AUTHOR=Pisanu Claudia , Squassina Alessio 

TITLE=Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2019.00617

DOI=10.3389/fphar.2019.00617

ISSN=1663-9812

ABSTRACT=Schizophrenia (SCZ) is a severe psychiatric disorder affecting approximately 23 million people worldwide. It is considered the 8th leading cause of disability according to the World Health Organization and is associated with a significant reduction in life expectancy. Antipsychotics represent the first-choice treatment in SCZ, but approximately 30% of patients fail to respond to acute treatment. These patients are generally defined as treatment resistant and are eligible for clozapine treatment.  Treatment resistant patients show a more severe course of the diseases, but it has been suggested that treatment resistant schizophrenia (TRS) may constitute a distinct phenotype more than just a more severe form of SCZ. TRS is heritable, and genetic has been shown to play an important role in modulating response to antipsychotics. Important efforts have been put into place in order to better understand the genetic architecture of TRS, with the main goal of identifying reliable predictive markers that might improve the management and quality of life of TRS patients. However, the number of candidate gene and genome-wide association studies specifically focused on TRS is limited, and to date findings do not allow to disentangle its polygenic nature. More recent studies implemented polygenic risk score, gene based and machine learning methods to explore the genetics of TRS, reporting promising findings. In this review, we present an overview on the genetics of TRS, particularly focusing our discussion on studies implementing polygenic approaches.