Abstract
Background
Common psychiatric disorders are characterized by complex disease architectures with
many small genetic effects that contribute and complicate biological understanding
of their etiology. There is therefore a pressing need for in vitro experimental systems
that allow for interrogation of polygenic psychiatric disease risk to study the underlying
biological mechanisms.
Methods
We have developed an analytical framework that integrates genome-wide disease risk
from genome-wide association studies with longitudinal in vitro gene expression profiles
of human neuronal differentiation.
Results
We demonstrate that the cumulative impact of risk loci of specific psychiatric disorders
is significantly associated with genes that are differentially expressed and upregulated
during differentiation. We find the strongest evidence for schizophrenia, a finding
that we replicate in an independent dataset. A longitudinal gene cluster involved
in synaptic function primarily drives the association with schizophrenia risk.
Conclusions
These findings reveal that in vitro human neuronal differentiation can be used to
translate the polygenic architecture of schizophrenia to biologically relevant pathways
that can be modeled in an experimental system. Overall, this work emphasizes the use
of longitudinal in vitro transcriptomic signatures as a cellular readout and the application
to the genetics of complex traits.
Keywords
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Article info
Publication history
Published online: September 05, 2018
Accepted:
August 9,
2018
Received in revised form:
July 18,
2018
Received:
February 23,
2018
Identification
Copyright
© 2018 Society of Biological Psychiatry.
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- Leveraging Human Induced Pluripotent Stem Cell–Based Models Provides Biological Context to Genome-wide Association Study FindingsBiological PsychiatryVol. 85Issue 7
- PreviewAs genetic studies of increasing scope identify a growing list of variants associated with risk for neurodegenerative and psychiatric disease, a critical challenge is understanding the complex interplay between genetic risk loci to facilitate improved diagnosis and better prediction of prognosis and treatment response.
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