rear_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Nursery_Stable_Obs + Nurs_Wt
##
## random effects equation:
## ~G + Nursery_Pen + In
##
## log-likelihood: -163.3065 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) -0.26571284 0.24766153 -1.0728870 2.833218e-01
## Sexg 0.12316117 0.06870141 1.7927023 7.302054e-02
## Rep3 0.49860215 0.18426767 2.7058579 6.812818e-03
## Rep4 2.13737118 0.17143480 12.4675459 0.000000e+00
## Rep5 0.95787973 0.17959227 5.3336356 9.626564e-08
## Rep6 0.23300880 0.17557604 1.3271105 1.844721e-01
## Rep7 0.72485014 0.18407628 3.9377705 8.224220e-05
## Rep8 1.06579728 0.19927447 5.3483883 8.874089e-08
## Rep9 1.14778616 0.18555116 6.1858203 6.178034e-10
## Nursery_Stable_ObsJS 0.43567000 0.15852723 2.7482344 5.991716e-03
## Nursery_Stable_ObsKW -0.03961583 0.13618886 -0.2908889 7.711363e-01
## Nurs_Wt 0.02443437 0.01127666 2.1668098 3.024936e-02
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.10198776 0.02666872 0.1805120
## Nursery_Pen 0.08243433 0.01867339 0.1459037
## In 0.38056939 0.02393627 0.6735843
## Estimate StdError prop.var se
## G 0.10198776 0.02666872 0.1805120 0.04473628
## Nursery_Pen 0.08243433 0.01867339 0.1459037 0.03042458
## In 0.38056939 0.02393627 0.6735843 0.05790299
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Nursery_Stable_Total_ls"
## gblup analysis of trait: Nursery_Stable_Total_ls
##
## fixed effects equation:
## y ~ Sex + Rep + Nursery_Stable_Obs + Nurs_Wt
##
## random effects equation:
## ~G + Nursery_Pen + In
##
## log-likelihood: -255.3529 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 0.55071490 0.27404687 2.009565 4.447729e-02
## Sexg 0.19020444 0.07427768 2.560721 1.044551e-02
## Rep3 0.69866071 0.21515239 3.247283 1.165124e-03
## Rep4 2.79428026 0.19802040 14.111073 0.000000e+00
## Rep5 1.04462631 0.20566385 5.079290 3.788485e-07
## Rep6 0.21688616 0.20394573 1.063450 2.875778e-01
## Rep7 1.09479267 0.21541399 5.082273 3.729452e-07
## Rep8 1.32760009 0.22982553 5.776556 7.624503e-09
## Rep9 1.31756307 0.21144102 6.231350 4.624314e-10
## Nursery_Stable_ObsJS 0.47429105 0.17127728 2.769142 5.620419e-03
## Nursery_Stable_ObsKW -0.16871427 0.14721883 -1.146010 2.517910e-01
## Nurs_Wt 0.03921775 0.01234299 3.177330 1.486377e-03
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.18900894 0.03859557 0.2710296
## Nursery_Pen 0.09582746 0.02186155 0.1374119
## In 0.41253749 0.02852634 0.5915586
## Estimate StdError prop.var se
## G 0.18900894 0.03859557 0.2710296 0.05059867
## Nursery_Pen 0.09582746 0.02186155 0.1374119 0.02929678
## In 0.41253749 0.02852634 0.5915586 0.05829610
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Sow_Post_Front_ls"
