Comprehensive integrative analyses identify GLT8D1 and CSNK2B as schizophrenia risk genes

Yang, Cuiping; Li, Xiaoyan; Wu, Yong; Shen, Qingwu; Zeng, Yong; Xiong, Qian; Wei, Mengping; Chen, Chuansheng; Liu, Jiewei; Huo, Yongxia; Li, Kaiqin; Xue, Gui; Yao, Yong-Gang; Zhang, Chen; Li, Ming; Chen, Yongbin; Luo, Xiong-Jian

doi:10.1038/s41467-018-03247-3

Cited by 80 publications

(80 citation statements)

References 86 publications

(198 reference statements)

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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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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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“…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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“…Evidence suggest that noncoding variants identified by GWASs may be involved in transcriptional mechanisms (Maurano & Stamatoyannopoulos, ; Lucas D. Ward & Kellis, ) and thus co‐localizing the noncoding variants with expression quantitative trait loci (eQTL) could provide insights into gene expression regulation (Westra et al, ) and biological basis for disease associations (Cookson, Liang, Abecasis, Moffatt, & Lathrop, ; Degner et al, ). Such integrative studies have been performed successfully in multiple psychiatric disorders (Cross‐Disorder Group of the Psychiatric Genomics Consortium, ), including schizophrenia (Luo et al, ; Yang, Li, et al, ), autism spectrum disorder (Wang et al, ), attention deficit‐hyperactivity disorder (Yang, Chang, et al, ), bipolar disorder (Li et al, ), and major depressive disorder (Huo et al, ). Integrating GWAS signals of DD with eQTLs derived from human brain tissues will be useful to identify any functional variants.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, neurodevelopmental disorders (e.g., schizophrenia, autism, epilepsy and intellectual disability) could share a genetic base (Brainstorm et al, ) and some core clinical features and molecular regulatory networks (Parikshak, Gandal, & Geschwind, ), particularly in human brain tissues (Johnson et al, ). It is therefore plausible to borrow functional genomic information from other neurodevelopmental disorders such as schizophrenia (Yang, Li, et al, ) to inform research in DD where sample resources (e.g., human brain tissues) and cell models are generally lacking. For example, differential gene expression data derived from a well‐powered study of schizophrenia might be valuable to study functional roles of the DD susceptible genes.…”

Section: Introductionmentioning

confidence: 99%

Dyslexia associated functional variants in Europeans are not associated with dyslexia in Chinese

Liu

Hou

et al. 2019

American J of Med Genetics Pt B

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Genome-wide association studies (GWAS) of developmental dyslexia (DD) often used European samples and identified only a handful associations with moderate or weak effects. This study aims to identify DD functional variants by integrating the GWAS associations with tissue-specific functional data and test the variants in a Chinese DD study cohort named READ. We colocalized associations from nine DD related GWAS with expression quantitative trait loci (eQTL) derived from brain tissues and identified two eSNPs rs349045 and rs201605. Both eSNPs had supportive evidence of chromatin interactions observed in human hippocampus tissues and their respective target genes ZNF45 and DNAH9 both had lower expression in brain tissues in schizophrenia patients than controls. In contrast, an eSNP rs4234898 previously identified based on eQTL from the lymphoblastic cell lines of dyslexic children had no chromatin interaction with its target gene SLC2A3 in hippocampus tissues and SLC2A3 expressed higher in the schizophrenia patients than controls. We genotyped the three eSNPs in the READ cohort of 372 cases and 354 controls and discovered only weak associations in rs201605 and rs4234898 with three DD symptoms (p < .05). The lack of associations could be due to low power in READ but could also implicate different etiology of DD in Chinese. K E Y W O R D SDNAH9, dyslexia, eQTL, GWAS, integrative analysis, SLC2A3

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Comprehensive integrative analyses identify GLT8D1 and CSNK2B as schizophrenia risk genes

Cited by 80 publications

References 86 publications

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

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

Dyslexia associated functional variants in Europeans are not associated with dyslexia in Chinese

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