Path Analysis Frühling Rijsdijk. Biometrical Genetic Theory Aims of session: Derivation of...
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![Page 1: Path Analysis Frühling Rijsdijk. Biometrical Genetic Theory Aims of session: Derivation of Predicted Var/Cov matrices Using: (1)Path Tracing Rules (2)Covariance.](https://reader036.fdocuments.net/reader036/viewer/2022062715/56649d795503460f94a5c817/html5/thumbnails/1.jpg)
Path Analysis
Frühling Rijsdijk
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BiometricalGeneticTheory
Aims of session:
Derivation of Predicted Var/Cov matrices Using:
(1) Path Tracing Rules
(2) Covariance Algebra
Predicted Var/Cov of the Model
Path TracingRules
System of Linear
Equations
Path Diagrams
Observed Var/Cov of the Data
CovarianceAlgebra
Twin Model
SEM
model building
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Method of Path Analysis
• Allows us to represent linear models for the relationship between variables in diagrammatic form, e.g. a genetic model; a factor model; a regression model
• Makes it easy to derive expectations for the variances and covariances of variables in terms of the parameters of the proposed linear model
• Permits easy translation into matrix formulation as used by programs such as Mx, OpenMx.
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Conventions of Path Analysis I• Squares or rectangles denote observed variables• Circles or ellipses denote latent (unmeasured) variables• Upper-case letters are used to denote variables• Lower-case letters (or numeric values) are used
to denote covariances or path coefficients• Single-headed arrows or paths (–>) represent hypothesized
causal relationships
- where the variable at the tail
is hypothesized to have a direct
causal influence on the variable
at the head a
Y = aX + eE
Y
VX
X
Ee
VE=1
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Conventions of Path Analysis II• Double-headed arrows (<–>) are used to represent a
covariance between two variables, which may arise through common causes not represented in the model.
• Double-headed arrows may also be used to represent the variance of a variable.
Y = aX + bZ + eE
a
Y
VX
X
Ee
VE=1
Z
VZ
b
r
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Conventions of Path Analysis III• Variables that do not receive causal input from any
one variable in the diagram are referred to as independent, or predictor or exogenous variables.
• Variables that do, are referred to as dependent or endogenous variables.
• Only independent variables are connected by double-headed arrows.
• Single-headed arrows may be drawn from independent to dependent variables or from dependent variables to other dependent variables.
a
Y
VX
X
Ee
VE=1
Z
VZ
b
r
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Conventions of Path Analysis IV
• Omission of a two-headed arrow between two independent variables implies the assumption that the covariance of those variables is zero
• Omission of a direct path from an independent (or dependent) variable to a dependent variable implies that there is no direct causal effect of the former on the latter variable
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Path TracingThe covariance between any two variables
is the sum of all legitimate chains
connecting the variables
The numerical value of a chain is the product of all traced path coefficients in it
A legitimate chain
is a path along arrows that follow 3 rules:
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(i) Trace backward, then forward, or simply forward from one variable to another. NEVER forward then backward!
Include double-headed arrows from the independent variables to itself. These variances will be
1 for latent variables
(ii) Loops are not allowed, i.e. we can not trace twice through
the same variable
(iii) There is a maximum of one curved arrow per path.
So, the double-headed arrow from the independent
variable to itself is included, unless the chain includes
another double-headed arrow (e.g. a correlation path)
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Since the variance of a variable is
the covariance of the variable with
itself, the expected variance will be
the sum of all paths from the variable
to itself, which follow the path tracing
rules
The Variance
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• Cov AB = kl + mqn + mpl• Cov BC = no• Cov AC = mqo
• Var A = k2 + m2 + 2 kpm• Var B = l2 + n2
• Var C = o2
A B C
D E F1
p q
k n om l
1 1
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Path Diagramsfor the Classical
Twin Model
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Quantitative Genetic Theory
• There are two sources of Genetic influences: Additive (A) and non-additive or Dominance (D)
• There are two sources of environmental influences: Common or shared (C) and non-shared or unique (E)
cd
PHENOTYPE
