Polygenic dissection of the bipolar phenotype

Genomic findings in schizophrenia and their implications

There has been substantial progress in understanding the genetics of schizophrenia over the past 15 years. This has revealed a highly polygenic condition with the majority of the currently explained heritability coming from common alleles of small effect but with additional contributions from rare copy number and coding variants. Many specific genes and loci have been implicated that provide a firm basis upon which mechanistic research can proceed. These point to disturbances in neuronal, and particularly synaptic, functions that are not confined to a small number of brain regions and circuits. Genetic findings have also revealed the nature of schizophrenia's close relationship to other conditions, particularly bipolar disorder and childhood neurodevelopmental disorders, and provided an explanation for how common risk alleles persist in the population in the face of reduced fecundity. Current genomic approaches only potentially explain around 40% of heritability, but only a small proportion of this is attributable to robustly identified loci. The extreme polygenicity poses challenges for understanding biological mechanisms. The high degree of pleiotropy points to the need for more transdiagnostic research and the shortcomings of current diagnostic criteria as means of delineating biologically distinct strata. It also poses challenges for inferring causality in observational and experimental studies in both humans and model systems. Finally, the Eurocentric bias of genomic studies needs to be rectified to maximise benefits and ensure these are felt across diverse communities. Further advances are likely to come through the application of new and emerging technologies, such as whole-genome and long-read sequencing, to large and diverse samples. Substantive progress in biological understanding will require parallel advances in functional genomics and proteomics applied to the brain across developmental stages. For these efforts to succeed in identifying disease mechanisms and defining novel strata they will need to be combined with sufficiently granular phenotypic data.

Genetic Liabilities Differentiating Bipolar Disorder, Schizophrenia, and Major Depressive Disorder, and Phenotypic Heterogeneity in Bipolar Disorder

Importance

Understanding the origins of clinical heterogeneity in bipolar disorder (BD) will inform new approaches to stratification and studies of underlying mechanisms.

Objective

To identify components of genetic liability that are shared between BD, schizophrenia, and major depressive disorder (MDD) and those that differentiate each disorder from the others and to examine associations between heterogeneity for key BD symptoms and each component.

Design, Setting, and Participants

Using data from the Bipolar Disorder Research Network in the United Kingdom, components of liability were identified by applying genomic structural equation modeling to genome-wide association studies of schizophrenia, BD, and MDD. Polygenic risk scores (PRS) representing each component were tested for association with symptoms in an independent BD data set. Adults with DSM-IV BD or schizoaffective disorder, bipolar type, were included. Data were collected from January 2000 to December 2013, and data were analyzed from June 2020 to February 2022.

Main Outcomes and Measures

PRS representing the components of liability were tested for association with mania and depression, psychosis, and mood incongruence of psychosis in participants with BD, measured using the Bipolar Affective Disorder Dimensional Scale.

Results

Of 4429 included participants, 3012 (68.0%) were female, and the mean (SD) age was 46.2 (12.3) years. Mania and psychosis were associated with the shared liability component (mania β = 0.29; 95% CI, 0.23-0.34; P = 3.04 × 10-25; psychosis β = 0.05; 95% CI, 0.04-0.07; P = 2.33 × 10-13) and the components that differentiate each of schizophrenia (mania β = 0.08; 95% CI, 0.03-0.14; P = .002; psychosis β = 0.03; 95% CI, 0.01-0.04; P = 1.0 × 10-4) and BD (mania β = 0.14; 95% CI, 0.09-0.20; P = 1.99 × 10-7; psychosis β = 0.02; 95% CI, 0.01-0.03; P = .006) from the other disorders. The BD differentiating component was associated with mania independently of effects on psychosis (β = 0.14; 95% CI, 0.08-0.20; P = 4.32 × 10-6) but not with psychosis independently of mania. Conversely, the schizophrenia differentiating component was associated with psychosis independently of effects on mania (β = 0.01; 95% CI, 0.003-0.03; P = .02), but not with mania independently of psychosis. Mood incongruence of psychosis was associated only with the schizophrenia differentiating component (β = 0.03; 95% CI, 0.01-0.05; P = .005). Depression was associated with higher MDD differentiating component (β = 0.07; 95% CI, 0.01-0.12; P = .01) but lower BD differentiating component (β = -0.11; 95% CI, -0.17 to -0.06; P = 7.06 × 10-5).

Conclusions and Relevance

In this study of BD, clinical heterogeneity reflected the burden of liability to BD and the contribution of alleles that have differentiating effects on risk for other disorders; mania, psychosis, and depression were associated with the components of genetic liability differentiating BD, MDD, and schizophrenia, respectively. Understanding the basis of this etiological heterogeneity will be critical for identifying the different pathophysiological processes underlying BD, stratifying patients, and developing precision therapeutics.

Interactive effects of polygenic risk and cognitive subtype on brain morphology in schizophrenia spectrum and bipolar disorders

Grey matter volume (GMV) may be associated with polygenic risk for schizophrenia (PRS-SZ) and severe cognitive deficits in people with schizophrenia, schizoaffective disorder (collectively SSD), and bipolar disorder (BD). This study examined the interactive effects of PRS-SZ and cognitive subtypes of SSD and BD in relation to GMV. Two-step cluster analysis was performed on 146 clinical cases (69 SSD and 77 BD) assessed on eight cognitive domains (verbal and visual memory, executive function, processing speed, visual processing, language ability, working memory, and planning). Among them, 55 BD, 51 SSD, and 58 healthy controls (HC), contributed to focal analyses of the relationships between cognitive subtypes, PRS-SZ and their interaction on GMV. Two distinct cognitive subtypes were evident among the combined sample of cases: a 'cognitive deficit' group (CD; N = 31, 20SSD/11BD) showed severe impairment across all cognitive indices, and a 'cognitively spared' (CS; N = 75; 31SSD/44BD) group showed intermediate cognitive performance that was significantly worse than the HC group but better than the CD subgroup. A cognitive subgroup-by-PRS-SZ interaction was significantly associated with GMV in the left precentral gyrus. Moderation analyses revealed a significant negative relationship between PRS-SZ and GMV in the CD group only. At low and average (but not high) PRS-SZ, larger precentral GMV was evident in the CD group compared to both CS and HC groups, and in the CS group compared to HCs. This study provides evidence for a relationship between regional GMV changes and PRS-SZ in psychosis spectrum cases with cognitive deficits, but not in cases cognitively spared.

Pleiotropy between language impairment and broader behavioral disorders-an investigation of both common and rare genetic variants

BACKGROUND

Language plays a major role in human behavior. For this reason, neurodevelopmental and psychiatric disorders in which linguistic ability is impaired could have a big impact on the individual's social interaction and general wellbeing. Such disorders tend to have a strong genetic component, but most past studies examined mostly the linguistic overlaps across these disorders; investigations into their genetic overlaps are limited. The aim of this study was to assess the potential genetic overlap between language impairment and broader behavioral disorders employing methods capturing both common and rare genetic variants.

METHODS

We employ polygenic risk scores (PRS) trained on specific language impairment (SLI) to evaluate genetic overlap across several disorders in a large case-cohort sample comprising ~13,000 autism spectrum disorder (ASD) cases, including cases of childhood autism and Asperger's syndrome, ~15,000 attention deficit/hyperactivity disorder (ADHD) cases, ~3000 schizophrenia cases, and ~21,000 population controls. We also examine rare variants in SLI/language-related genes in a subset of the sample that was exome-sequenced using the SKAT-O method.

RESULTS

We find that there is little evidence for genetic overlap between SLI and ADHD, schizophrenia, and ASD, the latter being in line with results of linguistic analyses in past studies. However, we observe a small, significant genetic overlap between SLI and childhood autism specifically, which we do not observe for SLI and Asperger's syndrome. Moreover, we observe that childhood autism cases have significantly higher SLI-trained PRS compared to Asperger's syndrome cases; these results correspond well to the linguistic profiles of both disorders. Our rare variant analyses provide suggestive evidence of association for specific genes with ASD, childhood autism, and schizophrenia.

CONCLUSIONS

Our study provides, for the first time, to our knowledge, genetic evidence for ASD subtypes based on risk variants for language impairment.

