page_title: Twin study
section_title: Modeling the environment: MZ discordant designs
hierarchical_section_title: Twin study / Methods / Modern modeling / Modeling the environment: MZ discordant designs
caption_attribution_description: English: A cross-lagged longitudinal MZ discordant twin design. This model can take account of relationships among differences across traits at time one, and then examine the distinct hypotheses that increments in trait1 drive subsequent change in that trait in the future, or, importantly, in other traits.
context_page_description: Twin studies are studies conducted on identical or fraternal twins. They aim to reveal the importance of environmental and genetic influences for traits, phenotypes, and disorders. Twin research is considered a key tool in behavioral genetics and in content fields, from biology to psychology. Twin studies are part of the broader methodology used in behavior genetics, which uses all data that are genetically informative – siblings studies, adoption studies, pedigree, etc. These studies have been used to track traits ranging from personal behavior to the presentation of severe mental illnesses such as schizophrenia.
  Twins are a valuable source for observation because they allow the study of environmental influence and varying genetic makeup: "identical" or monozygotic twins share essentially 100% of their genes, which means that most differences between the twins are due to experiences that one twin has but not the other twin. "Fraternal" or dizygotic twins share only about 50% of their genes, the same as any other sibling. Twins also share many aspects of their environment because they are born into the same family.
context_section_description: As MZ twins share both their genes and their family-level environmental factors, any differences between MZ twins reflect E: the unique environment. Researchers can use this information to understand the environment in powerful ways, allowing epidemiological tests of causality that are otherwise typically confounded by factors such as gene-environment covariance, reverse causation and confounding.
  An example of a positive MZ discordant effect is shown below on the left. The twin who scores higher on trait 1 also scores higher on trait 2. This is compatible with a "dose" of trait 1 causing an increase in trait 2. Of course, trait 2 might also be affecting trait 1. Disentangling these two possibilities requires a different design (see below for an example). A null result is incompatible with a causal hypothesis.
  Take for instance the case of an observed link between depression and exercise (See Figure above on right). People who are depressed also reporting doing little physical activity. One might hypothesise that this is a causal link: that "dosing" patients with exercise would raise their mood and protect against depression. The next figure shows what empirical tests of this hypothesis have found: a null result.
  Longitudinal discordance designs
  As may be seen in the next Figure, this design can be extended to multiple measurements, with consequent increase in the kinds of information that one can learn. This is called a cross-lagged model (multiple traits measured over more than one time).
  In the longitudinal discordance model, differences between identical twins can be used to take account of relationships among differences across traits at time one (path A), and then examine the distinct hypotheses that increments in trait1 drive subsequent change in that trait in the future (paths B and  E), or, importantly, in other traits (paths C & D). In the example, the hypothesis that the observed correlation where depressed persons often also exercise less than average is causal, can be tested. If exercise is protective against depression, then path D should be significant, with a twin who exercises more showing less depression as a consequence.
caption: A cross-lagged longitudinal MZ discordant twin design. This model can take account of relationships among differences across traits at time one, and then examine the distinct hypotheses that increments in trait1 drive subsequent change in that trait in the future, or, importantly, in other traits.
exif: {}