Imaging Genetics and Genomics in Psychiatry: A Critical Review of Progress and Potential

Artificial Intelligence for Cardiac Imaging-Genetics Research

Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.

Monoamine and neuroendocrine gene-sets associate with frustration-based aggression in a gender-specific manner

Investigating phenotypic heterogeneity in aggression and understanding the molecular biological basis of aggression subtypes may lead to new prevention and treatment options. In the current study, we evaluated the taxonomy of aggression and examined specific genetic mechanisms underlying aggression subtypes in healthy males and females. Confirmatory Factor Analysis (CFA) was used to replicate a recently reported three-factor model of the Reactive Proactive Questionnaire (RPQ) in healthy adults (n = 661; median age 24.0 years; 41% male). Gene-set association analysis, aggregating common genetic variants within (a combination of) three molecular pathways previously implicated in aggression, i.e. serotonergic, dopaminergic, and neuroendocrine signaling, was conducted with MAGMA software in males and females separately (total n = 395) for aggression subtypes. We replicate the three-factor CFA model of the RPQ, and found males to score significantly higher on one of these factors compared to females: proactive aggression. The genetic association analysis showed a female-specific association of genetic variation in the combined gene-set with a different factor of the RPQ; reactive aggression due to internal frustration. Both the neuroendocrine and serotonergic gene-sets contributed significantly to this association. Our genetic findings are subtype- and sex-specific, stressing the value of efforts to reduce heterogeneity in research of aggression etiology. Importantly, subtype- and sex-differences in the underlying pathophysiology of aggression suggest that optimal treatment options will have to be tailored to the individual patient. Male and female needs of intervention might differ, stressing the need for sex-specific further research of aggression. Our work highlights opportunities for sample size maximization offered by population-based studies of aggression.

Allelic variant in SLC6A3 rs393795 affects cerebral regional homogeneity and gait dysfunction in patients with Parkinson's disease

Aims

We sought to explore the role of the SLC6A3rs393795 allelic variant in cerebral spontaneous activity and clinical features in Parkinson’s disease (PD) via imaging genetic approach.

Methods

Our study recruited 50 PD and 45 healthy control (HC) participants to provide clinical, genetic, and resting state functional magnetic resonance imaging (rs-fMRI) data. All subjects were separated into 16 PD-AA, 34 PD-CA/CC, 14 HC-AA, and 31 HC-CA/CC four subgroups according to SLC6A3rs393795 genotyping. Afterwards, main effects and interactions of groups (PD versus HC) and genotypes (AA versus CA/CC) on cerebral function reflected by regional homogeneity (ReHo) were explored using two-way analysis of covariance (ANCOVA) after controlling age and gender. Finally, Spearman’ s correlations were employed to investigate the relationships between significantly interactive brain regions and clinical manifestations in PD subgroups.

Results

Compared with HC subjects, PD patients exhibited increased ReHo signals in left middle temporal gyrus and decreased ReHo signals in left pallidum. Compared with CA/CC carriers, AA genotype individuals showed abnormal increased ReHo signals in right inferior frontal gyrus (IFG) and supplementary motor area (SMA). Moreover, significant interactions (affected by both disease factor and allelic variation) were detected in right inferior temporal gyrus (ITG). Furthermore, aberrant increased ReHo signals in right ITG were observed in PD-AA in comparison with PD-CA/CC. Notably, ReHo values in right ITG were negatively associated with Tinetti Mobility Test (TMT) gait subscale scores and positively related to Freezing of Gait Questionnaire (FOG-Q) scores in PD-AA subgroup.

Conclusions

Our findings suggested that SLC6A3rs393795 allelic variation might have a trend to aggravate the severity of gait disorders in PD patients by altering right SMA and IFG function, and ultimately result in compensatory activation of right ITG. It could provide us with a new perspective for exploring deeply genetic mechanisms of gait disturbances in PD.

Methylation of the dopamine transporter gene in blood is associated with striatal dopamine transporter availability in ADHD: A preliminary study

Dopamine transporters (DAT) are implicated in the pathogenesis and treatment of attention-deficit hyperactivity disorder (ADHD) and are upregulated by chronic treatment with methylphenidate, commonly prescribed for ADHD. Methylation of the DAT1 gene in brain and blood has been associated with DAT expression in rodents' brains. Here we tested the association between methylation of the DAT1 promoter derived from blood and DAT availability in the striatum of unmedicated ADHD adult participants and in that of healthy age-matched controls (HC) using Positron Emission Tomography (PET) and [¹¹ C]cocaine. Results showed no between-group differences in DAT1 promoter methylation or striatal DAT availability. However, the degree of methylation in the promoter region of DAT1 correlated negatively with DAT availability in caudate in ADHD participants only. DAT availability in VS correlated with inattention scores in ADHD participants. We verified in a postmortem cohort with ADHD diagnosis and without, that DAT1 promoter methylation in peripheral blood correlated positively with DAT1 promoter methylation extracted from substantia nigra (SN) in both groups. In the cohort without ADHD diagnosis, DAT1 gene expression in SN further correlated positively with DAT protein expression in caudate; however, the sample size of the cohort with ADHD was insufficient to investigate DAT1 and DAT expression levels. Overall, these findings suggest that peripheral DAT1 promoter methylation may be predictive of striatal DAT availability in adults with ADHD. Due to the small sample size, more work is needed to validate whether DAT1 methylation in blood predicts DAT1 methylation in SN in ADHD and controls.

The effect of a genetic variant at the schizophrenia associated AS3MT/BORCS7 locus on striatal dopamine function: A PET imaging study

One of the most statistically significant loci to result from large-scale GWAS of schizophrenia is 10q24.32. However, it is still unclear how this locus is involved in the pathoaetiology of schizophrenia. The hypothesis that presynaptic dopamine dysfunction underlies schizophrenia is one of the leading theories of the pathophysiology of the disorder. Supporting this, molecular imaging studies show evidence for elevated dopamine synthesis and release capacity. Thus, altered dopamine function could be a potential mechanism by which this genetic variant acts to increase the risk of schizophrenia. We therefore tested the hypothesis that the 10q24.32 region confers genetic risk for schizophrenia through an effect on striatal dopamine function. To this aim we investigated the in vivo relationship between a GWAS schizophrenia-associated SNP within this locus and dopamine synthesis capacity measured using [18F]-DOPA PET in healthy controls. 92 healthy volunteers underwent [18F]-DOPA PET scans to measure striatal dopamine synthesis capacity (indexed as Kicer) and were genotyped for the SNP rs7085104. We found a significant association between rs7085104 genotype and striatal Kicer. Our findings indicate that the mechanism mediating the 10q24.32 risk locus for schizophrenia could involve altered dopaminergic function. Future studies are needed to clarify the neurobiological pathway implicated in this association.

