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JuanJMV's picture
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Joined: 07/20/2016 - 13:13
Siblings

Hello,

I am trying to do a univariate analysis with a binary variable. I am not sure how to treat siblings
I have created different groups MZ, DZ, SIB (which I think is the same as introduce SIB in the DZ group).

I have also checked if DZ and SIB can be equated through the saturated model and there are not significant differences when I equated means and variances between DZ and Sib

I would like to know if I can use the siblings in my analysis (correlations are a little bit different) or should I do something different to introduce siblings in my analysis?

I have read this thread "https://openmx.ssri.psu.edu/thread/4086" but I am not sure if I need to check the "twin effects" and what should I change in the script

Thank you so much

Here the script:

# Select Variables for Analysis
vars      <- 'APN'                      # list of variables names
nv        <- 1                         # number of variables
ntv       <- nv*2                      # number of total variables
selVars   <- paste(vars,c(rep(1,nv),rep(2,nv)),sep="")
 
# Select Covariates for Analysis
covVars   <- c('age1',"age2", "Sex1" , "Sex2")
nc        <- 4   
 
# Select Data for Analysis
 
ordData   <- twinData
mzData    <- subset(ordData, Zyg==1| Zyg==3, c(selVars, covVars))
dzData    <- subset(ordData, Zyg==2 | Zyg==4| Zyg==5 , c(selVars, covVars))
sibData   <- subset(ordData,Zyg==6| Zyg==7 | Zyg==8 , c(selVars, covVars))
 
 
mzDataF   <- mzData 
dzDataF   <- dzData
mzDataF$APN1 <- mxFactor(mzDataF$APN1, levels =c(0,1))
mzDataF$APN2 <- mxFactor(mzDataF$APN2, levels =c(0,1))
dzDataF$APN1 <- mxFactor(dzDataF$APN1, levels =c(0,1))
dzDataF$APN2 <- mxFactor(dzDataF$APN2, levels =c(0,1))
sibData$APN1 <- mxFactor(sibData$APN1, levels =c(0,1))
sibData$APN2 <- mxFactor(sibData$APN2, levels =c(0,1))
 
# Set Starting Values
svTh      <- .8                        # start value for thresholds
svPa      <- .4                        # start value for path coefficient
svPe      <- .8                        # start value for path coefficient for e
lbPa      <- .0001                     # start value for lower bounds
 
# ------------------------------------------------------------------------------
# PREPARE MODEL
 
# ACE Model
# Create Matrices for Covariates and linear Regression Coefficients
 
 
 
defAge    <- mxMatrix( type="Full", nrow=1, ncol=2, free=FALSE, labels=c("data.age1", "data.age2"), name="Age" )
pathB1     <- mxMatrix( type="Full", nrow=1, ncol=2, free=TRUE, values=.01, label=c("b11", "b12"), name="b1" )
 
defSex    <- mxMatrix( type="Full", nrow=1, ncol=2, free=FALSE, labels=c("data.Sex1","data.Sex2"), name="Sex" )
pathB2     <- mxMatrix( type="Full", nrow=1, ncol=2, free=TRUE, values=.01, label=c("b21", "b22"), name="b2" )
 
 
# Create Algebra for expected Mean & Threshold Matrices
meanG     <- mxMatrix( type="Zero", nrow=1, ncol=ntv, name="meanG" )
expMean   <- mxAlgebra( expression= meanG +  b1*Age + b2*Sex, name="expMean" )
threG     <- mxMatrix( type="Full", nrow=1, ncol=ntv, free=TRUE, values=svTh, labels="tob", name="threG" )
 
# Create Matrices for Path Coefficients
pathA     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPa, label="a11", lbound=lbPa, name="a" ) 
pathC     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPa, label="c11", lbound=lbPa, name="c" )
pathE     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPe, label="e11", lbound=lbPa, name="e" )
 
# Create Algebra for Variance Components
covA      <- mxAlgebra( expression=a %*% t(a), name="A" )
covC      <- mxAlgebra( expression=c %*% t(c), name="C" ) 
covE      <- mxAlgebra( expression=e %*% t(e), name="E" )
 
# Create Algebra for expected Variance/Covariance Matrices in MZ & DZ twins
covP      <- mxAlgebra( expression= A+C+E, name="V" )
covMZ     <- mxAlgebra( expression= A+C, name="cMZ" )
covDZ     <- mxAlgebra( expression= 0.5%x%A+ C, name="cDZ" )
covSIB    <- mxAlgebra( expression= 0.5%x%A+ C, name="cSIB" )
expCovMZ  <- mxAlgebra( expression= rbind( cbind(V, cMZ), cbind(t(cMZ), V)), name="expCovMZ" )
expCovDZ  <- mxAlgebra( expression= rbind( cbind(V, cDZ), cbind(t(cDZ), V)), name="expCovDZ" )
expCovSIB <- mxAlgebra( expression= rbind( cbind(V, cSIB), cbind(t(cSIB), V)), name="expCovSIB" )
 
# Constrain Variance of Binary Variables
var1     <- mxConstraint( expression=diag2vec(V)==1, name="Var1" )
 
# Create Data Objects for Multiple Groups
dataMZ    <- mxData( observed=mzDataF, type="raw" )
dataDZ    <- mxData( observed=dzDataF, type="raw" )
dataSIB    <- mxData( observed=sibData, type="raw" )
 