## gblup analysis of trait: Sow_Post_Front_ls
##
## fixed effects equation:
## y ~ Rep + Sow_Pre_Front_ls + Sow_Wt + Sow_Pre_Obs:Sow_Post_Obs
##
## random effects equation:
## ~G + Sow_Pen + In
##
## log-likelihood: -26.24097 converged in: 8 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) 3.926836746 0.363913473 10.7905781
## Rep3 0.051813093 0.281497760 0.1840622
## Rep4 0.120422077 0.217703900 0.5531462
## Rep5 -0.274372183 0.210253663 -1.3049579
## Rep6 -0.205867548 0.206545944 -0.9967155
## Rep7 -0.064169162 0.226270790 -0.2835945
## Rep8 -0.036797580 0.225296770 -0.1633294
## Rep9 -0.221016275 0.194106194 -1.1386359
## Sow_Pre_Front_ls 0.151094424 0.043630335 3.4630590
## Sow_Wt -0.001098105 0.001895563 -0.5793027
## Sow_Pre_ObsCO:Sow_Post_ObsCO 0.596127648 0.120514433 4.9465249
## Sow_Pre_ObsJS:Sow_Post_ObsCO 0.247818181 0.131735795 1.8811757
## Sow_Pre_ObsKW:Sow_Post_ObsCO 0.252480523 0.100811567 2.5044797
## Sow_Pre_ObsCO:Sow_Post_ObsJS 0.047111724 0.241191994 0.1953287
## Sow_Pre_ObsJS:Sow_Post_ObsJS -0.211828952 0.260804849 -0.8122125
## Sow_Pre_ObsKW:Sow_Post_ObsJS 0.167554849 0.187601034 0.8931446
## Sow_Pre_ObsCO:Sow_Post_ObsKW -0.076096394 0.115006804 -0.6616686
## Sow_Pre_ObsJS:Sow_Post_ObsKW -0.282675604 0.129665113 -2.1800436
## p.value
## (Intercept) 0.000000e+00
## Rep3 8.539647e-01
## Rep4 5.801633e-01
## Rep5 1.919072e-01
## Rep6 3.189026e-01
## Rep7 7.767211e-01
## Rep8 8.702591e-01
## Rep9 2.548551e-01
## Sow_Pre_Front_ls 5.340713e-04
## Sow_Wt 5.623850e-01
## Sow_Pre_ObsCO:Sow_Post_ObsCO 7.555006e-07
## Sow_Pre_ObsJS:Sow_Post_ObsCO 5.994802e-02
## Sow_Pre_ObsKW:Sow_Post_ObsCO 1.226316e-02
## Sow_Pre_ObsCO:Sow_Post_ObsJS 8.451356e-01
## Sow_Pre_ObsJS:Sow_Post_ObsJS 4.166697e-01
## Sow_Pre_ObsKW:Sow_Post_ObsJS 3.717797e-01
## Sow_Pre_ObsCO:Sow_Post_ObsKW 5.081836e-01
## Sow_Pre_ObsJS:Sow_Post_ObsKW 2.925423e-02
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.18216408 0.04606445 0.39455808
## Sow_Pen 0.01342438 0.01165887 0.02907651
## In 0.26610296 0.03303881 0.57636541
## Estimate StdError prop.var se
## G 0.18216408 0.04606445 0.39455808 0.09186994
## Sow_Pen 0.01342438 0.01165887 0.02907651 0.02505188
## In 0.26610296 0.03303881 0.57636541 0.10179882
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Sow_Post_Middle_ls"
## gblup analysis of trait: Sow_Post_Middle_ls
##
## fixed effects equation:
## y ~ Rep + Sow_Pre_Middle_ls + Sow_Wt + Sow_Pre_Obs:Sow_Post_Obs
##
## random effects equation:
## ~G + Sow_Pen + In
##
## log-likelihood: -41.8234 converged in: 8 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) 4.169658367 0.371952103 11.21020244
## Rep3 0.544570124 0.282445621 1.92805299
## Rep4 -0.351549323 0.217113603 -1.61919529
## Rep5 -0.128767499 0.210021573 -0.61311558
## Rep6 -0.433896482 0.203798441 -2.12904711
## Rep7 -0.011699653 0.222657003 -0.05254563
## Rep8 -0.170157348 0.221044781 -0.76978677
## Rep9 -0.417441145 0.192388669 -2.16978031
## Sow_Pre_Middle_ls 0.173427091 0.041647772 4.16413850
## Sow_Wt -0.004635885 0.001965786 -2.35828578
## Sow_Pre_ObsCO:Sow_Post_ObsCO 