Endophenotypes in the Genetic Analyses of Mental Disorders

Endophenotyping social cognition in the broader autism phenotype

Relatives of individuals with autism spectrum disorder (ASD) may display milder social traits of the broader autism phenotype (BAP) providing potential endophenotypic markers of genetic risk for ASD. We performed a case-control comparison to quantify social cognition and pragmatic language difficulties in the BAP (n = 25 cases; n = 33 controls) using the Faux Pas test (FPT) and the Goldman-Eisler Cartoon task. Using deep phenotyping we then examined patterns of inheritance of social cognition in two large multiplex families and the spectrum of performance in 32 additional families (159 members; n = 51 ASD, n = 87 BAP, n = 21 unaffected). BAP individuals showed significantly poorer FPT performance and reduced verbal fluency with the absence of a compression effect in social discourse compared to controls. In multiplex families, we observed reduced FPT performance in 89% of autistic family members, 63% of BAP relatives and 50% of unaffected relatives. Across all affected families, there was a graded spectrum of difficulties, with ASD individuals showing the most severe FPT difficulties, followed by the BAP and unaffected relatives compared to community controls. We conclude that relatives of probands show an inherited pattern of graded difficulties in social cognition with atypical faux pas detection in social discourse providing a novel candidate endophenotype for ASD.

Unraveling metabolic stress response in dairy cows: genetic control of plasma biomarkers throughout lactation and the transition period

Breeding animals able to effectively respond to stress could be a long-term, sustainable, and affordable strategy to improve resilience and welfare in livestock systems. In the present study, the concentrations of 29 plasma biomarkers were used as candidate endophenotypes for metabolic stress response in single-SNP, gene- and haplotype-based GWAS using 739 healthy lactating Italian Holstein cows and 88,271 variants. Significant genetic associations were found in all the 3 GWAS approaches for plasma γ-glutamyl transferase concentration on BTA17, for paraoxonase on BTA4, and for alkaline phosphatase and zinc on BTA2. On these chromosomes, single-SNP and gene-based chromosome-wide association studies were performed, confirming GWAS findings. The signals identified for paraoxonase, γ-glutamyl transferase, and alkaline phosphatase were in proximity of the genes coding for them. The heritability of these 4 biomarkers ranged from moderate to high (from 0.39 to 0.54). Plasma biomarkers are known to undergo large changes in concentration during metabolic stress in the transition period, with an inter-individual variability in the rate of change and recovery time. Genetics may account in part for these differences. To assess this, we studied a subset of 139 periparturient cows homozygous at 3 SNPs known to be respectively associated with concentration of plasma ceruloplasmin, paraoxonase and γ-glutamyl transferase. We compared the immune-metabolic profile measured in plasma at -7, +5 and +30 d relative to calving between groups of opposite homozygotes. A significant effect of the genotype was found on paraoxonase and γ-glutamyl transferase plasma concentration at all the 3 time points. No evidence for genotype effect was detected for ceruloplasmin. Understanding the genetic control underlying metabolic stress response may suggest new approaches to foster resilience in dairy cows.

Gray matter volumetric correlates of the polygenic risk of depression: A study of the Human Connectome Project data

Genetic factors confer risks for depression. Understanding the neural endophenotypes, including brain morphometrics, of genetic predisposition to depression would help in unraveling the pathophysiology of depression. We employed voxel-based morphometry (VBM) to examine how gray matter volumes (GMVs) were correlated with the polygenic risk score (PRS) for depression in 993 young adults of the Human Connectome Project. The phenotype of depression was quantified with a DSM-oriented scale of the Achenbach Adult Self-Report. The PRS for depression was computed for each subject using the Psychiatric Genomics Association Study as the base sample. In multiple regression with age, sex, race, drinking severity, and total intracranial volume as covariates, regional GMVs in positive correlation with the PRS were observed in bilateral hippocampi and right gyrus rectus. Regional GMVs in negative correlation with the PRS were observed in a wide swath of brain regions, including bilateral frontal and temporal lobes, anterior cingulate cortex, thalamus, lingual gyri, cerebellum, and the left postcentral gyrus, cuneus, and parahippocampal gyrus. We also found sex difference in anterior cingulate volumes in manifesting the genetic risk of depression. In addition, the GMV of the right cerebellum crus I partially mediated the link from PRS to depression severity. These findings add to the literature by highlighting 1) a more diverse pattern of the volumetric markers of depression, with most regions showing lower but others higher GMVs in association with the genetic risks of depression, and 2) the cerebellar GMV as a genetically informed neural phenotype of depression, in neurotypical individuals.

Understanding copy number variations through their genes: a molecular view on 16p11.2 deletion and duplication syndromes

Neurodevelopmental disorders (NDDs) include a broad spectrum of pathological conditions that affect >4% of children worldwide, share common features and present a variegated genetic origin. They include clinically defined diseases, such as autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder (ADHD), motor disorders such as Tics and Tourette's syndromes, but also much more heterogeneous conditions like intellectual disability (ID) and epilepsy. Schizophrenia (SCZ) has also recently been proposed to belong to NDDs. Relatively common causes of NDDs are copy number variations (CNVs), characterised by the gain or the loss of a portion of a chromosome. In this review, we focus on deletions and duplications at the 16p11.2 chromosomal region, associated with NDDs, ID, ASD but also epilepsy and SCZ. Some of the core phenotypes presented by human carriers could be recapitulated in animal and cellular models, which also highlighted prominent neurophysiological and signalling alterations underpinning 16p11.2 CNVs-associated phenotypes. In this review, we also provide an overview of the genes within the 16p11.2 locus, including those with partially known or unknown function as well as non-coding RNAs. A particularly interesting interplay was observed between MVP and MAPK3 in modulating some of the pathological phenotypes associated with the 16p11.2 deletion. Elucidating their role in intracellular signalling and their functional links will be a key step to devise novel therapeutic strategies for 16p11.2 CNVs-related syndromes.

Influence of BDNF Val66Met genetic polymorphism in Major Depressive Disorder and Body Mass Index: Evidence from a meta-analysis of 6481 individuals

BACKGROUND

Major depressive disorder (MDD) and obesity are global health problems that frequently co-occur. Among shared etiological factors, genetic variation at the brain-derived neurotrophic factor (BDNF) gene is interesting since its implication in energy balance regulation, food intake and synaptic function. Thus, the aim of this study was to investigate the influence of the BDNF Val66Met polymorphism in relation to MDD and body mass index (BMI) in two large independent cohorts.

METHODS

The sample consisted of 2646 individuals with MDD and 3835 controls from the PISMA-ep and Radiant studies. Linear regressions were performed to test the association between the polymorphism and BMI and the interaction between the polymorphism and MDD on BMI. A meta-analysis across cohorts was conducted.

RESULTS

No association was found between the polymorphism and BMI. However, we found an association with MDD, showing these individuals higher BMI than controls in both cohorts. No differences were found in BMI depending on Val66Met genotype and no interaction between this polymorphism and MDD in relation to BMI was found. Although a tendency towards an interaction was found in the Radiant sample, the results of the meta-analysis did not support this finding.

LIMITATIONS

The use of self-reported height and weight measures to calculate BMI values.

CONCLUSIONS

We provide evidence for an association between BMI and MDD confirming previous results. Our meta-analysis including two large cohorts showed no interaction between BDNF, BMI and MDD. Future studies will be needed to confirm the role of this polymorphism in the relationship between BMI and MDD.

