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

Zhong, Jingmei; Li, Shiwu; Zeng, Wei-Bin; Li, Xiaoyan; Gu, Chunjie; Liu, Jiewei; Luo, Xiong‐Jian

doi:10.1038/s41386-019-0345-4

Cited by 39 publications

(29 citation statements)

References 81 publications

(96 reference statements)

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“…Interestingly, contrary to our results in LCLs, a previous study showed up-regulation of FLOT1 in the brain of depressed patients [38]. Differences between FLOT1 (gene) and flotilin-1 (protein) may reflect posttranslational modification or targeted degradation in depressed subjects.…”

Section: Human Lymphoblast Cell Lines (Lcls) Reveal Potential Depresscontrasting

confidence: 99%

“…Expression of flotillin-1 protein was suppressed in LCLs from depressive subjects, suggesting its use as a potential peripheral marker for depression. Curiously, FLOT1 was significantly upregulated in brains and peripheral blood of depressive cases compared with controls [38]. This was one of the clear differences we noted between LCLs and neuronal and glial cells.…”

Section: Discussionmentioning

confidence: 64%

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Potential depression and antidepressant-response biomarkers in human lymphoblast cell lines from treatment-responsive and treatment-resistant subjects: roles of SSRIs and omega-3 polyunsaturated fatty acids

Chukaew

Leow

Saengsawang

et al. 2020

Preprint

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AbstractWhile several therapeutic strategies exist for depression, most antidepressant drugs require several weeks before reaching full biochemical efficacy and remission is not entirely achieved in many patients. Therefore, biomarkers for depression and drug-response would help tailor treatment strategies. This study made use of banked human lymphoblast cell lines (LCLs) from normal and depressed subjects; the latter divided into remitters and non-remitters. Due to the fact that previous studies have shown effects on growth factors, cytokines and elements of the cAMP generating system as potential biomarkers for depression and antidepressant action, these were examined in LCLs. Initial gene and protein expression profiles for signaling cascades related to neuroendocrine and inflammatory functions differ among the three groups. Growth factor genes, including VEGFA and BDNF were significantly down-regulated in cells from depressed subjects. In addition, omega-3 polyunsaturated fatty acids (n-3 PUFAs) have been reported to act as both antidepressants and anti-inflammatories, but the mechanisms for these effects are not established. Here we showed that n-3 PUFAs and escitalopram (selective serotonin reuptake inhibitors, SSRIs) treatment increased adenylyl cyclase (AC) and BDNF gene expression in LCLs. These data are consistent with clinical observations showing that n-3 PUFA and SSRI have antidepressant affects, which may be additive. Contrary to observations made in neuronal and glial cells, n-3 PUFA treatment attenuated cAMP accumulation in LCLs. However, while lymphoblasts show paradoxical responses to neurons and glia, patient-derived lymphoblasts appear to carry potential depression biomarkers making them an important tool for studying precision medicine in depressive patients. Furthermore, these data validate usefulness of n-3 PUFAs in treatment for depression.

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Section: Human Lymphoblast Cell Lines (Lcls) Reveal Potential Depresscontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 64%

Potential depression and antidepressant-response biomarkers in human lymphoblast cell lines from treatment-responsive and treatment-resistant subjects: roles of SSRIs and omega-3 polyunsaturated fatty acids

Chukaew

Leow

Saengsawang

et al. 2020

Preprint

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“…Considering the fact that most of the variants identified by GWAS are located in non‐coding regions, it is reasonable to assume that these variants affect phenotypes through regulating gene expression. Previous studies (Luo et al, ; Wu et al, ; Yang et al, ; Zhong et al, ) have successfully prioritized plausible causal genes by using integrative approach of expression quantitative trait loci (eQTL) and disease association data. Nevertheless, to date, there is no relevant systematic study, incorporating data from different omics layers to identify periodontitis‐associated genes.…”

Section: Introductionmentioning

confidence: 99%

Integration of genome‐wide association study and expression quantitative trait loci data identifies AIM2 as a risk gene of periodontitis