0.443729185 0.126621050 3.50438717
## Sow_Pre_ObsJS:Sow_Post_ObsCO 0.497798545 0.135884474 3.66339532
## Sow_Pre_ObsKW:Sow_Post_ObsCO 0.580404008 0.104864718 5.53478825
## Sow_Pre_ObsCO:Sow_Post_ObsJS 0.723491244 0.252174430 2.86901112
## Sow_Pre_ObsJS:Sow_Post_ObsJS 0.689125590 0.271104515 2.54191853
## Sow_Pre_ObsKW:Sow_Post_ObsJS -0.059968412 0.195689593 -0.30644661
## Sow_Pre_ObsCO:Sow_Post_ObsKW -0.201399563 0.120259801 -1.67470395
## Sow_Pre_ObsJS:Sow_Post_ObsKW -0.223414932 0.133065327 -1.67898683
## p.value
## (Intercept) 0.000000e+00
## Rep3 5.384854e-02
## Rep4 1.054053e-01
## Rep5 5.397999e-01
## Rep6 3.325036e-02
## Rep7 9.580939e-01
## Rep8 4.414264e-01
## Rep9 3.002349e-02
## Sow_Pre_Middle_ls 3.125304e-05
## Sow_Wt 1.835955e-02
## Sow_Pre_ObsCO:Sow_Post_ObsCO 4.576594e-04
## Sow_Pre_ObsJS:Sow_Post_ObsCO 2.488940e-04
## Sow_Pre_ObsKW:Sow_Post_ObsCO 3.116046e-08
## Sow_Pre_ObsCO:Sow_Post_ObsJS 4.117573e-03
## Sow_Pre_ObsJS:Sow_Post_ObsJS 1.102459e-02
## Sow_Pre_ObsKW:Sow_Post_ObsJS 7.592646e-01
## Sow_Pre_ObsCO:Sow_Post_ObsKW 9.399233e-02
## Sow_Pre_ObsJS:Sow_Post_ObsKW 9.315461e-02
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.13350574 0.04217018 0.28361360
## Sow_Pen 0.01528884 0.01280856 0.03247893
## In 0.32193651 0.03506204 0.68390747
## Estimate StdError prop.var se
## G 0.13350574 0.04217018 0.28361360 0.08451652
## Sow_Pen 0.01528884 0.01280856 0.03247893 0.02691903
## In 0.32193651 0.03506204 0.68390747 0.10507261
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Sow_Post_Rear_ls"
## gblup analysis of trait: Sow_Post_Rear_ls
##
## fixed effects equation:
## y ~ Rep + Sow_Pre_Rear_ls + Sow_Wt + Sow_Pre_Obs:Sow_Post_Obs
##
## random effects equation:
## ~G + Sow_Pen + In
##
## log-likelihood: -46.82141 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) 3.736109821 0.433657490 8.6153471
## Rep3 0.499931936 0.334769960 1.4933596
## Rep4 -0.199620815 0.263831558 -0.7566222
## Rep5 -0.060735275 0.254675214 -0.2384813
## Rep6 -0.505125643 0.244379550 -2.0669718
## Rep7 -0.180146997 0.263313879 -0.6841531
## Rep8 -0.130643923 0.265102404 -0.4928055
## Rep9 -0.364860076 0.233964052 -1.5594707
## Sow_Pre_Rear_ls 0.140907124 0.044098461 3.1952844
## Sow_Wt -0.003332089 0.002276351 -1.4637849
## Sow_Pre_ObsCO:Sow_Post_ObsCO 0.118148243 0.126828407 0.9315598
## Sow_Pre_ObsJS:Sow_Post_ObsCO 0.244859726 0.134991012 1.8138965
## Sow_Pre_ObsKW:Sow_Post_ObsCO 0.220292368 0.104455520 2.1089586
## Sow_Pre_ObsCO:Sow_Post_ObsJS 0.322718873 0.303471998 1.0634222
## Sow_Pre_ObsJS:Sow_Post_ObsJS 0.621221365 0.321878090 1.9299896
## Sow_Pre_ObsKW:Sow_Post_ObsJS -0.186641634 0.239597959 -0.7789784
## Sow_Pre_ObsCO:Sow_Post_ObsKW -0.291079387 0.120701959 -2.4115548
## Sow_Pre_ObsJS:Sow_Post_ObsKW -0.018319650 0.132052593 -0.1387299
## p.value
## (Intercept) 0.000000000
## Rep3 0.135343081
## Rep4 0.449276235