Evolution, the Cognitive Sciences, and the Science of Victimization

Despite clear aversion to such labels, one of the most impactful criminological theories is rooted in cognitive science. Gottfredson and Hirschi's general theory has been repeatedly tested, replicated relatively well, and has since reached beyond its original scope to explain other important outcomes like victimization. However, the work never viewed itself as part of a larger scientific landscape and resisted the incursion of neuroscience, cognitive science, and evolutionary theory from the start. This missed opportunity contributes to some of the theory's shortcomings. We begin by considering relevant literatures that were originally excluded and then conduct a new analysis examining the cognitive underpinnings of victimization in a high-risk sample of adolescents. We used the Future of Families and Child Wellbeing Study (n = 3,444; 48% female; 49% Black, 25% Hispanic) which contained sound measures of self-control and intelligence, as well as four types of adolescent victimization. Self-control was robustly associated with all forms of victimization, whereas intelligence had generally no detectable effect. We discuss how these findings fit into a broader understanding about self-control and victimization.

Psychopathology in children: The transdiagnostic contribution of affiliative capacity and inhibitory control

Recent initiatives have focused on integrating transdiagnostic biobehavioral processes or dispositions with dimensional models of psychopathology. Toward this goal, biobehavioral traits of affiliative capacity (AFF) and inhibitory control (INH) hold particular promise as they demonstrate transdiagnostic stability and predictive validity across developmental stages and differing measurement modalities. The current study employed data from different modes of measurement in a sample of 1830 children aged 5-10 years to test for associations of AFF and INH, individually and interactively, with broad dimensions of psychopathology. Low AFF, assessed via parent-report, evidenced predictive relations with distress- and externalizing-related problems. INH as assessed by cognitive-task performance did not relate itself to either psychopathology dimension, but it moderated the effects observed for low AFF, such that high INH protected against distress symptoms in low-AFF participants, whereas low INH amplified distress and externalizing symptoms in low-AFF participants. Results are discussed in the context of the interface of general trait transdiagnostic risk factors with quantitatively derived dimensional models of psychopathology.

Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability

Disease heterogeneity poses a significant challenge for precision diagnostics in both clinical and sub-clinical stages. Recent work leveraging artificial intelligence (AI) has offered promise to dissect this heterogeneity by identifying complex intermediate phenotypes - herein called dimensional neuroimaging endophenotypes (DNEs) - which subtype various neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2)1, autism spectrum disorder (ASD1-3)2, late-life depression (LLD1-2)3, and schizophrenia (SCZ1-2)4, in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×1-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs and their polygenic risk scores significantly improved the prediction accuracy for 14 systemic disease categories and mortality. These findings underscore the potential of the nine DNEs to identify individuals at a high risk of developing the four brain diseases during preclinical stages for precision diagnostics. All results are publicly available at: http://labs.loni.usc.edu/medicine/.

Genome- and Exome-Wide Association Studies Revealed Candidate Genes Associated with DaTscan Imaging Features

Introduction

Despite remarkable progress in identifying Parkinson's disease (PD) genetic risk loci, the genetic basis of PD remains largely unknown. With the help of the endophenotype approach and using data from dopamine transporter single-photon emission computerized tomography (DaTscan), we identified potentially involved genes in PD.

Method

We conducted an imaging genetic study by performing exome-wide association study (EWAS) and genome-wide association study (GWAS) on the specific binding ratio (SBR) of six DaTscan anatomical areas between 489 and 559 subjects of Parkinson's progression markers initiative (PPMI) cohort and 83,623 and 36,845 single-nucleotide polymorphisms (SNPs)/insertion-deletion mutations (INDELs). We also investigated the association of cerebrospinal fluid (CSF) protein concentration of our significant genes with PD progression using PPMI CSF proteome data.

Results

Among 83,623 SNPs/INDELs in EWAS, one SNP (rs201465075) on 1 q32.1 locus was significantly (P value = 4.03 × 10-7) associated with left caudate DaTscan SBR, and 33 SNPs were suggestive. Among 36,845 SNPs in GWAS, one SNP (rs12450112) on 17 p.12 locus was significantly (P value = 1.34 × 10-6) associated with right anterior putamen DaTscan SBR, and 39 SNPs were suggestive among which 8 SNPs were intergenic. We found that rs201465075 and rs12450112 are most likely related to IGFN1 and MAP2K4 genes. The protein level of MAP2K4 in the CSF was significantly associated with PD progression in the PPMI cohort; however, proteomic data were not available for the IGFN1 gene.

Conclusion

We have shown that particular variants of IGFN1 and MAP2K4 genes may be associated with PD. Since DaTscan imaging could be positive in other Parkinsonian syndromes, caution should be taken when interpreting our results. Future experimental studies are also needed to verify these findings.

What are quantitative traits and how can they be used in autism research?

Quantitative traits are measurable characteristics distributed along a continuous scale thought to relate to underlying biology. There is growing interest in the use of quantitative traits in behavioral and psychiatric research, particularly in research on conditions diagnosed based on reports of behaviors, including autism. This brief commentary describes quantitative traits, including defining what they are, how we can measure them, and key considerations for their use in autism research. Examples of measures include behavioral report scales like the Social Responsiveness Scale and Broader Autism Phenotype Questionnaire, as well as biological measurements, like certain neuroimaging metrics; such measures can capture quantitative traits or constructs like the broader autism phenotype, social communication, and social cognition. Quantitative trait measures align with the Research Domain Criteria (RDoC) approach and can be used in autism research to help gain a better understanding of causal pathways and biological processes. They can also be used to aid identification of genetic and environmental factors involved in such pathways, and thereby lead to an understanding of influences on traits across the entire population. Finally, in some cases, they may be used to gauge treatment response, and assist screening and clinical characterization of phenotype. In addition, practical benefits of quantitative trait measures include improved statistical power relative to categorical classifications and (for some measures) efficiency. Ultimately, research across autism fields may benefit from incorporating quantitative trait measures as a complement to categorical diagnosis to advance understanding of autism and neurodevelopment.

Shared genetic liability for alcohol consumption, alcohol problems, and suicide attempt: Evaluating the role of impulsivity

Heavy drinking and diagnosis with alcohol use disorder (AUD) are consistently associated with risk for suicide attempt (SA). Though the shared genetic architecture among alcohol consumption and problems (ACP) and SA remains largely uncharacterized, impulsivity has been proposed as a heritable, intermediate phenotype for both alcohol problems and suicidal behavior. The present study investigated the extent to which shared liability for ACP and SA is genetically related to five dimensions of impulsivity. Analyses incorporated summary statistics from genome-wide association studies of alcohol consumption (N = 160,824), problems (N = 160,824), and dependence (N = 46,568), alcoholic drinks per week (N = 537,349), suicide attempt (N = 513,497), impulsivity (N = 22,861), and extraversion (N = 63,030). We used genomic structural equation modeling (Genomic SEM) to, first, estimate a common factor model with alcohol consumption, problems, and dependence, drinks per week, and SA included as indicators. Next, we evaluated the correlations between this common genetic factor and five factors representing genetic liability to negative urgency, positive urgency, lack of premeditation, sensation-seeking, and lack of perseverance. Common genetic liability to ACP and SA was significantly correlated with all five impulsive personality traits examined (rs = 0.24-0.53, ps < 0.002), and the largest correlation was with lack of premeditation, though supplementary analyses suggested that these findings were potentially more strongly influenced by ACP than SA. These analyses have potential implications for screening and prevention: Impulsivity can be comprehensively assessed in childhood, whereas heavy drinking and suicide attempt are quite rare prior to adolescence. Our findings provide preliminary evidence that features of impulsivity may serve as early indicators of genetic risk for alcohol problems and suicidality.

