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Exploring Measured Genotypes in Twin Studies of Nicotine Dependence: Insights from the NIDA Workshop

This summary highlights findings from the NIDA Workshop held in October 2012, focusing on measured genotypes in the context of nicotine dependence. The workshop discussed the integration of genetic data with twin studies and the extension of statistical models to include measured genotypes. It emphasized the power of association studies, including insights from the Tobacco and Genetics Consortium's meta-analyses. Models were developed to comprehend the distinct contributions of genetic and environmental factors, specifically examining the CHRNA3 and CHRNA5 gene markers and their association with nicotine dependence.

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Exploring Measured Genotypes in Twin Studies of Nicotine Dependence: Insights from the NIDA Workshop

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  1. SEM with Measured Genotypes • NIDA Workshop • VIPBG, October 2012 • Maes, H. H., Neale, M. C., Chen, X., Chen, J., Prescott, C. A., & Kendler, K. S. (2011). A twin association study of nicotine dependence with markers in the CHRNA3 and CHRNA5 genes. Behav Genet, 41(5), 680-690. • doi: 10.1007/s10519-011-9476-z

  2. What would ACE look like if we knew the genes and environments?

  3. Where we’d like to be

  4. Molecular Studies using Relatives • Association studies • Often based on genotyping unrelated individuals • Statistical models for relatives have been extended to included measured genotypes • van den Oord et al. (2002), Merlin (Abecasis, 2002) • Genotype data added to twin/family studies • Increased power from family design • Problem: • Some relatives with phenotypes without genotypes • Power of association studies can be increased if incompletely genotyped families are retained in analyses • (Visscher et al 2008)

  5. The Tobacco and Genetics Consortium Nature Genetics (2010)

  6. Meta-Analyses of GWAS of Smoking • Three Meta-analyses • TAG 2010; Thorgeirsonet al. 2010; Liu et al. 2010 • > 100,000 individuals • Several genome-wide significant results • Initiation: BDNF, Cessation: DBH • Consumption • Neuronal acetylcholine receptor subunit genes • SNPs in CHRNA5and CHRNA3 • rs16969968 • First identified by Sacconeet al (2007)

  7. Goal: test whether nicotine dependence is linked to nicotinic receptor variants • printACE(AceFit)a^2 c^2 e^2 aS^2[1,] 0.62 0 0.38 0 • printACE(AcegFit)a^2 c^2 e^2 aS^2[1,] 0.61 0 0.38 0.01 • mxCompare(AcegFit, AceFit)base comparison ep minus2LL df AIC diffLL diffdf p1 ACEg <NA> 15 3386.251 730 1926.251 NA NA NA2 ACEg ACEonly 14 3390.466 731 1928.466 4.214693 1 0.0401

  8. Twin Association Model • Traditional Twin Model • Measured Genotypes as covariates in Means Model • Quantify contributions of specific variants as well as background genetic and environmental factors

  9. Twin Association Model

  10. Expected Means based on allelic effects of SNPs • Population mean = “m” • Allele at a particular locus = either “A” or “a” • SNP effect modeled as deviations from “m” • Additive (aS) or dominant (dS) SNP effect model • Expected mean for • AA homozygote = m + aS • Aa heterozygotes = m + dS • aahomozygote = m – aS

  11. MZs: 1 of 3 classes +aS dS -aS

  12. DZs: 1 of 3x3 classes

  13. Twin Data Availability

  14. Missing Genotypes

  15. So, how do we do add substitute values for our missing genotype data? • Need to know the expected proportions of each genotype in the cases of twin1 and or twin2 missing for MZ and for DZ • Need to code our data in such as way as to allow us to fill in the three possible values for missing MZ data and the 9 possible values in the case of one or more missing DZ twin genotypes in a pair.

