Bipolar disorder (BD) is a highly heritable mental disorder and is estimated to affect about 50 million people worldwide. Our understanding of the genetic etiology of BD has greatly increased in recent years with advances in technology and methodology as well as the adoption of international consortiums and large population-based biobanks. It is clear that BD is also highly heterogeneous and polygenic and shows substantial genetic overlap with other psychiatric disorders. Genetic studies of BD suggest that the number of associated loci is expected to substantially increase in larger future studies and with it, improved genetic prediction of the disorder. Still, a number of challenges remain to fully characterize the genetic architecture of BD. First among these is the need to incorporate ancestrally-diverse samples to move research away from a Eurocentric bias that has the potential to exacerbate health disparities already seen in BD. Furthermore, incorporation of population biobanks, registry data, and electronic health records will be required to increase the sample size necessary for continued genetic discovery, while increased deep phenotyping is necessary to elucidate subtypes within BD. Lastly, the role of rare variation in BD remains to be determined. Meeting these challenges will enable improved identification of causal variants for the disorder and also allow for equitable future clinical applications of both genetic risk prediction and therapeutic interventions.

Psychiatric disorders overlap substantially at the genetic level, with family-based methods long pointing toward transdiagnostic risk pathways. Psychiatric genomics has progressed rapidly in the last decade, shedding light on the biological makeup of cross-disorder risk at multiple levels of analysis. Over a hundred genetic variants have been identified that affect multiple disorders, with many more to be uncovered as sample sizes continue to grow. Cross-disorder mechanistic studies build on these findings to cluster transdiagnostic variants into meaningful categories, including in what tissues or when in development these variants are expressed. At the upper-most level, methods have been developed to estimate the overall shared genetic signal across pairs of traits (i.e. single-nucleotide polymorphism-based genetic correlations) and subsequently model these relationships to identify overarching, genomic risk factors. These factors can subsequently be associated with external traits (e.g. functional imaging phenotypes) to begin to understand the makeup of these transdiagnostic risk factors. As psychiatric genomic efforts continue to expand, we can begin to gain even greater insight by including more fine-grained phenotypes (i.e. symptom-level data) and explicitly considering the environment. The culmination of these efforts will help to inform bottom-up revisions of our current nosology.

This short essay recounts the author’s interactions with Nick Martin in the years they both worked with Lindon Eaves at Virginia Commonwealth University. Although coming from very different academic traditions, they became close colleagues building their young careers together. Nick generously shared his statistical genetics expertise and the author taught Nick a thing or two about psychiatric illness.

Progress in identifying the genetic vulnerability factors in autism requires correct identification of the inherited phenotype(s). This can be achieved not only by the accurate description of the affected subject but also by the identification of vulnerability traits in non-affected relatives of autistic probands. This review will focus on this last strategy and principally on clinical, biochemical and cognitive traits.

The June 2016 death of our esteemed colleague, Dr Irving I. Gottesman, was felt as an extreme loss at so many levels by colleagues, students, friends, and family across the globe. Irv's stellar contributions to the field of twin research will continue to be remembered and cited for many years to come. In commemoration of his life and work, I organized a symposium at the 16th meeting of the International Society for Twin Studies, held in Madrid, Spain, November 16–18, 2017. The panelists included mostly former students, as well as colleagues, who presented their scientific research and personal remarks reflecting Irv's profound influence in shaping their lives and careers. A chronology of Irv's academic positions and honors is included in the introduction to this special issue of Twin Research and Human Genetics, followed by brief sketches of the panel participants; their scholarly papers and personal reflections follow.

Background: There continues to be significant investment in the detection of genotype × environment interaction (G × E) in psychiatric genetics. The implications of the method of assessment for the genetic analysis of psychiatric disorders are examined for simulated twin data on symptom scores and environmental covariates. Methods: Additive and independent genetic and environmental risks were simulated for 10,000 monozygotic (MZ) and 10,000 dizygotic (DZ) twin pairs and the ‘subjects’ administered typical simulated checklists of clinical symptoms and environmental factors. A variety of standard tests for G × E were applied to the simulated additive risk scores, sum scores derived from the checklists and transformed sum scores. Results: All analyses revealed no evidence for G × E for latent risk but marked evidence for G × E and other effects of modulation in the sum scores. These effects were all removed by transformation. An integrated genetic and psychometric model, accounting for both the causes of latent liability and a theory of measurement, was fitted to a sample of the simulated sum-score data and showed that there was no significant modulation of the parameters of the genetic model by environmental covariates (i.e., no G × E). Conclusions: Claims to detect G × E based on analytical methods that ignore the theory of measurement must be subjected to greater scrutiny prior to publication.