# Create Expectation Objects for Multiple Groups
expMZ     <- mxExpectationNormal( covariance="expCovMZ", means="expMean", dimnames=selVars, thresholds="threG" )
expDZ     <- mxExpectationNormal( covariance="expCovDZ", means="expMean", dimnames=selVars, thresholds="threG" )
expSIB    <- mxExpectationNormal( covariance="expCovSIB", means="expMean", dimnames=selVars, thresholds="threG" )
funML     <- mxFitFunctionML()
 
# Create Model Objects for Multiple Groups
pars      <- list(pathB1, pathB2, meanG, threG, pathA, pathC, pathE, covA, covC, covE, covP)
defs      <- list(defAge, defSex)
modelMZ   <- mxModel( name="MZ", pars, defs, expMean, covMZ, expCovMZ, dataMZ, expMZ, funML )
modelDZ   <- mxModel( name="DZ", pars, defs, expMean, covDZ, expCovDZ, dataDZ, expDZ, funML )
modelSIB  <- mxModel( name="SIB", pars, defs, expMean, covSIB, expCovSIB, dataSIB, expSIB, funML )
multi     <- mxFitFunctionMultigroup( c("MZ","DZ","SIB") )
 
# Create Algebra for Variance Components
rowVC     <- rep('VC',nv)
colVC     <- rep(c('A','C','E','SA','SC','SE'),each=nv)
estVC     <- mxAlgebra( expression=cbind(A,C,E,A/V,C/V,E/V), name="VC", dimnames=list(rowVC,colVC))
 
# Create Confidence Interval Objects
ciACE     <- mxCI( "VC" )
 
# Build Model with Confidence Intervals
modelACE  <- mxModel( "oneACEba", pars, var1, modelMZ, modelDZ,modelSIB, multi, estVC, ciACE )
 
# ------------------------------------------------------------------------------
# RUN MODEL
 
# Run ACE Model
fitACE    <- mxRun( modelACE, intervals=T )
sumACE    <- summary( fitACE )
AdminRobK's picture
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Joined: 01/24/2014 - 12:15
twin effects

At minimum, change the appropriate lines to this instead:

# Create Matrices for Path Coefficients
pathA     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPa, label="a11", lbound=lbPa, name="a" ) 
pathC     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPa, label="c11", lbound=lbPa, name="c" )
pathE     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPe, label="e11", lbound=lbPa, name="e" )
pathTw     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPa, label="tw11", lbound=lbPa, name="tw" )
 
# Create Algebra for Variance Components
covA      <- mxAlgebra( expression=a %*% t(a), name="A" )
covC      <- mxAlgebra( expression=c %*% t(c), name="C" ) 
covE      <- mxAlgebra( expression=e %*% t(e), name="E" )
covTw      <- mxAlgebra( expression=tw %*% t(tw), name="Tw" )
 
# Create Algebra for expected Variance/Covariance Matrices in MZ & DZ twins
covP      <- mxAlgebra( expression= A+C+E+Tw, name="V" )
covMZ     <- mxAlgebra( expression= A+C+Tw, name="cMZ" )
covDZ     <- mxAlgebra( expression= 0.5%x%A+ C+Tw, name="cDZ" )
covSIB    <- mxAlgebra( expression= 0.5%x%A+ C, name="cSIB" )
expCovMZ  <- mxAlgebra( expression= rbind( cbind(V, cMZ), cbind(t(cMZ), V)), name="expCovMZ" )
expCovDZ  <- mxAlgebra( expression= rbind( cbind(V, cDZ), cbind(t(cDZ), V)), name="expCovDZ" )
expCovSIB <- mxAlgebra( expression= rbind( cbind(V, cSIB), cbind(t(cSIB), V)), name="expCovSIB" )

If you were to fix parameter "tw11" to zero, siblings and DZ twins would have equivalent model-expectations.

JuanJMV's picture
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Joined: 07/20/2016 - 13:13
Thank you so much!

Thank you so much Rob.

I have used that in a univariate model and it works perfect!

I am also working with a tetravariate model (3 continuous variables and 1 binary).
I have made the changes and I have compared the ADE model (with TW effect) and ADE model (without TW effect) the comparison is non-significant p=0.96 and TW is less than 1% in all the variables
So, can I assume that there are not differences between sib and DZ and use Sib in my analysis?

I am also a little bit confuse about “Cholesky decomposition” and “correlated factor model” my results are from the Cholesky decomposition, right? How can I check the correlated factor model? Do I need a different script? I have searched it in Boulder materials but I am not completely sure.

Finally, can I know with this script the genetic/environmental influences shared by the four variables at the same time?
Thank you so much in advance!!!!

