Relationships between substance abuse/dependence and psychiatric disorders based on polygenic scores

Genetic susceptibility to substance use disorders (SUDs) is partially shared between substances. Heritability of any substance dependence, estimated as 54%, is partly explained by additive effects of common variants. Comorbidity between SUDs and other psychiatric disorders is frequent. The present study aims to analyze the additive role of common variants in this comorbidity using polygenic scores (PGSs) based on genome‐wide association study discovery samples of schizophrenia (SCZ), bipolar disorder, attention‐deficit/hyperactivity disorder, autism spectrum disorder, major depressive disorder and anxiety disorders, available from large consortia. PGSs were calculated for 534 patients meeting DSM‐IV criteria for dependence of a substance and abuse/dependence of another substance between alcohol, tobacco, cannabis, cocaine, opiates, hypnotics, stimulants, hallucinogens and solvents; and 587 blood donors from the same population, Iberians from Galicia, as controls. Significance of the PGS and percentage of variance explained were calculated by logistic regression. Using discovery samples of similar size, significant associations with SUDs were detected for SCZ PGS. SCZ PGS explained more variance in SUDs than in most psychiatric disorders. Cross‐disorder PGS based on five psychiatric disorders was significant after adjustment for the effect of SCZ PGS. SCZ PGS was significantly higher in women than in men abusing alcohol. Our findings indicate that SUDs share genetic susceptibility with SCZ to a greater extent than with other psychiatric disorders, including externalizing disorders such as attention‐deficit/hyperactivity disorder. Women have lower probability to develop substance abuse/dependence than men at similar PGS probably because of a higher social pressure against excessive drug use in women.     

Does menopausal transition really influence mental health? Findings from the prospective long‐term Zurich study

In the prospective long‐term Zurich study, we re‐examined the hypothesized association between mental health problems in women and the transition through menopausal stages. One hundred sixty‐eight women from a population‐based Swiss community cohort were prospectively followed up from age 21 to 50. At age 50, the occurrence of hot flushes/night sweats and sleep disturbances was significantly more frequent in peri‐ and post‐menopausal women. Irritability/nervousness was increased only in peri‐menopausal women, but that association was accounted for by neuroticism trait scores at age 30. Transitions to peri‐ or post‐menopause were not related to changes in either the prevalence rates of DSM major depressive episode or anxiety disorders, or the course of psychopathological syndromes as assessed by the Symptom Checklist 90 ‐ Revised. The null associations held when adjusting for duration of reproductive period or age at menopause. Preceding mental health problems between ages 21 and 41, increased neuroticism trait scores at age 30, and concurrent psychosocial distress were significantly related to mental health problems occurring between ages 41 and 50. Depending upon the cut‐off point that was chosen, the arbitrary dichotomization of a continuous depression outcome produced spurious associations with the menopausal transition. We conclude that mental health problems between ages 41 and 50 are probably not directly related to the menopausal transition, and that previously reported associations could be false positives due to inadequate dichotomizations, reporting bias, undisclosed multiple adjustments or overfitting.     

Genetic risk score analysis indicates migraine with and without comorbid depression are genetically different disorders

Migraine and major depressive disorder (MDD) are comorbid, moderately heritable and to some extent influenced by the same genes. In a previous paper, we suggested the possibility of causality (one trait causing the other) underlying this comorbidity. We present a new application of polygenic (genetic risk) score analysis to investigate the mechanisms underlying the genetic overlap of migraine and MDD. Genetic risk scores were constructed based on data from two discovery samples in which genome-wide association analyses (GWA) were performed for migraine and MDD, respectively. The Australian Twin Migraine GWA study (N = 6,350) included 2,825 migraine cases and 3,525 controls, 805 of whom met the diagnostic criteria for MDD. The RADIANT GWA study (N = 3,230) included 1,636 MDD cases and 1,594 controls. Genetic risk scores for migraine and for MDD were used to predict pure and comorbid forms of migraine and MDD in an independent Dutch target sample (NTR–NESDA, N = 2,966), which included 1,476 MDD cases and 1,058 migraine cases (723 of these individuals had both disorders concurrently). The observed patterns of prediction suggest that the ‘pure’ forms of migraine and MDD are genetically distinct disorders. The subgroup of individuals with comorbid MDD and migraine were genetically most similar to MDD patients. These results indicate that in at least a subset of migraine patients with MDD, migraine may be a symptom or consequence of MDD.     

