├── .gitignore ├── AllDiap_April2016.pdf ├── Day1 ├── .DS_Store ├── Alfalfa │ ├── ALFALFA.txt │ ├── Alfalfa.R │ ├── AlfalfaLRT.R │ └── Alfalfa_estimating_variances.R ├── Clonal │ ├── CLONES.txt │ ├── Clonal.R │ └── PEDPAR.txt ├── ContPol │ ├── CONTPOL.txt │ ├── ContPol.R │ └── DUMMYPED.txt ├── FamilyM │ └── FISHF.txt ├── Fish │ ├── FISHAB.txt │ ├── Fish.R │ ├── PEDIND.txt │ └── PEDPAR.txt └── OpenPol │ ├── OPENPOL.txt │ └── OpenPol.R ├── Day2 ├── Bivar │ ├── BivarOpen.R │ └── OPENPOL.txt ├── GBLUPFull │ ├── AINV.txt │ ├── Clonaldata.txt │ ├── GBLUP_Fruit.R │ ├── GenoMatrix │ │ ├── Genotypic.txt │ │ ├── MarkerID.txt │ │ └── SubjectID.txt │ ├── Peddummy.txt │ └── Pedgenot.txt ├── GBLUPTest │ ├── DATAG.txt │ ├── DUMMYPED.txt │ ├── GBLUP_Test.R │ ├── GINVM4.txt │ ├── GINVM6.txt │ └── PEDSIRE.txt ├── MultiEnv │ ├── MET.R │ └── TRIALS4.txt ├── RepMeas │ ├── MVCOLS.txt │ ├── Multiv.R │ ├── REPCOLS.txt │ └── RepMeas.R ├── Spatial │ ├── ROWCOL.txt │ └── Spatial.R ├── Unreplicated │ ├── PEPPER.txt │ └── Unrep.R └── VarStruct │ ├── LEAFAREA.txt │ └── Leafarea.R ├── LICENSE ├── Practicals.pdf ├── Practicals ├── .DS_Store ├── Alfalfa │ ├── ALFALFA.txt │ └── Files_Code.docx ├── FieldT │ ├── FIELDT.txt │ └── Fieldt.R ├── Fish │ ├── FISHAB.txt │ ├── Fish.R │ ├── PEDIND.txt │ └── PEDPAR.txt ├── Indiv │ ├── OPENPOLs.txt │ └── PEDIND.txt ├── PineMET │ └── PINEMET.txt ├── Rubber │ └── RUBBER.txt └── Willow │ └── WILLOW.txt ├── README.md └── asreml-R.pdf /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | # Any R package builds (as compress project directories): 3 | *.tar.gz 4 | *.tgz 5 | *.zip 6 | *.Rproj 7 | 8 | # Ignore mac-specific and R / Rstudio environment files 9 | **/.DS_Store 10 | **/.RData 11 | **/.Rapp.history 12 | **/.Rhistory 13 | .Rproj.user 14 | **/*.Rcheck/ 15 | **/texput.log 16 | **/**.Rproj 17 | 18 | # Working (scratch) directory that shouldn't be uploaded 19 | scratch/ 20 | 21 | -------------------------------------------------------------------------------- /AllDiap_April2016.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ConradStack/ASReml-R-Short-Course/cad54bc6ee3c41dee9f492877440ab3cf6ed99dc/AllDiap_April2016.pdf -------------------------------------------------------------------------------- /Day1/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ConradStack/ASReml-R-Short-Course/cad54bc6ee3c41dee9f492877440ab3cf6ed99dc/Day1/.DS_Store -------------------------------------------------------------------------------- /Day1/Alfalfa/ALFALFA.txt: -------------------------------------------------------------------------------- 1 | Source Variety Block Resp 2 | 1 A 1 2.17 3 | 1 B 1 1.58 4 | 1 C 1 2.29 5 | 1 D 1 2.23 6 | 1 A 2 1.88 7 | 1 B 2 1.26 8 | 1 C 2 1.6 9 | 1 D 2 2.01 10 | 1 A 3 1.62 11 | 1 B 3 1.22 12 | 1 C 3 1.67 13 | 1 D 3 1.82 14 | 1 A 4 2.34 15 | 1 B 4 1.59 16 | 1 C 4 1.91 17 | 1 D 4 2.1 18 | 1 A 5 1.58 19 | 1 B 5 1.25 20 | 1 C 5 1.39 21 | 1 D 5 1.66 22 | 1 A 6 1.66 23 | 1 B 6 0.94 24 | 1 C 6 1.12 25 | 1 D 6 1.1 26 | 2 E 1 2.33 27 | 2 F 1 1.38 28 | 2 G 1 1.86 29 | 2 H 1 2.27 30 | 2 E 2 2.01 31 | 2 F 2 1.3 32 | 2 G 2 1.7 33 | 2 H 2 1.81 34 | 2 E 3 1.7 35 | 2 F 3 1.85 36 | 2 G 3 1.81 37 | 2 H 3 2.01 38 | 2 E 4 1.78 39 | 2 F 4 1.09 40 | 2 G 4 1.54 41 | 2 H 4 1.4 42 | 2 E 5 1.42 43 | 2 F 5 1.13 44 | 2 G 5 1.67 45 | 2 H 5 1.31 46 | 2 E 6 1.35 47 | 2 F 6 1.06 48 | 2 G 6 0.88 49 | 2 H 6 1.06 50 | 3 I 1 1.75 51 | 3 J 1 1.52 52 | 3 K 1 1.55 53 | 3 L 1 1.56 54 | 3 I 2 1.95 55 | 3 J 2 1.47 56 | 3 K 2 1.61 57 | 3 L 2 1.72 58 | 3 I 3 2.13 59 | 3 J 3 1.8 60 | 3 K 3 1.82 61 | 3 L 3 1.99 62 | 3 I 4 1.78 63 | 3 J 4 1.37 64 | 3 K 4 1.56 65 | 3 L 4 1.55 66 | 3 I 5 1.31 67 | 3 J 5 1.01 68 | 3 K 5 1.23 69 | 3 L 5 1.51 70 | 3 I 6 1.3 71 | 3 J 6 1.31 72 | 3 K 6 1.13 73 | 3 L 6 1.33 74 | -------------------------------------------------------------------------------- /Day1/Alfalfa/Alfalfa.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Alfalfa Experiment 3 | ############################### 4 | 5 | rm(list=ls()) # Removes all variables in memory 6 | setwd("./Distribute/Day1/Alfalfa/") 7 | 8 | alfalfa<-read.table("ALFALFA.TXT",header=TRUE) 9 | head(alfalfa) 10 | summary(alfalfa) 11 | str(alfalfa) 12 | 13 | # Boxplot for Each variety and general histogram 14 | boxplot(Resp~Variety,data=alfalfa) 15 | hist(alfalfa$Resp, main='Alfalfa Experiment',xlab='Yield') 16 | 17 | # Creating factors 18 | str(alfalfa) 19 | alfalfa$Variety<-as.factor(alfalfa$Variety) 20 | alfalfa$Source<-as.factor(alfalfa$Source) 21 | alfalfa$Block<-as.factor(alfalfa$Block) 22 | str(alfalfa) 23 | 24 | # Fitting model with blocks fixed 25 | alflm<-lm(Resp~Block+Variety,data=alfalfa) 26 | summary(alflm) 27 | anova(alflm) 28 | 29 | 30 | # Analysis using ASReml 31 | 32 | library(asreml) 33 | 34 | # asreml(fixed=y~, 35 | # random=~. 36 | # rcov=) 37 | model1 <- asreml(fixed=Resp ~ Block, random= ~Variety, data=alfalfa) 38 | 39 | # the variance component estimates. 40 | # NB -> the conf intervals (z-ratio) are calculated assuming infinite df, this is NOT valid do not use these! 41 | summary(model1)$varcomp 42 | 43 | # Wald method to calculate the variance of the variance components 44 | wald(model1,denDF="default") # 45 | 46 | # BLUPs and BLUEs 47 | ls(model1) # everything available within model1 object (cool!) 48 | model1$coefficients$fixed # BLUEs 49 | model1$coefficients$random # BLUPs 50 | 51 | # Another way to get the BLUPs (with standard errors) 52 | # NB -> the z ratio again is not valid (it is based on approximated SE and assumes an infinite degrees of freedom). 53 | BLUP <- (summary(model1, all=TRUE)$coef.random) 54 | 55 | 56 | # Predictions (asreml) 57 | predictions <- predict(model1, classify="Variety") # same as asking for lsmeans 58 | predictions$predictions$pvals # incorporate BLUPs and fixed effects 59 | # mean of a fixed effect is always zero (Salvador) 60 | # prediction is = mu + (mean of Block effects == 0) + V_a (the BLUP value for each variety) 61 | 62 | 63 | # Using Block as a random effect along with Variety 64 | model2 <- asreml(fixed=Resp ~ 1, random= ~ Block + Variety, data=alfalfa) 65 | BLUP2 <- (summary(model2, all=TRUE)$coef.random) 66 | pred.block <- predict(model2, classify="Block") # not meaningful / interesting 67 | pred.block$predictions$pvals 68 | pred.variety <- predict(model2, classify="Variety") 69 | pred.variety$predictions$pvals 70 | 71 | # Using blocks as random effects can only increase the SE's of the predictions (of each variety). There appears to be substantial variety among blocks, incorporating this information is importance for prediction 72 | # 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | -------------------------------------------------------------------------------- /Day1/Alfalfa/AlfalfaLRT.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Alfalfa Experiment 3 | ############################### 4 | 5 | rm(list=ls()) # Removes all variables in memory 6 | setwd("./Distribute/Day1/Alfalfa/") 7 | 8 | alfalfa<-read.table("ALFALFA.TXT",header=TRUE) 9 | head(alfalfa) 10 | summary(alfalfa) 11 | str(alfalfa) 12 | 13 | # Creating factors 14 | str(alfalfa) 15 | alfalfa$Variety<-as.factor(alfalfa$Variety) 16 | alfalfa$Source<-as.factor(alfalfa$Source) 17 | alfalfa$Block<-as.factor(alfalfa$Block) 18 | str(alfalfa) 19 | 20 | library(asreml) 21 | 22 | # Full Model 23 | model1<-asreml(fixed=Resp~Block,random=~Variety,data=alfalfa) 24 | summary(model1) 25 | log1<-model1$loglik 26 | 27 | # Restricted Model 28 | model0<-asreml(fixed=Resp~Block,data=alfalfa) 29 | summary(model0) 30 | log0<-model0$loglik 31 | -------------------------------------------------------------------------------- /Day1/Alfalfa/Alfalfa_estimating_variances.R: -------------------------------------------------------------------------------- 1 | 2 | library(nadiv) 3 | library(asremlPlus) 4 | library(asreml) 5 | 6 | # rm(list=ls()) # Removes all variables in memory 7 | # setwd("./Distribute/Day1/Alfalfa/") 8 | alfalfa<-read.table("ALFALFA.TXT",header=TRUE) 9 | 10 | 11 | # Creating factors 12 | str(alfalfa) 13 | alfalfa$Variety<-as.factor(alfalfa$Variety) 14 | alfalfa$Source<-as.factor(alfalfa$Source) 15 | alfalfa$Block<-as.factor(alfalfa$Block) 16 | str(alfalfa) 17 | 18 | # Fitting asreml model 19 | model1 <- asreml(fixed=Resp ~ Block, random= ~Variety, data=alfalfa) 20 | summary(model1) 21 | 22 | # H2 = var(Variety) / (var(Variety) + var(Error)) 23 | H2 <- nadiv:::pin(model1, tmp ~ V1 / (V1 + V2)) 24 | # NB -> the standard error is not *exactly* correct: the heritability estimate will not necessarily be normally distributed (they only will if a very large number of samples are available.) 25 | 26 | # Likelihood ratio test (Full vs restricted model) 27 | # NB -> the below is equivalent to testing whether heritability is > 0 28 | model0 <- asreml(fixed=Resp ~ Block, data=alfalfa) 29 | reml.lrt.asreml(full.asreml.obj = model1, model0, positive.zero = TRUE) 30 | 31 | # Get the AIC and BIC of both models 32 | # (smaller is better) 33 | info.crit.asreml(model1) 34 | info.crit.asreml(model0) 35 | 36 | 37 | 38 | 39 | 40 | -------------------------------------------------------------------------------- /Day1/Clonal/Clonal.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Clonal Model 3 | ############################### 4 | 5 | rm(list=ls()) 6 | setwd("./Distribute/Day1/Clonal/") 7 | library(asreml) 8 | library(nadiv) 9 | library(plyr) 10 | 11 | # Reading Files and generationg AINVERSE 12 | clonal<-read.table("CLONES.txt",h=T) 13 | pedpar<-read.table("PEDPAR.txt",h=T) 14 | ainv<-asreml.Ainverse(pedpar)$ginv 15 | 16 | require(igraph) 17 | gr = graph.edgelist(unique(as.matrix(clonal[,c("Female","Male")]))) 18 | plot(gr) 19 | is.connected(gr,"weak") 20 | 21 | head(clonal) 22 | head(pedpar) 23 | clonal$Rep<-as.factor(clonal$Rep) 24 | clonal$IncBlock<-as.factor(clonal$IncBlock) 25 | clonal$Female<-as.factor(clonal$Female) 26 | clonal$Male<-as.factor(clonal$Male) 27 | clonal$FamilyID<-as.factor(clonal$FamilyID) 28 | clonal$cloneid<-as.factor(clonal$cloneid) 29 | str(clonal) 30 | 31 | # Fitting Simple Clonal Model - no pedigree 32 | model1 = asreml(VOL ~ Rep, 33 | random = ~ Rep:IncBlock + Female + Male + FamilyID + cloneid, data=clonal) 34 | 35 | BLUP = model1$coefficients$random 36 | BLUP = BLUP[grepl("^cloneid",names(BLUP))] 37 | 38 | pred.clones = predict(model1, classify="cloneid") 39 | pred.clones$predictions$pvals 40 | 41 | 42 | # Fitting Full Clonal Model - with pedigree 43 | model2 = asreml(VOL ~ Rep, 44 | random = ~ Rep:IncBlock + ped(Female) + ped(Male) + FamilyID + cloneid, 45 | ginverse=list(Male=ainv,Female = ainv), 46 | data=clonal) 47 | plot(model2) 48 | 49 | # Overlay parental effects (using the "and(...)" function, which says to incorporate the female and male effects together using the pedigree) 50 | model3 = asreml(VOL ~ Rep, 51 | random = ~ Rep:IncBlock + ped(Female) + and(ped(Male)) + FamilyID + cloneid, 52 | ginverse=list(Male=ainv,Female = ainv), 53 | data=clonal) 54 | 55 | # NB -> there is often a slight positive correlation between fitted values and residuals when using the pedigree. why? 56 | 57 | 58 | BLUP = summary(model3, all=TRUE)$coef.random 59 | # clonal effect is everything left over after removing male + female + family effects (i.e., they are not directly comparable...) 60 | 61 | # Genetic components 62 | summary(model3)$varcomp 63 | (h2 <- nadiv:::pin(model3, tmp ~ (4*V1) / (2*V1 + V2 + V3 + V4 + V5) ) ) 64 | (d2 <- nadiv:::pin(model3, tmp ~ (4*V2) / (2*V1 + V2 + V3 + V4 + V5) ) ) 65 | (i2 <- nadiv:::pin(model3, tmp ~ (V4 - 2*V1 - 3*V2) / (2*V1 + V2 + V3 + V4 + V5) ) ) 66 | (H2 <- nadiv:::pin(model3, tmp ~ (2*V1 + V2 + V4 ) / (2*V1 + V2 + V3 + V4 + V5) ) ) 67 | 68 | 69 | # Easy model - ignoring everything but cloneid 70 | model4 = asreml(VOL ~ Rep, 71 | random = ~ Rep:IncBlock + cloneid, 72 | data=clonal) 73 | preds = predict(model4,classify="cloneid")$prediction$pvals 74 | summary(model4)$varcomp 75 | (H2 <- nadiv:::pin(model4, tmp ~ (V2) / (V1 + V2 + V3) ) ) 76 | # NB -> this one is handy if you want to rank clones only. However, if you want to estimate anything from the parents or understand the breakdown of the genetic variance components 77 | 78 | 79 | -------------------------------------------------------------------------------- /Day1/Clonal/PEDPAR.txt: -------------------------------------------------------------------------------- 1 | Indiv Male Female IDSORT 2 | Par901 0 0 1 3 | Par933 0 0 2 4 | Par934 0 0 3 5 | Par902 0 0 4 6 | Par936 0 0 5 7 | Par937 0 0 6 8 | Par903 0 0 7 9 | Par935 0 0 8 10 | Par904 0 0 9 11 | Par905 0 0 10 12 | Par938 0 0 11 13 | Par939 0 0 12 14 | Par940 0 0 13 15 | Par941 0 0 14 16 | Par942 0 0 15 17 | Par906 0 0 16 18 | Par943 0 0 17 19 | Par907 0 0 18 20 | Par908 0 0 19 21 | Par909 0 0 20 22 | Par910 0 0 21 23 | Par911 0 0 22 24 | Par912 0 0 23 25 | Par913 0 0 24 26 | Par914 0 0 25 27 | Par915 0 0 26 28 | Par916 0 0 27 29 | Par917 0 0 28 30 | Par918 0 0 29 31 | Par919 0 0 30 32 | Par920 0 0 31 33 | Par921 0 0 32 34 | Par922 0 0 33 35 | Par944 0 0 34 36 | Par923 Par936 Par901 35 37 | Par924 Par903 Par936 36 38 | Par925 Par937 Par933 37 39 | Par926 Par935 Par934 38 40 | Par927 Par944 0 39 41 | Par928 Par938 Par940 40 42 | Par929 Par939 Par940 41 43 | Par930 Par943 Par941 42 44 | Par931 Par942 Par943 43 45 | Par932 Par903 Par906 44 -------------------------------------------------------------------------------- /Day1/ContPol/ContPol.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Full Sib Model 3 | ############################### 4 | 5 | rm(list=ls()) 6 | setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami/Distribute/Day1/ContPol") 7 | library(asreml) 8 | 9 | fs<-read.table("CONTPOL.txt", h=T) 10 | head(fs) 11 | fs$REP<-as.factor(fs$REP) 12 | fs$FEMALE<-as.factor(fs$FEMALE) 13 | fs$MALE<-as.factor(fs$MALE) 14 | fs$FAMILY<-as.factor(fs$FAMILY) 15 | fs$CHECKLOT<-as.factor(fs$CHECKLOT) 16 | str(fs) 17 | 18 | # Analysis Full-Sib - Two additive terms 19 | FSIB<-asreml(fixed=YIELD~REP,random=~FEMALE+MALE+FEMALE:MALE,data=fs) 20 | summary(FSIB)$varcomp 21 | View(FSIB$coefficients$random) 22 | pred.MALE<-predict(FSIB,classify="FEMALE",sed=TRUE) 23 | pred.FEMALE<-predict(FSIB,classify="FEMALE",sed=TRUE) 24 | View(pred.FEMALE$predictions$pvals) 25 | 26 | -------------------------------------------------------------------------------- /Day1/ContPol/DUMMYPED.txt: -------------------------------------------------------------------------------- 1 | PARENT SIRE DAM 2 | C0 0 0 3 | C1 0 0 4 | C2 0 0 5 | C3 0 0 6 | C4 0 0 7 | C10 0 0 8 | C20 0 0 9 | C30 0 0 10 | C40 0 0 11 | FILLER 0 0 12 | PAR0001 0 0 13 | PAR0002 0 0 14 | PAR0003 0 0 15 | PAR0004 0 0 16 | PAR0005 0 0 17 | PAR0006 0 0 18 | PAR0007 0 0 19 | PAR0008 0 0 20 | PAR0009 0 0 21 | PAR0010 0 0 22 | PAR0011 0 0 23 | PAR0012 0 0 24 | PAR0013 0 0 25 | PAR0014 0 0 26 | PAR0015 0 0 27 | PAR0016 0 0 28 | PAR0017 0 0 29 | PAR0018 0 0 30 | PAR0019 0 0 31 | PAR0020 0 0 32 | PAR0021 0 0 33 | PAR0022 0 0 34 | PAR0023 0 0 35 | PAR0024 0 0 36 | PAR0025 0 0 37 | PAR0026 0 0 38 | PAR0027 0 0 39 | PAR0028 0 0 40 | PAR0029 0 0 41 | PAR0030 0 0 42 | PAR0031 0 0 43 | PAR0032 0 0 44 | PAR0033 0 0 45 | PAR0034 0 0 46 | PAR0035 0 0 47 | PAR0036 0 0 48 | PAR0037 0 0 49 | PAR0038 0 0 50 | PAR0039 0 0 51 | PAR0040 0 0 52 | PAR0041 0 0 53 | PAR0042 0 0 54 | PAR0043 0 0 55 | PAR0044 0 0 56 | PAR0045 0 0 57 | PAR0046 0 0 58 | PAR0047 0 0 59 | PAR0048 0 0 60 | PAR0049 0 0 61 | PAR0050 0 0 62 | PAR0051 0 0 63 | PAR0052 0 0 64 | PAR0053 0 0 65 | PAR0054 0 0 66 | PAR0055 0 0 67 | PAR0056 0 0 68 | PAR0057 0 0 69 | PAR0058 0 0 70 | PAR0059 0 0 71 | PAR0060 0 0 72 | PAR0061 0 0 73 | PAR0063 0 0 74 | PAR0064 0 0 75 | -------------------------------------------------------------------------------- /Day1/FamilyM/FISHF.txt: -------------------------------------------------------------------------------- 1 | ID SireID DamID Family Weight 2 | 1001 120 125 22 88.3 3 | 1002 120 125 22 84.9 4 | 1003 120 125 22 76.8 5 | 1004 121 114 23 95.4 6 | 1005 121 114 23 85.4 7 | 1006 121 114 23 74.8 8 | 1007 121 114 23 103.4 9 | 1008 121 114 23 78.7 10 | 1009 121 114 23 109.5 11 | 1010 121 114 23 113.1 12 | 1011 121 114 23 95.4 13 | 1012 121 114 23 91.1 14 | 1013 121 114 23 85.4 15 | 1014 121 114 23 85.4 16 | 1015 121 114 23 86.0 17 | 1016 121 114 23 99.0 18 | 1017 121 114 23 78.1 19 | 1018 121 114 23 78.1 20 | 1019 121 114 23 93.8 21 | 1020 121 114 23 91.1 22 | 1021 121 114 23 89.4 23 | 1022 121 114 23 104.9 24 | 1023 121 114 23 90.0 25 | 1024 121 114 23 84.3 26 | 1025 121 114 23 92.2 27 | 1026 121 114 23 87.7 28 | 1027 121 114 23 101.5 29 | 1028 121 114 23 84.9 30 | 1029 121 114 23 99.0 31 | 1030 121 114 23 86.6 32 | 1031 121 114 23 98.0 33 | 1032 121 114 23 81.9 34 | 1033 121 114 23 101.0 35 | 1034 133 95 24 100.5 36 | 1035 133 95 24 81.2 37 | 1036 133 95 24 93.8 38 | 1037 133 95 24 94.3 39 | 1038 133 95 24 71.4 40 | 1039 133 95 24 81.9 41 | 1040 133 95 24 89.4 42 | 1041 133 95 24 74.2 43 | 1042 133 95 24 93.3 44 | 1043 133 95 24 82.5 45 | 1044 133 95 24 93.8 46 | 1045 133 95 24 70.7 47 | 1046 135 112 25 82.5 48 | 1047 135 112 25 81.2 49 | 1048 135 112 25 84.9 50 | 1049 135 112 25 84.3 51 | 1050 135 112 25 103.0 52 | 1051 135 112 25 91.7 53 | 1052 135 112 25 74.2 54 | 1053 135 112 25 101.0 55 | 1054 135 112 25 86.6 56 | 1055 135 112 25 76.2 57 | 1056 135 112 25 92.2 58 | 1057 135 112 25 82.5 59 | 1058 135 112 25 102.5 60 | 1059 135 112 25 86.6 61 | 1060 135 112 25 83.7 62 | 1061 135 112 25 92.7 63 | 1062 135 112 25 76.8 64 | 1063 135 112 25 91.1 65 | 1064 135 112 25 100.0 66 | 1065 144 116 26 83.1 67 | 1066 144 116 26 81.9 68 | 1067 144 116 26 99.5 69 | 1068 144 116 26 104.4 70 | 1069 144 116 26 100.0 71 | 1070 144 116 26 80.6 72 | 1071 144 116 26 79.4 73 | 1072 144 116 26 82.5 74 | 1073 144 116 26 87.2 75 | 1074 144 116 26 75.5 76 | 1075 144 116 26 97.0 77 | 1076 144 116 26 79.4 78 | 1077 144 116 26 80.6 79 | 1078 144 116 26 77.5 80 | 1079 144 116 26 86.6 81 | 1080 144 116 26 94.9 82 | 1081 144 116 26 74.2 83 | 1082 144 116 26 83.1 84 | 1083 144 116 26 80.6 85 | 1084 144 116 26 81.9 86 | 1085 144 116 26 70.7 87 | 1086 144 116 26 74.8 88 | 1087 144 116 26 83.7 89 | 1088 144 116 26 97.5 90 | 1089 144 116 26 80.0 91 | 1090 144 116 26 99.5 92 | 1091 144 116 26 103.4 93 | 1092 144 116 26 79.4 94 | 1093 144 116 26 88.9 95 | 1094 144 116 26 97.5 96 | 1095 144 116 26 72.8 97 | 1096 144 116 26 92.2 98 | 1097 144 116 26 87.7 99 | 1098 144 116 26 98.0 100 | 1099 144 116 26 84.3 101 | 1100 144 116 26 82.5 102 | 1101 144 116 26 90.6 103 | 1102 144 116 26 105.8 104 | 1103 144 116 26 67.1 105 | 1104 144 116 26 80.0 106 | 1105 148 52 27 88.9 107 | 1106 148 52 27 74.2 108 | 1107 148 52 27 102.0 109 | 1108 148 52 27 87.2 110 | 1109 148 52 27 76.8 111 | 1110 148 52 27 66.3 112 | 1111 578 137 28 100.0 113 | 1112 578 137 28 76.2 114 | 1113 578 137 28 77.5 115 | 1114 578 137 28 72.8 116 | 1115 578 137 28 76.2 117 | 1116 578 137 28 82.5 118 | 1117 578 137 28 88.3 119 | 1118 578 137 28 88.3 120 | 1119 578 137 28 101.0 121 | 1120 578 137 28 91.1 122 | 1121 578 137 28 94.9 123 | 1122 578 137 28 84.3 124 | 1123 578 137 28 97.5 125 | 1124 578 137 28 86.0 126 | 1125 578 137 28 78.7 127 | 1126 578 137 28 81.2 128 | 1127 592 138 29 78.7 129 | 1128 592 138 29 77.5 130 | 1129 592 138 29 72.8 131 | 1130 592 138 29 90.6 132 | 1131 592 138 29 76.8 133 | 1132 592 138 29 76.8 134 | 1133 592 138 29 92.7 135 | 1134 592 138 29 81.9 136 | 1135 602 80 30 64.8 137 | 1136 602 80 30 90.0 138 | 1137 602 80 30 93.3 139 | 1138 602 80 30 102.0 140 | 1139 602 80 30 67.1 141 | 1140 602 80 30 88.9 142 | 1141 602 80 30 110.9 143 | 1142 715 141 31 78.1 144 | 1143 715 141 31 98.0 145 | 1144 715 141 31 90.0 146 | 1145 715 141 31 84.9 147 | 1146 715 141 31 82.5 148 | 1147 715 141 31 73.5 149 | 1148 715 141 31 88.9 150 | 1149 715 141 31 77.5 151 | 1150 715 141 31 84.9 152 | 1151 717 53 32 86.6 153 | 1152 717 53 32 71.4 154 | 1153 717 53 32 88.9 155 | 1154 717 53 32 70.7 156 | 1155 717 53 32 101.0 157 | 1156 717 53 32 83.1 158 | 1157 717 53 32 94.9 159 | 1158 1001 100 1 60.0 160 | 1159 1001 100 1 94.9 161 | 1160 1001 100 1 74.8 162 | 1161 1001 100 1 102.5 163 | 1162 1001 100 1 80.6 164 | 1163 1001 100 1 89.4 165 | 1164 1010 18 10 79.4 166 | 1165 1010 18 10 78.7 167 | 1166 1010 18 10 83.7 168 | 1167 1010 18 10 76.2 169 | 1168 1010 18 10 77.5 170 | 1169 1010 18 10 78.1 171 | 1170 1010 18 10 74.2 172 | 1171 1010 18 10 120.4 173 | 1172 1010 18 10 88.9 174 | 1173 1010 18 10 74.2 175 | 1174 1010 18 10 87.7 176 | 1175 1010 18 10 72.8 177 | 1176 1011 63 11 87.2 178 | 1177 1011 63 11 114.5 179 | 1178 1012 16 12 119.2 180 | 1179 1012 16 12 117.5 181 | 1180 1012 16 12 102.5 182 | 1181 1012 16 12 78.1 183 | 1182 1012 16 12 89.4 184 | 1183 1012 16 12 81.9 185 | 1184 1012 16 12 92.2 186 | 1185 1012 16 12 70.7 187 | 1186 1012 16 12 91.1 188 | 1187 1012 16 12 97.5 189 | 1188 1012 16 12 115.8 190 | 1189 1012 16 12 110.0 191 | 1190 1012 16 12 70.0 192 | 1191 1012 16 12 83.7 193 | 1192 1012 16 12 100.0 194 | 1193 1012 16 12 94.9 195 | 1194 1012 16 12 86.6 196 | 1195 1012 16 12 114.0 197 | 1196 1012 16 12 95.9 198 | 1197 1012 16 12 103.9 199 | 1198 1012 16 12 99.0 200 | 1199 1012 16 12 108.2 201 | 1200 1012 16 12 94.3 202 | 1201 1012 16 12 94.3 203 | 1202 1012 16 12 67.1 204 | 1203 1012 16 12 88.3 205 | 1204 1015 8 13 90.0 206 | 1205 1015 8 13 83.7 207 | 1206 1015 8 13 99.0 208 | 1207 1015 8 13 109.1 209 | 1208 1015 8 13 74.8 210 | 1209 1015 8 13 90.0 211 | 1210 1015 8 13 97.5 212 | 1211 1016 34 14 79.4 213 | 1212 1016 34 14 90.0 214 | 1213 1016 34 14 88.3 215 | 1214 1016 34 14 95.9 216 | 1215 1016 34 14 94.9 217 | 1216 1016 34 14 80.6 218 | 1217 1017 117 15 81.9 219 | 1218 1017 117 15 80.0 220 | 1219 1017 117 15 78.7 221 | 1220 1017 117 15 75.5 222 | 1221 1017 117 15 75.5 223 | 1222 1017 117 15 81.2 224 | 1223 1002 24 2 92.2 225 | 1224 1002 24 2 101.0 226 | 1225 1002 24 2 89.4 227 | 1226 1002 24 2 89.4 228 | 1227 1002 24 2 105.4 229 | 1228 1002 24 2 90.0 230 | 1229 1002 24 2 77.5 231 | 1230 1002 24 2 94.3 232 | 1231 1002 24 2 68.6 233 | 1232 1002 24 2 88.3 234 | 1233 1002 24 2 91.7 235 | 1234 1002 24 2 97.0 236 | 1235 1002 24 2 72.1 237 | 1236 1002 24 2 88.9 238 | 1237 1002 24 2 75.5 239 | 1238 1002 24 2 73.5 240 | 1239 1002 24 2 74.8 241 | 1240 1002 24 2 86.6 242 | 1241 1002 24 2 84.9 243 | 1242 1002 24 2 94.9 244 | 1243 1002 24 2 78.1 245 | 1244 1002 24 2 81.2 246 | 1245 1002 24 2 91.1 247 | 1246 1002 24 2 78.7 248 | 1247 1002 24 2 76.2 249 | 1248 1002 24 2 66.3 250 | 1249 1002 24 2 91.7 251 | 1250 1022 55 16 86.0 252 | 1251 1022 55 16 100.0 253 | 1252 1022 55 16 89.4 254 | 1253 1022 55 16 78.7 255 | 1254 1022 55 16 86.6 256 | 1255 1024 161 17 81.2 257 | 1256 1024 161 17 107.7 258 | 1257 1024 161 17 86.6 259 | 1258 1024 161 17 88.3 260 | 1259 1024 161 17 85.4 261 | 1260 1025 60 18 76.8 262 | 1261 1025 60 18 75.5 263 | 1262 1025 60 18 94.9 264 | 1263 1025 60 18 72.1 265 | 1264 1025 60 18 88.9 266 | 1265 1025 60 18 81.9 267 | 1266 1025 60 18 88.3 268 | 1267 1025 60 18 72.1 269 | 1268 1025 60 18 74.8 270 | 1269 1026 23 19 76.8 271 | 1270 1026 23 19 107.2 272 | 1271 1026 23 19 84.9 273 | 1272 1026 23 19 73.5 274 | 1273 1026 23 19 88.3 275 | 1274 1026 23 19 83.1 276 | 1275 1026 23 19 90.6 277 | 1276 1026 23 19 94.9 278 | 1277 1026 23 19 63.2 279 | 1278 1028 129 20 82.5 280 | 1279 1028 129 20 91.7 281 | 1280 1028 129 20 86.6 282 | 1281 1028 129 20 111.4 283 | 1282 1028 129 20 78.7 284 | 1283 1028 129 20 101.0 285 | 1284 1028 129 20 99.0 286 | 1285 1028 129 20 80.6 287 | 1286 1028 129 