Use of a Bayesian Network Model to predict psychiatric illness in individuals with 'at risk mental states' from a general population cohort.

The 'at risk mental state' (ARMS) paradigm has been introduced in psychiatry to study prodromal phases of schizophrenia. With time it was seen that the ARMS state can also precede mental disorders other than schizophrenia, such as depression and anxiety. However, several problems hamper the paradigm's use in preventative medicine, such as varying transition rates across studies, the use of non-naturalistic samples, and the multifactorial nature of psychiatric disorders. To strengthen ARMS predictive power, there is a need for a holistic model incorporating-in an unbiased fashion-the small-effect factors that cause mental disorders. Bayesian networks, a probabilistic graphical model, was used in a populational cohort of 83 ARMS individuals to predict conversion to psychiatric illness. Nine predictors-including state, trait, biological and environmental factors-were inputted. Dopamine receptor 2 polymorphism, high private religiosity, and childhood trauma remained in the final model, which reached an 85.51% (SD = 0.1190) accuracy level in predicting conversion. This is the first time a robust model was produced with Bayesian networks to predict psychiatric illness among at risk individuals from the general population. This could be an important tool to strengthen predictive measures in psychiatry which should be replicated in larger samples to provide the model further learning.

Intragenic Deletion in the LIFR Gene in a Long-Term Survivor with Stüve-Wiedemann Syndrome.

Stüve-Wiedemann syndrome (SWS, OMIM 601559) is a rare autosomal recessive bent-bone dysplasia, caused by loss-of-function mutations in the leukemia inhibitory factor receptor (LIFR) gene, which usually leads to early death. Only few patients with long-term survival have been described in the literature. We report on a 5-year-old boy from a consanguineous marriage with molecular analysis for the LIFR gene. Sanger and next-generation sequencing (NGS) of LIFR were performed. Copy number variation analysis with NGS showed a novel mutation as the cause for the syndrome: an intragenic homozygous deletion in LIFR, involving exons 15-20. Bridging PCR was carried out to confirm the intragenic deletion. This is the first description of a large deletion in LIFR, broadening the spectrum of mutations in SWS. Besides the reported allelic heterogeneity, further studies such as exome sequencing are required to identify a novel gene in order to confirm the locus heterogeneity in SWS.