Polygenic risk for psychiatric disorders correlates with executive function in typical development

Executive functions are a diverse and critical suite of cognitive abilities that are often disrupted in individuals with psychiatric disorders. Despite their moderate to high heritability, little is known about the molecular genetic factors that contribute to variability in executive functions and how these factors may be related to those that predispose to psychiatric disorders. We examined the relationship between polygenic risk scores built from large genome‐wide association studies of psychiatric disorders and executive functioning in typically developing children. In our discovery sample (N = 417), consistent with previous reports on general cognitive abilities, polygenic risk for autism spectrum disorder was associated with better performance on the Dimensional Change Card Sort test from the NIH Cognition Toolbox, with the largest effect in the youngest children. Polygenic risk for major depressive disorder was associated with poorer performance on the Flanker test in the same sample. This second association replicated for performance on the Penn Conditional Exclusion Test in an independent cohort (N = 3681). Our results suggest that the molecular genetic factors contributing to variability in executive function during typical development are at least partially overlapping with those associated with psychiatric disorders, although larger studies and further replication are needed.     

The genetics of human personality

Personality traits are the relatively enduring patterns of thoughts, feelings and behaviors that reflect the tendency to respond in certain ways under certain circumstances. Twin and family studies have showed that personality traits are moderately heritable, and can predict various lifetime outcomes, including psychopathology. The Research Domain Criteria characterizes psychiatric diseases as extremes of normal tendencies, including specific personality traits. This implies that heritable variation in personality traits, such as neuroticism, would share a common genetic basis with psychiatric diseases, such as major depressive disorder. Despite considerable efforts over the past several decades, the genetic variants that influence personality are only beginning to be identified. We review these recent and increasingly rapid developments, which focus on the assessment of personality via several commonly used personality questionnaires in healthy human subjects. Study designs covered include twin, linkage, candidate gene association studies, genome‐wide association studies and polygenic analyses. Findings from genetic studies of personality have furthered our understanding about the genetic etiology of personality, which, like neuropsychiatric diseases themselves, is highly polygenic. Polygenic analyses have showed genetic correlations between personality and psychopathology, confirming that genetic studies of personality can help to elucidate the etiology of several neuropsychiatric diseases.     

Convergence and Divergence in the Etiology of Myelin Impairment in Psychiatric Disorders and Drug Addiction

Impairment of oligodendroglia (OL)-dependent myelination in the central nervous system (CNS) is a remarkable parallel recently identified in major psychiatric disorders and chronic drug abuse. Neuroimaging and neuropathological studies revealed myelin defects and microarray-profiling analysis demonstrated aberrant expression of myelin-related genes in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD) and cocaine addiction. However, the etiology underlying myelin impairment in these clinically distinct subjects remains elusive. This article reviews myelin impairment in line with dopaminergic dysfunction, a prime neuropathophysiological trait shared in psychiatric disorders and drug abuse, as well as the genetic and epigenetic alterations associated with these diseases. The current findings support the hypothesis that aberrant dopamine (DA) action on OLs is a common pathologic mechanism for myelin impairment in the aforementioned mental morbidities, whereas inherited genetic variations that specifically affect OL development and myelinogenesis may further increase myelin vulnerability in psychiatric disorders. Importantly, OL defect is not only a pathological consequence but also a causative factor for dopaminergic dysfunction. Hence, myelin impairment is a key factor in the pathogenic loop of psychiatric diseases and drug addiction. Special issue article in honor of Dr. Ji-Sheng Han.     

Are Genetic Risk Factors for Psychosis Also Associated with Dimension-Specific Psychotic Experiences in Adolescence?

