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Bivariate analysis

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Bivariate analysis. Mx class 2004. Univariate ACE model. Expected Covariance Matrices ... Dataset: NL MRI Study. 1990's. Working Memory, Gray & White Matter. N ... – PowerPoint PPT presentation

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Title: Bivariate analysis


1
Bivariate analysis
  • Mx class 2004

2
Univariate ACE model
3
Expected Covariance Matrices
4
Bivariate Questions I
  • Univariate Analysis What are the contributions
    of additive genetic, dominance/shared
    environmental and unique environmental factors to
    the variance?
  • Bivariate Analysis What are the contributions of
    genetic and environmental factors to the
    covariance between two traits?

5
Two Traits
6
Bivariate Questions II
  • Two or more traits can be correlated because they
    share common genes or common environmental
    influences
  • e.g. Are the same genetic/environmental factors
    influencing the traits?
  • With twin data on multiple traits it is possible
    to partition the covariation into its genetic and
    environmental components
  • Goal to understand what factors make sets of
    variables correlate or co-vary

7
Bivariate Twin Data
variance
twin covariance
trait covariance
cross-trait twin covariance
8
Bivariate Twin Covariance Matrix
VX1 CX1X2 CX2X1 VX2
CX1Y1 CX2Y2
CX1Y2 CX2Y1
VY1 CY1Y2 CY2Y1 VY2
CY1X1 CY2X2
CY1X2 CY2X1
9
Genetic Correlation
10
Alternative Representations
11
Cholesky Decomposition
12
More Variables
13
Bivariate AE Model
14
MZ Twin Covariance Matrix
a112e112
a112
a222a212 e222e212
a21a11 e21e11
a222a212
a21a11
15
DZ Twin Covariance Matrix
a112e112
.5a112
a222a212 e222e212
a21a11 e21e11
.5a222 .5a212
.5a21a11
16
Within-Twin Covariances Mx
17
Within-Twin Covariances
18
Cross-Twin Covariances
19
Cross-Trait Covariances
  • Within-twin cross-trait covariances imply common
    etiological influences
  • Cross-twin cross-trait covariances imply familial
    common etiological influences
  • MZ/DZ ratio of cross-twin cross-trait covariances
    reflects whether common etiological influences
    are genetic or environmental

20
Univariate Expected Covariances
21
Univariate Expected Covariances II
22
Bivariate Expected Covariances
23
Practical Example I
  • Dataset MCV-CVT Study
  • 1983-1993
  • BMI, skinfolds (bic,tri,calf,sil,ssc)
  • Longitudinal 11 years
  • N MZFY 107, DZF 60

24
Practical Example II
  • Dataset NL MRI Study
  • 1990s
  • Working Memory, Gray White Matter
  • N MZFY 68, DZF 21
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