  16. Expected proportion of each genotype based on allele frequencies expected proportion of each of genotypic categories of twin pairs calculated based on allele frequencies obtained from total sample of genotyped individuals

  17. Let’s look at the dataset… • str(selData)'data.frame': 850 obs. of 9 variables: $ zyg : int 1 3 5 3 4 2 1 4 5 3 ... $ rs10a11: int 1 1 1 1 0 1 1 1 1 0 ... $ rs10a12: int 1 1 1 1 1 1 1 1 1 0 ... $ rs10a13: int 1 1 1 1 0 1 1 1 1 1 ... $ rs10a21: int 1 1 1 1 1 1 1 1 1 0 ... $ rs10a22: int 1 1 1 1 1 1 1 1 1 0 ... $ rs10a23: int 1 1 1 1 1 1 1 1 1 1 ... $ ftnd1 : int NA 5 5 7 6 NA NA NA 4 NA ... $ ftnd2 : int NA NA 5 9 NA NA NA 8 NA NA ...

  18. Recode Genotypes into 3 columns (to map into the 9 genotype classes) • rs#11 rs#12 rs#13 • if rs10a1 = 2 [AA] 1, 0, 0 • if rs10a1 = 1 [Aa]  0, 1, 0 • if rs10a1 = 0 [aa] 0, 0, 1 • if rs10a1 = NA [??]  1, 1, 1 • mzGen1 = c(rs#11, rs#12, rs#13) • Now we can multiple these 1*3 matrices to get a 9-cell vector with 1s in the “possible” co-twin genotypes • vector[ t(Gen1) %*% Gen2 ]

  19. vector[t(Gen1)%*%Gen2]

  20. Individual Proportions • mzGen1| mzGen2 > mzGenProb • mzN x 6 mzN x 9 • # mzN = number of MZ pairs • mzGenComb = vector(t(mzGen1) %*% mzGen2) • mzGenProb = mzGenComb %*% mzProb/ (mzGenComb %*% (mzProb %*% U)) # note: “%*% U” Sums all the probabilities (Einstein addition)

  21. Matrices for Genotype # Matrices to store effect of genotype mxMatrix(name="mean" , type="Full", nrow=nv, ncol=nv, free=T, values=0, label="gm"), mxMatrix(name="addSNP", type="Full", nrow=nv, ncol=nv, free=T, values=0, label="aS"), mxMatrix(name="domSNP", type="Full", nrow=nv, ncol=nv, free=F, values=0, label="dS"), mxMatrix(name="pSNP" , type="Full", nrow=nv, ncol=nv, free=F, values=allelep), mxAlgebra(1-pSNP, name="qSNP"), mxAlgebra(2 * pSNP* qSNP* addSNP^2, name = "S"), mxAlgebra(V+S, name="totalV"), mxAlgebra((cbind(A,C,E,S)) %x% solve(totalV), name = "stVarCom"), mxAlgebra(cbind(V, A, C, E, S, stVarCom), name = "allVarCom"), mxMatrix(name="U9", type = "Unit", nrow= 9, ncol= 1),

  22. Expected Mean Vector • # Matrix & Algebra for expected means vector and expected thresholdsmxAlgebra(rbind(mean+addSNP,mean+domSNP,mean-addSNP), name="mean3"),mxAlgebra( cbind(mean+addSNP,mean+addSNP), name="expMean_AAAA"),mxAlgebra( cbind(mean+addSNP,mean+domSNP), name="expMean_AAAa"),mxAlgebra( cbind(mean+addSNP,mean-addSNP), name="expMean_AAaa"),mxAlgebra( cbind(mean+domSNP,mean+addSNP), name="expMean_AaAA"),mxAlgebra( cbind(mean+domSNP,mean+domSNP), name="expMean_AaAa"),mxAlgebra( cbind(mean+domSNP,mean-addSNP), name="expMean_Aaaa"), mxAlgebra( cbind(mean-addSNP,mean+addSNP), name="expMean_aaAA"),mxAlgebra( cbind(mean-addSNP,mean+domSNP), name="expMean_aaAa"),mxAlgebra( cbind(mean-addSNP,mean-addSNP), name="expMean_aaaa"),

  23. Expected Thresholds, Covariances • mxMatrix( type="Full", nrow= nth, ncol= nv, free = c(F,F,rep(T,nth-2)), values=thValues, lbound=thLBound, name="Thre”),mxMatrix( type="Lower", nrow=nth, ncol=nth, free=FALSE, values=1, name="Inc" ),mxAlgebra(Inc%*% Thre, name="ThreInc"),mxAlgebra(cbind(ThreInc,ThreInc), dimnames=list(thRows,selVars), name="expThre"),# Algebra for expected variance/covariance matricesmxAlgebra((rbind(cbind(A+C+E , A+C),cbind(A+C , A+C+E))), name="expCovMZ"),mxAlgebra((rbind (cbind(A+C+E, 0.5%x%A+C),cbind(0.5%x%A+C , A+C+E))), name="expCovDZ")

