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.
The current work presents investigation of genetic variation and disease association in a specific group of common complex diseases, namely mental and behavioral disorders. These disorders are of wide range and include conditions such as major depression, substance dependence and abuse, anxiety disorders, psychosis and schizophrenia. These disorders are highly prevalent worldwide and are major public health burdens [1–4]. Overall these disorders cause major distress and dysfunction due to alterations in behavior, mood and thinking. As indicated by National Institute of Mental Health: “An estimated 26.2 percent of Americans ages 18 and older about one in four adults – suffer from a diagnosable mental disorder in a given year” . Every one in four people is affected by mental or neurological conditions throughout their lifetime, hence the WHO indicates these groups of disorders as a ‘leading causes of ill health and disability worldwide’ .
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