Power, measurement error, and pleiotropy robustness in twin-design extensions to Mendelian Randomization.

Mendelian Randomization (MR) has become an important tool for causal inference in the health sciences. It takes advantage of the random segregation of alleles to control for background confounding factors. In brief, the method works by using genetic variants as instrumental variables, but it depends on the assumption of exclusion restriction, i.e., that the variants affect the outcome exclusively via the exposure variable. Equivalently, the assumption states that there is no horizontal pleiotropy from the variant to the outcome. This assumption is unlikely to hold in nature, so several extensions to MR have been developed to increase its robustness against horizontal pleiotropy, though not eliminating the problem entirely (Sanderson et al. 2022). The Direction of Causation (DoC) model, which affords information from the cross-twin cross-trait correlations to estimate causal paths, was extended with polygenic scores to explicitly model horizontal pleiotropy and a causal path (MR-DoC, Minica et al 2018). MR-DoC was further extended to accommodate bidirectional causation (MR-DoC2 ; Castro-de-Araujo et al. 2023). In the present paper, we compared the power of the DoC model, MR-DoC, and MR-DoC2. We investigated the effect of phenotypic measurement error and the effect of misspecification of unshared (individual-specific) environmental factors on the parameter estimates.

Feedback-loop between psychotic symptoms and brain volume: A cross-lagged panel model study.

Brain structural changes are known to be associated with psychotic symptoms, with worse symptoms consistently associated with brain volume loss in some areas. It is not clear whether volume and symptoms interfere with each other over the course of psychosis. In this paper, we analyse the temporal relationships between psychosis symptom severity and total gray matter volume. We applied a cross-lagged panel model to a public dataset from the NUSDAST cohorts. The subjects were assessed at three-time points: baseline, 24 months, and 48 months. Psychosis symptoms were measured by SANS and SAPS scores. The cohort contained 673 subjects with schizophrenia, healthy subjects and their siblings. There were significant effects of symptom severity on total gray matter volume and vice-versa. The worse the psychotic symptoms, the smaller the total gray volume, and the smaller the volume, the worse the symptomatology. There is a bidirectional temporal relationship between symptoms of psychosis and brain volume.

Subtyping schizophrenia based on symptomatology and cognition using a data driven approach.

Schizophrenia is a highly heterogeneous disorder, not only in its phenomenology but in its clinical course. This limits the usefulness of the diagnosis as a basis for both research and clinical management. Methods of reducing this heterogeneity may inform the diagnostic classification. With this in mind, we performed k-means clustering with symptom and cognitive measures to generate groups in a machine-driven way. We found that our data was best organised in three clusters: high cognitive performance, high positive symptomatology, low positive symptomatology. We hypothesized that these clusters represented biological categories, which we tested by comparing these groups in terms of brain volumetric information. We included all the groups in an ANCOVA analysis with post hoc tests, where brain volume areas were modelled as dependent variables, controlling for age and estimated intracranial volume. We found six brain volumes significantly differed between the clusters: left caudate, left cuneus, left lateral occipital, left inferior temporal, right lateral, and right pars opercularis. The k-means clustering provides a way of subtyping schizophrenia which appears to have a biological basis, though one that requires both replication and confirmation of its clinical significance.

RDoC and shift of reference.

OBJECTIVE: This paper aims to analyse in a philosophically informed way the recent National Institute of Mental Health proposal for the Research Domain Criteria (RDoC) framework. CONCLUSION: Current classification systems have helped unify psychiatry and the conditions that it is most concerned with. However, by relying too much on syndromes and symptoms, they too often do not define stable constructs. As a result, inclusions and removals from the manuals are not always backed by sound reasons. The RDoC framework is an important move towards ameliorating matters. This paper argues that it improves the current situation by re-referencing constructs to physical properties (biomarkers for disorders, for example), by allowing theoretical levels within the framework, and by treating psychiatry as a special case of the cognitive sciences.