Shared genetic architecture across psychiatric disorders

Generalized genetic liability to substance use disorders

Lifetime and temporal co-occurrence of substance use disorders (SUDs) is common and compared with individual SUDs is characterized by greater severity, additional psychiatric comorbidities, and worse outcomes. Here, we review evidence for the role of generalized genetic liability to various SUDs. Coaggregation of SUDs has familial contributions, with twin studies suggesting a strong contribution of additive genetic influences undergirding use disorders for a variety of substances (including alcohol, nicotine, cannabis, and others). GWAS have documented similarly large genetic correlations between alcohol, cannabis, and opioid use disorders. Extending these findings, recent studies have identified multiple genomic loci that contribute to common risk for these SUDs and problematic tobacco use, implicating dopaminergic regulatory and neuronal development mechanisms in the pathophysiology of generalized SUD genetic liability, with certain signals demonstrating cross-species and translational validity. Overlap with genetic signals for other externalizing behaviors, while substantial, does not explain the entirety of the generalized genetic signal for SUD. Polygenic scores (PGS) derived from the generalized genetic liability to SUDs outperform PGS for individual SUDs in prediction of serious mental health and medical comorbidities. Going forward, it will be important to further elucidate the etiology of generalized SUD genetic liability by incorporating additional SUDs, evaluating clinical presentation across the lifespan, and increasing the granularity of investigation (e.g., specific transdiagnostic criteria) to ultimately improve the nosology, prevention, and treatment of SUDs.

Classification of Schizophrenia, Bipolar Disorder and Major Depressive Disorder with Comorbid Traits and Deep Learning Algorithms

Recent GWASs have demonstrated that comorbid disorders share genetic liabilities. But whether and how these shared liabilities can be used for the classification and differentiation of comorbid disorders remains unclear. In this study, we use polygenic risk scores (PRSs) estimated from 42 comorbid traits and the deep neural networks (DNN) architecture to classify and differentiate schizophrenia (SCZ), bipolar disorder (BIP) and major depressive disorder (MDD). Multiple PRSs were obtained for individuals from the schizophrenia (SCZ) (cases = 6,317, controls = 7,240), bipolar disorder (BIP) (cases = 2,634, controls 4,425) and major depressive disorder (MDD) (cases = 1,704, controls = 3,357) datasets, and classification models were constructed with and without the inclusion of PRSs of the target (SCZ, BIP or MDD). Models with the inclusion of target PRSs performed well as expected. Surprisingly, we found that SCZ could be classified with only the PRSs from 35 comorbid traits (not including the target SCZ and directly related traits) (accuracy 0.760 ± 0.007, AUC 0.843 ± 0.005). Similar results were obtained for BIP (33 traits, accuracy 0.768 ± 0.007, AUC 0.848 ± 0.009), and MDD (36 traits, accuracy 0.794 ± 0.010, AUC 0.869 ± 0.004). Furthermore, these PRSs from comorbid traits alone could effectively differentiate unaffected controls, SCZ, BIP, and MDD patients (average categorical accuracy 0.861 ± 0.003, average AUC 0.961 ± 0.041). These results suggest that the shared liabilities from comorbid traits alone may be sufficient to classify SCZ, BIP and MDD. More importantly, these results imply that a data-driven and objective diagnosis and differentiation of SCZ, BIP and MDD may be feasible.

Heart rate variability: Evaluating a potential biomarker of anxiety disorders

Establishing quantifiable biological markers associated with anxiety will increase the objectivity of phenotyping and enhance genetic research of anxiety disorders. Heart rate variability (HRV) is a physiological measure reflecting the dynamic relationship between the sympathetic and parasympathetic nervous systems, and is a promising target for further investigation. This review summarizes evidence evaluating HRV as a potential physiological biomarker of anxiety disorders by highlighting literature related to anxiety and HRV combined with investigations of endophenotypes, neuroimaging, treatment response, and genetics. Deficient HRV shows promise as an endophenotype of pathological anxiety and may serve as a noninvasive index of prefrontal cortical control over the amygdala, and potentially aid with treatment outcome prediction. We propose that the genetics of HRV can be used to enhance the understanding of the genetics of pathological anxiety for etiological investigations and treatment prediction. Given the anxiety-HRV link, strategies are offered to advance genetic analytical approaches, including the use of polygenic methods, wearable devices, and pharmacogenetic study designs. Overall, HRV shows promising support as a physiological biomarker of pathological anxiety, potentially in a transdiagnostic manner, with the heart-brain entwinement providing a novel approach to advance anxiety treatment development.

Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for the 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs. The multivariate GWASs of these GIBNs identified 74 genome-wide significant (GWS) loci (p<5×10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed with attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), and insomnia, indicating genetic predisposition to a larger SA in the specific GIBN is associated with lower genetic risk of these disorders. CT GIBNs displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping the cortex into genetically informed networks.

Empathic disequilibrium as a new framework for understanding individual differences in psychopathology

Introduction

Empathy is part of basic social cognition and is central to everyday interactions. Indeed, emotional and cognitive empathy deficits are related to various psychopathologies, yet the links reported have been inconsistent. Thus, the mechanism underlying these inconsistent links is poorly understood. At least a partial answer may lie in that the dependency between cognitive and emotional empathy has been overlooked. Here, we examined the (dis)equilibrium between emotional and cognitive empathy and how it relates to individual differences in clinical traits. We further examined a possible mediator of these links-emotional reactivity.

Methods

Participants (N = 425) from the general population reported on their empathy, emotional reactivity, autistic traits, psychopathic tendencies, and symptoms of depression and anxiety.

Results

Beyond empathy, both extremes of empathic disequilibrium were associated with various features of clinical conditions; Higher emotional relative to cognitive empathy was related to the social domain of autism and anxiety, while higher cognitive relative to emotional empathy was related to the non-social domain of autism, depression symptoms, and psychopathic tendencies. The associations with autistic traits, anxiety, and psychopathic tendencies were mediated by emotional reactivity.

Discussion

Our findings suggest a new framework for understanding how individual variability in empathy is expressed in various psychopathologies.

Examining the shared etiology of psychopathology with genome-wide association studies

Genome-wide association studies (GWASs) have ushered in a new era of reproducible discovery in psychiatric genetics. The field has now identified hundreds of common genetic variants that are associated with mental disorders, and many of them influence more than one disorder. By advancing the understanding of causal biology underlying psychopathology, GWAS results are poised to inform the development of novel therapeutics, stratification of at-risk patients, and perhaps even the revision of top-down classification systems in psychiatry. Here, we provide a concise review of GWAS findings with an emphasis on findings that have elucidated the shared genetic etiology of psychopathology, summarizing insights at three levels of analysis: 1) genome-wide architecture; 2) networks, pathways, and gene sets; and 3) individual variants/genes. Three themes emerge from these efforts. First, all psychiatric phenotypes are heritable, highly polygenic, and influenced by many pleiotropic variants with incomplete penetrance. Second, GWAS results highlight the broad etiological roles of neuronal biology, system-wide effects over localized effects, and early neurodevelopment as a critical period. Third, many loci that are robustly associated with multiple forms of psychopathology harbor genes that are involved in synaptic structure and function. Finally, we conclude our review by discussing the implications that GWAS results hold for the field of psychiatry, as well as expected challenges and future directions in the next stage of psychiatric genetics.

Mental health impact of autism on families of children with intellectual and developmental disabilities of genetic origin

Background

Many children with an intellectual or developmental disability (IDD) have associated autism spectrum disorders (ASD), as well as an increased risk of mental health difficulties. In a cohort with IDD of genetic aetiology, we tested the hypothesis that excess risk attached to those with ASD + IDD, in terms of both children's mental health and parental psychological distress.

Methods

Participants with a copy number variant or single nucleotide variant (5-19 years) were recruited via UK National Health Service. 1904 caregivers competed an online assessment of child mental health and reported on their own psychological wellbeing. We used regression to examine the association between IDD with and without co-occurring ASD, and co-occurring mental health difficulties, as well as with parental psychological distress. We adjusted for children's sex, developmental level, physical health, and socio-economic deprivation.

