Time, memory and the heritability of major depression

Genetic and environmental contributions to psychopathological symptoms in adulthood: Clarifying the role of individual and parental risk factors

Psychopathology-related suicide attempts and deaths are increasing, and the research focus remains on mental disorder in childhood and adolescence. We examined the genetic and environmental contributions to internalizing, externalizing, and general psychopathological distress to clarify the role of individual/parental risk factors for suicide in young adults. Data from 1206 young adults were obtained from a publicly available dataset from the Human Connectome Project. Heritability estimates were analyzed by twin modelling using OpenMx and data from a subsample of 402 monozygotic and dizygotic twins. The estimated prevalence of psychopathological symptoms ranged from 9 to 16%. The heritability of internalizing, externalizing, and general psychopathological distress reached significance, with estimates ranging from 22 to 46%. Shared (common) environments contributed to aggressive behavior (30 %). Determining factors for psychopathological distress (internalizing, externalizing, general) were evaluated using logistic regression analysis. Household income (<$50,000), childhood conduct problems, and maternal drug or alcohol problems were common risk factors of internalizing, externalizing, and general psychopathological distress. Marijuana dependence and maternal anxiety were additional risk factors of externalizing distress. The presence of alcohol and maternal drug or alcohol problems was linked to general psychopathological distress. The results highlight risk factors associated with psychopathological symptoms that should be considered in the early detection of high-risk groups and implementation of family-based interventions. Providing continuous care and/or follow-up in at-risk children and young adults may improve mental health and well-being.

Measurement and genetic architecture of lifetime depression in the Netherlands as assessed by LIDAS (Lifetime Depression Assessment Self-report)

BACKGROUND

Major depressive disorder (MDD) is a common mood disorder, with a heritability of around 34%. Molecular genetic studies made significant progress and identified genetic markers associated with the risk of MDD; however, progress is slowed down by substantial heterogeneity as MDD is assessed differently across international cohorts. Here, we used a standardized online approach to measure MDD in multiple cohorts in the Netherlands and evaluated whether this approach can be used in epidemiological and genetic association studies of depression.

METHODS

Within the Biobank Netherlands Internet Collaboration (BIONIC) project, we collected MDD data in eight cohorts involving 31 936 participants, using the online Lifetime Depression Assessment Self-report (LIDAS), and estimated the prevalence of current and lifetime MDD in 22 623 unrelated individuals. In a large Netherlands Twin Register (NTR) twin-family dataset (n ≈ 18 000), we estimated the heritability of MDD, and the prediction of MDD in a subset (n = 4782) through Polygenic Risk Score (PRS).

RESULTS

Estimates of current and lifetime MDD prevalence were 6.7% and 18.1%, respectively, in line with population estimates based on validated psychiatric interviews. In the NTR heritability estimates were 0.34/0.30 (s.e. = 0.02/0.02) for current/lifetime MDD, respectively, showing that the LIDAS gives similar heritability rates for MDD as reported in the literature. The PRS predicted risk of MDD (OR 1.23, 95% CI 1.15-1.32, R2 = 1.47%).

CONCLUSIONS

By assessing MDD status in the Netherlands using the LIDAS instrument, we were able to confirm previously reported MDD prevalence and heritability estimates, which suggests that this instrument can be used in epidemiological and genetic association studies of depression.

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future.

Irritable bowel syndrome: a co-twin control analysis

OBJECTIVES

Among individuals who satisfy diagnostic criteria for both major depressive disorder (MDD) and irritable bowel syndrome (IBS), little is known about the role of familial tendency and environmental influences in disease susceptibility. Therefore, we aimed to examine if there is a genetic component to the co-occurrence of MDD and IBS in participants from the population-based Swedish Twin Registry.

METHODS

We implemented a nested case-control analysis and a co-twin control analysis. IBS cases were ascertained using an adapted version of the Rome II criteria and MDD cases were assessed using a shortened version of the Computerized International Diagnostic Interview. The case-control analysis included individuals with complete covariate information (N = 29,616), and adjusted for twin pair, 3-yr age band, and sex. The co-twin control analysis considered 288 twin pairs discordant for IBS. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using generalized estimating equations for the case-control analysis and conditional logistic regression for the co-twin control analysis.

RESULTS

In all analyses, comorbid disorders of chronic widespread pain, chronic fatigue-like illness, and MDD were more common in IBS cases than controls. In the case-control analysis, individuals with IBS had an increased odds of MDD (OR 2.7, 95% CI 2.3-3.2). In the co-twin analysis, the association was similar (OR 2.2, 95% CI 1.5-3.3) when both MZ and DZ twins were used, and larger among only MZ twins (OR 3.2, 95% CI 1.7-6.1).

CONCLUSIONS

From this analysis, we found no evidence that genetic and family environmental factors explain the association between MDD and IBS. Rather, one of these disorders appears to be part of the disease-causing sequence of events, or biologic disease pathway, for the other disorder. However, it was not possible to evaluate the timing of onset between MDD and IBS in this study.

Population stratification in the candidate gene study: fatal threat or red herring?

Advances in molecular genetics have provided behavioral scientists with a means of investigating the influence of genetic factors on human behavior. Unfortunately, recent candidate gene studies have produced inconsistent results, and a frequent scapegoat for the lack of replication across studies is the threat of population stratification. This review of the literature on population stratification suggests that the threat may be a red herring. Reliable findings will require improved specification and measurement of the behavioral phenotypes in question, a renewed focus on internal validity, and the specification and testing of genetic factors in the context of longitudinal multivariate models. In this respect, behavioral scientists are well suited to investigating genetic factors that influence psychological mechanisms.