1 Major Depressive Disorder in Twins

MZ | DZ | ||||

Twin 1 | Normal | Depressed | Normal | Depressed | |

Twin 2 | Normal | 329 | 83 | 201 | 94 |

Depressed | 95 | 83 | 82 | 63 |

PRELIS estimates of the correlation in liability to depression are .435 for MZ and .186 for DZ pairs. Details of using PRELIS to derive these statistics and associated estimates of their asymptotic variances are given in Section 2.3. The

`PMatrix`

command is used to read in the tetrachoric
correlation matrix, and the `ACov`

command reads the asymptotic
weight matrices. In both cases we use the `File=`

keyword in
order to read these data from files. Therefore our univariate Mx
input script is unchanged from that shown in Appendix
on page , except for the title and the dat file used.
Major depressive disorder in adult female MZ twins Data NInput_vars=2 NObservations=590 #Include mzdepsum.datwhere the dat file reads

PMatrix File=MZdep.cov ACov File=MZdep.asyin the MZ group, with the same commands for the DZ group except for the number of observations (

`NObs=440`

) and a global replacement
of DZ for MZ. For clarity, the comments at the beginning also should
be changed.
Results of fitting the ACE and ADE models and submodels are summarized
in
Table 6.10.
Parameter Estimates | Fit statistics | ||||||

Model | df | ||||||

-- | -- | 1.00 | -- | 56.40 | 2 | .00 | |

-- | 0.58 | 0.81 | -- | 6.40 | 1 | .01 | |

0.65 | -- | 0.76 | -- | .15 | 1 | .70 | |

0.65 | -- | 0.76 | -- | .15 | 0 | -- | |

0.56 | -- | 0.75 | 0.36 | .00 | 0 | -- |

First, note that the degrees of freedom for fitting to correlation matrices are fewer than when fitting to covariance matrices. Although we provide Mx with two correlation matrices, each consisting of 1's on the diagonal and a correlation on the off-diagonal, the 1's on the diagonal cannot be considered unique. In fact, only one of them conveys information which effectively `scales' the covariance. There is no information in the remaining three 1's on the diagonals of the MZ and DZ correlation matrices,

`Option DFreedom=-3`

. Another way of looking
at this is that the diagonal 1's convey no information whatsoever, but
that we use one parameter to estimate the diagonal elements (; it
appears only in the expected variances, not the expected covariances).
Thus, there are 4 imaginary variances and 1 parameter to estimate them
-- giving 3 statistics too many.
Second, the substantive interpretation of the results is that the
model with just random environment fails, indicating significant
familial aggregation for diagnoses of major depressive disorder. The
environmental explanation of familial covariance also fails
() but a model of additive genetic and random
environment effects fits well (). There is no possible
room for significant improvement with the addition of any other
parameter, since there are only .15 units left.
Nevertheless, we fitted both ACE and ADE models and found that
dominance genetic effects could account for the remaining variability
whereas shared environmental effects could not. This finding is in
agreement with the observation that the MZ correlation is slightly
greater than twice the DZ correlation. The heritability of liability
to Major Depressive Disorder is moderate but significant at 42%, with
the remaining variability associated with random environmental sources
including error of measurement. These results are not compatible with
the view that shared family experiences such as parental rearing,
social class, or parental loss are key factors in the etiology of
major depression. More modest effects of these factors may be
detected by including them in multivariate model fitting
(Kendler