Depression genetic risk score is associated with anhedonia-related markers across units of analysis

Investigations of pathophysiological mechanisms implicated in vulnerability to depression have been negatively impacted by the significant heterogeneity characteristic of psychiatric syndromes. Such challenges are also reflected in numerous null findings emerging from genome-wide association studies (GWAS) of depression. Bolstered by increasing sample sizes, recent GWAS studies have identified genetics variants linked to MDD. Among them, Okbay and colleagues (Nat. Genet. 2016 Jun;48(6):624-33) identified genetic variants associated with three well-validated depression-related phenotypes: subjective well-being, depressive symptoms, and neuroticism. Despite this progress, little is known about psychopathological and neurobiological mechanisms underlying such risk. To fill this gap, a genetic risk score (GRS) was computed from the Okbay's study for a sample of 88 psychiatrically healthy females. Across two sessions, participants underwent two well-validated psychosocial stressors, and performed two separate tasks probing reward learning both before and after stress. Analyses tested whether GRS scores predicted anhedonia-related phenotypes across three units of analyses: self-report (Snaith Hamilton Pleasure Scale), behavior (stress-induced changes in reward learning), and circuits (stress-induced changes in striatal reward prediction error; striatal volume). GRS scores were negatively associated with anhedonia-related phenotypes across all units of analyses but only circuit-level variables were significant. In addition, the amount of explained variance was systematically larger as variables were putatively closer to the effects of genes (self-report < behavior < neural circuitry). Collectively, findings implicate anhedonia-related phenotypes and neurobiological mechanisms in increased depression vulnerability, and highlight the value of focusing on fundamental dimensions of functioning across different units of analyses.

The Impact of Genes on Adolescent Substance Use: A Developmental Perspective

Purpose

This review discusses the importance of understanding the impact of genetic factors on adolescent substance use within a developmental framework. Methods for identifying genetic factors, relevant endophenotypes and intermediate phenotypes, and gene-environment interplay effects will be reviewed.

Findings

Prior work supports the role of polygenic variation on adolescent substance use. Mechanisms through which genes impact adolescent phenotypes consist of differences in neural structure and function, early temperamental differences, and problem behavior. Gene-environment interactions are characterized by increased vulnerability to both maladaptive and adaptive contexts.

Summary

Developmental considerations in genetic investigations highlight the critical role that polygenic variation has on adolescent substance use. Yet, determining what to do with this information, especially in terms of personalized medicine, poses ethical and logistic challenges.

rs1360780 of the FKBP5 gene modulates the association between maternal acceptance and regional gray matter volume in the thalamus in children and adolescents

Investigating the effects of gene–environment interactions (G × E) with regard to brain structure may help to elucidate the putative mechanisms associated with psychiatric risk. rs1360780 (C/T) is a functional single-nucleotide polymorphism (SNP) in the gene encoding FK506–binding protein 5 (FKBP5), which is involved in the regulation of the hypothalamic–pituitary–adrenal (HPA) axis stress responses. The minor (T) allele of FKBP5 is considered a risk allele for stress-related disorders, due to the overproduction of FKBP5, which results in impaired communication of stress signals with the HPA axis. Previous studies have reported that interactions between childhood maltreatment and the rs1360780 genotype affect structures in subcortical areas of the brain. However, it is unclear how this SNP modulates the association between non-adverse environments and brain structure. In this study, we examined the interactive effect of the rs1360780 genotype and maternal acceptance on the regional gray matter volume (rGMV) in 202 Japanese children. Maternal acceptance was assessed using a Japanese psychological questionnaire for mothers. Whole-brain multiple regression analysis using voxel-based morphometry showed a significant positive association between maternal acceptance and rGMV in the left thalamus of T-allele carriers, while a significant negative association was found in C/C homozygotes. Post-hoc analysis revealed that at or below the 70th percentiles of maternal acceptance, the T-allele carriers had a reduced thalamic rGMV compared with that of C/C homozygotes. Thus, our investigation indicated that the effect of the maternal acceptance level on brain development was different, depending on the rs1360780 genotype. Importantly, we found that the differences in brain structure between the T-allele carriers and C/C homozygotes at low to moderate levels of maternal acceptance, which is not equivalent to maltreatment. The present study contributes to the G × E research by highlighting the necessity to investigate the role of non-adverse environmental factors.

Interactions between FKBP5 variation and environmental stressors in adolescent Major Depression

OBJECTIVE

Major Depression (MD) results from a complex interplay between environmental stressors and biological factors. Previous studies in adults have shown that adverse life events interact with genetic variation in FKBP5, a gene implicated in the stress-response system, to predict depressive symptoms and MD. This is the first study to investigate interactions between FKBP5 variants and a range of environmental stressors in adolescents with a clinical diagnosis of MD.

METHOD

148 male and female adolescents with MD and 143 typically developing (TD) controls (13-18 years) were included in the present study. For self-reported environmental stressors, subjective severity was assessed to allow a classification of these factors as mild, moderate and severe. Sociodemographic stressors were assessed via parental-report.

RESULTS

With a heightened number of sociodemographic, moderate and total number of stressors, participants carrying at least one copy of the FKBP5 CATT haplotype or at least one minor allele of various FKBP5 SNPs had the highest risk for being in the MD group. No genetic main effects were found. Sociodemographic stressors as well as self-reported mild, moderate, and severe stressors were more common in depressed than in TD adolescents.

CONCLUSION

This is the first study to show interactions between genetic variation in FKBP5 and environmental stressors in a sample of clinically depressed adolescents. The current study provides important starting-points for preventive efforts and highlights the need for a fine-grained analysis of different forms and severities of environmental stressors and their interplay with genetic variation for understanding the complex etiology of (youth) MD.