Analysis of genetic differences between psychiatric disorders: exploring pathways and cell types/tissues involved and ability to differentiate the disorders by polygenic scores

Although displaying genetic correlations, psychiatric disorders are clinically defined as categorical entities as they each have distinguishing clinical features and may involve different treatments. Identifying differential genetic variations between these disorders may reveal how the disorders differ biologically and help to guide more personalized treatment. Here we presented a statistical framework and comprehensive analysis to identify genetic markers differentially associated with various psychiatric disorders/traits based on GWAS summary statistics, covering 18 psychiatric traits/disorders and 26 comparisons. We also conducted comprehensive analysis to unravel the genes, pathways and SNP functional categories involved, and the cell types and tissues implicated. We also assessed how well one could distinguish between psychiatric disorders by polygenic risk scores (PRS). SNP-based heritabilities (h2snp) were significantly larger than zero for most comparisons. Based on current GWAS data, PRS have mostly modest power to distinguish between psychiatric disorders. For example, we estimated that AUC for distinguishing schizophrenia from major depressive disorder (MDD), bipolar disorder (BPD) from MDD and schizophrenia from BPD were 0.694, 0.602 and 0.618, respectively, while the maximum AUC (based on h2snp) were 0.763, 0.749 and 0.726, respectively. We also uncovered differences in each pair of studied traits in terms of their differences in genetic correlation with comorbid traits. For example, clinically defined MDD appeared to more strongly genetically correlated with other psychiatric disorders and heart disease, when compared to non-clinically defined depression in UK Biobank. Our findings highlight genetic differences between psychiatric disorders and the mechanisms involved. PRS may help differential diagnosis of selected psychiatric disorders in the future with larger GWAS samples.

Epigenetic biotypes of post-traumatic stress disorder in war-zone exposed veteran and active duty males

Post-traumatic stress disorder (PTSD) is a heterogeneous condition evidenced by the absence of objective physiological measurements applicable to all who meet the criteria for the disorder as well as divergent responses to treatments. This study capitalized on biological diversity observed within the PTSD group observed following epigenome-wide analysis of a well-characterized Discovery cohort (N = 166) consisting of 83 male combat exposed veterans with PTSD, and 83 combat veterans without PTSD in order to identify patterns that might distinguish subtypes. Computational analysis of DNA methylation (DNAm) profiles identified two PTSD biotypes within the PTSD+ group, G1 and G2, associated with 34 clinical features that are associated with PTSD and PTSD comorbidities. The G2 biotype was associated with an increased PTSD risk and had higher polygenic risk scores and a greater methylation compared to the G1 biotype and healthy controls. The findings were validated at a 3-year follow-up (N = 59) of the same individuals as well as in two independent, veteran cohorts (N = 54 and N = 38), and an active duty cohort (N = 133). In some cases, for example Dopamine-PKA-CREB and GABA-PKC-CREB signaling pathways, the biotypes were oppositely dysregulated, suggesting that the biotypes were not simply a function of a dimensional relationship with symptom severity, but may represent distinct biological risk profiles underpinning PTSD. The identification of two novel distinct epigenetic biotypes for PTSD may have future utility in understanding biological and clinical heterogeneity in PTSD and potential applications in risk assessment for active duty military personnel under non-clinician-administered settings, and improvement of PTSD diagnostic markers.

Schizophrenia Polygenic Risk and Brain Structural Changes in Methamphetamine-Associated Psychosis in a South African Population

Background

The genetic architecture of psychotic disorders is complex, with hundreds of genetic risk loci contributing to a polygenic model of disease. Overlap in the genetics of psychotic disorders and brain measures has been found in European populations, but has not been explored in populations of African ancestry. The aim of this study was to determine whether a relationship exists between a schizophrenia-derived PRS and (i) methamphetamine associated psychosis (MAP), and (ii) brain structural measures, in a South African population.

Methods

The study sample consisted of three participant groups: 31 individuals with MAP, 48 with apsychotic methamphetamine dependence, and 49 healthy controls. Using PRSice, PRS was generated for each of the participants with GWAS summary statistics from the Psychiatric Genomics Consortium Schizophrenia working group (PGC-SCZ2) as the discovery dataset. Regression analyses were performed to determine associations of PRS, with diagnosis, whole brain, and regional gray and white matter measures.

Results

Schizophrenia-derived PRS did not significantly predict MAP diagnosis. After correction for multiple testing, no significant associations were found between PRS and brain measures across all groups.

Discussion

The lack of significant associations here may indicate that the study is underpowered, that brain volumes in MAP are due to factors other than polygenic risk for schizophrenia, or that PRS derived from a largely European discovery set has limited utility in individuals of African ancestry. Larger studies, that include diverse populations, and more nuanced brain measures, may help elucidate the relationship between schizophrenia-PRS, brain structural changes, and psychosis.

Conclusion

This research presents the first PRS study to investigate shared genetic effects across psychotic disorders and brain structural measures in an African population. Ancestrally comparable discovery datasets may be useful for future African genetic research.

Dissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores

Bipolar disorder (BD) has high clinical heterogeneity, frequent psychiatric comorbidities, and elevated suicide risk. To determine genetic differences between common clinical sub-phenotypes of BD, we performed a systematic polygenic risk score (PRS) analysis using multiple PRSs from a range of psychiatric, personality, and lifestyle traits to dissect differences in BD sub-phenotypes in two BD cohorts: the Mayo Clinic BD Biobank (N = 968) and Genetic Association Information Network (N = 1001). Participants were assessed for history of psychosis, early-onset BD, rapid cycling (defined as four or more episodes in a year), and suicide attempts using questionnaires and the Structured Clinical Interview for DSM-IV. In a combined sample of 1969 bipolar cases (45.5% male), those with psychosis had higher PRS for SCZ (OR = 1.3 per S.D.; p = 3e-5) but lower PRSs for anhedonia (OR = 0.87; p = 0.003) and BMI (OR = 0.87; p = 0.003). Rapid cycling cases had higher PRS for ADHD (OR = 1.23; p = 7e-5) and MDD (OR = 1.23; p = 4e-5) and lower BD PRS (OR = 0.8; p = 0.004). Cases with a suicide attempt had higher PRS for MDD (OR = 1.26; p = 1e-6) and anhedonia (OR = 1.22; p = 2e-5) as well as lower PRS for educational attainment (OR = 0.87; p = 0.003). The observed novel PRS associations with sub-phenotypes align with clinical observations such as rapid cycling BD patients having a greater lifetime prevalence of ADHD. Our findings confirm that genetic heterogeneity contributes to clinical heterogeneity of BD and consideration of genetic contribution to psychopathologic components of psychiatric disorders may improve genetic prediction of complex psychiatric disorders.

Polygenic risk for anxiety influences anxiety comorbidity and suicidal behavior in bipolar disorder

Bipolar disorder is often comorbid with anxiety, which is itself associated with poorer clinical outcomes, including suicide. A better etiologic understanding of this comorbidity could inform diagnosis and treatment. The present study aims to test whether comorbid anxiety in bipolar disorder reflects shared genetic risk factors. We also sought to assess the contribution of genetic risk for anxiety to suicide attempts in bipolar disorder. Polygenic risk scores (PRS) were calculated from published genome-wide association studies of samples of controls and cases with anxiety (n = 83,566) or bipolar disorder (n = 51,710), then scored in independent target samples (total n = 3369) of individuals with bipolar disorder who reported or denied lifetime anxiety disorders or suicidal attempts in research interviews. Participants were recruited from clinical and nonclinical settings and genotyped for common genetic variants. The results show that polygenic risk for anxiety was associated with comorbid anxiety disorders and suicide attempts in bipolar disorder, while polygenic risk for bipolar disorder was not associated with any of these variables. Our findings point out that comorbid anxiety disorders in bipolar disorder reflect a dual burden of bipolar and anxiety-related genes; the latter may also contribute to suicide attempts. Clinical care that recognizes and addresses this dual burden may help improve outcomes in people living with comorbid bipolar and anxiety disorders.

Developing and testing high‐efficacy patient subgroups within a clinical trial using risk scores

There is the potential for high-dimensional information about patients collected in clinical trials (such as genomic, imaging, and data from wearable technologies) to be informative for the efficacy of a new treatment in situations where only a subset of patients benefits from the treatment. The adaptive signature design (ASD) method has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using genetic data. The method requires selection of three tuning parameters which may be highly computationally expensive. We propose a variation to the ASD method, the cross-validated risk scores (CVRS) design method, that does not require selection of any tuning parameters. The method is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure.We assess the properties of CVRS against the originally proposed cross-validated ASD using simulation data and a real psychiatry trial. CVRS, as assessed for various sample sizes and response rates, has a substantial reduction in the computational time required. In many simulation scenarios, there is a substantial improvement in the ability to correctly identify the sensitive group and the power of the design to detect a treatment effect in the sensitive group.We illustrate the application of the CVRS method on the psychiatry trial.