11
D C
a
1
A
e
1
E
P
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In the preceding diagram…
• A, D, C, E are independent variables– A = Additive genetic influences– D = Non-additive genetic influences (i.e.,
dominance)– C = Shared environmental influences– E = Non-shared environmental influences– A, D, C, E have variances of 1
• Phenotype is a dependent variable– P = phenotype; the measured variable
• a, d, c, e are parameter estimates
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PTwin 1
E C A1 1 1
PTwin 2
A C E1 1 1
Model for MZ Pairs Reared Together
1
1
Note: a, c and e are the same cross twins
e ac a ec
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PTwin 1
E C A1 1 1
PTwin 2
A C E1 1 1
1
.5
Note: a, c and e are also the same cross groups
e ac a ec
Model for DZ Pairs Reared Together
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PTwin 1
E
e c
C A1 1 1
Variance of Twin 1 AND Twin 2 (for MZ and DZ pairs)
a
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PTwin 1
E
e c
C A1 1 1
Variance of Twin 1 AND Twin 2 (for MZ and DZ pairs)
a
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PTwin 1
E
e c
C A1 1 1
Variance of Twin 1 AND Twin 2 (for MZ and DZ pairs)
a
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PTwin 1
E
e c
C A1 1 1
Variance of Twin 1 AND Twin 2 (for MZ and DZ pairs)
a*1*a = a2
+a
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PTwin 1
E
e c
C A1 1 1
Variance of Twin 1 AND Twin 2 (for MZ and DZ pairs)
a*1*a = a2
+c*1*c = c2
e*1*e = e2+
Total Variance = a2 + c2 + e2
a
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PTwin 1
E C A1 1 1
Covariance Twin 1-2: MZ pairs
PTwin 2
A C E1 1 1
1
1
e ac a ec
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PTwin 1
E C A1 1 1
Covariance Twin 1-2: MZ pairs
PTwin 2
A C E1 1 1
1
1
e ac a ec
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PTwin 1
E C A1 1 1
Covariance Twin 1-2: MZ pairs
Total Covariance = a2 +
PTwin 2
A C E1 1 1
1
1
e ac a ec
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PTwin 1
E C A1 1 1
Covariance Twin 1-2: MZ pairs
Total Covariance = a2 + c2
PTwin 2
A C E1 1 1
1
1
e ac a ec
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PTwin 1
E C A1 1 1
Covariance Twin 1-2: DZ pairs
Total Covariance = .5a2 + c2
PTwin 2
A C E1 1 1
.5
1
e ac a ec
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22222
22222
ecaca
caecaMZCov
Tw1 Tw2
Tw1
Tw2
22222
22222
2
12
1
ecaca
caecaDZCov
Tw1 Tw2
Tw1
Tw2
Predicted Var-Cov Matrices
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PTwin 1
E D A1 1 1
PTwin 2
A D E1 1 1
ADE Model
1(MZ) / 0.25 (DZ)
1/.5
e ad a ed
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22222
22222
edada
daedaMZCov
Tw1 Tw2
Tw1
Tw2
22222
22222
4
1
2
14
1
2
1
edada
daedaDZCov
Tw1 Tw2
Tw1
Tw2
Predicted Var-Cov Matrices
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ACE or ADE
Cov(mz) = a2 + c2 or a2 + d2
Cov(dz) = ½ a2 + c2 or ½ a2 + ¼ d2
VP = a2 + c2 + e2 or a2 + d2 + e2
3 unknown parameters (a, c, e or a, d, e), and only 3 distinct predictive statistics:
Cov MZ, Cov DZ, Vp
this model is just identified
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The twin correlations indicate which of the two components is more likely to fit the data:
Cor(mz) = a2 + c2 or a2 + d2
Cor(dz) = ½ a2 + c2 or ½ a2 + ¼ d2
If a2 =.40, c2 =.20 rmz = 0.60 rdz = 0.40 If a2 =.40, d2 =.20 rmz = 0.60 rdz = 0.25
Effects of C and D are confounded
ADE
ACE
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ADCE: classical twin design + adoption data
Cov(mz) = a2 + d2 + c2
Cov(dz) = ½ a2 + ¼ d2 + c2
Cov(adopSibs) = c2
VP = a2 + d2 + c2 + e2
4 unknown parameters (a, c, d, e), and 4 distinct predictive statistics:
Cov MZ, Cov DZ, Cov adopSibs, Vp
this model is just identified
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Path Tracing Rules are based on
Covariance Algebra
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Three Fundamental Covariance Algebra Rules
Cov (aX,bY) = ab Cov(X,Y)
Cov (X,Y+Z) = Cov (X,Y) + Cov (X,Z)
Var (X) = Cov(X,X)
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2
2
2
2
1
a
a
AVara
AACova
aAaACov
aAVarYVar
*
)(
),(
),(
)()(
The variance of a dependent variable (Y) caused by independent variable A, is the squared regression coefficient multiplied
by the variance of the independent variable
Example 1
Y
a
Y = aA
A
1
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52
2
.*
,,
a
Cov(A,A)a
aA)Cov(aAZ)Cov(Y
Example 2
Y
a
Y = aA
A
Z
a
Z = aA
A11
.5
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Summary• Path Tracing and Covariance Algebra
have the same aim :to work out the predicted Variances and Covariances of variables, given the specified model
• The Ultimate Goal is to fit Predicted Variances / Covariances to observed Variances / Covariances of the data in order to estimate model parameters :– regression coefficients, correlations