Pathways to depression: Dynamic associations between neural responses to appetitive cues in the environment, stress, and the development of illness

This review focuses on research my colleagues and I have conducted on etiological pathways to depression. Much of this work has focused on the measurement of neural responses to appetitive cues, using two event-related brain potential (ERP) components, the Late Positive Potential (LPP) and the Reward Positivity (RewP). Reductions in each of these components have been associated with current symptoms of depression, and in some cases have been shown to differentiate anxious from depressive phenotypes. In this review, I will describe three broad and related approaches we have taken in our research to address a series of interdependent issuess. The first attempts to understand different sources of variation in the LPP and RewP, and how these sources interact with one another. The second tries to identify whether variation in the processes measured by these ERP components might reflect a latent vulnerability to depression and its symptoms, that is evident prior to illness onset. And the third examines the possibility that the processes reflected in the LPP and RewP might play a mechanistic role in the development of depression.

Genetic Influences on Cognitive Dysfunction in Schizophrenia

Schizophrenia is a severe and debilitating psychotic disorder that is highly heritable and relatively common in the population. The clinical heterogeneity associated with schizophrenia is substantial, with patients exhibiting a broad range of deficits and symptom severity. Large-scale genomic studies employing a case-control design have begun to provide some biological insight. However, this strategy combines individuals with clinically diverse symptoms and ignores the genetic risk that is carried by many clinically unaffected individuals. Consequently, the majority of the genetic architecture underlying schizophrenia remains unexplained, and the pathways by which the implicated variants contribute to the clinically observable signs and symptoms are still largely unknown. Parsing the complex, clinical phenotype of schizophrenia into biologically relevant components may have utility in research aimed at understanding the genetic basis of liability. Cognitive dysfunction is a hallmark symptom of schizophrenia that is associated with impaired quality of life and poor functional outcome. Here, we examine the value of quantitative measures of cognitive dysfunction to objectively target the underlying neurobiological pathways and identify genetic variants and gene networks contributing to schizophrenia risk. For a complex disorder, quantitative measures are also more efficient than diagnosis, allowing for the identification of associated genetic variants with fewer subjects. Such a strategy supplements traditional analyses of schizophrenia diagnosis, providing the necessary biological insight to help translate genetic findings into actionable treatment targets. Understanding the genetic basis of cognitive dysfunction in schizophrenia may thus facilitate the development of novel pharmacological and procognitive interventions to improve real-world functioning.

Biomarkers of Frailty: miRNAs as Common Signatures of Impairment in Cognitive and Physical Domains

The past years have seen an increasing concern about frailty, owing to the growing number of elderly people and the major impact of this syndrome on health and social care. The identification of frail people passes through the use of different tests and biomarkers, whose concerted analysis helps to stratify the populations of patients according to their risk profile. However, their efficiency in prognosis and their capability to reflect the multisystemic impairment of frailty is discussed. Recent works propose the use of miRNAs as biological hallmarks of physiological impairment in different organismal districts. Changes in miRNAs expression have been described in biological processes associated with phenotypic outcomes of frailty, opening intriguing possibilities for their use as biomarkers of fragility. Here, with the aim of finding reliable biomarkers of frailty, while considering its complex nature, we revised the current literature on the field, for uncovering miRNAs shared across physical and cognitive frailty domains. By applying in silico analyses, we retrieved the top-ranked shared miRNAs and their targets, finally prioritizing the most significant ones. From this analysis, ten miRNAs emerged which converge into two main biological processes: inflammation and energy homeostasis. Such markers, if validated, may offer promising capabilities for early diagnosis of frailty in the elderly population.

Feasibility and application of polygenic score analysis to the morphology of human-induced pluripotent stem cells

Genome-wide association studies have identified thousands of significant associations between genetic variants and complex traits. Inferring biological insights from these associations has been challenging. One approach attempted has been to examine the effects of individual variants in cellular models. Here, I demonstrate the feasibility of examining the aggregate effect of many variants on cellular phenotypes. I examine the effects of polygenic scores for cross-psychiatric disorder risk, schizophrenia, body mass index and height on cellular morphology, using 1.5 million induced pluripotent stem cells (iPSC) from 60 European-ancestry donors from the Human iPSC Initiative dataset. I show that measuring multiple cells per donor provides sufficient power for polygenic score analyses, and that cross-psychiatric disorder risk is associated with cell area (p = 0.004). Combined with emerging methods of high-throughput iPSC phenotyping, cellular polygenic scoring is a promising method for understanding potential biological effects of the polygenic component of complex traits.

Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits

The expanding use of the phenome-wide association study (PheWAS) faces challenges in the context of using International Classification of Diseases billing codes for phenotype definition, imbalanced study population ethnicity, and constrained application of the results in research. We performed a PheWAS utilizing 136 deep phenotypes corroborated by comprehensive health check-ups in a Korean population, along with trans-ethnic comparisons through using the UK Biobank and Biobank Japan Project. Meta-analysis with Korean and Japanese population was done. The PheWAS associated 65 phenotypes with 14,101 significant variants (P < 4.92 × 10-10). Network analysis, visualization of cross-phenotype mapping, and causal inference mapping with Mendelian randomization were conducted. Among phenotype pairs from the genotype-driven cross-phenotype associations, we evaluated penetrance in correlation analysis using a clinical database. We focused on the application of PheWAS in order to make it robust and to aid the derivation of biological meaning post-PheWAS. This comprehensive analysis of PheWAS results based on a health check-up database will provide researchers and clinicians with a panoramic overview of the networks among multiple phenotypes and genetic variants, laying groundwork for the practical application of precision medicine.

Relationship between impulsivity, uncontrolled eating and body mass index: a hierarchical model

BACKGROUND

Impulsivity increases the risk for obesity and weight gain. However, the precise role of impulsivity in the aetiology of overeating behavior and obesity is currently unknown. Here we examined the relationships between personality-related measures of impulsivity, Uncontrolled Eating, body mass index (BMI), and longitudinal weight changes. In addition, we analyzed the associations between general impulsivity domains and cortical thickness to elucidate brain vulnerability factors related to weight gain.

METHODS

Students (N = 2318) in their first year of university-a risky period for weight gain-completed questionnaire measures of impulsivity and eating behavior at the beginning of the school year. We also collected their weight at the end of the term (N = 1177). Impulsivity was divided into three factors: stress reactivity, reward sensitivity and lack of self-control. Using structural equation models, we tested a hierarchical relationship, in which impulsivity traits were associated with Uncontrolled Eating, which in turn predicted BMI and weight change. Seventy-one participants underwent T1-weighted MRI to investigate the correlation between impulsivity and cortical thickness.

RESULTS

Impulsivity traits showed positive correlations with Uncontrolled Eating. Higher scores in Uncontrolled Eating were in turn associated with higher BMI. None of the impulsivity-related measurements nor Uncontrolled Eating were correlated with longitudinal weight gain. Higher stress sensitivity was associated with increased cortical thickness in the superior temporal gyrus. Lack of self-control was positively associated with increased thickness in the superior medial frontal gyrus. Finally, higher reward sensitivity was associated with lower thickness in the inferior frontal gyrus.

CONCLUSION

The present study provides a comprehensive characterization of the relationships between different facets of impulsivity and obesity. We show that differences in impulsivity domains might be associated with BMI via Uncontrolled Eating. Our results might inform future clinical strategies aimed at fostering self-control abilities to prevent and/or treat unhealthy weight gain.