Psychosis has been hypothesised to be a continuously distributed quantitative phenotype and disorders such as schizophrenia and bipolar disorder represent its extreme manifestations. Evidence suggests that common genetic variants play an important role in liability to both schizophrenia and bipolar disorder. Here we tested the hypothesis that these common variants would also influence psychotic experiences measured dimensionally in adolescents in the general population. Our aim was to test whether schizophrenia and bipolar disorder polygenic risk scores (PRS), as well as specific single nucleotide polymorphisms (SNPs) previously identified as risk variants for schizophrenia, were associated with adolescent dimension-specific psychotic experiences. Self-reported Paranoia, Hallucinations, Cognitive Disorganisation, Grandiosity, Anhedonia, and Parent-rated Negative Symptoms, as measured by the Specific Psychotic Experiences Questionnaire (SPEQ), were assessed in a community sample of 2,152 16-year-olds. Polygenic risk scores were calculated using estimates of the log of odds ratios from the Psychiatric Genomics Consortium GWAS stage-1 mega-analysis of schizophrenia and bipolar disorder. The polygenic risk analyses yielded no significant associations between schizophrenia and bipolar disorder PRS and the SPEQ measures. The analyses on the 28 individual SNPs previously associated with schizophrenia found that two SNPs in TCF4 returned a significant association with the SPEQ Paranoia dimension, rs17512836 (p-value = 2.57×10⁻⁴) and rs9960767 (p-value = 6.23×10⁻⁴). Replication in an independent sample of 16-year-olds (N = 3,427) assessed using the Psychotic-Like Symptoms Questionnaire (PLIKS-Q), a composite measure of multiple positive psychotic experiences, failed to yield significant results. Future research with PRS derived from larger samples, as well as larger adolescent validation samples, would improve the predictive power to test these hypotheses further. The challenges of relating adult clinical diagnostic constructs such as schizophrenia to adolescent psychotic experiences at a genetic level are discussed.     

Autistic-Like Traits in Adult Patients with Mood Disorders and Schizophrenia

Autism spectrum disorder often co-occurs with other psychiatric disorders. Although a high prevalence of autistic-like traits/symptoms has been identified in the pediatric psychiatric population of normal intelligence, there are no reports from adult psychiatric population. This study examined whether there is a greater prevalence of autistic-like traits/symptoms in patients with adult-onset psychiatric disorders such as major depressive disorder (MDD), bipolar disorder, or schizophrenia, and whether such an association is independent of symptom severity. The subjects were 290 adults of normal intelligence between 25 and 59 years of age (MDD, n=125; bipolar disorder, n=56; schizophrenia, n=44; healthy controls, n=65). Autistic-like traits/symptoms were measured using the Social Responsiveness Scale for Adults. Symptom severity was measured using the Positive and Negative Symptoms Scale, the Hamilton Depression Rating Scale, and/or the Young Mania Rating Scale. Almost half of the clinical subjects, except those with remitted MDD, exhibited autistic-like traits/symptoms at levels typical for sub-threshold or threshold autism spectrum disorder. Furthermore, the proportion of psychiatric patients that demonstrated high autistic-like traits/symptoms was significantly greater than that of healthy controls, and not different between that of remitted or unremitted subjects with bipolar disorder or schizophrenia. On the other hand, remitted subjects with MDD did not differ from healthy controls with regard to the prevalence or degree of high autistic-like traits/symptoms. A substantial proportion of adults with bipolar disorder and schizophrenia showed high autistic-like traits/symptoms independent of symptom severity, suggesting a shared pathophysiology among autism spectrum disorder and these psychiatric disorders. Conversely, autistic-like traits among subjects with MDD were associated with the depressive symptom severity. These findings suggest the importance of evaluating autistic-like traits/symptoms underlying adult-onset psychiatric disorders for the best-suited treatment. Further studies with a prospective design and larger samples are needed.     

The genetics of neuroticism and human values

Human values and personality have been shown to share genetic variance in twin studies. However, there is a lack of evidence about the genetic components of this association. This study examined the interplay between genes, values and personality in the case of neuroticism, because polygenic scores were available for this personality trait. First, we replicated prior evidence of a positive association between the polygenic neuroticism score (PNS) and neuroticism. Second, we found that the PNS was significantly associated with the whole human value space in a sinusoidal waveform that was consistent with Schwartz's circular model of human values. These results suggest that it is useful to consider human values in the analyses of genetic contributions to personality traits. They also pave the way for an investigation of the biological mechanisms contributing to human value orientations.     

