In addition we have quantified the rate of DNA
In addition, we have quantified the rate of DNA methylations across 450,000 CpG sites in a subset of the adolescents (n = 132) and their parents (n = 280); this was accomplished by hybridizing DNA to the Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA). This chip interrogates methylation at >485,000 CpG sites, providing coverage of >99% RefSeq genes; the CpG sites are targeted across gene regions including the promoter, 5′UTR, first exon, gene body, and 3′UTR, as well as intergenic sequences (Sandoval et al., 2011).
Saguenay Youth Study: highlights Before proceeding with the description of the parent arm of the SYS cohort (Wave 2: Parents), let us briefly highlight some of the published observations made in Wave 1: Adolescents. We will focus here on findings relevant to maternal smoking during pregnancy and addictive behavior; our work on sex differences in the maturation of white matter (Herve et al., 2009; Perrin et al., 2008, 2009), puberty-related changes in the face morphology (Mareckova et al., 2011, 2013) and cardio-metabolic health (Goodwin et al., 2013; Melka et al., 2013a, 2012; Pausova et al., 2010, 2012; Syme et al., 2008, 2009) can be found elsewhere. One of the most common consequences of MSP is intra-uterine growth retardation (Lowe, 1959); this is not surprising given the multiple effects of cigarette smoking on the supply of nutrients and oxygen to the fetus (reviewed in Pausova et al., 2007; Slotkin, 1998). As shown with fetal imaging, 7 8-dihydroxyflavone growth does not appear to escape this global phenomenon (Anblagan et al., 2013). By the time the exposed offspring reaches adolescence, however, the brain size appears to be the same as that of non-exposed adolescents. Nonetheless, we asked whether this is the case also for individuals with a particular genetic variation associated with brain size, as revealed in a genome-wide association study (GWAS) in the SYS adolescents. We found that this was not so: exposed female adolescents with the KCTD8 risk-variant had smaller surface area of the cerebral cortex than non-exposed females without this variant (Fig. 6; Paus et al., 2012). We have speculated that this gene-environment interaction reflects an accelerated apoptosis of progenitor cells in the developing brains of embryos/fetuses who possess this particular genetic variant (Paus et al., 2012). Above and beyond global brain growth, we have observed differences between the exposed and non-exposed (female) adolescents in the (relative) size of the corpus callosum (Paus et al., 2008b) and the thickness of the orbitofrontal cortex, OFC (Toro et al., 2008). We followed up the latter finding and asked whether there is a relationship between the OFC thickness and drug experimentation; in this context, we have examined the role of the known functional polymorphism in the BDNF gene (and the methylation status of its promoters) in moderating this relationship (Lotfipour et al., 2009; Toledo-Rodriguez et al., 2010). We also investigated a relationship between genetic variations in alpha 6 nicotinic receptor gene (CHRNA6), striatal volume and drug experimentation (Lotfipour et al., 2010). Prenatal exposure to maternal smoking during pregnancy is a well-established risk factor for obesity (Al Mamun et al., 2006; Ino, 2010; Leary et al., 2006; Oken et al., 2005, 2008; Power et al., 2010; Power and Jefferis, 2002; Syme et al., 2010; von Kries et al., 2002; Weng et al., 2012). In an exposed individual, it increases the likelihood for developing obesity by 50%, (Ino, 2010; Weng et al., 2012). Given this higher risk (odds ratio of 1.5), and the prevalence of MSP in the 1960s and 1970s (40%) and currently (16%), we estimated that – at present – up to 16% of obesity in middle-aged adults and 7.4% of obesity in children is attributable to MSP. The underlying mechanisms of the link between MSP and obesity are not clear, however. Our findings in the SYS suggest that reward-related mechanisms may be at play. We showed that MSP is associated with substantial increases in body adiposity (Syme et al., 2010), and higher preference for fat accompanied by smaller amygdalae volumes (Haghighi et al., 2013). In a genome-wide association study, we also showed that dietary preference for fat (as well as body adiposity) is associated with genetic variation in the opioid receptor mu 1 gene (OPRM1) (Haghighi et al., 2014). Finally, we have demonstrated that MSP is associated with modifications of DNA methylation that persist into adolescence of the exposed offspring (Lee et al., 2014a), and that some of these modifications are present in OPRM1, and may inhibit expression of the protective (fat intake-lowering) allele of this gene (Lee et al., 2014b). Taken together, these observations suggest (a) the presence of relationships between the brain-reward system, dietary preference for fat and obesity; (b) perturbations of these relationships by MSP and genetic variations in OPRM1; and (c) DNA methylation as a possible molecular mechanism underlying interactions between environment (MSP) and genes (OPRM1).