## Rep5 0.811507820
## Rep6 0.038736809
## Rep7 0.493878518
## Rep8 0.622150024
## Rep9 0.118885022
## Sow_Pre_Rear_ls 0.001396931
## Sow_Wt 0.143252717
## Sow_Pre_ObsCO:Sow_Post_ObsCO 0.351564074
## Sow_Pre_ObsJS:Sow_Post_ObsCO 0.069693660
## Sow_Pre_ObsKW:Sow_Post_ObsCO 0.034948154
## Sow_Pre_ObsCO:Sow_Post_ObsJS 0.287590509
## Sow_Pre_ObsJS:Sow_Post_ObsJS 0.053608127
## Sow_Pre_ObsKW:Sow_Post_ObsJS 0.435992442
## Sow_Pre_ObsCO:Sow_Post_ObsKW 0.015884665
## Sow_Pre_ObsJS:Sow_Post_ObsKW 0.889663551
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.12871513 0.04169870 0.2558732
## Sow_Pen 0.05581951 0.02330404 0.1109638
## In 0.31850804 0.03480933 0.6331631
## Estimate StdError prop.var se
## G 0.12871513 0.04169870 0.2558732 0.07892359
## Sow_Pen 0.05581951 0.02330404 0.1109638 0.04350077
## In 0.31850804 0.03480933 0.6331631 0.09688989
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Sow_Post_Total_ls"
## gblup analysis of trait: Sow_Post_Total_ls
##
## fixed effects equation:
## y ~ Rep + Sow_Pre_Total_ls + Sow_Wt + Sow_Pre_Obs:Sow_Post_Obs
##
## random effects equation:
## ~G + Sow_Pen + In
##
## log-likelihood: 32.06971 converged in: 8 iterations
##
## estimated fixed effects:
## Estimate StdError test.st
## (Intercept) 4.932059582 0.333365206 14.7947641
## Rep3 0.301615964 0.251847106 1.1976154
## Rep4 -0.090267601 0.194790377 -0.4634089
## Rep5 -0.177845759 0.188337860 -0.9442911
## Rep6 -0.304276856 0.184843363 -1.6461335
## Rep7 -0.028756284 0.202102299 -0.1422858
## Rep8 -0.099046678 0.200860390 -0.4931120
## Rep9 -0.306042772 0.173910068 -1.7597760
## Sow_Pre_Total_ls 0.176888212 0.042211829 4.1904892
## Sow_Wt -0.003206767 0.001710267 -1.8750101
## Sow_Pre_ObsCO:Sow_Post_ObsCO 0.468774919 0.107564962 4.3580633
## Sow_Pre_ObsJS:Sow_Post_ObsCO 0.312543331 0.116806818 2.6757285
## Sow_Pre_ObsKW:Sow_Post_ObsCO 0.342394548 0.089888598 3.8090988
## Sow_Pre_ObsCO:Sow_Post_ObsJS 0.311437016 0.218274588 1.4268130
## Sow_Pre_ObsJS:Sow_Post_ObsJS 0.259363250 0.234951426 1.1039016
## Sow_Pre_ObsKW:Sow_Post_ObsJS 0.060846914 0.169610817 0.3587443
## Sow_Pre_ObsCO:Sow_Post_ObsKW -0.154932323 0.102568470 -1.5105258
## Sow_Pre_ObsJS:Sow_Post_ObsKW -0.218015894 0.114860636 -1.8980906
## p.value
## (Intercept) 0.000000e+00
## Rep3 2.310668e-01
## Rep4 6.430713e-01
## Rep5 3.450209e-01
## Rep6 9.973627e-02
## Rep7 8.868543e-01
## Rep8 6.219334e-01
## Rep9 7.844579e-02
## Sow_Pre_Total_ls 2.783537e-05
## Sow_Wt 6.079133e-02
## Sow_Pre_ObsCO:Sow_Post_ObsCO 1.312184e-05
## Sow_Pre_ObsJS:Sow_Post_ObsCO 7.456703e-03
## Sow_Pre_ObsKW:Sow_Post_ObsCO 1.394742e-04
## Sow_Pre_ObsCO:Sow_Post_ObsJS 1.536338e-01
## Sow_Pre_ObsJS:Sow_Post_ObsJS 2.696358e-01
## Sow_Pre_ObsKW:Sow_Post_ObsJS 7.197864e-01
## Sow_Pre_ObsCO:Sow_Post_ObsKW 1.309093e-01
## Sow_Pre_ObsJS:Sow_Post_ObsKW 5.768415e-02
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.13679494 0.035716597 0.37567499