A role for the CD38 rs3796863 polymorphism in alcohol and monetary reward: evidence from CD38 knockout mice and alcohol self-administration, [11C]-raclopride binding, and functional MRI in humans

Background: Cluster of differentiation 38 (CD38) is a transmembrane protein expressed in dopaminergic reward pathways in the brain, including the nucleus accumbens (NAc). The GG genotype of a common single nucleotide polymorphism (SNP) within CD38, rs3796863, is associated with increased social reward.Objective: Examine whether CD38 rs3796863 and Cd38 knockout (KO) are associated with reward-related neural and behavioral phenotypes.Methods: Data from four independent human studies were used to test whether rs3796863 genotype is associated with: (1) intravenous alcohol self-administration (n = 64, 30 females), (2) alcohol-stimulated dopamine (DA) release measured using ¹¹C-raclopride positron emission tomography (n = 22 men), (3) ventral striatum (VS) response to positive feedback measured using a card guessing functional magnetic resonance imaging (fMRI) paradigm (n = 531, 276 females), and (4) resting state functional connectivity (rsfc) of the VS (n = 51, 26 females). In a fifth study, we used a mouse model to examine whether cd38 knockout influences stimulated DA release in the NAc core and dorsal striatum using fast-scanning cyclic voltammetry.Results: Relative to T allele carriers, G homozygotes at rs3796863 within CD38 were characterized by greater alcohol self-administration, alcohol-stimulated dopamine release, VS response to positive feedback, and rsfc between the VS and anterior cingulate cortex. High-frequency stimulation reduced DA release among Cd38 KO mice had reduced dopamine release in the NAc.Conclusion: Converging evidence suggests that CD38 rs3796863 genotype may increase DA-related reward response and alcohol consumption.

Translating basic research knowledge on the biological embedding of early-life stress into novel approaches for the developmental programming of lifelong health

This review integrates scientific knowledge obtained over the past few decades on the biological mechanisms that contribute to the profound association between exposure to early adversity, including childhood trauma and prenatal stress, and the lifelong elevated risk to develop a broad range of diseases. We further discuss insights into gene-environment interactions moderating the association between early adversity and disease manifestation and we discuss the role of epigenetic and other molecular processes in the biological embedding of early adversity. Based on these findings, we propose potential mechanisms that may contribute to the intergenerational transmission of risk related to early adversity from the mother to the fetus. Finally, we argue that basic research knowledge on the biological embedding of early adversity must now be translated into novel intervention strategies that are mechanism-driven and sensitive to developmental timing. Indeed, to date, there are no diagnostic biomarkers of risk or mechanism-informed interventions that we can offer to victims of early adversity in order to efficiently prevent or reverse adverse health outcomes. Such translational efforts can be expected to have significant impact on both clinical practice and the public health system, and will promote precision medicine in pediatrics and across the lifespan.

Topological gene expression networks recapitulate brain anatomy and function

Understanding how gene expression translates to and affects human behavior is one of the ultimate goals of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to analyze gene co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene set and find that co-expression networks produced by Mapper return a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that network based descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenges.

Results of the European Group for the Study of Resistant Depression (GSRD) - basis for further research and clinical practice

Objectives: The overview outlines two decades of research from the European Group for the Study of Resistant Depression (GSRD) that fundamentally impacted evidence-based algorithms for diagnostics and psychopharmacotherapy of treatment-resistant depression (TRD). Methods: The GSRD staging model characterising response, non-response and resistance to antidepressant (AD) treatment was applied to 2762 patients in eight European countries. Results: In case of non-response, dose escalation and switching between different AD classes did not show superiority over continuation of original AD treatment. Predictors for TRD were symptom severity, duration of the current major depressive episode (MDE), suicidality, psychotic and melancholic features, comorbid anxiety and personality disorders, add-on treatment, non-response to the first AD, adverse effects, high occupational level, recurrent disease course, previous hospitalisations, positive family history of MDD, early age of onset and novel associations of single nucleoid polymorphisms (SNPs) within the PPP3CC, ST8SIA2, CHL1, GAP43 and ITGB3 genes and gene pathways associated with neuroplasticity, intracellular signalling and chromatin silencing. A prediction model reaching accuracy of above 0.7 highlighted symptom severity, suicidality, comorbid anxiety and lifetime MDEs as the most informative predictors for TRD. Applying machine-learning algorithms, a signature of three SNPs of the BDNF, PPP3CC and HTR2A genes and lacking melancholia predicted treatment response. Conclusions: The GSRD findings offer a unique and balanced perspective on TRD representing foundation for further research elaborating on specific clinical and genetic hypotheses and treatment strategies within appropriate study-designs, especially interaction-based models and randomized controlled trials.

Mapping causal pathways from genetics to neuropsychiatric disorders using genome-wide imaging genetics: Current status and future directions

Imaging genetics aims to identify genetic variants associated with the structure and function of the human brain. Recently, collaborative consortia have been successful in this goal, identifying and replicating common genetic variants influencing gross human brain structure as measured through magnetic resonance imaging. In this review, we contextualize imaging genetic associations as one important link in understanding the causal chain from genetic variant to increased risk for neuropsychiatric disorders. We provide examples in other fields of how identifying genetic variant associations to disease and multiple phenotypes along the causal chain has revealed a mechanistic understanding of disease risk, with implications for how imaging genetics can be similarly applied. We discuss current findings in the imaging genetics research domain, including that common genetic variants can have a slightly larger effect on brain structure than on risk for disorders like schizophrenia, indicating a somewhat simpler genetic architecture. Also, gross brain structure measurements share a genetic basis with some, but not all, neuropsychiatric disorders, invalidating the previously held belief that they are broad endophenotypes, yet pinpointing brain regions likely involved in the pathology of specific disorders. Finally, we suggest that in order to build a more detailed mechanistic understanding of the effects of genetic variants on the brain, future directions in imaging genetics research will require observations of cellular and synaptic structure in specific brain regions beyond the resolution of magnetic resonance imaging. We expect that integrating genetic associations at biological levels from synapse to sulcus will reveal specific causal pathways impacting risk for neuropsychiatric disorders.

SNCA rs11931074 polymorphism correlates with spontaneous brain activity and motor symptoms in Chinese patients with Parkinson's disease

The α-synuclein (SNCA) gene is thought to be involved in levels of α-synuclein and influence the susceptibility for the development of Parkinson's disease (PD). The aim of the present study is to explore the association among SNCA rs1193074 polymorphism, spontaneous brain activity and clinical symptoms in PD patients. 62 PD patients and 47 healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. Also blood sample of each participant was genotyped for rs11931074 polymorphism (PD: TT = 19, GT = 32, GG = 11; HC: TT = 10, GT = 25, GG = 12) and then examined to ascertain the influence of different genotypes on regional brain activity with amplitude low-frequency fluctuation analysis (ALFF). Furthermore, we evaluated the relationship among genotypes, interactive brain region and clinical symptoms in PD. Compared with HC subjects, PD patients showed decreased ALFF values in right lingual gyrus and increased ALFF values in right cerebellum posterior lobe. Significant interaction of ''groups × genotypes'' was found in the right angular gyrus, where there were higher ALFF values in TT genotype than in GT or GG genotype in the PD group and there was a contrary trend in the HC group. And further Spearman's correlative analyses revealed that ALFF values in right angular gyrus were negatively associated with unified Parkinson's disease rating scale (UPDRS) III score in PD-TT genotype. Our study shows for the first time that SNCA rs11931074 polymorphism might modulate brain functional alterations and correlate with motor symptoms in Chinese PD patients.