Working memory and reaction time variability mediate the relationship between polygenic risk and ADHD traits in a general population sample

Endophenotypes are heritable and quantifiable traits indexing genetic liability for a disorder. Here, we examined three potential endophenotypes, working memory function, response inhibition, and reaction time variability, for attention-deficit hyperactivity disorder (ADHD) measured as a dimensional latent trait in a large general population sample derived from the Adolescent Brain Cognitive Development^SM Study. The genetic risk for ADHD was estimated using polygenic risk scores (PRS) whereas ADHD traits were quantified as a dimensional continuum using Bartlett factor score estimates, derived from Attention Problems items from the Child Behaviour Checklist and Effortful Control items from the Early Adolescent Temperament Questionnaire-Revised. The three candidate cognitive endophenotypes were quantified using task-based performance measures. Higher ADHD PRSs were associated with higher ADHD traits, as well as poorer working memory performance and increased reaction time variability. Lower working memory performance, poorer response inhibition, and increased reaction time variability were associated with more pronounced ADHD traits. Working memory and reaction time variability partially statistically mediated the relationship between ADHD PRS and ADHD traits, explaining 14% and 16% of the association, respectively. The mediation effect was specific to the genetic risk for ADHD and did not generalise to genetic risk for four other major psychiatric disorders. Together, these findings provide robust evidence from a large general population sample that working memory and reaction time variability can be considered endophenotypes for ADHD that mediate the relationship between ADHD PRS and ADHD traits.

Towards the understanding of state-independent neural traits underlying psychiatric disorders

Hampered by the symptom complexity and diversity, the understanding of fundamental mechanisms underlying psychiatric disorders remains elusive. Traditional neuroscience research focusing on each behavioral domain separately may lack an overarching view of the pathogenesis of an entire disorder, offering limited power to identify core neuropathology that could possibly account for the disorder's various symptoms. The search for neural traits that are robustly present across different brain functional states and disease stages may provide insights into the rudimentary changes beneath manifest clinical phenotypes and thus help penetrate the causal mechanisms underlying a complex disorder. In this review, I briefly summarize previous research on this topic, emphasize how neural traits may help boost the understanding of biological mechanisms underlying psychiatric disorders, and exemplify how the observed traits may aid individualized predictions for diagnosis and prognosis in precision psychiatry, in particular related to schizophrenia. I also discuss a proposed research framework that can be leveraged for future studies on neural traits, as well as considerations for future applications of this nascent research strategy.

Genetics on the neurodiversity spectrum: Genetic, phenotypic and endophenotypic continua in autism and ADHD

How we ought to diagnose, categorise and respond to spectrum disabilities such as autism and Attention Deficit/Hyperactivity Disorder (ADHD) is a topic of lively debate. The heterogeneity associated with ADHD and autism is described as falling on various continua of behavioural, neural, and genetic difference. These continua are varyingly described either as extending into the general population, or as being continua within a given disorder demarcation. Moreover, the interrelationships of these continua are likewise often vague and subject to diverse interpretations. In this paper, I explore geneticists' and self-advocates' perspectives concerning autism and ADHD as continua. These diagnoses are overwhelmingly analysed as falling on a continuum or continua of underlying traits, which supports the notion of "the neurodiversity spectrum", i.e., a broader swath of human neural and behavioural diversity on which some concentrations of different functioning are diagnosed. I offer a taxonomy of conceptions of the genetic, phenotypic, and endophenotypic dimensionality within and beyond these diagnostic categories, and suggest that the spectrum of neurodiversity is characteristically endophenotypic.

Genetic associations of fatigue and other symptoms following breast cancer treatment: A prospective study

Background

Cancer-related fatigue, mood disturbances, pain and cognitive disturbance are common after adjuvant cancer therapy, but vary considerably between individuals despite common disease features and treatment exposures. A genetic basis for this variability was explored in a prospective cohort.

Methods

Physical and psychological health of women were assessed prospectively following therapy for early stage breast cancer with self-report questionnaires. Participation in a genetic association sub-study was offered. Indices for the key symptom domains of fatigue, pain, depression, anxiety, and neurocognitive difficulties were empirically derived by principal components analysis from end-treatment questionnaires, and then applied longitudinally. Genetic associations were sought with functional single nucleotide polymorphisms (SNPs) in pro- and anti-inflammatory cytokine genes - tumour necrosis factor (TNF)-α (−308 ​GG), interferon (IFN)-ɣ (+874 ​TA), interleukin (IL)-10 (1082 ​GA and −592 CA), IL-6 (−174 ​GC), IL-1β (−511 ​GA).

Results

Questionnaire data was available for 210 participants, of whom 111 participated in the genetic sub-study. As expected, symptom domain scores generally improved over several months following treatment completion. Tumour and adjuvant treatment related factors were unassociated with either severity or duration of the individual symptom domains, but severity of symptoms at end-treatment was strongly associated with duration for each domain (all p ​< ​0.05). In multivariable analyses, risk genotypes were independently associated with: fatigue with IL-6 -174 ​GG/GC and IL-10 -1082 GG; depression and anxiety with IL-10 -1082 AA; neurocognitive disturbance: TNF-α −308 GG; depression IL-1β (all p ​< ​0.05). The identified SNPs also had cumulative effects in prolonging the time to recovery from the associated symptom domain.

Conclusions

Genetic factors contribute to the severity and duration of common symptom domains after cancer therapy.

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Common symptoms following breast cancer treatment can be grouped into symptom domains.

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Symptom domains are useful to describe patterns and trajectories of symptoms following breast cancer treatment.

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Cytokine gene polymorphisms are associated with the severity and duration of symptom domains following cancer treatment.

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The symptom severity at final treatment predicts the duration of symptoms.

SNX29, a new susceptibility gene shared with major mental disorders in Han Chinese population

OBJECTIVES

Environmental and genetic factors play important roles in the development of schizophrenia (SCZ), bipolar disorder (BPD) or major depressive disorder (MDD). Some risk loci are identified with shared genetic effects on major psychiatric disorders. To investigate whether SNX29 gene played a significant role in these psychiatric disorders in the Han Chinese population.

METHODS

We focussed on 11 single-nucleotide polymorphisms (SNPs) harbouring SNX29 gene and carried out case-control studies in patients with SCZ (n = 1248), BPD (n = 1344), or MDD (n = 1056), and 1248 healthy controls (HC) recruited from the Han Chinese population. We constructed weighted gene co-expression network analysis (WGCNA) and extracted significant modules by R package.

RESULTS

We found that rs3743592 was significantly associated with MDD and rs6498263 with BPD in both allele and genotype distributions. Before correction, rs3743592 showed allelic and genotypic significance with SCZ, rs6498263 showed allelic significance with SCZ. WGCNA identified top 10 modules of co-expressed genes. Gene Ontology (GO) and pathway analysis were used to examine the functions of SNX29, which revealed that SNX29 was involved in the regulation of a number of biological processes, such as TGF-beta, ErbB, and Wnt signalling pathway, etc.

CONCLUSIONS

Our results supported common risk factors in SNX29 might share among these three mental disorders in the Han Chinese population.

Induced pluripotent stem cells for 2D and 3D modelling the biological basis of schizophrenia and screening possible therapeutics

Induced pluripotent stem cells (iPSCs) are providing unprecedented insight into complex neuropsychiatric disorders such as schizophrenia (SZ). Here we review the use of iPSCs for investigating the etiopathology and treatment of SZ, beginning with conventional in vitro two-dimensional (2D; monolayer) cell modelling, through to more advanced 3D tissue studies. With the advent of 3D modelling, utilising advanced differentiation paradigms and additive manufacturing technologies, inclusive of patient-specific cerebral/neural organoids and bioprinted neural tissues, such live disease-relevant tissue systems better recapitulate "within-body" tissue function and pathobiology. We posit that by enabling better understanding of biological causality, these evolving strategies will yield novel therapeutic targets and accordingly, drug candidates.

