Comparing GWAS and Brain Structure-Specific Gene Expression Profiles Identifies Psychiatric Disorder-Related Brain Structures at Different Developmental Stages.
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ABSTRACT:
SUBMITTER: Qi X
PROVIDER: S-EPMC7475161 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
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Comparing GWAS and Brain Structure-Specific Gene Expression Profiles Identifies Psychiatric Disorder-Related Brain Structures at Different Developmental Stages.
Qi Xin X Wu Cuiyan C Du Yanan Y Cheng Shiqiang S Wen Yan Y Ma Mei M Liang Chujun C Liu Li L Cheng Bolun B Zhang Lu L Li Ping P Zhang Feng F
Neuroscience bulletin 20200530 9
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Project description:Major depressive disorder (MDD) is the most prevalent mental disorder that affects more than 200 million people worldwide. Recent large-scale genome-wide association studies (GWAS) have identified multiple risk variants that show robust association with MDD. Nevertheless, how the identified risk variants confer risk of MDD remains largely unknown. To identify risk variants that are associated with gene expression in human brain and to identify genes whose expression change may contribute to the susceptibility of MDD, we systematically integrated the genetic associations from a large-scale MDD GWAS (N = 480,359) and brain expression quantitative trait loci (eQTL) data (N = 494) using a Bayesian statistical framework (Sherlock). Sherlock integrative analysis showed that FLOT1 was significantly associated with MDD (P = 6.02 × 10-6), suggesting that risk variants may contribute to MDD susceptibility through affecting FLOT1 expression. We further examined the expression level of FLOT1 in MDD cases and controls and found that FLOT1 was significantly upregulated in brains and peripheral blood of MDD cases compared with controls (European sample). Interestingly, we found that FLOT1 expression was also significantly upregulated in peripheral blood of first-episode drug-naive MDD cases compared with controls (P = 1.01 × 10-7, Chinese sample). Our study identified FLOT1 as a novel MDD risk gene whose expression level may play a role in MDD. In addition, our findings also suggest that risk variants may confer risk of MDD through affecting expression of FLOT1. Further functional investigation of FLOT1 may provide new insights for MDD pathogenesis.
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Project description:As psychiatric genetics enters an era where gene identification is finally yielding robust, replicable genetic associations and polygenic risk scores, it is important to consider next steps and delineate how that knowledge will be applied to ultimately ameliorate suffering associated with substance use and psychiatric disorders. Much of the post-genome-wide association study discussion has focused on the potential of genetic information to elucidate the underlying biology and use this information for the development of more effective pharmaceutical treatments. In this review we focus on additional areas of research that should follow gene identification. By taking genetic findings into longitudinal, developmental studies, we can map the pathways by which genetic risk manifests across development, elucidating the early behavioral manifestations of risk, and studying how various environments and interventions moderate that risk across developmental stages. The delineation of risk across development will advance our understanding of mechanism, sex differences and risk and resilience processes in different racial/ethnic groups. Here, we review how the extant twin study literature can be used to guide these efforts. Together, these new lines of research will enable us to develop more informed, tailored prevention and intervention efforts.
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Project description:We show here that combining two existing genome wide association studies (GWAS) yields additional biologically relevant information, beyond that obtained by either GWAS separately. We propose Joint GWAS Analysis, a method that compares a pair of GWAS for similarity among the top SNP associations, top genes identified, gene functional clusters, and top biological pathways. We show that Joint GWAS Analysis identifies additional enriched biological pathways that would be missed by traditional Single-GWAS analysis. Furthermore, we examine the similarities of six complex genetic disorders at the SNP-level, gene-level, gene-cluster-level, and pathway-level. We make concrete hypotheses regarding novel pathway associations for several complex disorders considered, based on the results of Joint GWAS Analysis. Together, these results demonstrate that common complex disorders share substantially more genomic architecture than has been previously realized and that the meta-analysis of GWAS needs not be limited to GWAS of the same phenotype to be informative.
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