20 103.9 288 | 1287 1028 129 20 75.5 289 | 1288 1028 129 20 94.3 290 | 1289 1028 129 20 87.7 291 | 1290 1028 129 20 83.1 292 | 1291 1028 129 20 97.5 293 | 1292 1028 129 20 99.0 294 | 1293 1028 129 20 78.7 295 | 1294 1028 129 20 77.5 296 | 1295 1028 129 20 86.6 297 | 1296 1003 3 3 96.4 298 | 1297 1003 3 3 76.8 299 | 1298 1003 3 3 90.6 300 | 1299 1003 3 3 74.8 301 | 1300 1003 3 3 92.2 302 | 1301 1003 3 3 90.6 303 | 1302 1003 3 3 70.0 304 | 1303 1003 3 3 86.6 305 | 1304 1003 3 3 79.4 306 | 1305 1003 3 3 83.7 307 | 1306 1003 3 3 94.9 308 | 1307 1003 3 3 86.0 309 | 1308 1003 3 3 92.2 310 | 1309 1003 3 3 79.4 311 | 1310 1003 3 3 86.0 312 | 1311 1003 3 3 86.6 313 | 1312 1003 3 3 76.8 314 | 1313 1003 3 3 71.4 315 | 1314 1003 3 3 84.9 316 | 1315 1003 3 3 93.8 317 | 1316 1003 3 3 81.2 318 | 1317 1003 3 3 84.9 319 | 1318 1003 3 3 84.9 320 | 1319 1003 3 3 100.0 321 | 1320 1003 3 3 86.0 322 | 1321 1003 3 3 82.5 323 | 1322 1003 3 3 84.9 324 | 1323 1003 3 3 89.4 325 | 1324 1003 3 3 91.7 326 | 1325 1003 3 3 88.3 327 | 1326 1003 3 3 65.6 328 | 1327 1003 3 3 78.7 329 | 1328 1003 3 3 64.8 330 | 1329 1003 3 3 67.1 331 | 1330 1003 3 3 88.3 332 | 1331 1003 3 3 93.8 333 | 1332 1003 3 3 98.5 334 | 1333 1003 3 3 78.1 335 | 1334 1003 3 3 60.8 336 | 1335 1031 56 21 81.2 337 | 1336 1031 56 21 84.3 338 | 1337 1031 56 21 90.6 339 | 1338 1031 56 21 100.0 340 | 1339 1031 56 21 94.9 341 | 1340 1031 56 21 105.8 342 | 1341 1031 56 21 87.7 343 | 1342 1031 56 21 91.7 344 | 1343 1031 56 21 94.3 345 | 1344 1031 56 21 90.0 346 | 1345 1031 56 21 90.0 347 | 1346 1031 56 21 70.7 348 | 1347 1031 56 21 80.0 349 | 1348 1031 56 21 95.4 350 | 1349 1031 56 21 74.2 351 | 1350 1031 56 21 79.4 352 | 1351 1031 56 21 106.3 353 | 1352 1031 56 21 88.3 354 | 1353 1031 56 21 94.9 355 | 1354 1031 56 21 92.2 356 | 1355 1031 56 21 83.1 357 | 1356 1031 56 21 70.7 358 | 1357 1031 56 21 95.4 359 | 1358 1031 56 21 90.6 360 | 1359 1031 56 21 87.2 361 | 1360 1031 56 21 91.7 362 | 1361 1031 56 21 85.4 363 | 1362 1031 56 21 72.1 364 | 1363 1031 56 21 78.7 365 | 1364 1004 21 4 78.1 366 | 1365 1004 21 4 93.8 367 | 1366 1004 21 4 95.4 368 | 1367 1004 21 4 93.8 369 | 1368 1004 21 4 81.9 370 | 1369 1004 21 4 89.4 371 | 1370 1004 21 4 73.5 372 | 1371 1004 21 4 72.8 373 | 1372 1004 21 4 76.2 374 | 1373 1004 21 4 74.2 375 | 1374 1004 21 4 84.3 376 | 1375 1004 21 4 73.5 377 | 1376 1004 21 4 79.4 378 | 1377 1004 21 4 77.5 379 | 1378 1004 21 4 74.8 380 | 1379 1004 21 4 90.0 381 | 1380 1004 21 4 84.9 382 | 1381 1004 21 4 75.5 383 | 1382 1004 21 4 102.0 384 | 1383 1004 21 4 74.2 385 | 1384 1004 21 4 90.6 386 | 1385 1004 21 4 84.9 387 | 1386 1004 21 4 72.1 388 | 1387 1004 21 4 90.6 389 | 1388 1004 21 4 77.5 390 | 1389 1004 21 4 79.4 391 | 1390 1004 21 4 97.5 392 | 1391 1004 21 4 72.1 393 | 1392 1004 21 4 83.7 394 | 1393 1004 21 4 72.8 395 | 1394 1004 21 4 80.6 396 | 1395 1004 21 4 75.5 397 | 1396 1004 21 4 80.6 398 | 1397 1005 2001 5 96.4 399 | 1398 1005 2001 5 74.8 400 | 1399 1005 2001 5 88.3 401 | 1400 1005 2001 5 91.7 402 | 1401 1005 2001 5 110.9 403 | 1402 1005 2001 5 73.5 404 | 1403 1005 2001 5 88.3 405 | 1404 1005 2001 5 80.0 406 | 1405 1005 2001 5 77.5 407 | 1406 1005 2001 5 103.0 408 | 1407 1005 2001 5 77.5 409 | 1408 1005 2001 5 72.1 410 | 1409 1006 11 6 79.4 411 | 1410 1006 11 6 90.0 412 | 1411 1006 11 6 108.2 413 | 1412 1006 11 6 96.4 414 | 1413 1006 11 6 101.0 415 | 1414 1007 9 7 94.3 416 | 1415 1007 9 7 84.3 417 | 1416 1007 9 7 72.1 418 | 1417 1007 9 7 74.2 419 | 1418 1007 9 7 89.4 420 | 1419 1007 9 7 87.2 421 | 1420 1007 9 7 74.8 422 | 1421 1007 9 7 76.8 423 | 1422 1007 9 7 72.1 424 | 1423 1007 9 7 94.3 425 | 1424 1007 9 7 90.6 426 | 1425 1007 9 7 76.8 427 | 1426 1007 9 7 76.8 428 | 1427 1007 9 7 82.5 429 | 1428 1008 15 8 101.0 430 | 1429 1008 15 8 81.9 431 | 1430 1008 15 8 77.5 432 | 1431 1008 15 8 87.2 433 | 1432 1008 15 8 83.7 434 | 1433 1008 15 8 73.5 435 | 1434 1008 15 8 71.4 436 | 1435 1008 15 8 74.8 437 | 1436 1008 15 8 77.5 438 | 1437 1008 15 8 86.0 439 | 1438 1008 15 8 90.0 440 | 1439 1008 15 8 86.0 441 | 1440 1008 15 8 70.7 442 | 1441 1008 15 8 91.7 443 | 1442 1008 15 8 98.5 444 | 1443 1008 15 8 90.0 445 | 1444 1008 15 8 97.5 446 | 1445 1008 15 8 61.6 447 | 1446 1008 15 8 80.0 448 | 1447 1008 15 8 86.0 449 | 1448 1008 15 8 84.9 450 | 1449 1008 15 8 61.6 451 | 1450 1008 15 8 72.1 452 | 1451 1008 15 8 86.0 453 | 1452 1008 15 8 93.8 454 | 1453 1009 13 9 81.9 455 | 1454 1009 13 9 86.6 456 | 1455 1009 13 9 96.4 457 | 1456 1009 13 9 78.7 458 | 1457 1009 13 9 87.7 459 | 1458 1009 13 9 79.4 460 | 1459 1009 13 9 82.5 -------------------------------------------------------------------------------- /Day1/Fish/Fish.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Animal Model 3 | ############################### 4 | 5 | rm(list=ls()) 6 | library(asreml) 7 | 8 | # Fish Example 9 | fish<-read.table("./Distribute/Day1/Fish/FISHAB.txt", header=T) 10 | head(fish) 11 | fish$Sex<-as.factor(fish$Sex) 12 | fish$INDIV<-as.factor(fish$INDIV) 13 | fish$Sire<-as.factor(fish$Sire) 14 | fish$Dam<-as.factor(fish$Dam) 15 | fish$FAM<-as.factor(fish$FAM) 16 | str(fish) 17 | 18 | ######### 19 | # Part 1 - PARENTAL MODEL WITH PEDIGREE 20 | pedpar<-read.table("./Distribute/Day1/Fish/PEDPAR.txt",h=T) 21 | ainvpar<-asreml.Ainverse(pedpar)$ginv 22 | 23 | # Fitting a Parental model 24 | parentalmodel<-asreml(fixed=DaysM~Sex, 25 | random=~ped(Sire)+and(ped(Dam))+FAM, 26 | ginverse=list(Sire=ainvpar,Dam=ainvpar), 27 | data=fish,workspace=64e06) 28 | plot(parentalmodel) 29 | 30 | 31 | ######### 32 | # Part 2 - Individual MODEL WITH PEDIGREE 33 | pedind<-read.table("./Distribute/Day1/Fish/PEDIND.txt",h=T) 34 | ainv<-asreml.Ainverse(pedind)$ginv 35 | 36 | model2<-asreml(fixed=DaysM~Sex, 37 | random=~ped(INDIV)+FAM, 38 | ginverse=list(INDIV=ainv), 39 | data=fish,workspace=64e06) 40 | 41 | plot(model2) 42 | 43 | ## Compare parental and individual models: 44 | summary(parentalmodel)$varcomp 45 | summary(model2)$varcomp 46 | 47 | summary(parentalmodel)$varcomp 48 | (h2 <- nadiv:::pin(parentalmodel, tmp ~ (4*V1) / (2*V1 + V2 + V3) ) ) 49 | blup <- summary(parentalmodel,all=TRUE)$coef.random 50 | 51 | summary(model2)$varcomp 52 | (h2 <- nadiv:::pin(model2, tmp ~ (V2) / (V1 + V2 + V3) ) ) 53 | blup2 <- summary(model2,all=TRUE)$coef.random 54 | 55 | # NB -> see how the individual ped variance component is 4 times larger than the parental model. 56 | # NB -> the residual variance of the parental model is larger BECAUSE it incorporates 3/4 of the additive genetic variance. This is very important point! It relates back to how the heritability and genetic variance components are derived. 57 | 58 | 59 | head(blup);head(blup2) 60 | head( blup[grepl("^ped",rownames(blup)),] ) 61 | head( blup2[grepl("^ped",rownames(blup2)),] ) 62 | 63 | head( blup[grepl("^FAM",rownames(blup)),] ) 64 | head( blup2[grepl("^FAM",rownames(blup2)),] ) 65 | 66 | -------------------------------------------------------------------------------- /Day1/Fish/PEDIND.txt: -------------------------------------------------------------------------------- 1 | Parent Sire Dam 2 | 501 0 0 3 | 502 0 0 4 | 503 0 0 5 | 504 0 0 6 | 505 0 0 7 | 506 0 0 8 | 507 0 0 9 | 508 0 0 10 | 509 0 0 11 | 510 0 0 12 | 511 0 0 13 | 512 0 0 14 | 513 0 0 15 | 514 0 0 16 | 515 0 0 17 | 516 0 0 18 | 517 1 105 19 | 518 1 105 20 | 519 1 105 21 | 520 2 106 22 | 521 3 107 23 | 522 4 108 24 | 523 5 109 25 | 524 6 110 26 | 525 6 110 27 | 526 7 111 28 | 527 8 112 29 | 528 8 112 30 | 529 8 112 31 | 530 8 112 32 | 531 9 113 33 | 532 9 113 34 | 533 9 113 35 | 534 10 114 36 | 535 10 114 37 | 536 11 115 38 | 537 12 116 39 | 538 13 117 40 | 539 13 117 41 | 540 13 117 42 | 541 13 117 43 | 542 13 117 44 | 543 13 117 45 | 544 13 117 46 | 545 14 118 47 | 546 14 118 48 | 547 15 119 49 | 548 15 119 50 | 549 15 119 51 | 550 16 120 52 | 551 16 120 53 | 552 17 121 54 | 553 17 121 55 | 554 17 121 56 | 555 17 121 57 | 556 18 122 58 | 557 19 123 59 | 558 20 124 60 | 559 21 125 61 | 560 21 125 62 | 561 21 125 63 | 562 21 125 64 | 563 22 126 65 | 564 23 127 66 | 565 23 127 67 | 566 24 128 68 | 567 25 129 69 | 568 25 129 70 | 569 25 129 71 | 570 25 129 72 | 571 26 130 73 | 572 27 131 74 | 573 28 132 75 | 574 29 133 76 | 575 29 133 77 | 576 29 133 78 | 577 29 133 79 | 578 30 134 80 | 579 30 134 81 | 580 31 135 82 | 581 32 136 83 | 582 32 136 84 | 583 33 137 85 | 584 33 137 86 | 585 33 137 87 | 586 33 137 88 | 587 34 138 89 | 588 35 139 90 | 589 36 140 91 | 590 36 140 92 | 591 37 141 93 | 592 38 142 94 | 593 38 142 95 | 594 38 142 96 | 595 38 142 97 | 596 38 142 98 | 597 39 143 99 | 598 40 144 100 | 599 41 145 101 | 600 41 145 102 | 601 41 145 103 | 602 42 146 104 | 603 42 146 105 | 604 42 146 106 | 605 43 147 107 | 606 43 147 108 | 607 43 147 109 | 608 44 148 110 | 609 45 149 111 | 610 46 150 112 | 611 46 150 113 | 612 47 151 114 | 613 47 151 115 | 614 47 151 116 | 615 47 151 117 | 616 48 152 118 | 617 49 153 119 | 618 49 153 120 | 619 49 153 121 | 620 50 154 122 | 621 50 154 123 | 622 51 155 124 | 623 51 155 125 | 624 52 156 126 | 625 52 156 127 | 626 52 156 128 | 627 53 157 129 | 628 53 157 130 | 629 54 158 131 | 630 55 159 132 | 631 55 159 133 | 632 55 159 134 | 633 56 160 135 | 634 56 160 136 | 635 56 160 137 | 636 57 161 138 | 637 58 162 139 | 638 58 162 140 | 639 59 163 141 | 640 59 163 142 | 641 59 163 143 | 642 60 164 144 | 643 61 165 145 | 644 61 165 146 | 645 62 166 147 | 646 63 167 148 | 647 63 167 149 | 648 63 167 150 | 649 63 167 151 | 650 64 168 152 | 651 64 168 153 | 652 64 168 154 | 653 64 168 155 | 654 65 169 156 | 655 66 170 157 | 656 66 170 158 | 657 67 171 159 | 658 68 172 160 | 659 69 173 161 | 660 70 174 162 | 661 70 174 163 | 662 70 174 164 | 663 71 175 165 | 664 71 175 166 | 665 72 176 167 | 666 72 176 168 | 667 73 177 169 | 668 73 177 170 | 669 73 177 171 | 670 74 178 172 | 671 74 178 173 | 672 74 178 174 | 673 75 179 175 | 674 75 179 176 | 675 76 180 177 | 676 76 180 178 | 677 77 181 179 | 678 77 181 180 | 679 77 181 181 | 680 78 182 182 | 681 78 182 183 | 682 78 182 184 | 683 78 182 185 | 684 79 183 186 | 685 80 184 187 | 686 81 185 188 | 687 81 185 189 | 688 82 186 190 | 689 83 187 191 | 690 83 187 192 | 691 83 187 193 | 692 84 188 194 | 693 84 188 195 | 694 85 189 196 | 695 85 189 197 | 696 86 190 198 | 697 86 190 199 | 698 86 190 200 | 699 87 191 201 | 700 87 191 202 | 701 87 191 203 | 702 87 191 204 | 703 87 191 205 | 704 88 192 206 | 705 88 192 207 | 706 88 192 208 | 707 89 193 209 | 708 89 193 210 | 709 89 193 211 | 710 90 194 212 | 711 91 195 213 | 712 91 195 214 | 713 92 196 215 | 714 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1293 546 620 534 | 1294 546 620 535 | 1295 546 620 536 | 1296 546 620 537 | 1297 546 620 538 | 1298 546 620 539 | 1299 546 620 540 | 1300 546 620 541 | 1301 546 620 542 | 1302 546 620 543 | 1303 507 605 544 | 1304 507 605 545 | 1305 507 605 546 | 1306 507 605 547 | 1307 507 605 548 | 1308 507 605 549 | 1309 507 605 550 | 1310 507 605 551 | 1311 507 605 552 | 1312 507 605 553 | 1313 507 605 554 | 1314 507 605 555 | 1315 507 605 556 | 1316 706 559 557 | 1317 706 559 558 | 1318 706 559 559 | 1319 706 559 560 | 1320 706 559 561 | 1321 706 559 562 | 1322 706 559 563 | 1323 706 559 564 | 1324 706 559 565 | 1325 573 560 566 | 1326 573 560 567 | 1327 573 560 568 | 1328 573 560 569 | 1329 573 560 570 | 1330 573 560 571 | 1331 566 604 572 | 1332 566 604 573 | 1333 566 604 574 | 1334 566 604 575 | 1335 566 604 576 | 1336 566 604 577 | 1337 656 662 578 | 1338 656 662 579 | 1339 656 662 580 | 1340 656 662 581 | 1341 656 662 582 | 1342 656 662 583 | 1343 656 662 584 | 1344 540 692 585 | 1345 540 692 586 | 1346 540 692 587 | 1347 540 692 588 | 1348 540 692 589 | 1349 540 692 590 | 1350 540 692 591 | 1351 540 692 592 | 1352 540 692 593 | 1353 540 692 594 | 1354 540 692 595 | 1355 511 677 596 | 1356 511 677 597 | 1357 511 677 598 | 1358 511 677 599 | 1359 511 677 600 | 1360 511 677 601 | 1361 511 677 602 | 1362 511 677 603 | 1363 511 677 604 | 1364 511 677 605 | 1365 634 657 606 | 1366 634 657 607 | 1367 634 657 608 | 1368 634 657 609 | 1369 634 657 610 | 1370 634 657 611 | 1371 581 597 612 | 1372 581 597 613 | 1373 581 597 614 | 1374 581 597 615 | 1375 581 597 616 | 1376 581 597 617 | 1377 581 597 618 | 1378 699 624 619 | 1379 699 624 620 | 1380 699 624 621 | 1381 699 624 622 | 1382 699 624 623 | 1383 589 671 624 | 1384 589 671 625 | 1385 589 671 626 | 1386 589 671 627 | 1387 589 671 628 | 1388 589 671 629 | 1389 650 606 630 | 1390 650 606 631 | 1391 650 606 632 | 1392 650 606 633 | 1393 650 606 634 | 1394 650 606 635 | 1395 650 606 636 | 1396 650 606 637 | 1397 650 606 638 | 1398 557 712 639 | 1399 557 712 640 | 1400 557 712 641 | 1401 557 712 642 | 1402 557 712 643 | 1403 557 712 644 | 1404 557 712 645 | 1405 557 712 646 | 1406 526 558 647 | 1407 526 558 648 | 1408 526 558 649 | 1409 526 558 650 | 1410 526 558 651 | 1411 526 558 652 | 1412 526 558 653 | 1413 526 558 654 | 1414 526 558 655 | 1415 526 558 656 | 1416 526 558 657 | 1417 526 558 658 | 1418 510 648 659 | 1419 510 648 660 | 1420 510 648 661 | 1421 510 648 662 | 1422 510 648 663 | 1423 510 648 664 | 1424 510 648 665 | 1425 502 681 666 | 1426 502 681 667 | 1427 502 681 668 | 1428 502 681 669 | 1429 502 681 670 | 1430 502 681 671 | 1431 502 681 672 | 1432 502 681 673 | 1433 502 681 674 | 1434 502 681 675 | 1435 502 681 676 | 1436 502 681 677 | 1437 502 681 678 | 1438 592 731 679 | 1439 592 731 680 | 1440 592 731 681 | 1441 592 731 682 | 1442 592 731 683 | 1443 592 731 684 | 1444 592 731 685 | 1445 592 731 686 | 1446 710 612 687 | 1447 710 612 688 | 1448 710 612 689 | 1449 710 612 690 | 1450 710 612 691 | 1451 710 612 692 | 1452 710 612 693 | 1453 710 612 694 | 1454 665 545 695 | 1455 665 545 696 | 1456 665 545 697 | 1457 665 545 698 | 1458 665 545 699 | 1459 665 545 700 | 1460 665 545 701 | 1461 611 739 702 | 1462 611 739 703 | 1463 611 739 704 | 1464 611 739 705 | 1465 611 739 706 | 1466 611 739 707 | 1467 611 739 708 | 1468 611 739 709 | 1469 611 739 710 | 1470 518 617 711 | 1471 518 617 712 | 1472 518 617 713 | 1473 518 617 714 | 1474 518 617 715 | 1475 518 617 716 | 1476 518 617 717 | 1477 518 617 718 | 1478 652 627 719 | 1479 652 627 720 | 1480 652 627 721 | 1481 652 627 722 | 1482 652 627 723 | 1483 651 555 724 | 1484 651 555 725 | 1485 651 555 726 | 1486 651 555 727 | 1487 651 555 728 | 1488 651 555 729 | 1489 591 595 730 | 1490 591 595 731 | 1491 591 595 732 | 1492 591 595 733 | 1493 591 595 734 | 1494 674 542 735 | 1495 674 542 736 | 1496 674 542 737 | 1497 674 542 738 | 1498 674 542 739 | 1499 674 542 740 | 1500 674 542 741 | 1501 701 539 742 | 1502 701 539 743 | 1503 701 539 744 | 1504 701 539 745 | 1505 701 539 746 | 1506 722 501 747 | 1507 722 501 748 | 1508 722 501 749 | 1509 722 501 750 | 1510 722 501 751 | 1511 722 501 752 | 1512 722 501 753 | 1513 728 683 754 | 1514 728 683 755 | 1515 728 683 756 | 1516 728 683 757 | 1517 728 683 758 | 1518 728 683 759 | 1519 626 594 760 | 1520 626 594 761 | 1521 626 594 762 | 1522 626 594 763 | 1523 626 594 764 | 1524 623 504 765 | 1525 623 504 766 | 1526 623 504 767 | 1527 623 504 768 | 1528 623 504 769 | 1529 623 504 770 | 1530 623 504 771 | 1531 610 682 772 | 1532 610 682 773 | 1533 610 682 774 | 1534 610 682 775 | 1535 610 682 776 | 1536 610 682 777 | 1537 610 682 778 | 1538 610 682 779 | 1539 610 682 780 | 1540 664 716 781 | 1541 664 716 782 | 1542 664 716 783 | 1543 664 716 784 | 1544 664 716 785 | 1545 664 716 786 | 1546 664 716 787 | 1547 528 520 788 | 1548 528 520 789 | 1549 528 520 790 | 1550 528 520 791 | 1551 528 520 792 | 1552 528 520 793 | 1553 631 690 794 | 1554 631 690 795 | 1555 631 690 796 | 1556 631 690 797 | 1557 631 690 798 | 1558 631 690 799 | 1559 588 629 800 | 1560 588 629 801 | 1561 588 629 802 | 1562 588 629 803 | 1563 588 629 804 | 1564 513 515 805 | 1565 513 515 806 | 1566 513 515 807 | 1567 513 515 808 | 1568 513 515 809 | 1569 513 515 810 | 1570 663 729 811 | 1571 663 729 812 | 1572 663 729 813 | 1573 663 729 814 | 1574 663 729 815 | 1575 663 729 816 | 1576 663 729 817 | 1577 663 729 818 | 1578 663 729 819 | 1579 663 729 820 | 1580 609 583 821 | 1581 609 583 822 | 1582 609 583 823 | 1583 609 583 824 | 1584 509 709 825 | 1585 509 709 826 | 1586 509 709 827 | 1587 509 709 828 | 1588 509 709 829 | 1589 509 709 830 | 1590 641 695 831 | 1591 641 695 832 | 1592 641 695 833 | 1593 641 695 834 | 1594 641 695 835 | 1595 641 695 836 | 1596 641 695 837 | 1597 641 695 838 | 1598 641 695 839 | 1599 641 695 840 | 1600 641 695 841 | 1601 714 738 842 | 1602 714 738 843 | 1603 714 738 844 | 1604 714 738 845 | 1605 714 738 846 | 1606 714 738 847 | 1607 724 544 848 | 1608 724 544 849 | 1609 724 544 850 | 1610 724 544 851 | 1611 724 544 852 | 1612 724 544 853 | 1613 600 734 854 | 1614 600 734 855 | 1615 600 734 856 | 1616 600 734 857 | 1617 600 734 858 | 1618 600 734 859 | 1619 600 734 860 | 1620 600 734 861 | 1621 642 670 862 | 1622 642 670 863 | 1623 642 670 864 | 1624 642 670 865 | 1625 642 670 866 | 1626 642 670 867 | 1627 642 670 868 | 1628 642 670 869 | 1629 642 670 870 | 1630 642 670 871 | 1631 642 670 872 | 1632 730 550 873 | 1633 508 534 874 | 1634 508 534 875 | 1635 508 534 876 | 1636 508 534 877 | 1637 508 534 878 | 1638 508 534 879 | 1639 508 534 880 | 1640 508 534 881 | 1641 508 534 882 | 1642 668 720 883 | 1643 668 720 884 | 1644 668 720 885 | 1645 668 720 886 | 1646 668 720 887 | 1647 668 720 888 | 1648 613 517 889 | 1649 613 517 890 | 1650 613 517 891 | 1651 613 517 892 | 1652 613 517 893 | 1653 613 517 894 | 1654 613 517 895 | 1655 621 569 896 | 1656 621 569 897 | 1657 621 569 898 | 1658 621 569 899 | 1659 621 569 900 | 1660 621 569 901 | 1661 621 569 902 | 1662 563 512 903 | 1663 563 512 904 | 1664 563 512 905 | 1665 563 512 906 | 1666 563 512 907 | 1667 563 512 908 | 1668 563 512 909 | 1669 531 561 910 | 1670 531 561 911 | 1671 531 561 912 | 1672 531 561 913 | 1673 531 561 914 | 1674 531 561 915 | 1675 531 561 916 | 1676 531 561 917 | 1677 531 561 918 | 1678 531 561 919 | 1679 531 561 920 | 1680 679 698 921 | 1681 679 698 922 | 1682 679 698 923 | 1683 679 698 924 | 1684 679 698 925 | 1685 679 698 926 | 1686 679 698 927 | 1687 679 698 928 | 1688 679 698 929 | 1689 679 698 930 | 1690 679 698 931 | 1691 614 649 932 | 1692 614 649 933 | 1693 614 649 934 | 1694 614 649 935 | 1695 614 649 936 | 1696 614 649 937 | 1697 614 649 938 | 1698 614 649 939 | 1699 586 661 940 | 1700 586 661 941 | 1701 586 661 942 | 1702 586 661 943 | 1703 586 661 944 | 1704 586 661 945 | 1705 586 661 946 | 1706 586 661 947 | 1707 715 643 948 | 1708 715 643 949 | 1709 715 643 950 | 1710 715 643 951 | 1711 715 643 952 | 1712 715 643 953 | 1713 715 643 954 | 1714 525 529 955 | 1715 525 529 956 | 1716 525 529 957 | 1717 525 529 958 | 1718 525 529 959 | 1719 525 529 960 | 1720 525 529 961 | 1721 525 529 962 | 1722 525 529 963 | 1723 525 529 964 | 1724 525 529 965 | 1725 525 529 966 | 1726 525 529 967 | 1727 630 523 968 | 1728 630 523 969 | 1729 630 523 970 | 1730 630 523 971 | 1731 630 523 972 | 1732 630 523 973 | 1733 630 523 974 | 1734 711 549 975 | 1735 711 549 976 | 1736 711 549 977 | 1737 711 549 978 | 1738 711 549 979 | 1739 711 549 980 | 1740 711 549 981 | 1741 582 533 982 | 1742 582 533 983 | 1743 582 533 984 | 1744 582 533 985 | 1745 582 533 986 | 1746 678 717 987 | 1747 678 717 988 | 1748 678 717 989 | 1749 678 717 990 | 1750 556 543 991 | 1751 556 543 992 | 1752 556 543 993 | 1753 556 543 994 | 1754 556 543 995 | 1755 556 543 996 | 1756 556 543 997 | 1757 556 543 998 | 1758 556 543 999 | 1759 556 543 1000 | 1760 556 543 1001 | 1761 556 543 1002 | 1762 556 543 1003 | 1763 556 543 1004 | 1764 708 735 1005 | 1765 708 735 1006 | 1766 708 735 1007 | 1767 708 735 1008 | 1768 708 735 1009 | 1769 708 735 1010 | 1770 708 735 1011 | 1771 708 735 1012 | 1772 708 735 1013 | 1773 708 735 1014 | 1774 590 567 1015 | 1775 603 535 1016 | 1776 603 535 1017 | 1777 603 535 1018 | 1778 603 535 1019 | 1779 603 535 1020 | 1780 603 535 1021 | 1781 603 535 1022 | 1782 603 535 1023 | 1783 644 516 1024 | 1784 644 516 1025 | 1785 644 516 1026 | 1786 644 516 1027 | 1787 644 516 1028 | 1788 598 506 1029 | 1789 598 506 1030 | 1790 598 506 1031 | 1791 598 506 1032 | 1792 598 506 1033 | 1793 598 506 1034 | 1794 593 615 1035 | 1795 593 615 1036 | 1796 593 615 1037 | 1797 593 615 1038 | 1798 593 615 1039 | 1799 593 615 1040 | 1800 593 615 1041 | 1801 593 615 1042 | 1802 593 615 1043 | 1803 593 615 1044 | 1804 593 615 1045 | 1805 593 615 1046 | 1806 737 639 1047 | 1807 737 639 1048 | 1808 737 639 1049 | 1809 737 639 1050 | 1810 737 639 1051 | 1811 737 639 1052 | 1812 737 639 1053 | 1813 737 639 1054 | 1814 737 639 1055 | 1815 737 639 1056 | 1816 713 601 1057 | 1817 713 601 1058 | 1818 713 601 1059 | 1819 713 601 1060 | 1820 713 601 1061 | 1821 713 601 1062 | 1822 713 601 1063 | 1823 713 601 1064 | 1824 713 601 1065 | 1825 645 675 1066 | 1826 645 675 1067 | 1827 645 675 1068 | 1828 645 675 1069 | 1829 645 675 1070 | 1830 645 675 1071 | 1831 645 532 1072 | 1832 645 532 1073 | 1833 645 532 1074 | 1834 645 532 1075 | 1835 645 532 1076 | 1836 645 532 1077 | 1837 554 632 1078 | 1838 554 632 1079 | 1839 554 632 1080 | 1840 554 632 1081 | 1841 554 632 1082 | 1842 554 632 1083 | 1843 554 632 1084 | 1844 554 632 1085 | 1845 554 632 1086 | 1846 554 684 1087 | 1847 554 684 1088 | 1848 554 684 1089 | 1849 554 684 1090 | 1850 554 684 1091 | 1851 554 684 1092 | 1852 554 684 1093 | 1853 554 684 1094 | 1854 538 672 1095 | 1855 538 672 1096 | 1856 538 672 1097 | 1857 538 672 1098 | 1858 538 672 1099 | 1859 538 672 1100 | 1860 538 672 1101 | 1861 538 503 1102 | 1862 538 503 1103 | 1863 538 503 1104 | 1864 538 503 1105 | 1865 538 503 1106 | 1866 538 503 1107 | 1867 538 503 1108 | 1868 685 667 1109 | 1869 685 667 1110 | 1870 685 667 1111 | 1871 685 667 1112 | 1872 685 667 1113 | 1873 685 667 1114 | 1874 685 667 1115 | 1875 685 667 1116 | 1876 685 667 1117 | 1877 685 505 1118 | 1878 685 505 1119 | 1879 685 505 1120 | 1880 685 505 1121 | 1881 685 505 1122 | 1882 685 505 1123 | 1883 685 505 1124 | 1884 685 505 1125 | 1885 685 505 1126 | 1886 685 505 1127 | 1887 700 628 1128 | 1888 700 628 1129 | 1889 700 628 1130 | 1890 700 628 1131 | 1891 700 628 1132 | 1892 700 726 1133 | 1893 660 721 1134 | 1894 660 721 1135 | 1895 660 721 1136 | 1896 660 647 1137 | 1897 660 647 1138 | 1898 660 647 1139 | 1899 660 647 1140 | 1900 660 647 1141 | 1901 660 647 1142 | 1902 660 647 1143 | 1903 660 647 1144 | 1904 687 548 1145 | 1905 687 548 1146 | 1906 687 548 1147 | 1907 687 548 1148 | 1908 687 548 1149 | 1909 687 673 1150 | 1910 687 673 1151 | 1911 687 673 1152 | 1912 687 673 1153 | 1913 687 673 1154 | 1914 687 673 1155 | 1915 687 673 1156 | 1916 687 673 1157 | 1917 687 673 1158 | 1918 725 704 1159 | 1919 725 704 1160 | 1920 725 704 1161 | 1921 725 704 1162 | 1922 725 704 1163 | 1923 725 704 1164 | 1924 725 704 1165 | 1925 732 625 1166 | 1926 732 625 1167 | 1927 732 625 1168 | 1928 732 625 1169 | 1929 732 570 1170 | 1930 732 570 1171 | 1931 732 570 1172 | 1932 732 570 1173 | 1933 732 570 1174 | -------------------------------------------------------------------------------- /Day1/Fish/PEDPAR.txt: -------------------------------------------------------------------------------- 1 | Parent Sire Dam 2 | 501 0 0 3 | 502 0 0 4 | 503 0 0 5 | 504 0 0 6 | 505 0 0 7 | 506 0 0 8 | 507 0 0 9 | 508 0 0 10 | 509 0 0 11 | 510 0 0 12 | 511 0 0 13 | 512 0 0 14 | 513 0 0 15 | 514 0 0 16 | 515 0 0 17 | 516 0 0 18 | 517 1 105 19 | 518 1 105 20 | 519 1 105 21 | 520 2 106 22 | 521 3 107 23 | 522 4 108 24 | 523 5 109 25 | 524 6 110 26 | 525 6 110 27 | 526 7 111 28 | 527 