A systematic review of associations between functional MRI activity and polygenic risk for schizophrenia and bipolar disorder

Genetic factors account for up to 80% of the liability for schizophrenia (SCZ) and bipolar disorder (BD). Genome-wide association studies have successfully identified several genes associated with increased risk for both disorders. This has allowed researchers to model the aggregate effect of genes associated with disease status and create a polygenic risk score (PGRS) for each individual. The interest in imaging genetics using PGRS has grown in recent years, with several studies now published. We have conducted a systematic review to examine the effects of PGRS of SCZ, BD and cross psychiatric disorders on brain function and connectivity using fMRI data. Results indicate that the effect of genetic load for SCZ and BD on brain function affects task-related recruitment, with frontal areas having a more prominent role, independent of task. Additionally, the results suggest that the polygenic architecture of psychotic disorders is not regionally confined but impacts on the task-dependent recruitment of multiple brain regions. Future imaging genetics studies with large samples, especially population studies, would be uniquely informative in mapping the spatial distribution of the genetic risk to psychiatric disorders on brain processes during various cognitive tasks and may lead to the discovery of biological pathways that could be crucial in mediating the link between genetic factors and alterations in brain networks.

The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders

Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.

Whole-exome sequencing identifies variants associated with structural MRI markers in patients with bipolar disorders

BACKGROUND

Bipolar disorder (BD) is one of the most heritable psychiatric disorders. A growing number of whole-exome sequencing (WES) studies for BD has been performed, however, no research has examined the association between single nucleotide variants (SNVs) from WES and structural magnetic resonance imaging (MRI) data.

METHODS

We sequenced whole-exomes in 53 patients with BD and 82 healthy control participants at an initial discovery stage and investigated the impacts of SNVs in risk genes from WES analysis on the cortical gray-matter thickness and integrity of white matter tracts and in the following stage. Cortical thickness and white matter integrity were investigated using the FreeSurfer and TRACULA (Tracts Constrained by UnderLying Anatomy).

RESULTS

We identified 122 BD-related genes including KMT2C, AHNAK, CDH23, DCHS1, FRAS1, MACF1 and RYR3 and observed 27 recurrent copy number alteration regions including gain on 8p23.1 and loss on 15q11.1 - q11.2. Among them, single nucleotide polymorphism (SNP) rs4639425 in KMT2C gene, which regulates histone H3 lysine 4 (H3K4) methylation involved in chromatin remodeling, was associated with widespread alterations of white matter integrity including the cingulum, uncinate fasciculus, cortico-spinal tract, and superior longitudinal fasciculus.

LIMITATION

The small sample size of patients with BD in the genome data may cause our study to be underpowered when searching for putative rare mutations.

CONCLUSION

This study first combined a WES approach and neuroimaging findings in psychiatric disorders. We postulate the rs4639425 may be associated with BD-related microstructural changes of white matter tracts.

Brain Imaging-Guided Analysis Reveals DNA Methylation Profiles Correlated with Insular Surface Area and Alcohol Use Disorder

BACKGROUND

Alcohol use disorder (AUD) is a wide-spread, heritable brain disease, but few studies have linked genetic variants or epigenetic factors to brain structures related to AUD in humans, due to many factors including the high-dimensional nature of imaging and genomic data.

METHODS

To provide potential insights into the links among epigenetic regulation, brain structure, and AUD, we have performed an integrative analysis of brain structural imaging and blood DNA methylome data from 52 AUD and 58 healthy control (HC) subjects collected in the Nathan Kline Institute-Rockland Sample.

RESULTS

We first found that AUD subjects had significantly larger insular surface area than HC in both left and right hemispheres. We then found that 7,827 DNA methylation probes on the HumanMethylation450K BeadChip had significant correlations with the right insular surface area (false discovery rate [FDR] < 0.05). Furthermore, we showed that 44 of the insular surface area-correlated methylation probes were also strongly correlated with AUD status (FDR < 0.05). These AUD-correlated probes are annotated to 36 protein-coding genes, with 16 genes (44%) having been reported by others to be related to AUD or alcohol response, including TAS2R16 and PER2. The remaining 20 genes, in particular ARHGAP22, might represent novel genes involved in AUD or responsive to alcohol.

CONCLUSIONS

We have identified 36 insular surface area- and AUD-correlated protein-coding genes that are either known to be AUD- or alcohol-related or not yet reported by prior studies. Therefore, our study suggests that the brain imaging-guided epigenetic analysis has a potential of identifying possible epigenetic mechanisms involved in AUD.

Interaction of schizophrenia polygenic risk and cortisol level on pre-adolescent brain structure

The etiology of schizophrenia is multi-factorial with early neurodevelopmental antecedents, likely to result from a complex interaction of genetic and environmental risk. However, few studies have examined how schizophrenia polygenic risk scores (PRS) are moderated by environmental factors in shaping neurodevelopmental brain structure, prior to the onset of psychotic symptoms. Here, we examined whether hair cortisol, a quantitative metric of chronic stress, moderated the association between genetic risk for schizophrenia and pre-adolescent brain structure. This study was embedded within the Generation R Study, involving pre-adolescents of European ancestry assessed regarding schizophrenia PRS, hair cortisol, and brain imaging (n = 498 structural; n = 526 diffusion tensor imaging). Linear regression was performed to determine the association between schizophrenia PRS, hair cortisol level, and brain imaging outcomes. Although no single measure exceeded the multiple testing threshold, nominally significant interactions were observed for total ventricle volume (P_interaction = 0.02) and global white matter microstructure (P_interaction = 0.01) - two of the most well replicated brain structural findings in schizophrenia. These findings provide suggestive evidence for the joint effects of schizophrenia liability and cortisol levels on brain correlates in the pediatric general population. Given the widely replicated finding of ventricular enlargement and lower white matter integrity among schizophrenia patients, our findings generate novel hypotheses for future research on gene-environment interactions affecting the neurodevelopmental pathophysiology of schizophrenia.