The role of the FTO gene in the relationship between depression and obesity. A systematic review

Depression and obesity are major global health problems that frequently co-occur. The FTO gene has one of the strongest links with obesity and high body mass index (BMI) in humans. Besides, this gene is highly expressed in the brain, may play a role in the nervous system, and could confer risk for depression, although scarce literature is available in this respect. We perform a systematic review of the relationship between FTO and both conditions. We selected original articles with observational design or reviews, where depression was assessed with ICD-10, DSM-5 or previous versions, published from 2012 (when the first related paper was published) to November 2020, performed in adults, in English or Spanish and having an optimal methodological quality (evaluated with SIGN checklist). Five original studies were finally included. The results regarding the role of FTO in depression-obesity comorbidity were inconclusive. This leads us to endorse further research covering the role of this gene on both conditions, emphasising a more precise characterization of depression, in order to confirm this role.

CNS genomic profiling in the mouse chronic social stress model implicates a novel category of candidate genes integrating affective pathogenesis

Despite high prevalence, medical impact and societal burden, anxiety, depression and other affective disorders remain poorly understood and treated. Clinical complexity and polygenic nature complicate their analyses, often revealing genetic overlap and cross-disorder heritability. However, the interplay or overlaps between disordered phenotypes can also be based on shared molecular pathways and 'crosstalk' mechanisms, which themselves may be genetically determined. We have earlier predicted (Kalueff et al., 2014) a new class of 'interlinking' brain genes that do not affect the disordered phenotypes per se, but can instead specifically determine their interrelatedness. To test this hypothesis experimentally, here we applied a well-established rodent chronic social defeat stress model, known to progress in C57BL/6J mice from the Anxiety-like stage on Day 10 to Depression-like stage on Day 20. The present study analyzed mouse whole-genome expression in the prefrontal cortex and hippocampus during the Day 10, the Transitional (Day 15) and Day 20 stages in this model. Our main question here was whether a putative the Transitional stage (Day 15) would reveal distinct characteristic genomic responses from Days 10 and 20 of the model, thus reflecting unique molecular events underlining the transformation or switch from anxiety to depression pathogenesis. Overall, while in the Day 10 (Anxiety) group both brain regions showed major genomic alterations in various neurotransmitter signaling pathways, the Day 15 (Transitional) group revealed uniquely downregulated astrocyte-related genes, and the Day 20 (Depression) group demonstrated multiple downregulated genes of cell adhesion, inflammation and ion transport pathways. Together, these results reveal a complex temporal dynamics of mouse affective phenotypes as they develop. Our genomic profiling findings provide first experimental support to the idea that novel brain genes (activated here only during the Transitional stage) may uniquely integrate anxiety and depression pathogenesis and, hence, determine the progression from one pathological state to another. This concept can potentially be extended to other brain conditions as well. This preclinical study also further implicates cilial and astrocytal mechanisms in the pathogenesis of affective disorders.

Shared alterations in resting-state brain connectivity in adults with attention-deficit/hyperactivity disorder and their unaffected first-degree relatives

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a developmental condition that often persists into adulthood with extensive negative consequences on quality of life. Despite emerging evidence indicating the genetic basis of ADHD, investigations into the familial expression of latent neurocognitive traits remain limited.

METHODS

In a group of adult ADHD probands (n = 20), their unaffected first-degree relatives (n = 20) and typically developing control participants (n = 20), we assessed endophenotypic alterations in the default mode network (DMN) connectivity during resting-state functional magnetic resonance imaging in relation to cognitive performance and clinical symptoms. In an external validation step, we also examined the dimensional nature of this neurocognitive trait in a sample of unrelated healthy young adults (n = 100) from the Human Connectome Project (HCP).

RESULTS

The results illustrated reduced anti-correlations between the posterior cingulate cortex/precuneus and right middle frontal gyrus that was shared between adult ADHD probands and their first-degree relatives, but not with healthy controls. The observed connectivity alterations were linked to higher ADHD symptoms that was mediated by performance in a sustained attention task. Moreover, this brain-based neurocognitive trait dimensionally explained ADHD symptom variability in the HCP sample.

CONCLUSIONS

Alterations in the default mode connectivity may represent a dimensional endophenotype of ADHD, hence a significant aspect of the neuropathophysiology of this disorder. As such, brain network organisation can potentially be employed as an important neurocognitive trait to enhance statistical power of genetic studies in ADHD and as a surrogate efficacy endpoint in the development of novel pharmaceuticals.

Challenges and Strategies for Current Classifications of Depressive Disorders: Proposal for Future Diagnostic Standards

The Diagnostic and Statistical Manual of Mental Disorder, Fourth Edition (DSM-IV) was revised based on a combination of a categorical and a dimensional approach such that in the DSM, Fifth Edition (DSM-5), depressive disorders have been separated as a distinctive disease entity from bipolar disorders, consistent with the deconstruction of Kraepelinian dualism. Additionally, the diagnostic thresholds of depressive disorders may be reduced due to the addition of "hopelessness" to the subjective descriptors of depressed mood and the removal of the "bereavement exclusion." Manic/hypomanic, psychotic, and anxious symptoms in major depressive disorder (MDD) and other depressive disorders are described using the transdiagnostic specifiers of "with mixed features," "with psychotic features," and "with anxious distress," respectively. Additionally, due to the polythetic and operational characteristics of the DSM-5 diagnostic criteria, the heterogeneity of MDD is inevitable. Thus, 227 different symptom combinations fulfill the DSM-5 diagnostic criteria for MDD. This heterogeneity of MDD is criticized in view of the Wittgensteinian analogy of language game. Depression subtypes determined by disturbances in monoamine levels and the severity of the disease have been identified in the literature. According to a review of the Gottesman and Gould criteria, neuroticism, morning cortisol, cortisol awakening response, asymmetry in frontal cortical activity on electroencephalography (EEG), and probabilistic reward learning, among other variables, are evidenced as endophenotypes for depressive disorders. Network analysis has been proposed as a potential method to compliment the limitations of current diagnostic criteria and to explore the pathways between depressive symptoms, as well as to identify novel and interesting relationships between depressive symptoms. Based on the literature on network analysis in this field, no differences in the centrality index of the DSM and non-DSM symptoms were repeatedly present among patients with MDD. Furthermore, MDD and other depressive syndromes include two of the Research Domain Criteria (RDoC), including the Loss construct within the Negative Valence Systems domains and various Reward constructs within the Positive Valence Systems domain.

The evolutionary genetics of personality

Genetic influences on personality differences are ubiquitous, but their nature is not well understood. A theoretical framework might help, and can be provided by evolutionary genetics. We assess three evolutionary genetic mechanisms that could explain genetic variance in personality differences: selective neutrality, mutation‐selection balance, and balancing selection. Based on evolutionary genetic theory and empirical results from behaviour genetics and personality psychology, we conclude that selective neutrality is largely irrelevant, that mutation‐selection balance seems best at explaining genetic variance in intelligence, and that balancing selection by environmental heterogeneity seems best at explaining genetic variance in personality traits. We propose a general model of heritable personality differences that conceptualises intelligence as fitness components and personality traits as individual reaction norms of genotypes across environments, with different fitness consequences in different environmental niches. We also discuss the place of mental health in the model. This evolutionary genetic framework highlights the role of gene‐environment interactions in the study of personality, yields new insight into the person‐situation‐debate and the structure of personality, and has practical implications for both quantitative and molecular genetic studies of personality. Copyright © 2007 John Wiley & Sons, Ltd.

