Shared Genetics of Psychiatric Disorders

Prevalence and correlates of common mental disorders among participants of the Uganda Genome Resource: Opportunities for psychiatric genetics research

Genetics research has potential to alleviate the burden of mental disorders in low- and middle-income-countries through identification of new mechanistic pathways which can lead to efficacious drugs or new drug targets. However, there is currently limited genetics data from Africa. The Uganda Genome Resource provides opportunity for psychiatric genetics research among underrepresented people from Africa. We aimed at determining the prevalence and correlates of major depressive disorder (MDD), suicidality, post-traumatic stress disorder (PTSD), alcohol abuse, generalised anxiety disorder (GAD) and probable attention-deficit hyperactivity disorder (ADHD) among participants of the Uganda Genome Resource. Standardised tools assessed for each mental disorder. Prevalence of each disorder was calculated with 95% confidence intervals. Multivariate logistic regression models evaluated the association between each mental disorder and associated demographic and clinical factors. Among 985 participants, prevalence of the disorders were: current MDD 19.3%, life-time MDD 23.3%, suicidality 10.6%, PTSD 3.1%, alcohol abuse 5.7%, GAD 12.9% and probable ADHD 9.2%. This is the first study to determine the prevalence of probable ADHD among adult Ugandans from a general population. We found significant association between sex and alcohol abuse (adjusted odds ratio [AOR] = 0.26 [0.14,0.45], p < 0.001) and GAD (AOR = 1.78 [1.09,2.49], p = 0.019) respectively. We also found significant association between body mass index and suicidality (AOR = 0.85 [0.73,0.99], p = 0.041), alcohol abuse (AOR = 0.86 [0.78,0.94], p = 0.003) and GAD (AOR = 0.93 [0.87,0.98], p = 0.008) respectively. We also found a significant association between high blood pressure and life-time MDD (AOR = 2.87 [1.08,7.66], p = 0.035) and probable ADHD (AOR = 1.99 [1.00,3.97], p = 0.050) respectively. We also found a statistically significant association between tobacco smoking and alcohol abuse (AOR = 3.2 [1.56,6.67], p = 0.002). We also found ever been married to be a risk factor for probable ADHD (AOR = 2.12 [0.88,5.14], p = 0.049). The Uganda Genome Resource presents opportunity for psychiatric genetics research among underrepresented people from Africa.

Neuropsychiatric polygenic scores are weak predictors of professional categories

Polygenic scores (PGS) enable the exploration of pleiotropic effects and genomic dissection of complex traits. Here, in 421,889 individuals with European ancestry from the Million Veteran Program and UK Biobank, we examine how PGS of 17 neuropsychiatric traits are related to membership in 22 broad professional categories. Overall, we find statistically significant but weak (the highest odds ratio is 1.1 per PGS standard deviation) associations between most professional categories and genetic predisposition for at least one neuropsychiatric trait. Secondary analyses in UK Biobank revealed independence of these associations from observed fluid intelligence and sex-specific effects. By leveraging aggregate population trends, we identified patterns in the public interest, such as the mediating effect of education attainment on the association of attention-deficit/hyperactivity disorder PGS with multiple professional categories. However, at the individual level, PGS explained less than 0.5% of the variance of professional membership, and almost none after we adjusted for education and socio-economic status.

Identifying risk loci for obsessive-compulsive disorder and shared genetic component with schizophrenia: A large-scale multi-trait association analysis with summary statistics

Due to limited samples, no genetic loci have been identified for obsessive-compulsive disorder (OCD) in genome-wide association studies. Additionally, although co-morbidities between OCD and schizophrenia (SCZ) were observed, their common genetic etiology was not completely known. Here, we conducted a comprehensive investigation regarding the genetic architecture of OCD and the common genetic foundation shared by OCD and SCZ using summary statistics data (2688 cases and 7037 controls for OCD; 53,386 cases and 77,258 controls for SCZ). We discovered significant genetic correlation between OCD and SCZ (r̂g=0.296, P = 2.82 × 10-11). We then performed two multi-trait association analyses to detect OCD-associated loci and colocalization analysis to detect causal variants. Parallel gene-level analyses were also implemented. We identified 323 OCD-relevant variants located within 12 loci, with four loci shared the same causal variants between OCD and SCZ. Further, the gene-level analyses discovered 8 OCD-associated genes. Finally, multiple functional analyses at both SNP and gene levels showed that these genetic association signals had significant enrichments in the regions of left ventricle and anterior cingulate cortex, and suggested an important role of pathways involving regulation of telomere maintenance, histone phosphorylation, and GnRH secretion. Overall, this study identified new genetic loci for OCD and provided substantial evidence supporting common genetic foundation underlying OCD and SCZ. The findings advanced our understanding of genetic architecture and pathophysiology of OCD as well as shedding light on shared genetic etiology of the two disorders.

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.

