The genetic architecture of psychophysiological phenotypes

Genetic Influences on Hippocampal Subfields: An Emerging Area of Neuroscience Research

There is clear evidence that hippocampal subfield volumes have partly distinct genetic determinants associated with specific biological processes. The identification of genetic correlates of hippocampal subfield volumes may help to elucidate the mechanisms of neurologic diseases, as well as aging and neurodegenerative processes. However, despite the emerging interest in this area of research, the current knowledge of the genetic architecture of hippocampal subfields has not yet been consolidated. We aimed to provide a review of the current evidence from genetic studies of hippocampal subfields, highlighting current priorities and upcoming challenges. The limited number of studies investigating the influential genetic effects on hippocampal subfields, a lack of replicated results and longitudinal designs, and modest sample sizes combined with insufficient standardization of protocols are identified as the most pressing challenges in this emerging area of research.

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

Replication in Imaging Genetics: The Case of Threat-Related Amygdala Reactivity

BACKGROUND

Low replication rates are a concern in most, if not all, scientific disciplines. In psychiatric genetics specifically, targeting intermediate brain phenotypes, which are more closely associated with putative genetic effects, was touted as a strategy leading to increased power and replicability. In the current study, we attempted to replicate previously published associations between single nucleotide polymorphisms and threat-related amygdala reactivity, which represents a robust brain phenotype not only implicated in the pathophysiology of multiple disorders, but also used as a biomarker of future risk.

METHODS

We conducted a literature search for published associations between single nucleotide polymorphisms and threat-related amygdala reactivity and found 37 unique findings. Our replication sample consisted of 1117 young adult volunteers (629 women, mean age 19.72 ± 1.25 years) for whom both genetic and functional magnetic resonance imaging data were available.

RESULTS

Of the 37 unique associations identified, only three replicated as previously reported. When exploratory analyses were conducted with different model parameters compared to the original findings, significant associations were identified for 28 additional studies: eight of these were for a different contrast/laterality; five for a different gender and/or race/ethnicity; and 15 in the opposite direction and for a different contrast, laterality, gender, and/or race/ethnicity. No significant associations, regardless of model parameters, were detected for six studies. Notably, none of the significant associations survived correction for multiple comparisons.

CONCLUSIONS

We discuss these patterns of poor replication with regard to the general strategy of targeting intermediate brain phenotypes in genetic association studies and the growing importance of advancing the replicability of imaging genetics findings.

Strategies for integrated analysis in imaging genetics studies

Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual, deepening our knowledge of the biological mechanisms behind neurodevelopmental domains and neurological disorders. Although the literature on IG has exponentially grown over the past years, the majority of studies have mainly analyzed associations between candidate brain regions and individual genetic variants. However, this strategy is not designed to deal with the complexity of neurobiological mechanisms underlying behavioral and neurodevelopmental domains. Moreover, larger sample sizes and increased multidimensionality of this type of data represents a challenge for standardizing modeling procedures in IG research. This review provides a systematic update of the methods and strategies currently used in IG studies, and serves as an analytical framework for researchers working in this field. To complement the functionalities of the Neuroconductor framework, we also describe existing R packages that implement these methodologies. In addition, we present an overview of how these methodological approaches are applied in integrating neuroimaging and genetic data.

The Leiden Family Lab study on Social Anxiety Disorder: A multiplex, multigenerational family study on neurocognitive endophenotypes

OBJECTIVES

Social anxiety disorder (SAD) is a serious and prevalent psychiatric condition, with a heritable component. However, little is known about the characteristics that are associated with the genetic component of SAD, the so-called "endophenotypes". These endophenotypes could advance our insight in the genetic susceptibility to SAD, as they are on the pathway from genotype to phenotype. The Leiden Family Lab study on Social Anxiety Disorder (LFLSAD) is the first multiplex, multigenerational study aimed to identify neurocognitive endophenotypes of social anxiety.

METHODS

The LFLSAD is characterized by a multidisciplinary approach and encompasses a variety of measurements, including a clinical interview, functional and structural magnetic resonance imaging and an electroencephalography experiment. Participants are family members from 2 generations, from families genetically enriched for SAD.

RESULTS

The sample (n = 132 participants, from 9 families) was characterized by a high prevalence of SAD, in both generations (prevalence (sub)clinical SAD: 38.3%). Furthermore, (sub)clinical SAD was positively related to self-reported social anxiety, fear of negative evaluation, trait anxiety, behavioral inhibition, negative affect, and the level of depressive symptoms.

CONCLUSIONS

By the multidimensional character of the measurements and thorough characterization of the sample, the LFLSAD offers unique opportunities to investigate candidate neurocognitive endophenotypes of SAD.