[Breakthrough in understanding the molecular causes of psychiatric disorders]

A long-established hypothesis is that genetic factors contribute to the development of psychiatric diseases, including common illnesses such as schizophrenia and the affective disorders; however, reliable molecular identification of these factors represents a far more recent innovation. This has been rendered possible by technological advances in the individual characterization of the human genome and the combining of large genetic datasets at the international level. For the first time, the results of genome-wide analyses provide researchers with systematic insights into disease-relevant biological mechanisms. Here, the integrated analysis of different omics level data generates important insights into the functional interpretation of the genetic findings. The results of genetic studies also demonstrated the degree of etiological overlap between differing psychiatric disorders, with the greatest commonality having been observed to date between schizophrenia and bipolar affective disorder. Although the translation of genetic findings into routine clinical practice is being pursued at various levels, elaborate follow-up studies are typically necessary. The diagnostic investigation of rare genomic deletions/duplications (so-called copy number variants) in patients with schizophrenia is likely to represent one of the first examples of routine clinical application. The necessary prerequisites for this are currently being defined.

BST1 rs4698412 allelic variant increases the risk of gait or balance deficits in patients with Parkinson's disease

Aims

We aimed to explore effects of bone marrow stromal cell antigen‐1 (BST1) rs4698412 allelic variant on brain activation and associative clinical symptoms in Parkinson’s disease (PD).

Methods

A total of 49 PD patients and 47 healthy control (HC) subjects were recruited for clinical evaluations, blood samples collection for genotypes, and resting‐state functional MRI (rs‐fMRI) scans. Based on BST1 rs4698412 allelic variant (G → A), participants were further divided into 18 PD‐GG, 31 PD‐GA/AA, 20 HC‐GG, and 27 HC‐GA/AA carriers, which respectively indicated subjects carrying ancestral or risk allele in that locus in PD or HC. Two‐way analysis of covariance (ANCOVA) was applied to investigate main effects and interactions between PD and BST1 rs4698412 allelic variant on brain function via amplitude of low‐frequency fluctuations (ALFF). Spearman’s correlations were then utilized to detect associations between interactive brain regions and clinical symptoms.

Results

Compared to HC subjects, PD patients exhibited increased ALFF values in left cerebellum_8 and cerebellum_9. Significant interaction was in right lingual gyrus, where there were the lowest ALFF values and ALFF values were only negatively associated with Timed Up and Go (TUG) test time in PD‐GA/AA subgroup.

Conclusion

BST1 rs4698412‐modulated lingual gyrus functional alterations could be related to gait and balance dysfunction in PD.

Shared Genetic Risk of Schizophrenia and Gray Matter Reduction in 6p22.1

Genetic factors are known to influence both risk for schizophrenia (SZ) and variation in brain structure. A pressing question is whether the genetic underpinnings of brain phenotype and the disorder overlap. Using multivariate analytic methods and focusing on 1,402 common single-nucleotide polymorphisms (SNPs) mapped from the Psychiatric Genomics Consortium (PGC) 108 regions, in 777 discovery samples, we identified 39 SNPs to be significantly associated with SZ-discriminating gray matter volume (GMV) reduction in inferior parietal and superior temporal regions. The findings were replicated in 609 independent samples. These 39 SNPs in chr6:28308034-28684183 (6p22.1), the most significant SZ-risk region reported by PGC, showed regulatory effects on both DNA methylation and gene expression of postmortem brain tissue and saliva. Furthermore, the regulated methylation site and gene showed significantly different levels of methylation and expression in the prefrontal cortex between cases and controls. In addition, for one regulated methylation site we observed a significant in vivo methylation-GMV association in saliva, suggesting a potential SNP-methylation-GMV pathway. Notably, the risk alleles inferred for GMV reduction from in vivo imaging are all consistent with the risk alleles for SZ inferred from postmortem data. Collectively, we provide evidence for shared genetic risk of SZ and regional GMV reduction in 6p22.1 and demonstrate potential molecular mechanisms that may drive the observed in vivo associations. This study motivates dissecting SZ-risk variants to better understand their associations with focal brain phenotypes and the complex pathophysiology of the illness.

Cortico-limbic connectivity as a possible biomarker for bipolar disorder: where are we now?

INTRODUCTION

The fronto-limbic network has been suggested as a key circuitry in the pathophysiology and maintenance of bipolar disorder. In the past decade, a disrupted connectivity within prefrontal-limbic structures was identified as a promising candidate biomarker for the disorder. Areas Covered: In this review, the authors examine current literature in terms of the structural, functional and effective connectivity in bipolar disorder, integrating recent findings of imaging genetics and machine learning. This paper profiles the current knowledge and identifies future perspectives to provide reliable and usable neuroimaging biomarkers for bipolar psychopathology in clinical practice. Expert Opinion: The replication and the translation of acquired knowledge into useful and usable tools represents one of the current greatest challenges in biomarker research applied to psychiatry.

Imaging Genetics Towards a Refined Diagnosis of Schizophrenia

Current diagnoses of schizophrenia and related psychiatric disorders are classified by phenomenological principles and clinical descriptions while ruling out other symptoms and conditions. Specific biomarkers are needed to assist the current diagnostic system. However, complicated gene and environment interactions induce great disease heterogeneity. This unclear etiology and heterogeneity raise difficulties in distinguishing schizophrenia-related effects. Simultaneously, the overlap in symptoms, genetic variations, and brain alterations in schizophrenia and related psychiatric disorders raises similar difficulties in determining disease-specific effects. Imaging genetics is a unique methodology to assess the impact of genetic factors on both brain structure and function. More importantly, imaging genetics builds a bridge to understand the behavioral and clinical implications of genetics and neuroimaging. By characterizing and quantifying the brain measures affected in psychiatric disorders, imaging genetics is contributing to identifying potential biomarkers for schizophrenia and related disorders. To date, candidate gene analysis, genome-wide association studies, polygenetic risk score analysis, and large-scale collaborative studies have made contributions to the understanding of schizophrenia with the potential to serve as biomarkers. Despite limitations, imaging genetics remains promising as more aggregative, clustering methods and imaging genetics-compatible clinical assessments are employed in future studies. We review imaging genetics' contribution to our understanding of the heterogeneity within schizophrenia and the commonalities across schizophrenia and other diagnostic borders, and we will discuss whether imaging genetics is ready to form its own diagnostic system.