# Select Continuous Variables
varsc     <- c('DE_Ln',"AN_Ln", 'EXT_Ln')                   # list of continuous variables names
nvc       <- 3                         # number of continuous variables
ntvc      <- nvc*2                     # number of total continuous variables
conVars   <- paste(varsc,c(rep(1,nvc),rep(2,nvc)),sep="")
 
# Select Ordinal Variables
nth       <- 1                        # number of thresholds
varso     <- c('APN')                   # list of ordinal variables names
nvo       <- 1                         # number of ordinal variables
ntvo      <- nvo*2                     # number of total ordinal variables
ordVars   <- paste(varso,c(rep(1,nvo),rep(2,nvo)),sep="")
ordData   <- twinData
 
 
# Select Variables for Analysis
vars      <- c('DE_Ln',"AN_Ln", 'EXT_Ln','APN')              # list of variables names
nv        <- nvc+nvo                   # number of variables
ntv       <- nv*2                      # number of total variables
oc        <- c(0,0,0,1)                    # 0 for continuous, 1 for ordinal variables
selVars   <- paste(vars,c(rep(1,nv),rep(2,nv)),sep="")
 
# Select Covariates for Analysis
 
covVars   <- c('age1',"age2", "Sex1" , "Sex2")
nc        <- 4                         # number of covariates                    # number of covariates
 
# Select Data for Analysis
mzData    <- subset(ordData, Zyg==1| Zyg==3, c(selVars, covVars))
dzData    <- subset(ordData, Zyg==2 | Zyg==4| Zyg==5, c(selVars, covVars))
sibData   <- subset(ordData,Zyg==6| Zyg==7 | Zyg==8 , c(selVars, covVars))
 
mzDataF   <- mzData 
dzDataF   <- dzData
mzDataF$APN1 <- mxFactor(mzDataF$APN1, levels =c(0,1))
mzDataF$APN2 <- mxFactor(mzDataF$APN2, levels =c(0,1))
dzDataF$APN1 <- mxFactor(dzDataF$APN1, levels =c(0,1))
dzDataF$APN2 <- mxFactor(dzDataF$APN2, levels =c(0,1))
sibData$APN1 <- mxFactor(sibData$APN1, levels =c(0,1))
sibData$APN2 <- mxFactor(sibData$APN2, levels =c(0,1))
# Generate Descriptive Statistics
 
# Set Starting Values
frMV      <- c(TRUE,FALSE)             # free status for variables
frCvD     <- diag(frMV,ntv,ntv)        # lower bounds for diagonal of covariance matrix
frCvD[lower.tri(frCvD)] <- TRUE        # lower bounds for below diagonal elements
frCvD[upper.tri(frCvD)] <- TRUE        # lower bounds for above diagonal elements
frCv      <- matrix(as.logical(frCvD),4)
svMe      <- c(2,4,2,0)                   # start value for means
svPa      <- .5                        # start value for path coefficient
svPaD     <- vech(diag(svPa,nv,nv))    # start values for diagonal of covariance matrix
svPe      <- .5                        # start value for path coefficient for e
svPeD     <- vech(diag(svPe,nv,nv))    # start values for diagonal of covariance matrix
lbPa      <- .00001                     # start value for lower bounds
lbPaD     <- diag(lbPa,nv,nv)          # lower bounds for diagonal of covariance matrix
lbPaD[lower.tri(lbPaD)] <- -10         # lower bounds for below diagonal elements
lbPaD[upper.tri(lbPaD)] <- NA          # lower bounds for above diagonal elements
svLTh     <- -1                     # start value for first threshold
svITh     <- 1                         # start value for increments
svTh      <- matrix(rep(c(svLTh,(rep(svITh,nth-1)))),nrow=nth,ncol=nv)     # start value for thresholds
lbTh      <- matrix(rep(c(-3,(rep(0.001,nth-1))),nv),nrow=nth,ncol=nv)     # lower bounds for thresholds
 
# Create Labels
labMe     <- paste("mean",vars,sep="_")
labTh     <- paste(paste("t",1:nth,sep=""),rep(varso,each=nth),sep="_")
labBe     <- labFull("beta",nc,1)
 
# ------------------------------------------------------------------------------
# PREPARE MODEL
 
# ADE Model
# Create Matrices for Covariates and linear Regression Coefficients
defAge    <- mxMatrix( type="Full", nrow=1, ncol=8, free=FALSE, labels=c("data.age1", "data.age1", "data.age1","data.age1","data.age2", "data.age2", "data.age2","data.age2"), name="Age" )
pathB1     <- mxMatrix( type="Full", nrow=1, ncol=8, free=TRUE, values=.01, label=c("b11", "b12","b13","b14", "b11", "b12", "b13","b14"), name="b1" )
 
defSex    <- mxMatrix( type="Full", nrow=1, ncol=8, free=FALSE, labels=c("data.Sex1", "data.Sex1","data.Sex1", "data.Sex1","data.Sex2", "data.Sex2", "data.Sex2","data.Sex2"), name="Sex" )
pathB2     <- mxMatrix( type="Full", nrow=1, ncol=8, free=TRUE, values=.01, label=c("b21", "b22","b23","b24", "b21", "b22", "b23","b24"), name="b2" )
 
# Create Algebra for expected Mean Matrices
meanG     <- mxMatrix( type="Full", nrow=1, ncol=ntv, free=TRUE, values=svMe, labels=labMe, name="meanG" )
expMean   <- mxAlgebra( expression= meanG + b1*Age + b2*Sex , name="expMean" )
thinG <- mxMatrix( type="Full", nrow=1, ncol=ntvo, free=FALSE, values=0, name="thinG" )
inc       <- mxMatrix( type="Lower", nrow=nth, ncol=nth, free=FALSE, values=1, name="inc" )
threG     <- mxAlgebra( expression= inc %*% thinG, name="threG" )
 