Derivation of poly-methylomic profile scores for schizophrenia

Schizophrenia and bipolar disorder share biological features and environmental risk factors that may be associated with altered DNA methylation. In this study we sought to: 1) construct a novel 'Poly-Methylomic Profile Score (PMPS)' by transforming schizophrenia-associated epigenome-wide methylation from a previously published epigenome-wide association study (EWAS) into a single quantitative metric; and 2) examine associations between the PMPS and clinical status in an independent sample of 57 schizophrenia (SZ) cases, 59 bipolar disorder (BD) cases and 55 healthy controls (HC) for whom blood-derived DNA methylation was quantified using the Illumina 450 K methylation beadchip. We constructed five PMPSs at different p-value thresholds by summing methylation beta-values weighted by individual-CpG effect sizes from the meta-analysis of a previously published schizophrenia EWAS (comprising three separate cohorts with 675 [353 SZ and 322 HC] discovery cohort participants, 847 [414 SZ and 433 HC] replication cohort participants, and 96 monozygotic twin-pairs discordant for SZ). All SZ PMPSs were elevated in SZ participants relative to HCs, with the score calculated at a p-value threshold of 1 × 10^-5 accounting for the greatest amount of variance. All PMPSs were elevated in SZ relative to BD and none of the PMPSs were increased in BD, or in a combined cohort of BD and SZ cases, relative to HCs. PMPSs were also not associated with positive or negative symptom severity. That this SZ-derived PMPSs was elevated in SZ, but not BD, suggests that epigenome-wide methylation patterns may represent distinct pathophysiology that is yet to be elucidated.

The emerging field of polygenic risk scores and perspective for use in clinical care

Genetic testing is used widely for diagnostic, carrier and predictive testing in monogenic diseases. Until recently, there were no genetic testing options available for multifactorial complex diseases like heart disease, diabetes and cancer. Genome-wide association studies (GWAS) have been invaluable in identifying single-nucleotide polymorphisms (SNPs) associated with increased or decreased risk for hundreds of complex disorders. For a given disease, SNPs can be combined to generate a cumulative estimation of risk known as a polygenic risk score (PRS). After years of research, PRSs are increasingly used in clinical settings. In this article, we will review the literature on how both genome-wide and restricted PRSs are developed and the relative merit of each. The validation and evaluation of PRSs will also be discussed, including the recognition that PRS validity is intrinsically linked to the methodological and analytical approach of the foundation GWAS together with the ethnic characteristics of that cohort. Specifically, population differences may affect imputation accuracy, risk magnitude and direction. Even as PRSs are being introduced into clinical practice, there is a push to combine them with clinical and demographic risk factors to develop a holistic disease risk. The existing evidence regarding the clinical utility of PRSs is considered across four different domains: informing population screening programs, guiding therapeutic interventions, refining risk for families at high risk, and facilitating diagnosis and predicting prognostic outcomes. The evidence for clinical utility in relation to five well-studied disorders is summarized. The potential ethical, legal and social implications are also highlighted.

Methodological challenges in constructing DNA methylation risk scores

Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.

Polygenic risk scores in psychiatry: Will they be useful for clinicians?

Major psychiatric disorders are heritable but they are genetically complex. This means that, with certain exceptions, single gene markers will not be helpful for diagnosis. However, we are learning more about the large number of gene variants that, in combination, are associated with risk for disorders such as schizophrenia, bipolar disorder, and other psychiatric conditions. The presence of those risk variants may now be combined into a polygenic risk score (PRS). Such a score provides a quantitative index of the genomic burden of risk variants in an individual, which relates to the likelihood that a person has a particular disorder. Currently, such scores are quite useful in research, and they are telling us much about the relationships between different disorders and other indices of brain function. In the future, as the datasets supporting the development of such scores become larger and more diverse and as methodological developments improve predictive capacity, we expect that PRS will have substantial clinical utility in the assessment of risk for disease, subtypes of disease, and even treatment response. Here, we provide an overview of PRS in general terms (including a glossary suitable for informed non-geneticists) and discuss the use of PRS in psychiatry, including their limitations and cautions for interpretation, as well as their applications now and in the future.

Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data

Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome-wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10-fold cross validation to assess predictive performance, with nested 10-fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.

Association of schizophrenia polygenic risk score with manic and depressive psychosis in bipolar disorder

Bipolar disorder (BD) is highly heterogeneous in symptomatology. Narrowing the clinical phenotype may increase the power to identify risk genes that contribute to particular BD subtypes. This study was designed to test the hypothesis that genetic overlap between schizophrenia (SZ) and BD is higher for BD with a history of manic psychosis. Analyses were conducted using a Mayo Clinic Bipolar Biobank cohort of 957 bipolar cases (including 333 with history of psychosis during mania, 64 with history of psychosis only during depression, 547 with no history of psychosis, and 13 with unknown history of psychosis) and 778 controls. Polygenic risk score (PRS) analysis was performed by calculating a SZ-PRS for the BD cases and controls, and comparing the calculated SZ risk between different psychosis subgroups and bipolar types. The SZ-PRS was significantly higher for BD-I cases with manic psychosis than BD-I cases with depressive psychosis (Nagelkerke's R² = 0.021; p = 0.045), BD-I cases without psychosis (R² = 0.015; p = 0.007), BD-II cases without psychosis (R² = 0.014; p = 0.017), and controls (R² = 0.065; p = 2 × 10^-13). No other significant differences were found. Our results show that BD-I with manic psychosis is genetically more similar to SZ than any other tested BD subgroup. Further investigations on genetics of distinct clinical phenotypes composing major psychoses may help refine the current diagnostic classification system.

Glucocorticoid receptor gene (NR3C1) DNA methylation in association with trauma, psychopathology, transcript expression, or genotypic variation: A systematic review

The glucocorticoid receptor gene (NR3C1) is a critical component of the stress response system. Cytosine methylation of NR3C1 has been repeatedly associated with trauma and mental disorders, including major depression, post-traumatic stress disorder, anxiety, and personality disorders, suggesting that NR3C1 methylation may play a role in stress-related psychopathology. We systematically reviewed 55 studies examining NR3C1 DNA methylation in association with trauma exposure, psychopathology, gene expression, and/or common genetic variants. Overall, a number of NR3C1 CpG sites were significantly associated with trauma or psychopathology, but significant findings were often inconsistent across studies. This lack of consistency is likely influenced by significant methodological variability - experimentally and analytically - across studies. Selected common genetic variants show no significant effect on NR3C1 CpG methylation. In contrast, there was ample evidence linking increased methylation of NR3C1 to reduced expression of this gene. The inverse association between methylation and gene expression shown across eight out of ten studies supports the notion that methylation in the promoter region of NR3C1 is associated with transcriptional silencing.

The use of polygenic risk scores to identify phenotypes associated with genetic risk of schizophrenia: Systematic review

Studying the phenotypic manifestations of increased genetic liability for schizophrenia can increase our understanding of this disorder. Specifically, information from alleles identified in genome-wide association studies can be collapsed into a polygenic risk score (PRS) to explore how genetic risk is manifest within different samples. In this systematic review, we provide a comprehensive assessment of studies examining associations between schizophrenia PRS (SZ-PRS) and several phenotypic measures. We searched EMBASE, Medline and PsycINFO (from August 2009-14th March 2016) plus references of included studies, following PRISMA guidelines. Study inclusion was based on predetermined criteria and data were extracted independently and in duplicate. Overall, SZ-PRS was associated with increased risk for psychiatric disorders such as depression and bipolar disorder, lower performance IQ and negative symptoms. SZ-PRS explained up to 6% of genetic variation in psychiatric phenotypes, compared to <0.7% in measures of cognition. Future gains from using the PRS approach may be greater if used for examining phenotypes that are more closely related to biological substrates, for scores based on gene-pathways, and where PRSs are used to stratify individuals for study of treatment response. As it was difficult to interpret findings across studies due to insufficient information provided by many studies, we propose a framework to guide robust reporting of PRS associations in the future.

Polygenic risk score: use in migraine research

BACKGROUND

The latest Genome-Wide Association Study identified 38 genetic variants associated with migraine. In this type of studies the significance level is very difficult to achieve (5 × 10^- 8) due to multiple testing. Thus, the identified variants only explain a small fraction of the genetic risk. It is expected that hundreds of thousands of variants also confer an increased risk but do not reach significance levels. One way to capture this information is by constructing a Polygenic Risk Score. Polygenic Risk Score has been widely used with success in genetics studies within neuropsychiatric disorders. The use of polygenic scores is highly relevant as data from a large migraine Genome-Wide Association Study are now available, which will form an excellent basis for Polygenic Risk Score in migraine studies.

RESULTS

Polygenic Risk Score has been used in studies of neuropsychiatric disorders to assess prediction of disease status in case-control studies, shared genetic correlation between co-morbid diseases, and shared genetic correlation between a disease and specific endophenotypes.

CONCLUSION

Polygenic Risk Score provides an opportunity to investigate the shared genetic risk between known and previously unestablished co-morbidities in migraine research, and may lead to better and personalized treatment of migraine if used as a clinical assistant when identifying responders to specific drugs. Polygenic Risk Score can be used to analyze the genetic relationship between different headache types and migraine endophenotypes. Finally, Polygenic Risk Score can be used to assess pharmacogenetic effects, and perhaps help to predict efficacy of the Calcitonin Gene-Related Peptide monoclonal antibodies that soon become available as migraine treatment.