## Sow_Pen 0.01219085 0.009656551 0.03347928
## In 0.21514529 0.026170859 0.59084573
## Estimate StdError prop.var se
## G 0.13679494 0.035716597 0.37567499 0.09068209
## Sow_Pen 0.01219085 0.009656551 0.03347928 0.02625973
## In 0.21514529 0.026170859 0.59084573 0.10220867
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Sow_Stable_Front_ls"
## gblup analysis of trait: Sow_Stable_Front_ls
##
## fixed effects equation:
## y ~ Rep + Sow_Stable_Obs + Sow_Wt
##
## random effects equation:
## ~G + Sow_Pen + In
##
## log-likelihood: -90.59176 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 1.507945382 0.465561737 3.2389805 1.199578e-03
## Rep3 0.091052639 0.317382953 0.2868857 7.741998e-01
## Rep4 2.053145914 0.253431533 8.1013830 4.440892e-16
## Rep5 -0.356847081 0.284616205 -1.2537834 2.099207e-01
## Rep6 -0.445009090 0.264597107 -1.6818366 9.260054e-02
## Rep7 -0.462216250 0.294731135 -1.5682641 1.168195e-01
## Rep8 -0.453380119 0.295721361 -1.5331328 1.252431e-01
## Rep9 -0.322122003 0.255770695 -1.2594172 2.078797e-01
## Sow_Stable_ObsJS 0.156703786 0.155586888 1.0071786 3.138489e-01
## Sow_Stable_ObsKW 0.257505458 0.086050351 2.9924975 2.767050e-03
## Sow_Wt 0.002167906 0.002440583 0.8882739 3.743934e-01
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.10586982 0.04314596 0.1803979
## Sow_Pen 0.07132743 0.02854838 0.1215391
## In 0.40967111 0.04093395 0.6980630
## Estimate StdError prop.var se
## G 0.10586982 0.04314596 0.1803979 0.07120330
## Sow_Pen 0.07132743 0.02854838 0.1215391 0.04513525
## In 0.40967111 0.04093395 0.6980630 0.09572826
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Sow_Stable_Middle_ls"
## gblup analysis of trait: Sow_Stable_Middle_ls
##
## fixed effects equation:
## y ~ Rep + Sow_Stable_Obs + Sow_Wt
##
## random effects equation:
## ~G + Sow_Pen + In
##
## log-likelihood: -77.53878 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 1.229286346 0.400560305 3.0689170 0.0021483624
## Rep3 0.126971643 0.261296967 0.4859285 0.6270178612
## Rep4 1.879183876 0.201141846 9.3425805 0.0000000000
## Rep5 -0.226567314 0.233684032 -0.9695456 0.3322730630
## Rep6 -0.758527689 0.213143133 -3.5587714 0.0003725936
## Rep7 -0.379274125 0.241735188 -1.5689653 0.1166560216
## Rep8 -0.481105247 0.242755534 -1.9818508 0.0474959431
## Rep9 -0.233868017 0.205127119 -1.1401126 0.2542393861
## Sow_Stable_ObsJS -0.066630819 0.141122939 -0.4721473 0.6368216275
## Sow_Stable_ObsKW -0.058268398 0.080728316 -0.7217839 0.4704273576
## Sow_Wt 0.002104293 0.002142674 0.9820872 0.3260568948
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.06617994 0.03620363 0.12636097
## Sow_Pen 0.02970918 0.01765112 0.05672536
## In 0.42784809 0.03939485 0.81691367
## Estimate StdError prop.var se
## G 0.06617994 0.03620363 0.12636097 0.06765143
## Sow_Pen 0.02970918 0.01765112 0.05672536 0.03281720
## In 0.42784809 0.03939485 0.81691367 0.09913527
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Sow_Stable_Rear_ls"