 
# Create Matrices for Path Coefficients
pathA     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPaD, label=labLower("a",nv), lbound=lbPaD, name="a" ) 
pathD     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPaD, label=labLower("d",nv), lbound=lbPaD, name="d" )
pathE     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPeD, label=labLower("e",nv), lbound=lbPaD, name="e" )
pathTw     <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPaD,label=labLower("tw",nv), lbound=lbPaD, name="tw" )
 
# Create Algebra for Variance Comptwonts
covA      <- mxAlgebra( expression=a %*% t(a), name="A" )
covD      <- mxAlgebra( expression=d %*% t(d), name="D" ) 
covE      <- mxAlgebra( expression=e %*% t(e), name="E" )
covTw      <- mxAlgebra( expression=tw %*% t(tw), name="TW" )
 
# Create Algebra for expected Variance/Covariance Matrices in MZ & DZ twins
covP      <- mxAlgebra( expression= A+D+E+TW, name="V" )
covMZ     <- mxAlgebra( expression= A+D+TW, name="cMZ" )
covDZ     <- mxAlgebra( expression= 0.5%x%A+ 0.25%x%D+TW, name="cDZ" )
covSIB    <- mxAlgebra( expression= 0.5%x%A+ 0.25%x%D, name="cSIB" )
 
expCovMZ  <- mxAlgebra( expression= rbind( cbind(V, cMZ), cbind(t(cMZ), V)), name="expCovMZ" )
expCovDZ  <- mxAlgebra( expression= rbind( cbind(V, cDZ), cbind(t(cDZ), V)), name="expCovDZ" )
expCovSIB <- mxAlgebra( expression= rbind( cbind(V, cSIB), cbind(t(cSIB), V)), name="expCovSIB" )
 
# Create Algebra for Standardization
matI      <- mxMatrix( type="Iden", nrow=nv, ncol=nv, name="I")
invSD     <- mxAlgebra( expression=solve(sqrt(I*V)), name="iSD")
 
# Calculate genetic and environmental correlations
corA      <- mxAlgebra( expression=solve(sqrt(I*A))%&%A, name ="rA" ) #cov2cor()
corD      <- mxAlgebra( expression=solve(sqrt(I*D))%&%D, name ="rD" )
corE      <- mxAlgebra( expression=solve(sqrt(I*E))%&%E, name ="rE" )
 
# Constrain Variance of Binary Variables
matUnv    <- mxMatrix( type="Unit", nrow=nvo, ncol=1, name="Unv1" )
matOc     <- mxMatrix( type="Full", nrow=1, ncol=nv, free=FALSE, values=oc, name="Oc" )
var1      <- mxConstraint( expression=diag2vec(Oc%&%V)==Unv1, name="Var1" )
 
# Create Data Objects for Multiple Groups
dataMZ    <- mxData( observed=mzDataF, type="raw" )
dataDZ    <- mxData( observed=dzDataF, type="raw" )
dataSIB    <- mxData( observed=sibData, type="raw" )
 
# Create Expectation Objects for Multiple Groups
expMZ     <- mxExpectationNormal( covariance="expCovMZ", means="expMean", dimnames=selVars, thresholds="thinG", threshnames=ordVars )
expDZ     <- mxExpectationNormal( covariance="expCovDZ", means="expMean", dimnames=selVars, thresholds="thinG", threshnames=ordVars )
expSIB    <- mxExpectationNormal( covariance="expCovSIB", means="expMean", dimnames=selVars, thresholds="thinG",threshnames=ordVars )
funML     <- mxFitFunctionML()
 
 
# Create Model Objects for Multiple Groups
pars      <- list(pathB1,pathB2, meanG, thinG, matI, invSD, matUnv, matOc,
                  pathA, pathD, pathE, covA, covD, covE, covP, corA, corD, corE, pathTw,covTw)
defs      <- list(defAge,defSex)
modelMZ   <- mxModel( name="MZ", pars, defs, expMean, covMZ, expCovMZ, dataMZ, expMZ, funML )
modelDZ   <- mxModel( name="DZ", pars, defs, expMean, covDZ, expCovDZ, dataDZ, expDZ, funML )
modelSIB   <- mxModel( name="SIB", pars, defs, expMean, covSIB, expCovSIB, dataSIB, expSIB, funML )
multi     <- mxFitFunctionMultigroup( c("MZ","DZ","SIB") )
 
# Create Algebra for Variance Components
rowVC     <- rep('VC',nv)
colVC     <- rep(c('A','D','E',"TW",'SA','SD','SE',"STW"),each=nv)
estVC     <- mxAlgebra( expression=cbind(A,D,E,TW,A/V,D/V,E/V,TW/V), name="VC", dimnames=list(rowVC,colVC))
# Create Confidence Interval Objects
ciADE <- mxCI(c("VC[1,1]", "MZ.rA", "MZ.rD", "MZ.rE","MZ.A","MZ.D","MZ.E","VC"))
 
# Build Model with Confidence Intervals
modelADE  <- mxModel( "twoACEja", pars, var1, modelMZ, modelDZ,modelSIB, multi, estVC, ciADE )
 
# ------------------------------------------------------------------------------
# RUN MODEL
 
# Run ACE Model
fitADE    <- mxTryHardOrdinal( modelADE, intervals=F, extraTries = 31 )
sumADE    <- summary( fitADE )
 
# Compare with Saturated Model
mxCompare( fit, fitADE )
 
# Print Goodness-of-fit Statistics & Parameter Estimates
fitGofs(fitADE)
fitEsts(fitADE)
 
# ------------------------------------------------------------------------------
# RUN SUBMODELS
#Without TW
 
modelADENOTW   <- mxModel( fitADE, name="twoADENOTWja" )
modelADENOTW   <- omxSetParameters( modelADENOTW, labels=labLower("tw",nv), free=FALSE, values=0 )
fitADENOTW     <- mxTryHardOrdinal( modelADENOTW, intervals=F, extraTries = 11 )
mxCompare( fitADE, fitADENOTW )
AdminRobK's picture
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Joined: 01/24/2014 - 12:15
4-trait script
I have made the changes and I have compared the ADE model (with TW effect) and ADE model (without TW effect) the comparison is non-significant p=0.96 and TW is less than 1% in all the variables
So, can I assume that there are not differences between sib and DZ and use Sib in my analysis?

It would be more accurate to say that you find no evidence of twin-specific environmental effects, and therefore, you chose to treat full sibs like DZ twins in subsequent analyses.

I am also a little bit confuse about “Cholesky decomposition” and “correlated factor model” my results are from the Cholesky decomposition, right? How can I check the correlated factor model? Do I need a different script? I have searched it in Boulder materials but I am not completely sure.

Why do you ask? In most cases, the Cholesky and correlated-factor "models" are equivalent parameterizations of the same model. The only exception I can think of offhand is in the case of "non-scalar" or "qualitative" sex-limitation.

Finally, can I know with this script the genetic/environmental influences shared by the four variables at the same time?

Yes, in the sense that it will estimate for you a 4x4 A, D, and E covariance matrix .

JuanJMV's picture
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Joined: 07/20/2016 - 13:13
Thank you!

I thought they were different models (I am confused about it, sorry). Where can I find more information about that? more suggestions apart from Boulder’s materials and Neale’s book?

Here my results from the 4-trait script.