A polygenic risk score analysis of psychosis endophenotypes across brain functional, structural, and cognitive domains

This large multi-center study investigates the relationships between genetic risk for schizophrenia and bipolar disorder, and multi-modal endophenotypes for psychosis. The sample included 4,242 individuals; 1,087 patients with psychosis, 822 unaffected first-degree relatives of patients, and 2,333 controls. Endophenotypes included the P300 event-related potential (N = 515), lateral ventricular volume (N = 798), and the cognitive measures block design (N = 3,089), digit span (N = 1,437), and the Ray Auditory Verbal Learning Task (N = 2,406). Data were collected across 11 sites in Europe and Australia; all genotyping and genetic analyses were done at the same laboratory in the United Kingdom. We calculated polygenic risk scores for schizophrenia and bipolar disorder separately, and used linear regression to test whether polygenic scores influenced the endophenotypes. Results showed that higher polygenic scores for schizophrenia were associated with poorer performance on the block design task and explained 0.2% (p = 0.009) of the variance. Associations in the same direction were found for bipolar disorder scores, but this was not statistically significant at the 1% level (p = 0.02). The schizophrenia score explained 0.4% of variance in lateral ventricular volumes, the largest across all phenotypes examined, although this was not significant (p = 0.063). None of the remaining associations reached significance after correction for multiple testing (with alpha at 1%). These results indicate that common genetic variants associated with schizophrenia predict performance in spatial visualization, providing additional evidence that this measure is an endophenotype for the disorder with shared genetic risk variants. The use of endophenotypes such as this will help to characterize the effects of common genetic variation in psychosis.

Genome-wide association study of facial emotion recognition in children and association with polygenic risk for mental health disorders

Emotion recognition is disrupted in many mental health disorders, which may reflect shared genetic aetiology between this trait and these disorders. We explored genetic influences on emotion recognition and the relationship between these influences and mental health phenotypes. Eight-year-old participants (n = 4,097) from the Avon Longitudinal Study of Parents and Children (ALSPAC) completed the Diagnostic Analysis of Non-Verbal Accuracy (DANVA) faces test. Genome-wide genotype data was available from the Illumina HumanHap550 Quad microarray. Genome-wide association studies were performed to assess associations with recognition of individual emotions and emotion in general. Exploratory polygenic risk scoring was performed using published genomic data for schizophrenia, bipolar disorder, depression, autism spectrum disorder, anorexia, and anxiety disorders. No individual genetic variants were identified at conventional levels of significance in any analysis although several loci were associated at a level suggestive of significance. SNP-chip heritability analyses did not identify a heritable component of variance for any phenotype. Polygenic scores were not associated with any phenotype. The effect sizes of variants influencing emotion recognition are likely to be small. Previous studies of emotion identification have yielded non-zero estimates of SNP-heritability. This discrepancy is likely due to differences in the measurement and analysis of the phenotype.

Redefining the endophenotype concept to accommodate transdiagnostic vulnerabilities and etiological complexity

In psychopathology research, endophenotypes are a subset of biomarkers that indicate genetic vulnerability independent of clinical state. To date, an explicit expectation is that endophenotypes be specific to single disorders. We evaluate this expectation considering recent advances in psychiatric genetics, recognition that transdiagnostic vulnerability traits are often more useful than clinical diagnoses in psychiatric genetics, and appreciation for etiological complexity across genetic, neural, hormonal and environmental levels of analysis. We suggest that the disorder-specificity requirement of endophenotypes be relaxed, that neural functions are preferable to behaviors as starting points in searches for endophenotypes, and that future research should focus on interactive effects of multiple endophenotypes on complex psychiatric disorders, some of which are ‘phenocopies’ with distinct etiologies.

Enhancing the Informativeness and Replicability of Imaging Genomics Studies

Imaging genomics is a new field of investigation that seeks to gain insights into the impact of human genetic variation on the structure, chemistry, and function of neural systems in health and disease. Because publications in this field have increased over the past decade, increasing concerns have been raised about false-positive results entering the literature. Here, we provide an overview of the field of imaging genomic and genetic approaches and discuss factors related to research design and analysis that can enhance the informativeness and replicability of these studies. We conclude that imaging genetic studies can provide important insights into the role of human genetic variation on neural systems and circuits, both in the context of normal quantitative variation and in relation to neuropsychiatric disease. We also argue that demonstrating genetic association to imaging-derived traits is subject to the same constraints as any other genetic study, including stringent type I error control. Adequately powered studies are necessary; however, there are currently limited data available to allow precise estimates of effect sizes for candidate gene studies. Independent replication is necessary before a result can be considered definitive, and for studies with small sample sizes it is necessary before publication. Increased transparency of methods and enhanced data sharing will further enhance replicability.

ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide

In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.