Zheng

Meng

et al. 2020

J Clinic Periodontology

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Aim:To identify risk variants associated with gene expression in peripheral blood and to identify genes whose expression change may contribute to the susceptibility to periodontitis. Material and Methods:We systematically integrated the genetic associations from a recent large-scale periodontitis GWAS and blood expression quantitative trait loci (eQTL) data using Sherlock, a Bayesian statistical framework. We then validated the potential causal genes in independent gene expression data sets. Gene co-expression analysis was used to explore the functional relationship for the identified causal genes.Results: Sherlock analysis identified 10 genes (rs7403881 for MT1L, rs12459542 for SIGLEC5, rs12459542 for SIGLEC14, rs6680386 for S100A12, rs10489524 for TRIM33, rs11962642 for HIST1H3E, rs2814770 for AIM2, rs7593959 for FASTKD2, rs10416904 for PKN1, and rs10508204 for WDR37) whose expression may influence periodontitis. Among these genes, AIM2 was consistent significantly upregulated in periodontium of periodontitis patients across four data sets. The cis-eQTL (rs2814770, ~16 kb upstream of AIM2) showed significant association with AIM2 (p = 6.63 × 10 -6 ) and suggestive association with periodontitis (p = 7.52 × 10 -4 ). We also validated the significant association between rs2814770 and AIM2 expression in independent expression data set. Pathway analysis revealed that genes co-expressed with AIM2 were significantly enriched in immune-and inflammation-related pathways. Conclusion:Our findings implicate that AIM2 is a susceptibility gene, expression of which in gingiva may influence periodontitis risk. Further functional investigation of AIM2 may provide new insight for periodontitis pathogenesis. K E Y W O R D SAIM2, eQTL, GWAS, periodontitis, Sherlock

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“…Furthermore, only based on the findings of GWAS analysis is impossible to infer whether the detected disease-associated SNPs contain regulatory functions. Thus, Sherlock integrative analysis is a good and effective method for combining the information of GWAS with eQTL data and has been applied to identify many novel risk genes of many complex diseases [15,[19][20][21][22], which cannot be detected by GWAS alone.…”

Section: Discussionmentioning

confidence: 99%

“…He et al [15] introduced a Bayesian statistical approach of Sherlock to systematically reveal the cis-and trans-regulatory effects of risk genes on complex diseases based on GWAS summary data and eQTL data. By using this bioinformatics tool, numerous studies have identified many novel risk genes, which cannot be found with the use of the GWAS approach alone, for different complex traits, such as schizophrenia [19], gout disease [20], and major depressive disorders [21,22].…”

Section: Introductionmentioning

confidence: 99%

Integrative genomics analysis of eQTL and GWAS summary data identifies PPP1CB as a novel bone mineral density risk genes

Zhai

Shao

et al. 2020

Bioscience Reports

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In recent years, multiple genome-wide association studies (GWAS) have identified numerous susceptibility variants and risk genes that demonstrate significant associations with bone mineral density (BMD). However, exploring how these genetic variants contribute risk to BMD remains a major challenge. We systematically integrated two independent expression quantitative trait loci (eQTL) data (N = 1890) and GWAS summary statistical data of BMD (N = 142,487) using Sherlock integrative analysis to reveal whether expression-associated variants confer risk to BMD. By using Sherlock integrative analysis and MAGMA gene-based analysis, we found there existed 36 promising genes, for example, PPP1CB, XBP1, and FDFT1, whose expression alterations may contribute susceptibility to BMD. Through a protein–protein interaction (PPI) network analysis, we further prioritized the PPP1CB as a hub gene that has interactions with predicted genes and BMD-associated genes. Two eSNPs of rs9309664 (PeQTL = 1.42 × 10−17 and PGWAS = 1.40 × 10−11) and rs7475 (PeQTL = 2.10 × 10−6 and PGWAS = 1.70 × 10−7) in PPP1CB were identified to be significantly associated with BMD risk. Consistently, differential gene expression analysis found that the PPP1CB gene showed significantly higher expression in low BMD samples than that in high BMD samples based on two independent expression datasets (P = 0.0026 and P = 0.043, respectively). Together, we provide a convergent line of evidence to support that the PPP1CB gene involves in the etiology of osteoporosis.

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

Cited by 39 publications

References 81 publications

Potential depression and antidepressant-response biomarkers in human lymphoblast cell lines from treatment-responsive and treatment-resistant subjects: roles of SSRIs and omega-3 polyunsaturated fatty acids

Potential depression and antidepressant-response biomarkers in human lymphoblast cell lines from treatment-responsive and treatment-resistant subjects: roles of SSRIs and omega-3 polyunsaturated fatty acids

Integration of genome‐wide association study and expression quantitative trait loci data identifies AIM2 as a risk gene of periodontitis

Integrative genomics analysis of eQTL and GWAS summary data identifies PPP1CB as a novel bone mineral density risk genes

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