Studies of the genetics of psychiatric disorders have become one of the most exciting and fast-moving areas in human genetics. A decade ago, there were few reproducible findings, and now there ...are hundreds. In this review, we focus on the findings that have illuminated the genetic architecture of psychiatric disorders and the challenges of using these findings to inform our understanding of pathophysiology. The evidence is now overwhelming that psychiatric disorders are “polygenic”—that many genetic loci contribute to risk. With the exception of a subset of those with ASD, few individuals with a psychiatric disorder have a single, deterministic genetic cause; rather, developing a psychiatric disorder is influenced by hundreds of different genetic variants, consistent with a polygenic model. As progressively larger studies have uncovered more about their genetic architecture, the need to elucidate additional architectures has become clear. Even if we were to have complete knowledge of the genetic architecture of a psychiatric disorder, full understanding requires deep knowledge of the functional genomic architecture—the implicated loci impact regulatory processes that influence gene expression and the functional coordination of genes that control biological processes. Following from this is cellular architecture: of all brain regions, cell types, and developmental stages, where and when are the functional architectures operative? Given that the genetic architectures of different psychiatric disorders often strongly overlap, we are challenged to re-evaluate and refine the diagnostic architectures of psychiatric disorders using fundamental genetic and neurobiological data.
Genetic and genomic approaches provide new layers of insights toward the study, diagnosis, and treatment of complex psychiatric disorders
Psychiatric disorders are among the most intractable enigmas in medicine. In the past 5 years, there has been unprecedented progress on the genetics of many of these conditions. In this Review, we ...discuss the genetics of nine cardinal psychiatric disorders (namely, Alzheimer's disease, attention-deficit hyperactivity disorder, alcohol dependence, anorexia nervosa, autism spectrum disorder, bipolar disorder, major depressive disorder, nicotine dependence and schizophrenia). Empirical approaches have yielded new hypotheses about aetiology and now provide data on the often debated genetic architectures of these conditions, which have implications for future research strategies. Further study using a balanced portfolio of methods to assess multiple forms of genetic variation is likely to yield many additional new findings.
The Psychiatric GWAS Consortium was founded with the aim of conducting statistically rigorous and comprehensive GWAS meta-analyses for five major psychiatric disorders: ADHD, autism, bipolar ...disorder, major depressive disorder, and schizophrenia. In the era of GWAS and high-throughput genomics, a major trend has been the emergence of collaborative, consortia approaches. Taking advantage of the scale that collaborative consortia approaches can bring to a problem, the PGC has been a major driver in psychiatric genetics and provides a model for how similar approaches may be applied to other disease communities.
Summary Background Whether schizophrenia and bipolar disorder are the clinical outcomes of discrete or shared causative processes is much debated in psychiatry. We aimed to assess genetic and ...environmental contributions to liability for schizophrenia, bipolar disorder, and their comorbidity. Methods We linked the multi-generation register, which contains information about all children and their parents in Sweden, and the hospital discharge register, which includes all public psychiatric inpatient admissions in Sweden. We identified 9 009 202 unique individuals in more than 2 million nuclear families between 1973 and 2004. Risks for schizophrenia, bipolar disorder, and their comorbidity were assessed for biological and adoptive parents, offspring, full-siblings and half-siblings of probands with one of the diseases. We used a multivariate generalised linear mixed model for analysis of genetic and environmental contributions to liability for schizophrenia, bipolar disorder, and the comorbidity. Findings First-degree relatives of probands with either schizophrenia (n=35 985) or bipolar disorder (n=40 487) were at increased risk of these disorders. Half-siblings had a significantly increased risk (schizophrenia: relative risk RR 3·6, 95% CI 2·3–5·5 for maternal half-siblings, and 2·7, 1·9–3·8 for paternal half-siblings; bipolar disorder: 4·5, 2·7–7·4 for maternal half-siblings, and 2·4, 1·4–4·1 for paternal half-siblings), but substantially lower than that of the full-siblings (schizophrenia: 9·0, 8·5–11·6; bipolar disorder: 7·9, 7·1–8·8). When relatives of probands with bipolar disorder were analysed, increased risks for schizophrenia existed for all relationships, including adopted children to biological parents with bipolar disorder. Heritability for schizophrenia and bipolar disorder was 64% and 59%, respectively. Shared environmental effects were small but substantial (schizophrenia: 4·5%, 4·4%–7·4%; bipolar disorder: 3·4%, 2·3%–6·2%) for both disorders. The comorbidity between disorders was mainly (63%) due to additive genetic effects common to both disorders. Interpretation Similar to molecular genetic studies, we showed evidence that schizophrenia and bipolar disorder partly share a common genetic cause. These results challenge the current nosological dichotomy between schizophrenia and bipolar disorder, and are consistent with a reappraisal of these disorders as distinct diagnostic entities. Funding Swedish Council for Working Life and Social Research, and the Swedish Research Council.