Comparative personality research: methodological approaches

In the broadest sense, personality refers to stable inter‐individual variability in behavioural organisation within a particular population. Researching personality in human as well as nonhuman species provides unique possibilities for comparisons across species with different phylogenies, ecologies and social systems. It also allows insights into mechanisms and processes of the evolution of population differences within and between species. The enormous diversity across species entails particular challenges to methodology. This paper explores theoretical approaches and analytical methods of deriving dimensions of inter‐individual variability on different population levels from a personality trait perspective. The existing diversity suggests that some populations, especially some species, may exhibit different or even unique trait domains. Therefore, a methodology is needed that identifies ecologically valid and comprehensive representations of the personality variation within each population. I taxonomise and compare current approaches in their suitability for this task. I propose a new bottom–up approach—the behavioural repertoire approach—that is tailored to the specific methodological requirements of comparative personality research. Initial empirical results in nonhuman primates emphasise the viability of this approach and highlight interesting implications for human personality research. Copyright © 2008 John Wiley & Sons, Ltd.

Depression Heterogeneity and Its Biological Underpinnings: Toward Immunometabolic Depression

Epidemiological evidence indicates the presence of dysregulated homeostatic biological pathways in depressed patients, such as increased inflammation and disrupted energy-regulating neuroendocrine signaling (e.g., leptin, insulin). Alterations in these biological pathways may explain the considerable comorbidity between depression and cardiometabolic conditions (e.g., obesity, metabolic syndrome, diabetes) and represent a promising target for intervention. This review describes how immunometabolic dysregulations vary as a function of depression heterogeneity by illustrating that such biological dysregulations map more consistently to atypical behavioral symptoms reflecting altered energy intake/expenditure balance (hyperphagia, weight gain, hypersomnia, fatigue, and leaden paralysis) and may moderate the antidepressant effects of standard or novel (e.g., anti-inflammatory) therapeutic approaches. These lines of evidence are integrated in a conceptual model of immunometabolic depression emerging from the clustering of immunometabolic biological dysregulations and specific behavioral symptoms. The review finally elicits questions to be answered by future research and describes how the immunometabolic depression dimension could be used to dissect the heterogeneity of depression and potentially to match subgroups of patients to specific treatments with higher likelihood of clinical success.

Immune Factor, TNFα, Disrupts Human Brain Organoid Development Similar to Schizophrenia-Schizophrenia Increases Developmental Vulnerability to TNFα

Schizophrenia (SZ) is a neurodevelopmental genetic disorder in which maternal immune activation (MIA) and increased tumor necrosis factor-α (TNF-α) may contribute. Previous studies using iPSC-derived cerebral organoids and neuronal cells demonstrated developmental malformation and transcriptional dysregulations, including TNF receptors and their signaling genes, common to SZ patients with diverse genetic backgrounds. In the present study, we examined the significance of the common TNF receptor dysregulations by transiently exposing cerebral organoids from embryonic stem cells (ESC) and from representative control and SZ patient iPSCs to TNF. In control iPSC organoids, TNF produced malformations qualitatively similar in, but generally less pronounced than, the malformations of the SZ iPSC-derived organoids. TNF and SZ alone disrupted subcortical rosettes and dispersed proliferating Ki67⁺ neural progenitor cells (NPC) from the organoid ventricular zone (VZ) into the cortical zone (CZ). In the CZ, the absence of large ramified pan-Neu⁺ neurons coincided with loss of myelinated neurites despite increased cortical accumulation of O4⁺ oligodendrocytes. The number of calretinin⁺ interneurons increased; however, they lacked the preferential parallel orientation to the organoid surface. SZ and SZ+TNF affected fine cortical and subcortical organoid structure by replacing cells with extracellular matrix (ECM)-like fibers The SZ condition increased developmental vulnerability to TNF, leading to more pronounced changes in NPC, pan-Neu⁺ neurons, and interneurons. Both SZ- and TNF-induced malformations were associated with the loss of nuclear (n)FGFR1 form in the CZ and its upregulation in deep IZ regions, while in earlier studies blocking nFGFR1 reproduced cortical malformations observed in SZ. Computational analysis of ChiPseq and RNAseq datasets shows that nFGFR1 directly targets neurogenic, oligodendrogenic, cell migration, and ECM genes, and that the FGFR1-targeted TNF receptor and signaling genes are overexpressed in SZ NPC. Through these changes, the developing brain with the inherited SZ genome dysregulation may suffer increased vulnerability to TNF and thus, MIA.

Genetic Associations Between Executive Functions and a General Factor of Psychopathology

OBJECTIVE

Symptoms of psychopathology covary across diagnostic boundaries, and a family history of elevated symptoms for a single psychiatric disorder places an individual at heightened risk for a broad range of other psychiatric disorders. Both twin-based and genome-wide molecular methods indicate a strong genetic basis for the familial aggregation of psychiatric disease. This has led researchers to prioritize the search for highly heritable childhood risk factors for transdiagnostic psychopathology. Cognitive abilities that involve the selective control and regulation of attention, known as executive functions (EFs), are a promising set of risk factors.

METHOD

In a population-based sample of child and adolescent twins (n = 1,913, mean age = 13.1 years), we examined genetic overlap between both EFs and general intelligence (g) and a transdiagnostic dimension of vulnerability to psychopathology, comprising symptoms of anxiety, depression, neuroticism, aggression, conduct disorder, oppositional defiant disorder, hyperactivity, and inattention. Psychopathology symptoms in children were rated by children and their parents.

RESULTS

Latent factors representing general EF and g were highly heritable (h2 = 86%-92%), and genetic influences on both sets of cognitive abilities were robustly correlated with transdiagnostic genetic influences on psychopathology symptoms (genetic r values ranged from -0.20 to -0.38).

CONCLUSION

General EF and g robustly index genetic risk for transdiagnostic symptoms of psychopathology in childhood. Delineating the developmental and neurobiological mechanisms underlying observed associations between cognitive abilities and psychopathology remains a priority for ongoing research.

White Matter Microstructure across the Psychosis Spectrum

Diffusion-weighted imaging (DWI) is a neuroimaging technique that has allowed us an unprecedented look at the role that white matter microstructure may play in mental illnesses, such as psychosis. Psychosis-related illnesses, including schizophrenia, are increasingly viewed as existing along a spectrum; spectrums may be defined based on factors such as stage of illness, symptom severity, or genetic liability. This review first focuses on an overview of some of the recent findings from DWI studies. Then, it examines the ways in which DWI analyses have been extended across the broader psychosis spectrum, or spectrums, and what we have learned from such approaches.

Real-Time Monitoring: A Key Element in Personalized Health and Precision Health

Current management of psychiatric disorders relies heavily on retrospective, subjective reports provided by patients and their families. Consequently, psychiatric services are often provisioned inefficiently and with suboptimal outcomes. Recent advances in computing and sensor technologies have enabled the development of real-time monitoring systems for the diagnosis and management of psychiatric disorders. The state of these technologies is rapidly evolving, with passive monitoring and predictive modeling as two areas that have great potential to affect psychiatric care. Although outpatient psychiatry probably stands to benefit the most from the use of real-time monitoring technologies, there are also several ways in which inpatient psychiatry may also benefit. As the capabilities of these technologies increase and their use becomes more common, many ethical and legal issues will need to be considered. The role of governmental regulatory bodies and nongovernmental organizations in providing oversight of the implementation of these technologies is an active area of discussion.

