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 GWAS 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 SCZ 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.
Mapping genetic variants that regulate gene expression (eQTLs) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-Seq limits sample size, sequencing depth, and therefore, discovery power. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-Seq of whole blood tissue across 1490 individuals at low-coverage (5.9 million reads/sample) and show that the effective power is higher than an RNA-Seq study of 570 individuals at high-coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-Seq data to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power. Our study suggests that lowering coverage while increasing the number of individuals is an effective approach to increase discovery power in RNA-Seq studies.
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