HPA-axis multilocus genetic variation moderates associations between environmental stress and depressive symptoms among adolescents

Research suggests that genetic variants linked to hypothalamic-pituitary-adrenal (HPA)-axis functioning moderate the association between environmental stressors and depression, but examining gene–environment interactions with single polymorphisms limits power. The current study used a multilocus genetic profile score (MGPS) approach to measuring HPA-axis–related genetic variation and examined interactions with acute stress, chronic stress, and childhood adversity (assessed using contextual threat interview methods) with depressive symptoms as outcomes in an adolescent sample (ages 14–17, N = 241; White subsample n = 192). Additive MGPSs were calculated using 10 single nucleotide polymorphisms within HPA-axis genes (CRHR1, NR3C2, NR3C1, FKBP5). Higher MGPS directly correlated with adolescent depressive symptoms. Moreover, MGPS predicted stronger associations between acute and chronic stress and adolescent depressive symptoms and also moderated the effect of interpersonal, but not noninterpersonal, childhood adversity. Gene–environment interactions individually accounted for 5%–8% of depressive symptom variation. All results were retained following multiple test correction and stratification by race. Results suggest that using MGPSs provides substantial power to examine gene–environmental interactions linked to affective outcomes among adolescents.

Psychiatry and developmental psychopathology: Unifying themes and future directions

In the past 35 years, developmental psychopathology has grown into a flourishing discipline that shares a scientific agenda with contemporary psychiatry. In this editorial, which introduces the special issue, we describe the history of developmental psychopathology, including core principles that bridge allied disciplines. These include (1) emphasis on interdisciplinary research, (2) elucidation of multicausal pathways to seemingly single disorders (phenocopies), (3) description of divergent multifinal outcomes from common etiological start points (pathoplasticity), and (4) research conducted across multiple levels of analysis spanning genes to environments. Next, we discuss neurodevelopmental models of psychopathology, and provide selected examples. We emphasize differential neuromaturation of subcortical and cortical neural networks and connectivity, and how both acute and protracted environmental insults can compromise neural structure and function. To date, developmental psychopathology has placed greater emphasis than psychiatry on neuromaturational models of mental illness. However, this gap is closing rapidly as advances in technology render etiopathophysiologies of psychopathology more interrogable. We end with suggestions for future interdisciplinary research, including the need to evaluate measurement invariance across development, and to construct more valid assessment methods where indicated.

Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.

Sparse multiple factor analysis to integrate genetic data, neuroimaging features, and attention-deficit/hyperactivity disorder domains

OBJECTIVES

We proposed the application of a multivariate cross-sectional framework based on a combination of a variable selection method and a multiple factor analysis (MFA) in order to identify complex meaningful biological signals related to attention-deficit/hyperactivity disorder (ADHD) symptoms and hyperactivity/inattention domains.

METHODS

The study included 135 children from the general population with genomic and neuroimaging data. ADHD symptoms were assessed using a questionnaire based on ADHD-DSM-IV criteria. In all analyses, the raw sum scores of the hyperactivity and inattention domains and total ADHD were used. The analytical framework comprised two steps. First, zero-inflated negative binomial linear model via penalized maximum likelihood (LASSO-ZINB) was performed. Second, the most predictive features obtained with LASSO-ZINB were used as input for the MFA.

RESULTS

We observed significant relationships between ADHD symptoms and hyperactivity and inattention domains with white matter, gray matter regions, and cerebellum, as well as with loci within chromosome 1.

CONCLUSIONS

Multivariate methods can be used to advance the neurobiological characterization of complex diseases, improving the statistical power with respect to univariate methods, allowing the identification of meaningful biological signals in Imaging Genetic studies.

Imaging genetics paradigms in depression research: Systematic review and meta-analysis

Imaging genetics studies involving participants with major depressive disorder (MDD) have expanded. Nevertheless, findings have been inconsistent. Thus, we conducted a systematic review and meta-analysis of imaging genetics studies that enrolled MDD participants across major databases through June 30th, 2017. Sixty-five studies met eligibility criteria (N = 4034 MDD participants and 3293 controls), and there was substantial between-study variability in the methodological quality of included studies. However, few replicated findings emerged from this literature with only 22 studies providing data for meta-analyses (882 participants with MDD and 616 controls). Total hippocampal volumes did not significantly vary in MDD participants or controls carrying either the BDNF Val66Met 'Met' (386 participants with MDD and 376 controls) or the 5-HTTLPR short 'S' (310 participants with MDD and 230 controls) risk alleles compared to non-carriers. Heterogeneity across studies was explored through meta-regression and subgroup analyses. Gender distribution, the use of medications, segmentation methods used to measure the hippocampus, and age emerged as potential sources of heterogeneity across studies that assessed the association of 5-HTTLPR short 'S' alleles and hippocampal volumes. Our data also suggest that the methodological quality of included studies, publication year, and the inclusion of brain volume as a covariate contributed to the heterogeneity of studies that assessed the association of the BDNF Val66Met 'Met' risk allele and hippocampal volumes. In exploratory voxel-wise meta-analyses, MDD participants carrying the 5-HTTLPR short 'S' allele had white matter microstructural abnormalities predominantly in the corpus callosum, while carriers of the BDNF Val66Met 'Met' allele had larger gray matter volumes and hyperactivation of the right middle frontal gyrus compared to non-carriers. In conclusion, few replicated findings emerged from imaging genetics studies that included participants with MDD. Nevertheless, we explored and identified specific sources of heterogeneity across studies, which could provide insights to enhance the reproducibility of this emerging field.

A Perspective of the Cross-Tissue Interplay of Genetics, Epigenetics, and Transcriptomics, and Their Relation to Brain Based Phenotypes in Schizophrenia

Genetic association studies of psychiatric disorders have provided unprecedented insight into disease risk profiles with high confidence. Yet, the next research challenge is how to translate this rich information into mechanisms of disease, and further help interventions and treatments. Given other comprehensive reviews elsewhere, here we want to discuss the research approaches that integrate information across various tissue types. Taking schizophrenia as an example, the tissues, cells, or organisms being investigated include postmortem brain tissues or neurons, peripheral blood and saliva, in vivo brain imaging, and in vitro cell lines, particularly human induced pluripotent stem cells (iPSC) and iPSC derived neurons. There is a wealth of information on the molecular signatures including genetics, epigenetics, and transcriptomics of various tissues, along with neuronal phenotypic measurements including neuronal morphometry and function, together with brain imaging and other techniques that provide data from various spatial temporal points of disease development. Through consistent or complementary processes across tissues, such as cross-tissue methylation quantitative trait loci (QTL) and expression QTL effects, systemic integration of such information holds the promise to put the pieces of puzzle together for a more complete view of schizophrenia disease pathogenesis.