G × E in psychiatry may explain why environmental risk factors have big impact in some individuals but not in others, and conversely why relatives that are genetically at risk for disease do not all develop disease. Here we discuss two novel methods that use an aggregate genome-wide measure of genetic risk to detect G × E and estimate its effect in the population using data currently available and data we anticipate will be available in the near future. The first method exploits summary statistics from large-scale genome-wide association studies ignorant of the environmental conditions and detects G × E in an out-of-sample risk-profiling framework. The second method relies on larger samples and is based on a mixed linear model framework. It estimates variance explained directly from single nucleotide polymorphisms and environmental measures. Both methods have great potential to improve public health interventions focusing on risk-based screening that is informed by both genetic and environmental risk factors.

Nature and nurture stand in reciprocity, not opposition. Offspring inherit, along with their parents' genes, their parents, their peers, and the places they inhabit. The ontogenetic niche is a crucial link between parents and offspring, an envelope of life chances. Development is at one and the same time a social and a psychological and a biological process. If we psychiatrists allow ourselves to become mere pill pushers — or psychotherapists who fit all patients into one Procrustean bed — each as the exclusive road to mental health, we will have abandoned the duty we owe our patients and our integrity as physicians.

Objective – To present an overview on current progress and future directions in psychiatric genetics. Methods — The review of studies that have demonstrated a genetic contribution to a wide range of psychopathology using family, twin, adoption studies and exploration of the methods and limitations of molecular genetic studies. Results – Single gene disorders has been the area that is most straightforward with striking advances in disorders such as Huntington Disease and early onset familial Alzheimer disease. Complex phenotypes such as schizophrenia and affective disorder have presented greater difficulties but late onset Alzheimer disease and dyslexia are examples where replicated molecular genetic findings suggest that gene identification is feasible even for multifactorial disorders. Conclusion – The combination of increasingly complete information on the genome together with accessibility to this on the internet provide the essential tools for the search for susceptibility genes. Another essential requirement in trying to identify genes of small effect is well characterized large scale collections of cases and this demands the interaction of epidemiological and clinical researchers. Advances in genomics will also allow tailoring of Pharmaceuticals pointing at treatment response and side effects. Hopefully all this perspectives together, will improve our understanding of the neurobiological pathogenesis of diseases such as Schizophrenia, Depression and Bipolar disorder ‘legitimizing’ them in the public view.

There have been nearly 400 genome-wide association studies (GWAS) published since 2005. The GWAS approach has been exceptionally successful in identifying common genetic variants that predispose to a variety of complex human diseases and biochemical and anthropometric traits. Although this approach is relatively new, there are many excellent reviews of different aspects of the GWAS method. Here, we provide a primer, an annotated overview of the GWAS method with particular reference to psychiatric genetics. We dissect the GWAS methodology into its components and provide a brief description with citations and links to reviews that cover the topic in detail.

Given that natural selection is so powerful at optimizing complex adaptations, why does it seem unable to eliminate genes (susceptibility alleles) that predispose to common, harmful, heritable mental disorders, such as schizophrenia or bipolar disorder? We assess three leading explanations for this apparent paradox from evolutionary genetic theory: (1) ancestral neutrality (susceptibility alleles were not harmful among ancestors), (2) balancing selection (susceptibility alleles sometimes increased fitness), and (3) polygenic mutation-selection balance (mental disorders reflect the inevitable mutational load on the thousands of genes underlying human behavior). The first two explanations are commonly assumed in psychiatric genetics and Darwinian psychiatry, while mutation-selection has often been discounted. All three models can explain persistent genetic variance in some traits under some conditions, but the first two have serious problems in explaining human mental disorders. Ancestral neutrality fails to explain low mental disorder frequencies and requires implausibly small selection coefficients against mental disorders given the data on the reproductive costs and impairment of mental disorders. Balancing selection (including spatio-temporal variation in selection, heterozygote advantage, antagonistic pleiotropy, and frequency-dependent selection) tends to favor environmentally contingent adaptations (which would show no heritability) or high-frequency alleles (which psychiatric genetics would have already found). Only polygenic mutation-selection balance seems consistent with the data on mental disorder prevalence rates, fitness costs, the likely rarity of susceptibility alleles, and the increased risks of mental disorders with brain trauma, inbreeding, and paternal age. This evolutionary genetic framework for mental disorders has wide-ranging implications for psychology, psychiatry, behavior genetics, molecular genetics, and evolutionary approaches to studying human behavior.

Schizophrenia is perhaps the most debilitating mental disease and determining the underlying cause has become a challenging area of psychiatric research. It is relatively well established that genes play a role in the aetiology of schizophrenia. In this article, a review of important findings related to schizophrenia as a genetic trait will be provided, including a discussion of family, twin and adoption studies. Molecular genetic studies of specific candidate genes are then reviewed. Some controversies within the literature are examined and possible directions for future research are discussed.