Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia

MOTIVATION

Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account.

RESULTS

We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.

Key role for lipids in cognitive symptoms of schizophrenia

Schizophrenia (SZ) is a psychiatric disorder with a convoluted etiology that includes cognitive symptoms, which arise from among others a dysfunctional dorsolateral prefrontal cortex (dlPFC). In our search for the molecular underpinnings of the cognitive deficits in SZ, we here performed RNA sequencing of gray matter from the dlPFC of SZ patients and controls. We found that the differentially expressed RNAs were enriched for mRNAs involved in the Liver X Receptor/Retinoid X Receptor (LXR/RXR) lipid metabolism pathway. Components of the LXR/RXR pathway were upregulated in gray matter but not in white matter of SZ dlPFC. Intriguingly, an analysis for shared genetic etiology, using two SZ genome-wide association studies (GWASs) and GWAS data for 514 metabolites, revealed genetic overlap between SZ and acylcarnitines, VLDL lipids, and fatty acid metabolites, which are all linked to the LXR/RXR signaling pathway. Furthermore, analysis of structural T₁-weighted magnetic resonance imaging in combination with cognitive behavioral data showed that the lipid content of dlPFC gray matter is lower in SZ patients than in controls and correlates with a tendency towards reduced accuracy in the dlPFC-dependent task-switching test. We conclude that aberrations in LXR/RXR-regulated lipid metabolism lead to a decreased lipid content in SZ dlPFC that correlates with reduced cognitive performance.

Risk Stratification for Bipolar Disorder Using Polygenic Risk Scores Among Young High-Risk Adults

Objective: Identifying high-risk groups with an increased genetic liability for bipolar disorder (BD) will provide insights into the etiology of BD and contribute to early detection of BD. We used the BD polygenic risk score (PRS) derived from BD genome-wide association studies (GWAS) to explore how such genetic risk manifests in young, high-risk adults. We postulated that BD-PRS would be associated with risk factors for BD. Methods: A final sample of 185 young, high-risk German adults (aged 18-35 years) were grouped into three risk groups and compared to a healthy control group (n = 1,100). The risk groups comprised 117 cases with attention deficit hyperactivity disorder (ADHD), 45 with major depressive disorder (MDD), and 23 help-seeking adults with early recognition symptoms [ER: positive family history for BD, (sub)threshold affective symptomatology and/or mood swings, sleeping disorder]. BD-PRS was computed for each participant. Logistic regression models (controlling for sex, age, and the first five ancestry principal components) were used to assess associations of BD-PRS and the high-risk phenotypes. Results: We observed an association between BD-PRS and combined risk group status (OR = 1.48, p < 0.001), ADHD diagnosis (OR = 1.32, p = 0.009), MDD diagnosis (OR = 1.96, p < 0.001), and ER group status (OR = 1.7, p = 0.025; not significant after correction for multiple testing) compared to healthy controls. Conclusions: In the present study, increased genetic risk for BD was a significant predictor for MDD and ADHD status, but not for ER. These findings support an underlying shared risk for both MDD and BD as well as ADHD and BD. Improving our understanding of the underlying genetic architecture of these phenotypes may aid in early identification and risk stratification.

Mitochondria under the spotlight: On the implications of mitochondrial dysfunction and its connectivity to neuropsychiatric disorders

Neuropsychiatric disorders (NPDs) such as bipolar disorder (BD), schizophrenia (SZ) and mood disorder (MD) are hard to manage due to overlapping symptoms and lack of biomarkers. Risk alleles of BD/SZ/MD are emerging, with evidence suggesting mitochondrial (mt) dysfunction as a critical factor for disease onset and progression. Mood stabilizing treatments for these disorders are scarce, revealing the need for biomarker discovery and artificial intelligence approaches to design synthetically accessible novel therapeutics. Here, we show mt involvement in NPDs by associating 245 mt proteins to BD/SZ/MD, with 7 common players in these disease categories. Analysis of over 650 publications suggests that 245 NPD-linked mt proteins are associated with 800 other mt proteins, with mt impairment likely to rewire these interactions. High dosage of mood stabilizers is known to alleviate manic episodes, but which compounds target mt pathways is another gap in the field that we address through mood stabilizer-gene interaction analysis of 37 prescriptions and over-the-counter psychotropic treatments, which we have refined to 15 mood-stabilizing agents. We show 26 of the 245 NPD-linked mt proteins are uniquely or commonly targeted by one or more of these mood stabilizers. Further, induced pluripotent stem cell-derived patient neurons and three-dimensional human brain organoids as reliable BD/SZ/MD models are outlined, along with multiomics methods and machine learning-based decision making tools for biomarker discovery, which remains a bottleneck for precision psychiatry medicine.

Gene-set Enrichment with Mathematical Biology (GEMB)

BACKGROUND

Gene-set analyses measure the association between a disease of interest and a "set" of genes related to a biological pathway. These analyses often incorporate gene network properties to account for differential contributions of each gene. We extend this concept further-defining gene contributions based on biophysical properties-by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function.

RESULTS

We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We demonstrate in simulation how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular calcium ion concentration, that is significantly related to bipolar disorder in a small dataset (P = 0.04; n = 544). We reproduce this finding using publicly available summary data from the Psychiatric Genomics Consortium (P = 1.7 × 10-4; n = 41,653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder (P = 0.08). The weighted gene-set approach based on intracellular calcium ion concentration did not detect a significant relationship with schizophrenia (P = 0.09; n = 65,967) or major depression disorder (P = 0.30; n = 500,199).

CONCLUSIONS

Together, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders.

Clinical Features of Psychotic Disorders: Comparing Categorical and Dimensional Models

Objective

Despite research demonstrating the value of dimensional approaches, standard systems for classifying psychotic disorders rely primarily on categorization of patients into distinct diagnoses. We present the first study comparing analyses of dimensional features, categories, and standard diagnoses, all derived from the same sample.

Methods

Using symptom ratings from 934 patients hospitalized for psychosis, we examined dimensional models, fit using factor analysis, categorical models, fit to factor-based scores from the dimensional model, and their correspondence with DSM-defined diagnoses. We compared the ability of each model to discriminate patients' assignment to medication regimen as a clinical validator.

Results

Dimensional modeling identified four factors (manic, depressive, negative symptoms, and positive symptoms), which corresponded to factors in prior studies and appeared robust to statistical approach. Scores based on these factors overlapped substantially among DSM diagnoses. Patients assigned to clusters had less overlap in factor-based scores. However, categorical models were sensitive to statistical approach. The addition of DSM diagnoses, but not cluster assignments, improved the fits of models with dimensional scores alone as the clinical predictors for some medication classes.

Conclusions

The results highlight the variability of symptom presentation within DSM-defined diagnostic categories, the utility of symptom dimensions or factors, and a potential lack of robustness of data-driven categorical approaches. Findings support initiatives to develop updated diagnostic systems that complement categorical classification of psychotic illness with factors representing dimensional ratings of symptoms.