Replication in Imaging Genetics: The Case of Threat-Related Amygdala Reactivity

BACKGROUND

Low replication rates are a concern in most, if not all, scientific disciplines. In psychiatric genetics specifically, targeting intermediate brain phenotypes, which are more closely associated with putative genetic effects, was touted as a strategy leading to increased power and replicability. In the current study, we attempted to replicate previously published associations between single nucleotide polymorphisms and threat-related amygdala reactivity, which represents a robust brain phenotype not only implicated in the pathophysiology of multiple disorders, but also used as a biomarker of future risk.

METHODS

We conducted a literature search for published associations between single nucleotide polymorphisms and threat-related amygdala reactivity and found 37 unique findings. Our replication sample consisted of 1117 young adult volunteers (629 women, mean age 19.72 ± 1.25 years) for whom both genetic and functional magnetic resonance imaging data were available.

RESULTS

Of the 37 unique associations identified, only three replicated as previously reported. When exploratory analyses were conducted with different model parameters compared to the original findings, significant associations were identified for 28 additional studies: eight of these were for a different contrast/laterality; five for a different gender and/or race/ethnicity; and 15 in the opposite direction and for a different contrast, laterality, gender, and/or race/ethnicity. No significant associations, regardless of model parameters, were detected for six studies. Notably, none of the significant associations survived correction for multiple comparisons.

CONCLUSIONS

We discuss these patterns of poor replication with regard to the general strategy of targeting intermediate brain phenotypes in genetic association studies and the growing importance of advancing the replicability of imaging genetics findings.

Strategies for integrated analysis in imaging genetics studies

Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual, deepening our knowledge of the biological mechanisms behind neurodevelopmental domains and neurological disorders. Although the literature on IG has exponentially grown over the past years, the majority of studies have mainly analyzed associations between candidate brain regions and individual genetic variants. However, this strategy is not designed to deal with the complexity of neurobiological mechanisms underlying behavioral and neurodevelopmental domains. Moreover, larger sample sizes and increased multidimensionality of this type of data represents a challenge for standardizing modeling procedures in IG research. This review provides a systematic update of the methods and strategies currently used in IG studies, and serves as an analytical framework for researchers working in this field. To complement the functionalities of the Neuroconductor framework, we also describe existing R packages that implement these methodologies. In addition, we present an overview of how these methodological approaches are applied in integrating neuroimaging and genetic data.

Polygenic Risk Scores in Clinical Psychology: Bridging Genomic Risk to Individual Differences

Genomewide association studies (GWASs) across psychiatric phenotypes have shown that common genetic variants generally confer risk with small effect sizes (odds ratio < 1.1) that additively contribute to polygenic risk. Summary statistics derived from large discovery GWASs can be used to generate polygenic risk scores (PRS) in independent, target data sets to examine correlates of polygenic disorder liability (e.g., does genetic liability to schizophrenia predict cognition?). The intuitive appeal and generalizability of PRS have led to their widespread use and new insights into mechanisms of polygenic liability. However, when currently applied across traits they account for small amounts of variance (<3%), are relatively uninformative for clinical treatment, and, in isolation, provide no insight into molecular mechanisms. Larger GWASs are needed to increase the precision of PRS, and novel approaches integrating various data sources (e.g., multitrait analysis of GWASs) may improve the utility of current PRS.

Discovery biology of neuropsychiatric syndromes (DBNS): a center for integrating clinical medicine and basic science

BACKGROUND

There is emerging evidence that there are shared genetic, environmental and developmental risk factors in psychiatry, that cut across traditional diagnostic boundaries. With this background, the Discovery biology of neuropsychiatric syndromes (DBNS) proposes to recruit patients from five different syndromes (schizophrenia, bipolar disorder, obsessive compulsive disorder, Alzheimer's dementia and substance use disorders), identify those with multiple affected relatives, and invite these families to participate in this study. The families will be assessed: 1) To compare neuro-endophenotype measures between patients, first degree relatives (FDR) and healthy controls., 2) To identify cellular phenotypes which differentiate the groups., 3) To examine the longitudinal course of neuro-endophenotype measures., 4) To identify measures which correlate with outcome, and 5) To create a unified digital database and biorepository.

METHODS

The identification of the index participants will occur at well-established specialty clinics. The selected individuals will have a strong family history (with at least another affected FDR) of mental illness. We will also recruit healthy controls without family history of such illness. All recruited individuals (N = 4500) will undergo brief clinical assessments and a blood sample will be drawn for isolation of DNA and peripheral blood mononuclear cells (PBMCs). From among this set, a subset of 1500 individuals (300 families and 300 controls) will be assessed on several additional assessments [detailed clinical assessments, endophenotype measures (neuroimaging- structural and functional, neuropsychology, psychophysics-electroencephalography, functional near infrared spectroscopy, eye movement tracking)], with the intention of conducting repeated measurements every alternate year. PBMCs from this set will be used to generate lymphoblastoid cell lines, and a subset of these would be converted to induced pluripotent stem cell lines and also undergo whole exome sequencing.

DISCUSSION

We hope to identify unique and overlapping brain endophenotypes for major psychiatric syndromes. In a proportion of subjects, we expect these neuro-endophenotypes to progress over time and to predict treatment outcome. Similarly, cellular assays could differentiate cell lines derived from such groups. The repository of biomaterials as well as digital datasets of clinical parameters, will serve as a valuable resource for the broader scientific community who wish to address research questions in the area.

ZNF804A Variation May Affect Hippocampal-Prefrontal Resting-State Functional Connectivity in Schizophrenic and Healthy Individuals

The ZNF804A variant rs1344706 has consistently been associated with schizophrenia and plays a role in hippocampal-prefrontal functional connectivity during working memory. Whether the effect exists in the resting state and in patients with schizophrenia remains unclear. In this study, we investigated the ZNF804A polymorphism at rs1344706 in 92 schizophrenic patients and 99 healthy controls of Han Chinese descent, and used resting-state functional magnetic resonance imaging to explore the functional connectivity in the participants. We found a significant main effect of genotype on the resting-state functional connectivity (RSFC) between the hippocampus and the dorsolateral prefrontal cortex (DLPFC) in both schizophrenic patients and healthy controls. The homozygous ZNF804A rs1344706 genotype (AA) conferred a high risk of schizophrenia, and also exhibited significantly decreased resting functional coupling between the left hippocampus and right DLPFC (F(2,165) = 13.43, P < 0.001). The RSFC strength was also correlated with cognitive performance and the severity of psychosis in schizophrenia. The current findings identified the neural impact of the ZNF804A rs1344706 on hippocampal-prefrontal RSFC associated with schizophrenia.

