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Bivariate analysis

Mx class 2004
Univariate ACE model
                                     1 or .5
                                        1
    1           1        1                1            1        1

        A           C            E             E           C            A
            a       c        e                     e       c        a

                    T1                                     T2
Expected Covariance Matrices

          a2+c2+e2    a2+c2
 E MZ =                         2x2
          a2+c2    a2+c2+e2


          a2+c2+e2    .5a2+c2
 E DZ =                         2x2
          .5a2+c2    a2+c2+e2
Bivariate Questions I
n   Univariate Analysis: What are the contributions
    of additive genetic, dominance/shared
    environmental and unique environmental factors
    to the variance?
n   Bivariate Analysis: What are the contributions of
    genetic and environmental factors to the
    covariance between two traits?
Two Traits

             AX   AC   AY



             X         Y


             EX   EC   EY
Bivariate Questions II
n   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?
n   With twin data on multiple traits it is possible to
    partition the covariation into its genetic and
    environmental components
n   Goal: to understand what factors make sets of
    variables correlate or co-vary
Bivariate Twin Data
                  individual twin

                  within            between

trait   within    variance          twin covariance

        between   trait covariance cross-trait
                                   twin covariance
Bivariate Twin Covariance Matrix

       X1      Y1      X2      Y2
X1     VX1     CX1Y1   CX1X2   CX1Y2
Y1     CY1X1   VY1     CY1X2   CY1Y2
X2     CX2X1   CX2Y1   VX2     CX2Y2
Y2     CY2X1   CY2Y1   CY2X2   VY2
Genetic Correlation
                          1 or .5   1 or .5

            rg                                     rg
   1             1                     1                1

       AX            AY                    AX               AY

       ax            ay                       ax            ay

       X1            Y1                    X2               Y2
Alternative Representations
                               1

         rg                        AC
1             1        1              1             1             1

    AX            AY       ASX             A   SY       A1            A2
                                   ac ac
    ax            ay       asx             asy          a11 a21       a22

    X1            Y1       X1              Y1           X1            Y1
Cholesky Decomposition
                       1 or .5   1 or .5


   1             1                  1             1

       A1            A2                 A1            A2

       a11 a21       a22                a11 a21       a22

       X1            Y1                 X2            Y2
More Variables


  1          1         1           1         1

      A1         A2        A3          A4        A5

      a11 a21 aa22 a32 a4233 a43
               31       a              a44

      X1         X2        X3          X4        X5
Bivariate AE Model
                       1 or .5   1 or .5
   1             1                  1             1

       A1            A2                 A1            A2

       a11 a21       a22                a11 a21       a22

       X1            Y1                 X2            Y2
       e11 e21       e22                e11 e21       e22

       E1            E2                 E1            E2
   1             1                  1             1
MZ Twin Covariance Matrix

      X1          Y1     X2         Y2
X1    a112+e112           a112

Y1    a21*a11+ a222+a212+ a21*a11   a222+a212
      e21*e11 e222+e212

X2
Y2
DZ Twin Covariance Matrix

      X1          Y1     X2        Y2
X1    a112+e112           .5a112

Y1    a21*a11+ a222+a212+ .5a21*a11 .5a222+
      e21*e11 e222+e212             .5a212

X2
Y2
Within-Twin Covariances [Mx]
                       1              1

                           A1             A2               X Lower 2 2

                                                                 A1 A2
                           a11 a21        a22
                                                          X1     a11   0
                           X1             Y1              Y1     a21 a22
A=X*X'

         a11   0           a11 a21               a112           a11*a21
EA=                *                      =
         a21 a22           0    a22             a21*a11        a222+a212
Within-Twin Covariances
               a112      a11*a21
       EA=
              a21*a11   a222+a212


               e112      e11*e21
       EE=
              e21*e11   e222+e212


                  a112+ e112         a11*a21 + e11*e21
EP= EA+EE =
               a21*a11 + e21*e11    a222+a212 +e222+e212
Cross-Twin Covariances
                a112       a11*a21
MZ      EA=
              a21*a11     a222+a212


               .5a112     .5a11*a21
DZ   .5@EA=
              .5a21*a11   .5a222+.5a212
Cross-Trait Covariances
n Within-twin cross-trait covariances imply
  common etiological influences
n Cross-twin cross-trait covariances imply
  familial common etiological influences
n MZ/DZ ratio of cross-twin cross-trait
  covariances reflects whether common
  etiological influences are genetic or
  environmental
Univariate Expected Covariances

          a2+c2+e2    a2+c2
 E MZ =                         2x2
          a2+c2    a2+c2+e2


          a2+c2+e2    .5a2+c2
 E DZ =                         2x2
          .5a2+c2    a2+c2+e2
Univariate Expected Covariances II


          EA+EC+EC    EA+EC
 E MZ =                         2x2
          EA+EC    EA+EC+EC




          EA+EC+EC   .5@EA+EC
 E DZ =                         2x2
          .5@EA+EC   EA+EC+EC
Bivariate Expected Covariances