The search for personalized antidepressant treatments: what have we learned and where are we going

Over 20 years after the initial report of gene variants within the central nervous system modulating antidepressant response, we are now facing for the first time routine clinical pharmacogenetic applications. The scientific community is divided between enthusiasm and skepticism. It seems clear that the benefit of existing tools is not huge, at least for the central nervous system gene variants, while it is generally accepted for the metabolic gene variants. Findings from large international consortia suggest for the first time in psychiatric genetic research history that cumulative scores comprising many variants across the whole genome may reliably constitute liability factors for psychiatric disorders, this approach will most likely improve also present pharmacogenetic tools. A composite genetic score complemented with clinical risk factors for each patient is the most promising approach for a more effective method of targeted treatment for patients with depression.

Polygenic Risk and Progression to Bipolar or Psychotic Disorders Among Individuals Diagnosed With Unipolar Depression in Early Life

OBJECTIVE

The authors investigated the associations between polygenic liability and progression to bipolar disorder or psychotic disorders among individuals diagnosed with unipolar depression in early life.

METHODS

A cohort comprising 16,949 individuals (69% female, 10-35 years old at the first depression diagnosis) from the iPSYCH Danish case-cohort study (iPSYCH2012) who were diagnosed with depression in Danish psychiatric hospitals from 1994 to 2016 was examined. Polygenic risk scores (PRSs) for major depression, bipolar disorder, and schizophrenia were generated using the most recent results from the Psychiatric Genomics Consortium. Hazard ratios for each disorder-specific PRS were estimated using Cox regressions with adjustment for the other two PRSs. Absolute risk of progression was estimated using the cumulative hazard.

RESULTS

Patients were followed for up to 21 years (median=7 years, interquartile range, 5-10 years). The absolute risks of progression to bipolar disorder and psychotic disorders were 7.3% and 13.8%, respectively. After mutual adjustment for the other PRSs, only the PRS for bipolar disorder predicted progression to bipolar disorder (adjusted hazard ratio for a one-standard-deviation increase in PRS=1.11, 95% CI=1.03, 1.21), and only the PRS for schizophrenia predicted progression to psychotic disorders (adjusted hazard ratio=1.10, 95% CI=1.04, 1.16). After adjusting for PRSs, parental history still strongly predicted progression to bipolar disorder (adjusted hazard ratio=5.02, 95% CI=3.53, 7.14) and psychotic disorders (adjusted hazard ratio=1.63, 95% CI=1.30, 2.06).

CONCLUSIONS

PRSs for bipolar disorder and schizophrenia are associated with risk for progression to bipolar disorder or psychotic disorders, respectively, among individuals diagnosed with depression; however, the effects are small compared with parental history, particularly for bipolar disorder.

A meta-review of "lifestyle psychiatry": the role of exercise, smoking, diet and sleep in the prevention and treatment of mental disorders

There is increasing academic and clinical interest in how "lifestyle factors" traditionally associated with physical health may also relate to mental health and psychological well-being. In response, international and national health bodies are producing guidelines to address health behaviors in the prevention and treatment of mental illness. However, the current evidence for the causal role of lifestyle factors in the onset and prognosis of mental disorders is unclear. We performed a systematic meta-review of the top-tier evidence examining how physical activity, sleep, dietary patterns and tobacco smoking impact on the risk and treatment outcomes across a range of mental disorders. Results from 29 meta-analyses of prospective/cohort studies, 12 Mendelian randomization studies, two meta-reviews, and two meta-analyses of randomized controlled trials were synthesized to generate overviews of the evidence for targeting each of the specific lifestyle factors in the prevention and treatment of depression, anxiety and stress-related disorders, schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder. Standout findings include: a) convergent evidence indicating the use of physical activity in primary prevention and clinical treatment across a spectrum of mental disorders; b) emerging evidence implicating tobacco smoking as a causal factor in onset of both common and severe mental illness; c) the need to clearly establish causal relations between dietary patterns and risk of mental illness, and how diet should be best addressed within mental health care; and d) poor sleep as a risk factor for mental illness, although with further research required to understand the complex, bidirectional relations and the benefits of non-pharmacological sleep-focused interventions. The potentially shared neurobiological pathways between multiple lifestyle factors and mental health are discussed, along with directions for future research, and recommendations for the implementation of these findings at public health and clinical service levels.

Association Analysis Between Common Variants of the TRPM1 Gene and Three Mental Disorders in the Han Chinese Population

Objective: Our study was designed to determine if the TRPM1 gene is associated with any of three mental disorders. The project included a cross disorder meta-analysis and association analysis including 141701 cases and 175248 controls. Materials and Methods: We genotyped eight tag single nucleotide polymorphisms (SNPs) in 1248 unrelated schizophrenia (SCZ) patients, 1056 major depressive disorder patients, 1344 bipolar disorder patients, and 1248 normal controls. We then performed a meta-analysis of 10 GWASs to identify common genetic factors among these three mental disorders. Finally, we performed a meta-analysis of six GWASs to explore the role of rs10162727 in SCZ. Result: Although two haplotypes of the TRPM1 gene, rs1035706-rs10162727 and rs10162727-rs3784599, were identified in the control group, as well as all three disease groups, none of the eight tag SNP associations remained significant after correction for multiple tests. In this cross-disorder meta-analysis of the three diseases, none of the tag SNPs were confirmed to be common among the diseases. In addition, in the meta-analysis conducted for the rs10162727 locus in SCZ, there was no significant association (p-value = 0.84, odds ratio = 1.02 [95% CI = 0.87-1.19]). Conclusion: In the Han Chinese population, no significant evidence was found linking variants of the TRPM1 gene with any of the mental disorders examined.

Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data

The 'discovery' stage of genome-wide association studies required amassing large, homogeneous cohorts. In order to attain clinically useful insights, we must now consider the presentation of disease within our clinics and, by extension, within our medical records. Large-scale use of electronic health record (EHR) data can help to understand phenotypes in a scalable manner, incorporating lifelong and whole-phenome context. However, extending analyses to incorporate EHR and biobank-based analyses will require careful consideration of phenotype definition. Judgements and clinical decisions that occur 'outside' the system inevitably contain some degree of bias and become encoded in EHR data. Any algorithmic approach to phenotypic characterization that assumes non-biased variables will generate compounded biased conclusions. Here, we discuss and illustrate potential biases inherent within EHR analyses, how these may be compounded across time and suggest frameworks for large-scale phenotypic analysis to minimize and uncover encoded bias.

LDB2 locus disruption on 4p16.1 as a risk factor for schizophrenia and bipolar disorder

We had previously reported the case of a male patient with schizophrenia, having de-novo balanced translocation. Here, we determined the exact breakpoints in chromosomes 4 and 13. The breakpoint within chromosome 4 was mapped to a region 32.6 kbp upstream of the LDB2 gene encoding Lim domain binding 2. Variant screening in LDB2 revealed a rare novel missense variant in patients with psychiatric disorder.

Risk prediction of late-onset Alzheimer's disease implies an oligogenic architecture

Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer's disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.

