Heterogeneity ii
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Heterogeneity II. Danielle Dick, Tim York, Marleen de Moor, Dorret Boomsma Boulder Twin Workshop March 2010. Heterogeneity Questions I.

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Heterogeneity ii

Heterogeneity II

Danielle Dick, Tim York,

Marleen de Moor, Dorret Boomsma

Boulder Twin Workshop

March 2010


Heterogeneity questions i

Heterogeneity Questions I

  • Standard Univariate Analysis: What are the contributions of additive genetic, dominance/shared environmental and unique environmental factors to the variance?

  • Heterogeneity: Are the contributions of genetic and environmental factors equal for different groups, such as sex, race, ethnicity, SES, environmental exposure, etc.?


Ways to model heterogeneity in twin data

Ways to Model Heterogeneity in Twin Data

  • Multiple Group Models

    • Sex Effects

    • Divorced/Not Divorced

    • Young/Old cohorts

    • Urban/Rural residency

    • Etc…


Sex effects

Sex Effects

Females

Males


Sex effects1

Sex Effects

Females

Males

aF = aM ? cF = cM ? eF = eM ?


Divorce effects

Divorce Effects

Divorced

Not Divorced

aD = aND ? cD = cND ? eD = eND ?


Problem

Problem:

  • Many variables of interest do not fall into groups

    • Age

    • Socioeconomic status

    • Regional alcohol sales

    • Parental warmth

    • Parental monitoring

      • Grouping these variables into high/low categories may lose information


Definition variables in mx

‘Definition variables’ in Mx

  • General definition: Definition variables are variables that may vary per subject and that are not dependent variables

  • In Mx:The specific value of the def var for a specific individual is read into a matrix in Mx when analyzing the data of that particular individual


Definition variables in mx1

‘Definition variables’ in Mx

create dynamic var/cov structure

  • Common uses:

  • To model changes in variance components as function of some variable (e.g., age, SES, etc)

  • As covariates/effects on the means (e.g. age and sex)


Standard model

Standard model

  • Means vector

  • Covariance matrix


Model fitting approach to gxe

Model-fitting approach to GxE

A

C

E

A

C

E

c

a

e

a

c

e

m

m

Twin 1

Twin 2

M

M


Model fitting approach to gxe1

Model-fitting approach to GxE

A

C

E

A

C

E

c

a+XM

e

a+XM

c

e

m

m

Twin 1

Twin 1

Twin 2

Twin 2

M

M


Individual specific moderators

Individual specific moderators

A

C

E

A

C

E

c

a+XM1

e

a+XM2

c

e

m

m

Twin 1

Twin 1

Twin 2

Twin 2

M

M


E x e interactions

E x E interactions

A

C

E

A

C

E

c+YM1

c+YM2

a+XM1

a+XM2

e+ZM1

e+ZM2

m

m

Twin 1

Twin 1

Twin 2

Twin 2

M

M


Heterogeneity ii

Definition Variables in Mx

X

a

M1

Total genetic variance = a+XM1


Heterogeneity ii

  • Classic Twin Model:

    Var (P) = a2 + c2 + e2

  • Moderation Model:

    Var (P) =

    (a + βXM)2 + (c + βYM)2 + (e + βZM)2

Purcell 2002,

Twin Research


Heterogeneity ii

Var (T) = (a + βXM)2 + (c + βYM)2 (e + βZM)2

Where M is the value of the moderator and

Significance of βX indicates genetic moderation

Significance of βY indicates common environmental moderation

Significance of βZ indicates unique environmental moderation


Unstandardized versus standardized effects

Unstandardized versus standardized effects


Matrix letters as specified in mx script

Matrix Letters as Specified in Mx Script

A

C

E

A

C

E

c+YM1

c+YM2

a+XM1

a+XM2

c+cM*D1

c+cM*D2

e+ZM1

e+ZM2

a +aM*D1

a+aM*D2

e+eM*D2

e+eM*D1

Twin 1

Twin 2

M

M

m

m

mu

mu


Definition variables in mx2

‘Definition variables’ in Mx

create dynamic var/cov structure

  • Common uses:

  • To model changes in variance components as function of some variable (e.g., age, SES, etc)

  • As covariates/effects on the means (e.g. age and sex)


Definition variables used as covariates

Definition variables used as covariates

General model with age and sex as covariates:

yi =  + 1(agei) + 2 (sexi) + 

Where yi is the observed score of individual i, is theintercept or grand mean, 1is the regression weight of age, ageiis the age of individual i, 2 is the deviation of males (if sex is coded 0= female; 1=male), sexiis the sex ofindividual i, and is the residual that is not explained by the covariates (and can be decomposed further into ACE etc).


Allowing for a main effect of x

Allowing for a main effect of X

  • Means vector

  • Covariance matrix


Common uses of definition variables in the means model

Common uses of definition variables in the means model

  • Incorporating covariates (sex, age, etc)

  • Testing the effect of SNPs (association)

  • In the context of GxE, controlling for rGE


Adding covariates to means model

Adding Covariates to Means Model

A

C

E

A

C

E

c

a

e

a

c

e

m+MM1

m+MM2

Twin 1

Twin 2

M

M


Matrix letters as specified in mx script1

Matrix Letters as Specified in Mx Script

A

C

E

A

C

E

c+YM1

c+YM2

a+XM1

a+XM2

c+cM*D1

c+cM*D2

e+ZM1

e+ZM2

a +aM*D1

a+aM*D2

e+eM*D2

e+eM*D1

Twin 1

Twin 2

M

M

m+MM2

m+MM1

mu+b*D2

mu+b*D1

Main effects and moderating effects


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