Results

Of the 1904 participants with IDD, 701 (36.8%) had co-occurring ASD. Children with both IDD and ASD were at higher risk of associated disorders than those with IDD alone (ADHD: OR = 1.84, 95% confidence interval [CI] 1.46-2.32, p < 0.0001; emotional disorders: OR = 1.85, 95%CI 1.36-2.5, p < 0.0001; disruptive behaviour disorders: OR = 1.79, 95%CI 1.36-2.37, p < 0.0001). The severity of associated symptoms was also greater in those with ASD (hyperactivity: B = 0.25, 95%CI 0.07-0.34, p = 0.006; emotional difficulties: B = 0.91, 95%CI 0.67 to 1.14, p < 0.0001; conduct problems: B = 0.25, 95%CI 0.05 to 0.46, p = 0.013). Parents of children with IDD and ASD also reported greater psychological distress than those with IDD alone (β = 0.1, 95% CI 0.85 to 2.21, p < 0.0001). Specifically, in those with ASD, symptoms of hyperactivity (β = 0.13, 95% CI 0.29-0.63, p < 0.0001), emotional difficulties (β = 0.15, 95% CI 0.26-0.51, p < 0.0001) and conduct difficulties (β = 0.07, 95% CI 0.07-0.37, p < 0.004) all significantly contributed to parental psychological distress.

Conclusions

Among children with IDD of genetic aetiology, one third have co-occurring ASD. Not only do those with co-occurring ASD present with a wider range of associated mental health disorders and more severe mental health difficulties than those with IDD alone, but their parents also experience more psychological distress. Our findings suggest that the additional mental health and behavioural symptoms in those with ASD contributed to the degree of parental psychological distress.

Whole exome sequencing in dense families suggests genetic pleiotropy amongst Mendelian and complex neuropsychiatric syndromes

Whole Exome Sequencing (WES) studies provide important insights into the genetic architecture of serious mental illness (SMI). Genes that are central to the shared biology of SMIs may be identified by WES in families with multiple affected individuals with diverse SMI (F-SMI). We performed WES in 220 individuals from 75 F-SMI families and 60 unrelated controls. Within pedigree prioritization employed criteria of rarity, functional consequence, and sharing by ≥ 3 affected members. Across the sample, gene and gene-set-wide case-control association analysis was performed with Sequence Kernel Association Test (SKAT). In 14/16 families with ≥ 3 sequenced affected individuals, we identified a total of 78 rare predicted deleterious variants in 78 unique genes shared by ≥ 3 members with SMI. Twenty (25%) genes were implicated in monogenic CNS syndromes in OMIM (OMIM-CNS), a fraction that is a significant overrepresentation (Fisher's Exact test OR = 2.47, p = 0.001). In gene-set SKAT, statistically significant association was noted for OMIM-CNS gene-set (SKAT-p = 0.005) but not the synaptic gene-set (SKAT-p = 0.17). In this WES study in F-SMI, we identify private, rare, protein altering variants in genes previously implicated in Mendelian neuropsychiatric syndromes; suggesting pleiotropic influences in neurodevelopment between complex and Mendelian syndromes.

The Polygenic Nature and Complex Genetic Architecture of Specific Learning Disorder

Specific Learning Disorder (SLD) is a multifactorial, neurodevelopmental disorder which may involve persistent difficulties in reading (dyslexia), written expression and/or mathematics. Dyslexia is characterized by difficulties with speed and accuracy of word reading, deficient decoding abilities, and poor spelling. Several studies from different, but complementary, scientific disciplines have investigated possible causal/risk factors for SLD. Biological, neurological, hereditary, cognitive, linguistic-phonological, developmental and environmental factors have been incriminated. Despite worldwide agreement that SLD is highly heritable, its exact biological basis remains elusive. We herein present: (a) an update of studies that have shaped our current knowledge on the disorder’s genetic architecture; (b) a discussion on whether this genetic architecture is ‘unique’ to SLD or, alternatively, whether there is an underlying common genetic background with other neurodevelopmental disorders; and, (c) a brief discussion on whether we are at a position of generating meaningful correlations between genetic findings and anatomical data from neuroimaging studies or specific molecular/cellular pathways. We conclude with open research questions that could drive future research directions.