 b11         b12         b13         b14         b21         b22         b23 
    -0.0249      0.0092     -0.0353     -0.0185      0.2984      0.4055     -0.0904 
        b24 mean_DE_Ln mean_AN_Ln mean_EXT_Ln    mean_APN       a_1_1       a_2_1 
    -0.0638      2.4840      3.6142      3.3896     -0.1613      0.7674      0.4689 
      a_3_1       a_4_1       a_2_2       a_3_2       a_4_2       a_3_3       a_4_3 
     0.7036      0.2898      0.3788     -0.1192     -0.1943      0.0115     -0.4787 
      a_4_4       e_1_1       e_2_1       e_3_1       e_4_1       e_2_2       e_3_2 
     0.0001      0.9260      0.3311     -0.1696      0.0531      0.5751     -0.0437 
      e_4_2       e_3_3       e_4_3       e_4_4 
     0.1980      0.8366      0.0591      0.7769 
        A      A      A      A D D D D       E       E       E      E TW TW TW TW     SA
VC 0.5890 0.3598 0.5400 0.2224 0 0 0 0  0.8575  0.3066 -0.1571 0.0492  0  0  0  0 0.4072
VC 0.3598 0.3633 0.2848 0.0623 0 0 0 0  0.3066  0.4404 -0.0813 0.1315  0  0  0  0 0.5399
VC 0.5400 0.2848 0.5094 0.2215 0 0 0 0 -0.1571 -0.0813  0.7306 0.0318  0  0  0  0 1.4101
VC 0.2224 0.0623 0.2215 0.3508 0 0 0 0  0.0492  0.1315  0.0318 0.6492  0  0  0  0 0.8189
       SA     SA     SA SD SD SD SD      SE      SE      SE     SE STW STW STW STW
VC 0.5399 1.4101 0.8189  0  0  0  0  0.5928  0.4601 -0.4101 0.1811   0   0   0   0
VC 0.4520 1.3994 0.3214  0  0  0  0  0.4601  0.5480 -0.3994 0.6786   0   0   0   0
VC 1.3994 0.4108 0.8744  0  0  0  0 -0.4101 -0.3994  0.5892 0.1256   0   0   0   0
VC 0.3214 0.8744 0.3508  0  0  0  0  0.1811  0.6786  0.1256 0.6492   0   0   0   0
                      lbound estimate  ubound
twoAENOTWja.VC[1,1]   0.4639   0.5890  0.7224
MZ.rA[1,1]                NA   1.0000      NA
MZ.rA[2,1]            0.6836   0.7779  0.8612
MZ.rA[3,1]            0.9156   0.9858  0.9999
MZ.rA[4,1]            0.1929   0.4892  0.9797
MZ.rA[1,2]            0.6836   0.7779  0.8612
MZ.rA[2,2]                NA   1.0000      NA
MZ.rA[3,2]            0.5106   0.6620  0.8055
MZ.rA[4,2]           -0.1524   0.1744  0.6370
MZ.rA[1,3]            0.9156   0.9858  0.9999
MZ.rA[2,3]            0.5106   0.6620  0.8055
MZ.rA[3,3]                NA   1.0000      NA
MZ.rA[4,3]            0.2093   0.5240  0.9994
MZ.rA[1,4]            0.1929   0.4892  0.9787
MZ.rA[2,4]           -0.1524   0.1744  0.6370
MZ.rA[3,4]            0.2093   0.5240  0.9994
MZ.rA[4,4]                NA   1.0000      NA
MZ.rD[1,1]                NA      NaN      NA
MZ.rD[2,1]                NA      NaN      NA
MZ.rD[3,1]                NA      NaN      NA
MZ.rD[4,1]                NA      NaN      NA
MZ.rD[1,2]                NA      NaN      NA
MZ.rD[2,2]                NA   0.0000      NA
MZ.rD[3,2]                NA   0.0000      NA
MZ.rD[4,2]                NA   0.0000      NA
MZ.rD[1,3]                NA      NaN      NA
MZ.rD[2,3]                NA   0.0000      NA
MZ.rD[3,3]                NA   0.0000      NA
MZ.rD[4,3]                NA   0.0000      NA
MZ.rD[1,4]                NA      NaN      NA
MZ.rD[2,4]                NA   0.0000      NA
MZ.rD[3,4]                NA   0.0000      NA
MZ.rD[4,4]                NA   0.0000      NA
MZ.rE[1,1]                NA   1.0000      NA
MZ.rE[2,1]            0.4199   0.4989  0.5704
MZ.rE[3,1]           -0.2885  -0.1984 -0.1036
MZ.rE[4,1]           -0.1117   0.0659  0.2268
MZ.rE[1,2]            0.4199   0.4989  0.5704
MZ.rE[2,2]                NA   1.0000      NA
MZ.rE[3,2]           -0.2442  -0.1433 -0.0373
MZ.rE[4,2]            0.0401   0.2459  0.4559
MZ.rE[1,3]           -0.2885  -0.1984 -0.1036
MZ.rE[2,3]           -0.2442  -0.1433 -0.0373
MZ.rE[3,3]                NA   1.0000      NA
MZ.rE[4,3]           -0.1298   0.0462  0.2431
MZ.rE[1,4]           -0.1117   0.0659  0.2267
MZ.rE[2,4]            0.0401   0.2459  0.4576
MZ.rE[3,4]           -0.1298   0.0462  0.2431
MZ.rE[4,4]                NA   1.0000      NA
MZ.A[1,1]             0.4639   0.5890  0.7224
MZ.A[2,1]             0.2771   0.3598  0.4463
MZ.A[3,1]             0.4568   0.5400  0.6259
MZ.A[4,1]             0.0894   0.2224  0.3512
MZ.A[1,2]             0.2771   0.3598  0.4463
MZ.A[2,2]             0.2850   0.3633  0.4439
MZ.A[3,2]             0.2153   0.2848  0.3543
MZ.A[4,2]            -0.0471   0.0623  0.1745
MZ.A[1,3]             0.4568   0.5400  0.6259
MZ.A[2,3]             0.2153   0.2848  0.3543
MZ.A[3,3]             0.3947   0.5094  0.6334
MZ.A[4,3]             0.0938   0.2215  0.3365
MZ.A[1,4]             0.0894   0.2224  0.3512
MZ.A[2,4]            -0.0471   0.0623  0.1745
MZ.A[3,4]             0.0938   0.2215  0.3365
MZ.A[4,4]             0.0626   0.3508  0.6413
MZ.D[1,1]                 NA   0.0000      NA
MZ.D[2,1]                 NA   0.0000      NA
MZ.D[3,1]                 NA   0.0000      NA
MZ.D[4,1]                 NA   0.0000      NA
MZ.D[1,2]                 NA   0.0000      NA
MZ.D[2,2]                 NA   0.0000      NA
MZ.D[3,2]                 NA   0.0000      NA
MZ.D[4,2]                 NA   0.0000      NA
MZ.D[1,3]                 NA   0.0000      NA
MZ.D[2,3]                 NA   0.0000      NA
MZ.D[3,3]                 NA   0.0000      NA
MZ.D[4,3]                 NA   0.0000      NA
MZ.D[1,4]                 NA   0.0000      NA
MZ.D[2,4]                 NA   0.0000      NA
MZ.D[3,4]                 NA   0.0000      NA
MZ.D[4,4]                 NA   0.0000      NA
MZ.E[1,1]             0.7506   0.8575  0.9762
MZ.E[2,1]             0.2394   0.3066  0.3812
MZ.E[3,1]            -0.2214  -0.1571 -0.0860
MZ.E[4,1]            -0.0798   0.0492  0.1704
MZ.E[1,2]             0.2394   0.3066  0.3812
MZ.E[2,2]             0.3776   0.4404  0.5133
MZ.E[3,2]            -0.1381  -0.0813 -0.0216
MZ.E[4,2]             0.0215   0.1315  0.2399
MZ.E[1,3]            -0.2214  -0.1571 -0.0860
MZ.E[2,3]            -0.1381  -0.0813 -0.0216
MZ.E[3,3]             0.6327   0.7306  0.8392
MZ.E[4,3]            -0.0944   0.0318  0.1627
MZ.E[1,4]            -0.0798   0.0492  0.1704