  24. mxModel(“MZ_”, mxData(mzData, type="raw" ),mxModel("MZ_AA",mxFIMLObjective( "ACE.expCovMZ", "ACE.expMean_AAAA", selVars,"ACE.expThre", vector=T)),mxModel("MZ_Aa",mxFIMLObjective("ACE.expCovMZ", "ACE.expMean_AaAa", selVars,"ACE.expThre", vector=T)),mxModel("MZ_aa",mxFIMLObjective("ACE.expCovMZ","ACE.expMean_aaaa", selVars,"ACE.expThre", vector=T)),mxMatrix("Full",mzN,9,F, values=mzGenProb, name="mzWeights"),mxMatrix("Zero",mzN,1, name="Zero"),mxAlgebra(-2 * sum(log((mzWeights* cbind( MZ_AA.objective, Zero , Zero, Zero , MZ_Aa.objective, Zero, Zero , Zero , MZ_aa.objective)) %*%ACE.U9)), name="MZmix"),mxAlgebraObjective("MZmix")

  25. mxModel(“DZ_”, mxData(dzData, type="raw"),mxModel("DZ_AAAA", mxFIMLObjective("ACE.expCovDZ", "ACE.expMean_AAAA", selVars, "ACE.expThre", vector=T)),mxModel("DZ_AAAa", mxFIMLObjective("ACE.expCovDZ", "ACE.expMean_AAAa", selVars, "ACE.expThre", vector=T)),mxModel("DZ_AAaa", mxFIMLObjective("ACE.expCovDZ", "ACE.expMean_AAaa", selVars, "ACE.expThre", vector=T)),mxModel("DZ_AaAA”, mxFIMLObjective("ACE.expCovDZ", "ACE.expMean_AaAA", selVars, "ACE.expThre", vector=T)),mxModel("DZ_AaAa", mxFIMLObjective("ACE.expCovDZ", "ACE.expMean_AaAa", selVars, "ACE.expThre", vector=T)),mxModel("DZ_Aaaa”, mxFIMLObjective("ACE.expCovDZ", "ACE.expMean_Aaaa", selVars, "ACE.expThre", vector=T)),mxModel("DZ_aaAA”, mxFIMLObjective("ACE.expCovDZ", "ACE.expMean_aaAA", selVars, "ACE.expThre", vector=T)),mxModel("DZ_aaAa”, mxFIMLObjective("ACE.expCovDZ", "ACE.expMean_aaAa", selVars, "ACE.expThre", vector=T)),mxModel("DZ_aaaa”, mxFIMLObjective("ACE.expCovDZ", "ACE.expMean_aaaa", selVars, "ACE.expThre", vector=T)),mxMatrix(name="dzWeights", type= "Full",nrow=dzN,ncol=9,free=F, values=dzGenProb),mxMatrix(name="Zero", type="Zero",nrow=dzN,ncol=1),mxAlgebra(name="DZmix", expression = -2*sum(log((dzWeights*cbind(DZ_AAAA.objective, DZ_AAAa.objective, DZ_AAaa.objective,DZ_AaAA.objective, DZ_AaAa.objective, DZ_Aaaa.objective,DZ_aaAA.objective, DZ_aaAa.objective, DZ_aaaa.objective)) %*%ACE.U9)), ),mxAlgebraObjective("DZmix”)

  26. Goal: test whether nicotine dependence is linked to nicotinic receptor variants • mxCompare(AcegFit, AceFit)base comparison ep minus2LL df AIC diffLL diffdf p1 ACEg <NA> 15 3386.251 730 1926.251 NA NA NA2 ACEg ACEonly 14 3390.466 731 1928.466 4.214693 1 0.0401 • printACE(AceFit)a^2 c^2 e^2 aS^2[1,] 0.62 0 0.38 0 • printACE(AcegFit)a^2 c^2 e^2 aS^2[1,] 0.61 0 0.38 0.01

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