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Review
, 36, 89-98

New Statistical Approaches Exploit the Polygenic Architecture of Schizophrenia--Implications for the Underlying Neurobiology

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Review

New Statistical Approaches Exploit the Polygenic Architecture of Schizophrenia--Implications for the Underlying Neurobiology

Andrew J Schork et al. Curr Opin Neurobiol.

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.

Figures

Box Figure
Box Figure. The relationship between power, sample size, and polygenicity
The power to detect a causal locus, assuming fixed heritability, depends on both the sample and the number of causal loci. When 1,000 causal loci were assumed the power to detect each causal locus was the highest (A) and much of the heritability was captured by genome-wide significant loci with reasonable sample sizes (D). When 10,000 causal loci were assumed, power was intermediate (B) and with realistic sample sizes, some heritability was discovered by genome-wide significant loci, but most was distributed among loci with intermediate significance (E). When 100,000 loci were assumed the power to detect each causal locus was greatly diminished (C) and even with 1,000,000 subjects, only a small fraction of heritability was captured by genome-wide significant loci (F).

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