A Genetic Deconstruction of Neurocognitive Traits in Schizophrenia and Bipolar Disorder

Background

Impairments in cognitive functions are common in patients suffering from psychiatric disorders, such as schizophrenia and bipolar disorder. Cognitive traits have been proposed as useful for understanding the biological and genetic mechanisms implicated in cognitive function in healthy individuals and in the dysfunction observed in psychiatric disorders.

Methods

Sets of genes associated with a range of cognitive functions often impaired in schizophrenia and bipolar disorder were generated from a genome-wide association study (GWAS) on a sample comprising 670 healthy Norwegian adults who were phenotyped for a broad battery of cognitive tests. These gene sets were then tested for enrichment of association in GWASs of schizophrenia and bipolar disorder. The GWAS data was derived from three independent single-centre schizophrenia samples, three independent single-centre bipolar disorder samples, and the multi-centre schizophrenia and bipolar disorder samples from the Psychiatric Genomics Consortium.

Results

The strongest enrichments were observed for visuospatial attention and verbal abilities sets in bipolar disorder. Delayed verbal memory was also enriched in one sample of bipolar disorder. For schizophrenia, the strongest evidence of enrichment was observed for the sets of genes associated with performance in a colour-word interference test and for sets associated with memory learning slope.

Conclusions

Our results are consistent with the increasing evidence that cognitive functions share genetic factors with schizophrenia and bipolar disorder. Our data provides evidence that genetic studies using polygenic and pleiotropic models can be used to link specific cognitive functions with psychiatric disorders.     

Lifetime prevalence of mood and anxiety disorders in twin pairs discordant for schizophrenia.

There have been long questions about the relationship of schizophrenia to other mental disorders. Lifetime DSM-III-R diagnoses of mood and anxiety disorders in twins with clinically diagnosed schizophrenia (n = 24) and their non-affected co-twins (n = 24) were compared with twins from pairs without schizophrenia (n = 3327) using a sample from the Vietnam Era Twin Registry. Schizophrenic probands had significantly elevated rates of all included disorders (bipolar disorder, major depression, dysthymia, generalized anxiety disorder, panic disorder, and PTSD) compared with controls (P<0.01). The odd ratios comparing co-twins of schizophrenic probands with controls was greater than three for every disorder, but did not attain statistical significance. A similar pattern was observed when analyses were restricted to only monozygotic twins (n = 12). Consistent with other studies, schizophrenics appeared to have higher rates of a range of mental disorders. Our results suggest that schizophrenia per se represents a risk factor for other psychiatric disorders, but the absence of significantly elevated risk among non-schizophrenic co-twins suggested that family environmental and/or genetic factors that contribute to risk of schizophrenia do not increase the risk of mood and anxiety disorders to the same extent that the risk of these other disorders is increased by the presence of schizophrenia.     

Etiology in psychiatry: embracing the reality of poly‐gene‐environmental causation of mental illness

Intriguing findings on genetic and environmental causation suggest a need to reframe the etiology of mental disorders. Molecular genetics shows that thousands of common and rare genetic variants contribute to mental illness. Epidemiological studies have identified dozens of environmental exposures that are associated with psychopathology. The effect of environment is likely conditional on genetic factors, resulting in gene‐environment interactions. The impact of environmental factors also depends on previous exposures, resulting in environment‐environment interactions. Most known genetic and environmental factors are shared across multiple mental disorders. Schizophrenia, bipolar disorder and major depressive disorder, in particular, are closely causally linked. Synthesis of findings from twin studies, molecular genetics and epidemiological research suggests that joint consideration of multiple genetic and environmental factors has much greater explanatory power than separate studies of genetic or environmental causation. Multi‐factorial gene‐environment interactions are likely to be a generic mechanism involved in the majority of cases of mental illness, which is only partially tapped by existing gene‐environment studies. Future research may cut across psychiatric disorders and address poly‐causation by considering multiple genetic and environmental measures across the life course with a specific focus on the first two decades of life. Integrative analyses of poly‐causation including gene‐environment and environment‐environment interactions can realize the potential for discovering causal types and mechanisms that are likely to generate new preventive and therapeutic tools.     

Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia,Bipolar Disorder,and Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.     