Association Between Schizophrenia-Related Polygenic Liability and the Occurrence and Level of Mood-Incongruent Psychotic Symptoms in Bipolar Disorder

Importance

Bipolar disorder (BD) overlaps schizophrenia in its clinical presentation and genetic liability. Alternative approaches to patient stratification beyond current diagnostic categories are needed to understand the underlying disease processes and mechanisms.

Objective

To investigate the association between common-variant liability for schizophrenia, indexed by polygenic risk scores (PRSs), and psychotic presentations of BD.

Design, Setting, and Participants

This case-control study in the United Kingdom used multinomial logistic regression to estimate differential PRS associations across categories of cases and controls. Participants included in the final analyses were 4436 cases of BD from the Bipolar Disorder Research Network. These cases were compared with the genotypic data for 4976 cases of schizophrenia and 9012 controls from the Type 1 Diabetes Genetics Consortium study and the Generation Scotland study. Data were collected between January 1, 2000, and December 31, 2013. Data analysis was conducted from March 1, 2016, to February 28, 2017.

Exposures

Standardized PRSs, calculated using alleles with an association threshold of P < .05 in the second Psychiatric Genomics Consortium genome-wide association study of schizophrenia, were adjusted for the first 10 population principal components and genotyping platforms.

Main Outcomes and Measures

Multinomial logit models estimated PRS associations with BD stratified by Research Diagnostic Criteria subtypes of BD, by lifetime occurrence of psychosis, and by lifetime mood-incongruent psychotic features. Ordinal logistic regression examined PRS associations across levels of mood incongruence. Ratings were derived from the Schedules for Clinical Assessment in Neuropsychiatry interview and the Bipolar Affective Disorder Dimension Scale.

Results

Of the 4436 cases of BD, 2966 (67%) were female patients, and the mean (SD) age at interview was 46 [12] years. Across clinical phenotypes, there was an exposure-response gradient, with the strongest PRS association for schizophrenia (risk ratio [RR] = 1.94; 95% CI, 1.86-2.01), followed by schizoaffective BD (RR = 1.37; 95% CI, 1.22-1.54), bipolar I disorder subtype (RR = 1.30; 95% CI, 1.24-1.36), and bipolar II disorder subtype (RR = 1.04; 95% CI, 0.97-1.11). Within BD cases, there was an effect gradient, indexed by the nature of psychosis. Prominent mood-incongruent psychotic features had the strongest association (RR = 1.46; 95% CI, 1.36-1.57), followed by mood-congruent psychosis (RR = 1.24; 95% CI, 1.17-1.33) and BD with no history of psychosis (RR = 1.09; 95% CI, 1.04-1.15).

Conclusions and Relevance

For the first time to date, a study shows a polygenic-risk gradient across schizophrenia and BD, indexed by the occurrence and level of mood-incongruent psychotic symptoms.

Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies

BACKGROUND

Weighted genetic risk scores (GRS), defined as weighted sums of risk alleles of single nucleotide polymorphisms (SNPs), are statistically powerful for detection gene-environment (GxE) interactions. To assign weights, the gold standard is to use external weights from an independent study. However, appropriate external weights are not always available. In such situations and in the presence of predominant marginal genetic effects, we have shown in a previous study that GRS with internal weights from marginal genetic effects ("GRS-marginal-internal") are a powerful and reliable alternative to single SNP approaches or the use of unweighted GRS. However, this approach might not be appropriate for detecting predominant interactions, i.e. interactions showing an effect stronger than the marginal genetic effect.

METHODS

In this paper, we present a weighting approach for such predominant interactions ("GRS-interaction-training") in which parts of the data are used to estimate the weights from the interaction terms and the remaining data are used to determine the GRS. We conducted a simulation study for the detection of GxE interactions in which we evaluated power, type I error and sign-misspecification. We compared this new weighting approach to the GRS-marginal-internal approach and to GRS with external weights.

RESULTS

Our simulation study showed that in the absence of external weights and with predominant interaction effects, the highest power was reached with the GRS-interaction-training approach. If marginal genetic effects were predominant, the GRS-marginal-internal approach was more appropriate. Furthermore, the power to detect interactions reached by the GRS-interaction-training approach was only slightly lower than the power achieved by GRS with external weights. The power of the GRS-interaction-training approach was confirmed in a real data application to the Traffic, Asthma and Genetics (TAG) Study (N = 4465 observations).

CONCLUSION

When appropriate external weights are unavailable, we recommend to use internal weights from the study population itself to construct weighted GRS for GxE interaction studies. If the SNPs were chosen because a strong marginal genetic effect was hypothesized, GRS-marginal-internal should be used. If the SNPs were chosen because of their collective impact on the biological mechanisms mediating the environmental effect (hypothesis of predominant interactions) GRS-interaction-training should be applied.

Personalized management of bipolar disorder

Bipolar disorder (BD) is one of the most serious psychiatric disorders. The rates of disability, the risk of suicide attempts and their high lethality, as well as frequent and severe psychiatric and medical comorbidities, put it among the major causes of mortality and disability worldwide. At the same time, many patients can do well when treated properly. In this review, we focus on those aspects of the clinical care that offer the potential of individualized approach, in the context of the recent technology driven advances in the comprehension of the neurobiological underpinnings of BD. We first review those clinical and biological factors that can help identifying individuals at high risk of developing BD. Among these are a family history of BD and/or completed suicide, prodromal symptoms (in childhood and/or adolescence) such as anxiety and mood lability, early onset, and poor response to antidepressants. Panels of genetic markers are also being studied to identify subjects at risk for BD. Further, neuroimaging studies have found an increased gray matter density in the right Inferior Frontal Gyrus (rIFG) as a possible risk marker of BD. We then examine clinical factors that influence the initiation, selection and possibly discontinuation of long-term treatment. Lastly, we discuss the risk of side effects in BD, and their relevance for treatment adherence and for treatment monitoring. In summary, we discuss how a personalized approach in BD can be implemented through the identification of specific clinical and molecular predictors. We show that the realization of a personalized management of BD is not only of a theoretical value, but has substantial clinical repercussions, resulting in a significant reduction of the long-term morbidity and mortality associated to BD.

Genome-wide significant locus for Research Diagnostic Criteria Schizoaffective Disorder Bipolar type

Studies have suggested that Research Diagnostic Criteria for Schizoaffective Disorder Bipolar type (RDC-SABP) might identify a more genetically homogenous subgroup of bipolar disorder. Aiming to identify loci associated with RDC-SABP, we have performed a replication study using independent RDC-SABP cases (n = 144) and controls (n = 6,559), focusing on the 10 loci that reached a p-value <10^-5 for RDC-SABP in the Wellcome Trust Case Control Consortium (WTCCC) bipolar disorder sample. Combining the WTCCC and replication datasets by meta-analysis (combined RDC-SABP, n = 423, controls, n = 9,494), we observed genome-wide significant association at one SNP, rs2352974, located within the intron of the gene TRAIP on chromosome 3p21.31 (p-value, 4.37 × 10^-8 ). This locus did not reach genome-wide significance in bipolar disorder or schizophrenia large Psychiatric Genomic Consortium datasets, suggesting that it may represent a relatively specific genetic risk for the bipolar subtype of schizoaffective disorder.

Effects of bonding with parents and home culture on intercultural adaptations and the moderating role of genes

In the current age of globalization, living abroad is becoming an increasingly common and highly sought after experience. Sojourners' ability to adjust to a new culture can be affected by their existing attachments, internalized as intrapsychic environment, as well as their biological sensitivity to environment. This sensitivity can be partly attributed to one's genomic endowments. As such, this prospective study sought to examine the differential effects of early experiences with parents and affection for home culture on young adults' ability to adapt to a foreign culture (n=305, students who studied overseas for a semester) - specifically, the difficulties they experience - moderated by genetic susceptibility. An additional 258 students who did not travel overseas were included as a comparison group to demonstrate the uniqueness of intercultural adaptation. Current findings suggest that the maternal, paternal and cultural bondings or affections affect different aspects of intercultural adjustment. Maternal bonding affected sojourners' relationships with host nationals, while paternal bonding affected sojourners' adjustment to a new physical environment. Moreover, individuals' genetic predispositions significantly moderate these main effects regarding how much difficulty the sojourners experienced overseas.