Interaction between adverse childhood experiences and polygenic risk in patients with bipolar disorder

The interaction between genes and environment often occurs when they depend on one another. We hypothesized that adverse childhood experiences (ACEs) would interact with genetic predispositions to bipolar disorder (BD), demonstrating earlier age at onset (AAO) and worse clinical outcomes. We aimed to clarify the effects of the interaction between ACEs and genetic susceptibility using polygenic risk score (PRS) on AAO and clinical outcomes. Single nucleotide polymorphisms and clinical data, including ACEs, were obtained from the Bipolar Genomic Study, which contains a large sample of BD participants. A total of 1615 subjects with BD I were obtained and divided into two groups according to the presence or absence of ACEs and an additional four groups based on the number of ACEs (none versus one versus two versus ≥ three types). ACEs was evaluated using the childhood life events scale (CLES). BD-PRS was obtained from the Psychiatric Genomics Consortium, which compared BD patients and healthy controls. The BD-PRS was higher in the group with ACEs than without ACEs at most p-value thresholds. In multivariate linear regression analyses, both groups with more ACEs and higher BD-PRS were independently and interactively associated with an earlier AAO of BD; however, only greater ACEs were associated with worsened clinical outcome. These findings highlight the clinical importance of evaluating ACEs and polygenic risk in research of the etiology of BD.

Identifying 5 Common Psychiatric Disorders Associated Chemicals Through Integrative Analysis of Genome-Wide Association Study and Chemical-Gene Interaction Datasets

Psychiatric disorders are a group of complex psychological syndromes whose etiology remains unknown. Previous study suggested that various chemicals contributed to the development of psychiatric diseases through affecting gene expression. This study aims to systematically explore the potential relationships between 5 major psychiatric disorders and more than 11 000 chemicals. The genome-wide association studies (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depression disorder (MDD), and schizophrenia (SCZ) were driven from the Psychiatric GWAS Consortium and iPSYCH website. The chemicals related gene sets were obtained from the comparative toxicogenomics database (CTD). First, transcriptome-wide association studies (TWAS) were performed by FUSION to calculate the expression association testing statistics utilizing GWAS summary statistics of the 5 common psychiatric disorders. Chemical-related gene set enrichment analysis (GSEA) was then conducted to explore the relationships between chemicals and each of the psychiatric diseases. We observed several significant correlations between chemicals and each of the psychiatric disorders. We also detected common chemicals between every 4 of the 5 major psychiatric disorders, such as androgen antagonists for ADHD (P value = .0098), ASD (P value = .0330), BD (P value = .0238), and SCZ (P value = .0062), and imipramine for ADHD (P value = .0054), ASD (P value = .0386), MDD (P value = .0438), and SCZ (P value = .0008). Our study results provide new clues for revealing the roles of environmental chemicals in the development of psychiatric disorders.

Polygenic risk for autism spectrum disorder affects left amygdala activity and negative emotion in schizophrenia

Although the diagnoses based on phenomenology have many practical advantages, accumulating evidence shows that schizophrenia and autism spectrum disorder (ASD) share some overlap in genetics and clinical presentation. It remains largely unknown how ASD-associated polygenetic risk contributes to the pathogenesis of schizophrenia. In the present study, we calculated high-resolution ASD polygenic risk scores (ASD PRSs) and selected optimal ten ASD PRS with minimal P values in the association analysis of PRSs, with schizophrenia to assess the effect of ASD PRS on brain neural activity in schizophrenia cases and controls. We found that amplitude of low-frequency fluctuation in left amygdala was positively associated with ASD PRSs in our cohort. Correlation analysis of ASD PRSs with facial emotion recognition test identified the negative correlation of ASD PRSs with negative emotions in schizophrenia cases and controls. Finally, functional enrichment analysis of PRS genes revealed that neural system function and development, as well as signal transduction, were mainly enriched in PRS genes. Our results provide empirical evidence that polygenic risk for ASD contributes to schizophrenia by the intermediate phenotypes of left amygdala function and emotion recognition. It provides a promising strategy to understand the relationship between phenotypes and genotypes shared in mental disorders.

Molecular Genetic Risk for Psychosis Is Associated With Psychosis Risk Symptoms in a Population-Based UK Cohort: Findings From Generation Scotland

OBJECTIVE

Subthreshold psychosis risk symptoms in the general population may be associated with molecular genetic risk for psychosis. This study sought to optimize the association of risk symptoms with genetic risk for psychosis in a large population-based cohort in the UK (N = 9104 individuals 18-65 years of age) by properly accounting for population stratification, factor structure, and sex.

METHODS

The newly expanded Generation Scotland: Scottish Family Health Study includes 5391 females and 3713 males with age M [SD] = 45.2 [13] with both risk symptom data and genetic data. Subthreshold psychosis symptoms were measured using the Schizotypal Personality Questionnaire-Brief (SPQ-B) and calculation of polygenic risk for schizophrenia was based on 11 425 349 imputed common genetic variants passing quality control. Follow-up examination of other genetic risks included attention-deficit hyperactivity disorder (ADHD), autism, bipolar disorder, major depression, and neuroticism.

RESULTS

Empirically derived symptom factor scores reflected interpersonal/negative symptoms and were positively associated with polygenic risk for schizophrenia. This signal was largely sex specific and limited to males. Across both sexes, scores were positively associated with neuroticism and major depressive disorder.

CONCLUSIONS

A data-driven phenotypic analysis enabled detection of association with genetic risk for schizophrenia in a population-based sample. Multiple polygenic risk signals and important sex differences suggest that genetic data may be useful in improving future phenotypic risk assessment.

Leveraging correlations between variants in polygenic risk scores to detect heterogeneity in GWAS cohorts

Evidence from both GWAS and clinical observation has suggested that certain psychiatric, metabolic, and autoimmune diseases are heterogeneous, comprising multiple subtypes with distinct genomic etiologies and Polygenic Risk Scores (PRS). However, the presence of subtypes within many phenotypes is frequently unknown. We present CLiP (Correlated Liability Predictors), a method to detect heterogeneity in single GWAS cohorts. CLiP calculates a weighted sum of correlations between SNPs contributing to a PRS on the case/control liability scale. We demonstrate mathematically and through simulation that among i.i.d. homogeneous cases generated by a liability threshold model, significant anti-correlations are expected between otherwise independent predictors due to ascertainment on the hidden liability score. In the presence of heterogeneity from distinct etiologies, confounding by covariates, or mislabeling, these correlation patterns are altered predictably. We further extend our method to two additional association study designs: CLiP-X for quantitative predictors in applications such as transcriptome-wide association, and CLiP-Y for quantitative phenotypes, where there is no clear distinction between cases and controls. Through simulations, we demonstrate that CLiP and its extensions reliably distinguish between homogeneous and heterogeneous cohorts when the PRS explains as low as 3% of variance on the liability scale and cohorts comprise 50, 000 − 100, 000 samples, an increasingly practical size for modern GWAS. We apply CLiP to heterogeneity detection in schizophrenia cohorts totaling > 50, 000 cases and controls collected by the Psychiatric Genomics Consortium. We observe significant heterogeneity in mega-analysis of the combined PGC data (p-value 8.54 × 0⁻⁴), as well as in individual cohorts meta-analyzed using Fisher’s method (p-value 0.03), based on significantly associated variants. We also apply CLiP-Y to detect heterogeneity in neuroticism in over 10, 000 individuals from the UK Biobank and detect heterogeneity with a p-value of 1.68 × 10⁻⁹. Scores were not significantly reduced when partitioning by known subclusters (“Depression” and “Worry”), suggesting that these factors are not the primary source of observed heterogeneity.

