A bootstrapped commingling analysis of platelet monoamine oxidase activity levels corrected for cigarette smoking

E. Warwick Daw, John P. Rice, Robert M. Anthenelli, Marc A. Schuckit, Jayson Tipp, Nancy L. Saccone, Theodore Reich, John I. Nurnberger, Ting Kai Li

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1 Scopus citations


Monoamine oxidase (MAO) activity levels have been suggested as a possible biological marker for alcohol dependence and abuse, as well as for schizophrenia and other psychiatric conditions. Using platelet MAO activities in the Collaborative Study on the Genetics of Alcoholism data set, we applied bootstrapping methods as a novel way to test for admixture in families. This bootstrapping involved resampling in family units and hypothesis testing of the resampled datasets for commingling in the distribution of MAO activity levels. Prior to commingling analysis, we used linear models to find covariates of greatest effect on MAO activity levels. While an alcoholism diagnosis was significant in men (n = 1151, P < 0.0001) and women (n = 1254, P = 0.0003), the effect lost significance after controlling for cigarette smoking, indicating alcoholism and smoking behavior to be highly confounded. When smoking histories were compared, former smokers had levels (mean = 7.1) closer to those who never smoked (mean = 7.0) than to current smokers (mean = 5.4). Furthermore, current daily smoking and time since smoking cessation were significantly related to MAO levels, indicating smoking probably has a direct effect on MAO levels, rather than the reverse. These results suggest that studies using MAO levels as a biological marker should consider smoking as an important covariate. Finally, admixture was found in MAO levels controlled for smoking and sex, possibly indicating a major genetic locus; this confirms previous evidence for admixture.

Original languageEnglish
Pages (from-to)177-185
Number of pages9
JournalPsychiatric genetics
Issue number4
StatePublished - 2001


  • Admixture
  • Alcoholism
  • Family data
  • Mixed model
  • Quantitative trait
  • Resampling


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