Aim: Investigation of association studies within the field of mental and behavioral disorders is of value given their complex molecular etiology including epistatic interactions of multiple genes with small effects.
Materials & methods: Utilizing biomedical text mining, associations are uncovered for all mental and behavioral conditions listed in Diagnostic and Statistical Manual of Mental Disorders Text Revision. Specifically, a computational pipeline is designed to retrieve neurotransmitter receptor variations from biomedical literature with a text mining approach, where unique polymorphisms are also mined.
Results: Analyses of 1337 unique neurotransmitter receptors and 465 distinct conditions yield 1568 unique gene-disease associations.
Conclusion: This study takes an unconventional approach to association studies and generates a novel dataset of associations for disorders such as major depression and schizophrenia, which provides a global perspective for their genetic etiology.
Keywords: biomedical literature; genetic etiology; gene–disease associations; mental and behavioral disorders; neurotransmitter receptors; polymorphisms; text mining.