A gene-based analysis of variants in the Brain-derived Neurotrophic Factor gene with psychological distress in a Taiwanese population

The association between smoking cessation and lifestyle/genetic variant rs6265 among the adult population in Taiwan

Recent studies showed significant associations between socio-demographic, lifestyle factors, polymorphic variant rs6265, and smoking cessation behaviours. We examined rs6265 TT, TC and CC genotypes and their association with socio-demographic and other variables, including mental health status, drinking, exercise, and smoking behaviour among Taiwanese adults. Data on rs6265 were retrieved from the Taiwan Biobank, which contained genetic data collected between 2008 to 2019 from 20,584 participants (aged 30-70 years). Participants who smoked for more than 6 months prior to enrolment were categorized as smokers. If they had smoked and later quit for more than 6 months, they were classified as former smokers. Information regarding drinking, exercise, depression, and bipolar disorder were obtained through questionnaires and were categorized as either as affirmative (yes) or negative (no) responses. In contrast to previous studies, we found that the association between the polymorphism rs6265 and smoking behaviour was not significant (P-value = 0.8753). Males with lower education levels, young persons, and alcohol drinkers showed significant smoking behaviours (P-value < .0001). This population-based study indicates that rs6265 has no significant correlations with smoking cessation behaviour among adults in Taiwan.

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.