Several traits, such as bipolar disease, are known to be heterogeneous and comprise distinct subtypes with unique genomic associations. For other traits such as schizophrenia, heterogeneity may be suspected, but specific subtypes are less well characterized. Furthermore, conventional mixture model-based methods to detect subtypes in genome-wide association data struggle with the high polygenicity of complex traits. We propose CLiP (Correlated Liability Predictors), a method that does not identify subtype-specific effects, but is very well-powered to detect heterogeneity of any kind within the very weak signals of GWAS. CLiP serves as a method to flag particular phenotypes for potential further study into the genomic factors driving heterogeneity, as well as a means to evaluate the transferability of polygenic risk scores. We also develop extensions of CLiP applicable to scoring heterogeneity in quantitative phenotypes and quantitative predictors such as gene expression. We apply CLiP to scoring heterogeneity in schizophrenia cohorts from the Psychiatric Genomics Consortium and neuroticism in individuals in the UK Biobank and find significant heterogeneity in both phenotypes, warranting further study.

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 autism spectrum disorder associates with anger recognition in a neurodevelopment-focused phenome-wide scan of unaffected youths from a population-based cohort

The polygenic nature and the contribution of common genetic variation to autism spectrum disorder (ASD) allude to a high degree of pleiotropy between ASD and other psychiatric and behavioral traits. In a pleiotropic system, a single genetic variant contributes small effects to several phenotypes or disorders. While analyzed broadly, there is a paucity of research studies investigating the shared genetic information between specific neurodevelopmental domains and ASD. We performed a phenome-wide association study of ASD polygenetic risk score (PRS) against 491 neurodevelopmental subdomains ascertained in 4,309 probands from the Philadelphia Neurodevelopmental Cohort (PNC) who lack an ASD diagnosis. Our main analysis calculated ASD PRS in 4,309 PNC probands using the per-SNP effects reported in a recent genome-wide association study of ASD in a case-control design. In a high-resolution manner, our main analysis regressed ASD PRS against 491 neurodevelopmental phenotypes with age, sex, and ten principal components of ancestry as covariates. Follow-up analyses included in the regression model PRS derived from brain-related traits genetically correlated with ASD. Our main finding demonstrated that 11-17-year old probands with the highest ASD genetic risk were able to identify angry faces (R² = 1.06%, p = 1.38 × 10⁻⁷, p_{Bonferroni-corrected} = 1.9 × 10⁻³). This ability replicated in older probands (>18 years; R² = 0.55%, p = 0.036) and persisted after covarying with other psychiatric disorders, brain imaging traits, and educational attainment (R² = 0.2%, p = 0.019). We also detected several suggestive associations between ASD PRS and emotionality and connectedness with others. These data (i) indicate how genetic liability to ASD may influence neurodevelopment in the general population, (ii) reinforce epidemiological findings of heightened ability of ASD cases to predict certain social psychological events based on increased systemizing skills, and (iii) recapitulate theories of imbalance between empathizing and systemizing in ASD etiology.

Large-scale genetic studies have identified many regions of the genome associated with autism spectrum disorder that are considered common in the general population. We investigated how the additive effects of these genetic variations associate with neurodevelopment in youths who lack an ASD diagnosis to better understand how genetic risk for ASD may contribute to other aspects of mental health. We uncovered a relationship between greater genetic risk for ASD and more accurate recognition of angry emotions in others, which persists after considering genetic associations with other psychiatric disorders, educational attainment, and brain region volume. This finding is consistent with existing theories of the relationship between ASD genetic liability and a person’s ability to build generalizable and impulse driven models for responding to social phenomena.

Autism spectrum disorder at the crossroad between genes and environment: contributions, convergences, and interactions in ASD developmental pathophysiology

The complex pathophysiology of autism spectrum disorder encompasses interactions between genetic and environmental factors. On the one hand, hundreds of genes, converging at the functional level on selective biological domains such as epigenetic regulation and synaptic function, have been identified to be either causative or risk factors of autism. On the other hand, exposure to chemicals that are widespread in the environment, such as endocrine disruptors, has been associated with adverse effects on human health, including neurodevelopmental disorders. Interestingly, experimental results suggest an overlap in the regulatory pathways perturbed by genetic mutations and environmental factors, depicting convergences and complex interplays between genetic susceptibility and toxic insults. The pervasive nature of chemical exposure poses pivotal challenges for neurotoxicological studies, regulatory agencies, and policy makers. This highlights an emerging need of developing new integrative models, including biomonitoring, epidemiology, experimental, and computational tools, able to capture real-life scenarios encompassing the interaction between chronic exposure to mixture of substances and individuals' genetic backgrounds. In this review, we address the intertwined roles of genetic lesions and environmental insults. Specifically, we outline the transformative potential of stem cell models, coupled with omics analytical approaches at increasingly single cell resolution, as converging tools to experimentally dissect the pathogenic mechanisms underlying neurodevelopmental disorders, as well as to improve developmental neurotoxicology risk assessment.

Polygenic prediction of the risk of perinatal depressive symptoms

BACKGROUND

Perinatal depression carries adverse effects on maternal health and child development, but genetic underpinnings remain unclear. We investigated the polygenic risk of perinatal depressive symptoms.

METHODS

About 742 women from the prospective Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction cohort were genotyped and completed the Center for Epidemiologic Studies Depression scale 14 times during the prenatal period and twice up to 12 months postpartum. Polygenic risk scores for major depressive disorder, bipolar disorder, schizophrenia, and cross-disorder were calculated using multiple p-value thresholds.

RESULTS

Polygenic risk scores for major depressive disorder, schizophrenia, and cross-disorder, but not bipolar disorder, were associated with higher prenatal and postpartum depressive symptoms (0.8%-1% increase per one standard deviation increase in polygenic risk scores). Prenatal depressive symptoms accounted for and mediated the associations between the polygenic risk scores and postpartum depressive symptoms (effect size proportions-mediated: 52.2%-88.0%). Further, the polygenic risk scores were associated with 1.24-1.45-fold odds to belong to the group displaying consistently high compared with consistently low depressive symptoms through out the prenatal and postpartum periods.

CONCLUSIONS

Polygenic risk scores for major depressive disorder, schizophrenia, and cross-disorder in non-perinatal populations generalize to perinatal depressive symptoms and may afford to identify women for timely preventive interventions.