CRISPR/Cas-Based Approaches to Study Schizophrenia and Other Neurodevelopmental Disorders

The study of diseases of the central nervous system (CNS) at the molecular level is challenging because of the complexity of neural circuits and the huge number of specialized cell types. Moreover, genomic association studies have revealed the complex genetic architecture of schizophrenia and other genetically determined mental disorders. Investigating such complex genetic architecture to decipher the molecular basis of CNS pathologies requires the use of high-throughput models such as cells and their derivatives. The time is coming for high-throughput genetic technologies based on CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)/Cas systems to manipulate multiple genomic targets. CRISPR/Cas systems provide the desired complexity, versatility, and flexibility to create novel genetic tools capable of both altering the DNA sequence and affecting its function at higher levels of genetic information flow. CRISPR/Cas tools make it possible to find and investigate the intricate relationship between the genotype and phenotype of neuronal cells. The purpose of this review is to discuss innovative CRISPR-based approaches for studying the molecular mechanisms of CNS pathologies using cellular models.

The shared genetic architecture between epidemiological and behavioral traits with lung cancer

The complex polygenic nature of lung cancer is not fully characterized. Our study seeks to identify novel phenotypes associated with lung cancer using cross-trait linkage disequilibrium score regression (LDSR). We measured pairwise genetic correlation (rg) and SNP heritability (h2) between 347 traits and lung cancer risk using genome-wide association study summary statistics from the UKBB and OncoArray consortium. Further, we conducted analysis after removing genomic regions previously associated with smoking behaviors to mitigate potential confounding effects. We found significant negative genetic correlations between lung cancer risk and dietary behaviors, fitness metrics, educational attainment, and other psychosocial traits. Alcohol taken with meals (rg = - 0.41, h2 = 0.10, p = 1.33 × 10-16), increased fluid intelligence scores (rg = - 0.25, h2 = 0.22, p = 4.54 × 10-8), and the age at which full time education was completed (rg = - 0.45, h2 = 0.11, p = 1.24 × 10-20) demonstrated negative genetic correlation with lung cancer susceptibility. The body mass index was positively correlated with lung cancer risk (rg = 0.20, h2 = 0.25, p = 2.61 × 10-9). This analysis reveals shared genetic architecture between several traits and lung cancer predisposition. Future work should test for causal relationships and investigate common underlying genetic mechanisms across these genetically correlated traits.

A meta-study on transcription factor networks in the suicidal brain

There is evidence supporting the presence of brain gene expression differences between suicides and non-suicides. Such differences have been implicated in suicide pathophysiology. However, regulatory factors underlying these gene expression differences have not been fully understood. Therefore, the identification of differences in regulatory mechanisms, i.e., transcriptional factors between suicides and non-suicides is crucial for the understanding of suicide neurobiology. In this study, we conducted a transcription factor network meta-study with freely available data from the prefrontal cortex of suicides and non-suicides with different mental disorders, including major depression disorder, bipolar disorder and schizophrenia, as well as healthy controls. Disorder-specific characteristics of suicides and non-suicides transcription factor networks were detected, i.e., the presence of immune response genes in both suicides and non-suicides with major depression disorder networks. Also, we found the presence of ESR1, which has been implicated to give resilience to social stress, in the non-suicides network but not in the suicides with major depression network. Suicides and non-suicides with bipolar disorder shared only three genes in common: FOS, CRY1 and PER2. In addition, we found a higher number of genes involved in immune response in the non-suicides with bipolar disorder compared to the suicides with bipolar disorder network. The suicides and non-suicides with schizophrenia networks exhibited clear differences, including the presence of circadian cycle genes in the suicides with schizophrenia network and their absence in the non-suicides with schizophrenia network. The results of this study provide insight on the regulatory mechanisms underpinning transcriptional changes in the suicidal brain.

Genome-wide association study of psychiatric and substance use comorbidity in Mexican individuals

The combination of substance use and psychiatric disorders is one of the most common comorbidities. The objective of this study was to perform a genome-wide association study of this comorbidity (Com), substance use alone (Subs), and psychiatric symptomatology alone (Psych) in the Mexican population. The study included 3914 individuals of Mexican descent. Genotyping was carried out using the PsychArray microarray and genome-wide correlations were calculated. Genome-wide associations were analyzed using multiple logistic models, polygenic risk scores (PRSs) were evaluated using multinomial models, and vertical pleiotropy was evaluated by generalized summary-data-based Mendelian randomization. Brain DNA methylation quantitative loci (brain meQTL) were also evaluated in the prefrontal cortex. Genome-wide correlation and vertical pleiotropy were found between all traits. No genome-wide association signals were found, but 64 single-nucleotide polymorphism (SNPs) reached nominal associations (p < 5.00e-05). The SNPs associated with each trait were independent, and the individuals with high PRSs had a higher prevalence of tobacco and alcohol use. In the multinomial models all of the PRSs (Subs-PRS, Com-PRS, and Psych-PRS) were associated with all of the traits. Brain meQTL of the Subs-associated SNPs had an effect on the genes enriched in insulin signaling pathway, and that of the Psych-associated SNPs had an effect on the Fc gamma receptor phagocytosis pathway.

Genomic Variation, Evolvability, and the Paradox of Mental Illness

Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.