## gblup analysis of trait: Sow_Stable_Rear_ls
##
## fixed effects equation:
## y ~ Rep + Sow_Stable_Obs + Sow_Wt
##
## random effects equation:
## ~G + Sow_Pen + In
##
## log-likelihood: -42.39347 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 0.587181525 0.365626155 1.60596149 1.082824e-01
## Rep3 -0.018902057 0.236094138 -0.08006153 9.361883e-01
## Rep4 1.712161601 0.180520475 9.48458394 0.000000e+00
## Rep5 -0.328804379 0.211510863 -1.55455079 1.200531e-01
## Rep6 -0.858687193 0.191757820 -4.47797745 7.535356e-06
## Rep7 -0.804357712 0.217958006 -3.69042517 2.238795e-04
## Rep8 -0.583803838 0.219139064 -2.66407927 7.719937e-03
## Rep9 -0.071050315 0.184820881 -0.38442797 7.006613e-01
## Sow_Stable_ObsJS 0.051425351 0.130165993 0.39507517 6.927874e-01
## Sow_Stable_ObsKW 0.359942054 0.074819810 4.81078545 1.503383e-06
## Sow_Wt 0.004470833 0.001963846 2.27656956 2.281194e-02
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.04669147 0.02974880 0.10345434
## Sow_Pen 0.02239839 0.01450983 0.04962813
## In 0.38223458 0.03423806 0.84691753
## Estimate StdError prop.var se
## G 0.04669147 0.02974880 0.10345434 0.06482753
## Sow_Pen 0.02239839 0.01450983 0.04962813 0.03142937
## In 0.38223458 0.03423806 0.84691753 0.09808184
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
## [1] "Sow_Stable_Total_ls"
## gblup analysis of trait: Sow_Stable_Total_ls
##
## fixed effects equation:
## y ~ Rep + Sow_Stable_Obs + Sow_Wt
##
## random effects equation:
## ~G + Sow_Pen + In
##
## log-likelihood: -30.67933 converged in: 7 iterations
##
## estimated fixed effects:
## Estimate StdError test.st p.value
## (Intercept) 2.01081427 0.408999212 4.91642578 8.813859e-07
## Rep3 0.01923475 0.277932363 0.06920659 9.448252e-01
## Rep4 2.00525911 0.221193657 9.06562663 0.000000e+00
## Rep5 -0.33957782 0.248975225 -1.36390205 1.725984e-01
## Rep6 -0.76498786 0.231259864 -3.30791454 9.399349e-04
## Rep7 -0.66836472 0.258117108 -2.58938558 9.614737e-03
## Rep8 -0.60707481 0.258936147 -2.34449619 1.905281e-02
## Rep9 -0.23944887 0.223312682 -1.07225828 2.836040e-01
## Sow_Stable_ObsJS 0.11570795 0.137265766 0.84294832 3.992573e-01
## Sow_Stable_ObsKW 0.25221119 0.076156921 3.31173040 9.272086e-04
## Sow_Wt 0.00365306 0.002147646 1.70095985 8.895053e-02
##
## estimated variance components:
## Estimate StdError prop.var
## G 0.08394056 0.03412803 0.1824440
## Sow_Pen 0.05243401 0.02155945 0.1139648
## In 0.32371486 0.03234323 0.7035912
## Estimate StdError prop.var se
## G 0.08394056 0.03412803 0.1824440 0.07178145
## Sow_Pen 0.05243401 0.02155945 0.1139648 0.04377214
## In 0.32371486 0.03234323 0.7035912 0.09642974
## <0.001 <0.01 <0.025 <0.05 <0.1
## signif.values 0 0 0 0 0
## attr(,"class")
## [1] "summary.gwas"
