Letters to the Editor
March 2007

Promoting Measured Genes and Measured Environments: On the Importance of Careful Statistical Analyses and Biological Relevance

Author Affiliations

Copyright 2007 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.2007

Arch Gen Psychiatry. 2007;64(3):377-378. doi:10.1001/archpsyc.64.3.377

Research testing the interaction between measured genes and measured environments in psychiatric disorders was promoted in a recent review by Moffitt et al1 in the ARCHIVES. In presenting the emerging gene × environment interaction findings, Caspi et al2 cite their finding of an interaction between the genetic variants of monoamine oxidase A conferring low enzymatic activity and childhood maltreatment to increase the risk for violent behavior. Moffitt et al cite one replication of this finding by Foley et al3 that was published in the ARCHIVES. A careful perusal of this latter study, which has now been cited 19 times, shows that it is flawed both in its analyses and interpretation. First, Foley et al argued that the results obtained with logistic regression are likely to be more robust than results obtained within a linear regression framework. However, logistic regression has the disadvantage of collapsing ordinal or count response variables into a dichotomous variable, which may result in loss of information. Ordinal regression, negative binomial regression, or Poisson regression models are robust and more appropriate techniques when analyzing disorder symptom counts.4 Second, although Foley et al favored logistic over linear regression to avoid false-positive interactions due to scaling artifacts (heteroscedasticity), they did not assess or report the fit of their model. The presence of zero or small cell counts in interaction terms (as evident from Table 2 in the Foley et al article) may cause numerical problems in the modeling stage of the analysis.5 Using the raw data provided in the Foley et al Table 2, we found that the logistic regression model presented has poor fit (Hosmer-Lemeshow test, χ24 = 8.9; P = .06). However, model fit improved when we grouped categories 2, 3, and 4 of environmental adversity and used 3 (0, 1, and 2-4) instead of 5 categories (χ23 = 5.5; P = .14). However, the interaction between monoamine oxidase A and environmental adversity was nonsignificant (P = .36, 2-sided test). This is not surprising and could have been suspected simply by noticing that too many cells (7 [35%] of 20) in Table 2 had between 0 and 4 observations. It is also surprising to see the misleading Figure published in the highly reputed ARCHIVES, where a strong visual effect of interaction is in fact due to 1 observation made on a sample size of n = 1 (1/1 = 100%!). Finally, in this study, the monoamine oxidase A genotypes conferring low enzymatic activity are associated with a decreased risk of antisocial behavior whereas the same genotypes in combination with environmental adversity are associated with the opposite effect. In contradiction with the principal of parsimony, Foley et al interpreted this observation as an important finding indicating the complicated nature of psychiatric genetics. This may simply reflect the lack of rigor in the application of statistical methods to complex psychiatric disorders.

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