A haplotype of glycogen synthase kinase 3β is associated with early onset of unipolar major depression

Recent findings suggest that glycogen synthase kinase 3β (GSK3β) may play a role in the pathophysiology and treatment of mood disorders. Various genetic studies have shown the association of GSK3β polymorphisms with different mood disorder phenotypes. We hypothesized that genetic variants in the GSK3β gene could partially underlie the susceptibility to mood disorders. We performed a genetic case–control study of 440 psychiatrically screened control subjects and 445 mood disorder patients [256 unipolar major depressive disorder (MDD) and 189 bipolar disorder (BD)]. We genotyped a set of 11 single nucleotide polymorphisms (SNPs) and determined the relative frequency of a known copy number variant (CNV) overlapping the GSK3β by quantitative real‐time polymerase chain reaction (PCR). We found no evidence of association with MDD or BD diagnosis, and we further investigated the age at onset (AAO) of the disorder and severity of depressive index episode. We found that rs334555, located in intron 1 of GSK3β, was nominally associated with an earlier AAO of the disease in MDD (P = 0.001). We also identified a haplotype containing three SNPs (rs334555, rs119258668 and rs11927974) associated with AAO of the disorder (permutated P = 0.0025). We detected variability for the CNV, but we could not detect differences between patients and controls for any of the explored phenotypes. This study presents further evidence of the contribution of GSK3β to mood disorders, implicating a specific SNP and a haplotype with an earlier onset of the disorder in a group of well‐characterized patients with unipolar MDD. Further replication studies in patients with the same phenotypic characteristics should confirm the results reported here.     

Genetic and Environmental Risk for Chronic Pain and the Contribution of Risk Variants for Major Depressive Disorder: A Family-Based Mixed-Model Analysis

BackgroundChronic pain is highly prevalent and a significant source of disability, yet its genetic and environmental risk factors are poorly understood. Its relationship with major depressive disorder (MDD) is of particular importance. We sought to test the contribution of genetic factors and shared and unique environment to risk of chronic pain and its correlation with MDD in Generation Scotland: Scottish Family Health Study (GS:SFHS). We then sought to replicate any significant findings in the United Kingdom Biobank study.ConclusionsGenetic factors, as well as chronic pain in a partner or spouse, contribute substantially to the risk of chronic pain for an individual. Chronic pain is genetically correlated with MDD, has a polygenic architecture, and is associated with polygenic risk of MDD.     

Genetics of schizophrenia and smoking: an approach to studying their comorbidity based on epidemiological findings

The association between schizophrenia and tobacco smoking has been described in more than 1,000 articles, many with inadequate methodology. The studies on this association can focus on: (1) current smoking, ever smoking or smoking cessation; (2) non-psychiatric controls or controls with severe mental illness (e.g., bipolar disorder); and (3) higher smoking frequency or greater usage in smokers. The association with the most potential for genetic studies is that between ever daily smoking and schizophrenia; it may reflect a shared genetic vulnerability. To reduce the number of false-positive genes, we propose a three-stage approach derived from epidemiological knowledge. In the first stage, only genetic variations associated with ever daily smoking that are simultaneously significant within the non-psychiatric controls, the bipolar disorder controls and the schizophrenia cases will be selected. Only those genetic variations that are simultaneously significant in the three hypothesis tests will be tested in the second stage, where the prevalence of the genes must be significantly higher in schizophrenia than in bipolar disorder, and significantly higher in bipolar disorder than in controls. The genes simultaneously significant in the second stage will be included in a third stage where the gene variations must be significantly more frequent in schizophrenia patients who did not start smoking daily until their 20s (late start) versus those who had an early start. Any genetic approach to psychiatric disorders may fail if attention is not given to comorbidity and epidemiological studies that suggest which comorbidities are likely to be explained by genetics and which are not. Our approach, which examines the results of epidemiological studies on comorbidities and then looks for genes that simultaneously satisfy epidemiologically suggested sets of hypotheses, may also apply to the study of other major illnesses.     

Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank

BackgroundSleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort.Methods and findingsIn this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10⁻⁵⁶, FDR = 6 × 10⁻⁵⁵) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry.ConclusionsIn this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.

In a cross-sectional study, Michael Wainberg and colleagues investigate the association between accelerometer-derived sleep measures and lifetime psychiatric diagnoses.     

Anxiety and depression in twin and sib pairs extremely discordant and concordant for neuroticism: prodromus to a linkage study.