8 112 29 | 528 8 112 30 | 529 8 112 31 | 530 8 112 32 | 531 9 113 33 | 532 9 113 34 | 533 9 113 35 | 534 10 114 36 | 535 10 114 37 | 536 11 115 38 | 537 12 116 39 | 538 13 117 40 | 539 13 117 41 | 540 13 117 42 | 541 13 117 43 | 542 13 117 44 | 543 13 117 45 | 544 13 117 46 | 545 14 118 47 | 546 14 118 48 | 547 15 119 49 | 548 15 119 50 | 549 15 119 51 | 550 16 120 52 | 551 16 120 53 | 552 17 121 54 | 553 17 121 55 | 554 17 121 56 | 555 17 121 57 | 556 18 122 58 | 557 19 123 59 | 558 20 124 60 | 559 21 125 61 | 560 21 125 62 | 561 21 125 63 | 562 21 125 64 | 563 22 126 65 | 564 23 127 66 | 565 23 127 67 | 566 24 128 68 | 567 25 129 69 | 568 25 129 70 | 569 25 129 71 | 570 25 129 72 | 571 26 130 73 | 572 27 131 74 | 573 28 132 75 | 574 29 133 76 | 575 29 133 77 | 576 29 133 78 | 577 29 133 79 | 578 30 134 80 | 579 30 134 81 | 580 31 135 82 | 581 32 136 83 | 582 32 136 84 | 583 33 137 85 | 584 33 137 86 | 585 33 137 87 | 586 33 137 88 | 587 34 138 89 | 588 35 139 90 | 589 36 140 91 | 590 36 140 92 | 591 37 141 93 | 592 38 142 94 | 593 38 142 95 | 594 38 142 96 | 595 38 142 97 | 596 38 142 98 | 597 39 143 99 | 598 40 144 100 | 599 41 145 101 | 600 41 145 102 | 601 41 145 103 | 602 42 146 104 | 603 42 146 105 | 604 42 146 106 | 605 43 147 107 | 606 43 147 108 | 607 43 147 109 | 608 44 148 110 | 609 45 149 111 | 610 46 150 112 | 611 46 150 113 | 612 47 151 114 | 613 47 151 115 | 614 47 151 116 | 615 47 151 117 | 616 48 152 118 | 617 49 153 119 | 618 49 153 120 | 619 49 153 121 | 620 50 154 122 | 621 50 154 123 | 622 51 155 124 | 623 51 155 125 | 624 52 156 126 | 625 52 156 127 | 626 52 156 128 | 627 53 157 129 | 628 53 157 130 | 629 54 158 131 | 630 55 159 132 | 631 55 159 133 | 632 55 159 134 | 633 56 160 135 | 634 56 160 136 | 635 56 160 137 | 636 57 161 138 | 637 58 162 139 | 638 58 162 140 | 639 59 163 141 | 640 59 163 142 | 641 59 163 143 | 642 60 164 144 | 643 61 165 145 | 644 61 165 146 | 645 62 166 147 | 646 63 167 148 | 647 63 167 149 | 648 63 167 150 | 649 63 167 151 | 650 64 168 152 | 651 64 168 153 | 652 64 168 154 | 653 64 168 155 | 654 65 169 156 | 655 66 170 157 | 656 66 170 158 | 657 67 171 159 | 658 68 172 160 | 659 69 173 161 | 660 70 174 162 | 661 70 174 163 | 662 70 174 164 | 663 71 175 165 | 664 71 175 166 | 665 72 176 167 | 666 72 176 168 | 667 73 177 169 | 668 73 177 170 | 669 73 177 171 | 670 74 178 172 | 671 74 178 173 | 672 74 178 174 | 673 75 179 175 | 674 75 179 176 | 675 76 180 177 | 676 76 180 178 | 677 77 181 179 | 678 77 181 180 | 679 77 181 181 | 680 78 182 182 | 681 78 182 183 | 682 78 182 184 | 683 78 182 185 | 684 79 183 186 | 685 80 184 187 | 686 81 185 188 | 687 81 185 189 | 688 82 186 190 | 689 83 187 191 | 690 83 187 192 | 691 83 187 193 | 692 84 188 194 | 693 84 188 195 | 694 85 189 196 | 695 85 189 197 | 696 86 190 198 | 697 86 190 199 | 698 86 190 200 | 699 87 191 201 | 700 87 191 202 | 701 87 191 203 | 702 87 191 204 | 703 87 191 205 | 704 88 192 206 | 705 88 192 207 | 706 88 192 208 | 707 89 193 209 | 708 89 193 210 | 709 89 193 211 | 710 90 194 212 | 711 91 195 213 | 712 91 195 214 | 713 92 196 215 | 714 92 196 216 | 715 92 196 217 | 716 93 197 218 | 717 94 198 219 | 718 95 199 220 | 719 96 200 221 | 720 97 201 222 | 721 97 201 223 | 722 97 201 224 | 723 97 201 225 | 724 98 202 226 | 725 98 202 227 | 726 98 202 228 | 727 99 203 229 | 728 99 203 230 | 729 99 203 231 | 730 99 203 232 | 731 100 204 233 | 732 101 205 234 | 733 101 205 235 | 734 102 206 236 | 735 102 206 237 | 736 103 207 238 | 737 104 208 239 | 738 104 208 240 | 739 104 208 -------------------------------------------------------------------------------- /Day1/OpenPol/OpenPol.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Sire Model (Female) 3 | ############################### 4 | 5 | rm(list=ls()) 6 | setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami/Distribute/Day1/OpenPol") 7 | library(asreml) 8 | library(ggplot2) 9 | 10 | # Open Pollinated Example 11 | openp<-read.table("OPENPOL.txt", h=T) 12 | head(openp) 13 | openp$REP<-as.factor(openp$REP) 14 | openp$FEMALE<-as.factor(openp$FEMALE) 15 | openp$PLOT<-as.factor(openp$PLOT) 16 | openp$TYPE<-as.factor(openp$TYPE) 17 | str(openp) 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | # Looking at some of the predictions 30 | ggplot(preds,aes(x=FEMALE,y=predicted.value,fill=FEMALE)) + 31 | geom_bar(position=position_dodge(), stat="identity") + 32 | geom_errorbar(aes(ymin=predicted.value-standard.error, ymax=predicted.value+standard.error), 33 | width=.1, # Width of the error bars 34 | position=position_dodge(.9)) 35 | 36 | -------------------------------------------------------------------------------- /Day2/Bivar/BivarOpen.R: -------------------------------------------------------------------------------- 1 | ################################# 2 | ## Sire Model (Female) Bivariate 3 | ################################# 4 | 5 | rm(list=ls()) 6 | #setwd("./Distribute/Day2/Bivar") 7 | library(asreml) 8 | library(nadiv) 9 | 10 | # Open Pollinated Example 11 | openp<-read.table("./Distribute/Day2/Bivar/OPENPOL.txt", h=T) 12 | head(openp) 13 | openp$REP<-as.factor(openp$REP) 14 | openp$FEMALE<-as.factor(openp$FEMALE) 15 | openp$PLOT<-as.factor(openp$PLOT) 16 | openp$TYPE<-as.factor(openp$TYPE) 17 | str(openp) 18 | 19 | # Single Trait Analysis 20 | model.HT<-asreml(fixed=HT~REP,random=~FEMALE+PLOT,data=openp) 21 | plot(model.HT) 22 | summary(model.HT)$varcomp 23 | 24 | model.DBH<-asreml(fixed=DBH~REP,random=~FEMALE+PLOT,data=openp) 25 | plot(model.DBH) 26 | summary(model.DBH)$varcomp 27 | 28 | # (TODO) calculate heritability, clean outliers, etc.... 29 | 30 | ## Bivariate Analysis ### 31 | with(openp, cor(HT,DBH,use="na") ) # phenotypic correlation (not very useful for gauging indirect selection) 32 | 33 | # Model to Start 34 | # NB -> "trait" in the fixed effects indicates that each response variable (trait) will have its own intercept (mean). So, basically, the two traits are fit with two different models ... except they are tied together 35 | ### trait:REP -> "REP nested withing trait" 36 | ### us(trait,initf):FEMALE -> we want to gauge the correlation 37 | ### diag(trait,init=initp):PLOT -> diag finds different variance for each level of plot 38 | ### id(...) is unnecessary (it is the default), but it is good to be more deliberate / explicit ... forces you to think about what you are actually modeling 39 | ### with 2+ traits requires a flexible rcov (error variance structure) 40 | ### -> everything is conditional on trait... 41 | # NB -> **** starting values of variances can come from earlier, univariate analyses!!! **** 42 | # NB -> In salvador's experience, corgh() works best for random effects and us() work best for error variances 43 | initf<-c(0.19,0.31,0.69) # starting values for un(structured) variance matrix for FEMALE; for corgh, start with correlations, then finish with variances (see ASReml workshop booklet as a reference for how to order these things). 44 | initp<-c(0.05,0.001) # starting values for diag(onal) variance matrix for PLOT 45 | inite<-c(1.02,1.82,7.27) # starting values for un(structured) variance matrix for Residuals 46 | modelb1<-asreml(fixed = cbind(HT,DBH) ~ trait + trait:REP, 47 | random = ~corgh(trait,initf):FEMALE + diag(trait,init=initp):PLOT, 48 | rcov = ~units:us(trait,init=inite), # if units:at(trait) is used, then you are assuming that the observations have to separated because they don't / shouldn't share residual variance... 49 | maxiter = 20,workspace=256e06,data=openp) 50 | modelb1 <- update.asreml(modelb1) 51 | # plot(modelb1) # Does not work even if you try! 52 | summary(modelb1)$varcomp 53 | 54 | # NB -> why are NA's for std.error? Because of numerical issues, where a variance is too close to 0 or 1. Seeing this SHOULD feedback into your modeling (corgh -> diag , say, if correlation from corgh is close to 0 or 1) 55 | # 56 | # NB -> You don't need to specify initial values, but it can be more convenient if you've already run a number of univariate analyses. Also, the univariate analyses can tell you if a biariate analysis is even worthwhile (e.g., if one trait has an extremely low heritability, then it prob isn't worth trying to find the genetic correlation between it and another trait ... **** ) 57 | # NB -> If data are not "clean", and you see that results have not diverged from the initial values, then you might get worried that your inital value choices are having too much impact on your results... this can be checked by choosing different set of initial values 58 | # NB -> Salvador said it is often easier to do many bivariate analyses if you can't get the multivariate model likelihood to converge 59 | # NB -> the response variables need to be close to the same scale ( put on standard normal with unit variance scale. ) 60 | 61 | 62 | # Calculating Genetic parameters 63 | library(nadiv) 64 | (h2_1<-nadiv:::pin(modelb1,h2_1~4*V1/(V1+V4+V7))) 65 | (h2_2<-nadiv:::pin(modelb1,h2_2~4*V3/(V3+V5+V9))) 66 | (rA2<-nadiv:::pin(modelb1,rA2~V2/sqrt(V1*V3))) 67 | (rP2<-nadiv:::pin(modelb1,rP2~(V2+0+V8)/sqrt((V1+V4+V7)*(V3+V5+V9)))) 68 | -------------------------------------------------------------------------------- /Day2/GBLUPFull/GBLUP_Fruit.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Genomic Selection: GBLUP 3 | ############################### 4 | 5 | rm(list=ls()) 6 | #setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami") 7 | library(asreml) 8 | library(nadiv) 9 | 10 | # Reading data 11 | datag<-read.table("./Distribute/Day2/GBLUPFull/Clonaldata.txt",h=T,na.strings='.') 12 | ped<-read.table("./Distribute/Day2/GBLUPFull/pedgenot.txt",h=T) 13 | peddummy<-read.table("./Distribute/Day2/GBLUPFull/peddummy.txt",h=T) 14 | 15 | #datag<-datag[with(datag,order(datag$clone)), ] # Sorting by clone 16 | 17 | # Creating Factors (if necessary) 18 | head(datag) 19 | datag$Rep<-as.factor(datag$Rep) 20 | datag$Block<-as.factor(datag$Block) 21 | datag$Row<-as.factor(datag$Row) 22 | datag$Col<-as.factor(datag$Col) 23 | str(datag) 24 | 25 | # TRADITIONAL ANALYSIS 26 | 27 | # Fitting a simple animal model - using pedigree file 28 | head(ped) 29 | ainv<-asreml.Ainverse(ped)$ginv 30 | head(ainv) 31 | 32 | # Fiting simple model 33 | -------------------------------------------------------------------------------- /Day2/GBLUPFull/GenoMatrix/SubjectID.txt: -------------------------------------------------------------------------------- 1 | Gen0344 2 | Gen0087 3 | Gen0189 4 | Gen0090 5 | Gen0627 6 | Gen0540 7 | Gen0639 8 | Gen0192 9 | Gen0188 10 | Gen0168 11 | Gen0036 12 | Gen0121 13 | Gen0596 14 | Gen0054 15 | Gen0238 16 | Gen0293 17 | Gen0056 18 | Gen0185 19 | Gen0069 20 | Gen0119 21 | Gen0650 22 | Gen0002 23 | Gen0100 24 | Gen0213 25 | Gen0524 26 | Gen0046 27 | Gen0517 28 | Gen0039 29 | Gen0610 30 | Gen0614 31 | Gen0073 32 | Gen0355 33 | Ref004 34 | Gen0077 35 | Gen0107 36 | Gen0059 37 | Gen0320 38 | Gen0216 39 | Gen0515 40 | Gen0209 41 | Gen0558 42 | Gen0218 43 | Gen0561 44 | Gen0323 45 | Gen0634 46 | Gen0412 47 | Gen0114 48 | Gen0360 49 | Gen0263 50 | Gen0016 51 | Gen0110 52 | Gen0569 53 | Gen0038 54 | Gen0563 55 | Gen0089 56 | Gen0275 57 | Gen0222 58 | Gen0654 59 | Gen0012 60 | Gen0013 61 | Gen0454 62 | Gen0011 63 | Gen0015 64 | Gen0045 65 | Gen0648 66 | Gen0394 67 | Gen0309 68 | Gen0044 69 | Gen0050 70 | Gen0201 71 | Gen0373 72 | Gen0585 73 | Gen0640 74 | Gen0331 75 | Gen0520 76 | Gen0171 77 | Gen0057 78 | Gen0186 79 | Gen0142 80 | Gen0357 81 | Gen0542 82 | Gen0033 83 | Gen0159 84 | Gen0170 85 | Gen0297 86 | Gen0637 87 | Gen0638 88 | Gen0035 89 | Gen0032 90 | Gen0601 91 | Gen0144 92 | Gen0126 93 | Gen0656 94 | Gen0562 95 | Gen0554 96 | Gen0138 97 | Gen0420 98 | Gen0456 99 | Gen0022 100 | Gen0462 101 | Gen0457 102 | Gen0493 103 | Gen0580 104 | Gen0495 105 | Gen0348 106 | Gen0147 107 | Gen0624 108 | Gen0426 109 | Gen0579 110 | Gen0476 111 | Gen0559 112 | Gen0583 113 | Gen0581 114 | Gen0535 115 | Gen0528 116 | Gen0146 117 | Gen0378 118 | Gen0386 119 | Gen0575 120 | Gen0279 121 | Gen0313 122 | Gen0621 123 | Gen0458 124 | Gen0421 125 | Gen0227 126 | Gen0278 127 | Gen0481 128 | Gen0622 129 | Gen0574 130 | Gen0466 131 | Gen0469 132 | Gen0132 133 | Gen0434 134 | Gen0470 135 | Gen0438 136 | Gen0474 137 | Gen0193 138 | Gen0631 139 | Gen0402 140 | Gen0289 141 | Gen0576 142 | Gen0428 143 | Gen0538 144 | Gen0181 145 | Gen0304 146 | Gen0157 147 | Gen0342 148 | Gen0301 149 | Gen0414 150 | Gen0389 151 | Gen0145 152 | Gen0483 153 | Gen0200 154 | Gen0452 155 | Gen0395 156 | Gen0390 157 | Gen0217 158 | Gen0031 159 | Gen0592 160 | Gen0451 161 | Gen0586 162 | Gen0150 163 | Gen0482 164 | Gen0447 165 | Gen0612 166 | Gen0025 167 | Gen0094 168 | Gen0034 169 | Gen0272 170 | Gen0269 171 | Gen0391 172 | Gen0028 173 | Gen0276 174 | Gen0027 175 | Gen0460 176 | Gen0633 177 | Gen0287 178 | Gen0632 179 | Gen0265 180 | Gen0247 181 | Gen0092 182 | Gen0049 183 | Gen0231 184 | Gen0140 185 | Gen0448 186 | Gen0387 187 | Gen0154 188 | Gen0490 189 | Gen0589 190 | Gen0625 191 | Gen0400 192 | Gen0261 193 | Gen0136 194 | Gen0595 195 | Gen0340 196 | Gen0208 197 | Gen0439 198 | Gen0281 199 | Gen0243 200 | Gen0363 201 | Gen0550 202 | Gen0347 203 | Gen0590 204 | Gen0437 205 | Gen0155 206 | Gen0246 207 | Gen0651 208 | Gen0009 209 | Gen0519 210 | Gen0413 211 | Gen0260 212 | Gen0010 213 | Gen0570 214 | Gen0219 215 | Gen0626 216 | Gen0183 217 | Gen0029 218 | Gen0446 219 | Gen0496 220 | Gen0441 221 | Gen0417 222 | Gen0623 223 | Gen0541 224 | Gen0369 225 | Gen0534 226 | Gen0484 227 | Gen0401 228 | Gen0516 229 | Gen0547 230 | Gen0111 231 | Gen0048 232 | Gen0358 233 | Gen0162 234 | Gen0055 235 | Gen0163 236 | Gen0133 237 | Gen0134 238 | Gen0167 239 | Gen0040 240 | Gen0093 241 | Gen0067 242 | Gen0513 243 | Gen0244 244 | Gen0600 245 | Gen0095 246 | Gen0195 247 | Gen0280 248 | Gen0365 249 | Gen0337 250 | Gen0392 251 | Gen0443 252 | Gen0180 253 | Gen0488 254 | Gen0652 255 | Gen0383 256 | Gen0151 257 | Gen0499 258 | Gen0226 259 | Gen0248 260 | Gen0537 261 | Gen0491 262 | Gen0176 263 | Gen0141 264 | Gen0267 265 | Gen0324 266 | Gen0384 267 | Gen0430 268 | Gen0433 269 | Gen0264 270 | Gen0512 271 | Gen0636 272 | Gen0017 273 | Gen0084 274 | Gen0105 275 | Gen0453 276 | Gen0333 277 | Gen0223 278 | Gen0649 279 | Gen0642 280 | Gen0037 281 | Gen0308 282 | Gen0486 283 | Gen0302 284 | Gen0464 285 | Gen0404 286 | Gen0480 287 | Gen0398 288 | Gen0405 289 | Gen0253 290 | Gen0306 291 | Gen0602 292 | Gen0356 293 | Gen0230 294 | Gen0343 295 | Gen0449 296 | Gen0153 297 | Gen0235 298 | Gen0225 299 | Gen0229 300 | Gen0307 301 | Gen0158 302 | Gen0258 303 | Gen0286 304 | Gen0282 305 | Gen0353 306 | Gen0532 307 | Gen0616 308 | Gen0143 309 | Gen0619 310 | Gen0388 311 | Gen0179 312 | Gen0429 313 | Gen0197 314 | Gen0198 315 | Gen0199 316 | Gen0406 317 | Gen0445 318 | Gen0212 319 | Gen0120 320 | Gen0214 321 | Gen0096 322 | Gen0125 323 | Gen0647 324 | Gen0215 325 | Gen0240 326 | Gen0473 327 | Gen0318 328 | Gen0296 329 | Gen0115 330 | Gen0064 331 | Gen0122 332 | Gen0149 333 | Gen0283 334 | Gen0285 335 | Gen0572 336 | Gen0573 337 | Gen0415 338 | Gen0424 339 | Gen0598 340 | Gen0206 341 | Gen0501 342 | Gen0117 343 | Gen0071 344 | Gen0444 345 | Gen0112 346 | Gen0259 347 | Gen0523 348 | Gen0047 349 | Gen0256 350 | Gen0628 351 | Gen0245 352 | Gen0367 353 | Gen0058 354 | Gen0053 355 | Gen0210 356 | Gen0052 357 | Gen0556 358 | Gen0442 359 | Gen0555 360 | Gen0578 361 | Gen0431 362 | Gen0422 363 | Gen0203 364 | Gen0613 365 | Gen0314 366 | Gen0030 367 | Gen0266 368 | Gen0450 369 | Gen0611 370 | Gen0118 371 | Gen0085 372 | Gen0078 373 | Gen0043 374 | Gen0236 375 | Gen0191 376 | Gen0106 377 | Gen0557 378 | Gen0525 379 | Gen0560 380 | Gen0362 381 | Gen0277 382 | Gen0273 383 | Gen0086 384 | Gen0066 385 | Gen0618 386 | Gen0529 387 | Gen0083 388 | Gen0531 389 | Gen0527 390 | Gen0108 391 | Gen0137 392 | Gen0101 393 | Gen0521 394 | Gen0292 395 | Gen0148 396 | Gen0328 397 | Gen0317 398 | Gen0291 399 | Gen0290 400 | Gen0099 401 | Gen0375 402 | Gen0327 403 | Gen0630 404 | Gen0371 405 | Gen0549 406 | Gen0322 407 | Gen0603 408 | Gen0242 409 | Gen0504 410 | Gen0024 411 | Gen0165 412 | Gen0129 413 | Gen0166 414 | Gen0500 415 | Gen0503 416 | Gen0160 417 | Gen0220 418 | Gen0211 419 | Gen0505 420 | Gen0350 421 | Gen0128 422 | Gen0440 423 | Gen0063 424 | Gen0351 425 | Gen0072 426 | Gen0526 427 | Gen0494 428 | Gen0164 429 | Gen0629 430 | Gen0423 431 | Gen0104 432 | Gen0255 433 | Gen0080 434 | Gen0123 435 | Gen0161 436 | Gen0475 437 | Gen0182 438 | Gen0545 439 | Gen0295 440 | Gen0506 441 | Gen0316 442 | Gen0042 443 | Gen0116 444 | Gen0511 445 | Gen0326 446 | Gen0076 447 | Gen0319 448 | Gen0522 449 | Gen0349 450 | Gen0653 451 | Gen0361 452 | Gen0567 453 | Gen0510 454 | Gen0492 455 | Gen0552 456 | Gen0658 457 | Gen0019 458 | Gen0321 459 | Gen0644 460 | Gen0615 461 | Gen0020 462 | Gen0127 463 | Gen0597 464 | Gen0617 465 | Gen0593 466 | Gen0062 467 | Gen0023 468 | Gen0061 469 | Gen0021 470 | Gen0655 471 | Gen0169 472 | Gen0345 473 | Gen0376 474 | Gen0374 475 | Gen0502 476 | Gen0130 477 | Gen0594 478 | Gen0131 479 | Gen0359 480 | Gen0338 481 | Gen0346 482 | Gen0582 483 | Gen0425 484 | Gen0403 485 | Gen0408 486 | Gen0202 487 | Gen0380 488 | Gen0478 489 | Gen0407 490 | Gen0465 491 | Gen0604 492 | Gen0237 493 | Gen0416 494 | Gen0224 495 | Gen0533 496 | Gen0178 497 | Gen0546 498 | Gen0539 499 | Gen0382 500 | Gen0548 501 | Gen0184 502 | Gen0239 503 | Gen0271 504 | Gen0543 505 | Gen0577 506 | Gen0397 507 | Gen0251 508 | Gen0435 509 | Gen0234 510 | Gen0232 511 | Gen0330 512 | Gen0221 513 | Gen0364 514 | Gen0354 515 | Gen0325 516 | Gen0204 517 | Gen0190 518 | Gen0065 519 | Gen0288 520 | Gen0536 521 | Gen0461 522 | Gen0609 523 | Gen0497 524 | Gen0311 525 | Gen0565 526 | Gen0571 527 | Gen0274 528 | Gen0477 529 | Gen0606 530 | Gen0566 531 | Gen0207 532 | Gen0250 533 | Gen0074 534 | Gen0051 535 | Gen0014 536 | Gen0177 537 | Gen0352 538 | Gen0079 539 | Gen0174 540 | Gen0175 541 | Gen0172 542 | Gen0305 543 | Gen0620 544 | Gen0564 545 | Gen0436 546 | Gen0553 547 | Gen0068 548 | Gen0635 549 | Gen0315 550 | Gen0591 551 | Gen0041 552 | Gen0156 553 | Gen0196 554 | Gen0487 555 | Gen0463 556 | Gen0419 557 | Gen0139 558 | Gen0479 559 | Gen0607 560 | Gen0608 561 | Gen0060 562 | Gen0098 563 | Gen0368 564 | Gen0205 565 | Gen0075 566 | Gen0485 567 | Gen0508 568 | Gen0187 569 | Gen0135 570 | Gen0551 571 | Gen0088 572 | -------------------------------------------------------------------------------- /Day2/GBLUPFull/Peddummy.txt: -------------------------------------------------------------------------------- 1 | Genot Female Male 2 | Ref015 0 0 3 | Ref029 0 0 4 | Ref019 0 0 5 | Ref012 0 0 6 | Ref011 0 0 7 | Ref030 0 0 8 | Ref010 0 0 9 | Ref028 0 0 10 | Ref031 0 0 11 | Ref013 0 0 12 | Gen0001 0 0 13 | Ref026 0 0 14 | Ref033 0 0 15 | Ref048 0 0 16 | Ref032 0 0 17 | Ref017 0 0 18 | Ref020 0 0 19 | Gen0002 0 0 20 | Ref016 0 0 21 | Ref045 0 0 22 | Ref041 0 0 23 | Ref014 0 0 24 | Ref022 0 0 25 | Ref027 0 0 26 | Ref025 0 0 27 | Ref035 0 0 28 | Ref034 0 0 29 | Ref008 0 0 30 | Ref005 0 0 31 | Ref009 0 0 32 | Ref007 0 0 33 | Ref042 0 0 34 | Ref040 0 0 35 | Ref038 0 0 36 | Ref039 0 0 37 | Ref036 0 0 38 | Ref006 0 0 39 | Ref001 0 0 40 | Ref002 0 0 41 | Gen0019 0 0 42 | Gen0020 0 0 43 | Gen0021 0 0 44 | Gen0022 0 0 45 | Gen0023 0 0 46 | Gen0024 0 0 47 | Gen0025 0 0 48 | Gen0027 0 0 49 | Gen0028 0 0 50 | Gen0029 0 0 51 | Gen0030 0 0 52 | Gen0031 0 0 53 | Gen0032 0 0 54 | Gen0033 0 0 55 | Gen0034 0 0 56 | Gen0035 0 0 57 | Gen0036 0 0 58 | Gen0037 0 0 59 | Gen0038 0 0 60 | Gen0039 0 0 61 | Gen0040 0 0 62 | Gen0041 0 0 63 | Gen0042 0 0 64 | Gen0043 0 0 65 | Gen0044 0 0 66 | Gen0045 0 0 67 | Gen0046 0 0 68 | Gen0047 0 0 69 | Gen0048 0 0 70 | Gen0049 0 0 71 | Gen0050 0 0 72 | Gen0051 0 0 73 | Gen0052 0 0 74 | Gen0053 0 0 75 | Gen0054 0 0 76 | Gen0055 0 0 77 | Gen0056 0 0 78 | Gen0057 0 0 79 | Gen0058 0 0 80 | Gen0059 0 0 81 | Gen0060 0 0 82 | Gen0061 0 0 83 | Gen0062 0 0 84 | Gen0063 0 0 85 | Gen0064 0 0 86 | Gen0065 0 0 87 | Gen0066 0 0 88 | Gen0067 0 0 89 | Gen0077 0 0 90 | Gen0078 0 0 91 | Gen0079 0 0 92 | Gen0080 0 0 93 | Gen0083 0 0 94 | Gen0084 0 0 95 | Gen0085 0 0 96 | Gen0086 0 0 97 | Gen0087 0 0 98 | Gen0088 0 0 99 | Gen0089 0 0 100 | Gen0090 0 0 101 | Gen0092 0 0 102 | Gen0093 0 0 103 | Gen0094 0 0 104 | Gen0095 0 0 105 | Gen0096 0 0 106 | Gen0098 0 0 107 | Gen0099 0 0 108 | Gen0100 0 0 109 | Gen0101 0 0 110 | Gen0104 0 0 111 | Gen0105 0 0 112 | Gen0106 0 0 113 | Gen0107 0 0 114 | Gen0108 0 0 115 | Gen0110 0 0 116 | Gen0111 0 0 117 | Gen0121 0 0 118 | Gen0125 0 0 119 | Gen0126 0 0 120 | Gen0127 0 0 121 | Gen0128 0 0 122 | Gen0129 0 0 123 | Gen0130 0 0 124 | Gen0131 0 0 125 | Gen0132 0 0 126 | Gen0133 0 0 127 | Gen0134 0 0 128 | Gen0135 0 0 129 | Gen0136 0 0 130 | Gen0137 0 0 131 | Gen0138 0 0 132 | Gen0139 0 0 133 | Gen0140 0 0 134 | Gen0141 0 0 135 | Gen0142 0 0 136 | Gen0143 0 0 137 | Gen0144 0 0 138 | Gen0145 0 0 139 | Gen0146 0 0 140 | Gen0147 0 0 141 | Gen0148 0 0 142 | Gen0149 0 0 143 | Gen0150 0 0 144 | Gen0151 0 0 145 | Gen0153 0 0 146 | Gen0154 0 0 147 | Gen0155 0 0 148 | Gen0156 0 0 149 | Gen0157 0 0 150 | Gen0158 0 0 151 | Gen0159 0 0 152 | Gen0160 0 0 153 | Gen0161 0 0 154 | Gen0162 0 0 155 | Gen0163 0 0 156 | Gen0164 0 0 157 | Gen0165 0 0 158 | Gen0166 0 0 159 | Gen0167 0 0 160 | Gen0168 0 0 161 | Gen0169 0 0 162 | Gen0170 0 0 163 | Gen0171 0 0 164 | Gen0172 0 0 165 | Gen0174 0 0 166 | Gen0175 0 0 167 | Gen0176 0 0 168 | Gen0177 0 0 169 | Gen0178 0 0 170 | Gen0179 0 0 171 | Gen0180 0 0 172 | Gen0181 0 0 173 | Gen0187 0 0 174 | Gen0195 0 0 175 | Gen0196 0 0 176 | Gen0197 0 0 177 | Gen0198 0 0 178 | Gen0199 0 0 179 | Gen0200 0 0 180 | Gen0201 0 0 181 | Gen0202 0 0 182 | Gen0203 0 0 183 | Gen0204 0 0 184 | Gen0205 0 0 185 | Gen0206 0 0 186 | Gen0207 0 0 187 | Gen0208 0 0 188 | Gen0209 0 0 189 | Gen0210 0 0 190 | Gen0211 0 0 191 | Gen0212 0 0 192 | Gen0213 0 0 193 | Gen0214 0 0 194 | 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0 250 | Gen0281 0 0 251 | Gen0282 0 0 252 | Gen0283 0 0 253 | Gen0285 0 0 254 | Gen0286 0 0 255 | Gen0287 0 0 256 | Gen0288 0 0 257 | Gen0293 0 0 258 | Gen0301 0 0 259 | Gen0304 0 0 260 | Gen0305 0 0 261 | Gen0306 0 0 262 | Gen0307 0 0 263 | Gen0308 0 0 264 | Gen0309 0 0 265 | Gen0311 0 0 266 | Gen0313 0 0 267 | Gen0314 0 0 268 | Gen0315 0 0 269 | Gen0316 0 0 270 | Gen0317 0 0 271 | Gen0318 0 0 272 | Gen0319 0 0 273 | Gen0320 0 0 274 | Gen0321 0 0 275 | Gen0322 0 0 276 | Gen0323 0 0 277 | Gen0324 0 0 278 | Gen0325 0 0 279 | Gen0326 0 0 280 | Gen0327 0 0 281 | Gen0328 0 0 282 | Gen0338 0 0 283 | Gen0340 0 0 284 | Gen0342 0 0 285 | Gen0343 0 0 286 | Gen0344 0 0 287 | Gen0345 0 0 288 | Gen0346 0 0 289 | Gen0347 0 0 290 | Gen0348 0 0 291 | Gen0349 0 0 292 | Gen0350 0 0 293 | Gen0351 0 0 294 | Gen0352 0 0 295 | Gen0353 0 0 296 | Gen0354 0 0 297 | Gen0355 0 0 298 | Gen0356 0 0 299 | Gen0357 0 0 300 | Gen0358 0 0 301 | Gen0359 0 0 302 | Gen0360 0 0 303 | Gen0361 0 0 304 | Gen0362 0 0 305 | Gen0363 0 0 306 | Gen0364 0 0 307 | Gen0365 0 0 308 | Gen0367 0 0 309 | Gen0368 0 0 310 | Gen0369 0 0 311 | Gen0371 0 0 312 | Gen0373 0 0 313 | Gen0374 0 0 314 | Gen0375 0 0 315 | Gen0376 0 0 316 | Gen0378 0 0 317 | Gen0380 0 0 318 | Gen0382 0 0 319 | Gen0383 0 0 320 | Gen0384 0 0 321 | Gen0386 0 0 322 | Gen0387 0 0 323 | Gen0388 0 0 324 | Gen0389 0 0 325 | Gen0390 0 0 326 | Gen0391 0 0 327 | Gen0392 0 0 328 | Gen0394 0 0 329 | Gen0395 0 0 330 | Gen0397 0 0 331 | Gen0398 0 0 332 | Gen0412 0 0 333 | Gen0413 0 0 334 | Gen0414 0 0 335 | Gen0415 0 0 336 | Gen0416 0 0 337 | Gen0417 0 0 338 | Gen0419 0 0 339 | Gen0420 0 0 340 | Gen0421 0 0 341 | Gen0422 0 0 342 | Gen0423 0 0 343 | Gen0424 0 0 344 | Gen0425 0 0 345 | Gen0426 0 0 346 | Gen0428 0 0 347 | Gen0429 0 0 348 | Gen0440 0 0 349 | Gen0442 0 0 350 | Gen0475 0 0 351 | Gen0477 0 0 352 | Gen0484 0 0 353 | Gen0485 0 0 354 | Gen0486 0 0 355 | Gen0487 0 0 356 | Gen0488 0 0 357 | Gen0490 0 0 358 | Gen0491 0 0 359 | Gen0492 0 0 360 | Gen0493 0 0 361 | Gen0494 0 0 362 | Gen0495 0 0 363 | Gen0496 0 0 364 | Gen0497 0 0 365 | Gen0499 0 0 366 | Gen0500 0 0 367 | Gen0501 0 0 368 | Gen0502 0 0 369 | Gen0503 0 0 370 | Gen0504 0 0 371 | Gen0505 0 0 372 | Gen0506 0 0 373 | Gen0508 0 0 374 | Gen0510 0 0 375 | Gen0519 0 0 376 | Gen0520 0 0 377 | Gen0521 0 0 378 | Gen0522 0 0 379 | Gen0523 0 0 380 | Gen0524 0 0 381 | Gen0525 0 0 382 | Gen0526 0 0 383 | Gen0527 0 0 384 | Gen0528 0 0 385 | Gen0529 0 0 386 | Gen0531 0 0 387 | Gen0532 0 0 388 | Gen0533 0 0 389 | Gen0534 0 0 390 | Gen0535 0 0 391 | Gen0536 0 0 392 | Gen0537 0 0 393 | Gen0538 0 0 394 | Gen0539 0 0 395 | Gen0540 0 0 396 | Gen0541 0 0 397 | Gen0542 0 0 398 | Gen0543 0 0 399 | Gen0545 0 0 400 | Gen0546 0 0 401 | Gen0547 0 0 402 | Gen0548 0 0 403 | Gen0549 0 0 404 | Gen0550 0 0 405 | Gen0551 0 0 406 | Gen0552 0 0 407 | Gen0553 0 0 408 | Gen0555 0 0 409 | Gen0556 0 0 410 | Gen0557 0 0 411 | Gen0558 0 0 412 | Gen0560 0 0 413 | Gen0561 0 0 414 | Gen0562 0 0 415 | Gen0565 0 0 416 | Gen0566 0 0 417 | Gen0567 0 0 418 | Gen0569 0 0 419 | Gen0571 0 0 420 | Gen0572 0 0 421 | Gen0573 0 0 422 | Gen0574 0 0 423 | Gen0575 0 0 424 | Gen0576 0 0 425 | Gen0577 0 0 426 | Gen0578 0 0 427 | Gen0579 0 0 428 | Gen0580 0 0 429 | Gen0581 0 0 430 | Gen0582 0 0 431 | Gen0583 0 0 432 | Gen0585 0 0 433 | Gen0586 0 0 434 | Gen0589 0 0 435 | Gen0590 0 0 436 | Gen0592 0 0 437 | Gen0604 0 0 438 | Gen0606 0 0 439 | Gen0607 0 0 440 | Gen0608 0 0 441 | Gen0609 0 0 442 | Gen0611 0 0 443 | Gen0612 0 0 444 | Gen0613 0 0 445 | Gen0614 0 0 446 | Gen0615 0 0 447 | Gen0616 0 0 448 | Gen0618 0 0 449 | Gen0621 0 0 450 | Gen0622 0 0 451 | Gen0623 0 0 452 | Gen0624 0 0 453 | Gen0625 0 0 454 | Gen0638 0 0 455 | Gen0639 0 0 456 | Gen0640 0 0 457 | Gen0642 0 0 458 | Gen0644 0 0 459 | Gen0647 0 0 460 | Ref004 0 0 461 | Gen0005 0 0 462 | Gen0006 0 0 463 | Gen0008 0 0 464 | Gen0026 0 0 465 | Gen0081 0 0 466 | Gen0082 0 0 467 | Gen0091 0 0 468 | Gen0097 0 0 469 | Gen0102 0 0 470 | Gen0103 0 0 471 | Gen0109 0 0 472 | Gen0152 0 0 473 | Gen0173 0 0 474 | Gen0228 0 0 475 | Gen0233 0 0 476 | Gen0241 0 0 477 | Gen0249 0 0 478 | Gen0252 0 0 479 | Gen0254 0 0 480 | Gen0257 0 0 481 | Gen0262 0 0 482 | Gen0268 0 0 483 | Gen0270 0 0 484 | Gen0284 0 0 485 | Gen0310 0 0 486 | Gen0312 0 0 487 | Gen0339 0 0 488 | Gen0341 0 0 489 | Gen0366 0 0 490 | Gen0370 0 0 491 | Gen0372 0 0 492 | Gen0377 0 0 493 | Gen0379 0 0 494 | Gen0381 0 0 495 | Gen0385 0 0 496 | Gen0393 0 0 497 | Gen0396 0 0 498 | Gen0409 0 0 499 | Gen0410 0 0 500 | Gen0411 0 0 501 | Gen0418 0 0 502 | Gen0427 0 0 503 | Gen0467 0 0 504 | Gen0489 0 0 505 | Gen0498 0 0 506 | Gen0507 0 0 507 | Gen0509 0 0 508 | Gen0530 0 0 509 | Gen0544 0 0 510 | Gen0568 0 0 511 | Gen0584 0 0 512 | Gen0587 0 0 513 | Gen0588 0 0 514 | Gen0605 0 0 515 | Gen0641 0 0 516 | Gen0643 0 0 517 | Gen0645 0 0 518 | Gen0646 0 0 519 | Ref003 0 0 520 | Ref018 0 0 521 | Ref021 0 0 522 | Ref024 0 0 523 | Ref043 0 0 524 | Ref044 0 0 525 | Ref047 0 0 526 | Gen0009 0 0 527 | Gen0010 0 0 528 | Gen0011 0 0 529 | Gen0012 0 0 530 | Gen0013 0 0 531 | Gen0014 0 0 532 | Gen0015 0 0 533 | Gen0016 0 0 534 | Gen0017 0 0 535 | Gen0289 0 0 536 | Gen0290 0 0 537 | Gen0291 0 0 538 | Gen0292 0 0 539 | Gen0295 0 0 540 | Gen0296 0 0 541 | Gen0297 0 0 542 | Gen0302 0 0 543 | Gen0443 0 0 544 | Gen0444 0 0 545 | Gen0445 0 0 546 | Gen0446 0 0 547 | Gen0447 0 0 548 | Gen0448 0 0 549 | Gen0449 0 0 550 | Gen0450 0 0 551 | Gen0451 0 0 552 | Gen0452 0 0 553 | Gen0453 0 0 554 | Gen0454 0 0 555 | Gen0627 0 0 556 | Gen0628 0 0 557 | Gen0629 0 0 558 | Gen0630 0 0 559 | Gen0631 0 0 560 | Gen0632 0 0 561 | Gen0633 0 0 562 | Gen0634 0 0 563 | Gen0635 0 0 564 | Gen0636 0 0 565 | Gen0637 0 0 566 | Gen0648 0 0 567 | Gen0649 0 0 568 | Gen0650 0 0 569 | Gen0651 0 0 570 | Gen0652 0 0 571 | Gen0653 0 0 572 | Gen0654 0 0 573 | Gen0655 0 0 574 | Gen0656 0 0 575 | Gen0658 0 0 576 | Gen0003 0 0 577 | Gen0018 0 0 578 | Gen0294 0 0 579 | Gen0298 0 0 580 | Gen0299 0 0 581 | Gen0300 0 0 582 | Gen0303 0 0 583 | Gen0657 0 0 584 | Ref023 0 0 585 | Ref049 0 0 586 | Gen0182 0 0 587 | Gen0183 0 0 588 | Gen0184 0 0 589 | Gen0185 0 0 590 | Gen0186 0 0 591 | Gen0188 0 0 592 | Gen0189 0 0 593 | Gen0190 0 0 594 | Gen0191 0 0 595 | Gen0192 0 0 596 | Gen0193 0 0 597 | Gen0330 0 0 598 | Gen0331 0 0 599 | Gen0333 0 0 600 | Gen0337 0 0 601 | Gen0400 0 0 602 | Gen0401 0 0 603 | Gen0402 0 0 604 | Gen0403 0 0 605 | Gen0404 0 0 606 | Gen0405 0 0 607 | Gen0406 0 0 608 | Gen0407 0 0 609 | Gen0408 0 0 610 | Gen0004 0 0 611 | Gen0194 0 0 612 | Gen0329 0 0 613 | Gen0332 0 0 614 | Gen0334 0 0 615 | Gen0335 0 0 616 | Gen0336 0 0 617 | Gen0399 0 0 618 | Ref046 0 0 619 | Gen0068 0 0 620 | Gen0069 0 0 621 | Gen0071 0 0 622 | Gen0072 0 0 623 | Gen0073 0 0 624 | Gen0074 0 0 625 | Gen0075 0 0 626 | Gen0076 0 0 627 | Gen0112 0 0 628 | Gen0114 0 0 629 | Gen0115 0 0 630 | Gen0116 0 0 631 | Gen0117 0 0 632 | Gen0118 0 0 633 | Gen0119 0 0 634 | Gen0120 0 0 635 | Gen0122 0 0 636 | Gen0123 0 0 637 | Gen0430 0 0 638 | Gen0431 0 0 639 | Gen0433 0 0 640 | Gen0434 0 0 641 | Gen0435 0 0 642 | Gen0436 0 0 643 | Gen0437 0 0 644 | Gen0438 0 0 645 | Gen0439 0 0 646 | Gen0441 0 0 647 | Gen0456 0 0 648 | Gen0457 0 0 649 | Gen0458 0 0 650 | Gen0460 0 0 651 | Gen0461 0 0 652 | Gen0462 0 0 653 | Gen0463 0 0 654 | Gen0464 0 0 655 | Gen0465 0 0 656 | Gen0466 0 0 657 | Gen0469 0 0 658 | Gen0470 0 0 659 | Gen0473 0 0 660 | Gen0474 0 0 661 | Gen0476 0 0 662 | Gen0478 0 0 663 | Gen0479 0 0 664 | Gen0480 0 0 665 | Gen0481 0 0 666 | Gen0482 0 0 667 | Gen0483 0 0 668 | Gen0511 0 0 669 | Gen0512 0 0 670 | Gen0513 0 0 671 | Gen0515 0 0 672 | Gen0516 0 0 673 | Gen0517 0 0 674 | Gen0554 0 0 675 | Gen0559 0 0 676 | Gen0563 0 0 677 | Gen0564 0 0 678 | Gen0570 0 0 679 | Gen0591 0 0 680 | Gen0593 0 0 681 | Gen0594 0 0 682 | Gen0595 0 0 683 | Gen0596 0 0 684 | Gen0597 0 0 685 | Gen0598 0 0 686 | Gen0600 0 0 687 | Gen0601 0 0 688 | Gen0602 0 0 689 | Gen0603 0 0 690 | Gen0610 0 0 691 | Gen0617 0 0 692 | Gen0619 0 0 693 | Gen0620 0 0 694 | Gen0626 0 0 695 | Gen0007 0 0 696 | Gen0070 0 0 697 | Gen0113 0 0 698 | Gen0124 0 0 699 | Gen0432 0 0 700 | Gen0455 0 0 701 | Gen0459 0 0 702 | Gen0468 0 0 703 | Gen0471 0 0 704 | Gen0472 0 0 705 | Gen0514 0 0 706 | Gen0518 0 0 707 | Gen0599 0 0 708 | -------------------------------------------------------------------------------- /Day2/GBLUPFull/Pedgenot.txt: -------------------------------------------------------------------------------- 1 | Genot Female Male 2 | Ref015 0 0 3 | Ref029 0 0 4 | Ref019 0 0 5 | Ref012 0 0 6 | Ref011 0 0 7 | Ref030 0 0 8 | Ref010 0 0 9 | Ref028 0 0 10 | Ref031 0 0 11 | Ref013 0 0 12 | Gen0001 0 0 13 | Ref026 0 0 14 | Ref033 0 0 15 | Ref048 0 0 16 | Ref032 0 0 17 | Ref017 0 0 18 | Ref020 0 0 19 | Gen0002 0 0 20 | Ref016 0 0 21 | Ref045 0 0 22 | Ref041 0 0 23 | Ref014 0 0 24 | Ref022 0 0 25 | Ref027 0 0 26 | Ref025 0 0 27 | Ref035 0 0 28 | Ref034 0 0 29 | Ref008 0 0 30 | Ref005 0 0 31 | Ref009 0 0 32 | Ref007 0 0 33 | Ref042 0 0 34 | Ref040 0 0 35 | Ref038 0 0 36 | Ref039 0 0 37 | Ref036 0 0 38 | Ref006 0 0 39 | Ref001 0 0 40 | Ref002 0 0 41 | Gen0019 Ref029 Ref019 42 | Gen0020 Ref029 Ref019 43 | Gen0021 Ref029 Ref019 44 | Gen0022 Ref029 Ref019 45 | Gen0023 Ref029 Ref019 46 | Gen0024 Ref012 Ref019 47 | Gen0025 Ref012 Ref019 48 | Gen0027 Ref012 Ref019 49 | Gen0028 Ref012 Ref019 50 | Gen0029 Ref029 Ref019 51 | Gen0030 Ref012 Ref019 52 | Gen0031 Ref012 Ref019 53 | Gen0032 Ref012 Ref019 54 | Gen0033 Ref012 Ref019 55 | Gen0034 Ref012 Ref019 56 | Gen0035 Ref011 Ref015 57 | Gen0036 Ref011 Ref015 58 | Gen0037 Ref011 Ref015 59 | Gen0038 Ref011 Ref015 60 | Gen0039 Ref011 Ref015 61 | Gen0040 Ref011 Ref015 62 | Gen0041 Ref011 Ref015 63 | Gen0042 Ref011 Ref015 64 | Gen0043 Ref011 Ref015 65 | Gen0044 Ref011 Ref015 66 | Gen0045 Ref030 Ref029 67 | Gen0046 Ref030 Ref029 68 | Gen0047 Ref030 Ref029 69 | Gen0048 Ref030 Ref029 70 | Gen0049 Ref030 Ref029 71 | Gen0050 Ref030 Ref029 72 | Gen0051 Ref030 Ref029 73 | Gen0052 Ref030 Ref029 74 | Gen0053 Ref030 Ref029 75 | Gen0054 Ref030 Ref029 76 | Gen0055 Ref030 Ref029 77 | Gen0056 Ref029 Ref019 78 | Gen0057 Ref029 Ref019 79 | Gen0058 Ref019 Ref011 80 | Gen0059 Ref019 Ref011 81 | Gen0060 Ref019 Ref011 82 | Gen0061 Ref029 Ref019 83 | Gen0062 Ref029 Ref019 84 | Gen0063 Ref019 Ref011 85 | Gen0064 Ref019 Ref011 86 | Gen0065 Ref019 Ref011 87 | Gen0066 Ref019 Ref011 88 | Gen0067 Ref019 Ref011 89 | Gen0077 Ref010 Ref030 90 | Gen0078 Ref010 Ref030 91 | Gen0079 Ref010 Ref030 92 | Gen0080 Ref010 Ref030 93 | Gen0083 Ref010 Ref030 94 | Gen0084 Ref010 Ref030 95 | Gen0085 Ref010 Ref030 96 | Gen0086 Ref010 Ref030 97 | Gen0087 Ref010 Ref030 98 | Gen0088 Ref028 Ref031 99 | Gen0089 Ref028 Ref031 100 | Gen0090 Ref028 Ref031 101 | Gen0092 Ref028 Ref031 102 | Gen0093 Ref028 Ref031 103 | Gen0094 Ref028 Ref031 104 | Gen0095 Ref028 Ref031 105 | Gen0096 Ref028 Ref031 106 | Gen0098 Ref029 Ref011 107 | Gen0099 Ref029 Ref011 108 | Gen0100 Ref013 Ref010 109 | Gen0101 Ref029 Ref011 110 | Gen0104 Ref013 Ref010 111 | Gen0105 Ref013 Ref010 112 | Gen0106 Ref013 Ref010 113 | Gen0107 Ref013 Ref010 114 | Gen0108 Ref013 Ref010 115 | Gen0110 Ref029 Ref011 116 | Gen0111 Ref013 Ref010 117 | Gen0121 Ref029 Ref011 118 | Gen0125 Ref026 Ref033 119 | Gen0126 Ref026 Ref033 120 | Gen0127 Ref026 Ref033 121 | Gen0128 Ref026 Ref033 122 | Gen0129 Ref026 Ref033 123 | Gen0130 Ref026 Ref033 124 | Gen0131 Ref026 Ref033 125 | Gen0132 Ref026 Ref033 126 | Gen0133 Ref026 Ref033 127 | Gen0134 Ref026 Ref033 128 | Gen0135 Ref026 Ref033 129 | Gen0136 Ref026 Ref033 130 | Gen0137 Ref029 Ref011 131 | Gen0138 Ref015 Ref013 132 | Gen0139 Ref015 Ref013 133 | Gen0140 Ref015 Ref013 134 | Gen0141 Ref015 Ref013 135 | Gen0142 Ref015 Ref013 136 | Gen0143 Ref015 Ref013 137 | Gen0144 Ref015 Ref013 138 | Gen0145 Ref015 Ref013 139 | Gen0146 Ref015 Ref013 140 | Gen0147 Ref015 Ref013 141 | Gen0148 Ref029 Ref011 142 | Gen0149 Ref010 Ref012 143 | Gen0150 Ref010 Ref012 144 | Gen0151 Ref010 Ref012 145 | Gen0153 Ref010 Ref012 146 | Gen0154 Ref010 Ref012 147 | Gen0155 Ref010 Ref012 148 | Gen0156 Ref010 Ref012 149 | Gen0157 Ref010 Ref012 150 | Gen0158 Ref010 Ref012 151 | Gen0159 Ref010 Ref012 152 | Gen0160 Ref011 Ref026 153 | Gen0161 Ref011 Ref026 154 | Gen0162 Ref011 Ref026 155 | Gen0163 Ref011 Ref026 156 | Gen0164 Ref011 Ref026 157 | Gen0165 Ref011 Ref026 158 | Gen0166 Ref011 Ref026 159 | Gen0167 Ref011 Ref026 160 | Gen0168 Ref011 Ref026 161 | Gen0169 Ref011 Ref026 162 | Gen0170 Ref029 Ref011 163 | Gen0171 Ref012 Ref029 164 | Gen0172 Ref012 Ref029 165 | Gen0174 Ref012 Ref029 166 | Gen0175 Ref012 Ref029 167 | Gen0176 Ref012 Ref029 168 | Gen0177 Ref012 Ref029 169 | Gen0178 Ref012 Ref029 170 | Gen0179 Ref012 Ref029 171 | Gen0180 Ref012 Ref029 172 | Gen0181 Ref012 Ref029 173 | Gen0187 Ref029 Ref011 174 | Gen0195 Ref033 Ref028 175 | Gen0196 Ref033 Ref028 176 | Gen0197 Ref033 Ref028 177 | Gen0198 Ref033 Ref028 178 | Gen0199 Ref033 Ref028 179 | Gen0200 Ref033 Ref028 180 | Gen0201 Ref033 Ref028 181 | Gen0202 Ref033 Ref028 182 | Gen0203 Ref033 Ref028 183 | Gen0204 Ref033 Ref028 184 | Gen0205 Ref029 Ref011 185 | Gen0206 Ref033 Ref028 186 | Gen0207 Ref029 Ref011 187 | Gen0208 Ref033 Ref028 188 | Gen0209 Ref048 Ref019 189 | Gen0210 Ref048 Ref019 190 | Gen0211 Ref048 Ref019 191 | Gen0212 Ref048 Ref019 192 | Gen0213 Ref048 Ref019 193 | Gen0214 Ref048 Ref019 194 | Gen0215 Ref048 Ref019 195 | Gen0216 Ref048 Ref019 196 | Gen0217 Ref048 Ref019 197 | Gen0218 Ref048 Ref019 198 | Gen0219 Ref048 Ref019 199 | Gen0220 Ref048 Ref019 200 | Gen0221 Ref048 Ref011 201 | Gen0222 Ref048 Ref011 202 | Gen0223 Ref048 Ref011 203 | Gen0224 Ref030 Ref048 204 | Gen0225 Ref030 Ref048 205 | Gen0226 Ref030 Ref048 206 | Gen0227 Ref030 Ref048 207 | Gen0229 Ref030 Ref048 208 | Gen0230 Ref030 Ref048 209 | Gen0231 Ref048 Ref011 210 | Gen0232 Ref030 Ref048 211 | Gen0234 Ref030 Ref048 212 | Gen0235 Ref030 Ref048 213 | Gen0236 Ref030 Ref048 214 | Gen0237 Ref030 Ref048 215 | Gen0238 Ref030 Ref048 216 | Gen0239 Ref048 Ref011 217 | Gen0240 Ref011 Ref033 218 | Gen0242 Ref011 Ref033 219 | Gen0243 Ref011 Ref033 220 | Gen0244 Ref011 Ref033 221 | Gen0245 Ref011 Ref033 222 | Gen0246 Ref011 Ref033 223 | Gen0247 Ref048 Ref011 224 | Gen0248 Ref011 Ref033 225 | Gen0250 Ref011 Ref033 226 | Gen0251 Ref011 Ref033 227 | Gen0253 Ref011 Ref033 228 | Gen0255 Ref029 Ref032 229 | Gen0256 Ref029 Ref032 230 | Gen0258 Ref029 Ref032 231 | Gen0259 Ref029 Ref032 232 | Gen0260 Ref029 Ref032 233 | Gen0261 Ref029 Ref032 234 | Gen0263 Ref029 Ref032 235 | Gen0264 Ref029 Ref032 236 | Gen0265 Ref019 Ref032 237 | Gen0266 Ref019 Ref032 238 | Gen0267 Ref019 Ref032 239 | Gen0269 Ref019 Ref032 240 | Gen0271 Ref019 Ref032 241 | Gen0272 Ref019 Ref032 242 | Gen0273 Ref019 Ref032 243 | Gen0274 Ref019 Ref032 244 | Gen0275 Ref019 Ref032 245 | Gen0276 Ref019 Ref032 246 | Gen0277 Ref033 Ref015 247 | Gen0278 Ref033 Ref015 248 | Gen0279 Ref033 Ref015 249 | Gen0280 Ref033 Ref015 250 | Gen0281 Ref033 Ref015 251 | Gen0282 Ref033 Ref015 252 | Gen0283 Ref033 Ref015 253 | Gen0285 Ref033 Ref015 254 | Gen0286 Ref033 Ref015 255 | Gen0287 Ref033 Ref015 256 | Gen0288 Ref033 Ref015 257 | Gen0293 Ref048 Ref011 258 | Gen0301 Ref048 Ref011 259 | Gen0304 Ref019 Gen0001 260 | Gen0305 Ref019 Gen0001 261 | Gen0306 Ref019 Gen0001 262 | Gen0307 Ref019 Gen0001 263 | Gen0308 Ref019 Gen0001 264 | Gen0309 Ref019 Gen0001 265 | Gen0311 Ref019 Gen0001 266 | Gen0313 Ref019 Gen0001 267 | Gen0314 Ref019 Gen0001 268 | Gen0315 Ref019 Gen0001 269 | Gen0316 Ref033 Ref017 270 | Gen0317 Ref033 Ref017 271 | Gen0318 Ref033 Ref017 272 | Gen0319 Ref033 Ref017 273 | Gen0320 Ref033 Ref017 274 | Gen0321 Ref033 Ref017 275 | Gen0322 Ref033 Ref017 276 | Gen0323 Ref033 Ref017 277 | Gen0324 Ref033 Ref017 278 | Gen0325 Ref048 Ref011 279 | Gen0326 Ref033 Ref017 280 | Gen0327 Ref033 Ref017 281 | Gen0328 Ref033 Ref017 282 | Gen0338 Ref015 Ref010 283 | Gen0340 Ref015 Ref010 284 | Gen0342 Ref015 Ref010 285 | Gen0343 Ref015 Ref010 286 | Gen0344 Ref015 Ref010 287 | Gen0345 Ref015 Ref010 288 | Gen0346 Ref015 Ref010 289 | Gen0347 Ref015 Ref010 290 | Gen0348 Ref015 Ref010 291 | Gen0349 Ref020 Ref019 292 | Gen0350 Ref020 Ref019 293 | Gen0351 Ref020 Ref019 294 | Gen0352 Ref019 Ref026 295 | Gen0353 Ref019 Ref026 296 | Gen0354 Ref019 Ref026 297 | Gen0355 Ref019 Ref026 298 | Gen0356 Ref019 Ref026 299 | Gen0357 Ref019 Ref026 300 | Gen0358 Ref019 Ref026 301 | Gen0359 Ref019 Ref026 302 | Gen0360 Ref020 Ref019 303 | Gen0361 Ref020 Ref019 304 | Gen0362 Ref020 Ref019 305 | Gen0363 Ref019 Ref026 306 | Gen0364 Ref019 Ref026 307 | Gen0365 Ref019 Ref026 308 | Gen0367 Ref029 Gen0002 309 | Gen0368 Ref029 Gen0002 310 | Gen0369 Ref029 Gen0002 311 | Gen0371 Ref029 Gen0002 312 | Gen0373 Ref029 Gen0002 313 | Gen0374 Ref029 Gen0002 314 | Gen0375 Ref029 Gen0002 315 | Gen0376 Ref029 Gen0002 316 | Gen0378 Ref026 Ref032 317 | Gen0380 Ref026 Ref032 318 | Gen0382 Ref026 Ref032 319 | Gen0383 Ref026 Ref032 320 | Gen0384 Ref026 Ref032 321 | Gen0386 Ref026 Ref032 322 | Gen0387 Ref026 Ref032 323 | Gen0388 Ref013 Ref030 324 | Gen0389 Ref013 Ref030 325 | Gen0390 Ref013 Ref030 326 | Gen0391 Ref013 Ref030 327 | Gen0392 Ref013 Ref030 328 | Gen0394 Ref013 Ref030 329 | Gen0395 Ref013 Ref030 330 | Gen0397 Ref013 Ref030 331 | Gen0398 Ref013 Ref030 332 | Gen0412 Ref011 Ref028 333 | Gen0413 Ref011 Ref028 334 | Gen0414 Ref011 Ref028 335 | Gen0415 Ref020 Ref019 336 | Gen0416 Ref011 Ref028 337 | Gen0417 Ref011 Ref028 338 | Gen0419 Ref011 Ref028 339 | Gen0420 Ref028 Ref013 340 | Gen0421 Ref028 Ref013 341 | Gen0422 Ref028 Ref013 342 | Gen0423 Ref028 Ref013 343 | Gen0424 Ref028 Ref013 344 | Gen0425 Ref028 Ref013 345 | Gen0426 Ref028 Ref013 346 | Gen0428 Ref028 Ref013 347 | Gen0429 Ref028 Ref013 348 | Gen0440 Ref020 Ref019 349 | Gen0442 Ref020 Ref019 350 | Gen0475 Ref011 Ref032 351 | Gen0477 Ref011 Ref032 352 | Gen0484 Ref011 Ref032 353 | Gen0485 Ref011 Ref032 354 | Gen0486 Ref020 Ref029 355 | Gen0487 Ref020 Ref029 356 | Gen0488 Ref020 Ref029 357 | Gen0490 Ref020 Ref029 358 | Gen0491 Ref020 Ref029 359 | Gen0492 Ref020 Ref029 360 | Gen0493 Ref020 Ref029 361 | Gen0494 Ref020 Ref029 362 | Gen0495 Ref020 Ref029 363 | Gen0496 Ref020 Ref029 364 | Gen0497 Ref020 Ref029 365 | Gen0499 Ref048 Ref029 366 | Gen0500 Ref048 Ref029 367 | Gen0501 Ref048 Ref029 368 | Gen0502 Ref048 Ref029 369 | Gen0503 Ref048 Ref029 370 | Gen0504 Ref048 Ref029 371 | Gen0505 Ref048 Ref029 372 | Gen0506 Ref048 Ref029 373 | Gen0508 Ref011 Ref032 374 | Gen0510 Ref048 Ref029 375 | Gen0519 Ref029 Ref026 376 | Gen0520 Ref029 Ref026 377 | Gen0521 Ref029 Ref026 378 | Gen0522 Ref029 Ref026 379 | Gen0523 Ref029 Ref026 380 | Gen0524 Ref029 Ref026 381 | Gen0525 Ref029 Ref026 382 | Gen0526 Ref011 Ref032 383 | Gen0527 Ref029 Ref026 384 | Gen0528 Ref029 Ref026 385 | Gen0529 Ref011 Ref032 386 | Gen0531 Ref029 Ref026 387 | Gen0532 Ref029 Ref016 388 | Gen0533 Ref029 Ref016 389 | Gen0534 Ref011 Ref032 390 | Gen0535 Ref029 Ref016 391 | Gen0536 Ref029 Ref016 392 | Gen0537 Ref029 Ref016 393 | Gen0538 Ref029 Ref016 394 | Gen0539 Ref029 Ref016 395 | Gen0540 Ref011 Ref032 396 | Gen0541 Ref029 Ref016 397 | Gen0542 Ref029 Ref016 398 | Gen0543 Ref029 Ref016 399 | Gen0545 Ref010 Ref020 400 | Gen0546 Ref010 Ref020 401 | Gen0547 Ref010 Ref020 402 | Gen0548 Ref010 Ref020 403 | Gen0549 Ref010 Ref020 404 | Gen0550 Ref010 Ref020 405 | Gen0551 Ref010 Ref020 406 | Gen0552 Ref010 Ref020 407 | Gen0553 Ref010 Ref020 408 | Gen0555 Ref016 Ref010 409 | Gen0556 Ref016 Ref010 410 | Gen0557 Ref016 Ref010 411 | Gen0558 Ref016 Ref010 412 | Gen0560 Ref016 Ref010 413 | Gen0561 Ref016 Ref010 414 | Gen0562 Ref016 Ref010 415 | Gen0565 Ref015 Ref016 416 | Gen0566 Ref015 Ref016 417 | Gen0567 Ref015 Ref016 418 | Gen0569 Ref015 Ref016 419 | Gen0571 Ref015 Ref016 420 | Gen0572 Ref015 Ref016 421 | Gen0573 Ref015 Ref016 422 | Gen0574 Ref028 Ref010 423 | Gen0575 Ref028 Ref010 424 | Gen0576 Ref028 Ref010 425 | Gen0577 Ref028 Ref010 426 | Gen0578 Ref028 Ref010 427 | Gen0579 Ref028 Ref010 428 | Gen0580 Ref028 Ref010 429 | Gen0581 Ref028 Ref010 430 | Gen0582 Ref028 Ref010 431 | Gen0583 Ref028 Ref010 432 | Gen0585 Ref019 Ref028 433 | Gen0586 Ref019 Ref028 434 | Gen0589 Ref019 Ref028 435 | Gen0590 Ref019 Ref028 436 | Gen0592 Ref019 Ref028 437 | Gen0604 Ref017 Ref028 438 | Gen0606 Ref017 Ref028 439 | Gen0607 Ref017 Ref028 440 | Gen0608 Ref017 Ref028 441 | Gen0609 Ref017 Ref028 442 | Gen0611 Ref017 Ref015 443 | Gen0612 Ref017 Ref015 444 | Gen0613 Ref017 Ref015 445 | Gen0614 Ref017 Ref015 446 | Gen0615 Ref017 Ref015 447 | Gen0616 Ref017 Ref015 448 | Gen0618 Ref017 Ref015 449 | Gen0621 Ref016 Ref030 450 | Gen0622 Ref016 Ref030 451 | Gen0623 Ref016 Ref030 452 | Gen0624 Ref016 Ref030 453 | Gen0625 Ref016 Ref030 454 | Gen0638 Ref032 Ref033 455 | Gen0639 Ref032 Ref033 456 | Gen0640 Ref032 Ref033 457 | Gen0642 Ref032 Ref033 458 | Gen0644 Ref032 Ref033 459 | Gen0647 Ref032 Ref033 460 | Ref004 Ref045 Ref041 461 | Gen0005 Ref014 Ref022 462 | Gen0006 Ref027 Ref029 463 | Gen0008 Ref027 Ref022 464 | Gen0026 Ref012 Ref019 465 | Gen0081 Ref010 Ref030 466 | Gen0082 Ref010 Ref030 467 | Gen0091 Ref028 Ref031 468 | Gen0097 Ref028 Ref031 469 | Gen0102 Ref013 Ref010 470 | Gen0103 Ref013 Ref010 471 | Gen0109 Ref013 Ref010 472 | Gen0152 Ref010 Ref012 473 | Gen0173 Ref012 Ref029 474 | Gen0228 Ref030 Ref048 475 | Gen0233 Ref048 Ref011 476 | Gen0241 Ref011 Ref033 477 | Gen0249 Ref011 Ref033 478 | Gen0252 Ref011 Ref033 479 | Gen0254 Ref029 Ref032 480 | Gen0257 Ref029 Ref032 481 | Gen0262 Ref029 Ref032 482 | Gen0268 Ref019 Ref032 483 | Gen0270 Ref019 Ref032 484 | Gen0284 Ref033 Ref015 485 | Gen0310 Ref019 Gen0001 486 | Gen0312 Ref019 Gen0001 487 | Gen0339 Ref015 Ref010 488 | Gen0341 Ref015 Ref010 489 | Gen0366 Ref029 Gen0002 490 | Gen0370 Ref029 Gen0002 491 | Gen0372 Ref029 Gen0002 492 | Gen0377 Ref026 Ref032 493 | Gen0379 Ref026 Ref032 494 | Gen0381 Ref026 Ref032 495 | Gen0385 Ref026 Ref032 496 | Gen0393 Ref013 Ref030 497 | Gen0396 Ref013 Ref030 498 | Gen0409 Ref011 Ref028 499 | Gen0410 Ref011 Ref028 500 | Gen0411 Ref011 Ref028 501 | Gen0418 Ref011 Ref028 502 | Gen0427 Ref028 Ref013 503 | Gen0467 Ref011 Ref032 504 | Gen0489 Ref020 Ref029 505 | Gen0498 Ref048 Ref029 506 | Gen0507 Ref048 Ref029 507 | Gen0509 Ref048 Ref029 508 | Gen0530 Ref029 Ref026 509 | Gen0544 Ref010 Ref020 510 | Gen0568 Ref015 Ref016 511 | Gen0584 Ref019 Ref028 512 | Gen0587 Ref019 Ref028 513 | Gen0588 Ref019 Ref028 514 | Gen0605 Ref017 Ref028 515 | Gen0641 Ref032 Ref033 516 | Gen0643 Ref032 Ref033 517 | Gen0645 Ref032 Ref033 518 | Gen0646 Ref032 Ref033 519 | Ref003 Ref035 Ref034 520 | Ref018 Ref008 Ref005 521 | Ref021 Ref009 Ref007 522 | Ref024 Ref040 Ref048 523 | Ref043 Ref038 Ref039 524 | Ref044 Ref048 Ref036 525 | Ref047 Ref001 Ref041 526 | Gen0009 Ref015 Ref044 527 | Gen0010 Ref015 Ref044 528 | Gen0011 Ref015 Ref044 529 | Gen0012 Ref015 Ref044 530 | Gen0013 Ref015 Ref044 531 | Gen0014 Ref015 Ref044 532 | Gen0015 Ref015 Ref044 533 | Gen0016 Ref015 Ref044 534 | Gen0017 Ref015 Ref044 535 | Gen0289 Ref018 Ref048 536 | Gen0290 Ref018 Ref048 537 | Gen0291 Ref018 Ref048 538 | Gen0292 Ref018 Ref048 539 | Gen0295 Ref018 Ref048 540 | Gen0296 Ref018 Ref048 541 | Gen0297 Ref018 Ref048 542 | Gen0302 Ref018 Ref048 543 | Gen0443 Ref010 Ref018 544 | Gen0444 Ref010 Ref018 545 | Gen0445 Ref010 Ref018 546 | Gen0446 Ref010 Ref018 547 | Gen0447 Ref010 Ref018 548 | Gen0448 Ref010 Ref018 549 | Gen0449 Ref010 Ref018 550 | Gen0450 Ref010 Ref018 551 | Gen0451 Ref010 Ref018 552 | Gen0452 Ref010 Ref018 553 | Gen0453 Ref010 Ref018 554 | Gen0454 Ref010 Ref018 555 | Gen0627 Ref028 Ref018 556 | Gen0628 Ref028 Ref018 557 | Gen0629 Ref028 Ref018 558 | Gen0630 Ref028 Ref018 559 | Gen0631 Ref028 Ref018 560 | Gen0632 Ref028 Ref018 561 | Gen0633 Ref028 Ref018 562 | Gen0634 Ref028 Ref018 563 | Gen0635 Ref028 Ref018 564 | Gen0636 Ref028 Ref018 565 | Gen0637 Ref028 Ref018 566 | Gen0648 Ref018 Ref030 567 | Gen0649 Ref018 Ref030 568 | Gen0650 Ref018 Ref030 569 | Gen0651 Ref018 Ref030 570 | Gen0652 Ref018 Ref030 571 | Gen0653 Ref018 Ref030 572 | Gen0654 Ref018 Ref030 573 | Gen0655 Ref018 Ref030 574 | Gen0656 Ref018 Ref030 575 | Gen0658 Ref018 Ref030 576 | Gen0003 Ref004 Ref024 577 | Gen0018 Ref015 Ref044 578 | Gen0294 Ref018 Ref048 579 | Gen0298 Ref018 Ref048 580 | Gen0299 Ref018 Ref048 581 | Gen0300 Ref018 Ref048 582 | Gen0303 Ref018 Ref048 583 | Gen0657 Ref018 Ref030 584 | Ref023 Ref042 Ref004 585 | Ref049 Ref044 Ref002 586 | Gen0182 Ref049 Ref028 587 | Gen0183 Ref049 Ref028 588 | Gen0184 Ref049 Ref028 589 | Gen0185 Ref049 Ref028 590 | Gen0186 Ref049 Ref028 591 | Gen0188 Ref049 Ref028 592 | Gen0189 Ref049 Ref028 593 | Gen0190 Ref049 Ref028 594 | Gen0191 Ref049 Ref028 595 | Gen0192 Ref049 Ref028 596 | Gen0193 Ref049 Ref028 597 | Gen0330 Ref049 Ref015 598 | Gen0331 Ref049 Ref015 599 | Gen0333 Ref049 Ref015 600 | Gen0337 Ref049 Ref015 601 | Gen0400 Ref033 Ref049 602 | Gen0401 Ref033 Ref049 603 | Gen0402 Ref033 Ref049 604 | Gen0403 Ref033 Ref049 605 | Gen0404 Ref033 Ref049 606 | Gen0405 Ref033 Ref049 607 | Gen0406 Ref033 Ref049 608 | Gen0407 Ref033 Ref049 609 | Gen0408 Ref033 Ref049 610 | Gen0004 Ref049 Ref011 611 | Gen0194 Ref049 Ref028 612 | Gen0329 Ref049 Ref015 613 | Gen0332 Ref049 Ref015 614 | Gen0334 