Gene–environment interaction of monoamine oxidase A in relation to antisocial behaviour: current and future directions

Since the pioneering finding of Caspi and co-workers in 2002 that exposure to childhood maltreatment predicted later antisocial behaviour (ASB) in male carriers of the low-activity MAOA -uVNTR allele, frequent replication studies have been published. Two meta-analyses, one in 2006 and the other in 2...

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Veröffentlicht in:Journal of Neural Transmission 2018-11, Vol.125 (11), p.1601-1626
Hauptverfasser: Nilsson, Kent W., Åslund, Cecilia, Comasco, Erika, Oreland, Lars
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Sprache:eng
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Zusammenfassung:Since the pioneering finding of Caspi and co-workers in 2002 that exposure to childhood maltreatment predicted later antisocial behaviour (ASB) in male carriers of the low-activity MAOA -uVNTR allele, frequent replication studies have been published. Two meta-analyses, one in 2006 and the other in 2014, confirmed the original findings by Caspi and co-workers. In the present paper, we review the literature, note some methodological aspects of candidate gene–environment interaction (cG×E) studies and suggest some future directions. Our conclusions are as follows. (1) The direction of the effect in a cG×E model may differ according to the positive and negative environmental background of the population. (2) There is a predictor-intersection problem such that when measuring one type of maltreatment in a person, other kinds of maltreatment often co-occur. Other forms of abuse are implicitly considered in statistical models; therefore, it is difficult to draw conclusions about the effects of timing and the severity of different forms of stressful life events in relation to ASB. (3) There is also an outcome-intersection problem because of the major intersection of ASB and other forms of mental health problems. It is likely that the G×E with MAOA is related to a common unmeasured factor. (4) For the G×E model, in which the effect of the gene on the outcome variable is dependent on other predictor variables, theoretically, hypothesis-driven statistical modelling is needed.
ISSN:0300-9564
1435-1463
1435-1463
DOI:10.1007/s00702-018-1892-2