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Integration of GWAS and brain eQTL identifies FLOT1 as a risk gene for major depressive disorder.

ABSTRACT: 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.

SUBMITTER: Zhong J

PROVIDER: S-EPMC6785150 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Integration of GWAS and brain eQTL identifies FLOT1 as a risk gene for major depressive disorder.

Zhong Jingmei J Li Shiwu S Zeng Wanli W Li Xiaoyan X Gu Chunjie C Liu Jiewei J Luo Xiong-Jian XJ

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 20190216 9

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 ...[more]

PMID: 30771789

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Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Project description:BACKGROUND:A challenge of understanding the mechanisms underlying cognition including neurodevelopmental and neuropsychiatric disorders is mainly given by the potential severity of cognitive disorders for the quality of life and their prevalence. However, the field has been focused predominantly on protein coding variation until recently. Given the importance of tightly controlled gene expression for normal brain function, the goal of the study was to assess the functional variation including non-coding variation in human genome that is likely to play an important role in cognitive functions. To this end, we organized and utilized available genome-wide datasets from genomic, transcriptomic and association studies into a comprehensive data corpus. We focused on genomic regions that are enriched in regulatory activity-overlapping transcriptional factor binding regions and repurpose our data collection especially for identification of the regulatory SNPs (rSNPs) that showed associations both with allele-specific binding and allele-specific expression. We matched these rSNPs to the nearby and distant targeted genes and then selected the variants that could implicate the etiology of cognitive disorders according to Genome-Wide Association Studies (GWAS). Next, we use DeSeq 2.0 package to test the differences in the expression of the certain targeted genes between the controls and the patients that were diagnosed bipolar affective disorder and schizophrenia. Finally, we assess the potential biological role for identified drivers of cognition using DAVID and GeneMANIA. RESULTS:As a result, we selected fourteen regulatory SNPs locating within the loci, implicated from GWAS for cognitive disorders with six of the variants unreported previously. Grouping of the targeted genes according to biological functions revealed the involvement of processes such as 'posttranscriptional regulation of gene expression', 'neuron differentiation', 'neuron projection development', 'regulation of cell cycle process' and 'protein catabolic processes'. We identified four rSNP-targeted genes that showed differential expression between patient and control groups depending on brain region: NRAS-in schizophrenia cohort, CDC25B, DDX21 and NUCKS1-in bipolar disorder cohort. CONCLUSIONS:Overall, our findings are likely to provide the keys for unraveling the mechanisms that underlie cognitive functions including major depressive disorder, bipolar disorder and schizophrenia etiopathogenesis.

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Integration of GWAS and brain eQTL identifies FLOT1 as a risk gene for major depressive disorder.

Publications

Integration of GWAS and brain eQTL identifies FLOT1 as a risk gene for major depressive disorder.

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