Ref049 Ref015 615 | Gen0335 Ref049 Ref015 616 | Gen0336 Ref049 Ref015 617 | Gen0399 Ref033 Ref049 618 | Ref046 Ref049 Ref006 619 | Gen0068 Ref046 Ref015 620 | Gen0069 Ref046 Ref015 621 | Gen0071 Ref046 Ref015 622 | Gen0072 Ref046 Ref015 623 | Gen0073 Ref046 Ref015 624 | Gen0074 Ref046 Ref015 625 | Gen0075 Ref046 Ref015 626 | Gen0076 Ref046 Ref015 627 | Gen0112 Ref046 Gen0001 628 | Gen0114 Ref046 Gen0001 629 | Gen0115 Ref046 Gen0001 630 | Gen0116 Ref046 Gen0001 631 | Gen0117 Ref046 Gen0001 632 | Gen0118 Ref046 Gen0001 633 | Gen0119 Ref046 Gen0001 634 | Gen0120 Ref046 Gen0001 635 | Gen0122 Ref046 Gen0001 636 | Gen0123 Ref046 Gen0001 637 | Gen0430 Ref032 Ref046 638 | Gen0431 Ref032 Ref046 639 | Gen0433 Ref032 Ref046 640 | Gen0434 Ref032 Ref046 641 | Gen0435 Ref032 Ref046 642 | Gen0436 Ref032 Ref046 643 | Gen0437 Ref032 Ref046 644 | Gen0438 Ref032 Ref046 645 | Gen0439 Ref032 Ref046 646 | Gen0441 Ref032 Ref046 647 | Gen0456 Ref046 Ref033 648 | Gen0457 Ref046 Ref033 649 | Gen0458 Ref046 Ref033 650 | Gen0460 Ref046 Ref033 651 | Gen0461 Ref046 Ref033 652 | Gen0462 Ref046 Ref033 653 | Gen0463 Ref046 Ref033 654 | Gen0464 Ref046 Ref033 655 | Gen0465 Ref046 Ref033 656 | Gen0466 Ref046 Ref033 657 | Gen0469 Ref046 Ref029 658 | Gen0470 Ref046 Ref029 659 | Gen0473 Ref046 Ref029 660 | Gen0474 Ref046 Ref029 661 | Gen0476 Ref046 Ref029 662 | Gen0478 Ref046 Ref029 663 | Gen0479 Ref046 Ref029 664 | Gen0480 Ref046 Ref029 665 | Gen0481 Ref046 Ref029 666 | Gen0482 Ref046 Ref029 667 | Gen0483 Ref046 Ref029 668 | Gen0511 Ref011 Ref046 669 | Gen0512 Ref011 Ref046 670 | Gen0513 Ref011 Ref046 671 | Gen0515 Ref011 Ref046 672 | Gen0516 Ref011 Ref046 673 | Gen0517 Ref011 Ref046 674 | Gen0554 Ref019 Ref046 675 | Gen0559 Ref019 Ref046 676 | Gen0563 Ref019 Ref046 677 | Gen0564 Ref019 Ref046 678 | Gen0570 Ref019 Ref046 679 | Gen0591 Ref019 Ref046 680 | Gen0593 Ref046 Ref028 681 | Gen0594 Ref046 Ref028 682 | Gen0595 Ref046 Ref028 683 | Gen0596 Ref046 Ref028 684 | Gen0597 Ref046 Ref028 685 | Gen0598 Ref046 Ref028 686 | Gen0600 Ref046 Ref028 687 | Gen0601 Ref046 Ref028 688 | Gen0602 Ref046 Ref028 689 | Gen0603 Ref019 Ref046 690 | Gen0610 Ref019 Ref046 691 | Gen0617 Ref019 Ref046 692 | Gen0619 Ref019 Ref046 693 | Gen0620 Ref019 Ref046 694 | Gen0626 Ref019 Ref046 695 | Gen0007 Ref025 Ref046 696 | Gen0070 Ref046 Ref015 697 | Gen0113 Ref046 Gen0001 698 | Gen0124 Ref046 Gen0001 699 | Gen0432 Ref032 Ref046 700 | Gen0455 Ref046 Ref033 701 | Gen0459 Ref046 Ref033 702 | Gen0468 Ref046 Ref029 703 | Gen0471 Ref046 Ref029 704 | Gen0472 Ref046 Ref029 705 | Gen0514 Ref011 Ref046 706 | Gen0518 Ref011 Ref046 707 | Gen0599 Ref046 Ref028 708 | -------------------------------------------------------------------------------- /Day2/GBLUPTest/DATAG.txt: -------------------------------------------------------------------------------- 1 | Indiv Sire Dam Resp 2 | 1001 10 50 155 3 | 1002 10 60 121 4 | 1003 10 70 130 5 | 1004 20 50 141 6 | 1005 20 60 130 7 | 1006 20 70 162 8 | 1007 30 50 118 9 | 1008 30 60 108 10 | 1009 30 70 119 11 | 1010 40 80 143 12 | -------------------------------------------------------------------------------- /Day2/GBLUPTest/DUMMYPED.txt: -------------------------------------------------------------------------------- 1 | Indiv Sire Dam 2 | 10 0 0 3 | 20 0 0 4 | 30 0 0 5 | 40 0 0 -------------------------------------------------------------------------------- /Day2/GBLUPTest/GBLUP_Test.R: -------------------------------------------------------------------------------- 1 | ################################### 2 | ## Genomic Selection: GBLUP - Test 3 | ################################## 4 | 5 | rm(list=ls()) 6 | #setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami") 7 | library(asreml) 8 | 9 | # Reading data 10 | datag<-read.table("./Distribute/Day2/GBLUPTest/DATAG.txt",h=T) 11 | head(datag) 12 | dummyped<-read.table("./Distribute/Day2/GBLUPTest/DUMMYPED.txt",h=T) 13 | head(dummyped) 14 | 15 | # Creating Factors 16 | head(datag) 17 | datag$Indiv<-as.factor(datag$Indiv) 18 | datag$Sire<-as.factor(datag$Sire) 19 | datag$Dam<-as.factor(datag$Dam) 20 | str(datag) 21 | 22 | # Obtain the relatioship matrix - from dummy pedigree 23 | ainvsire<-asreml.Ainverse(dummyped)$ginv 24 | ainvsire 25 | attr(ainvsire,"rowNames") 26 | 27 | # Reading Ginverse (4 indivs) and assigning attr 28 | GINV4<-read.table("./Distribute/Day2/GBLUPTest/GINVM4.txt",h=T) 29 | gimatrix4<-data.frame(GINV4) 30 | gimatrix4 31 | attr(gimatrix4,"rowNames")<-c("10","20","30","40") 32 | attr(gimatrix4,"rowNames")<-attr(ainvsire,"rowNames") 33 | 34 | # Performing GBLUP (4) 35 | mGBLUP1<-asreml(fixed=Resp~1, 36 | random=~giv(Sire)+Dam, 37 | ginverse=list(Sire=gimatrix4),data=datag) 38 | summary(mGBLUP1)$varcomp 39 | (pred1<-predict(mGBLUP1,classify="Sire",sed=T)$predictions$pvals) 40 | 41 | 42 | -------------------------------------------------------------------------------- /Day2/GBLUPTest/GINVM4.txt: -------------------------------------------------------------------------------- 1 | Row Column GINV 2 | 1 1 1.130249 3 | 2 1 -0.020490 4 | 2 2 1.062319 5 | 3 1 0.072807 6 | 3 2 -0.236971 7 | 3 3 1.045793 8 | 4 1 -0.421368 9 | 4 2 -0.000872 10 | 4 3 -0.093379 11 | 4 4 1.175023 12 | -------------------------------------------------------------------------------- /Day2/GBLUPTest/GINVM6.txt: -------------------------------------------------------------------------------- 1 | Row Column GINV 2 | 1 1 1.1329 3 | 2 1 -0.0304 4 | 2 2 1.5564 5 | 3 1 0.0723 6 | 3 2 -0.2487 7 | 3 3 1.0465 8 | 4 1 -0.4295 9 | 4 2 0.4851 10 | 4 3 -0.1054 11 | 4 4 1.6556 12 | 5 1 -0.0415 13 | 5 2 -0.0204 14 | 5 3 0.0227 15 | 5 4 -0.1073 16 | 5 5 1.018 17 | 6 1 0.0218 18 | 6 2 -0.9804 19 | 6 3 0.0214 20 | 6 4 -0.9585 21 | 6 5 -0.0498 22 | 6 6 1.9501 23 | 24 | -------------------------------------------------------------------------------- /Day2/GBLUPTest/PEDSIRE.txt: -------------------------------------------------------------------------------- 1 | Indiv Sire Dam 2 | 10 1 0 3 | 20 2 0 4 | 30 2 0 5 | 40 1 0 6 | -------------------------------------------------------------------------------- /Day2/MultiEnv/MET.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Multi-Environment Trials 3 | ############################### 4 | 5 | rm(list=ls()) 6 | #setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami/Distribute/Day2/MultiEnv") 7 | library(asreml) 8 | #library(nadiv) 9 | 10 | # Reading data 11 | datam<-read.table("./Distribute/Day2/MultiEnv/TRIALS4.txt",h=T) 12 | head(datam) 13 | datam$Rep<-as.factor(datam$Rep) 14 | datam$Iblock<-as.factor(datam$Iblock) 15 | datam$Test<-as.factor(datam$Test) 16 | datam$Genotype<-as.factor(datam$Genotype) 17 | str(datam) 18 | 19 | # Performing some EDA 20 | boxplot(HT~Test,data=datam) 21 | aggregate(HT~Test,mean,data=datam) 22 | table(datam$Test,datam$Genotype) 23 | meanst<-aggregate(HT~Genotype+Test,mean,data=datam) 24 | interaction.plot(meanst$Test,meanst$Genotype,meanst$HT) 25 | 26 | 27 | # Model for a single test 28 | model1<-asreml(fixed=HT~Rep, 29 | random=~Rep:Iblock+Genotype,data=datam, 30 | subset=Test==2) 31 | summary(model1)$varcomp 32 | plot(model1) 33 | (h2b<-nadiv:::pin(model1,h2b~4*V1/(V1+V2+V3))) 34 | # heritability is biased when you calculate it for a single site 35 | 36 | 37 | 38 | ### Explicit Model # 39 | 40 | # Model for a single test 41 | model1all<-asreml(fixed=HT~ Test + Test:Rep, 42 | random= ~ at(Test):Rep:Iblock+Genotype + Test:Genotype, 43 | data=datam) 44 | summary(model1all)$varcomp 45 | plot(model1all) 46 | (h2b<-nadiv:::pin(model1all,h2b~4*V1/(V1+V2+V3))) 47 | 48 | 49 | # Simple Model for all sites # 50 | # NB -> the at(Test, [1-4]) calls allow you to specify different random effects for the different sites. You might use this, for example, if 3 trials are incomplete blocks and 1 isnt' 51 | model2<-asreml(fixed=HT~Test+Test:Rep, 52 | random=~at(Test,1):Rep:Iblock+at(Test,2):Rep:Iblock+ 53 | at(Test,3):Rep:Iblock+at(Test,4):Rep:Iblock+ 54 | Genotype+Test:Genotype, 55 | rcov=~units:at(Test),data=datam) 56 | summary(model2)$varcomp 57 | plot(model2) 58 | (r2B<-nadiv:::pin(model2,r2B~V5/(V5+V6))) 59 | 60 | # Some predictions 61 | ppG2<-predict(model2,classify="Genotype") 62 | View(ppG2$predictions) 63 | View(model2$coefficient$random) 64 | ppGE2<-predict(model2,classify="Testf:Genotype") 65 | View(ppGE2$predictions) 66 | 67 | ### Implicit Model ### 68 | 69 | # Simple corv similar to Explicit 70 | initg<-c(0.65,400) 71 | model2b<-asreml(fixed=HT~Test+Test:Rep, 72 | random=~at(Test):Rep:Iblock+ 73 | corv(Test,init=initg):Genotype, 74 | rcov=~at(Test):units,data=datam) 75 | summary(model2b)$varcomp 76 | 77 | # US model 78 | initg<-c(520.7,392.2,563.6,256.7,376.6,392.1,384.1,268.8,200.0,356.8) 79 | initg = sample(1000:5000,10)/10 80 | 81 | model3<-asreml(fixed=HT~Test+Test:Rep, 82 | random=~at(Test):Rep:Iblock+ 83 | us(Test,init=initg):Genotype, 84 | rcov=~at(Test):units,data=datam) 85 | model3<-update.asreml(model3) 86 | summary(model3)$varcomp 87 | 88 | 89 | initg = c( sample(5:95,6) / 100, 90 | c(sample(1000:5000,4)/10) 91 | ) 92 | 93 | model4<-asreml(fixed=HT~Test+Test:Rep, 94 | random=~at(Test):Rep:Iblock+ 95 | corgh(Test,init=initg):Genotype, 96 | rcov=~at(Test):units,data=datam) 97 | model4<-update.asreml(model4) 98 | summary(model4)$varcomp 99 | 100 | 101 | # LRT 102 | require(asremlPlus) 103 | reml.lrt.asreml(model3,model2b,positive.zero = FALSE) # if you are testing covariances or correlations, then positive.zero must be FALSE. If all variances, then set to TRUE 104 | info.crit.asreml(model2b) 105 | info.crit.asreml(model3) 106 | 107 | reml.lrt.asreml(model4,model2b,positive.zero = FALSE) # if you are testing covariances 108 | 109 | 110 | -------------------------------------------------------------------------------- /Day2/RepMeas/Multiv.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Repeated Measures as MV 3 | ############################### 4 | 5 | rm(list=ls()) 6 | #setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami") 7 | library(asreml) 8 | #library(nadiv) 9 | 10 | # Reading and Preparing the Data 11 | mvcol<-read.table("./Distribute/Day2/RepMeas/MVCOLS.txt",h=T,na.string="*") 12 | head(mvcol) 13 | mvcol$Rep<-as.factor(mvcol$Rep) 14 | mvcol$Female<-as.factor(mvcol$Female) 15 | mvcol$Indiv<-as.factor(mvcol$Indiv) 16 | str(mvcol) 17 | 18 | # Model for single time point 19 | rmodel1<-asreml(fixed=HT4~Rep,random=~Female,data=mvcol) 20 | plot(rmodel1) 21 | summary(rmodel1)$varcomp 22 | (h2<-nadiv:::pin(rmodel1,h2~4*V1/(V1+V2))) 23 | # (TODO) Check for outliers, get initial values for variance components, etc 24 | 25 | 26 | # MV 2 - Starting with a bivariate model 27 | initf<-c(0.1,36,74) # again, the variance element here come from the univariate model 28 | inite<-c(0.1,419,1405) 29 | mvmodel2<-asreml(fixed= cbind(HT1,HT2) ~ trait + trait:Rep, 30 | random= ~corh(trait,init=initf):Female, 31 | rcov=~units:corh(trait,init=inite),data=mvcol) 32 | summary(mvmodel2)$varcomp 33 | # (TODO) Check for outliers, get initial values for variance components, etc 34 | 35 | 36 | # MV 4 - More challenging model but still corh (maybe change to coruh) 37 | initf<-c(0.8,36,74,117,210) 38 | inite<-c(0.8,419,1405,3801,5154) 39 | mvmodel4<-asreml(fixed=cbind(HT1,HT2,HT3,HT4)~trait+trait:Rep, 40 | random=~corh(trait,init=initf):Female, 41 | rcov=~units:corh(trait,init=inite),data=mvcol) 42 | summary(mvmodel4)$varcomp 43 | # NB -> corh(trait,init=initf):Female is the "genetic correlation" between the traits. It is conditioning 44 | 45 | predf = predict(mvmodel4, classify="trait:Female")$predictions$pvals 46 | 47 | # HW -> replace corh with corgh, starting with trait:Female 48 | initf<-c( sample(seq(0.5,0.9,by=0.05), 6) 49 | ,36,74,117,210) 50 | inite<-c( 0.8 ,419,1405,3801,5154) 51 | mvmodel5 <- asreml(fixed = cbind(HT1,HT2,HT3,HT4) ~ trait + trait:Rep, 52 | random = ~corgh(trait,init=initf):Female, 53 | rcov = ~units:corh(trait,init=inite),data=mvcol) 54 | #summary(mvmodel5)$varcomp 55 | -------------------------------------------------------------------------------- /Day2/RepMeas/RepMeas.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Repeated Measures Analysis 3 | ############################### 4 | 5 | rm(list=ls()) 6 | setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami/Distribute/Day2/RepMeas/") 7 | library(asreml) 8 | library(nadiv) 9 | 10 | # Reading data 11 | repcol<-read.table("REPCOLS.txt",h=T,na.string="*") 12 | head(repcol) 13 | repcol$Rep<-as.factor(repcol$Rep) 14 | repcol$Timef<-as.factor(repcol$Time) # Time as a factor 15 | repcol$Timec<-repcol$Time # Tiem as a variate 16 | repcol$Female<-as.factor(repcol$Female) 17 | repcol$Indiv<-as.factor(repcol$Indiv) 18 | str(repcol) 19 | 20 | # Performing some EDA 21 | boxplot(HT~Timef,data=repcol) 22 | aggregate(HT~Timef,mean,data=repcol) 23 | aggregate(HT~Timef,sd,data=repcol) 24 | table(repcol$Timef,repcol$Female) 25 | meanst<-aggregate(HT~Female+Timef,mean,data=repcol) 26 | interaction.plot(meanst$Timef,meanst$Female,meanst$HT) 27 | 28 | # TRADITIONAL REPEATEAD MEASURES 29 | 30 | # Model for single time point 31 | rmodel1<-asreml(fixed=HT~Rep,random=~Female, 32 | subset=Timef==4,data=repcol) 33 | plot(rmodel1) 34 | summary(rmodel1)$varcomp 35 | (h2<-nadiv:::pin(rmodel1,h2~4*V1/(V1+V2))) 36 | 37 | # Fitting model with AR1 Error Structure - No transformation 38 | inite<-c(0.1,1,1,1,1) # Very dummy 39 | allmodel1<-asreml(fixed=HT~Rep+Timef+Timef:Rep, 40 | random=~Female+Female:Timef, 41 | rcov=~Indiv:ar1h(Timef,init=inite),data=repcol) 42 | allmodel1<-update.asreml(allmodel1) # Sometimes it works 43 | plot(allmodel1) 44 | summary(allmodel1)$varcomp 45 | 46 | # Some predictions 47 | predFem<-predict(allmodel1,classify="Female",sed=FALSE)$predictions$pvals 48 | head(predFem) 49 | predFemTime<-predict(allmodel1,classify="Female:Timef",sed=FALSE)$predictions$pvals 50 | head(predFemTime) 51 | 52 | # Calculating Genetic parameters 53 | (h2<-nadiv:::pin(allmodel1,h2~4*V1/(V1+V2+V4+(V5+V6+V7+V8)/4))) 54 | (rt2<-nadiv:::pin(allmodel1,rt2~V1/(V1+V2))) 55 | 56 | # TRANSFORMING data and some EDA 57 | repcol$logHT<-log(repcol$HT+1)*100 58 | boxplot(logHT~Timef,data=repcol) 59 | aggregate(logHT~Timef,mean,data=repcol) 60 | aggregate(logHT~Timef,sd,data=repcol) 61 | 62 | # Fitting model with AR1 Error Structure - with TRANSFORMATION 63 | inite<-c(0.1,1,1,1,1) # Very dummy 64 | allmodel2<-asreml(fixed=logHT~Rep+Timef+Timef:Rep, 65 | random=~Female+Female:Timef, 66 | rcov=~Indiv:ar1h(Timef,init=inite),data=repcol) 67 | allmodel2<-update.asreml(allmodel2) # Sometimes it works 68 | plot(allmodel2) 69 | summary(allmodel2)$varcomp 70 | 71 | # Calculating Genetic parameters 72 | (h2<-nadiv:::pin(allmodel2,h2~4*V1/(V1+V2+V4+(V5+V6+V7+V8)/4))) 73 | (rt2<-nadiv:::pin(allmodel2,rt2~V1/(V1+V2))) 74 | 75 | 76 | # RANDOM REGRESSION APPROACH 77 | 78 | # Changing to Random Regression (uncorrelated random terms) 79 | inite<-c(0.1,1,1,1,1) 80 | rreg1<-asreml(fixed=logHT~Rep+Timec+Timec:Rep, 81 | random=~Female+Female:Timec, 82 | rcov=~Indiv:ar1h(Timef,init=inite),data=repcol) 83 | rreg1<-update.asreml(rreg1) # Sometimes it works 84 | plot(rreg1) 85 | summary(rreg1)$varcomp 86 | anova(rreg1,denDF='default') 87 | 88 | # Random Regression (correlated random terms) 89 | inite<-c(0.85,863,1625,1341,465) 90 | rreg2<-asreml(fixed=logHT~Rep+Timec+Timec:Rep, 91 | random=~str(~Female/Timec,~us(2,init=c(30,-1,1)):id(26)), 92 | rcov=~Indiv:ar1h(Timef,init=inite),data=repcol) 93 | summary(rreg2)$varcomp 94 | anova(rreg1,denDF='default') 95 | 96 | # Some results 97 | predFem<-predict(rreg2,classify="Female",sed=FALSE)$predictions$pvals 98 | head(predFem) 99 | predFemTimec<-predict(rreg2,classify="Female:Timec",levels=list(Timec=4),sed=FALSE)$predictions$pvals 100 | View(predFemTimec) 101 | rreg2$coefficients$fixed 102 | rreg2$coefficients$random -------------------------------------------------------------------------------- /Day2/Spatial/ROWCOL.txt: -------------------------------------------------------------------------------- 1 | ID REP ROW COL PLOT TREE FEMALE X Y YA 2 | 1 2 4 1 14 2 4 1 1 8.628352 3 | 2 2 4 1 14 1 4 1 2 7.718902 4 | 3 2 3 1 26 2 7 1 3 8.041164 5 | 4 2 3 1 26 1 7 1 4 9.593278 6 | 5 2 2 1 62 2 16 1 5 8.739841 7 | 6 2 2 1 62 1 16 1 6 8.456119 8 | 7 2 1 1 50 2 13 1 7 9.557565 9 | 8 2 1 1 50 1 13 1 8 10.639179 10 | 9 1 4 1 1 2 1 1 9 9.938713 11 | 10 1 4 1 1 1 1 1 10 8.332414 12 | 11 1 3 1 53 2 14 1 11 10.495654 13 | 12 1 3 1 53 1 14 1 12 10.130853 14 | 13 1 2 1 37 2 10 1 13 11.983712 15 | 14 1 2 1 37 1 10 1 14 12.080121 16 | 15 1 1 1 33 2 9 1 15 11.203263 17 | 16 1 1 1 33 1 9 1 16 10.757546 18 | 17 2 4 1 14 4 4 2 1 9.797591 19 | 18 2 4 1 14 3 4 2 2 9.206996 20 | 19 2 3 1 26 4 7 2 3 8.786462 21 | 20 2 3 1 26 3 7 2 4 10.650088 22 | 21 2 2 1 62 4 16 2 5 9.091012 23 | 22 2 2 1 62 3 16 2 6 10.439434 24 | 23 2 1 1 50 4 13 2 7 10.101078 25 | 24 2 1 1 50 3 13 2 8 9.877924 26 | 25 1 4 1 1 4 1 2 9 9.396685 27 | 26 1 4 1 1 3 1 2 10 8.833243 28 | 27 1 3 1 53 4 14 2 11 9.675085 29 | 28 1 3 1 53 3 14 2 12 10.048748 30 | 29 1 2 1 37 4 10 2 13 11.29504 31 | 30 1 2 1 37 3 10 2 14 11.957024 32 | 31 1 1 1 33 4 9 2 15 10.727204 33 | 32 1 1 1 33 3 9 2 16 9.563843 34 | 33 2 4 2 42 2 11 3 1 8.763885 35 | 34 2 4 2 42 1 11 3 2 8.887516 36 | 35 2 3 2 58 2 15 3 3 8.754085 37 | 36 2 3 2 58 1 15 3 4 9.332127 38 | 37 2 2 2 18 2 5 3 5 9.015499 39 | 38 2 2 2 18 1 5 3 6 10.134031 40 | 39 2 1 2 22 2 6 3 7 10.823659 41 | 40 2 1 2 22 1 6 3 8 10.516701 42 | 41 1 4 2 5 2 2 3 9 10.881542 43 | 42 1 4 2 5 1 2 3 10 10.837548 44 | 43 1 3 2 29 2 8 3 11 10.010517 45 | 44 1 3 2 29 1 8 3 12 9.482563 46 | 45 1 2 2 9 2 3 3 13 10.31252 47 | 46 1 2 2 9 1 3 3 14 10.379925 48 | 47 1 1 2 45 2 12 3 15 9.376038 49 | 48 1 1 2 45 1 12 3 16 9.083294 50 | 49 2 4 2 42 4 11 4 1 9.376558 51 | 50 2 4 2 42 3 11 4 2 9.655747 52 | 51 2 3 2 58 4 15 4 3 8.981834 53 | 52 2 3 2 58 3 15 4 4 10.112855 54 | 53 2 2 2 18 4 5 4 5 8.818453 55 | 54 2 2 2 18 3 5 4 6 9.364 56 | 55 2 1 2 22 4 6 4 7 9.925738 57 | 56 2 1 2 22 3 6 4 8 9.92102 58 | 57 1 4 2 5 4 2 4 9 9.950983 59 | 58 1 4 2 5 3 2 4 10 10.238108 60 | 59 1 3 2 29 4 8 4 11 10.059038 61 | 60 1 3 2 29 3 8 4 12 9.918777 62 | 61 1 2 2 9 4 3 4 13 9.785952 63 | 62 1 2 2 9 3 3 4 14 10.512299 64 | 63 1 1 2 45 4 12 4 15 10.577824 65 | 64 1 1 2 45 3 12 4 16 8.975016 66 | 65 2 4 3 30 2 8 5 1 9.368379 67 | 66 2 4 3 30 1 8 5 2 8.45701 68 | 67 2 3 3 46 2 12 5 3 8.527266 69 | 68 2 3 3 46 1 12 5 4 9.094307 70 | 69 2 2 3 54 2 14 5 5 8.632895 71 | 70 2 2 3 54 1 14 5 6 9.892142 72 | 71 2 1 3 34 2 9 5 7 9.940912 73 | 72 2 1 3 34 1 9 5 8 9.984166 74 | 73 1 4 3 21 2 6 5 9 10.33309 75 | 74 1 4 3 21 1 6 5 10 9.770513 76 | 75 1 3 3 25 2 7 5 11 9.160495 77 | 76 1 3 3 25 1 7 5 12 10.891092 78 | 77 1 2 3 41 2 11 5 13 10.165703 79 | 78 1 2 3 41 1 11 5 14 9.701261 80 | 79 1 1 3 61 2 16 5 15 9.314549 81 | 80 1 1 3 61 1 16 5 16 8.478538 82 | 81 2 4 3 30 4 8 6 1 9.58983 83 | 82 2 4 3 30 3 8 6 2 7.716371 84 | 83 2 3 3 46 4 12 6 3 9.124738 85 | 84 2 3 3 46 3 12 6 4 8.689306 86 | 85 2 2 3 54 4 14 6 5 9.476977 87 | 86 2 2 3 54 3 14 6 6 9.369512 88 | 87 2 1 3 34 4 9 6 7 9.938339 89 | 88 2 1 3 34 3 9 6 8 9.792687 90 | 89 1 4 3 21 4 6 6 9 10.576264 91 | 90 1 4 3 21 3 6 6 10 9.824338 92 | 91 1 3 3 25 4 7 6 11 10.04053 93 | 92 1 3 3 25 3 7 6 12 10.043429 94 | 93 1 2 3 41 4 11 6 13 9.738282 95 | 94 1 2 3 41 3 11 6 14 9.877991 96 | 95 1 1 3 61 4 16 6 15 9.67295 97 | 96 1 1 3 61 3 16 6 16 9.658095 98 | 97 2 4 4 2 2 1 7 1 10.013968 99 | 98 2 4 4 2 1 1 7 2 9.899751 100 | 99 2 3 4 38 2 10 7 3 11.726108 101 | 100 2 3 4 38 1 10 7 4 11.103123 102 | 101 2 2 4 6 2 2 7 5 8.190233 103 | 102 2 2 4 6 1 2 7 6 9.926885 104 | 103 2 1 4 10 2 3 7 7 9.56915 105 | 104 2 1 4 10 1 3 7 8 9.468208 106 | 105 1 4 4 57 2 15 7 9 10.434051 107 | 106 1 4 4 57 1 15 7 10 8.860704 108 | 107 1 3 4 49 2 13 7 11 9.382523 109 | 108 1 3 4 49 1 13 7 12 9.212392 110 | 109 1 2 4 17 2 5 7 13 11.11139 111 | 110 1 2 4 17 1 5 7 14 10.167317 112 | 111 1 1 4 13 2 4 7 15 9.014179 113 | 112 1 1 4 13 1 4 7 16 10.332474 114 | 113 2 4 4 2 4 1 8 1 8.962478 115 | 114 2 4 4 2 3 1 8 2 9.091917 116 | 115 2 3 4 38 4 10 8 3 10.412102 117 | 116 2 3 4 38 3 10 8 4 10.955486 118 | 117 2 2 4 6 4 2 8 5 9.861749 119 | 118 2 2 4 6 3 2 8 6 9.875505 120 | 119 2 1 4 10 4 3 8 7 9.331495 121 | 120 2 1 4 10 3 3 8 8 9.889827 122 | 121 1 4 4 57 4 15 8 9 9.879418 123 | 122 1 4 4 57 3 15 8 10 9.517501 124 | 123 1 3 4 49 4 13 8 11 10.419062 125 | 124 1 3 4 49 3 13 8 12 9.190754 126 | 125 1 2 4 17 4 5 8 13 10.4993 127 | 126 1 2 4 17 3 5 8 14 11.259905 128 | 127 1 1 4 13 4 4 8 15 10.039157 129 | 128 1 1 4 13 3 4 8 16 11.004918 130 | 129 4 4 1 36 2 9 9 1 10.633529 131 | 130 4 4 1 36 1 9 9 2 9.582154 132 | 131 4 3 1 12 2 3 9 3 8.734384 133 | 132 4 3 1 12 1 3 9 4 9.76195 134 | 133 4 2 1 28 2 7 9 5 9.692085 135 | 134 4 2 1 28 1 7 9 6 8.668332 136 | 135 4 1 1 60 2 15 9 7 9.041004 137 | 136 4 1 1 60 1 15 9 8 9.547458 138 | 137 3 4 1 39 2 10 9 9 10.143782 139 | 138 3 4 1 39 1 10 9 10 10.684579 140 | 139 3 3 1 15 2 4 9 11 8.041727 141 | 140 3 3 1 15 1 4 9 12 8.249841 142 | 141 3 2 1 55 2 14 9 13 10.249982 143 | 142 3 2 1 55 1 14 9 14 10.156726 144 | 143 3 1 1 23 2 6 9 15 10.878982 145 | 144 3 1 1 23 1 6 9 16 10.297066 146 | 145 4 4 1 36 4 9 10 1 9.856127 147 | 146 4 4 1 36 3 9 10 2 9.999287 148 | 147 4 3 1 12 4 3 10 3 10.33889 149 | 148 4 3 1 12 3 3 10 4 9.606686 150 | 149 4 2 1 28 4 7 10 5 8.759858 151 | 150 4 2 1 28 3 7 10 6 10.92152 152 | 151 4 1 1 60 4 15 10 7 9.050145 153 | 152 4 1 1 60 3 15 10 8 9.363998 154 | 153 3 4 1 39 4 10 10 9 9.303189 155 | 154 3 4 1 39 3 10 10 10 10.109196 156 | 155 3 3 1 15 4 4 10 11 9.62643 157 | 156 3 3 1 15 3 4 10 12 8.891653 158 | 157 3 2 1 55 4 14 10 13 10.041357 159 | 158 3 2 1 55 3 14 10 14 10.246855 160 | 159 3 1 1 23 4 6 10 15 10.728642 161 | 160 3 1 1 23 3 6 10 16 11.912587 162 | 161 4 4 2 20 2 5 11 1 12.006079 163 | 162 4 4 2 20 1 5 11 2 10.914502 164 | 163 4 3 2 64 2 16 11 3 9.187954 165 | 164 4 3 2 64 1 16 11 4 10.67689 166 | 165 4 2 2 4 2 1 11 5 9.883661 167 | 166 4 2 2 4 1 1 11 6 10.519932 168 | 167 4 1 2 32 2 8 11 7 9.759038 169 | 168 4 1 2 32 1 8 11 8 10.574339 170 | 169 3 4 2 43 2 11 11 9 9.361324 171 | 170 3 4 2 43 1 11 11 10 8.598323 172 | 171 3 3 2 47 2 12 11 11 10.035435 173 | 172 3 3 2 47 1 12 11 12 9.282715 174 | 173 3 2 2 51 2 13 11 13 11.029017 175 | 174 3 2 2 51 1 13 11 14 11.245745 176 | 175 3 1 2 7 2 2 11 15 11.275587 177 | 176 3 1 2 7 1 2 11 16 11.95192 178 | 177 4 4 2 20 4 5 12 1 10.609466 179 | 178 4 4 2 20 3 5 12 2 10.134669 180 | 179 4 3 2 64 4 16 12 3 9.72136 181 | 180 4 3 2 64 3 16 12 4 9.278139 182 | 181 4 2 2 4 4 1 12 5 8.641557 183 | 182 4 2 2 4 3 1 12 6 9.775293 184 | 183 4 1 2 32 4 8 12 7 11.165159 185 | 184 4 1 2 32 3 8 12 8 10.164156 186 | 185 3 4 2 43 4 11 12 9 10.426836 187 | 186 3 4 2 43 3 11 12 10 9.768244 188 | 187 3 3 2 47 4 12 12 11 9.607801 189 | 188 3 3 2 47 3 12 12 12 8.783087 190 | 189 3 2 2 51 4 13 12 13 11.828571 191 | 190 3 2 2 51 3 13 12 14 10.607922 192 | 191 3 1 2 7 4 2 12 15 11.559122 193 | 192 3 1 2 7 3 2 12 16 11.366182 194 | 193 4 4 3 8 2 2 13 1 10.063203 195 | 194 4 4 3 8 1 2 13 2 11.009304 196 | 195 4 3 3 56 2 14 13 3 9.940028 197 | 196 4 3 3 56 1 14 13 4 9.836019 198 | 197 4 2 3 44 2 11 13 5 8.728169 199 | 198 4 2 3 44 1 11 13 6 10.891188 200 | 199 4 1 3 16 2 4 13 7 10.408353 201 | 200 4 1 3 16 1 4 13 8 11.764786 202 | 201 3 4 3 63 2 16 13 9 10.091091 203 | 202 3 4 3 63 1 16 13 10 9.91753 204 | 203 3 3 3 11 2 3 13 11 9.227667 205 | 204 3 3 3 11 1 3 13 12 10.33574 206 | 205 3 2 3 59 2 15 13 13 10.32113 207 | 206 3 2 3 59 1 15 13 14 11.185717 208 | 207 3 1 3 31 2 8 13 15 11.041117 209 | 208 3 1 3 31 1 8 13 16 11.620385 210 | 209 4 4 3 8 4 2 14 1 11.722186 211 | 210 4 4 3 8 3 2 14 2 9.019002 212 | 211 4 3 3 56 4 14 14 3 9.354793 213 | 212 4 3 3 56 3 14 14 4 8.766493 214 | 213 4 2 3 44 4 11 14 5 8.862507 215 | 214 4 2 3 44 3 11 14 6 10.023009 216 | 215 4 1 3 16 4 4 14 7 9.329479 217 | 216 4 1 3 16 3 4 14 8 11.425705 218 | 217 3 4 3 63 4 16 14 9 11.390129 219 | 218 3 4 3 63 3 16 14 10 10.61373 220 | 219 3 3 3 11 4 3 14 11 9.789946 221 | 220 3 3 3 11 3 3 14 12 9.994186 222 | 221 3 2 3 59 4 15 14 13 9.853207 223 | 222 3 2 3 59 3 15 14 14 10.060657 224 | 223 3 1 3 31 4 8 14 15 11.920458 225 | 224 3 1 3 31 3 8 14 16 11.696133 226 | 225 4 4 4 52 2 13 15 1 10.354988 227 | 226 4 4 4 52 1 13 15 2 9.811425 228 | 227 4 3 4 24 2 6 15 3 9.480197 229 | 228 4 3 4 24 1 6 15 4 10.621611 230 | 229 4 2 4 48 2 12 15 5 9.610756 231 | 230 4 2 4 48 1 12 15 6 9.860979 232 | 231 4 1 4 40 2 10 15 7 10.638375 233 | 232 4 1 4 40 1 10 15 8 11.591254 234 | 233 3 4 4 35 2 9 15 9 10.036305 235 | 234 3 4 4 35 1 9 15 10 9.431673 236 | 235 3 3 4 19 2 5 15 11 9.944301 237 | 236 3 3 4 19 1 5 15 12 10.63602 238 | 237 3 2 4 3 2 1 15 13 10.202418 239 | 238 3 2 4 3 1 1 15 14 11.488337 240 | 239 3 1 4 27 2 7 15 15 11.569614 241 | 240 3 1 4 27 1 7 15 16 11.647069 242 | 241 4 4 4 52 4 13 16 1 9.866679 243 | 242 4 4 4 52 3 13 16 2 9.918114 244 | 243 4 3 4 24 4 6 16 3 9.193467 245 | 244 4 3 4 24 3 6 16 4 11.019156 246 | 245 4 2 4 48 4 12 16 5 8.608866 247 | 246 4 2 4 48 3 12 16 6 10.167071 248 | 247 4 1 4 40 4 10 16 7 10.757353 249 | 248 4 1 4 40 3 10 16 8 10.51029 250 | 249 3 4 4 35 4 9 16 9 10.615567 251 | 250 3 4 4 35 3 9 16 10 10.508053 252 | 251 3 3 4 19 4 5 16 11 10.797376 253 | 252 3 3 4 19 3 5 16 12 11.331565 254 | 253 3 2 4 3 4 1 16 13 11.541865 255 | 254 3 2 4 3 3 1 16 14 10.440261 256 | 255 3 1 4 27 4 7 16 15 12.462323 257 | 256 3 1 4 27 3 7 16 16 11.182156 -------------------------------------------------------------------------------- /Day2/Spatial/Spatial.R: -------------------------------------------------------------------------------- 1 | ##################################### 2 | ## Spatial Analysis - Replicated ## 3 | ##################################### 4 | 5 | rm(list=ls()) 6 | setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami/Distribute/Day2/Spatial") 7 | library(asreml) 8 | library(nadiv) 9 | library(asremlPlus) 10 | 11 | spatial<-read.table("ROWCOL.txt",h=T) 12 | head(spatial) 13 | spatial$ROW<-as.factor(spatial$ROW) 14 | spatial$COL<-as.factor(spatial$COL) 15 | spatial$REP<-as.factor(spatial$REP) 16 | spatial$REP.PLOT<-as.factor(paste(spatial$REP,spatial$PLOT,sep=".")) 