MZ.E[2,4]             0.0215   0.1315  0.2399
MZ.E[3,4]            -0.0944   0.0318  0.1627
MZ.E[4,4]             0.3587   0.6492      NA
twoAENOTWja.VC[2,1]   0.2771   0.3598  0.4463
twoAENOTWja.VC[3,1]   0.4568   0.5400  0.6259
twoAENOTWja.VC[4,1]   0.0894   0.2224  0.3512
twoAENOTWja.VC[1,2]   0.2771   0.3598  0.4463
twoAENOTWja.VC[2,2]   0.2850   0.3633  0.4439
twoAENOTWja.VC[3,2]   0.2153   0.2848  0.3543
twoAENOTWja.VC[4,2]  -0.0471   0.0623  0.1745
twoAENOTWja.VC[1,3]   0.4568   0.5400  0.6259
twoAENOTWja.VC[2,3]   0.2153   0.2848  0.3543
twoAENOTWja.VC[3,3]   0.3947   0.5094  0.6334
twoAENOTWja.VC[4,3]   0.0938   0.2215  0.3365
twoAENOTWja.VC[1,4]   0.0894   0.2224  0.3512
twoAENOTWja.VC[2,4]  -0.0471   0.0623  0.1745
twoAENOTWja.VC[3,4]   0.0938   0.2215  0.3365
twoAENOTWja.VC[4,4]   0.0626   0.3508  0.6413
twoAENOTWja.VC[1,5]       NA   0.0000      NA
twoAENOTWja.VC[2,5]       NA   0.0000      NA
twoAENOTWja.VC[3,5]       NA   0.0000      NA
twoAENOTWja.VC[4,5]       NA   0.0000      NA
twoAENOTWja.VC[1,6]       NA   0.0000      NA
twoAENOTWja.VC[2,6]       NA   0.0000      NA
twoAENOTWja.VC[3,6]       NA   0.0000      NA
twoAENOTWja.VC[4,6]       NA   0.0000      NA
twoAENOTWja.VC[1,7]       NA   0.0000      NA
twoAENOTWja.VC[2,7]       NA   0.0000      NA
twoAENOTWja.VC[3,7]       NA   0.0000      NA
twoAENOTWja.VC[4,7]       NA   0.0000      NA
twoAENOTWja.VC[1,8]       NA   0.0000      NA
twoAENOTWja.VC[2,8]       NA   0.0000      NA
twoAENOTWja.VC[3,8]       NA   0.0000      NA
twoAENOTWja.VC[4,8]       NA   0.0000      NA
twoAENOTWja.VC[1,9]   0.7506   0.8575  0.9762
twoAENOTWja.VC[2,9]   0.2394   0.3066  0.3812
twoAENOTWja.VC[3,9]  -0.2214  -0.1571 -0.0860
twoAENOTWja.VC[4,9]  -0.0798   0.0492  0.1704
twoAENOTWja.VC[1,10]  0.2394   0.3066  0.3812
twoAENOTWja.VC[2,10]  0.3776   0.4404  0.5133
twoAENOTWja.VC[3,10] -0.1381  -0.0813 -0.0216
twoAENOTWja.VC[4,10]  0.0215   0.1315  0.2399
twoAENOTWja.VC[1,11] -0.2214  -0.1571 -0.0860
twoAENOTWja.VC[2,11] -0.1381  -0.0813 -0.0216
twoAENOTWja.VC[3,11]  0.6327   0.7306  0.8392
twoAENOTWja.VC[4,11] -0.0944   0.0318  0.1627
twoAENOTWja.VC[1,12] -0.0798   0.0492  0.1704
twoAENOTWja.VC[2,12]  0.0215   0.1315  0.2399
twoAENOTWja.VC[3,12] -0.0944   0.0318  0.1627
twoAENOTWja.VC[4,12]  0.3587   0.6492      NA
twoAENOTWja.VC[1,13]      NA   0.0000      NA
twoAENOTWja.VC[2,13]      NA   0.0000      NA
twoAENOTWja.VC[3,13]      NA   0.0000      NA
twoAENOTWja.VC[4,13]      NA   0.0000      NA
twoAENOTWja.VC[1,14]      NA   0.0000      NA
twoAENOTWja.VC[2,14]      NA   0.0000      NA
twoAENOTWja.VC[3,14]      NA   0.0000      NA
twoAENOTWja.VC[4,14]      NA   0.0000      NA
twoAENOTWja.VC[1,15]      NA   0.0000      NA
twoAENOTWja.VC[2,15]      NA   0.0000      NA
twoAENOTWja.VC[3,15]      NA   0.0000      NA
twoAENOTWja.VC[4,15]      NA   0.0000      NA
twoAENOTWja.VC[1,16]      NA   0.0000      NA
twoAENOTWja.VC[2,16]      NA   0.0000      NA
twoAENOTWja.VC[3,16]      NA   0.0000      NA
twoAENOTWja.VC[4,16]      NA   0.0000      NA
twoAENOTWja.VC[1,17]  0.3290   0.4072  0.4820
twoAENOTWja.VC[2,17]  0.4307   0.5399  0.6405
twoAENOTWja.VC[3,17]  1.2078   1.4101  1.6595
twoAENOTWja.VC[4,17]  0.4146   0.8189  1.3582
twoAENOTWja.VC[1,18]  0.4307   0.5399  0.6405
twoAENOTWja.VC[2,18]  0.3634   0.4520  0.5325
twoAENOTWja.VC[3,18]  1.0977   1.3994  1.8025
twoAENOTWja.VC[4,18] -0.2987   0.3214  0.8795
twoAENOTWja.VC[1,19]  1.2078   1.4101  1.6595
twoAENOTWja.VC[2,19]  1.0977   1.3994  1.8025
twoAENOTWja.VC[3,19]  0.3270   0.4108  0.4917
twoAENOTWja.VC[4,19]  0.3981   0.8744  1.4805
twoAENOTWja.VC[1,20]  0.4146   0.8189  1.3582
twoAENOTWja.VC[2,20] -0.2987   0.3214  0.8795
twoAENOTWja.VC[3,20]  0.3981   0.8744  1.4805
twoAENOTWja.VC[4,20]      NA   0.3508  0.6413
twoAENOTWja.VC[1,21]      NA   0.0000      NA
twoAENOTWja.VC[2,21]      NA   0.0000      NA
twoAENOTWja.VC[3,21]      NA   0.0000      NA
twoAENOTWja.VC[4,21]      NA   0.0000      NA
twoAENOTWja.VC[1,22]      NA   0.0000      NA
twoAENOTWja.VC[2,22]      NA   0.0000      NA
twoAENOTWja.VC[3,22]      NA   0.0000      NA
twoAENOTWja.VC[4,22]      NA   0.0000      NA
twoAENOTWja.VC[1,23]      NA   0.0000      NA
twoAENOTWja.VC[2,23]      NA   0.0000      NA
twoAENOTWja.VC[3,23]      NA   0.0000      NA
twoAENOTWja.VC[4,23]      NA   0.0000      NA
twoAENOTWja.VC[1,24]      NA   0.0000      NA
twoAENOTWja.VC[2,24]      NA   0.0000      NA
twoAENOTWja.VC[3,24]      NA   0.0000      NA
twoAENOTWja.VC[4,24]      NA   0.0000      NA
twoAENOTWja.VC[1,25]  0.5180   0.5928  0.6710
twoAENOTWja.VC[2,25]  0.3595   0.4601  0.5693
twoAENOTWja.VC[3,25] -0.6595  -0.4101 -0.2078
twoAENOTWja.VC[4,25] -0.3582   0.1811  0.6434
twoAENOTWja.VC[1,26]  0.3595   0.4601  0.5693
twoAENOTWja.VC[2,26]  0.4675   0.5480  0.6367
twoAENOTWja.VC[3,26] -0.8025  -0.3994 -0.0977
twoAENOTWja.VC[4,26]  0.1205   0.6786  1.2987
twoAENOTWja.VC[1,27] -0.6595  -0.4101 -0.2078
twoAENOTWja.VC[2,27] -0.8025  -0.3994 -0.0977
twoAENOTWja.VC[3,27]  0.5083   0.5892  0.6730
twoAENOTWja.VC[4,27] -0.4805   0.1256  0.6019
twoAENOTWja.VC[1,28] -0.3582   0.1811  0.6434
twoAENOTWja.VC[2,28]  0.1205   0.6786  1.2987
twoAENOTWja.VC[3,28] -0.4805   0.1256  0.6019
twoAENOTWja.VC[4,28]  0.3587   0.6492      NA
twoAENOTWja.VC[1,29]      NA   0.0000      NA
twoAENOTWja.VC[2,29]      NA   0.0000      NA
twoAENOTWja.VC[3,29]      NA   0.0000      NA
twoAENOTWja.VC[4,29]      NA   0.0000      NA
twoAENOTWja.VC[1,30]      NA   0.0000      NA
twoAENOTWja.VC[2,30]      NA   0.0000      NA
twoAENOTWja.VC[3,30]      NA   0.0000      NA
twoAENOTWja.VC[4,30]      NA   0.0000      NA
twoAENOTWja.VC[1,31]      NA   0.0000      NA
twoAENOTWja.VC[2,31]      NA   0.0000      NA
twoAENOTWja.VC[3,31]      NA   0.0000      NA
twoAENOTWja.VC[4,31]      NA   0.0000      NA
twoAENOTWja.VC[1,32]      NA   0.0000      NA
twoAENOTWja.VC[2,32]      NA   0.0000      NA
twoAENOTWja.VC[3,32]      NA   0.0000      NA
twoAENOTWja.VC[4,32]      NA   0.0000      NA