The effect of 5-HTTLPR and a serotonergic multi-marker score on amygdala, prefrontal and anterior cingulate cortex reactivity and habituation in a large, healthy fMRI cohort

Major depressive disorder (MDD) is characterized by low mood for at least two weeks. Impaired emotion regulation has been suggested to be the consequence of dysfunctional serotonergic regulation of limbic and prefrontal regions, especially the amygdala, the anterior cingulate cortex (ACC) and the prefrontal cortex (PFC). The impact of genetic variation on brain function can be investigated with intermediate phenotypes. A suggested intermediate phenotype of MDD is emotion recognition: The 5-HTTLPR polymorphism of SLC6A4 as well as other serotonergic genes have been associated with amygdala and prefrontal function during emotion recognition. Previously, it has been suggested that habituation is a more reliable index of emotion recognition than functional activation. We examined the relationship of genes involved in serotonergic signaling with amygdala as well as prefrontal functional activation and habituation during an emotion recognition task in 171 healthy subjects. While effects of 5-HTTLPR and of a serotonergic multi-marker score (5-HTTLPR, TPH1(rs1800532), TPH2(rs4570625), HTR1A(rs6295) and HTR2A(rs6311)) on amygdala activation did not withstand correction for multiple regions of interest, we observed a strong correlation of the multi-marker score and habituation in the amygdala, DLPFC, and ACC. We replicated a well-studied intermediate phenotype for association with 5-HTTLPR and provided additional evidence for polygenic involvement. Furthermore, we showed that task habituation may be influenced by genetic variation in serotonergic signaling, particularly by a serotonergic multi-marker score. We provided preliminary evidence that PFC activation is an important intermediate phenotype of MDD. Future studies are needed to corroborate the results in larger samples.

Biosignature Discovery for Substance Use Disorders Using Statistical Learning

There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts.

Mapping the Schizophrenia Genes by Neuroimaging: The Opportunities and the Challenges

Schizophrenia (SZ) is a heritable brain disease originating from a complex interaction of genetic and environmental factors. The genes underpinning the neurobiology of SZ are largely unknown but recent data suggest strong evidence for genetic variations, such as single nucleotide polymorphisms, making the brain vulnerable to the risk of SZ. Structural and functional brain mapping of these genetic variations are essential for the development of agents and tools for better diagnosis, treatment and prevention of SZ. Addressing this, neuroimaging methods in combination with genetic analysis have been increasingly used for almost 20 years. So-called imaging genetics, the opportunities of this approach along with its limitations for SZ research will be outlined in this invited paper. While the problems such as reproducibility, genetic effect size, specificity and sensitivity exist, opportunities such as multivariate analysis, development of multisite consortia for large-scale data collection, emergence of non-candidate gene (hypothesis-free) approach of neuroimaging genetics are likely to contribute to a rapid progress for gene discovery besides to gene validation studies that are related to SZ.

Neuroimaging genomics in psychiatry-a translational approach

Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene–gene epistasis, and gene–environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics—we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders.

Mediating Role of the Reward Network in the Relationship between the Dopamine Multilocus Genetic Profile and Depression

Multiple genetic loci in the dopamine (DA) pathway have been associated with depression symptoms in patients with major depressive disorder (MDD). However, the neural mechanisms underlying the polygenic effects of the DA pathway on depression remain unclear. We used an imaging genetic approach to investigate the polygenic effects of the DA pathway on the reward network in MDD. Fifty-three patients and 37 cognitively normal (CN) subjects were recruited and underwent resting-state functional magnetic resonance imaging (R-fMRI) scans. Multivariate linear regression analysis was employed to measure the effects of disease and multilocus genetic profile scores (MGPS) on the reward network, which was constructed using the nucleus accumbens (NAc) functional connectivity (NAFC) network. DA-MGPS was widely associated within the NAFC network, mainly in the inferior frontal cortex, insula, hypothalamus, superior temporal gyrus, and occipital cortex. The pattern of DA-MGPS effects on the fronto-striatal pathway differed in MDD patients compared with CN subjects. More importantly, NAc-putamen connectivity mediates the association between DA MGPS and anxious depression traits in MDD patients. Our findings suggest that the DA multilocus genetic profile makes a considerable contribution to the reward network and anxious depression in MDD patients. These results expand our understanding of the pathophysiology of polygenic effects underlying brain network abnormalities in MDD.

Hypothalamic-pituitary-adrenal axis genetic variation and early stress moderates amygdala function

Early life stress may precipitate psychopathology, at least in part, by influencing amygdala function. Converging evidence across species suggests that links between childhood stress and amygdala function may be dependent upon hypothalamic-pituitary-adrenal (HPA) axis function. Using data from college-attending non-Hispanic European-Americans (n=308) who completed the Duke Neurogenetics Study, we examined whether early life stress (ELS) and HPA axis genetic variation interact to predict threat-related amygdala function as well as psychopathology symptoms. A biologically-informed multilocus profile score (BIMPS) captured HPA axis genetic variation (FKBP5 rs1360780, CRHR1 rs110402; NR3C2 rs5522/rs4635799) previously associated with its function (higher BIMPS are reflective of higher HPA axis activity). BOLD fMRI data were acquired while participants completed an emotional face matching task. ELS and depression and anxiety symptoms were measured using the childhood trauma questionnaire and the mood and anxiety symptom questionnaire, respectively. The interaction between HPA axis BIMPS and ELS was associated with right amygdala reactivity to threat-related stimuli, after accounting for multiple testing (empirical-p=0.016). Among individuals with higher BIMPS (i.e., the upper 21.4%), ELS was positively coupled with threat-related amygdala reactivity, which was absent among those with average or low BIMPS. Further, higher BIMPS were associated with greater self-reported anxious arousal, though there was no evidence that amygdala function mediated this relationship. Polygenic variation linked to HPA axis function may moderate the effects of early life stress on threat-related amygdala function and confer risk for anxiety symptomatology. However, what, if any, neural mechanisms may mediate the relationship between HPA axis BIMPS and anxiety symptomatology remains unclear.