Neurogenetics of depression: a focus on reward processing and stress sensitivity

Abstract

Major depressive disorder (MDD) is etiologically complex and has a heterogeneous presentation. This heterogeneity hinders the ability of molecular genetic research to reliably detect the small effects conferred by common genetic variation. As a result, significant research efforts have been directed at investigating more homogenous intermediate phenotypes believed to be more proximal to gene function and lie between genes and/or environmental effects and disease processes. In the current review we survey and integrate research on two promising intermediate phenotypes linked to depression: reward processing and stress sensitivity. A synthesis of this burgeoning literature indicates that a molecular genetic approach focused on intermediate phenotypes holds significant promise to fundamentally improve our understanding of the pathophysiology and etiology of depression, which will be required for improved diagnostic definitions and the development of novel and more efficacious treatment and prevention strategies. We conclude by highlighting challenges facing intermediate phenotype research and future development that will be required to propel this pivotal research into new directions.

Figure 1

A biologically-informed multilocus dopamine profile across five polymorphic loci accounts for 10.9% of the variance in right ventral striatal reactivity (shown on the left) to reward within a card guessing paradigm. Summation used to capture increased DA signaling: DRD2 rs1799732 (deletion = 1, insertion = 0), rs1800497 (C/C = 1, C/T = 0.5, T/T = 0); DRD4 exon 3 VNTR (7-R = 1, all others = 0); SLC6A3 3’ UTR VNTR (9-R = 1, 10/10 = 0); COMT rs4680 (Met/Met = 1, Val/Met = 0.5, Val/Val = 0). Adapted from Nikolova et al. (2011).

Figure 2

A homozygosity at rs12938031 within CRHR1 is associated with deficits in reward learning (as represented by response bias) under stress. Stress was manipulated experimentally (threat-of-shock) in a within-subject design. Adapted from Bogdan et al. (2011).