Genetic and Environmental Factors Associated with Cannabis Involvement

Approximately 50-70% of the variation in cannabis use and use disorders can be attributed to heritable factors. For cannabis use, the remaining variance can be parsed in to familial and person-specific environmental factors while for use disorders, only the latter contribute. While numerous candidate gene studies have identified the role of common variation influencing liability to cannabis involvement, replication has been elusive. To date, no genomewide association study has been sufficiently powered to identify significant loci. Despite this, studies adopting polygenic techniques and integrating genetic variation with neural phenotypes and measures of environmental risk, such as childhood adversity, are providing promising new leads. It is likely that the small effect sizes associated with variants related to cannabis involvement will only be robustly identified in substantially larger samples. Results of such large-scale efforts will provide valuable single variant targets for translational research in neurogenetic, pharmacogenetic and non-human animal models as well as polygenic risk indices that can be used to explore a host of other genetic hypotheses related to cannabis use and misuse.

Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders--promises and limitations

BACKGROUND

Psychiatric science remains descriptive, with a categorical nosology intended to enhance interobserver reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles.

FINDINGS

A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain-behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality, and heterogeneity of neuropsychiatric data collected from multiple sources ('broad' data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits, and behaviors ('deep' data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders.

CONCLUSIONS

We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis.

Electrophysiological Endophenotypes for Schizophrenia

Endophenotypes are quantitative, heritable traits that may help to elucidate the pathophysiologic mechanisms underlying complex disease syndromes, such as schizophrenia. They can be assessed at numerous levels of analysis; here, we review electrophysiological endophenotypes that have shown promise in helping us understand schizophrenia from a more mechanistic point of view. For each endophenotype, we describe typical experimental procedures, reliability, heritability, and reported gene and neurobiological associations. We discuss recent findings regarding the genetic architecture of specific electrophysiological endophenotypes, as well as converging evidence from EEG studies implicating disrupted balance of glutamatergic signaling and GABAergic inhibition in the pathophysiology of schizophrenia. We conclude that refining the measurement of electrophysiological endophenotypes, expanding genetic association studies, and integrating data sets are important next steps for understanding the mechanisms that connect identified genetic risk loci for schizophrenia to the disease phenotype.

Longitudinal stability and predictive utility of the visual P3 response in adults with externalizing psychopathology

We determined whether time-domain P3 amplitude and time-frequency principal component (TF-PC) reductions could serve as stable and predictive developmental endophenotypes of externalizing psychopathology. Participants from the Minnesota Twin Family Study were assessed at age 17 and again at age 29 for lifetime externalizing (EXT) disorders. Comparisons of P3 amplitude and TF-PCs at delta and theta frequencies were made between EXT and unaffected comparison subjects. P3 amplitude and all five extracted TF-PCs were significantly reduced in those presenting lifetime EXT disorders at both ages 17 and 29 and showed substantial 12-year rank-order stability. P3 amplitude and delta TF-PCs measured at age 17 also predicted subsequent development of EXT by age 29, with every 1-microvolt decrease in age 17 amplitude associated with an approximately 5% increase in risk for an EXT diagnosis by age 29. Overall, results from this study further confirm that these P3-derived brain measures maintain their potential as putative EXT endophenotypes through the third decade of life.

Endophenotypes in Schizophrenia for the Perinatal Period: Criteria for Validation

Endophenotypes are disease-associated phenotypes that are thought to reflect the neurobiological or other mechanisms that underlie the more overt symptoms of a psychiatric illness. Endophenotypes have been critical in understanding the genetics, neurobiology, and treatment of schizophrenia. Because psychiatric illnesses have multiple causes, including both genetic and nongenetic risk factors, an endophenotype linked to one of the mechanisms may be expressed more frequently than the disease itself. However, in schizophrenia research, endophenotypes have almost exclusively been studied in older adolescents or adults who have entered or passed through the age of risk for the disorder. Yet, schizophrenia is a neurodevelopmental disorder where prenatal development starts a cascade of brain changes across the lifespan. Endophenotypes have only minimally been utilized to explore the perinatal development of vulnerability. One major impediment to the development of perinatally-useful endophenotypes has been the established validity criteria. For example, the criterion that the endophenotype be more frequently present in those with disease than those without is difficult to demonstrate when there can be a decades-long period between endophenotype measurement and the age of greatest risk for onset of the disorder. This article proposes changes to the endophenotype validity criteria appropriate to perinatal research and reviews how application of these modified criteria helped identify a perinatally-usable phenotype of risk for schizophrenia, P50 sensory gating, which was then used to propose a novel perinatal primary prevention intervention.

Knowns and unknowns for psychophysiological endophenotypes: integration and response to commentaries

We review and summarize seven molecular genetic studies of 17 psychophysiological endophenotypes that comprise this special issue of Psychophysiology, address criticisms raised in accompanying Perspective and Commentary pieces, and offer suggestions for future research. Endophenotypes are polygenic, and possibly influenced by rare genetic variants. Because they are not simpler genetically than clinical phenotypes, they are unlikely to assist gene discovery for psychiatric disorder. Once genetic variants for clinical phenotypes are identified, associated endophenotypes are likely to provide valuable insights into the psychological and neural mechanisms important to disorder pathology. This special issue provides a foundation for informed future steps in endophenotype genetics, including the formation of large sample consortia capable of fleshing out the many genetic variants contributing to individual differences in psychophysiological measures.