Crystallized and fluid intelligence are predicted by microstructure of specific white-matter tracts

Studies of the neural basis of intelligence have focused on comparing brain imaging variables with global scales instead of the cognitive domains integrating these scales or quotients. Here, the relation between mean tract-based fractional anisotropy (mTBFA) and intelligence indices was explored. Deterministic tractography was performed using a regions of interest approach for 10 white-matter fascicles along which the mTBFA was calculated. The study sample included 83 healthy individuals from the second wave of the Cuban Human Brain Mapping Project, whose WAIS-III intelligence quotients and indices were obtained. Inspired by the "Watershed model" of intelligence, we employed a regularized hierarchical Multiple Indicator, Multiple Causes model (MIMIC), to assess the association of mTBFA with intelligence scores, as mediated by latent variables summarizing the indices. Regularized MIMIC, used due to the limited sample size, selected relevant mTBFA by means of an elastic net penalty and achieved good fits to the data. Two latent variables were necessary to describe the indices: Fluid intelligence (Perceptual Organization and Processing Speed indices) and Crystallized Intelligence (Verbal Comprehension and Working Memory indices). Regularized MIMIC revealed effects of the forceps minor tract on crystallized intelligence and of the superior longitudinal fasciculus on fluid intelligence. The model also detected the significant effect of age on both latent variables.

Genomics of periodontal disease and tooth morbidity

In this review we critically summarize the evidence base and the progress to date regarding the genomic basis of periodontal disease and tooth morbidity (ie, dental caries and tooth loss), and discuss future applications and research directions in the context of precision oral health and care. Evidence for these oral/dental traits from genome-wide association studies first emerged less than a decade ago. Basic and translational research activities in this domain are now under way by multiple groups around the world. Key departure points in the oral health genomics discourse are: (a) some heritable variation exists for periodontal and dental diseases; (b) the environmental component (eg, social determinants of health and behavioral risk factors) has a major influence on the population distribution but probably interacts with factors of innate susceptibility at the person-level; (c) sizeable, multi-ethnic, well-characterized samples or cohorts with high-quality measures on oral health outcomes and genomics information are required to make decisive discoveries; (d) challenges remain in the measurement of oral health and disease, with current periodontitis and dental caries traits capturing only a part of the health-disease continuum, and are little or not informed by the underlying biology; (e) the substantial individual heterogeneity that exists in the clinical presentation and lifetime trajectory of oral disease can be identified and leveraged in a precision medicine framework or, if unappreciated, can hamper translational efforts. In this review we discuss how composite or biologically informed traits may offer improvements over clinically defined ones for the genomic interrogation of oral diseases. We demonstrate the utility of the results of genome-wide association studies for the development and testing of a genetic risk score for severe periodontitis. We conclude that exciting opportunities lie ahead for improvements in the oral health of individual patients and populations via advances in our understanding of the genomic basis of oral health and disease. The pace of new discoveries and their equitable translation to practice will largely depend on investments in the education and training of the oral health care workforce, basic and population research, and sustained collaborative efforts..

A Hierarchical Watershed Model of Fluid Intelligence in Childhood and Adolescence

Fluid intelligence is the capacity to solve novel problems in the absence of task-specific knowledge and is highly predictive of outcomes like educational attainment and psychopathology. Here, we modeled the neurocognitive architecture of fluid intelligence in two cohorts: the Centre for Attention, Leaning and Memory sample (CALM) (N = 551, aged 5-17 years) and the Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) (N = 335, aged 6-17 years). We used multivariate structural equation modeling to test a preregistered watershed model of fluid intelligence. This model predicts that white matter contributes to intermediate cognitive phenotypes, like working memory and processing speed, which, in turn, contribute to fluid intelligence. We found that this model performed well for both samples and explained large amounts of variance in fluid intelligence (R2CALM = 51.2%, R2NKI-RS = 78.3%). The relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7-12 years. This age effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that intelligence is part of a complex hierarchical system of partially independent effects.

Are candidate neurocognitive endophenotypes of OCD present in paediatric patients? A systematic review

To-date it has been difficult to ascertain the exact cognitive profile of childhood OCD as studies report variable results. Adult OCD research lately utilises the endophenotype approach; studying cognitive traits that are present in both patients and their unaffected first-degree relatives, and are thought to lie closer to the genotype than the full-blown disorder. By observing whether candidate endopenotypes of adult OCD are present in child patients, we can determine whether the two subtypes show cognitive overlap. We conducted a systematic review of the paediatric OCD literature focussing on proposed neurocognitive endophenotypes of OCD: cognitive flexibility, response inhibition, memory, planning, decision-making, action monitoring, and reversal learning. We found that paediatric patients present robust increases in brain error related negativity associated with abnormal action monitoring, impaired decision-making under uncertainty, planning, and visual working memory, but there is less evidence for deficits in other cognitive domains. This implies that children with OCD show some cognitive similarities with adult patients, but other dysfunctions may only manifest later in the disorder trajectory.

The association of endocannabinoid receptor genes (CNR1 and CNR2) polymorphisms with depression: A meta-analysis

Studies investigating the association between gene variants and depression susceptibility found inconsistent data. The present study aimed to clarify whether CNR1rs1049353, CNR1 AAT triplet repeat, and CNR2rs2501432 polymorphisms confer higher risk for depressive disorder.Literature from PubMed, Medline, Embase, Scopus, Cochrance Library, and Wanfang databases was searched (up to August 20, 2018). Seven case-control studies with various comorbidities were eligible. We targeted CNR single-nucleotide polymorphisms (SNPs) that have been reported by 2 or more studies to be involved in the current meta-analysis, resulting in a final list of 3 SNPs: CNR1rs1049353, CNR1 AAT triplet repeat polymorphism, and CNR2rs2501432. Odds ratios (ORs) and 95% confidence intervals (CIs) for allele and homozygote comparisons, dominant and recessive models, and triplet repeat polymorphism ((AAT)n≥5, ≥5 vs (AAT)n<5, <5 or <5, ≥5) were assessed using a random effect model as measures of association. Heterogeneity among included studies was analyzed using sensitivity test. Publication bias was also explored by Egger and rank correlation test.overall, no significant association was found between depression and CNR1rs1049353 (G vs A: OR [95% CI] = 1.09 [0.61-1.95]; GG vs AA: 1.29 [0.73-2.26]; GG vs GA+AA: 1.10 [0.57-2.10]; GG+GA vs AA: 1.25 [0.72-2.18]; and AAT triplet repeat polymorphism ((AAT)n≥5, ≥5 vs (AAT)n<5, <5 or <5, ≥5): 1.92 [0.59-6.27]. In contrast, a significant association between CNR2rs2501432 and depression was detected, and the ORs and 95% CIs are as follows: allele contrast (OR = 1.39, 95% CI = [1.12-1.72], P = .003); homozygous (OR = 2.19, 95% CI = [1.34-3.59], P = .002); dominant (OR = 1.93,95% CI = [1.23-3.04], P = .005); and recessive (OR = 1.41, 95% CI = [1.04-1.92], P = .03).This meta-analysis revealed that CNR1rs1049353 or AAT triplet repeat polymorphism had no association with susceptibility to depression, while CNR2rs2501432 polymorphism was a remarkable mark for depression patients.