There have been several recent studies addressing the genetic architecture of depression. This review serves to take stock of what is known now about the genetics of depression, how it has increased ...our knowledge and understanding of its mechanisms, and how the information and knowledge can be leveraged to improve the care of people affected. We identify four priorities for how the field of MD genetics research may move forward in future years, namely by increasing the sample sizes available for genome-wide association studies (GWASs), greater inclusion of diverse ancestries and low-income countries, the closer integration of psychiatric genetics with electronic medical records, and the development of the neuroscience toolkit for polygenic disorders.
A review by McIntosh et al. takes stock of recent rapid progress in the genetics of depression, how it has increased our mechanistic understanding, and how this information could be used to improve patient care in future.
The search for the genetic factors underlying complex neuropsychiatric disorders has proceeded apace in the past decade. Despite some advances in identifying genetic variants associated with ...psychiatric disorders, most variants have small individual contributions to risk. By contrast, disease risk increase appears to be less subtle for disease-predisposing environmental insults. In this study, we sought to identify associations between environmental pollution and risk of neuropsychiatric disorders. We present exploratory analyses of 2 independent, very large datasets: 151 million unique individuals, represented in a United States insurance claims dataset, and 1.4 million unique individuals documented in Danish national treatment registers. Environmental Protection Agency (EPA) county-level environmental quality indices (EQIs) in the US and individual-level exposure to air pollution in Denmark were used to assess the association between pollution exposure and the risk of neuropsychiatric disorders. These results show that air pollution is significantly associated with increased risk of psychiatric disorders. We hypothesize that pollutants affect the human brain via neuroinflammatory pathways that have also been shown to cause depression-like phenotypes in animal studies.
The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. ...In this context, we consider the recent “omnigenic” or “core genes” model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision medicine.
Frameworks for understanding how genes contribute to phenotypic traits have the power to shape experimental approaches and funding allocations. In contrast to the recent “omnigenic” model that emphasized contributions from a few core genes to complex disease, this Perspective argues for continued support for acquiring a broad range of patient data to link genetic variation with phenotypic diversity.
Objective:Anxiety disorders are common and often disabling. The goal of this study was to examine the genetic architecture of anxiety disorders and anxiety symptoms, which are also frequently ...comorbid with other mental disorders, such as major depressive disorder.Methods:Using one of the world’s largest biobanks including genetic, environmental, and medical information, the Million Veteran Program, the authors performed a genome-wide association study (GWAS) of a continuous trait for anxiety (based on score on the Generalized Anxiety Disorder 2-item scale GAD-2, N=199,611) as the primary analysis and self-report of physician diagnosis of anxiety disorder (N=224,330) as a secondary analysis.Results:The authors identified five genome-wide significant signals for European Americans and one for African Americans on GAD-2 score. The strongest were on chromosome 3 (rs4603973) near SATB1, a global regulator of gene expression, and on chromosome 6 (rs6557168) near ESR1, which encodes an estrogen receptor. The locus identified on chromosome 7 (rs56226325, MAF=0.17) near MAD1L1 was previously identified in GWASs of bipolar disorder and schizophrenia. The authors replicated these findings in the summary statistics of two major published GWASs for anxiety, and also found evidence of significant genetic correlation between the GAD-2 score results and previous GWASs for anxiety (rg=0.75), depression (rg=0.81), and neuroticism (rg=0.75).Conclusions:This is the largest GWAS of anxiety traits to date. The authors identified novel genome-wide significant associations near genes involved with global regulation of gene expression (SATB1) and the estrogen receptor alpha (ESR1). Additionally, the authors identified a locus (MAD1L1) that may have implications for genetic vulnerability across several psychiatric disorders. This work provides new insights into genetic risk mechanisms underpinning anxiety and related psychiatric disorders.
Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of genes and environment on human traits remain controversial. We report a ...meta-analysis of twin correlations and reported variance components for 17,804 traits from 2,748 publications including 14,558,903 partly dependent twin pairs, virtually all published twin studies of complex traits. Estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. For a majority (69%) of traits, the observed twin correlations are consistent with a simple and parsimonious model where twin resemblance is solely due to additive genetic variation. The data are inconsistent with substantial influences from shared environment or non-additive genetic variation. This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. All the results can be visualized using the MaTCH webtool.
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.
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