Theory of mind and executive functions in schizophrenia and bipolar disorder: A cross-diagnostic latent class analysis for identification of neuropsychological subtypes

OBJECTIVE

Executive dysfunction is a common feature of schizophrenia and bipolar disorder (BP). While deficits in social cognitive abilities, including theory of mind (ToM), have been suggested to be specific to schizophrenia, available evidence suggests that there is also a significant overlap in social cognitive performances of both disorders. However, there is significant heterogeneity of executive dysfunction and ToM deficits in BP and schizophrenia. Cross-diagnostic data-driven methods can reveal potential neurocognitive subtypes characterized by relatively selective deficits in social cognition.

METHODS

Neurocognitive subgroups were investigated using latent class analysis, based on executive functions and ToM, in a mixed sample of 97 clinically stable patients with schizophrenia or BP and 27 healthy controls.

RESULTS

Four neurocognitive subgroups, including a "neuropsychologically normal" cluster, a severe global impairment cluster and two clusters of mixed cognitive profiles were found. Severe impairment cluster was characterized by particularly severe ToM deficits and predominantly included patients with schizophrenia. Schizophrenia patients in this cluster had severe negative symptoms. In contrast, individuals with BP compared to schizophrenia patients were more likely to be included in the "neuropsychologically normal" cluster.

CONCLUSION

Identification of distinctive neurobiological subtypes of patients based on social and non-social cognitive profiles can improve classification of major psychoses. Neurocognitive subgroupings of patients might be also beneficial for intervention strategies including cognitive rehabilitation.

Polygenic Epidemiology

Much of the genetic basis of complex traits is present on current genotyping products, but the individual variants that affect the traits have largely not been identified. Several traditional problems in genetic epidemiology have recently been addressed by assuming a polygenic basis for disease and treating it as a single entity. Here I briefly review some of these applications, which collectively may be termed polygenic epidemiology. Methodologies in this area include polygenic scoring, linear mixed models, and linkage disequilibrium scoring. They have been used to establish a polygenic effect, estimate genetic correlation between traits, estimate how many variants affect a trait, stratify cases into subphenotypes, predict individual disease risks, and infer causal effects using Mendelian randomization. Polygenic epidemiology will continue to yield useful applications even while much of the specific variation underlying complex traits remains undiscovered.

[Adolescent manic-depressive disorders: Clinical aspects]

More than 50% of bipolar disorders diagnosed among adults first appeared before the age of 18. It is well established that adolescence is the high-risk period for the onset of major mood episodes associated with bipolar disorders. Even though there are few early-onset bipolar disorders, they are very severe. The most robust risk factor predicting bipolar disorder is a positive family history. Morbidity, mortality, and suicidality are high and have a severe impact on overall functioning, professional integration, family life, and affective relationships. Improving diagnosis of early symptoms should ameliorate these patients' prognosis.

Inherited determinants of Crohn's disease and ulcerative colitis phenotypes: a genetic association study

BACKGROUND

Crohn's disease and ulcerative colitis are the two major forms of inflammatory bowel disease; treatment strategies have historically been determined by this binary categorisation. Genetic studies have identified 163 susceptibility loci for inflammatory bowel disease, mostly shared between Crohn's disease and ulcerative colitis. We undertook the largest genotype association study, to date, in widely used clinical subphenotypes of inflammatory bowel disease with the goal of further understanding the biological relations between diseases.

METHODS

This study included patients from 49 centres in 16 countries in Europe, North America, and Australasia. We applied the Montreal classification system of inflammatory bowel disease subphenotypes to 34,819 patients (19,713 with Crohn's disease, 14,683 with ulcerative colitis) genotyped on the Immunochip array. We tested for genotype-phenotype associations across 156,154 genetic variants. We generated genetic risk scores by combining information from all known inflammatory bowel disease associations to summarise the total load of genetic risk for a particular phenotype. We used these risk scores to test the hypothesis that colonic Crohn's disease, ileal Crohn's disease, and ulcerative colitis are all genetically distinct from each other, and to attempt to identify patients with a mismatch between clinical diagnosis and genetic risk profile.

FINDINGS

After quality control, the primary analysis included 29,838 patients (16,902 with Crohn's disease, 12,597 with ulcerative colitis). Three loci (NOD2, MHC, and MST1 3p21) were associated with subphenotypes of inflammatory bowel disease, mainly disease location (essentially fixed over time; median follow-up of 10·5 years). Little or no genetic association with disease behaviour (which changed dramatically over time) remained after conditioning on disease location and age at onset. The genetic risk score representing all known risk alleles for inflammatory bowel disease showed strong association with disease subphenotype (p=1·65 × 10(-78)), even after exclusion of NOD2, MHC, and 3p21 (p=9·23 × 10(-18)). Predictive models based on the genetic risk score strongly distinguished colonic from ileal Crohn's disease. Our genetic risk score could also identify a small number of patients with discrepant genetic risk profiles who were significantly more likely to have a revised diagnosis after follow-up (p=6·8 × 10(-4)).

INTERPRETATION

Our data support a continuum of disorders within inflammatory bowel disease, much better explained by three groups (ileal Crohn's disease, colonic Crohn's disease, and ulcerative colitis) than by Crohn's disease and ulcerative colitis as currently defined. Disease location is an intrinsic aspect of a patient's disease, in part genetically determined, and the major driver to changes in disease behaviour over time.

FUNDING

International Inflammatory Bowel Disease Genetics Consortium members funding sources (see Acknowledgments for full list).

Assessment of first and second degree relatives of individuals with bipolar disorder shows increased genetic risk scores in both affected relatives and young At-Risk Individuals

Recent studies have revealed the polygenic nature of bipolar disorder (BP), and identified common risk variants associated with illness. However, the role of common polygenic risk in multiplex families has not previously been examined. The present study examined 249 European-ancestry families from the NIMH Genetics Initiative sample, comparing subjects with narrowly defined BP (excluding bipolar II and recurrent unipolar depression; n = 601) and their adult relatives without BP (n = 695). Unrelated adult controls (n = 266) were from the NIMH TGEN control dataset. We also examined a prospective cohort of young (12-30 years) offspring and siblings of individuals with BPI and BPII disorder (at risk; n = 367) and psychiatrically screened controls (n = 229), ascertained from five sites in the US and Australia and assessed with standardized clinical protocols. Thirty-two disease-associated SNPs from the PGC-BP Working Group report (2011) were genotyped and additive polygenic risk scores (PRS) derived. We show increased PRS in adult cases compared to unrelated controls (P = 3.4 × 10(-5) , AUC = 0.60). In families with a high-polygenic load (PRS score ≥32 in two or more subjects), PRS distinguished cases with BPI/SAB from other relatives (P = 0.014, RR = 1.32). Secondly, a higher PRS was observed in at-risk youth, regardless of affected status, compared to unrelated controls (GEE-χ(2) = 5.15, P = 0.012). This report is the first to explore common polygenic risk in multiplex families, albeit using only a small number of robustly associated risk variants. We show that individuals with BP have a higher load of common disease-associated variants than unrelated controls and first-degree relatives, and illustrate the potential utility of PRS assessment in a family context.

Polygenic risk scores in bipolar disorder subgroups

BACKGROUND

Bipolar disorder (BD) is a genetically and clinically heterogeneous disorder. Current classifications of BD rely on clinical presentations without any validating biomarkers, making homogenous and valid subtypes warranted. This study aims at investigating whether a BD polygenic risk score (PGRS) can validate BD subtypes including diagnostic sub-categories (BD-I versus BD-II), patients with and without psychotic symptoms, polarity of first presenting episode and age at onset based groups. We also wanted to investigate whether illness severity indicators were associated with a higher polygenic risk for BD.

METHODS

Analyze differences in BD PGRS scores between suggested subtypes of BD and between healthy controls (CTR) and BD in a sample of N=669 (255 BD and 414 CTR).

RESULTS

The BD PGRS significantly discriminates between BD and CTR (p<0.001). There were no differences in BD PGRS between groups defined by diagnostic sub-categories, presenting polarity and age at onset. Patients with psychotic BD had nominally significantly higher BD PGRS than patients with non-psychotic BD after controlling for diagnostic sub-category (p=0.019). These findings remained trend level significant after Bonferroni corrections (p=0.079).

LIMITATIONS

The low explained variance of the current PGRS method could lead to type II errors.

CONCLUSIONS

There are nominally significant differences in BD PGRS scores between patients with and without psychotic symptoms, indicating that these two forms of BD might represent distinct subtypes of BD based in its polygenic architecture and a division between BD with and without psychotic symptoms could represent a more valid subclassification of BD than current diagnostic sub-categories. If replicated, this finding could affect future research, diagnostics and clinical practice.

