Dopamine Genetic Risk Score Predicts Depressive Symptoms in Healthy Adults and Adults with Depression

Background: Depression is a common source of human disability for which etiologic insights remain limited. Although abnormalities of monoamine neurotransmission, including dopamine, are theorized to contribute to the pathophysiology of depression, evidence linking dopamine-related genes to depression has been mixed. The current study sought to address this knowledge-gap by examining whether the combined effect of dopamine polymorphisms was associated with depressive symptomatology in both healthy individuals and individuals with depression. Methods: Data were drawn from three independent samples: (1) a discovery sample of healthy adult participants (n = 273); (2) a replication sample of adults with depression (n = 1,267); and (3) a replication sample of healthy adult participants (n = 382). A genetic risk score was created by combining functional polymorphisms from five genes involved in synaptic dopamine availability (COMT and DAT) and dopamine receptor binding (DRD1, DRD2, DRD3). Results: In the discovery sample, the genetic risk score was associated with depressive symptomatology (β = −0.80, p = 0.003), with lower dopamine genetic risk scores (indicating lower dopaminergic neurotransmission) predicting higher levels of depression. This result was replicated with a similar genetic risk score based on imputed genetic data from adults with depression (β = −0.51, p = 0.04). Results were of similar magnitude and in the expected direction in a cohort of healthy adult participants (β = −0.86, p = 0.15). Conclusions: Sequence variation in multiple genes regulating dopamine neurotransmission may influence depressive symptoms, in a manner that appears to be additive. Further studies are required to confirm the role of genetic variation in dopamine metabolism and depression

Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures

The goal of the Brain Genomics Superstruct Project (GSP) is to enable large-scale exploration of the links between brain function, behavior, and ultimately genetic variation. To provide the broader scientific community data to probe these associations, a repository of structural and functional magnetic resonance imaging (MRI) scans linked to genetic information was constructed from a sample of healthy individuals. The initial release, detailed in the present manuscript, encompasses quality screened cross-sectional data from 1,570 participants ages 18 to 35 years who were scanned with MRI and completed demographic and health questionnaires. Personality and cognitive measures were obtained on a subset of participants. Each dataset contains a T1-weighted structural MRI scan and either one (n=1,570) or two (n=1,139) resting state functional MRI scans. Test-retest reliability datasets are included from 69 participants scanned within six months of their initial visit. For the majority of participants self-report behavioral and cognitive measures are included (n=926 and n=892 respectively). Analyses of data quality, structure, function, personality, and cognition are presented to demonstrate the dataset’s utility

Identification of common variants associated with human hippocampal and intracranial volumes

Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer\u27s disease(1,2) and is reduced in schizophrenia(3), major depression(4) and mesial temporal lobe epilepsy(5). Whereas many brain imaging phenotypes are highly heritable(6,7), identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 x 10(-16)) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 x 10(-12)). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 x 10(-7))

Adjusted association between dopamine score and depressive symptoms.

<p>Cell entries are beta coefficients, standard errors (s.e.), p-values and 95% confidence intervals (CI). The HS model controlled for race/ethnicity. The STAR*D model contained controls for age (continuous), sex (0 = male; 1 = female); marital status (0 = married/cohabiting; 1 = never married; 2 = divorced, widowed, or separated); and five principle components for genetic ancestry/population stratification. The GSP model controlled for age (continuous), sex (0 = male; 1 = female), and four principle components for genetic ancestry/population stratification. Depressive symptoms were measured by 3 scales: CES-D (HS), HAM-D (STAR*D), POMS short form (GSP).</p

Summary of polymorphisms and classification for the genetic risk score.

<p>Summary of polymorphisms and classification for the genetic risk score.</p

Adjusted association between individual dopamine variants and depressive symptoms.

<p>Cell entries are beta coefficients, standard errors (s.e.), p-values and 95% confidence intervals (CI). The HS model controlled for race/ethnicity.</p

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition