Schizophrenia (SZ) and bipolar disorder (BD) are severe psychiatric disorders which result from complex interplay between genetic and environmental factors. It is well-established that they are ...highly heritable disorders, and considerable progress has been made identifying their shared and distinct genetic risk factors. However, the 15-40% of risk that is derived from environmental sources is less definitively known. Environmental factors that have been repeatedly investigated and often associated with SZ include: obstetric complications, infections, winter or spring birth, migration, urban living, childhood adversity, and cannabis use. There is evidence that childhood adversity and some types of infections are also associated with BD. Evidence for other risk factors in BD is weaker due to fewer studies and often smaller sample sizes. Relatively few environmental exposures have ever been examined for SZ or BD, and additional ones likely remain to be discovered. A complete picture of how genetic and environmental risk factors confer risk for these disorders requires an understanding of how they interact. Early gene-by-environment interaction studies for both SZ and BD often involved candidate genes and were underpowered. Larger samples with genome-wide data and polygenic risk scores now offer enhanced prospects to reveal genetic interactions with environmental exposures that contribute to risk for these disorders. Overall, although some environmental risk factors have been identified for SZ, few have been for BD, and the extent to which these account for the total risk from environmental sources remains unknown. For both disorders, interactions between genetic and environmental risk factors are also not well understood and merit further investigation. Questions remain regarding the mechanisms by which risk factors exert their effects, and the ways in which environmental factors differ by sex. Concurrent investigations of environmental and genetic risk factors in SZ and BD are needed as we work toward a more comprehensive understanding of the ways in which these disorders arise.
Sequencing of gene-coding regions (the exome) is increasingly used for studying human disease, for which copy-number variants (CNVs) are a critical genetic component. However, detecting copy number ...from exome sequencing is challenging because of the noncontiguous nature of the captured exons. This is compounded by the complex relationship between read depth and copy number; this results from biases in targeted genomic hybridization, sequence factors such as GC content, and batching of samples during collection and sequencing. We present a statistical tool (exome hidden Markov model XHMM) that uses principal-component analysis (PCA) to normalize exome read depth and a hidden Markov model (HMM) to discover exon-resolution CNV and genotype variation across samples. We evaluate performance on 90 schizophrenia trios and 1,017 case-control samples. XHMM detects a median of two rare (<1%) CNVs per individual (one deletion and one duplication) and has 79% sensitivity to similarly rare CNVs overlapping three or more exons discovered with microarrays. With sensitivity similar to state-of-the-art methods, XHMM achieves higher specificity by assigning quality metrics to the CNV calls to filter out bad ones, as well as to statistically genotype the discovered CNV in all individuals, yielding a trio call set with Mendelian-inheritance properties highly consistent with expectation. We also show that XHMM breakpoint quality scores enable researchers to explicitly search for novel classes of structural variation. For example, we apply XHMM to extract those CNVs that are highly likely to disrupt (delete or duplicate) only a portion of a gene.
Objective:Both rare copy number variants (CNVs) and common single-nucleotide polymorphisms (SNPs) contribute to liability to schizophrenia, but their etiological relationship has not been fully ...elucidated. The authors evaluated an additive model whereby risk of schizophrenia requires less contribution from common SNPs in the presence of a rare CNV, and tested for interactions.Method:Genetic data from 21,094 case subjects with schizophrenia and 20,227 control subjects from the Psychiatric Genomics Consortium were examined. Three classes of rare CNVs were assessed: CNVs previously associated with schizophrenia, CNVs with large deletions ≥500 kb, and total CNV burden. The mean polygenic risk scores (PRSs) between study subjects with and without rare CNVs were compared, and joint effects of PRS and CNVs on schizophrenia liability were modeled by using logistic regression.Results:Schizophrenia case subjects carrying risk CNVs had a lower polygenic risk than case subjects without risk CNVs but a higher risk than control subjects. For case subjects carrying known risk CNVs, the PRS was diminished in proportion to the effect size of the CNV. The strongly associated 22q11.2 deletion required little added PRS to produce schizophrenia. Large deletions and increased CNV burden were also associated with lower polygenic risk in schizophrenia case subjects but not in control subjects or after removal of known risk CNV carriers.Conclusions:The authors found evidence for interactive effects of PRS and previously associated CNVs for risk for schizophrenia, and the results for large deletions and total CNV burden support an additive model. These findings offer insights into the genetic architecture of schizophrenia by illuminating how different established genetic risk factors act and interact to influence liability to schizophrenia.
Objectives
Bipolar disorder (BPD) shares genetic components with other psychiatric disorders; however, uncertainty remains about where in the psychiatric spectra BPD falls. To understand the etiology ...of BPD, we studied the familial aggregation of BPD and co‐aggregation between BPD and schizophrenia, depression, anxiety disorders, attention‐deficit hyperactivity disorder, drug abuse, personality disorders, and autism spectrum disorders.
Methods
A population‐based cohort was created by linking several Swedish national registers. A total of 54,723 individuals with BPD were identified among 8,141,033 offspring from 4,149,748 nuclear families. The relative risk of BPD in relatives and the co‐occurrence of other psychiatric disorders in patients with BPD and their relatives were compared to those of matched‐population controls. Structural equation modeling was used to estimate the heritability and tetrachoric correlation.
Results
The familial risks for relatives of BPD probands were 5.8–7.9 in first‐degree relatives, and decreased with genetic distance. Co‐occurrence risks for other psychiatric disorders were 9.7–22.9 in individuals with BPD and 1.7–2.8 in full siblings of BPD probands. Heritability for BPD was estimated at 58%. The correlations between BPD and other psychiatric disorders were considerable (0.37–0.62) and primarily due to genetic effects. The correlation with depression was the highest (0.62), and was 0.44 for schizophrenia.
Conclusions
The high familial risks provide evidence that genetic factors play an important role in the etiology of BPD, and the shared genetic determinants suggest pleiotropic effects across different psychiatric disorders. Results also indicate that BPD is in both the mood and psychotic spectra, but possibly more closely related to mood disorders.
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.
The relative proportions of genetic and environmental variance in behavioral measures have been studied extensively. A growing body of literature has examined changes in heritability measures over ...time, but we are unaware of any prior efforts to assess developmental heritability changes for multiple behavioral phenotypes using multiple data sources. We have chosen to explore the proportional genetic influences on a variety of behaviors during the genetically and environmentally labile adolescent and young adult years. This meta-analysis examined 8 behavioral domains and incorporated only primary research articles reporting two or more heritability time points in order to minimize the age-to-age error variability. Linear regression analyses revealed significant cross-time heritability increases for externalizing behaviors, anxiety symptoms, depressive symptoms, IQ, and social attitudes and nonsignificant increases for alcohol consumption, and nicotine initiation, but no evidence of heritability changes for attention-deficit/hyperactivity disorder. A variety of mechanisms may underlie these findings including the rising importance of active genotype-environment correlation, an increase in gene expression, or proportional reductions in environmental variance. Additional longitudinal studies and the inclusion of measures of total variance in primary research reports will aid in distinguishing between these possibilities. Further studies exploring heritability changes beyond young adulthood would also benefit our understanding of factors influencing heritability of behavioral traits over the lifespan.
Objective:Conclusions regarding lithium’s antisuicidal effect for bipolar disorder have been limited due to nonrepresentative subjects and potential confounding factors, including varying severity of ...illness. Findings regarding the effect of valproate, the most common alternative to lithium, are inconsistent for suicidal behavior. This study investigated the associations of these two drugs with the risk of suicide-related events, and possible differences between drugs, by using within-individual designs in a register-based longitudinal cohort.Method:Through linkage of multiple Swedish national registers, 51,535 individuals with bipolar disorder were followed from 2005 to 2013 for treatment with lithium and valproate. Stratified Cox regression was used to estimate the hazard ratios of suicide-related events during treated periods compared with untreated periods. For significant associations between medication and suicide-related events, the population attributable fraction was estimated to assess the public health impact for patients with bipolar disorder.Results:During follow-up, 10,648 suicide-related events occurred. The incidence rate was significantly decreased by 14% during lithium treatment (hazard ratio 0.86, 95% confidence interval CI 0.78–0.95) but not during valproate treatment (hazard ratio 1.02, 95% CI 0.89–1.15). The difference in hazard ratios of suicide-related events between lithium and valproate was statistically significant. Estimates of the population attributable fraction suggested that 12% (95% CI 4%−20%) of suicide-related events could have been avoided if patients had taken lithium during the entire follow-up.Conclusions:The results suggest that lithium should be considered for patients with bipolar disorder with suspected suicidal intentions, although risk for suicide is only one of the considerations when providing clinical care.
Objective:To evaluate previously reported associations of copy number variants (CNVs) with schizophrenia and to identify additional associations, the authors analyzed CNVs in the Molecular Genetics ...of Schizophrenia study (MGS) and additional available data.
Method:After quality control, MGS data for 3,945 subjects with schizophrenia or schizoaffective disorder and 3,611 screened comparison subjects were available for analysis of rare CNVs (<1% frequency). CNV detection thresholds were chosen that maximized concordance in 151 duplicate assays. Pointwise and genewise analyses were carried out, as well as analyses of previously reported regions. Selected regions were visually inspected and confirmed with quantitative polymerase chain reaction.
Results:In analyses of MGS data combined with other available data sets, odds ratios of 7.5 or greater were observed for previously reported deletions in chromosomes 1q21.1, 15q13.3, and 22q11.21, duplications in 16p11.2, and exon-disrupting deletions in NRXN1. The most consistently supported candidate associations across data sets included a 1.6-Mb deletion in chromosome 3q29 (21 genes, TFRC to BDH1) that was previously described in a mild-moderate mental retardation syndrome, exonic duplications in the gene for vasoactive intestinal peptide receptor 2 (VIPR2), and exonic duplications in C16orf72. The case subjects had a modestly higher genome-wide number of gene-containing deletions (>100 kb and >1 Mb) but not duplications.
Conclusions:The data strongly confirm the association of schizophrenia with 1q21.1, 15q13.3, and 22q11.21 deletions, 16p11.2 duplications, and exonic NRXN1 deletions. These CNVs, as well as 3q29 deletions, are also associated with mental retardation, autism spectrum disorders, and epilepsy. Additional candidate genes and regions, including VIPR2, were identified. Study of the mechanisms underlying these associations should shed light on the pathophysiology of schizophrenia.
IMPORTANCE: Early-onset depression has been linked to poor health outcomes. However, it is unclear the extent to which this disorder is associated with specific diseases and premature death and ...whether these associations remain after controlling for psychiatric comorbidity. OBJECTIVE: To quantify the association of youth depression with subsequent diagnoses of numerous somatic diseases and mortality. DESIGN, SETTING, AND PARTICIPANTS: A population-based cohort study was conducted using Swedish national registers containing data on all individuals born in Sweden between 1982 and 1996. A total of 1 487 964 participants were followed up from age 5 years through 2013 if no censoring occurred. Data analysis was performed from January 15, 2019, to August 10, 2020. EXPOSURES: Youth depression was defined as having received at least 1 diagnosis of depression from inpatient or outpatient care between ages 5 and 19 years. MAIN OUTCOMES AND MEASURES: This study examined 69 somatic conditions diagnosed after youth depression, as well as all-cause and cause-specific mortalities. Overall and sex-specific hazard ratios (HRs), together with 95% CIs, were estimated using Cox proportional hazards regression with attained age as underlying timescale and time-varying exposure, and adjusted for birth year and sex. All analyses were repeated controlling for psychiatric comorbidities. Absolute risk differences were calculated using standardization with Cox proportional hazards regression. RESULTS: Of 1 487 964 individuals included in the analysis, 51.2% were male. A total of 37 185 patients (2.5%; 67.4% female) had an inpatient or outpatient contact for depression between ages 5 and 19 years (mean SD age at first recorded diagnosis of depression, 16.7 2.1 years for males and 16.7 1.8 years for females). Age at the end of follow-up ranged between 17 and 31 years. Individuals with youth depression had higher relative risks for 66 of the 69 somatic diagnoses. Strong associations were observed for certain injuries, especially self-harm in females (HR, 14.4; 95% CI, 13.8-15.1), sleep disorders (HR, 8.1; 95% CI, 7.6-8.7), viral hepatitis (HR, 6.1; 95% CI, 5.4-6.8), all-cause mortality (HR, 5.9; 95% CI, 5.3-6.6), and cause-specific mortalities, especially death by intentional self-harm (HR, 14.6; 95% CI, 12.6-16.9). Most associations were attenuated but persisted after adjusting for psychiatric comorbidity. The absolute risk difference of a specific disease within 12 years from the first diagnosis of depression during youth ranged from −0.2% (95% CI, −1.0% to 0.6%) for arthropathies among males to 23.9% (95% CI, 22.7%-25.0%) for the broader category of injuries among females. CONCLUSIONS AND RELEVANCE: In this Swedish population cohort study, patients with depression diagnosed during their youth appeared to have increased risks for many somatic diseases as well as for mortality, even after controlling for other psychiatric disorders. These findings suggest that several medical conditions should be considered when investigating youth depression.
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