Refining the resolution of craniofacial dysmorphology in bipolar disorder as an index of brain dysmorphogenesis

As understanding of the genetics of bipolar disorder increases, controversy endures regarding whether the origins of this illness include early maldevelopment. Clarification would be facilitated by a 'hard' biological index of fetal developmental abnormality, among which craniofacial dysmorphology bears the closest embryological relationship to brain dysmorphogenesis. Therefore, 3D laser surface imaging was used to capture the facial surface of 21 patients with bipolar disorder and 45 control subjects; 21 patients with schizophrenia were also studied. Surface images were subjected to geometric morphometric analysis in non-affine space for more incisive resolution of subtle, localised dysmorphologies that might distinguish patients from controls. Complex and more biologically informative, non-linear changes distinguished bipolar patients from control subjects. On a background of minor dysmorphology of the upper face, maxilla, midface and periorbital regions, bipolar disorder was characterised primarily by the following dysmorphologies: (a) retrusion and shortening of the premaxilla, nose, philtrum, lips and mouth (the frontonasal prominences), with (b) some protrusion and widening of the mandible-chin. The topography of facial dysmorphology in bipolar disorder indicates disruption to early development in the frontonasal process and, on embryological grounds, cerebral dysmorphogenesis in the forebrain, most likely between the 10^th and 15^th week of fetal life.

Tutorial: a guide to performing polygenic risk score analyses

A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual's genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation-genetic liability-has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.

Genome-Wide Search for SNP Interactions in GWAS Data: Algorithm, Feasibility, Replication Using Schizophrenia Datasets

In this study, we looked for potential gene-gene interaction in susceptibility to schizophrenia by an exhaustive searching for SNP-SNP interactions in 3 GWAS datasets (phs000021:phg000013, phs000021:phg000014, phs000167) using our recently published algorithm. The search space for SNP-SNP interaction was confined to 8 biologically plausible ways of interaction under dominant-dominant or recessive-recessive modes. First, we performed our search of all pair-wise combination of 729,454 SNPs after filtering by SNP genotype quality. All possible pairwise interactions of any 2 SNPs (5 × 10¹¹) were exhausted to search for significant interaction which was defined by p-value of chi-square tests. Nine out the top 10 interactions, protein coding genes were partnered with non-coding RNA (ncRNA) which suggested a new alternative insight into interaction biology other than the frequently sought-after protein-protein interaction. Therefore, we extended to look for replication among the top 10,000 interaction SNP pairs and high proportion of concurrent genes forming the interaction pairs were found. The results indicated that an enrichment of signals over noise was present in the top 10,000 interactions. Then, replications of SNP-SNP interaction were confirmed for 14 SNPs-pairs in both replication datasets. Biological insight was highlighted by a potential binding between FHIT (protein coding gene) and LINC00969 (lncRNA) which showed a replicable interaction between their SNPs. Both of them were reported to have expression in brain. Our study represented an early attempt of exhaustive interaction analysis of GWAS data which also yield replicated interaction and new insight into understanding of genetic interaction in schizophrenia.

Genetic control of temperament traits across species: association of autism spectrum disorder risk genes with cattle temperament

BACKGROUND

Temperament traits are of high importance across species. In humans, temperament or personality traits correlate with psychological traits and psychiatric disorders. In cattle, they impact animal welfare, product quality and human safety, and are therefore of direct commercial importance. We hypothesized that genetic factors that contribute to variation in temperament among individuals within a species will be shared between humans and cattle. Using imputed whole-genome sequence data from 9223 beef cattle from three cohorts, a series of genome-wide association studies was undertaken on cattle flight time, a temperament phenotype measured as the time taken for an animal to cover a short-fixed distance after release from an enclosure. We also investigated the association of cattle temperament with polymorphisms in bovine orthologs of risk genes for neuroticism, schizophrenia, autism spectrum disorders (ASD), and developmental delay disorders in humans.

RESULTS

Variants with the strongest associations were located in the bovine orthologous region that is involved in several behavioural and cognitive disorders in humans. These variants were also partially validated in independent cattle cohorts. Genes in these regions (BARHL2, NDN, SNRPN, MAGEL2, ABCA12, KIFAP3, TOPAZ1, FZD3, UBE3A, and GABRA5) were enriched for the GO term neuron migration and were differentially expressed in brain and pituitary tissues in humans. Moreover, variants within 100 kb of ASD susceptibility genes were associated with cattle temperament and explained 6.5% of the total additive genetic variance in the largest cattle cohort. The ASD genes with the most significant associations were GABRB3 and CUL3. Using the same 100 kb window, a weak association was found with polymorphisms in schizophrenia risk genes and no association with polymorphisms in neuroticism and developmental delay disorders risk genes.

CONCLUSIONS

Our analysis showed that genes identified in a meta-analysis of cattle temperament contribute to neuron development functions and are differentially expressed in human brain tissues. Furthermore, some ASD susceptibility genes are associated with cattle temperament. These findings provide evidence that genetic control of temperament might be shared between humans and cattle and highlight the potential for future analyses to leverage results between species.

NRG1, PIP4K2A, and HTR2C as Potential Candidate Biomarker Genes for Several Clinical Subphenotypes of Depression and Bipolar Disorder

GSK3B, BDNF, NGF, NRG1, HTR2C, and PIP4K2A play important roles in molecular mechanisms of psychiatric disorders. GSK3B occupies a central position in these molecular mechanisms and is also modulated by psychotropic drugs. BDNF regulates a number of key aspects in neurodevelopment and synaptic plasticity. NGF exerts a trophic action and is implicated in cerebral alterations associated with psychiatric disorders. NRG1 is active in neural development, synaptic plasticity, and neurotransmission. HTR2C is another important psychopharmacological target. PIP4K2A catalyzes the phosphorylation of PI5P to form PIP2, the latter being implicated in various aspects of neuronal signal transduction. In the present study, the six genes were sequenced in a cohort of 19 patients with bipolar affective disorder, 41 patients with recurrent depressive disorder, and 55 patients with depressive episode. The study revealed a number of genetic variants associated with antidepressant treatment response, time to recurrence of episodes, and depression severity. Namely, alleles of rs35641374 and rs10508649 (NRG1 and PIP4K2A) may be prognostic biomarkers of time to recurrence of depressive and manic/mixed episodes among patients with bipolar affective disorder. Alleles of NC_000008.11:g.32614509_32614510del, rs61731109, and rs10508649 (also NRG1 and PIP4K2A) seem to be predictive biomarkers of response to pharmacological antidepressant treatment on the 28th day assessed by the HDRS-17 or CGI-I scale. In particular, the allele G of rs10508649 (PIP4K2A) may increase resistance to antidepressant treatment and be at the same time protective against recurrent manic/mixed episodes. These results support previous data indicating a biological link between resistance to antidepressant treatment and mania. Bioinformatic functional annotation of associated variants revealed possible impact for transcriptional regulation of PIP4K2A. In addition, the allele A of rs2248440 (HTR2C) may be a prognostic biomarker of depression severity. This allele decreases expression of the neighboring immune system gene IL13RA2 in the putamen according to the GTEx portal. The variant rs2248440 is near rs6318 (previously associated with depression and effects of psychotropic drugs) that is an eQTL for the same gene and tissue. Finally, the study points to several protein interactions relevant in the pathogenesis of mood disorders. Functional studies using cellular or animal models are warranted to support these results.