New developments in the genetics of bipolar disorder

The last several years have been breakthrough ones in bipolar disorder (BPD) genetics, as the field has identified robust risk variants for the first time. Leading the way have been genome-wide association studies (GWAS) that have assessed common genetic markers across very large groups of patients and controls. These have resulted in findings in genes including ANK3, CACNA1C, SYNE1, ODZ4, and TRANK1. Additional studies have begun to examine the biology of these genes and how risk variants influence aspects of brain and behavior that underlie BPD. For example, carriers of the CACNA1C risk variant have been found to exhibit hippocampal and anterior cingulate dysfunction during episodic memory recall. This work has shed additional light on the relationship of bipolar susceptibility variants to other disorders, particularly schizophrenia. Even larger BPD GWAS are expected with samples now amassed of 21,035 cases and 28,758 controls. Studies have examined the pharmacogenomics of BPD with studies of lithium response, yielding high profile results that remain to be confirmed. The next frontier in the field is the identification of rare bipolar susceptibility variants through large-scale DNA sequencing. While only a couple of papers have been published to date, many studies are underway. The Bipolar Sequencing Consortium has been formed to bring together all of the groups working in this area, and to perform meta-analyses of the data generated. The consortium, with 13 member groups, now has exome data on ~3,500 cases and ~5,000 controls, and on ~162 families. The focus will likely shift within several years from exome data to whole genome data as costs of obtaining such data continue to drop. Gene-mapping studies are now providing clear results that provide insights into the pathophysiology of the disorder. Sequencing studies should extend this process further. Findings could eventually set the stage for rational therapeutic development.

Prefrontal inefficiency is associated with polygenic risk for schizophrenia

Considering the diverse clinical presentation and likely polygenic etiology of schizophrenia, this investigation examined the effect of polygenic risk on a well-established intermediate phenotype for schizophrenia. We hypothesized that a measure of cumulative genetic risk based on additive effects of many genetic susceptibility loci for schizophrenia would predict prefrontal cortical inefficiency during working memory, a brain-based biomarker for the disorder. The present study combined imaging, genetic and behavioral data obtained by the Mind Clinical Imaging Consortium study of schizophrenia (n = 255). For each participant, we derived a polygenic risk score (PGRS), which was based on over 600 nominally significant single nucleotide polymorphisms, associated with schizophrenia in a separate discovery sample comprising 3322 schizophrenia patients and 3587 control participants. Increased polygenic risk for schizophrenia was associated with neural inefficiency in the left dorsolateral prefrontal cortex after covarying for the effects of acquisition site, diagnosis, and population stratification. We also provide additional supporting evidence for our original findings using scores based on results from the Psychiatric Genomics Consortium study. Gene ontology analysis of the PGRS highlighted genetic loci involved in brain development and several other processes possibly contributing to disease etiology. Our study permits new insights into the additive effect of hundreds of genetic susceptibility loci on a brain-based intermediate phenotype for schizophrenia. The combined impact of many common genetic variants of small effect are likely to better reveal etiologic mechanisms of the disorder than the study of single common genetic variants.

Genetic risk for attention-deficit/hyperactivity disorder contributes to neurodevelopmental traits in the general population

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) can be viewed as the extreme end of traits in the general population. Epidemiological and twin studies suggest that ADHD frequently co-occurs with and shares genetic susceptibility with autism spectrum disorder (ASD) and ASD-related traits. The aims of this study were to determine whether a composite of common molecular genetic variants, previously found to be associated with clinically diagnosed ADHD, predicts ADHD and ASD-related traits in the general population.

METHODS

Polygenic risk scores were calculated in the Avon Longitudinal Study of Parents and Children (ALSPAC) population sample (N = 8229) based on a discovery case-control genome-wide association study of childhood ADHD. Regression analyses were used to assess whether polygenic scores predicted ADHD traits and ASD-related measures (pragmatic language abilities and social cognition) in the ALSPAC sample. Polygenic scores were also compared in boys and girls endorsing any (rating ≥ 1) ADHD item (n = 3623).

RESULTS

Polygenic risk for ADHD showed a positive association with ADHD traits (hyperactive-impulsive, p = .0039; inattentive, p = .037). Polygenic risk for ADHD was also negatively associated with pragmatic language abilities (p = .037) but not with social cognition (p = .43). In children with a rating ≥ 1 for ADHD traits, girls had a higher polygenic score than boys (p = .003).

CONCLUSIONS

These findings provide molecular genetic evidence that risk alleles for the categorical disorder of ADHD influence hyperactive-impulsive and attentional traits in the general population. The results further suggest that common genetic variation that contributes to ADHD diagnosis may also influence ASD-related traits, which at their extreme are a characteristic feature of ASD.

Research review: Polygenic methods and their application to psychiatric traits

BACKGROUND

Despite evidence from twin and family studies for an important contribution of genetic factors to both childhood and adult onset psychiatric disorders, identifying robustly associated specific DNA variants has proved challenging. In the pregenomics era the genetic architecture (number, frequency and effect size of risk variants) of complex genetic disorders was unknown. Empirical evidence for the genetic architecture of psychiatric disorders is emerging from the genetic studies of the last 5 years.

METHODS AND SCOPE

We review the methods investigating the polygenic nature of complex disorders. We provide mini-guides to genomic profile (or polygenic) risk scoring and to estimation of variance (or heritability) from common SNPs; a glossary of key terms is also provided. We review results of applications of the methods to psychiatric disorders and related traits and consider how these methods inform on missing heritability, hidden heritability and still-missing heritability.

FINDINGS

Genome-wide genotyping and sequencing studies are providing evidence that psychiatric disorders are truly polygenic, that is they have a genetic architecture of many genetic variants, including risk variants that are both common and rare in the population. Sample sizes published to date are mostly underpowered to detect effect sizes of the magnitude presented by nature, and these effect sizes may be constrained by the biological validity of the diagnostic constructs.

CONCLUSIONS

Increasing the sample size for genome wide association studies of psychiatric disorders will lead to the identification of more associated genetic variants, as already found for schizophrenia. These loci provide the starting point of functional analyses that might eventually lead to new prevention and treatment options and to improved biological validity of diagnostic constructs. Polygenic analyses will contribute further to our understanding of complex genetic traits as sample sizes increase and as sample resources become richer in phenotypic descriptors, both in terms of clinical symptoms and of nongenetic risk factors.

Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia

Bipolar disorder and schizophrenia are two often severe disorders with high heritabilities. Recent studies have demonstrated a large overlap of genetic risk loci between these disorders but diagnostic and molecular distinctions still remain. Here, we perform a combined genome-wide association study (GWAS) of 19 779 bipolar disorder (BP) and schizophrenia (SCZ) cases versus 19 423 controls, in addition to a direct comparison GWAS of 7129 SCZ cases versus 9252 BP cases. In our case-control analysis, we identify five previously identified regions reaching genome-wide significance (CACNA1C, IFI44L, MHC, TRANK1 and MAD1L1) and a novel locus near PIK3C2A. We create a polygenic risk score that is significantly different between BP and SCZ and show a significant correlation between a BP polygenic risk score and the clinical dimension of mania in SCZ patients. Our results indicate that first, combining diseases with similar genetic risk profiles improves power to detect shared risk loci and second, that future direct comparisons of BP and SCZ are likely to identify loci with significant differential effects. Identifying these loci should aid in the fundamental understanding of how these diseases differ biologically. These findings also indicate that combining clinical symptom dimensions and polygenic signatures could provide additional information that may someday be used clinically.

Somatic and cognitive symptoms as indicators of potential endophenotypes in bipolar spectrum disorders: an exploratory and proof-of-concept study comparing bipolar II disorder with recurrent brief depression and healthy controls

BACKGROUND

We examined whether somatic symptoms reported by patients with bipolar spectrum disorder (BSD), in this study defined as bipolar II (BD-2) or recurrent brief depression with (RBD-H) or without (RBD-O) a history of hypomanic symptoms might point to the possible underlying disease markers (endophenotypes). We hypothesized that somatic symptoms that are possible indirect indicators of endophenotypes should be more prevalent among patients than among healthy controls; should not correlate with neuroticism; should not correlate with the severity of current mental status (e.g., anxiety, depression); and should not correlate with the use of psychotropic drugs including antiepileptics or be explained by co-morbid medical diseases.

METHODS

Sixty-one patients (BD-2: n=21; RBD-H: n=19; RBD-O: n=21) were compared with 21 healthy controls. Assessments included a 123-item somatic symptom checklist; assessments for neuroticism, anxiety and depression. Candidate somatic symptoms were selected using a 4-step inclusion/exclusion procedure.

RESULTS

Seven symptoms survived in all three groups: general (fatigue, feeling exhausted); sensory (leaden sensation in legs, pain in the body, impaired sense of smell); cognitive (loss of memory) and autonomic (excessive perspiration). In addition 15 symptoms survived in one or two groups (examples: impaired hearing, hypersensitivity to sound, inability to find words).

LIMITATIONS

Possible selection bias and small sample size precludes firm conclusions with regards to specific symptoms.