17 | spatial$Xf<-as.factor(spatial$X) # X coordinate as a factor 18 | spatial$Yf<-as.factor(spatial$Y) # Y coordinate as a factor 19 | spatial$FEMALE<-as.factor(spatial$FEMALE) 20 | head(spatial) 21 | str(spatial) 22 | 23 | # Some EDA for checking grid 24 | plot(spatial$X,spatial$Y) 25 | 26 | # Basic Model without spatial components but design components # 27 | nospatial<-asreml(fixed=YA~REP, 28 | random=~REP:ROW+REP:COL+REP.PLOT+FEMALE, 29 | data=spatial) 30 | summary(nospatial)$varcomp 31 | plot(nospatial) 32 | (nospatial$loglik) 33 | 34 | # Obtaining Variogram of basic nospatial model 35 | nospatial<-asreml(fixed=YA~REP, 36 | random=~REP:ROW+REP:COL+REP.PLOT+FEMALE, 37 | rcov=~id(Xf):id(Yf),data=spatial) 38 | plot(variogram(nospatial)) 39 | 40 | # Incorporating Spatial Autocorrelation on X and Y 41 | spatial1<-asreml(fixed=YA~REP, 42 | random=~REP:ROW+REP:COL+REP.PLOT+FEMALE, 43 | rcov=~ar1(Xf):ar1(Yf),data=spatial) 44 | summary(spatial1)$varcomp 45 | plot(spatial1) 46 | plot(variogram(spatial1)) 47 | 48 | # Using asremlplus 49 | reml.lrt.asreml(spatial1,nospatial,positive.zero=FALSE) # LRT with pvalues 50 | info.crit.asreml(nospatial) # AIC and BIC 51 | info.crit.asreml(spatial1) # AIC and BIC 52 | 53 | 54 | # Playing with fixed effects: Adding global trend fixed effects # 55 | spatial3<-asreml(fixed=YA~REP+X+Y,random=~REP:ROW+REP:COL+REP.PLOT+FEMALE, 56 | rcov=~ar1(Xf):ar1(Yf),data=spatial) 57 | plot(spatial3) 58 | summary(spatial3)$varcomp 59 | anova(spatial3,denDF='default') 60 | 61 | # Selected model with only autocorrelation for X # 62 | spatial4<-asreml(fixed=YA~REP+X+Y, 63 | random=~REP:ROW+REP:COL+REP.PLOT+FEMALE, 64 | rcov=~ar1(Xf,init=0.3):Yf,data=spatial) 65 | anova(spatial4,denDF='default') 66 | plot(variogram(spatial4)) 67 | summary(spatial4)$varcomp 68 | 69 | # Some predictions 70 | pred.nospatial<-predict(nospatial,classify="FEMALE",sed=TRUE)$predictions$pvals 71 | head(pred.nospatial) 72 | pred.spatial4<-predict(spatial4,classify="FEMALE",sed=TRUE)$predictions$pvals 73 | head(pred.spatial4) 74 | 75 | # Selected model with only autocorrelation for X -Incorporate NUGGET # 76 | spatial4nugg<-asreml(fixed=YA~REP+X+Y, 77 | random=~REP:ROW+REP:COL+REP.PLOT+FEMALE+units, 78 | rcov=~ar1(Xf,init=0.3):ar1(Yf,init=0.3),data=spatial) 79 | spatial4nugg<-update.asreml(spatial4nugg) 80 | summary(spatial4nugg)$varcomp 81 | plot(variogram(spatial4nugg)) 82 | -------------------------------------------------------------------------------- /Day2/Unreplicated/Unrep.R: -------------------------------------------------------------------------------- 1 | ######################################### 2 | # Spatial Analysis - Unreplicated Trial # 3 | ######################################### 4 | 5 | rm(list=ls()) 6 | setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami/Distribute/Day2/Unreplicated") 7 | library(asreml) 8 | library(nadiv) 9 | library(asremlPlus) 10 | 11 | unrep<-read.table("PEPPER.txt",h=T) 12 | unrep$Rep<-as.factor(unrep$Rep) 13 | unrep$Xf<-as.factor(unrep$X) 14 | unrep$Yf<-as.factor(unrep$Y) 15 | unrep$Genotype<-as.factor(unrep$Genotype) 16 | head(unrep) 17 | View(unrep) 18 | 19 | # Some EDA 20 | plot(unrep$X,unrep$Y) 21 | table(unrep$Rep,unrep$Genotype) 22 | 23 | # Traditional Augmented analysis - No spatial!! 24 | nospatial<-asreml(fixed=YD~1, 25 | random=~Rep+Genotype, 26 | rcov=~Xf:Yf,data=unrep) 27 | summary(nospatial)$varcomp 28 | plot(nospatial) 29 | plot(variogram(nospatial)) 30 | (rhos<-nadiv:::pin(nospatial,rhos~V1/(V1+V2+V3))) 31 | (H2<-nadiv:::pin(nospatial,H2~V2/(V1+V2+V3))) 32 | predtrad<-predict(nospatial,classify="Genotype")$predictions$pvals 33 | View(predtrad) 34 | 35 | # Basic Spatial Analysis for Augmented Designs 36 | 37 | -------------------------------------------------------------------------------- /Day2/VarStruct/LEAFAREA.txt: -------------------------------------------------------------------------------- 1 | id block pot variety diseas trt leafarea 2 | 1 1 1 P H H_P 147.7 3 | 2 2 1 P H H_P 110.6 4 | 3 3 1 P H H_P 93.9 5 | 4 4 1 P H H_P 89.6 6 | 5 5 1 P H H_P 98.5 7 | 6 6 1 P H H_P 88.9 8 | 7 7 1 P H H_P 107.4 9 | 8 8 1 P H H_P 160.4 10 | 9 9 1 P H H_P 161.1 11 | 10 10 1 P H H_P 193.8 12 | 11 1 2 P D D_P 46.7 13 | 12 2 2 P D D_P 9.9 14 | 13 3 2 P D D_P 16.4 15 | 14 4 2 P D D_P 20.5 16 | 15 5 2 P D D_P 21.8 17 | 16 6 2 P D D_P 9.7 18 | 17 7 2 P D D_P 9.1 19 | 18 8 2 P D D_P 17.5 20 | 19 9 2 P D D_P 9.5 21 | 20 10 2 P D D_P 12.2 22 | 21 1 3 K H H_K 86.3 23 | 22 2 3 K H H_K 111.8 24 | 23 3 3 K H H_K 52.5 25 | 24 4 3 K H H_K 58.4 26 | 25 5 3 K H H_K 69.7 27 | 26 6 3 K H H_K 43.7 28 | 27 7 3 K H H_K 53.6 29 | 28 8 3 K H H_K 44.6 30 | 29 9 3 K H H_K 84.7 31 | 30 10 3 K H H_K 99.5 32 | 31 1 4 K D D_K 6.8 33 | 32 2 4 K D D_K 7.9 34 | 33 3 4 K D D_K 6.6 35 | 34 4 4 K D D_K 8.4 36 | 35 5 4 K D D_K 12.7 37 | 36 6 4 K D D_K 12.4 38 | 37 7 4 K D D_K 10 39 | 38 8 4 K D D_K 11.9 40 | 39 9 4 K D D_K 8.1 41 | 40 10 4 K D D_K 5.4 42 | 41 1 5 UD21 H H_UD21 76.5 43 | 42 2 5 UD21 H H_UD21 61.1 44 | 43 3 5 UD21 H H_UD21 84 45 | 44 4 5 UD21 H H_UD21 68.8 46 | 45 5 5 UD21 H H_UD21 75.3 47 | 46 6 5 UD21 H H_UD21 116.3 48 | 47 7 5 UD21 H H_UD21 78.9 49 | 48 8 5 UD21 H H_UD21 98.9 50 | 49 9 5 UD21 H H_UD21 125.3 51 | 50 10 5 UD21 H H_UD21 101.4 52 | 51 1 6 UD21 D D_UD21 21.2 53 | 52 2 6 UD21 D D_UD21 4.1 54 | 53 3 6 UD21 D D_UD21 21.2 55 | 54 4 6 UD21 D D_UD21 20.8 56 | 55 5 6 UD21 D D_UD21 15.8 57 | 56 6 6 UD21 D D_UD21 20.1 58 | 57 7 6 UD21 D D_UD21 13.4 59 | 58 8 6 UD21 D D_UD21 14 60 | 59 9 6 UD21 D D_UD21 21.8 61 | 60 10 6 UD21 D D_UD21 24.4 62 | 61 1 7 KW33 H H_KW33 99.5 63 | 62 2 7 KW33 H H_KW33 163.7 64 | 63 3 7 KW33 H H_KW33 69.7 65 | 64 4 7 KW33 H H_KW33 111.6 66 | 65 5 7 KW33 H H_KW33 106.9 67 | 66 6 7 KW33 H H_KW33 117.6 68 | 67 7 7 KW33 H H_KW33 96.6 69 | 68 8 7 KW33 H H_KW33 68.3 70 | 69 9 7 KW33 H H_KW33 98.6 71 | 70 10 7 KW33 H H_KW33 90.3 72 | 71 1 8 KW33 D D_KW33 26 73 | 72 2 8 KW33 D D_KW33 30.5 74 | 73 3 8 KW33 D D_KW33 32.3 75 | 74 4 8 KW33 D D_KW33 33.1 76 | 75 5 8 KW33 D D_KW33 34.5 77 | 76 6 8 KW33 D D_KW33 31.5 78 | 77 7 8 KW33 D D_KW33 29.4 79 | 78 8 8 KW33 D D_KW33 30.4 80 | 79 9 8 KW33 D D_KW33 26.3 81 | 80 10 8 KW33 D D_KW33 29.9 82 | 81 1 9 LW26 H H_LW26 73.3 83 | 82 2 9 LW26 H H_LW26 66.7 84 | 83 3 9 LW26 H H_LW26 76.9 85 | 84 4 9 LW26 H H_LW26 56.7 86 | 85 5 9 LW26 H H_LW26 69.8 87 | 86 6 9 LW26 H H_LW26 57 88 | 87 7 9 LW26 H H_LW26 68.9 89 | 88 8 9 LW26 H H_LW26 86.3 90 | 89 9 9 LW26 H H_LW26 97.6 91 | 90 10 9 LW26 H H_LW26 92.1 92 | 91 1 10 LW26 D D_LW26 21.8 93 | 92 2 10 LW26 D D_LW26 11.2 94 | 93 3 10 LW26 D D_LW26 16.6 95 | 94 4 10 LW26 D D_LW26 11.9 96 | 95 5 10 LW26 D D_LW26 12.8 97 | 96 6 10 LW26 D D_LW26 14.4 98 | 97 7 10 LW26 D D_LW26 9.9 99 | 98 8 10 LW26 D D_LW26 22.8 100 | 99 9 10 LW26 D D_LW26 10.1 101 | 100 10 10 LW26 D D_LW26 13.1 102 | 101 1 11 MB23 H H_MB23 69.7 103 | 102 2 11 MB23 H H_MB23 74.9 104 | 103 3 11 MB23 H H_MB23 156.2 105 | 104 4 11 MB23 H H_MB23 87.9 106 | 105 5 11 MB23 H H_MB23 91.9 107 | 106 6 11 MB23 H H_MB23 93.5 108 | 107 7 11 MB23 H H_MB23 90.3 109 | 108 8 11 MB23 H H_MB23 69.5 110 | 109 9 11 MB23 H H_MB23 98 111 | 110 10 11 MB23 H H_MB23 99.4 112 | 111 1 12 MB23 D D_MB23 30.7 113 | 112 2 12 MB23 D D_MB23 35.6 114 | 113 3 12 MB23 D D_MB23 22.6 115 | 114 4 12 MB23 D D_MB23 21.8 116 | 115 5 12 MB23 D D_MB23 29.8 117 | 116 6 12 MB23 D D_MB23 30.5 118 | 117 7 12 MB23 D D_MB23 22 119 | 118 8 12 MB23 D D_MB23 24.5 120 | 119 9 12 MB23 D D_MB23 28.6 121 | 120 10 12 MB23 D D_MB23 32 122 | 121 1 13 GK48 H H_GK48 72.3 123 | 122 2 13 GK48 H H_GK48 80.8 124 | 123 3 13 GK48 H H_GK48 58.9 125 | 124 4 13 GK48 H H_GK48 63.4 126 | 125 5 13 GK48 H H_GK48 59.8 127 | 126 6 13 GK48 H H_GK48 57.5 128 | 127 7 13 GK48 H H_GK48 62.8 129 | 128 8 13 GK48 H H_GK48 52.4 130 | 129 9 13 GK48 H H_GK48 56.9 131 | 130 10 13 GK48 H H_GK48 51.4 132 | 131 1 14 GK48 D D_GK48 31.4 133 | 132 2 14 GK48 D D_GK48 40.4 134 | 133 3 14 GK48 D D_GK48 34.9 135 | 134 4 14 GK48 D D_GK48 35.8 136 | 135 5 14 GK48 D D_GK48 18.5 137 | 136 6 14 GK48 D D_GK48 25.8 138 | 137 7 14 GK48 D D_GK48 27.4 139 | 138 8 14 GK48 D D_GK48 22.6 140 | 139 9 14 GK48 D D_GK48 21.5 141 | 140 10 14 GK48 D D_GK48 23 142 | 141 1 15 KW37 H H_KW37 120.8 143 | 142 2 15 KW37 H H_KW37 82.7 144 | 143 3 15 KW37 H H_KW37 118.1 145 | 144 4 15 KW37 H H_KW37 95 146 | 145 5 15 KW37 H H_KW37 83.9 147 | 146 6 15 KW37 H H_KW37 96.4 148 | 147 7 15 KW37 H H_KW37 90 149 | 148 8 15 KW37 H H_KW37 74.7 150 | 149 9 15 KW37 H H_KW37 58.5 151 | 150 10 15 KW37 H H_KW37 56.1 152 | 151 1 16 KW37 D D_KW37 28 153 | 152 2 16 KW37 D D_KW37 21.9 154 | 153 3 16 KW37 D D_KW37 16.4 155 | 154 4 16 KW37 D D_KW37 15.7 156 | 155 5 16 KW37 D D_KW37 18.6 157 | 156 6 16 KW37 D D_KW37 18.7 158 | 157 7 16 KW37 D D_KW37 20.6 159 | 158 8 16 KW37 D D_KW37 29.3 160 | 159 9 16 KW37 D D_KW37 23.1 161 | 160 10 16 KW37 D D_KW37 21.1 162 | 161 1 17 GK49 H H_GK49 112.6 163 | 162 2 17 GK49 H H_GK49 116.8 164 | 163 3 17 GK49 H H_GK49 105.8 165 | 164 4 17 GK49 H H_GK49 81.3 166 | 165 5 17 GK49 H H_GK49 121.1 167 | 166 6 17 GK49 H H_GK49 117.8 168 | 167 7 17 GK49 H H_GK49 85.5 169 | 168 8 17 GK49 H H_GK49 121.1 170 | 169 9 17 GK49 H H_GK49 148.6 171 | 170 10 17 GK49 H H_GK49 97.6 172 | 171 1 18 GK49 D D_GK49 33.4 173 | 172 2 18 GK49 D D_GK49 66.5 174 | 173 3 18 GK49 D D_GK49 58.4 175 | 174 4 18 GK49 D D_GK49 59.9 176 | 175 5 18 GK49 D D_GK49 54.9 177 | 176 6 18 GK49 D D_GK49 35.5 178 | 177 7 18 GK49 D D_GK49 30.2 179 | 178 8 18 GK49 D D_GK49 34.5 180 | 179 9 18 GK49 D D_GK49 29.6 181 | 180 10 18 GK49 D D_GK49 61.1 182 | 181 1 19 TG7 H H_TG7 80.6 183 | 182 2 19 TG7 H H_TG7 69.7 184 | 183 3 19 TG7 H H_TG7 11.9 185 | 184 4 19 TG7 H H_TG7 82.5 186 | 185 5 19 TG7 H H_TG7 66.6 187 | 186 6 19 TG7 H H_TG7 80.5 188 | 187 7 19 TG7 H H_TG7 75.8 189 | 188 8 19 TG7 H H_TG7 63 190 | 189 9 19 TG7 H H_TG7 74.9 191 | 190 10 19 TG7 H H_TG7 57.8 192 | 191 1 20 TG7 D D_TG7 21 193 | 192 2 20 TG7 D D_TG7 27.8 194 | 193 3 20 TG7 D D_TG7 24.8 195 | 194 4 20 TG7 D D_TG7 23.7 196 | 195 5 20 TG7 D D_TG7 18.8 197 | 196 6 20 TG7 D D_TG7 37.1 198 | 197 7 20 TG7 D D_TG7 25.7 199 | 198 8 20 TG7 D D_TG7 27.5 200 | 199 9 20 TG7 D D_TG7 32 201 | 200 10 20 TG7 D D_TG7 21 202 | 201 1 21 KW35 H H_KW35 117.1 203 | 202 2 21 KW35 H H_KW35 108.9 204 | 203 3 21 KW35 H H_KW35 91.6 205 | 204 4 21 KW35 H H_KW35 85.8 206 | 205 5 21 KW35 H H_KW35 92.9 207 | 206 6 21 KW35 H H_KW35 131.1 208 | 207 7 21 KW35 H H_KW35 81.5 209 | 208 8 21 KW35 H H_KW35 85.7 210 | 209 9 21 KW35 H H_KW35 115.1 211 | 210 10 21 KW35 H H_KW35 144.8 212 | 211 1 22 KW35 D D_KW35 26.2 213 | 212 2 22 KW35 D D_KW35 28.8 214 | 213 3 22 KW35 D D_KW35 17.1 215 | 214 4 22 KW35 D D_KW35 17.4 216 | 215 5 22 KW35 D D_KW35 18 217 | 216 6 22 KW35 D D_KW35 22.4 218 | 217 7 22 KW35 D D_KW35 16.1 219 | 218 8 22 KW35 D D_KW35 19.9 220 | 219 9 22 KW35 D D_KW35 21.5 221 | 220 10 22 KW35 D D_KW35 23.3 222 | 221 1 23 MM14 H H_MM14 59.7 223 | 222 2 23 MM14 H H_MM14 73 224 | 223 3 23 MM14 H H_MM14 53.2 225 | 224 4 23 MM14 H H_MM14 57.9 226 | 225 5 23 MM14 H H_MM14 71.2 227 | 226 6 23 MM14 H H_MM14 58.5 228 | 227 7 23 MM14 H H_MM14 40.7 229 | 228 8 23 MM14 H H_MM14 52.1 230 | 229 9 23 MM14 H H_MM14 49.3 231 | 230 10 23 MM14 H H_MM14 67.1 232 | 231 1 24 MM14 D D_MM14 47.3 233 | 232 2 24 MM14 D D_MM14 31.5 234 | 233 3 24 MM14 D D_MM14 19.8 235 | 234 4 24 MM14 D D_MM14 17 236 | 235 5 24 MM14 D D_MM14 19.8 237 | 236 6 24 MM14 D D_MM14 12.5 238 | 237 7 24 MM14 D D_MM14 14.9 239 | 238 8 24 MM14 D D_MM14 28.6 240 | 239 9 24 MM14 D D_MM14 34.8 241 | 240 10 24 MB23 D D_MM14 23.3 242 | -------------------------------------------------------------------------------- /Day2/VarStruct/Leafarea.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Variance Structure 3 | ############################### 4 | 5 | rm(list=ls()) 6 | #setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami/Distribute/Day2/VarStruct") 7 | library(asreml) 8 | 9 | 10 | # Leafarea Example 11 | leafarea<-read.table("./Distribute/Day2/VarStruct/LEAFAREA.txt", h=T) 12 | head(leafarea) 13 | leafarea$block<-as.factor(leafarea$block) 14 | leafarea$pot<-as.factor(leafarea$pot) 15 | leafarea$variety<-as.factor(leafarea$variety) 16 | leafarea$disease<-as.factor(leafarea$diseas) 17 | 18 | # Some EDA 19 | hist(leafarea$leafarea) # Transformation? 20 | 21 | 22 | # Initial Analysis - All fixed! 23 | model0<-asreml(fixed=leafarea~block+disease+variety+variety:disease, 24 | data=leafarea) 25 | plot(model0) 26 | anova(model0,denDF='default') 27 | pred.model0<-predict(model0,classify="variety:disease",sed=TRUE) 28 | View(pred.model0$predictions$pvals) 29 | # NB -> the residuals look very skewed (not good). This model is assuming that all observations have the same background noise! This is clearly not correct, so move to reml analysis where we can assign different variances to variety and variety:disease error separately. 30 | # NB -> if we want to select varieties, then we will (probably?) need to use reml model (below) 31 | 32 | 33 | # Initial Analysis - Variety Random! 34 | model1<-asreml(fixed=leafarea~block+disease, 35 | random=~variety+variety:disease, 36 | data=leafarea) 37 | plot(model1) 38 | summary(model1)$varcomp 39 | anova(model1,denDF='default') 40 | 41 | 42 | #### DIRECT SUM #### 43 | # NB -> units is a keyword in asreml-r 44 | # NB -> rcov = ~units by itself is basically an indentity 45 | # NB -> rcov = ~ at(disease):units ; this command does a direct sum of indentity matrix (units) and different levels of disease 46 | # 47 | leafarea = leafarea[order(leafarea$disease),] 48 | model1b<-asreml(fixed=leafarea~block+disease, 49 | random=~variety+variety:disease, 50 | rcov = ~units:at(disease), # error variance, one for each disease state 51 | data=leafarea) 52 | 53 | plot(model1b) 54 | summary(model1b)$varcomp 55 | wald(model1b, denDF="default") # the anova: gauging significance (not 0) of the fixed effects. 56 | rhoB <- 41.98 / (41.98+173.38) 57 | rhoB <- nadiv:::pin(model1b, tmp ~ V1 / (V1 + V2) ) # indicates whether certain plants, G, do better in certain environments, GxE. If this correlation is near 0, then GxE is strong and we can only assume particular plants do well in particular environments. This case would be bad for breeding programs! 58 | # NB -> (extremely important!!!) The raw residuals are on different scales! (we need studentized residuals) 59 | # Note how the variances are VERY different btw the different disease states! This is critical to understand. Each site should have its own error variance. 60 | # Exploratory data analysis (EDA) should help to indicate how to get to model1b, where we are modeling different error variances for the different disease states. 61 | # Transforming the data is to make your life easier. Its a trick, makes model easier, but possibly interpretation more difficult. 62 | # NB -> Genetic Variance, G == variety!variety.var 63 | # NB -> GxE Variance == variety:disease!variety.var 64 | 65 | 66 | #### DIRECT PRODUCT #### 67 | # NB -> this will be a GxE analysis 68 | # NB -> id() says to use an identify matrix for variety 69 | # NB -> us() says to use an unstructured matrix for disease 70 | model1c<-asreml(fixed=leafarea~block+disease, 71 | random=~id(variety):us(disease), # This defines the structure of the G matrix (variety:disease interaction) == I_12 (kron) 2x2 matrix of disease variances 72 | rcov = ~units:at(disease), # error variance, one for each disease state 73 | data=leafarea) 74 | #plot(model1c) 75 | summary(model1c)$varcomp 76 | wald(model1c, denDF="default") # the anova: gauging significance (not 0) of the fixed effects. 77 | (rhoB <- nadiv:::pin(model1c, tmp ~ V2 / sqrt(V1 * V3) ) ) # type B correlation 78 | 79 | 80 | # using a different variance structure, which is estimating the correlation directly, instead of 3 variance components. 81 | # NB -> this is the same model as 1c, just a different parameterization! 82 | model1d<-asreml(fixed=leafarea~block+disease, 83 | random=~id(variety):corv(disease), 84 | rcov = ~units:at(disease), # error variance, one for each disease state 85 | data=leafarea) 86 | summary(model1d)$varcomp 87 | 88 | 89 | 90 | -------------------------------------------------------------------------------- /Practicals.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ConradStack/ASReml-R-Short-Course/cad54bc6ee3c41dee9f492877440ab3cf6ed99dc/Practicals.pdf -------------------------------------------------------------------------------- /Practicals/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ConradStack/ASReml-R-Short-Course/cad54bc6ee3c41dee9f492877440ab3cf6ed99dc/Practicals/.DS_Store -------------------------------------------------------------------------------- /Practicals/Alfalfa/ALFALFA.txt: -------------------------------------------------------------------------------- 1 | Source Variety Block Resp 2 | 1 A 1 2.17 3 | 1 B 1 1.58 4 | 1 C 1 2.29 5 | 1 D 1 2.23 6 | 1 A 2 1.88 7 | 1 B 2 1.26 8 | 1 C 2 1.6 9 | 1 D 2 2.01 10 | 1 A 3 1.62 11 | 1 B 3 1.22 12 | 1 C 3 1.67 13 | 1 D 3 1.82 14 | 1 A 4 2.34 15 | 1 B 4 1.59 16 | 1 C 4 1.91 17 | 1 D 4 2.1 18 | 1 A 5 1.58 19 | 1 B 5 1.25 20 | 1 C 5 1.39 21 | 1 D 5 1.66 22 | 1 A 6 1.66 23 | 1 B 6 0.94 24 | 1 C 6 1.12 25 | 1 D 6 1.1 26 | 2 E 1 2.33 27 | 2 F 1 1.38 28 | 2 G 1 1.86 29 | 2 H 1 2.27 30 | 2 E 2 2.01 31 | 2 F 2 1.3 32 | 2 G 2 1.7 33 | 2 H 2 1.81 34 | 2 E 3 1.7 35 | 2 F 3 1.85 36 | 2 G 3 1.81 37 | 2 H 3 2.01 38 | 2 E 4 1.78 39 | 2 F 4 1.09 40 | 2 G 4 1.54 41 | 2 H 4 1.4 42 | 2 E 5 1.42 43 | 2 F 5 1.13 44 | 2 G 5 1.67 45 | 2 H 5 1.31 46 | 2 E 6 1.35 47 | 2 F 6 1.06 48 | 2 G 6 0.88 49 | 2 H 6 1.06 50 | 3 I 1 1.75 51 | 3 J 1 1.52 52 | 3 K 1 1.55 53 | 3 L 1 1.56 54 | 3 I 2 1.95 55 | 3 J 2 1.47 56 | 3 K 2 1.61 57 | 3 L 2 1.72 58 | 3 I 3 2.13 59 | 3 J 3 1.8 60 | 3 K 3 1.82 61 | 3 L 3 1.99 62 | 3 I 4 1.78 63 | 3 J 4 1.37 64 | 3 K 4 1.56 65 | 3 L 4 1.55 66 | 3 I 5 1.31 67 | 3 J 5 1.01 68 | 3 K 5 1.23 69 | 3 L 5 1.51 70 | 3 I 6 1.3 71 | 3 J 6 1.31 72 | 3 K 6 1.13 73 | 3 L 6 1.33 74 | -------------------------------------------------------------------------------- /Practicals/Alfalfa/Files_Code.