          EA+EC+EC    EA+EC
 E MZ =                         4x4
          EA+EC    EA+EC+EC




          EA+EC+EC   .5@EA+EC
 E DZ =                         4x4
          .5@EA+EC   EA+EC+EC
Practical Example I
n Dataset: MCV-CVT Study
n 1983-1993
n BMI, skinfolds (bic,tri,calf,sil,ssc)
n Longitudinal: 11 years
n N MZFY: 107, DZF: 60
Practical Example II
n Dataset: NL MRI Study
n 1990’s
n Working Memory, Gray & White Matter


n   N MZFY: 68, DZF: 21

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Bivariate

  • 2. Univariate ACE model 1 or .5 1 1 1 1 1 1 1 A C E E C A a c e e c a T1 T2
  • 3. Expected Covariance Matrices a2+c2+e2 a2+c2 E MZ = 2x2 a2+c2 a2+c2+e2 a2+c2+e2 .5a2+c2 E DZ = 2x2 .5a2+c2 a2+c2+e2
  • 4. Bivariate Questions I n Univariate Analysis: What are the contributions of additive genetic, dominance/shared environmental and unique environmental factors to the variance? n Bivariate Analysis: What are the contributions of genetic and environmental factors to the covariance between two traits?
  • 5. Two Traits AX AC AY X Y EX EC EY
  • 6. Bivariate Questions II n 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? n With twin data on multiple traits it is possible to partition the covariation into its genetic and environmental components n Goal: to understand what factors make sets of variables correlate or co-vary
  • 7. Bivariate Twin Data individual twin within between trait within variance twin covariance between trait covariance cross-trait twin covariance
  • 8. Bivariate Twin Covariance Matrix X1 Y1 X2 Y2 X1 VX1 CX1Y1 CX1X2 CX1Y2 Y1 CY1X1 VY1 CY1X2 CY1Y2 X2 CX2X1 CX2Y1 VX2 CX2Y2 Y2 CY2X1 CY2Y1 CY2X2 VY2
  • 9. Genetic Correlation 1 or .5 1 or .5 rg rg 1 1 1 1 AX AY AX AY ax ay ax ay X1 Y1 X2 Y2
  • 10. Alternative Representations 1 rg AC 1 1 1 1 1 1 AX AY ASX A SY A1 A2 ac ac ax ay asx asy a11 a21 a22 X1 Y1 X1 Y1 X1 Y1
  • 11. Cholesky Decomposition 1 or .5 1 or .5 1 1 1 1 A1 A2 A1 A2 a11 a21 a22 a11 a21 a22 X1 Y1 X2 Y2
  • 12. More Variables 1 1 1 1 1 A1 A2 A3 A4 A5 a11 a21 aa22 a32 a4233 a43 31 a a44 X1 X2 X3 X4 X5
  • 13. Bivariate AE Model 1 or .5 1 or .5 1 1 1 1 A1 A2 A1 A2 a11 a21 a22 a11 a21 a22 X1 Y1 X2 Y2 e11 e21 e22 e11 e21 e22 E1 E2 E1 E2 1 1 1 1
  • 14. MZ Twin Covariance Matrix X1 Y1 X2 Y2 X1 a112+e112 a112 Y1 a21*a11+ a222+a212+ a21*a11 a222+a212 e21*e11 e222+e212 X2 Y2
  • 15. DZ Twin Covariance Matrix X1 Y1 X2 Y2 X1 a112+e112 .5a112 Y1 a21*a11+ a222+a212+ .5a21*a11 .5a222+ e21*e11 e222+e212 .5a212 X2 Y2
  • 16. Within-Twin Covariances [Mx] 1 1 A1 A2 X Lower 2 2 A1 A2 a11 a21 a22 X1 a11 0 X1 Y1 Y1 a21 a22 A=X*X' a11 0 a11 a21 a112 a11*a21 EA= * = a21 a22 0 a22 a21*a11 a222+a212
  • 17. Within-Twin Covariances a112 a11*a21 EA= a21*a11 a222+a212 e112 e11*e21 EE= e21*e11 e222+e212 a112+ e112 a11*a21 + e11*e21 EP= EA+EE = a21*a11 + e21*e11 a222+a212 +e222+e212
  • 18. Cross-Twin Covariances a112 a11*a21 MZ EA= a21*a11 a222+a212 .5a112 .5a11*a21 DZ .5@EA= .5a21*a11 .5a222+.5a212
  • 19. Cross-Trait Covariances n Within-twin cross-trait covariances imply common etiological influences n Cross-twin cross-trait covariances imply familial common etiological influences n MZ/DZ ratio of cross-twin cross-trait covariances reflects whether common etiological influences are genetic or environmental
  • 20. Univariate Expected Covariances a2+c2+e2 a2+c2 E MZ = 2x2 a2+c2 a2+c2+e2 a2+c2+e2 .5a2+c2 E DZ = 2x2 .5a2+c2 a2+c2+e2
  • 21. Univariate Expected Covariances II EA+EC+EC EA+EC E MZ = 2x2 EA+EC EA+EC+EC EA+EC+EC .5@EA+EC E DZ = 2x2 .5@EA+EC EA+EC+EC
  • 22. Bivariate Expected Covariances EA+EC+EC EA+EC E MZ = 4x4 EA+EC EA+EC+EC EA+EC+EC .5@EA+EC E DZ = 4x4 .5@EA+EC EA+EC+EC
  • 23. Practical Example I n Dataset: MCV-CVT Study n 1983-1993 n BMI, skinfolds (bic,tri,calf,sil,ssc) n Longitudinal: 11 years n N MZFY: 107, DZF: 60
  • 24. Practical Example II n Dataset: NL MRI Study n 1990’s n Working Memory, Gray & White Matter n N MZFY: 68, DZF: 21