CONCLUSION

Our approach identified symptoms for which an association with BSDs has been suggested previously, as well as symptoms not commonly associated with BSDs. The findings support the feasibility and validity of using assessment of somatic symptoms as an approach to identify potential endophenotypes in BSDs.

Association analysis between suicidal behaviour and candidate genes of bipolar disorder and schizophrenia

BACKGROUND

The present study investigated associations between the strongest joint genetic risk variants for bipolar disorder (BD) and schizophrenia (SCZ) and a history of suicide attempt in patients with BD, SCZ and related psychiatric disorders.

METHODS

A history of suicide attempt was assessed in a sample of 1009 patients with BD, SCZ and related psychosis spectrum disorders, and associations with the joint genetic risk variants for BD and SCZ (rs2239547 (ITIH3/4-region), rs10994359 (ANK3) and rs4765905 (CACNA1C)) were investigated. Previously reported susceptibility loci for suicide attempt in BD were also investigated. Associations were tested by logistic regression with Bonferroni correction for multiple testing.

RESULTS

The risk allele in rs2239547 (ITIH3/4-region) was significantly associated with a history of suicide attempt (p=0.01) after multiple testing correction (p threshold<0.017). The previous suicide attempt susceptibility loci were only nominally associated, but had the same direction of risk in the replication sample (sign test, p=0.02).

LIMITATIONS

Relatively small sample size and retrospective clinical assessment.

CONCLUSIONS

We detected a novel association between suicide attempt and the ITIH3/4-region in a combined group of patients with BD, SCZ and related psychosis spectrum disorders. This may be useful in understanding molecular mechanisms of suicidal behaviour in severe mental disorders, although replication is warranted.

Genetic relationships between schizophrenia, bipolar disorder, and schizoaffective disorder

There is substantial evidence for partial overlap of genetic influences on schizophrenia and bipolar disorder, with family, twin, and adoption studies showing a genetic correlation between the disorders of around 0.6. Results of genome-wide association studies are consistent with commonly occurring genetic risk variants, contributing to both the shared and nonshared aspects, while studies of large, rare chromosomal structural variants, particularly copy number variants, show a stronger influence on schizophrenia than bipolar disorder to date. Schizoaffective disorder has been less investigated but shows substantial familial overlap with both schizophrenia and bipolar disorder. A twin analysis is consistent with genetic influences on schizoaffective episodes being entirely shared with genetic influences on schizophrenic and manic episodes, while association studies suggest the possibility of some relatively specific genetic influences on broadly defined schizoaffective disorder, bipolar subtype. Further insights into genetic relationships between these disorders are expected as studies continue to increase in sample size and in technical and analytical sophistication, information on phenotypes beyond clinical diagnoses are increasingly incorporated, and approaches such as next-generation sequencing identify additional types of genetic risk variant.

Genome-wide association studies: findings at the major histocompatibility complex locus in psychosis

The major histocompatibility complex (MHC) is one of the most intensively investigated, genetically diverse regions of the genome. In its extended form, it encodes more than 400 genes critical to immunity but is also involved in many other functions. In 2009, three simultaneously published genome-wide association studies (GWAS) reported the first compelling evidence for involvement of the MHC in schizophrenia susceptibility. In this review, we describe the structure and function of the MHC, discuss some of the challenges for genetic analysis of the region, and provide an update on findings from GWAS studies before describing potential approaches to interpreting the role of the locus in schizophrenia etiology. The GWAS literature supports involvement of the MHC locus in schizophrenia susceptibility. Current evidence suggests that the MHC plays a more significant role in schizophrenia susceptibility than in other psychiatric disorders. Because of the substantial diversity at the locus, there are differences in the implicated risk variants between ancestral groups, as there are for many other disorders. This is somewhat different than the pattern emerging at other loci. The association findings presently capture large genomic regions, with at least some evidence to suggest that multiple signals may be involved. Based on notable successes in other disorders, we suggest approaches to refining association signals at the locus. Finally, we discuss that these genetic data may be used to understand how the MHC contributes to the complex etiology of schizophrenia.

The developmental trajectory of bipolar disorder

BACKGROUND

Bipolar disorder is highly heritable and therefore longitudinal observation of children of affected parents is important to mapping the early natural history.

AIMS

To model the developmental trajectory of bipolar disorder based on the latest findings from an ongoing prospective study of the offspring of parents with well-characterised bipolar disorder.

METHOD

A total of 229 offspring from families in which 1 parent had confirmed bipolar disorder and 86 control offspring were prospectively studied for up to 16 years. High-risk offspring were divided into subgroups based on the parental long-term response to lithium. Offspring were clinically assessed and DSM-IV diagnoses determined on masked consensus review using best estimate procedure. Adjusted survival analysis and generalised estimating equations were used to calculate differences in lifetime psychopathology. Multistate models were used to examine the progression through proposed clinical stages.

RESULTS

High-risk offspring had an increased lifetime risk of a broad spectrum of disorders including bipolar disorder (hazard ratio (HR) = 20.89; P = 0.04), major depressive disorder (HR = 17.16; P = 0.004), anxiety (HR = 2.20; P = 0.03), sleep (HR = 28.21; P = 0.02) and substance use disorders (HR = 2.60; P = 0.05) compared with controls. However, only offspring from lithium non-responsive parents developed psychotic disorders. Childhood anxiety disorder predicted an increased risk of major mood disorder and evidence supported a progressive transition through clinical stages, from non-specific psychopathology to depressive and then manic or psychotic episodes.

CONCLUSIONS

Findings underscore the importance of a developmental approach in conjunction with an appreciation of familial risk to facilitate earlier accurate diagnosis in symptomatic youth.

Genetic susceptibility for bipolar disorder and response to antidepressants in major depressive disorder

The high heterogeneity of response to antidepressant treatment in major depressive disorder (MDD) makes individual treatment outcomes currently unpredictable. It has been suggested that resistance to antidepressant treatment might be due to undiagnosed bipolar disorder or bipolar spectrum features. Here, we investigate the relationship between genetic susceptibility for bipolar disorder and response to treatment with antidepressants in MDD. Polygenic scores indexing risk for bipolar disorder were derived from the Psychiatric Genomics Consortium Bipolar Disorder whole genome association study. Linear regressions tested the effect of polygenic risk scores for bipolar disorder on proportional reduction in depression severity in two large samples of individuals with MDD, treated with antidepressants, NEWMEDS (n=1,791) and STAR*D (n=1,107). There was no significant association between polygenic scores for bipolar disorder and response to treatment with antidepressants. Our data indicate that molecular measure of genetic susceptibility to bipolar disorder does not aid in understanding non-response to antidepressants.

What psychiatric genetics has taught us about the nature of psychiatric illness and what is left to learn

Psychiatric genetics has taught us a great deal about the nature of psychiatric disorders. Traditional family, twin and adoption studies have demonstrated the substantial role of genetic factors in their etiology, clarified the role of genetic factors in comorbidity, elucidated development pathways, and documented the importance of gene-environment correlation and interaction. We have also received some hard lessons when we were unable to detect replicable genes of large effect size and found that our much-valued candidate genes did not live up to their expected promise. With more mature molecular and statistical methods, we are entering now a different era. Statistical analyses of aggregate molecular signals are validating earlier heritability estimates. Replicated findings from genome-wide association studies are beginning to emerge, as are discoveries of large-effect size rare genomic variants. The number of such findings is likely to soon grow dramatically. The most pressing question facing the field is what biological picture these results will reveal. I articulate four possible scenarios that reflect (i) no, (ii) minimal, (iii) moderate and (iv) high biological coherence in the replicated molecular variant findings, which are soon likely to emerge. I discuss the factors that will likely influence these patterns, including the problems of etiological heterogeneity and multiple realizability. These findings could provide critical insights into the underlying biology of our psychiatric syndromes and potentially permit us to perceive, 'through a glass darkly,' the levels of the mind-brain system that are disordered.

Shared polygenic contribution between childhood attention-deficit hyperactivity disorder and adult schizophrenia

BACKGROUND

There is recent evidence of some degree of shared genetic susceptibility between adult schizophrenia and childhood attention-deficit hyperactivity disorder (ADHD) for rare chromosomal variants.

AIMS

To determine whether there is overlap between common alleles conferring risk of schizophrenia in adults with those that do so for ADHD in children.

METHOD

We used recently published Psychiatric Genome-wide Association Study (GWAS) Consortium (PGC) adult schizophrenia data to define alleles over-represented in people with schizophrenia and tested whether those alleles were more common in 727 children with ADHD than in 2067 controls.

RESULTS

Schizophrenia risk alleles discriminated ADHD cases from controls (P = 1.04 × 10(-4), R(2) = 0.45%); stronger discrimination was given by alleles that were risk alleles for both adult schizophrenia and adult bipolar disorder (also derived from a PGC data-set) (P = 9.98 × 10(-6), R(2) = 0.59%).