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ConradStack/ASReml-R-Short-Course/cad54bc6ee3c41dee9f492877440ab3cf6ed99dc/Practicals/Alfalfa/Files_Code.docx -------------------------------------------------------------------------------- /Practicals/FieldT/FIELDT.txt: -------------------------------------------------------------------------------- 1 | ID REP IBLOCK PLOT INDIV GENOTYPE YD 2 | 1 1 1 1 1 1 8.33 3 | 2 1 1 1 2 1 9.94 4 | 3 1 1 1 3 1 8.83 5 | 4 1 1 1 4 1 9.40 6 | 5 1 1 2 1 2 10.84 7 | 6 1 1 2 2 2 10.88 8 | 7 1 1 2 3 2 10.24 9 | 8 1 1 2 4 2 9.95 10 | 9 1 1 3 1 3 10.38 11 | 10 1 1 3 2 3 10.31 12 | 11 1 1 3 3 3 10.51 13 | 12 1 1 3 4 3 9.79 14 | 13 1 1 4 1 4 10.33 15 | 14 1 1 4 2 4 9.01 16 | 15 1 1 4 3 4 11.00 17 | 16 1 1 4 4 4 10.04 18 | 17 1 2 5 1 5 10.17 19 | 18 1 2 5 2 5 11.11 20 | 19 1 2 5 3 5 11.26 21 | 20 1 2 5 4 5 10.50 22 | 21 1 2 6 1 6 9.77 23 | 22 1 2 6 2 6 10.33 24 | 23 1 2 6 3 6 9.82 25 | 24 1 2 6 4 6 10.58 26 | 25 1 2 7 1 7 10.89 27 | 26 1 2 7 2 7 9.16 28 | 27 1 2 7 3 7 10.04 29 | 28 1 2 7 4 7 10.04 30 | 29 1 2 8 1 8 9.48 31 | 30 1 2 8 2 8 10.01 32 | 31 1 2 8 3 8 9.92 33 | 32 1 2 8 4 8 10.06 34 | 33 1 3 9 1 9 10.76 35 | 34 1 3 9 2 9 11.20 36 | 35 1 3 9 3 9 9.56 37 | 36 1 3 9 4 9 10.73 38 | 37 1 3 10 1 10 12.08 39 | 38 1 3 10 2 10 11.98 40 | 39 1 3 10 3 10 11.96 41 | 40 1 3 10 4 10 11.30 42 | 41 1 3 11 1 11 9.70 43 | 42 1 3 11 2 11 10.17 44 | 43 1 3 11 3 11 9.88 45 | 44 1 3 11 4 11 9.74 46 | 45 1 3 12 1 12 9.08 47 | 46 1 3 12 2 12 9.38 48 | 47 1 3 12 3 12 8.98 49 | 48 1 3 12 4 12 10.58 50 | 49 1 4 13 1 13 9.21 51 | 50 1 4 13 2 13 9.38 52 | 51 1 4 13 3 13 9.19 53 | 52 1 4 13 4 13 10.42 54 | 53 1 4 14 1 14 10.13 55 | 54 1 4 14 2 14 10.50 56 | 55 1 4 14 3 14 10.05 57 | 56 1 4 14 4 14 9.68 58 | 57 1 4 15 1 15 8.86 59 | 58 1 4 15 2 15 10.43 60 | 59 1 4 15 3 15 9.52 61 | 60 1 4 15 4 15 9.88 62 | 61 1 4 16 1 16 8.48 63 | 62 1 4 16 2 16 9.31 64 | 63 1 4 16 3 16 9.66 65 | 64 1 4 16 4 16 9.67 66 | 65 2 1 1 1 1 9.90 67 | 66 2 1 1 2 1 10.01 68 | 67 2 1 1 3 1 9.09 69 | 68 2 1 1 4 1 8.96 70 | 69 2 1 2 1 2 9.93 71 | 70 2 1 2 2 2 8.19 72 | 71 2 1 2 3 2 9.88 73 | 72 2 1 2 4 2 9.86 74 | 73 2 1 3 1 3 9.47 75 | 74 2 1 3 2 3 9.57 76 | 75 2 1 3 3 3 9.89 77 | 76 2 1 3 4 3 9.33 78 | 77 2 1 4 1 4 7.72 79 | 78 2 1 4 2 4 8.63 80 | 79 2 1 4 3 4 9.21 81 | 80 2 1 4 4 4 9.80 82 | 81 2 2 5 1 5 10.13 83 | 82 2 2 5 2 5 9.02 84 | 83 2 2 5 3 5 9.36 85 | 84 2 2 5 4 5 8.82 86 | 85 2 2 6 1 6 10.52 87 | 86 2 2 6 2 6 10.82 88 | 87 2 2 6 3 6 9.92 89 | 88 2 2 6 4 6 9.93 90 | 89 2 2 7 1 7 9.59 91 | 90 2 2 7 2 7 8.04 92 | 91 2 2 7 3 7 10.65 93 | 92 2 2 7 4 7 8.79 94 | 93 2 2 8 1 8 8.46 95 | 94 2 2 8 2 8 9.37 96 | 95 2 2 8 3 8 7.72 97 | 96 2 2 8 4 8 9.59 98 | 97 2 3 9 1 9 9.98 99 | 98 2 3 9 2 9 9.94 100 | 99 2 3 9 3 9 9.79 101 | 100 2 3 9 4 9 9.94 102 | 101 2 3 10 1 10 11.10 103 | 102 2 3 10 2 10 11.73 104 | 103 2 3 10 3 10 10.96 105 | 104 2 3 10 4 10 10.41 106 | 105 2 3 11 1 11 8.89 107 | 106 2 3 11 2 11 8.76 108 | 107 2 3 11 3 11 9.66 109 | 108 2 3 11 4 11 9.38 110 | 109 2 3 12 1 12 9.09 111 | 110 2 3 12 2 12 8.53 112 | 111 2 3 12 3 12 8.69 113 | 112 2 3 12 4 12 9.12 114 | 113 2 4 13 1 13 10.64 115 | 114 2 4 13 2 13 9.56 116 | 115 2 4 13 3 13 9.88 117 | 116 2 4 13 4 13 10.10 118 | 117 2 4 14 1 14 9.89 119 | 118 2 4 14 2 14 8.63 120 | 119 2 4 14 3 14 9.37 121 | 120 2 4 14 4 14 9.48 122 | 121 2 4 15 1 15 9.33 123 | 122 2 4 15 2 15 8.75 124 | 123 2 4 15 3 15 10.11 125 | 124 2 4 15 4 15 8.98 126 | 125 2 4 16 1 16 8.46 127 | 126 2 4 16 2 16 8.74 128 | 127 2 4 16 3 16 10.44 129 | 128 2 4 16 4 16 9.09 130 | 129 3 1 1 1 1 11.49 131 | 130 3 1 1 2 1 10.20 132 | 131 3 1 1 3 1 10.44 133 | 132 3 1 1 4 1 11.54 134 | 133 3 1 2 1 2 11.95 135 | 134 3 1 2 2 2 11.28 136 | 135 3 1 2 3 2 11.37 137 | 136 3 1 2 4 2 11.56 138 | 137 3 1 3 1 3 10.34 139 | 138 3 1 3 2 3 9.23 140 | 139 3 1 3 3 3 9.99 141 | 140 3 1 3 4 3 9.79 142 | 141 3 1 4 1 4 8.25 143 | 142 3 1 4 2 4 8.04 144 | 143 3 1 4 3 4 8.89 145 | 144 3 1 4 4 4 -9.00 146 | 145 3 2 5 1 5 10.64 147 | 146 3 2 5 2 5 9.94 148 | 147 3 2 5 3 5 11.33 149 | 148 3 2 5 4 5 10.80 150 | 149 3 2 6 1 6 10.30 151 | 150 3 2 6 2 6 10.88 152 | 151 3 2 6 3 6 11.91 153 | 152 3 2 6 4 6 10.73 154 | 153 3 2 7 1 7 11.65 155 | 154 3 2 7 2 7 11.57 156 | 155 3 2 7 3 7 11.18 157 | 156 3 2 7 4 7 12.46 158 | 157 3 2 8 1 8 11.62 159 | 158 3 2 8 2 8 11.04 160 | 159 3 2 8 3 8 11.70 161 | 160 3 2 8 4 8 11.92 162 | 161 3 3 9 1 9 9.43 163 | 162 3 3 9 2 9 10.04 164 | 163 3 3 9 3 9 10.51 165 | 164 3 3 9 4 9 10.62 166 | 165 3 3 10 1 10 10.68 167 | 166 3 3 10 2 10 10.14 168 | 167 3 3 10 3 10 10.11 169 | 168 3 3 10 4 10 9.30 170 | 169 3 3 11 1 11 8.60 171 | 170 3 3 11 2 11 9.36 172 | 171 3 3 11 3 11 9.77 173 | 172 3 3 11 4 11 10.43 174 | 173 3 3 12 1 12 9.28 175 | 174 3 3 12 2 12 10.04 176 | 175 3 3 12 3 12 8.78 177 | 176 3 3 12 4 12 9.61 178 | 177 3 4 13 1 13 11.25 179 | 178 3 4 13 2 13 11.03 180 | 179 3 4 13 3 13 10.61 181 | 180 3 4 13 4 13 11.83 182 | 181 3 4 14 1 14 10.16 183 | 182 3 4 14 2 14 10.25 184 | 183 3 4 14 3 14 10.25 185 | 184 3 4 14 4 14 10.04 186 | 185 3 4 15 1 15 11.19 187 | 186 3 4 15 2 15 10.32 188 | 187 3 4 15 3 15 10.06 189 | 188 3 4 15 4 15 9.85 190 | 189 3 4 16 1 16 9.92 191 | 190 3 4 16 2 16 10.09 192 | 191 3 4 16 3 16 10.61 193 | 192 3 4 16 4 16 11.39 194 | 193 4 1 1 1 1 10.52 195 | 194 4 1 1 2 1 9.88 196 | 195 4 1 1 3 1 9.78 197 | 196 4 1 1 4 1 8.64 198 | 197 4 1 2 1 2 11.01 199 | 198 4 1 2 2 2 10.06 200 | 199 4 1 2 3 2 9.02 201 | 200 4 1 2 4 2 11.72 202 | 201 4 1 3 1 3 9.76 203 | 202 4 1 3 2 3 8.73 204 | 203 4 1 3 3 3 9.61 205 | 204 4 1 3 4 3 10.34 206 | 205 4 1 4 1 4 11.76 207 | 206 4 1 4 2 4 10.41 208 | 207 4 1 4 3 4 11.43 209 | 208 4 1 4 4 4 9.33 210 | 209 4 2 5 1 5 10.91 211 | 210 4 2 5 2 5 12.01 212 | 211 4 2 5 3 5 10.13 213 | 212 4 2 5 4 5 10.61 214 | 213 4 2 6 1 6 10.62 215 | 214 4 2 6 2 6 9.48 216 | 215 4 2 6 3 6 11.02 217 | 216 4 2 6 4 6 9.19 218 | 217 4 2 7 1 7 -9.00 219 | 218 4 2 7 2 7 9.69 220 | 219 4 2 7 3 7 10.92 221 | 220 4 2 7 4 7 8.76 222 | 221 4 2 8 1 8 10.57 223 | 222 4 2 8 2 8 9.76 224 | 223 4 2 8 3 8 10.16 225 | 224 4 2 8 4 8 11.17 226 | 225 4 3 9 1 9 9.58 227 | 226 4 3 9 2 9 10.63 228 | 227 4 3 9 3 9 10.00 229 | 228 4 3 9 4 9 9.86 230 | 229 4 3 10 1 10 11.59 231 | 230 4 3 10 2 10 10.64 232 | 231 4 3 10 3 10 10.51 233 | 232 4 3 10 4 10 10.76 234 | 233 4 3 11 1 11 10.89 235 | 234 4 3 11 2 11 8.73 236 | 235 4 3 11 3 11 10.02 237 | 236 4 3 11 4 11 8.86 238 | 237 4 3 12 1 12 9.86 239 | 238 4 3 12 2 12 9.61 240 | 239 4 3 12 3 12 10.17 241 | 240 4 3 12 4 12 8.61 242 | 241 4 4 13 1 13 9.81 243 | 242 4 4 13 2 13 10.35 244 | 243 4 4 13 3 13 9.92 245 | 244 4 4 13 4 13 9.87 246 | 245 4 4 14 1 14 9.84 247 | 246 4 4 14 2 14 9.94 248 | 247 4 4 14 3 14 8.77 249 | 248 4 4 14 4 14 9.35 250 | 249 4 4 15 1 15 9.55 251 | 250 4 4 15 2 15 -9.00 252 | 251 4 4 15 3 15 9.36 253 | 252 4 4 15 4 15 9.05 254 | 253 4 4 16 1 16 10.68 255 | 254 4 4 16 2 16 9.19 256 | 255 4 4 16 3 16 9.28 257 | 256 4 4 16 4 16 9.72 258 | -------------------------------------------------------------------------------- /Practicals/FieldT/Fieldt.R: -------------------------------------------------------------------------------- 1 | # Practical 1 2 | 3 | rm(list=ls()) # Removes all variables in memory 4 | setwd("Distribute/Practicals/FieldT") 5 | 6 | grape<-read.table("FIELDT.TXT",header=TRUE,na.strings="-9.00") 7 | head(grape) 8 | View(grape) 9 | 10 | library(asreml) 11 | # y = mu + REP + REP:IBLOCK + REP:IBLOCK:PLOT + GENOTYPE + e 12 | 13 | grape$REP<-as.factor(grape$REP) 14 | grape$IBLOCK<-as.factor(grape$IBLOCK) 15 | grape$PLOT<-as.factor(grape$PLOT) 16 | grape$GENOTYPE<-as.factor(grape$GENOTYPE) 17 | str(grape) 18 | #grape$YD[144]<-NA 19 | #grape$YD[217]<-NA 20 | #grape$YD[250]<-NA 21 | 22 | ## Interpretations: 23 | # REP:IBLOCK - IBLOCK within REP 24 | # REP:IBLOCK:PLOT - PLOT within IBLOCK within REP 25 | # 26 | model1 <- asreml(YD ~ REP, 27 | random = ~ REP:IBLOCK + REP:IBLOCK:PLOT + GENOTYPE, 28 | data = grape) 29 | 30 | hist(grape$YD) 31 | View(grape) 32 | 33 | 34 | model1<-asreml(fixed=YD~REP, 35 | random=~REP:IBLOCK+REP:IBLOCK:PLOT+GENOTYPE,data=grape) 36 | plot(model1) 37 | summary(model1) 38 | summary(model1)$varcomp 39 | Num<-summary(model1)$varcomp$component[3] 40 | Den<-sum(summary(model1)$varcomp$component) 41 | (H2<-Num/Den) 42 | 43 | wald(model1) 44 | wald(model1,denf='defualt',ssType='incremental') 45 | wald(model1,denf='defualt',ssType='conditional') 46 | 47 | 48 | 49 | -------------------------------------------------------------------------------- /Practicals/Fish/Fish.R: -------------------------------------------------------------------------------- 1 | ############################### 2 | ## Animal Model 3 | ############################### 4 | 5 | rm(list=ls()) 6 | setwd("E:/WORK_PARTIAL/ASReml/ASReml_2016_Miami/Distribute/Day1/Fish") 7 | library(asreml) 8 | library(nadiv) 9 | 10 | # Fish Example 11 | fish<-read.table("FISHAB.txt", h=T) 12 | head(fish) 13 | fish$Sex<-as.factor(fish$Sex) 14 | fish$INDIV<-as.factor(fish$INDIV) 15 | fish$Sire<-as.factor(fish$Sire) 16 | fish$Dam<-as.factor(fish$Dam) 17 | fish$FAM<-as.factor(fish$FAM) 18 | str(fish) 19 | 20 | ######### 21 | # Part 1 - PARENTAL MODEL WITH PEDIGREE 22 | pedpar<-read.table("PEDPAR.txt",h=T) 23 | ainvpar<-asreml.Ainverse(pedpar)$ginv 24 | 25 | # Fitting a Parental model 26 | parentalmodel<-asreml(fixed=DaysM~Sex, 27 | random=~ped(Sire)+and(ped(Dam))+FAM, 28 | ginverse=list(Sire=ainvpar,Dam=ainvpar), 29 | data=fish,workspace=64e06) 30 | plot(parentalmodel) 31 | summary(parentalmodel)$varcomp 32 | pedparentalmodel<-predict(parentalmodel,classify="Sire", sed=TRUE) 33 | View(pedparentalmodel$predictions$pvals) 34 | 35 | # Genetic Variances 36 | (h2<-nadiv:::pin(parentalmodel,h2~4*V1/(2*V1+V2+V3))) 37 | (d2<-nadiv:::pin(parentalmodel,d2~4*V2/(2*V1+V2+V3))) 38 | 39 | 40 | ######### 41 | # Part 2 - INDIVIDUAL MODEL WITH PEDIGREE 42 | pedind<-read.table("PEDIND.txt",h=T) 43 | ainvind<-asreml.Ainverse(pedind)$ginv 44 | 45 | # Fitting individual model 46 | Indvmodel<-asreml(fixed=DaysM~Sex, 47 | random=~ped(INDIV)+FAM, 48 | ginverse=list(INDIV=ainvind),data=fish) 49 | summary(Indvmodel)$varcomp 50 | plot(Indvmodel) 51 | pedIndvmodel<-predict(Indvmodel,classify="INDIV", sed=TRUE) 52 | View(pedIndvmodel$predictions$pvals) 53 | 54 | # Genetic Variances 55 | (h2<-nadiv:::pin(Indvmodel,h2~V1/(V1+V2+V3))) 56 | (d2<-nadiv:::pin(Indvmodel,d2~4*V2/(V1+V2+V3))) 57 | 58 | # Model No-family 59 | IndvmodelNF<-asreml(fixed=DaysM~Sex, 60 | random=~ped(INDIV), 61 | ginverse=list(INDIV=ainvind),data=fish) 62 | summary(IndvmodelNF)$varcomp 63 | 64 | # Using asremlplus 65 | library(asremlPlus) 66 | reml.lrt.asreml(Indvmodel,IndvmodelNF,positive.zero=TRUE) # LRT with pvalues 67 | info.crit.asreml(Indvmodel) # AIC and BIC - the larger the better 68 | info.crit.asreml(IndvmodelNF) 69 | -------------------------------------------------------------------------------- /Practicals/Fish/PEDIND.txt: -------------------------------------------------------------------------------- 1 | Parent Sire Dam 2 | 501 0 0 3 | 502 0 0 4 | 503 0 0 5 | 504 0 0 6 | 505 0 0 7 | 506 0 0 8 | 507 0 0 9 | 508 0 0 10 | 509 0 0 11 | 510 0 0 12 | 511 0 0 13 | 512 0 0 14 | 513 0 0 15 | 514 0 0 16 | 515 0 0 17 | 516 0 0 18 | 517 1 105 19 | 518 1 105 20 | 519 1 105 21 | 520 2 106 22 | 521 3 107 23 | 522 4 108 24 | 523 5 109 25 | 524 6 110 26 | 525 6 110 27 | 526 7 111 28 | 527 8 112 29 | 528 8 112 30 | 529 8 112 31 | 530 8 112 32 | 531 9 113 33 | 532 9 113 34 | 533 9 113 35 | 534 10 114 36 | 535 10 114 37 | 536 11 115 38 | 537 12 116 39 | 538 13 117 40 | 539 13 117 41 | 540 13 117 42 | 541 13 117 43 | 542 13 117 44 | 543 13 117 45 | 544 13 117 46 | 545 14 118 47 | 546 14 118 48 | 547 15 119 49 | 548 15 119 50 | 549 15 119 51 | 550 16 120 52 | 551 16 120 53 | 552 17 121 54 | 553 17 121 55 | 554 17 121 56 | 555 17 121 57 | 556 18 122 58 | 557 19 123 59 | 558 20 124 60 | 559 21 125 61 | 560 21 125 62 | 561 21 125 63 | 562 21 125 64 | 563 22 126 65 | 564 23 127 66 | 565 23 127 67 | 566 24 128 68 | 567 25 129 69 | 568 25 129 70 | 569 25 129 71 | 570 25 129 72 | 571 26 130 73 | 572 27 131 74 | 573 28 132 75 | 574 29 133 76 | 575 29 133 77 | 576 29 133 78 | 577 29 133 79 | 578 30 134 80 | 579 30 134 81 | 580 31 135 82 | 581 32 136 83 | 582 32 136 84 | 583 33 137 85 | 584 33 137 86 | 585 33 137 87 | 586 33 137 88 | 587 34 138 89 | 588 35 139 90 | 589 36 140 91 | 590 36 140 92 | 591 37 141 93 | 592 38 142 94 | 593 38 142 95 | 594 38 142 96 | 595 38 142 97 | 596 38 142 98 | 597 39 143 99 | 598 40 144 100 | 599 41 145 101 | 600 41 145 102 | 601 41 145 103 | 602 42 146 104 | 603 42 146 105 | 604 42 146 106 | 605 43 147 107 | 606 43 147 108 | 607 43 147 109 | 608 44 148 110 | 609 45 149 111 | 610 46 150 112 | 611 46 150 113 | 612 47 151 114 | 613 47 151 115 | 614 47 151 116 | 615 47 151 117 | 616 48 152 118 | 617 49 153 119 | 618 49 153 120 | 619 49 153 121 | 620 50 154 122 | 621 50 154 123 | 622 51 155 124 | 623 51 155 125 | 624 52 156 126 | 625 52 156 127 | 626 52 156 128 | 627 53 157 129 | 628 53 157 130 | 629 54 158 131 | 630 55 159 132 | 631 55 159 133 | 632 55 159 134 | 633 56 160 135 | 634 56 160 136 | 635 56 160 137 | 636 57 161 138 | 637 58 162 139 | 638 58 162 140 | 639 59 163 141 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1043 | 1803 593 615 1044 | 1804 593 615 1045 | 1805 593 615 1046 | 1806 737 639 1047 | 1807 737 639 1048 | 1808 737 639 1049 | 1809 737 639 1050 | 1810 737 639 1051 | 1811 737 639 1052 | 1812 737 639 1053 | 1813 737 639 1054 | 1814 737 639 1055 | 1815 737 639 1056 | 1816 713 601 1057 | 1817 713 601 1058 | 1818 713 601 1059 | 1819 713 601 1060 | 1820 713 601 1061 | 1821 713 601 1062 | 1822 713 601 1063 | 1823 713 601 1064 | 1824 713 601 1065 | 1825 645 675 1066 | 1826 645 675 1067 | 1827 645 675 1068 | 1828 645 675 1069 | 1829 645 675 1070 | 1830 645 675 1071 | 1831 645 532 1072 | 1832 645 532 1073 | 1833 645 532 1074 | 1834 645 532 1075 | 1835 645 532 1076 | 1836 645 532 1077 | 1837 554 632 1078 | 1838 554 632 1079 | 1839 554 632 1080 | 1840 554 632 1081 | 1841 554 632 1082 | 1842 554 632 1083 | 1843 554 632 1084 | 1844 554 632 1085 | 1845 554 632 1086 | 1846 554 684 1087 | 1847 554 684 1088 | 1848 554 684 1089 | 1849 554 684 1090 | 1850 554 684 1091 | 1851 554 684 1092 | 1852 554 684 1093 | 1853 554 684 1094 | 1854 538 672 1095 | 1855 538 672 1096 | 1856 538 672 1097 | 1857 538 672 1098 | 1858 538 672 1099 | 1859 538 672 1100 | 1860 538 672 1101 | 1861 538 503 1102 | 1862 538 503 1103 | 1863 538 503 1104 | 1864 538 503 1105 | 1865 538 503 1106 | 1866 538 503 1107 | 1867 538 503 1108 | 1868 685 667 1109 | 1869 685 667 1110 | 1870 685 667 1111 | 1871 685 667 1112 | 1872 685 667 1113 | 1873 685 667 1114 | 1874 685 667 1115 | 1875 685 667 1116 | 1876 685 667 1117 | 1877 685 505 1118 | 1878 685 505 1119 | 1879 685 505 1120 | 1880 685 505 1121 | 1881 685 505 1122 | 1882 685 505 1123 | 1883 685 505 1124 | 1884 685 505 1125 | 1885 685 505 1126 | 1886 685 505 1127 | 1887 700 628 1128 | 1888 700 628 1129 | 1889 700 628 1130 | 1890 700 628 1131 | 1891 700 628 1132 | 1892 700 726 1133 | 1893 660 721 1134 | 1894 660 721 1135 | 1895 660 721 1136 | 1896 660 647 1137 | 1897 660 647 1138 | 1898 660 647 1139 | 1899 660 647 1140 | 1900 660 647 1141 | 1901 660 647 1142 | 1902 660 647 1143 | 1903 660 647 1144 | 1904 687 548 1145 | 1905 687 548 1146 | 1906 687 548 1147 | 1907 687 548 1148 | 1908 687 548 1149 | 1909 687 673 1150 | 1910 687 673 1151 | 1911 687 673 1152 | 1912 687 673 1153 | 1913 687 673 1154 | 1914 687 673 1155 | 1915 687 673 1156 | 1916 687 673 1157 | 1917 687 673 1158 | 1918 725 704 1159 | 1919 725 704 1160 | 1920 725 704 1161 | 1921 725 704 1162 | 1922 725 704 1163 | 1923 725 704 1164 | 1924 725 704 1165 | 1925 732 625 1166 | 1926 732 625 1167 | 1927 732 625 1168 | 1928 732 625 1169 | 1929 732 570 1170 | 1930 732 570 1171 | 1931 732 570 1172 | 1932 732 570 1173 | 1933 732 570 1174 | -------------------------------------------------------------------------------- /Practicals/Fish/PEDPAR.txt: -------------------------------------------------------------------------------- 1 | Parent Sire Dam 2 | 501 0 0 3 | 502 0 0 4 | 503 0 0 5 | 504 0 0 6 | 505 0 0 7 | 506 0 0 8 | 507 0 0 9 | 508 0 0 10 | 509 0 0 11 | 510 0 0 12 | 511 0 0 13 | 512 0 0 14 | 513 0 0 15 | 514 0 0 16 | 515 0 0 17 | 516 0 0 18 | 517 1 105 19 | 518 1 105 20 | 519 1 105 21 | 520 2 106 22 | 521 3 107 23 | 522 4 108 24 | 523 5 109 25 | 524 6 110 26 | 525 6 110 27 | 526 7 111 28 | 527 8 112 29 | 528 8 112 30 | 529 8 112 31 | 530 8 112 32 | 531 9 113 33 | 532 9 113 34 | 533 9 113 35 | 534 10 114 36 | 535 10 114 37 | 536 11 115 38 | 537 12 116 39 | 538 13 117 40 | 539 13 117 41 | 540 13 117 42 | 541 13 117 43 | 542 13 117 44 | 543 13 117 45 | 544 13 117 46 | 545 14 118 47 | 546 14 118 48 | 547 15 119 49 | 548 15 119 50 | 549 15 119 51 | 550 16 120 52 | 551 16 120 53 | 552 17 121 54 | 553 17 121 55 | 554 17 121 56 | 555 17 121 57 | 556 18 122 58 | 557 19 123 59 | 558 20 124 60 | 559 21 125 61 | 560 21 125 62 | 561 21 125 63 | 562 21 125 64 | 563 22 126 65 | 564 23 127 66 | 565 23 127 67 | 566 24 128 68 | 567 25 129 69 | 568 25 129 70 | 569 25 129 71 | 570 25 129 72 | 571 26 130 73 | 572 27 131 74 | 573 28 132 75 | 574 29 133 76 | 575 29 133 77 | 576 29 133 78 | 577 29 133 79 | 578 30 134 80 | 579 30 134 81 | 580 31 135 82 | 581 32 136 83 | 582 32 136 84 | 583 33 137 85 | 584 33 137 86 | 585 33 137 87 | 586 33 137 88 | 587 34 138 89 | 588 35 139 90 | 589 36 140 91 | 590 36 140 92 | 591 37 141 93 | 592 38 142 94 | 593 38 142 95 | 594 38 142 96 | 595 38 142 97 | 596 38 142 98 | 597 39 143 99 | 598 40 144 100 | 599 41 145 101 | 600 41 145 102 | 601 41 145 103 | 602 42 146 104 | 603 42 146 105 | 604 42 146 106 | 605 43 147 107 | 606 43 147 108 | 607 43 147 109 | 608 44 148 110 | 609 45 149 111 | 610 46 150 112 | 611 46 150 113 | 612 47 151 114 | 613 47 151 115 | 614 47 151 116 | 615 47 151 117 | 616 48 152 118 | 617 49 153 119 | 618 49 153 120 | 619 49 153 121 | 620 50 154 122 | 621 50 154 123 | 622 51 155 124 | 623 51 155 125 | 624 52 156 126 | 625 52 156 127 | 626 52 156 128 | 627 53 157 129 | 628 53 157 130 | 629 54 158 131 | 630 55 159 132 | 631 55 159 133 | 632 55 159 134 | 633 56 160 135 | 634 56 160 136 | 635 56 160 137 | 636 57 161 138 | 637 58 162 139 | 638 58 162 140 | 639 59 163 141 | 640 59 163 142 | 641 59 163 143 | 642 60 164 144 | 643 61 165 145 | 644 61 165 146 | 645 62 166 147 | 646 63 167 148 | 647 63 167 149 | 648 63 167 150 | 649 63 167 151 | 650 64 168 152 | 651 64 168 153 | 652 64 168 154 | 653 64 168 155 | 654 65 169 156 | 655 66 170 157 | 656 66 170 158 | 657 67 171 159 | 658 68 172 160 | 659 69 173 161 | 660 70 174 162 | 661 70 174 163 | 662 70 174 164 | 663 71 175 165 | 664 71 175 166 | 665 72 176 167 | 666 72 176 168 | 667 73 177 169 | 668 73 177 170 | 669 73 177 171 | 670 74 178 172 | 671 74 178 173 | 672 74 178 174 | 673 75 179 175 | 674 75 179 176 | 675 76 180 177 | 676 76 180 178 | 677 77 181 179 | 678 77 181 180 | 679 77 181 181 | 680 78 182 182 | 681 78 182 183 | 682 78 182 184 | 683 78 182 185 | 684 79 183 186 | 685 80 184 187 | 686 81 185 188 | 687 81 185 189 | 688 82 186 190 | 689 83 187 191 | 690 83 187 192 | 691 83 187 193 | 692 84 188 194 | 693 84 188 195 | 694 85 189 196 | 695 85 189 197 | 696 86 190 198 | 697 86 190 199 | 698 86 190 200 | 699 87 191 201 | 700 87 191 202 | 701 87 191 203 | 702 87 191 204 | 703 87 191 205 | 704 88 192 206 | 705 88 192 207 | 706 88 192 208 | 707 89 193 209 | 708 89 193 210 | 709 89 193 211 | 710 90 194 212 | 711 91 195 213 | 712 91 195 214 | 713 92 196 215 | 714 92 196 216 | 715 92 196 217 | 716 93 197 218 | 717 94 198 219 | 718 95 199 220 | 719 96 200 221 | 720 97 201 222 | 721 97 201 223 | 722 97 201 224 | 723 97 201 225 | 724 98 202 226 | 725 98 202 227 | 726 98 202 228 | 727 99 203 229 | 728 99 203 230 | 729 99 203 231 | 730 99 203 232 | 731 100 204 233 | 732 101 205 234 | 733 101 205 235 | 734 102 206 236 | 735 102 206 237 | 736 103 207 238 | 737 104 208 239 | 738 104 208 240 | 739 104 208 -------------------------------------------------------------------------------- /Practicals/Rubber/RUBBER.txt: -------------------------------------------------------------------------------- 1 | IDSORT Rep female male family ht6 2 | 1 1 F5 M14 F5M14 21 3 | 2 1 F15 M10 F15M10 19 4 | 3 1 F18 M11 F18M11 20.5 5 | 4 1 F22 M11 F22M11 21 6 | 5 1 F10 M15 F10M15 17 7 | 6 1 F17 M6 F17M6 22 8 | 7 1 F13 M9 F13M9 21 9 | 8 1 F13 M9 F13M9 18 10 | 9 1 F18 M11 F18M11 22.5 11 | 10 1 F17 M14 F17M14 20.5 12 | 11 1 F11 M12 F11M12 24 13 | 12 1 F12 M3 F12M3 21 14 | 13 1 F7 M1 F7M1 22 15 | 14 1 F7 M4 F7M4 24 16 | 15 1 F19 M8 F19M8 24 17 | 16 1 F20 M14 F20M14 23.5 18 | 17 2 F9 M7 F9M7 21 19 | 18 2 F20 M14 F20M14 21 20 | 19 2 F5 M14 F5M14 24 21 | 20 2 F20 M14 F20M14 23 22 | 21 2 F17 M6 F17M6 25 23 | 22 2 F5 M14 F5M14 22 24 | 23 2 F7 M1 F7M1 23 25 | 24 2 F15 M10 F15M10 22 26 | 25 2 F5 M14 F5M14 24 27 | 26 2 F9 M7 F9M7 25 28 | 27 2 F3 M13 F3M13 22 29 | 28 2 F7 M4 F7M4 25 30 | 29 2 F11 M12 F11M12 25 31 | 30 2 F6 M5 F6M5 22 32 | 31 2 F12 M3 F12M3 17 33 | 32 3 F5 M14 F5M14 23.5 34 | 33 3 F11 M12 F11M12 23 35 | 34 3 F7 M1 F7M1 25 36 | 35 3 F8 M4 F8M4 23 37 | 36 3 F17 M14 F17M14 22.5 38 | 37 3 F9 M7 F9M7 26 39 | 38 3 F15 M10 F15M10 22 40 | 39 3 F18 M11 F18M11 21 41 | 40 3 F7 M4 F7M4 24 42 | 41 3 F4 M6 F4M6 22.5 43 | 42 3 F19 M8 F19M8 27 44 | 43 3 F21 M15 F21M15 21.5 45 | 44 3 F18 M11 F18M11 25 46 | 45 3 F6 M11 F6M11 26 47 | 46 3 F17 M6 F17M6 24 48 | 47 3 F22 M11 F22M11 24 49 | 48 3 F12 M3 F12M3 22 50 | 49 3 F3 M13 F3M13 23 51 | 50 3 F20 M14 F20M14 22 52 | 51 3 F10 M14 F10M14 24 53 | 52 4 F7 M1 F7M1 27 54 | 53 4 F20 M14 F20M14 23 55 | 54 4 F20 M14 F20M14 26 56 | 55 4 F21 M15 F21M15 20 57 | 56 4 F12 M3 F12M3 26 58 | 57 4 F5 M14 F5M14 25 59 | 58 4 F7 M4 F7M4 25 60 | 59 4 F4 M6 F4M6 22 61 | 60 4 F18 M11 F18M11 23 62 | 61 4 F17 M6 F17M6 26 63 | 62 4 F10 M14 F10M14 25 64 | 63 4 F8 M4 F8M4 22 65 | 64 5 F6 M11 F6M11 24 66 | 65 5 F17 M6 F17M6 22.5 67 | 66 5 F20 M14 F20M14 23 68 | 67 5 F7 M1 F7M1 24 69 | 68 5 F10 M14 F10M14 23 70 | 69 5 F5 M14 F5M14 28 71 | 70 5 F21 M15 F21M15 19 72 | 71 5 F4 M6 F4M6 25 73 | 72 5 F16 M7 F16M7 25 74 | 73 5 F22 M11 F22M11 23 75 | 74 5 F18 M11 F18M11 21 76 | 75 5 F17 M14 F17M14 24 77 | 76 5 F8 M4 F8M4 20 78 | 77 5 F15 M10 F15M10 25 79 | 78 5 F20 M14 F20M14 25 80 | 79 5 F13 M9 F13M9 24 81 | 80 5 F1 M2 F1M2 22 82 | 81 5 F19 M8 F19M8 27 83 | 82 5 F7 M4 F7M4 27 84 | 83 5 F13 M9 F13M9 22 85 | 84 6 F20 M14 F20M14 22 86 | 85 6 F17 M6 F17M6 20 87 | 86 6 F18 M11 F18M11 21 88 | 87 6 F4 M6 F4M6 20 89 | 88 6 F19 M8 F19M8 22 90 | 89 6 F22 M11 F22M11 18 91 | 90 6 F11 M12 F11M12 22 92 | 91 6 F7 M1 F7M1 24 93 | 92 6 F10 M14 F10M14 23 94 | 93 6 F13 M9 F13M9 22 95 | 94 6 F21 M15 F21M15 23 96 | 95 6 F5 M14 F5M14 24 97 | 96 6 F7 M4 F7M4 25 98 | 97 6 F12 M3 F12M3 23 99 | 98 6 F17 M14 F17M14 23 100 | 99 6 F15 M10 F15M10 25 101 | 100 6 F6 M11 F6M11 20 102 | 101 6 F20 M14 F20M14 23.5 103 | 102 7 F13 M9 F13M9 21 104 | 103 7 F5 M14 F5M14 22 105 | 104 7 F7 M4 F7M4 21 106 | 105 7 F18 M11 F18M11 24 107 | 106 7 F8 M4 F8M4 23 108 | 107 7 F21 M15 F21M15 24 109 | 108 7 F7 M1 F7M1 25 110 | 109 7 F10 M14 F10M14 24 111 | 110 7 F15 M10 F15M10 20 112 | 111 7 F11 M12 F11M12 20 113 | 112 7 F13 M9 F13M9 23 114 | 113 7 F18 M11 F18M11 22 115 | 114 7 F5 M14 F5M14 20 116 | 115 7 F4 M6 F4M6 24 117 | 116 8 F10 M14 F10M14 23 118 | 117 8 F5 M14 F5M14 25 119 | 118 8 F17 M6 F17M6 22 120 | 119 8 F9 M7 F9M7 29 121 | 120 8 F13 M9 F13M9 23 122 | 121 8 F4 M6 F4M6 24 123 | 122 8 F15 M10 F15M10 25 124 | 123 8 F16 M7 F16M7 24 125 | 124 8 F8 M4 F8M4 27 126 | 125 8 F22 M11 F22M11 23 127 | 126 8 F21 M15 F21M15 25 128 | 127 8 F12 M3 F12M3 24 129 | 128 8 F7 M4 F7M4 23 130 | 129 8 F4 M6 F4M6 24 131 | 130 8 F11 M12 F11M12 25 132 | 131 8 F18 M11 F18M11 21 133 | 132 8 F6 M11 F6M11 23 134 | 133 8 F7 M1 F7M1 23 135 | 134 8 F20 M14 F20M14 25 136 | 135 8 F18 M11 F18M11 22 137 | 136 9 F7 M4 F7M4 26 138 | 137 9 F22 M11 F22M11 24 139 | 138 9 F10 M15 F10M15 26 140 | 139 9 F20 M14 F20M14 25 141 | 140 9 F9 M7 F9M7 23 142 | 141 9 F13 M9 F13M9 22 143 | 142 9 F11 M12 F11M12 25 144 | 143 9 F10 M14 F10M14 22 145 | 144 9 F18 M11 F18M11 19 146 | 145 9 F5 M14 F5M14 25 147 | 146 9 F17 M6 F17M6 22 148 | 147 9 F4 M6 F4M6 27 149 | 148 9 F15 M10 F15M10 23 150 | 149 9 F12 M3 F12M3 26 151 | 150 9 F21 M15 F21M15 20 152 | 151 9 F7 M1 F7M1 26 153 | 152 9 F8 M4 F8M4 25 154 | 153 9 F14 M9 F14M9 23 155 | 154 9 F18 M11 F18M11 23 156 | 155 9 F17 M14 F17M14 26 157 | 156 10 F18 M11 F18M11 26 158 | 157 10 F5 M14 F5M14 26 159 | 158 10 F19 M8 F19M8 27 160 | 159 10 F20 M14 F20M14 24 161 | 160 10 F5 M14 F5M14 26 162 | 161 10 F9 M7 F9M7 25 163 | 162 10 F17 M6 F17M6 24 164 | 163 10 F15 M10 F15M10 23 165 | 164 10 F7 M1 F7M1 25 166 | 165 10 F21 M15 F21M15 23 167 | 166 10 F10 M15 F10M15 25 168 | 167 10 F4 M6 F4M6 27 169 | 168 10 F10 M14 F10M14 23 170 | 169 10 F7 M4 F7M4 24 171 | 170 10 F10 M14 F10M14 27 172 | 171 10 F6 M11 F6M11 24 173 | 172 10 F13 M9 F13M9 23 174 | 173 10 F5 M14 F5M14 23 175 | 174 10 F20 M14 F20M14 24 176 | 175 11 F20 M14 F20M14 23 177 | 176 11 F5 M14 F5M14 23 178 | 177 11 F20 M14 F20M14 22 179 | 178 11 F17 M14 F17M14 22 180 | 179 11 F5 M14 F5M14 23 181 | 180 11 F19 M8 F19M8 26 182 | 181 11 F11 M12 F11M12 23 183 | 182 11 F7 M1 F7M1 21 184 | 183 11 F8 M4 F8M4 25 185 | 184 11 F7 M4 F7M4 23 186 | 185 11 F4 M6 F4M6 20 187 | 186 11 F10 M15 F10M15 25 188 | 187 11 F15 M10 F15M10 27 189 | 188 11 F6 M11 F6M11 22 190 | 189 11 F13 M9 F13M9 23 191 | 190 11 F1 M2 F1M2 22 192 | 191 11 F18 M11 F18M11 21 193 | 192 11 F17 M6 F17M6 21 194 | 193 11 F10 M14 F10M14 26 195 | 194 11 F21 M15 F21M15 23 196 | 195 11 F22 M11 F22M11 24 197 | 196 12 F5 M14 F5M14 25 198 | 197 12 F10 M15 F10M15 25 199 | 198 12 F10 M14 F10M14 23 200 | 199 12 F18 M11 F18M11 21 201 | 200 12 F17 M14 F17M14 24 202 | 201 12 F12 M3 F12M3 22 203 | 202 12 F20 M14 F20M14 23 204 | 203 12 F19 M8 F19M8 24 205 | 204 12 F6 M11 F6M11 20 206 | 205 12 F11 M12 F11M12 24 207 | 206 12 F18 M11 F18M11 23 208 | 207 12 F7 M1 F7M1 24 209 | 208 12 F17 M6 F17M6 21 210 | 209 12 F10 M14 F10M14 25 211 | 210 12 F7 M4 F7M4 24 212 | 211 12 F21 M15 F21M15 25 213 | 212 12 F13 M9 F13M9 25 214 | 213 12 F16 M7 F16M7 23 215 | 214 12 F22 M11 F22M11 18 216 | 215 