Associations of cigarette smoking with psychiatric disorders: evidence from a two-sample Mendelian randomization study

We conducted a two-sample Mendelian randomization study to determine the association of smoking initiation with seven psychiatric disorders. We used 353 independent single-nucleotide polymorphisms associated with cigarette smoking initiation as instrumental variables at genome-wide significance threshold (p < 5 × 10⁻⁸) from a recent genome-wide association study in 1,232,091 European-origin participants. Summary-level data for seven psychiatric disorders, including anxiety, bipolar disorder, insomnia, major depressive disorder, posttraumatic stress disorder, suicide attempts, and schizophrenia, was obtained from large genetic consortia and genome-wide association studies. The odds ratios of genetically predicted smoking initiation were 1.96 for suicide attempts (95% CI 1.70, 2.27; p = 4.5 × 10⁻²⁰), 1.69 for post-traumatic stress disorder (95% CI 1.32, 2.16; p = 2.5 × 10⁻⁵), 1.54 for schizophrenia (95% CI 1.35, 1.75; p = 1.6 × 10⁻¹⁰), 1.41 for bipolar disorder (95% CI 1.25, 1.59; p = 1.8 × 10⁻⁸), 1.38 for major depressive disorder (95% CI 1.31, 1.45; p = 2.3 × 10⁻³⁸), 1.20 for insomnia (95% CI 1.14, 1.25; p = 6.0 × 10⁻¹⁴) and 1.17 for anxiety (95% CI 0.98, 1.40; p = 0.086). Results of sensitivity analyses were consistent and no horizontal pleiotropy was detected in MR-Egger analysis. However, the associations with suicide attempts, schizophrenia, bipolar disorder, and anxiety might be related to possible reverse causality or weak instrument bias. This study found that cigarette smoking was causally associated with increased risks of a number of psychiatric disorders. The causal effects of smoking on suicide attempts, schizophrenia, bipolar disorder and anxiety needs further research.

The shared genetic architecture of schizophrenia, bipolar disorder and lifespan

Psychiatric disorders such as Schizophrenia (SCZ) and Bipolar Disorder (BD) represent an evolutionary paradox, as they exhibit strong negative effects on fitness, such as decreased fecundity and early mortality, yet they persist at a worldwide prevalence of approximately 1%. Molecular mechanisms affecting lifespan, which may be widely common among complex diseases with fitness effects, can be studied by the integrated analysis of data from genome-wide association studies (GWAS) of human longevity together with any disease of interest. Here, we report the first of such studies, focusing on the genetic overlap-pleiotropy-between two psychiatric disorders with shortened lifespan, SCZ and BD, and human parental lifespan (PLS) as a surrogate of life expectancy. Our results are twofold: first, we demonstrate extensive polygenic overlap between SCZ and PLS and to a lesser extent between BD and PLS. Second, we identified novel loci shared between PLS and SCZ (n = 39), and BD (n = 8). Whereas most of the identified SCZ (66%) and BD (62%) pleiotropic risk alleles were associated with reduced lifespan, we also detected some antagonistic protective alleles associated to shorter lifespans. In fact, top-associated SNPs with SCZ seems to explain longevity variance explained (LVE) better than many other life-threatening diseases, including Type 2 diabetes and most cancers, probably due to a high overlap with smoking-related pathways. Overall, our study provides evidence of a genetic burden driven through premature mortality among people with SCZ, which can have profound implications for understanding, and potentially treating, the mortality gap associated with this psychiatric disorder.

Polygenic risk score as clinical utility in psychiatry: a clinical viewpoint

Genome-wide association studies (GWASs) have detected many susceptible variants for common diseases, including psychiatric disorders. However, because of the small effect size of each variant, clinical utility that aims for risk prediction and/or diagnostic assistance based on the individual "variants" is difficult to use. Therefore, to improve the statistical power, polygenic risk score (PRS) has been established and applied in the GWAS as a robust analytic tool. Although PRS has potential predictive ability, because of its current "insufficient" discriminative power at the individual level for clinical use, it remains limited solely in the research area, specifically in the psychiatric field. For a better understanding of the PRS, in this review, we (1) introduce the clinical features of psychiatric disorders, (2) summarize the recent GWAS/PRS findings in the psychiatric disorders, (3) evaluate the problems of PRS, and (4) propose its possible utility to apply PRS into the psychiatric clinical setting.

Cannabinoids and psychotic symptoms: A potential role for a genetic variant in the P2X purinoceptor 7 (P2RX7) gene

To investigate the biological mechanisms underlying the higher risk for psychosis in those that use cannabis, we conducted a genome-wide environment-interaction study (GWEIS). In a sample of individuals without a psychiatric disorder (N = 1262), we analyzed the interactions between regular cannabis use and genotype with psychotic-like experiences (PLE) as outcome. PLE were measured using the Community Assessment of Psychic Experiences (CAPE). The sample was enriched for those at the extremes of both cannabis use and PLE to increase power. A single nucleotide polymorphism in the P2RX7 gene (rs7958311) was associated with risk for a high level of psychotic experiences in regular cannabis users (p = 1.10 x10^-7) and in those with high levels of lifetime cannabis use (p = 4.5 × 10^-6). This interaction was replicated in individuals with high levels of lifetime cannabis use in the IMAGEN cohort (N = 1217, p = 0.020). Functional relevance of P2RX7 in cannabis users was suggested by in vitro experiments on activated monocytes. Exposure of these cells to tetrahydrocannabinol (THC) or cannabidiol (CBD) reduced the immunological response of the P2X7 receptor, which was dependent on the identified genetic variant. P2RX7 variants have been implicated in psychiatric disorders before and the P2X7 receptor is involved in pathways relevant to psychosis, such as neurotransmission, synaptic plasticity and immune regulation. We conclude that P2RX7 plays a role in vulnerability to develop psychotic symptoms when using cannabis and point to a new pathway that can potentially be targeted by newly developed P2X7 antagonists.

Psychiatric comorbidities in Asperger syndrome are related with polygenic overlap and differ from other Autism subtypes

There is great phenotypic heterogeneity within autism spectrum disorders (ASD), which has led to question their classification into a single diagnostic category. The study of the common genetic variation in ASD has suggested a greater contribution of other psychiatric conditions in Asperger syndrome (AS) than in the rest of the DSM-IV ASD subtypes (Non_AS). Here, using available genetic data from previously performed genome-wide association studies (GWAS), we aimed to study the genetic overlap between five of the most related disorders (schizophrenia (SCZ), major depression disorder (MDD), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorders (OCD) and anxiety (ANX)), and AS, comparing it with the overlap in Non_AS subtypes. A Spanish cohort of autism trios (N = 371) was exome sequenced as part of the Autism Sequencing Consortium (ASC) and 241 trios were extensively characterized to be diagnosed with AS following DSM-IV and Gillberg's criteria (N = 39) or not (N = 202). Following exome imputation, polygenic risk scores (PRS) were calculated for ASD, SCZ, ADHD, MDD, ANX, and OCD (from available summary data from Psychiatric Genomic Consortium (PGC) repository) in the Spanish trios' cohort. By using polygenic transmission disequilibrium test (pTDT), we reported that risk for SCZ (P_scz= 0.008, corrected-P_SCZ = 0.0409), ADHD (P_ADHD = 0.021, corrected-P_ADHD = 0.0301), and MDD (P_MDD = 0.039, corrected-P_MDD = 0.0501) is over-transmitted to children with AS but not to Non_AS. Indeed, agnostic clustering procedure with deviation values from pTDT tests suggested two differentiated clusters of subjects, one of which is significantly enriched in AS (P = 0.025). Subsequent analysis with S-Predixcan, a recently developed software to predict gene expression from genotype data, revealed a clear pattern of correlation between cortical gene expression in ADHD and AS (P < 0.001) and a similar strong correlation pattern between MDD and AS, but also extendable to another non-brain tissue such as lung (P < 0.001). Altogether, these results support the idea of AS being qualitatively distinct from Non_AS autism and consistently evidence the genetic overlap between AS and ADHD, MDD, or SCZ.