But I would like to know the genetic influences (also environmental influences) shared between the four variables. I mean I can know it for v1-v2, v1-v3,…… but can I know the shared genetic influences between v1, v2, v3 and v4 together?

By the way since I have a negative value for example in MZ.E[3,1] I can not know the % of the phenotypic correlation explained by genetic factors right?

Thank you so much again!!

AdminRobK's picture
Offline
Joined: 01/24/2014 - 12:15
Where can I find more
Where can I find more information about that? more suggestions apart from Boulder’s materials and Neale’s book?

Hmm. This is the kind of thing one typically learns from one's dissertation adviser and/or at behavior-genetics workshops (like the one in Boulder). Offhand, I can think of two articles that are somewhat relevant: Loehlin (1996) and Neale, Røysamb, & Jacobson (2006).

But I would like to know the genetic influences (also environmental influences) shared between the four variables. I mean I can know it for v1-v2, v1-v3,…… but can I know the shared genetic influences between v1, v2, v3 and v4 together?

Oh, I see. Then, it sounds like you want to fit a common-pathway and/or independent-pathway model.

By the way since I have a negative value for example in MZ.E[3,1] I can not know the % of the phenotypic correlation explained by genetic factors right?

You can still estimate its contribution to the phenotypic correlation, but interpreting it as a proportion doesn't really make sense because of the negative value.

JuanJMV's picture
Offline
Joined: 07/20/2016 - 13:13
Thank you so much!!

Thank you so much Rob!

I am going to work on the common-pathway and independent-pathway models.

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