13 F12 M3 F12M3 26 217 | 216 13 F17 M6 F17M6 21 218 | 217 13 F18 M11 F18M11 22 219 | 218 13 F20 M14 F20M14 22 220 | 219 13 F22 M11 F22M11 23 221 | 220 13 F6 M11 F6M11 25 222 | 221 13 F8 M4 F8M4 23 223 | 222 13 F7 M1 F7M1 24 224 | 223 13 F4 M6 F4M6 23 225 | 224 13 F13 M9 F13M9 24 226 | 225 13 F18 M11 F18M11 20 227 | 226 13 F15 M10 F15M10 23 228 | 227 13 F7 M4 F7M4 25 229 | 228 13 F10 M14 F10M14 24 230 | 229 13 F17 M14 F17M14 23 231 | 230 13 F10 M15 F10M15 23 232 | 231 13 F5 M14 F5M14 23 233 | 232 14 F18 M11 F18M11 21 234 | 233 14 F17 M6 F17M6 21 235 | 234 14 F13 M9 F13M9 24 236 | 235 14 F10 M14 F10M14 27 237 | 236 14 F3 M13 F3M13 24 238 | 237 14 F7 M1 F7M1 22 239 | 238 14 F8 M4 F8M4 25 240 | 239 14 F17 M14 F17M14 25 241 | 240 14 F20 M14 F20M14 21 242 | 241 14 F21 M15 F21M15 23 243 | 242 14 F4 M6 F4M6 24 244 | 243 14 F22 M11 F22M11 23 245 | 244 14 F6 M11 F6M11 23 246 | 245 15 F22 M11 F22M11 22 247 | 246 15 F4 M6 F4M6 21 248 | 247 15 F18 M11 F18M11 20 249 | 248 15 F6 M5 F6M5 20 250 | 249 15 F7 M1 F7M1 24 251 | 250 15 F12 M3 F12M3 23 252 | 251 15 F13 M9 F13M9 17 253 | 252 15 F14 M9 F14M9 20 254 | 253 15 F13 M9 F13M9 21 255 | 254 15 F21 M15 F21M15 26 256 | 255 15 F10 M14 F10M14 24 257 | 256 15 F9 M7 F9M7 22 258 | 257 15 F7 M4 F7M4 22 259 | 258 16 F7 M1 F7M1 20 260 | 259 16 F18 M11 F18M11 22 261 | 260 16 F17 M6 F17M6 19 262 | 261 16 F10 M14 F10M14 21 263 | 262 16 F4 M6 F4M6 25 264 | 263 16 F22 M11 F22M11 19 265 | 264 16 F7 M1 F7M1 20 266 | 265 16 F3 M13 F3M13 19 267 | 266 16 F13 M9 F13M9 22 268 | 267 16 F21 M15 F21M15 23 269 | 268 16 F8 M4 F8M4 21 270 | 269 16 F6 M11 F6M11 21 271 | 270 16 F20 M14 F20M14 23 272 | 271 17 F13 M9 F13M9 22 273 | 272 17 F9 M7 F9M7 18 274 | 273 17 F7 M1 F7M1 21 275 | 274 17 F15 M10 F15M10 22 276 | 275 17 F5 M14 F5M14 23 277 | 276 17 F11 M12 F11M12 23 278 | 277 17 F22 M11 F22M11 20 279 | 278 17 F10 M14 F10M14 23 280 | 279 17 F8 M4 F8M4 22 281 | 280 17 F7 M4 F7M4 22 282 | 281 17 F6 M11 F6M11 20 283 | 282 17 F17 M14 F17M14 21 284 | 283 17 F18 M11 F18M11 21 285 | 284 17 F18 M11 F18M11 21 286 | 285 17 F3 M13 F3M13 22 287 | 286 17 F2 M14 F2M14 21 288 | 287 17 F20 M14 F20M14 24 289 | 288 17 F19 M8 F19M8 21 290 | 289 17 F17 M6 F17M6 24 291 | 290 17 F9 M7 F9M7 25 292 | 291 18 F4 M6 F4M6 26 293 | 292 18 F15 M10 F15M10 20 294 | 293 18 F6 M11 F6M11 21 295 | 294 18 F22 M11 F22M11 22 296 | 295 18 F11 M12 F11M12 21 297 | 296 18 F10 M14 F10M14 25 298 | 297 18 F5 M14 F5M14 22 299 | 298 18 F18 M11 F18M11 22 300 | 299 18 F3 M13 F3M13 22 301 | 300 18 F17 M6 F17M6 24 302 | 301 18 F18 M11 F18M11 24 303 | 302 18 F21 M15 F21M15 25 304 | 303 18 F9 M7 F9M7 25 305 | 304 18 F10 M14 F10M14 23 306 | 305 18 F7 M4 F7M4 23 307 | 306 18 F7 M1 F7M1 22 308 | 307 18 F12 M3 F12M3 24 309 | 308 18 F20 M14 F20M14 24 310 | 309 18 F2 M14 F2M14 23 311 | 310 19 F21 M15 F21M15 25 312 | 311 19 F6 M11 F6M11 20 313 | 312 19 F7 M1 F7M1 22 314 | 313 19 F17 M6 F17M6 22 315 | 314 19 F5 M14 F5M14 23 316 | 315 19 F7 M1 F7M1 18 317 | 316 19 F18 M11 F18M11 20 318 | 317 19 F10 M14 F10M14 24 319 | 318 19 F7 M4 F7M4 22 320 | 319 19 F22 M11 F22M11 22 321 | 320 19 F15 M10 F15M10 23 322 | 321 19 F5 M14 F5M14 26 323 | 322 19 F4 M6 F4M6 25 324 | 323 19 F11 M12 F11M12 26 325 | 324 19 F9 M7 F9M7 25 326 | 325 19 F13 M9 F13M9 23 327 | 326 19 F20 M14 F20M14 22 328 | 327 19 F10 M14 F10M14 25 329 | 328 20 F4 M6 F4M6 27 330 | 329 20 F6 M11 F6M11 23 331 | 330 20 F3 M13 F3M13 23 332 | 331 20 F20 M14 F20M14 23 333 | 332 20 F21 M15 F21M15 25 334 | 333 20 F17 M6 F17M6 23 335 | 334 20 F7 M4 F7M4 24 336 | 335 20 F18 M11 F18M11 24 337 | 336 20 F22 M11 F22M11 22 338 | 337 20 F10 M14 F10M14 22 339 | 338 20 F5 M14 F5M14 20 340 | 339 20 F7 M1 F7M1 21 341 | 340 20 F8 M4 F8M4 23 342 | 341 20 F9 M7 F9M7 25 343 | -------------------------------------------------------------------------------- /Practicals/Willow/WILLOW.txt: -------------------------------------------------------------------------------- 1 | INDIV Male Female FAM Pop WT1 WT2 WT3 2 | 1 13 10013 13-10013 1 16.0 15.4 15.9 3 | 2 52 10052 52-10052 1 14.9 15.0 15.7 4 | 3 34 10034 34-10034 1 15.0 14.8 16.0 5 | 4 3 1003 3-1003 1 15.0 15.2 16.4 6 | 5 56 10056 56-10056 1 15.9 15.3 16.6 7 | 6 35 10035 35-10035 1 16.1 16.3 17.1 8 | 7 8 1008 8-1008 1 15.2 15.2 16.2 9 | 8 10 10010 10-10010 1 13.5 13.5 14.1 10 | 9 11 10011 11-10011 1 14.5 14.2 15.6 11 | 10 61 10061 61-10061 1 20.0 19.1 21.2 12 | 11 17 10017 17-10017 1 15.2 15.0 16.0 13 | 12 232 10232 232-10232 1 15.7 16.0 17.3 14 | 13 57 10057 57-10057 1 15.7 15.7 16.1 15 | 14 5 1005 5-1005 1 16.0 16.3 16.6 16 | 15 14 10014 14-10014 1 15.0 16.0 16.5 17 | 16 51 10051 51-10051 1 15.0 14.0 15.6 18 | 17 30 10030 30-10030 1 16.0 15.7 16.1 19 | 18 11 10011 11-10011 1 14.0 14.0 14.4 20 | 19 30 10030 30-10030 1 14.5 14.6 14.8 21 | 20 11 10011 11-10011 1 14.5 15.2 16.2 22 | 21 11 10011 11-10011 1 15.5 15.7 16.3 23 | 22 11 10011 11-10011 1 14.4 14.5 14.7 24 | 23 11 10011 11-10011 1 15.5 13.9 14.8 25 | 24 17 10017 17-10017 1 16.2 16.5 17.8 26 | 25 13 10013 13-10013 1 17.4 17.0 17.7 27 | 26 11 10011 11-10011 1 15.0 13.6 14.3 28 | 27 3 1003 3-1003 1 17.0 16.6 17.2 29 | 28 11 10011 11-10011 1 14.0 14.2 14.2 30 | 29 50 10050 50-10050 1 17.5 17.3 17.5 31 | 30 8 1008 8-1008 1 12.9 14.0 13.7 32 | 31 14 10014 14-10014 1 14.5 14.7 15.7 33 | 32 59 10059 59-10059 1 17.0 17.2 18.2 34 | 33 34 10034 34-10034 1 14.9 15.5 16.0 35 | 34 51 10051 51-10051 1 16.6 15.3 17.2 36 | 35 11 10011 11-10011 1 13.0 12.8 13.5 37 | 36 30 10030 30-10030 1 15.2 16.5 17.3 38 | 37 3 1003 3-1003 1 15.2 15.7 15.9 39 | 38 46 10046 46-10046 1 17.0 15.9 17.6 40 | 39 10 10010 10-10010 1 15.0 18.2 18.0 41 | 40 26 10026 26-10026 1 12.5 15.7 14.5 42 | 41 57 10057 57-10057 1 13.5 15.8 15.6 43 | 42 56 10056 56-10056 1 16.8 17.5 18.0 44 | 43 59 10059 59-10059 1 16.4 18.1 17.9 45 | 44 26 10026 26-10026 1 16.7 17.0 17.4 46 | 45 21 10021 21-10021 1 15.0 17.2 16.3 47 | 46 46 10046 46-10046 1 17.0 17.8 18.7 48 | 47 35 10035 35-10035 1 15.2 16.4 16.7 49 | 48 5 1005 5-1005 1 16.4 16.6 17.1 50 | 49 35 10035 35-10035 1 13.9 16.5 16.7 51 | 50 39 10039 39-10039 1 16.2 16.9 18.1 52 | 51 46 10046 46-10046 1 18.0 19.0 19.4 53 | 52 8 1008 8-1008 1 15.7 15.4 16.8 54 | 53 36 10036 36-10036 1 15.2 15.2 16.7 55 | 54 14 10014 14-10014 1 16.6 16.2 17.0 56 | 55 11 10011 11-10011 1 13.7 14.2 14.7 57 | 56 8 1008 8-1008 1 18.2 18.0 18.6 58 | 57 14 10014 14-10014 1 17.1 17.0 18.0 59 | 58 21 10021 21-10021 1 13.4 17.0 15.4 60 | 59 61 10061 61-10061 1 15.2 17.8 17.2 61 | 60 4 1004 4-1004 1 16.2 16.3 17.5 62 | 61 8 1008 8-1008 1 15.5 16.0 15.8 63 | 62 8 1008 8-1008 1 15.0 15.3 16.0 64 | 63 232 10232 232-10232 1 15.5 15.5 15.7 65 | 64 30 10030 30-10030 1 14.0 14.3 14.8 66 | 65 21 10021 21-10021 1 15.5 15.6 17.1 67 | 66 21 10021 21-10021 1 15.5 16.2 17.1 68 | 67 11 10011 11-10011 1 14.1 15.0 14.7 69 | 68 8 1008 8-1008 1 15.0 14.8 15.9 70 | 69 13 10013 13-10013 1 15.5 15.5 16.9 71 | 70 59 10059 59-10059 1 16.5 16.4 16.7 72 | 71 4 1004 4-1004 1 12.5 15.1 13.9 73 | 72 46 10046 46-10046 2 15.0 15.3 15.3 74 | 73 39 10039 39-10039 2 14.7 15.8 16.8 75 | 74 14 10014 14-10014 2 17.2 19.0 19.4 76 | 75 13 10013 13-10013 2 14.5 16.7 15.6 77 | 76 8 1008 8-1008 2 15.4 17.7 18.0 78 | 77 26 10026 26-10026 2 15.7 15.3 16.2 79 | 78 57 10057 57-10057 2 17.2 17.4 18.6 80 | 79 35 10035 35-10035 2 15.5 16.9 17.5 81 | 80 333 10333 333-10333 2 16.5 17.6 18.3 82 | 81 13 10013 13-10013 2 14.5 15.5 15.1 83 | 82 444 10444 444-10444 2 15.0 16.2 15.8 84 | 83 46 10046 46-10046 2 15.8 14.6 16.0 85 | 84 50 10050 50-10050 2 17.7 18.0 19.4 86 | 85 10 10010 10-10010 2 14.0 18.3 17.4 87 | 86 13 10013 13-10013 2 14.2 14.3 14.5 88 | 87 5 1005 5-1005 2 17.0 14.6 16.7 89 | 88 61 10061 61-10061 2 17.8 18.4 19.2 90 | 89 26 10026 26-10026 2 15.0 16.0 15.8 91 | 90 52 10052 52-10052 2 15.0 15.5 15.4 92 | 91 13 10013 13-10013 2 16.8 17.2 17.2 93 | 92 4 1004 4-1004 2 17.5 19.0 18.5 94 | 93 59 10059 59-10059 2 16.0 15.8 16.8 95 | 94 36 10036 36-10036 2 17.0 17.9 18.2 96 | 95 21 10021 21-10021 2 16.2 15.6 16.7 97 | 96 5 1005 5-1005 2 14.0 13.9 15.2 98 | 97 12 10012 12-10012 2 14.4 14.5 14.5 99 | 98 21 10021 21-10021 2 16.2 16.4 17.8 100 | 99 4 1004 4-1004 2 13.0 13.4 14.3 101 | 100 39 10039 39-10039 2 15.2 15.8 16.8 102 | 101 11 10011 11-10011 2 15.0 14.8 16.1 103 | 102 21 10021 21-10021 2 15.0 15.3 16.1 104 | 103 333 10333 333-10333 2 14.9 15.2 15.4 105 | 104 12 10012 12-10012 2 14.5 16.2 16.0 106 | 105 444 10444 444-10444 2 15.0 15.4 16.1 107 | 106 3 1003 3-1003 2 15.2 16.8 17.6 108 | 107 61 10061 61-10061 2 17.2 15.4 16.7 109 | 108 21 10021 21-10021 2 13.2 13.5 13.4 110 | 109 21 10021 21-10021 2 13.2 13.5 14.4 111 | 110 30 10030 30-10030 2 14.0 15.8 15.3 112 | 111 26 10026 26-10026 2 14.2 17.7 16.0 113 | 112 34 10034 34-10034 2 16.7 16.8 18.3 114 | 113 5 1005 5-1005 2 15.0 16.0 16.2 115 | 114 11 10011 11-10011 2 16.0 16.0 16.7 116 | 115 61 10061 61-10061 2 16.2 14.0 16.0 117 | 116 11 10011 11-10011 2 14.0 16.2 15.7 118 | 117 8 1008 8-1008 2 15.9 16.8 17.2 119 | 118 34 10034 34-10034 2 15.2 15.3 16.2 120 | 119 21 10021 21-10021 2 16.0 16.0 16.4 121 | 120 46 10046 46-10046 3 15.0 15.8 16.9 122 | 121 12 10012 12-10012 3 16.2 17.5 17.6 123 | 122 59 10059 59-10059 3 14.2 15.8 15.8 124 | 123 21 10021 21-10021 3 15.5 17.0 17.4 125 | 124 34 10034 34-10034 3 15.5 17.0 17.1 126 | 125 40 10040 40-10040 3 13.2 17.6 16.3 127 | 126 61 10061 61-10061 3 16.5 19.0 18.3 128 | 127 59 10059 59-10059 3 16.5 18.3 18.9 129 | 128 232 10232 232-10232 3 16.0 17.5 16.9 130 | 129 59 10059 59-10059 3 15.0 16.5 15.9 131 | 130 61 10061 61-10061 3 16.2 15.0 17.2 132 | 131 232 10232 232-10232 3 17.4 17.0 18.3 133 | 132 14 10014 14-10014 3 15.2 18.0 16.6 134 | 133 59 10059 59-10059 3 18.0 19.0 20.1 135 | 134 39 10039 39-10039 3 15.5 15.9 16.8 136 | 135 21 10021 21-10021 3 15.0 16.3 16.0 137 | 136 56 10056 56-10056 3 17.5 20.0 19.4 138 | 137 3 1003 3-1003 3 14.0 17.4 16.0 139 | 138 12 10012 12-10012 3 14.0 14.7 15.7 140 | 139 39 10039 39-10039 3 16.0 16.1 17.3 141 | 140 10 10010 10-10010 3 15.2 16.2 16.3 142 | 141 50 10050 50-10050 3 16.7 18.2 17.6 143 | 142 232 10232 232-10232 3 16.6 16.4 18.0 144 | 143 59 10059 59-10059 3 16.4 16.5 17.9 145 | 144 39 10039 39-10039 3 15.9 16.6 17.7 146 | 145 3 1003 3-1003 3 14.0 15.0 15.0 147 | 146 34 10034 34-10034 3 15.2 16.6 16.6 148 | 147 56 10056 56-10056 3 17.4 19.4 19.7 149 | 148 17 10017 17-10017 3 16.2 18.0 18.4 150 | 149 30 10030 30-10030 3 15.8 17.3 18.1 151 | 150 17 10017 17-10017 3 16.0 18.1 18.6 152 | 151 555 10555 555-10555 3 16.0 16.0 16.8 153 | 152 232 10232 232-10232 3 14.0 15.6 15.4 154 | 153 46 10046 46-10046 3 14.5 15.9 16.3 155 | 154 8 1008 8-1008 3 15.5 18.2 18.2 156 | 155 59 10059 59-10059 3 16.4 16.0 17.2 157 | 156 14 10014 14-10014 3 14.7 14.9 15.5 158 | 157 46 10046 46-10046 3 15.0 15.8 16.3 159 | 158 26 10026 26-10026 3 14.5 14.7 15.2 160 | 159 34 10034 34-10034 3 13.7 13.7 13.8 161 | 160 59 10059 59-10059 3 14.5 14.1 15.0 162 | 161 8 1008 8-1008 3 17.0 16.9 17.7 163 | 162 12 10012 12-10012 3 16.2 16.0 17.5 164 | 163 8 1008 8-1008 3 17.0 17.0 17.2 165 | 164 46 10046 46-10046 3 16.0 17.0 18.1 166 | 165 39 10039 39-10039 3 15.2 15.3 15.3 167 | 166 30 10030 30-10030 3 14.8 15.0 15.1 168 | 167 52 10052 52-10052 3 17.0 17.7 17.7 169 | 168 11 10011 11-10011 3 18.0 13.4 16.3 170 | 169 30 10030 30-10030 3 16.0 14.1 15.3 171 | 170 4 1004 4-1004 3 16.0 16.0 16.7 172 | 171 40 10040 40-10040 3 15.2 18.4 17.9 173 | 172 14 10014 14-10014 3 18.5 18.5 18.8 174 | 173 56 10056 56-10056 3 17.2 17.5 18.4 175 | 174 50 10050 50-10050 3 12.9 19.3 17.0 176 | 175 10 10010 10-10010 3 15.8 17.0 17.5 177 | 176 57 10057 57-10057 3 14.9 16.2 15.9 178 | 177 57 10057 57-10057 3 14.2 14.9 14.6 179 | 178 21 10021 21-10021 3 14.7 14.6 15.7 180 | 179 14 10014 14-10014 3 16.4 17.9 18.7 181 | 180 4 1004 4-1004 3 15.7 16.2 17.3 182 | 181 333 10333 333-10333 3 15.5 16.2 16.1 183 | 182 3 1003 3-1003 3 14.5 16.1 16.6 184 | 183 10 10010 10-10010 3 18.5 17.4 18.8 185 | 184 232 10232 232-10232 3 16.2 17.9 17.1 186 | 185 57 10057 57-10057 3 14.0 15.0 14.9 187 | 186 11 10011 11-10011 3 14.0 14.5 14.4 188 | 187 4 1004 4-1004 4 13.5 15.0 15.4 189 | 188 444 10444 444-10444 4 14.5 14.6 15.0 190 | 189 51 10051 51-10051 4 15.9 16.2 16.9 191 | 190 8 1008 8-1008 4 16.0 16.0 17.2 192 | 191 26 10026 26-10026 4 14.8 15.0 15.1 193 | 192 14 10014 14-10014 4 16.5 17.0 17.9 194 | 193 11 10011 11-10011 4 16.5 16.7 16.6 195 | 194 56 10056 56-10056 4 15.2 16.0 15.9 196 | 195 52 10052 52-10052 4 16.0 18.0 18.6 197 | 196 12 10012 12-10012 4 15.0 17.0 16.5 198 | 197 8 1008 8-1008 4 15.5 18.0 18.2 199 | 198 5 1005 5-1005 4 15.5 18.0 17.0 200 | 199 36 10036 36-10036 4 15.5 18.3 17.9 201 | 200 35 10035 35-10035 4 14.5 15.7 16.5 202 | 201 52 10052 52-10052 4 16.0 18.1 18.5 203 | 202 51 10051 51-10051 4 15.2 18.8 17.6 204 | 203 59 10059 59-10059 4 15.0 16.4 16.9 205 | 204 36 10036 36-10036 4 14.1 18.2 17.5 206 | 205 4 1004 4-1004 4 14.5 15.7 16.4 207 | 206 11 10011 11-10011 4 13.8 15.4 15.7 208 | 207 35 10035 35-10035 4 16.2 16.3 17.0 209 | 208 21 10021 21-10021 4 18.5 19.7 20.2 210 | 209 12 10012 12-10012 4 17.4 20.8 19.8 211 | 210 51 10051 51-10051 4 14.5 16.4 15.5 212 | 211 232 10232 232-10232 4 15.2 17.0 16.6 213 | 212 10 10010 10-10010 4 14.2 14.9 15.5 214 | 213 8 1008 8-1008 4 16.9 18.5 18.3 215 | 214 8 1008 8-1008 4 17.0 18.2 18.4 216 | 215 51 10051 51-10051 4 15.5 15.4 15.7 217 | 216 3 1003 3-1003 4 14.5 17.2 16.5 218 | 217 46 10046 46-10046 4 17.7 15.8 16.9 219 | 218 35 10035 35-10035 4 15.2 17.0 16.5 220 | 219 39 10039 39-10039 4 14.5 16.0 16.5 221 | 220 10 10010 10-10010 4 15.2 16.0 16.0 222 | 221 59 10059 59-10059 4 14.5 15.4 15.2 223 | 222 13 10013 13-10013 4 16.2 17.2 18.3 224 | 223 5 1005 5-1005 4 16.0 17.6 17.5 225 | 224 12 10012 12-10012 4 15.5 17.3 17.5 226 | 225 61 10061 61-10061 4 20.0 19.1 20.3 227 | 226 40 10040 40-10040 4 16.5 18.3 18.5 228 | 227 8 1008 8-1008 4 17.2 18.6 18.5 229 | 228 46 10046 46-10046 4 16.2 17.6 17.4 230 | 229 34 10034 34-10034 4 14.0 14.7 14.7 231 | 230 232 10232 232-10232 4 14.0 15.0 15.5 232 | 231 12 10012 12-10012 4 14.0 14.0 15.2 233 | 232 61 10061 61-10061 4 17.2 18.5 18.0 234 | 233 12 10012 12-10012 4 15.6 16.4 16.5 235 | 234 51 10051 51-10051 4 14.5 15.6 15.7 236 | 235 40 10040 40-10040 4 15.0 17.0 17.0 237 | 236 51 10051 51-10051 4 13.9 15.9 15.7 238 | 237 50 10050 50-10050 4 15.1 17.6 17.5 239 | 238 52 10052 52-10052 4 17.4 18.0 19.3 240 | 239 10 10010 10-10010 4 14.5 16.0 16.6 241 | 240 5 1005 5-1005 4 16.7 17.0 17.8 242 | 241 40 10040 40-10040 4 17.2 19.8 18.5 243 | 242 3 1003 3-1003 4 14.5 17.0 15.9 244 | 243 10 10010 10-10010 4 14.9 16.9 16.3 245 | 244 40 10040 40-10040 4 16.5 18.4 17.7 246 | 245 46 10046 46-10046 4 14.5 15.0 16.0 247 | 246 3 1003 3-1003 4 15.0 16.0 16.1 248 | 247 46 10046 46-10046 4 16.0 17.5 17.7 249 | 248 35 10035 35-10035 4 16.0 16.8 17.3 250 | 249 39 10039 39-10039 4 16.9 15.7 17.6 251 | 250 57 10057 57-10057 4 16.0 14.2 16.6 252 | 251 12 10012 12-10012 4 15.2 16.1 16.7 253 | 252 232 10232 232-10232 4 15.0 17.5 17.8 254 | 253 11 10011 11-10011 4 14.2 15.8 16.5 255 | 254 232 10232 232-10232 4 17.2 18.2 18.3 256 | 255 232 10232 232-10232 4 16.2 16.3 17.6 257 | 256 26 10026 26-10026 4 12.5 15.1 15.1 258 | 257 61 10061 61-10061 4 14.0 18.6 17.0 259 | 258 5 1005 5-1005 4 15.6 17.2 18.0 260 | 259 35 10035 35-10035 4 15.0 18.2 17.6 261 | 260 444 10444 444-10444 4 14.2 15.2 14.9 262 | 261 36 10036 36-10036 4 15.0 17.2 16.3 263 | 262 39 10039 39-10039 4 14.0 14.2 15.4 264 | 263 26 10026 26-10026 4 14.8 16.2 16.6 265 | 264 232 10232 232-10232 4 16.0 15.1 16.9 266 | 265 13 10013 13-10013 4 15.0 16.3 17.1 267 | 266 50 10050 50-10050 4 15.8 15.8 16.5 268 | 267 57 10057 57-10057 4 14.0 15.5 15.1 269 | 268 10 10010 10-10010 4 13.5 15.1 15.0 270 | 269 35 10035 35-10035 4 19.2 20.2 21.0 271 | 270 61 10061 61-10061 4 17.8 19.4 19.1 272 | 271 10 10010 10-10010 4 15.0 17.0 17.6 273 | 272 17 10017 17-10017 4 16.0 17.1 17.7 274 | 273 17 10017 17-10017 4 14.2 16.3 16.5 275 | 274 56 10056 56-10056 4 14.9 15.4 15.8 276 | 275 40 10040 40-10040 5 16.5 19.0 18.9 277 | 276 14 10014 14-10014 5 17.2 18.5 18.9 278 | 277 46 10046 46-10046 5 16.2 21.0 18.9 279 | 278 10 10010 10-10010 5 16.0 16.0 17.5 280 | 279 56 10056 56-10056 5 15.4 16.5 17.3 281 | 280 56 10056 56-10056 5 16.5 17.5 18.4 282 | 281 10 10010 10-10010 5 15.0 16.0 16.0 283 | 282 35 10035 35-10035 5 15.0 16.0 16.2 284 | 283 5 1005 5-1005 5 15.2 16.5 16.3 285 | 284 26 10026 26-10026 5 15.6 16.9 16.7 286 | 285 232 10232 232-10232 5 16.0 17.0 16.7 287 | 286 232 10232 232-10232 5 16.0 17.3 18.2 288 | 287 3 1003 3-1003 5 15.2 15.7 16.3 289 | 288 59 10059 59-10059 5 15.2 16.7 17.0 290 | 289 5 1005 5-1005 5 16.2 17.5 18.3 291 | 290 46 10046 46-10046 5 17.0 19.5 20.0 292 | 291 3 1003 3-1003 5 16.7 18.1 19.0 293 | 292 5 1005 5-1005 5 15.4 16.7 17.4 294 | 293 14 10014 14-10014 5 15.5 16.0 16.9 295 | 294 444 10444 444-10444 5 16.5 18.2 18.6 296 | 295 61 10061 61-10061 5 17.5 20.0 19.9 297 | 296 56 10056 56-10056 5 16.2 17.0 17.7 298 | 297 8 1008 8-1008 5 15.4 17.0 17.6 299 | 298 30 10030 30-10030 5 15.5 16.0 17.1 300 | 299 17 10017 17-10017 5 15.2 16.5 16.9 301 | 300 232 10232 232-10232 5 15.2 15.5 16.5 302 | 301 17 10017 17-10017 5 15.2 17.0 16.6 303 | 302 36 10036 36-10036 5 16.0 18.0 17.7 304 | 303 39 10039 39-10039 5 15.2 16.0 16.3 305 | 304 35 10035 35-10035 5 17.9 18.3 18.9 306 | 305 34 10034 34-10034 5 14.0 15.2 15.3 307 | 306 3 1003 3-1003 5 15.4 15.5 16.9 308 | 307 333 10333 333-10333 5 15.2 16.5 17.0 309 | 308 5 1005 5-1005 5 15.3 16.8 16.3 310 | 309 56 10056 56-10056 5 16.2 17.3 18.0 311 | 310 232 10232 232-10232 5 13.5 16.0 14.8 312 | 311 46 10046 46-10046 5 17.2 19.2 18.7 313 | 312 12 10012 12-10012 5 15.0 17.0 16.4 314 | 313 59 10059 59-10059 5 14.2 17.6 17.4 315 | 314 13 10013 13-10013 5 16.4 17.6 17.9 316 | 315 46 10046 46-10046 5 15.2 16.3 16.2 317 | 316 46 10046 46-10046 5 17.4 17.6 17.8 318 | 317 52 10052 52-10052 5 18.0 18.4 19.0 319 | 318 56 10056 56-10056 5 15.8 16.0 15.9 320 | 319 34 10034 34-10034 5 18.5 18.5 18.7 321 | 320 26 10026 26-10026 5 14.2 16.0 15.7 322 | 321 232 10232 232-10232 5 16.2 17.2 17.4 323 | 322 34 10034 34-10034 5 16.2 18.0 18.4 324 | 323 35 10035 35-10035 5 16.0 17.2 16.7 325 | 324 444 10444 444-10444 5 14.4 16.2 15.7 326 | 325 21 10021 21-10021 5 14.5 16.0 16.5 327 | 326 21 10021 21-10021 5 16.7 18.0 18.3 328 | 327 61 10061 61-10061 5 15.2 17.6 17.6 329 | 328 5 1005 5-1005 5 13.5 14.5 14.9 330 | 329 14 10014 14-10014 5 15.4 16.8 16.4 331 | 330 40 10040 40-10040 5 19.0 17.7 18.8 332 | 331 59 10059 59-10059 5 16.7 14.4 16.8 333 | 332 59 10059 59-10059 5 16.2 16.8 17.0 334 | 333 35 10035 35-10035 5 16.2 17.3 17.3 335 | 334 12 10012 12-10012 5 15.2 15.3 15.7 336 | 335 232 10232 232-10232 5 16.5 17.0 17.0 337 | 336 17 10017 17-10017 5 16.0 16.7 17.4 338 | 337 40 10040 40-10040 5 17.2 18.6 18.2 339 | 338 46 10046 46-10046 5 18.2 18.8 19.9 340 | 339 61 10061 61-10061 5 14.0 17.8 16.2 341 | 340 26 10026 26-10026 5 16.5 18.0 18.6 342 | 341 26 10026 26-10026 5 15.2 15.4 16.0 343 | 342 10 10010 10-10010 5 14.0 14.5 14.7 344 | 343 52 10052 52-10052 5 16.2 18.2 18.9 345 | 344 3 1003 3-1003 5 15.5 16.6 16.8 346 | 345 34 10034 34-10034 5 15.8 17.6 17.2 347 | 346 5 1005 5-1005 5 17.2 17.0 17.2 348 | 347 39 10039 39-10039 5 17.0 18.0 19.1 349 | 348 52 10052 52-10052 5 17.7 17.1 18.3 350 | 349 50 10050 50-10050 5 16.5 18.5 18.0 351 | 350 26 10026 26-10026 5 14.8 16.4 15.7 352 | 351 40 10040 40-10040 5 16.7 18.0 18.1 353 | 352 3 1003 3-1003 5 16.0 16.6 17.3 354 | 353 26 10026 26-10026 5 16.0 17.0 18.1 355 | 354 5 1005 5-1005 5 17.5 17.8 19.0 356 | 355 8 1008 8-1008 5 16.2 17.8 17.7 357 | 356 56 10056 56-10056 5 15.5 16.5 17.4 358 | 357 40 10040 40-10040 5 16.5 18.2 18.3 359 | 358 14 10014 14-10014 6 14.4 15.9 15.8 360 | 359 232 10232 232-10232 6 14.1 18.0 16.3 361 | 360 4 1004 4-1004 6 14.5 14.6 15.5 362 | 361 56 10056 56-10056 6 13.5 17.5 16.2 363 | 362 8 1008 8-1008 6 16.2 16.9 17.3 364 | 363 30 10030 30-10030 6 15.5 15.5 15.6 365 | 364 8 1008 8-1008 6 16.5 16.8 17.8 366 | 365 10 10010 10-10010 6 14.0 14.0 14.1 367 | 366 30 10030 30-10030 6 17.0 15.5 17.6 368 | 367 57 10057 57-10057 6 14.9 15.0 16.0 369 | 368 10 10010 10-10010 6 14.2 14.4 14.4 370 | 369 50 10050 50-10050 6 14.2 15.4 15.5 371 | 370 56 10056 56-10056 6 18.2 17.5 19.0 372 | 371 57 10057 57-10057 6 15.0 15.0 15.2 373 | 372 21 10021 21-10021 6 16.0 16.3 16.5 374 | 373 4 1004 4-1004 6 15.7 16.0 17.2 375 | 374 666 10666 666-10666 6 14.0 14.9 14.6 376 | 375 10 10010 10-10010 6 14.9 14.8 15.5 377 | 376 8 1008 8-1008 6 14.2 14.3 14.3 378 | 377 57 10057 57-10057 6 15.0 15.5 15.8 379 | 378 51 10051 51-10051 6 15.5 17.8 17.9 380 | 379 59 10059 59-10059 6 14.8 15.7 16.7 381 | 380 17 10017 17-10017 6 14.8 15.4 15.4 382 | 381 56 10056 56-10056 6 16.0 16.2 17.1 383 | 382 59 10059 59-10059 6 15.0 15.0 15.5 384 | 383 34 10034 34-10034 6 14.0 14.4 14.7 385 | 384 57 10057 57-10057 6 15.0 14.0 14.8 386 | 385 30 10030 30-10030 6 16.4 18.0 18.5 387 | 386 3 1003 3-1003 6 15.5 15.8 17.2 388 | 387 34 10034 34-10034 6 13.7 13.6 14.0 389 | 388 46 10046 46-10046 6 17.3 16.6 17.0 390 | 389 52 10052 52-10052 6 17.6 16.5 18.5 391 | 390 232 10232 232-10232 6 14.0 15.6 15.5 392 | 391 4 1004 4-1004 6 14.2 14.6 14.9 393 | 392 3 1003 3-1003 6 16.5 16.6 17.3 394 | 393 777 10777 777-10777 6 14.5 16.8 15.9 395 | 394 30 10030 30-10030 6 16.5 15.5 17.4 396 | 395 8 1008 8-1008 6 14.0 18.6 16.6 397 | 396 21 10021 21-10021 6 14.5 14.6 14.7 398 | 397 17 10017 17-10017 6 16.4 18.0 18.5 399 | 398 13 10013 13-10013 6 15.2 15.4 16.3 400 | 399 21 10021 21-10021 6 15.2 15.5 16.1 401 | 400 10 10010 10-10010 6 15.0 15.0 16.0 402 | 401 5 1005 5-1005 6 16.0 15.9 16.5 403 | 402 34 10034 34-10034 6 15.0 15.3 16.6 404 | 403 17 10017 17-10017 6 13.6 13.8 14.6 405 | 404 21 10021 21-10021 6 15.0 16.5 17.3 406 | 405 57 10057 57-10057 6 16.0 15.7 17.4 407 | 406 11 10011 11-10011 6 14.9 14.8 14.9 408 | 407 56 10056 56-10056 6 14.6 16.9 16.4 409 | 408 21 10021 21-10021 6 16.4 18.0 18.7 410 | 409 52 10052 52-10052 6 15.0 15.3 15.4 411 | 410 4 1004 4-1004 6 15.2 15.2 16.5 412 | 411 52 10052 52-10052 6 15.2 15.4 15.9 413 | 412 10 10010 10-10010 6 15.2 15.0 15.1 414 | 413 21 10021 21-10021 6 17.0 14.9 17.3 415 | 414 4 1004 4-1004 6 17.2 18.1 19.0 416 | 415 34 10034 34-10034 6 13.9 14.8 15.5 417 | 416 11 10011 11-10011 6 18.2 15.8 17.8 418 | 417 57 10057 57-10057 6 13.9 14.4 14.8 419 | 418 26 10026 26-10026 6 16.4 18.0 17.6 420 | 419 30 10030 30-10030 6 15.9 17.0 17.1 421 | 420 36 10036 36-10036 6 15.5 16.2 16.4 422 | 421 30 10030 30-10030 6 16.8 17.9 18.5 423 | 422 10 10010 10-10010 6 16.2 15.9 16.3 424 | 423 232 10232 232-10232 6 14.9 15.2 15.7 425 | 424 8 1008 8-1008 6 15.5 16.8 17.7 426 | 425 34 10034 34-10034 6 15.7 14.8 16.0 427 | 426 46 10046 46-10046 6 14.2 13.6 15.0 428 | 427 4 1004 4-1004 6 15.6 16.1 17.0 429 | 428 17 10017 17-10017 6 15.0 16.4 17.0 430 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ASReml-R-Short-Course 2 | Course notes and scripts from ASReml-R short course taught by [Salvador Gezan](https://scholar.google.com/citations?user=ur-md2kAAAAJ&hl=en "Salvador Gezan - Google Scholar") at the USDA-ARS station in Miami, FL. This course material is provided as-is, courtesy of Salvador Gezan 3 | 4 | (Documentation to follow) 5 | -------------------------------------------------------------------------------- /asreml-R.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ConradStack/ASReml-R-Short-Course/cad54bc6ee3c41dee9f492877440ab3cf6ed99dc/asreml-R.pdf --------------------------------------------------------------------------------