A principal component approach to improve association testing with polygenic risk scores

Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS-PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS-PCA approach with a permutation test and an a priori selected p-value threshold. The PRS-PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.

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.

Psychosocial implications of living with familial risk of a psychiatric disorder and attitudes to psychiatric genetic testing: A systematic review of the literature

The aim of this systematic review was to synthesize the existing evidence documenting the psychosocial implications of living with a familial risk of an adult-onset psychiatric disorder. Six databases were searched systematically to identify qualitative and quantitative studies, which explored perspectives of those at increased risk for psychiatric disorders, as well as the general public. Thematic analysis was used to identify major themes. Thirty-five articles met the eligibility criteria and reported on the views of 4,896 participants. The literature demonstrates strong interest in psychiatric genetic testing of adults as well as children, whereas attitudes toward prenatal testing were much less positive. Predictors of interest in testing, as well as perceived advantages and disadvantages were identified. Very few studies are available on anticipated and actual reactions to receiving results. Studies show that the majority of participants feel that having a genetic explanation would alleviate some of the stigma associated with mental illness. This review shows that interest in, and predictors of attitudes toward, psychiatric genetic testing are well researched, but the extent to which attitudes will translate into actual testing uptake is unknown. Future research also needs to assess the actual behavioral and psychological impact of genetic testing.

Adolescent residential mobility, genetic liability and risk of schizophrenia, bipolar disorder and major depression

BACKGROUND

Residential mobility during upbringing, and especially adolescence, is associated with multiple negative mental health outcomes. However, whether associations are confounded by unmeasured familial factors, including genetic liability, is unclear.

AIMS

We used a population-based case-cohort study to assess whether polygenic risk scores (PRSs) for schizophrenia, bipolar disorder and major depression were associated with mobility from ages 10-14 years, and whether PRS and parental history of mental disorder together explained associations between mobility and each disorder.

METHOD

Information on cases (n = 4207 schizophrenia, n = 1402 bipolar disorder, n = 18 215 major depression) and a random population sample (n = 17 582), born 1981-1997, was linked between Danish civil and psychiatric registries. Genome-wide data were obtained from the Danish Neonatal Screening Biobank and PRSs were calculated based on results of separate, large meta-analyses.

RESULTS

PRSs for schizophrenia and major depression were weakly associated with moving once (odds ratio 1.07, 95% CI 1.00-1.16; and odds ratio 1.10, 95% CI 1.04-1.17, respectively), but not twice or three or more times. Mobility was positively associated with each disorder, with more moves associated with greater risk. Adjustment for PRS produced slight reductions in the magnitude of associations. Adjustment for PRS and parental history of mental disorder together reduced estimates by 5-11%. In fully adjusted models mobility was associated with all three disorders; hazard ratios ranged from 1.33 (95% CI 1.08-1.62; one move and bipolar disorder) to 3.05 (95% CI 1.92-4.86; three or more moves and bipolar disorder).

CONCLUSIONS

Associations of mobility with schizophrenia, bipolar disorder and depression do not appear to be attributable to genetic liability as measured here. Potential familial confounding of mobility associations may be predominantly environmental in nature.

Synaptic and brain-expressed gene sets relate to the shared genetic risk across five psychiatric disorders

BACKGROUND

Mounting evidence shows genetic overlap between multiple psychiatric disorders. However, the biological underpinnings of shared risk for psychiatric disorders are not yet fully uncovered. The identification of underlying biological mechanisms is crucial for the progress in the treatment of these disorders.

METHODS

We applied gene-set analysis including 7372 gene sets, and 53 tissue-type specific gene-expression profiles to identify sets of genes that are involved in the etiology of multiple psychiatric disorders. We included genome-wide meta-association data of the five psychiatric disorders schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, and attention-deficit/hyperactivity disorder. The total dataset contained 159 219 cases and 262 481 controls.

RESULTS

We identified 19 gene sets that were significantly associated with the five psychiatric disorders combined, of which we excluded five sets because their associations were likely driven by schizophrenia only. Conditional analyses showed independent effects of several gene sets that in particular relate to the synapse. In addition, we found independent effects of gene expression levels in the cerebellum and frontal cortex.

CONCLUSIONS

We obtained novel evidence for shared biological mechanisms that act across psychiatric disorders and we showed that several gene sets that have been related to individual disorders play a role in a broader range of psychiatric disorders.

Multi-trait analysis for genome-wide association study of five psychiatric disorders

We conducted a cross-trait meta-analysis of genome-wide association study on schizophrenia (SCZ) (n = 65,967), bipolar disorder (BD) (n = 41,653), autism spectrum disorder (ASD) (n = 46,350), attention deficit hyperactivity disorder (ADHD) (n = 55,374), and depression (DEP) (n = 688,809). After the meta-analysis, the number of genomic loci increased from 14 to 19 in ADHD, from 3 to 10 in ASD, from 45 to 57 in DEP, from 8 to 54 in BD, and from 64 to 87 in SCZ. We observed significant enrichment of overlapping genes among different disorders and identified a panel of cross-disorder genes. A total of seven genes were found being commonly associated with four out of five psychiatric conditions, namely GABBR1, GLT8D1, HIST1H1B, HIST1H2BN, HIST1H4L, KCNB1, and DCC. The SORCS3 gene was highlighted due to the fact that it was involved in all the five conditions of study. Analysis of correlations unveiled the existence of two clusters of related psychiatric conditions, SCZ and BD that were separate from the other three traits, and formed another group. Our results may provide a new insight for genetic basis of the five psychiatric disorders.

An Activity-Mediated Transition in Transcription in Early Postnatal Neurons

The maturation of the mammalian brain occurs after birth, and this stage of neuronal development is frequently impaired in neurological disorders, such as autism and schizophrenia. However, the mechanisms that regulate postnatal brain maturation are poorly defined. By purifying neuronal subpopulations across brain development in mice, we identify a postnatal switch in the transcriptional regulatory circuits that operates in the maturing mammalian brain. We show that this developmental transition includes the formation of hundreds of cell-type-specific neuronal enhancers that appear to be modulated by neuronal activity. Once selected, these enhancers are active throughout adulthood, suggesting that their formation in early life shapes neuronal identity and regulates mature brain function.