Evidence for cerebello-thalamo-cortical hyperconnectivity as a heritable trait for schizophrenia

Our recent study has demonstrated that increased connectivity in the cerebello-thalamo-cortical (CTC) circuitry is a state-independent neural trait that can potentially predict the onset of psychosis. One possible cause of such "trait" abnormality would be genetic predisposition. Here, we tested this hypothesis using multi-paradigm functional magnetic resonance imaging (fMRI) data from two independent twin cohorts. In a sample of 85 monozygotic (MZ) and 52 dizygotic (DZ) healthy twin pairs acquired from the Human Connectome Project, we showed that the connectivity pattern of the identified CTC circuitry was more similar in the MZ twins (r = 0.54) compared with that in the DZ twins (r = 0.22). The structural equation modeling analysis revealed a heritability estimate of 0.52 for the CTC connectivity, suggesting a moderately strong genetic effect. Moreover, using an independent schizophrenia cotwin sample (10 discordant MZ cotwins, 30 discordant DZ cotwins, and 32 control cotwins), we observed a significant linear relationship between genetic distance to schizophrenia and the connectivity strength in the CTC circuitry (i.e., schizophrenia MZ cotwins > schizophrenia DZ cotwins > control twins, P = 0.045). The present data provide converging evidence that increased connectivity in the CTC circuitry is likely to be a heritable trait that is associated with the genetic risk of schizophrenia.

Liberal Acceptance Bias, Momentary Aberrant Salience, and Psychosis: An Experimental Experience Sampling Study

Cognitive models of psychosis posit that reasoning biases are an important mechanism contributing to the formation of psychotic symptoms, in part through transforming anomalous experiences of aberrant salience into frank psychotic symptoms. This study aimed to investigate the interplay of liberal acceptance (LA) bias, which is a specific type of reasoning bias, and momentary aberrant salience in the development of paranoid and psychotic experiences in daily life in first-episode psychosis patients (FEP), at-risk mental state participants (ARMS), and controls. We used a novel experimental Experience Sampling Methodology (eESM) task for measuring LA bias (ie, decisions based on low probability estimates) and ESM measures of momentary aberrant salience and paranoid and psychotic experiences in 51 FEP, 46 ARMS, and 53 controls. We found evidence that LA bias was more likely to occur in FEP than in controls. Further, LA bias was associated with psychotic and paranoid experiences (all P < .007) and modified the association between momentary aberrant salience and psychotic experiences (χ2(df) = 7.4(2), P = .025) in ARMS, such that momentary salience was associated with more intense psychotic experiences in the presence of LA bias in ARMS, but not in FEP and controls. Our findings suggest that LA bias may be central for anomalous experiences such as momentary aberrant salience to increase intensity of psychotic experiences in at-risk individuals. Further, LA bias appears to be more likely to be present, but not directly linked to current intensity of psychotic experiences, in treated FEP. Novel eESM tasks open new avenues for targeting psychological processes under real-world conditions.

Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection

It becomes increasingly important in using genome-wide association studies (GWAS) to select important genetic information associated with qualitative or quantitative traits. Currently, the discovery of biological association among SNPs motivates various strategies to construct SNP-sets along the genome and to incorporate such set information into selection procedure for a higher selection power, while facilitating more biologically meaningful results. The aim of this paper is to propose a novel Bayesian framework for hierarchical variable selection at both SNP-set (group) level and SNP (within group) level. We overcome a key limitation of existing posterior updating scheme in most Bayesian variable selection methods by proposing a novel sampling scheme to explicitly accommodate the ultrahigh-dimensionality of genetic data. Specifically, by constructing an auxiliary variable selection model under SNP-set level, the new procedure utilizes the posterior samples of the auxiliary model to subsequently guide the posterior inference for the targeted hierarchical selection model. We apply the proposed method to a variety of simulation studies and show that our method is computationally efficient and achieves substantially better performance than competing approaches in both SNP-set and SNP selection. Applying the method to the Alzheimers Disease Neuroimaging Initiative (ADNI) data, we identify biologically meaningful genetic factors under several neuroimaging volumetric phenotypes. Our method is general and readily to be applied to a wide range of biomedical studies.

Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances

Posttraumatic stress responses are characterized by a heterogeneity in clinical appearance and etiology. This heterogeneity impacts the field's ability to characterize, predict, and remediate maladaptive responses to trauma. Machine learning (ML) approaches are increasingly utilized to overcome this foundational problem in characterization, prediction, and treatment selection across branches of medicine that have struggled with similar clinical realities of heterogeneity in etiology and outcome, such as oncology. In this article, we review and evaluate ML approaches and applications utilized in the areas of posttraumatic stress, stress pathology, and resilience research, and present didactic information and examples to aid researchers interested in the relevance of ML to their own research. The examined studies exemplify the high potential of ML approaches to build accurate predictive and diagnostic models of posttraumatic stress and stress pathology risk based on diverse sources of available information. The use of ML approaches to integrate high-dimensional data demonstrates substantial gains in risk prediction even when the sources of data are the same as those used in traditional predictive models. This area of research will greatly benefit from collaboration and data sharing among researchers of posttraumatic stress disorder, stress pathology, and resilience.

Endophenotypes in Schizophrenia: Digging Deeper to Identify Genetic Mechanisms

Schizophrenia (SZ) is a severe psychotic disorder that is highly heritable and common in the general population. The genetic heterogeneity of SZ is substantial, with contributions from common, rare, and de novo variants, in addition to environmental factors. Large genome-wide association studies have detected many variants that are associated with SZ, yet the pathways by which these variants influence risk remain largely unknown. SZ is also clinically heterogeneous, with patients exhibiting a broad range of deficits and symptom severity that vary over the course of illness and treatment, which has complicated efforts to identify risk variants. However, the underlying brain dysfunction forms a more stable trait marker that quantitative neurocognitive and neurophysiological endophenotypes may be able to objectively measure. These endophenotypes are less likely to be heterogeneous than the disorder and provide a neurobiological context to detect risk variants and underlying pathways among genes associated with SZ diagnosis. Furthermore, many endophenotypes are translational into animal model systems, allowing for direct evaluation of the neural circuit dysfunctions and neurobiological substrates. We review a selection of the most promising SZ endophenotypes, including prepulse inhibition, mismatch negativity, oculomotor antisaccade, letter-number sequencing, and continuous performance tests. We also highlight recent findings from large consortia that suggest the potential role of genes, particularly in the neuregulin and glutamate pathways, in several of these endophenotypes. Although endophenotypes require additional time and effort to assess, the insight into the underlying neurobiology that they provide may ultimately reveal the underlying genetic architecture for SZ and suggest novel treatment targets.

Primary Cilia-An Underexplored Topic in Major Mental Illness

Though much progress has been made in recent years towards understanding the function and physiology of primary cilia, they remain a somewhat elusive organelle. Some studies have explored the role of primary cilia in the developing nervous system, and their dysfunction has been linked with several neurosensory deficits. Yet, very little has been written on their potential role in psychiatric disorders. This article provides an overview of some of the functions of primary cilia in signalling pathways, and demonstrates that they are a worthy candidate in psychiatric research. The links between primary cilia and major mental illness have been demonstrated to exist at several levels, spanning genetics, signalling pathways, and pharmacology as well as cell division and migration. The primary focus of this review is on the sensory role of the primary cilium and the neurodevelopmental hypothesis of psychiatric disease. As such, the primary cilium is demonstrated to be a key link between the cellular environment and cell behaviour, and hence of key importance in the considerations of the nature and nurture debate in psychiatric research.

A Review of Statistical Methods in Imaging Genetics

With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi-modality imaging, genetic, neurocognitive, and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function, and brain-related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention, and treatment of numerous complex brain-related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high-dimensional imaging phenotypes and high-dimensional genetic data, a big data squared (BD2) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high-dimensional nature of BD2, various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel-wise approaches, reduced rank regression, mixture models, and group sparse multi-task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research.