New statistical approaches exploit the polygenic architecture of schizophrenia — implications for the underlying neurobiology. (February 2016)

• Record Type: Journal Article
• Authors: Schork, Andrew J; Wang, Yunpeng; Thompson, Wesley K; Dale, Anders M; Andreassen, Ole A
• Is Part Of: Current opinion in neurobiology. Volume 36(2016)
• Page Start: 89
• Page End: 98
• Publication Date: 2016-02
• Subjects: Neurobiology -- Periodicals; 573.8
• DOI: 10.1016/j.conb.2015.10.008
• Languages: English
• ISSNs: 0959-4388

Abstract

Highlights: Schizophrenia is highly polygenic with much missing heritability. New statistical tools are tailored to polygenic investigation of GWAS data. Extensive enrichment is present within functional genome elements, pathways and among shared traits. Pathway enrichment converges on neurotransmission, immune and neurodevelopmental pathways. Continued functional studies are needed to add clarity and context to statistical findings. Abstract : Schizophrenia is a complex disorder with high heritability. Recent findings from several large genetic studies suggest a large number of risk variants are involved (i.e. schizophrenia is a polygenic disorder) and analytic approaches could be tailored for this scenario. Novel statistical approaches for analyzing GWAS data have recently been developed to be more sensitive to polygenic traits. These approaches have provided intriguing new insights into neurobiological pathways and support for the involvement of regulatory mechanisms, neurotransmission (glutamate, dopamine, GABA), and immune and neurodevelopmental pathways. Integrating the emerging statistical genetics evidence with sound neurobiological experiments will be a crucial, and challenging, next step in deciphering the specific disease mechanisms of schizophrenia.