Using the tools of genetic epidemiology to understand sex differences in neuropsychiatric disorders

Many neuropsychiatric disorders exhibit differences in prevalence, age of onset, symptoms or course of illness between males and females. For the most part, the origins of these differences are not well understood. In this article, we provide an overview of sex differences in psychiatric disorders including autism spectrum disorder (ASD), attention deficit/hyperactivity disorder (ADHD), anxiety, depression, alcohol and substance abuse, schizophrenia, eating disorders and risk of suicide. We discuss both genetic and nongenetic mechanisms that have been hypothesized to underlie these differences, including ascertainment bias, environmental stressors, X‐ or Y‐linked risk loci, and differential liability thresholds in males and females. We then review the use of twin, family and genome‐wide association approaches to study potential genetic mechanisms of sex differences and the extent to which these designs have been employed in studies of psychiatric disorders. We describe the utility of genetic epidemiologic study designs, including classical twin and family studies, large‐scale studies of population registries, derived recurrence risks, and molecular genetic analyses of genome‐wide variation that may enhance our understanding sex differences in neuropsychiatric disorders.

A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

Variation is rampant throughout human genomes: some of it affects disease risk, and most does not; to separate the two requires a plethora of hypothesis tests. This challenge of multiple testing—limiting false positives while maximizing power—arises in many “omics” studies and sciences. One approach is to control the false discovery rate (FDR), and a recent selective inference method for controlling FDR, adaptive P-value thresholding (AdaPT), facilitates incorporation of auxiliary information (covariates) related to each hypothesis test. How AdaPT performs on data is an open question. We apply AdaPT to results from genomic association studies and include many covariates. This adaptive search discovers a more complex and interpretable model with far greater power than classic multiple testing procedures.

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptive P-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS association P values play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene–gene coexpression, captured by subnetwork (module) membership. In all, 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefrontal cortex. We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

deTS: tissue-specific enrichment analysis to decode tissue specificity

MOTIVATION

Diseases and traits are under dynamic tissue-specific regulation. However, heterogeneous tissues are often collected in biomedical studies, which reduce the power in the identification of disease-associated variants and gene expression profiles.

RESULTS

We present deTS, an R package, to conduct tissue-specific enrichment analysis with two built-in reference panels. Statistical methods are developed and implemented for detecting tissue-specific genes and for enrichment test of different forms of query data. Our applications using multi-trait genome-wide association studies data and cancer expression data showed that deTS could effectively identify the most relevant tissues for each query trait or sample, providing insights for future studies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/bsml320/deTS and CRAN https://cran.r-project.org/web/packages/deTS/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Shared polygenic risk for ADHD, executive dysfunction and other psychiatric disorders

Many psychiatric disorders are associated with impaired executive functioning (EF). The associated EF component varies by psychiatric disorders, and this variation might be due to genetic liability. We explored the genetic association between five psychiatric disorders and EF in clinically-recruited attention deficit hyperactivity disorder (ADHD) children using polygenic risk score (PRS) methodology. Genome-wide association study (GWAS) summary data for ADHD, major depressive disorder (MDD), schizophrenia (SZ), bipolar disorder (BIP) and autism were used to calculate the PRSs. EF was evaluated by the Stroop test for inhibitory control, the trail-making test for cognitive flexibility, and the digital span test for working memory in a Chinese ADHD cohort (n = 1147). Exploratory factor analysis of the three measures identified one principal component for EF (EF-PC). Linear regression models were used to analyze the association between each PRS and the EF measures. The role of EF measures in mediating the effects of the PRSs on ADHD symptoms was also analyzed. The result showed the PRSs for MDD, ADHD and BIP were all significantly associated with the EF-PC. For each EF component, the association results were different for the PRSs of the five psychiatric disorders: the PRSs for ADHD and MDD were associated with inhibitory control (adjusted P = 0.0183 and 0.0313, respectively), the PRS for BIP was associated with working memory (adjusted P = 0.0416), and the PRS for SZ was associated with cognitive flexibility (adjusted P = 0.0335). All three EF measures were significantly correlated with ADHD symptoms. In mediation analyses, the ADHD and MDD PRSs, which were associated with inhibitory control, had significant indirect effects on ADHD symptoms through the mediation of inhibitory control. These findings indicate that the polygenic risks for several psychiatric disorders influence specific executive dysfunction in children with ADHD. The results helped to clarify the relationship between risk genes of each mental disorder and the intermediate cognitive domain, which may further help elucidate the risk genes and motivate efforts to develop EF measures as a diagnostic marker and future treatment target.

Polygenic risk scores: pleiotropy and the effect of environment

Polygenic risk scores (PRSs) discriminate trait risks better than single genetic markers because they aggregate the effects of risk alleles from multiple genetic loci. Constructing pleiotropic PRSs and understanding heterogeneity, and the replication of PRS-trait associations can strengthen its applications. By using variational Bayesian multivariate high-dimensional regression, we constructed pleiotropic PRSs jointly associated with body mass index, systolic and diastolic blood pressure, total and high-density lipoprotein cholesterol in a sample of 18,108 Caucasians from three independent cohorts. We found that dissecting heterogeneity associated with birth year, which is a proxy of exogenous exposures, improved the replication of significant PRS-trait associations from 37.5% (6 of 16) in the entire sample to 90% (18 of 20) in the more homogeneous sample of individuals born before the year 1925. Our findings suggest that secular changes in exogenous exposures may substantially modify pleiotropic risk profiles affecting translation of genetic discoveries into health care.

Characterizing the Causal Pathway for Genetic Variants Associated with Neurological Phenotypes Using Human Brain-Derived Proteome Data

Leveraging high-dimensional molecular datasets can help us develop mechanistic insight into associations between genetic variants and complex traits. In this study, we integrated human proteome data derived from brain tissue to evaluate whether targeted proteins putatively mediate the effects of genetic variants on seven neurological phenotypes (Alzheimer disease, amyotrophic lateral sclerosis, depression, insomnia, intelligence, neuroticism, and schizophrenia). Applying the principles of Mendelian randomization (MR) systematically across the genome highlighted 43 effects between genetically predicted proteins derived from the dorsolateral prefrontal cortex and these outcomes. Furthermore, genetic colocalization provided evidence that the same causal variant at 12 of these loci was responsible for variation in both protein and neurological phenotype. This included genes such as DCC, which encodes the netrin-1 receptor and has an important role in the development of the nervous system (p = 4.29 × 10-11 with neuroticism), as well as SARM1, which has been previously implicated in axonal degeneration (p = 1.76 × 10-08 with amyotrophic lateral sclerosis). We additionally conducted a phenome-wide MR study for each of these 12 genes to assess potential pleiotropic effects on 700 complex traits and diseases. Our findings suggest that genes such as SNX32, which was initially associated with increased risk of Alzheimer disease, may potentially influence other complex traits in the opposite direction. In contrast, genes such as CTSH (which was also associated with Alzheimer disease) and SARM1 may make worthwhile therapeutic targets because they did not have genetically predicted effects on any of the other phenotypes after correcting for multiple testing.