Background: The application of network analysis to explore the interactions among symptoms of mental disorders has demonstrated great promise in treatment intervention. However, current network studies are restricted at the phenotypic level, without incorporating genetic risk into network framework albeit mental disorders are highly heritable. Major Depressive Disorder (MDD) is a common mental disorder with great symptom heterogeneity. The current conceptualization of MDD as a single entity faces great challenges in elucidating the mechanism of symptoms clustering. An alternative approach using network analysis is a unique way to investigate causal relations between MDD symptoms.
Aim: To understand how genetic risk for MDD can alter the network structure for MDD symptoms in people with the diagnosis
Method: Polygenic risk score (PRS) for MDD patients will be calculated and used to split individuals into those with high vs low genetic risk for MDD. A symptom level network analysis will be conducted for each group respectively, followed by comparing the network structure (e.g., connectivity) between the two groups. Centrality metrics (e.g., strength) to identify the most central symptoms for each network will be evaluated.
Expected outcome: The group with high genetic liability to MDD is expected to have greater symptom connectivity compared to the low genetic liability group. Genetic risk (i.e., PRS) for MDD is expected to have direct associations with some MDD symptoms but not all. Central symptoms that play important roles in symptom clustering will be identified for each group.
Implication: This study can build on evidence for the association between network connectivity and risk for psychopathology. Central symptoms in each network can be considered as the target for intervention for MDD patients.