CONCLUSIONS

This increasing evidence for a small, but significant, shared genetic susceptibility between adult schizophrenia and childhood ADHD highlights the importance of research work across traditional diagnostic boundaries.

High loading of polygenic risk for ADHD in children with comorbid aggression

OBJECTIVE Although attention deficit hyperactivity disorder (ADHD) is highly heritable, genome-wide association studies (GWAS) have not yet identified any common genetic variants that contribute to risk. There is evidence that aggression or conduct disorder in children with ADHD indexes higher genetic loading and clinical severity. The authors examine whether common genetic variants considered en masse as polygenic scores for ADHD are especially enriched in children with comorbid conduct disorder. METHOD Polygenic scores derived from an ADHD GWAS meta-analysis were calculated in an independent ADHD sample (452 case subjects, 5,081 comparison subjects). Multivariate logistic regression analyses were employed to compare polygenic scores in the ADHD and comparison groups and test for higher scores in ADHD case subjects with comorbid conduct disorder relative to comparison subjects and relative to those without comorbid conduct disorder. Association with symptom scores was tested using linear regression. RESULTS Polygenic risk for ADHD, derived from the meta-analysis, was higher in the independent ADHD group than in the comparison group. Polygenic score was significantly higher in ADHD case subjects with conduct disorder relative to ADHD case subjects without conduct disorder. ADHD polygenic score showed significant association with comorbid conduct disorder symptoms. This relationship was explained by the aggression items. CONCLUSIONS Common genetic variation is relevant to ADHD, especially in individuals with comorbid aggression. The findings suggest that the previously published ADHD GWAS meta-analysis contains weak but true associations with common variants, support for which falls below genome-wide significance levels. The findings also highlight the fact that aggression in ADHD indexes genetic as well as clinical severity.

Power and predictive accuracy of polygenic risk scores

Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.

Recently there has been much interest in combining multiple genetic markers into a single score for predicting disease risk. Even if many of the individual markers have no detected effect, the combined score could be a strong predictor of disease. This has allowed researchers to demonstrate that some diseases have a strong genetic basis, even if few actual genes have been identified, and it has also revealed a common genetic basis for distinct diseases. These analyses have so far been performed opportunistically, with mixed results. Here I derive formulae based on the heritability of disease and size of the study, allowing researchers to plan their analyses from a more informed position. I show that discouraging results in some previous studies were due to the low number of subjects studied, but a modest increase in study size would allow more successful analysis. However, I also show that, for genetics to become useful for predicting individual risk of disease, hundreds of thousands of subjects may be needed to estimate the gene effects. This is larger than most existing studies, but will become more common in the near future, so that gene scores will become more useful for predicting disease than has appeared to date.

Deletion at the SLC1A1 glutamate transporter gene co-segregates with schizophrenia and bipolar schizoaffective disorder in a 5-generation family

Growing evidence for genetic overlap between schizophrenia (SCZ) and bipolar disorder (BPD) suggests that causal variants of large effect on disease risk may cross traditional diagnostic boundaries. Extended multigenerational families with both SCZ and BPD cases can be a valuable resource for discovery of shared biological pathways because they can reveal the natural evolution of the underlying genetic disruptions and their phenotypic expression. We investigated a deletion at the SLC1A1 glutamate transporter gene originally identified as a copy number variant exclusively carried by members of a 5-generation Palauan family. Using an expanded sample of 21 family members, quantitative PCR confirmed the deletion in all seven individuals with psychosis, three "obligate-carrier" parents and one unaffected sibling, while four marry-in parents were non-carriers. Linkage analysis under an autosomal dominant model generated a LOD-score of 3.64, confirming co-segregation of the deletion with psychosis. For more precise localization, we determined the approximate deletion end points using alignment of next-generation sequencing data for one affected deletion-carrier and then designed PCR amplicons to span the entire deletion locus. These probes established that the deletion spans 84,298 bp, thus eliminating the entire promoter, the transcription start site, and the first 59 amino acids of the protein, including the first transmembrane Na(2+)/dicarboxylate symporter domain, one of the domains that perform the glutamate transport action. Discovery of this functionally relevant SLC1A1 mutation and its co-segregation with psychosis in an extended multigenerational pedigree provides further support for the important role played by glutamatergic transmission in the pathophysiology of psychotic disorders.

CACNA1C risk variant and amygdala activity in bipolar disorder, schizophrenia and healthy controls

OBJECTIVES

Several genetic studies have implicated the CACNA1C SNP rs1006737 in bipolar disorder (BD) and schizophrenia (SZ) pathology. This polymorphism was recently found associated with increased amygdala activity in healthy controls and patients with BD. We performed a functional Magnetic Resonance Imaging (fMRI) study in a sample of BD and SZ cases and healthy controls to test for altered amygdala activity in carriers of the rs1006737 risk allele (AA/AG), and to investigate if there were differences across the diagnostic groups.

METHODS

Rs1006737 was genotyped in 250 individuals (N = 66 BD, 61 SZ and 123 healthy controls), all of Northern European origin, who underwent an fMRI negative faces matching task. Statistical tests were performed with a model correcting for sex, age, diagnostic category and medication status in the total sample, and then in each diagnostic group.

RESULTS

In the total sample, carriers of the risk allele had increased activation in the left amygdala. Group-wise analyses showed that this effect was significant in the BD group, but not in the other diagnostic groups. However, there was no significant interaction effect for the risk allele between BD and the other groups.

CONCLUSIONS

These results indicate that CACNA1C SNP rs1006737 affects amygdala activity during emotional processing across all diagnostic groups. The current findings add to the growing body of knowledge of the pleiotropic effect of this polymorphism, and further support that ion channel dysregulation is involved in the underlying mechanisms of BD and SZ.

How does a Developmental Perspective inform us about the early Natural History of Bipolar Disorder?

OBJECTIVE

The focus of this paper is to explore how a developmental perspective can advance understanding of the clinical trajectory into bipolar disorder (BD) and clarify controversies regarding the diagnosis in youth.

METHOD

In this selective review, we focus on findings from longitudinal studies of general population and high-risk pediatric cohorts in order to inform our understanding of the development of BD in youth. Also highlighted are related aspects of the debate about the diagnosis in young children and a discussion of the implications of the findings for advancing early detection and intervention clinical and research efforts.

RESULTS

Evidence overwhelmingly suggests that BD typically onsets in adolescence and early adulthood, with the depressive polarity of the illness dominating the early course. Non-specific childhood antecedents have been noted in some high-risk individuals. However, in youth without a confirmed familial risk of BD, manic-like symptoms have little prognostic significance for BD and not uncommonly form part of the normative adolescent experience. Over-emphasis of symptoms and reliance on parent report alone, alongside the relative neglect of the child's developmental stage and risk profile, contributes to the over diagnosis in young children and under recognition of BD early in the clinical course.

CONCLUSIONS

Longitudinal population and high-risk studies over development have made major contributions to our understanding of the early natural history of BD in youth. Implications call for a different diagnostic approach to facilitate accurate identification of youth in the early clinical stages of psychiatric disorders and to differentiate between the emerging illness trajectories and transient normative symptoms in childhood and adolescence.

Genome-wide association of mood-incongruent psychotic bipolar disorder

Mood-incongruent psychotic features (MICP) are familial symptoms of bipolar disorder (BP) that also occur in schizophrenia (SZ), and may represent manifestations of shared etiology between the major psychoses. In this study we have analyzed three large samples of BP with imputed genome-wide association data and have performed a meta-analysis of 2196 cases with MICP and 8148 controls. We found several regions with suggestive evidence of association (P<10(-6)), although no marker met genome-wide significance criteria. The top associations were on chromosomes: 6q14.2 within the PRSS35/SNAP91 gene complex (rs1171113, P=9.67 × 10(-8)); 3p22.2 downstream of TRANK/LBA1 (rs9834970, P=9.71 × 10(-8)); and 14q24.2 in an intron of NUMB (rs2333194, P=7.03 × 10(-7)). These associations were present in all three samples, and both rs1171113 and rs2333194 were found to be overrepresented in an analysis of MICP cases compared with all other BP cases. To test the relationship of MICP with SZ, we performed polygenic analysis using the Psychiatric GWAS Consortium SZ results and found evidence of association between SZ polygenes and the presence of MICP in BP cases (meta-analysis P=0.003). In summary, our analysis of the MICP phenotype in BP has provided suggestive evidence for association of common variants in several genes expressed in the nervous system. The results of our polygenic analysis provides support for a modest degree of genetic overlap between BP with MICP and SZ, highlighting that phenotypic correlations across syndromes may be due to the influence of polygenic risk factors.