Functional Validation II: Neuroimaging x genetics
The goal of the multicenter subproject 10 of the eMED Alcohol Addiction Consortium - A Systems-Oriented Approach is (i) to study neuroimaging x genetics predictions in an existing sample (NGFNplus) of tightly endophenotyped and genome-wide genotyped alcohol dependent subjects (N=240) and controls (N=240); (ii) to translate the results of neuroimaging and genetic analyses from an adolescent risk sample (IMAGEN) to adult disease (NGFNplus sample) by examining related MRI-paradigms tagging the same functional brain systems in both samples (e.g. reward system, inhibitory control system, emotion processing, working memory); (iii) to conduct a follow-up neuroimaging study on the NGFNplus sample validating the neurobehavioral risk profiles predictive for juvenile harmful alcohol use in adult patients with alcohol addiction, (iv) to expand the NGFNplus sample by including a new set of healthy subjects with high genetic risk (1st degree relatives of patients with alcohol addiction). We will do so by using elaborate imaging genetic methods that are already available and successfully used in other multicenter studies by our group (e.g. univariate analyses, functional and effective connectivity analyses, polygenetic scores, network topology) as well as by using complex computational algorithms and mathematical models, in particular advanced machine learning methods, developed in TP 6. Our approach aims in the long run to predict and characterize longitudinal outcomes in patients with alcohol addiction (5 years following our index session) and to complement the NGFN-sample with an add-on study with 1st degree relatives that will allow us to test the generalizability of the identified predictive risk profiles for early risk identification.
In the first period of the project, we have identified potential genetic risk factors for alcohol use disorder (AUD). Our results so far highlight how the interaction between genetic profiles and neural activations to alcohol cues and structural brain alterations can help to understand alcoholism and predict relapse. Specifically, in AA-genotype carriers of the GATA4 gene, lower gray matter volumes in the amygdala and the caudate were related to higher relapse rates (Zois et al., 2016). In the G-genotype carriers however, higher relapse rates were associated with higher gray matter volume in these regions. Additionally, single nucleotide polymorphisms (SNPs) in the glutamatergic neurotransmission genes GRIN2C and GRIK1 were differentially related to altered neural responses in the anterior cingulate cortex and dorsolateral prefrontal cortex to alcoholic cues, which were further associated with relapse risk and craving for alcohol (Bach et al., 2015a). Also, G-allele carriers of the mu-opioid receptor gene OPRM1 showed increased neural responses to alcohol cues in the striatum and insula, which were further correlated with alcohol craving and predicted time to relapse (Bach et al., 2015b). Overall, our findings to date provide deeper insights into AUD and the contribution of genetic factors and brain function and structure (imaging genetics) in the development and continuation of AUD and addiction.
Keywords: alcoholism, genetics, endophenotyping, MRT, imaging genetics