Multivariate modelling of anxiety and depression data in twins has suggested that the two phenotypes are largely underpinned by one genetic factor, while other studies have indicated a relationship between these disorders and the neuroticism personality trait. As part of a study to identify quantitative trait loci for anxiety and depression, questionnaire responses and interviews of 15,027 Australian twins and 11,389 of their family members conducted during the past 20 years were reviewed to identify individuals with neuroticism, anxiety and depression scores in the upper or lower deciles of the population. This information was then used to identify extreme discordant and concordant (EDAC) sib pairs. 1373 high-scoring and 1571 low-scoring subjects (2357 sib pairs) were selected for participation, and extremely high participation rates were achieved, with over 90% of contactable prospective participants completing the interview phase, and over 90% of these providing blood or buccal samples. Participation bias arising from the nature of the selection variables was minimal, with only a small difference between rates of interview participation among prospective participants with high and low selection scores (89.4% vs 91.6%). The interview permitted the diagnosis of depression and several anxiety disorders (OCD, agoraphobia, panic disorder, generalised anxiety disorder) in this sample according to DSM-IV criteria. The methodology for selection of prospective subjects was demonstrated to be extremely successful, with highly significant differences in depression and anxiety disorder prevalence rates between individuals in the two selection groups. The success of this EDAC sampling scheme will enhance the power for QTL linkage and association analysis in this sample.     

Neuronal cell adhesion genes

The major mental disorders, schizophrenia and bipolar disorder are substantially heritable. Recent genomic studies have identified a small number of common and rare risk genes contributing to both disorders and support epidemiological evidence that genetic susceptibility overlaps between them. Prompted by the question of whether risk genes cluster in specific molecular pathways or implicate discrete mechanisms we and others have developed hypothesis-free methods of investigating genome-wide association datasets at a pathway-level. The application of our method to the 212 experimentally-derived pathways in the Kyoto Encycolpaedia of Genes and Genomes (KEGG) database identified significant association between the cell adhesion molecule (CAM) pathway and both schizophrenia and bipolar disorder susceptibility across three GWAS datasets. Interestingly, a similar approach applied to an autistic spectrum disorders (ASDs) sample identified a similar pathway and involved many of the same genes. Disruption of a number of these genes (including NRXN1, CNTNAP2 and CASK) are known to cause diverse neurodevelopmental brain disorder phenotypes including schizophenia, autism, learning disability and specific language disorder. Taken together these studies bring the CAM pathway sharply into focus for more comprehensive DNA sequencing to identify the critical genes, and investigate their relationships and interaction with environmental risk factors in the expression of many seemingly different neurodevelopmental disorders.     

Schizophrenia and the neurodevelopmental continuum:evidence from genomics

The idea that disturbances occurring early in brain development contribute to the pathogenesis of schizophrenia, often referred to as the neurodevelopmental hypothesis, has become widely accepted. Despite this, the disorder is viewed as being distinct nosologically, and by implication pathophysiologically and clinically, from syndromes such as autism spectrum disorders, attention‐deficit/hyperactivity disorder (ADHD) and intellectual disability, which typically present in childhood and are grouped together as “neurodevelopmental disorders”. An alternative view is that neurodevelopmental disorders, including schizophrenia, rather than being etiologically discrete entities, are better conceptualized as lying on an etiological and neurodevelopmental continuum, with the major clinical syndromes reflecting the severity, timing and predominant pattern of abnormal brain development and resulting functional abnormalities. It has also been suggested that, within the neurodevelopmental continuum, severe mental illnesses occupy a gradient of decreasing neurodevelopmental impairment as follows: intellectual disability, autism spectrum disorders, ADHD, schizophrenia and bipolar disorder. Recent genomic studies have identified large numbers of specific risk DNA changes and offer a direct and robust test of the predictions of the neurodevelopmental continuum model and gradient hypothesis. These findings are reviewed in detail. They not only support the view that schizophrenia is a disorder whose origins lie in disturbances of brain development, but also that it shares genetic risk and pathogenic mechanisms with the early onset neurodevelopmental disorders (intellectual disability, autism spectrum disorders and ADHD). They also support the idea that these disorders lie on a gradient of severity, implying that they differ to some extent quantitatively as well as qualitatively. These findings have important implications for nosology, clinical practice and research.