This document discusses bivariate analysis of twin data to examine the genetic and environmental contributions to the covariance between two traits. It introduces bivariate twin models that partition the covariance into additive genetic (A), shared environmental (C), and unique environmental (E) components. The expected covariance matrices for MZ and DZ twins are presented. The goal of bivariate analysis is to understand what factors make sets of variables correlate through examining within-twin and cross-twin covariances. Examples of applications to real twin datasets are given.
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?
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
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