Pleiotropic locus for emotion recognition and amygdala volume identified using univariate and bivariate linkage

Genome-wide mapping of brain phenotypes in extended pedigrees with strong genetic loading for bipolar disorder

Bipolar disorder is a highly heritable illness, associated with alterations of brain structure. As such, identification of genes influencing inter-individual differences in brain morphology may help elucidate the underlying pathophysiology of bipolar disorder (BP). To identify quantitative trait loci (QTL) that contribute to phenotypic variance of brain structure, structural neuroimages were acquired from family members (n = 527) of extended pedigrees heavily loaded for bipolar disorder ascertained from genetically isolated populations in Latin America. Genome-wide linkage and association analysis were conducted on the subset of heritable brain traits that showed significant evidence of association with bipolar disorder (n = 24) to map QTL influencing regional measures of brain volume and cortical thickness. Two chromosomal regions showed significant evidence of linkage; a QTL on chromosome 1p influencing corpus callosum volume and a region on chromosome 7p linked to cortical volume. Association analysis within the two QTLs identified three SNPs correlated with the brain measures.

Clinical correlates of subsyndromal depression in African American individuals with psychosis: The relationship with positive symptoms and comorbid substance dependence

Patients with psychosis exhibit subsyndromal depressive symptoms during the course of illness and yet the clinical correlates of these symptoms remain under-investigated. We aimed to investigate the clinical correlates of subsyndromal depression in psychosis including the extent to which they mediate commonly observed comorbid substance dependence. We developed a model of depression in a non-clinical sample recruited via Amazon's Mechanical Turk (N = 266), and confirmed that model in a locally recruited African-American clinical sample comprising psychotic and non-psychotic individuals (N = 256). Using scores from this model we tested: the strength of relationships between depressive symptomatology and positive, negative and disorganized symptoms in a range of psychotic disorders; whether depressive symptoms were higher in individuals with affective psychoses versus schizophrenia; and if depressive symptomatology mediated the relationship between psychosis and substance dependence. Subsyndromal depressive symptomatology was significantly higher in individuals with psychosis than without psychosis, but did not significantly differ between affective and non-affective psychotic groups. Depressive symptomatology was significantly related to positive (but not negative or disorganized) psychotic symptoms, and mediated the relationship between psychosis and substance dependence. The present study underlines the importance of assessing subsyndromal depression in patients with psychosis, and generates a number of testable predictions for future work. In particular, the examination of the relationships between comorbid psychopathology, namely depression and substance abuse, may improve insight into the neurobiology of psychosis.

Genetic correlations between wellbeing, depression and anxiety symptoms and behavioral responses to the emotional faces task in healthy twins

Currently there is a very limited understanding of how mental wellbeing versus anxiety and depression symptoms are associated with emotion processing behaviour. For the first time, we examined these associations using a behavioural emotion task of positive and negative facial expressions in 1668 healthy adult twins. Linear mixed model results suggested faster reaction times to happy facial expressions was associated with higher wellbeing scores, and slower reaction times with higher depression and anxiety scores. Multivariate twin modelling identified a significant genetic correlation between depression and anxiety symptoms and reaction time to happy facial expressions, in the absence of any significant correlations with wellbeing. We also found a significant negative phenotypic relationship between depression and anxiety symptoms and accuracy for identifying neutral emotions, although the genetic or environment correlations were not significant in the multivariate model. Overall, the phenotypic relationships between speed of identifying happy facial expressions and wellbeing on the one hand, versus depression and anxiety symptoms on the other, were in opposing directions. Twin modelling revealed a small common genetic correlation between response to happy faces and depression and anxiety symptoms alone, suggesting that wellbeing and depression and anxiety symptoms show largely independent relationships with emotion processing at the behavioral level.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes.

Social cognition as an RDoC domain

While the bulk of research into neural substrates of behavior and psychopathology has focused on cognitive, memory and executive functions, there has been a recent surge of interest in emotion processing and social cognition, manifested in designating Social Cognition as a major RDoC domain. We describe the origins of this field's influence on cognitive neuroscience and highlight the most salient findings leading to the characterization of the "social brain" and the establishments of parameters that quantify normative and aberrant behaviors. Such parameters of behavior and neurobiology are required for a potentially successful RDoC construct, especially if heritability is established, because of the need to link with genomic systems. We proceed to illustrate how a social cognition measure can be used within the RDoC framework by presenting a task of facial emotion identification. We show that performance is sensitive to normative individual differences related to age and sex and to deficits associated with schizophrenia and other psychotic disorders. Neuroimaging studies with this task demonstrate that it recruits limbic and frontal regulatory activation in healthy samples as well as abnormalities in psychiatric populations. Evidence for its heritability was documented in genomic family studies and in patients with the 22q11.2 deletion syndrome. Measures that meet such criteria can help build translational bridges between cellular molecular mechanisms and behavior that elucidate aberrations related to psychopathology. Such links will transcend current diagnostic classifications and ultimately lead to a mechanistically based diagnostic nomenclature. Establishing such bridges will provide the elements necessary for early detection and scientifically grounded intervention.

Differing brain structural correlates of familial and environmental risk for major depressive disorder revealed by a combined VBM/pattern recognition approach

BACKGROUND

Neuroimaging traits of either familial or environmental risk for major depressive disorder (MDD) have been interpreted as possibly useful vulnerability markers. However, the simultaneous occurrence of familial and environmental risk might prove to be a major obstacle in the attempt of recent studies to confine the precise impact of each of these conditions on brain structure. Moreover, the exclusive use of group-level analyses does not permit prediction of individual illness risk which would be the basic requirement for the clinical application of imaging vulnerability markers. Hence, we aimed to distinguish between brain structural characteristics of familial predisposition and environmental stress by using both group- and individual-level analyses.

METHOD

We investigated grey matter alterations between 20 healthy control subjects (HC) and 20 MDD patients; 16 healthy first-degree relatives of MDD patients (FH+) and 20 healthy subjects exposed to former childhood maltreatment (CM+) by using a combined VBM/pattern recognition approach.

RESULTS

We found similar grey matter reductions in the insula and the orbitofrontal cortex in patients and FH+ subjects and in the hippocampus in patients and CM+ subjects. No direct overlap in grey matter alterations was found between FH+ and CM+ subjects. Pattern classification successfully detected subjects at risk for the disease even by strictly focusing on morphological traits of MDD.

CONCLUSIONS

Familial and environmental risk factors for MDD are associated with differing morphometric anomalies. Pattern recognition might be a promising instrument in the search for and future application of vulnerability markers for MDD.