Integration of eQTL Analysis and GWAS Highlights Regulation Networks in Cotton under Stress Condition
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ABSTRACT:
SUBMITTER: Han X
PROVIDER: S-EPMC9324452 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.
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Project description:The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs) that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network identified computationally by our method under SNP perturbations is well supported by the results from experimental perturbation studies related to DNA replication stress response.
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Project description:Genome-wide association studies (GWAS) have identified several susceptibility loci for gastric cancer (GC), but the majority of identified single-nucleotide polymorphisms (SNPs) fall within the non-coding region and are likely to exert their biological function by modulating gene expression. To systematically estimate expression-associated SNPs (eSNPs) that confer genetic predisposition to GC, we evaluated the associations of 314,203 stomach tissue-specific eSNPs with GC risk in three GWAS datasets (2,631 cases and 4,373 controls). Subsequently, we conducted a gene-based analysis to calculate the cumulative effect of eSNPs through sequence kernel association combined test and Sherlock integrative analysis. At the SNP-level, we identified two novel variants (rs836545 at 7p22.1 and rs1892252 at 6p22.2) associated with GC risk. The risk allele carriers of rs836545-T and rs1892252-G exhibited higher expression levels of DAGLB (P = 3.70 × 10-18) and BTN3A2 (P = 3.20 × 10-5), respectively. Gene-based analyses identified DAGLB and FBXO43 as novel susceptibility genes for GC. DAGLB and FBXO43 were significantly overexpressed in GC tissues than in their adjacent tissues (P = 5.59 × 10-7 and P = 3.90 × 10-6, respectively), and high expression level of these two genes was associated with an unfavorable prognosis of GC patients (P = 1.30 × 10-7 and P = 7.60 × 10-3, respectively). Co-expression genes with these two novel genes in normal stomach tissues were significantly enriched in several cancer-related pathways, including P53, MAPK and TGF-beta pathways. In summary, our findings confirm the importance of eSNPs in dissecting the genetic basis of GC, and the identified eSNPs and relevant genes will provide new insight into the genetic and biological basis for the mechanism of GC development.
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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:PURPOSE:Osteoporosis is a common global health problem characterized by low bone mineral density (BMD) and increased risk of fracture. Genome-wide association studies (GWAS) have identified >100 genetic loci associated with BMD. However, the functional genes responsible for most associations remain largely unknown. We conducted an innovative summary statistic data-based Mendelian randomization (SMR) analysis to identify novel causal genes associated with BMD and explored their potential functional significance. METHODS:After quality control of the largest GWAS meta-analysis data of BMD and the largest expression quantitative trait loci (eQTL) meta-analysis data from peripheral blood samples, 5967 genes were tested using the SMR method. Another eQTL data was used to verify the results. Next we performed a fine-mapping association analysis to investigate the functional SNP in the identified loci. Weighted gene co-expression network analysis (WGCNA) was used to explore functional relationships for the identified novel genes with known putative osteoporosis genes. Further, we assessed functions of the identified genes through in vitro cellular study or previous functional studies. RESULTS:We identified two potentially causal genes (ASB16-AS1 and SYN2) associated with BMD. SYN2 was a novel osteoporosis candidate gene and ASB16-AS1 locus was known to be associated with BMD but was not the nearest gene to the top GWAS SNP. Fine-mapping association analysis showed that rs184478 and rs795000 was predicted to be possible causal SNPs in ASB16-AS1 and SYN2, respectively. ASB16-AS1 co-expressed with several known putative osteoporosis risk genes. In vitro cellular study showed that over-expressed ASB16-AS1 increased the expression of osteoblastogenesis related genes (BMP2 and ALPL), indicating its functional significance. CONCLUSION:Our findings support that ASB16-AS1 and SYN2 may represent two novel functional genes underlying BMD variation. The findings provide a basis for further functional mechanistic studies.
| S-EPMC6346739 | biostudies-literature
Project description:AbstractSeveral genetic loci have been reported to be significantly associated with coronary artery disease (CAD) by multiple genome-wide association studies (GWAS). Nevertheless, the biological and functional effects of these genetic variants on CAD remain largely equivocal. In the current study, we performed an integrative genomics analysis by integrating large-scale GWAS data (N = 459,534) and 2 independent expression quantitative trait loci (eQTL) datasets (N = 1890) to determine whether CAD-associated risk single nucleotide polymorphisms (SNPs) exert regulatory effects on gene expression. By using Sherlock Bayesian, MAGMA gene-based, multidimensional scaling (MDS), functional enrichment, and in silico permutation analyses for independent technical and biological replications, we highlighted 4 susceptible genes (CHCHD1, TUBG1, LY6G6C, and MRPS17) associated with CAD risk. Based on the protein-protein interaction (PPI) network analysis, these 4 genes were found to interact with each other. We detected a remarkably altered co-expression pattern among these 4 genes between CAD patients and controls. In addition, 3 genes of CHCHD1 (P = .0013), TUBG1 (P = .004), and LY6G6C (P = .038) showed significantly different expressions between CAD patients and controls. Together, we provide evidence to support that these identified genes such as CHCHD1 and TUBG1 are indicative factors of CAD.
| S-EPMC7982177 | biostudies-literature
Project description:BackgroundSevere asthma is a chronic disease contributing to disproportionate disease morbidity and mortality. From the year of 2007, many genome-wide association studies (GWAS) have documented a large number of asthma-associated genetic variants and related genes. Nevertheless, the molecular mechanism of these identified variants involved in asthma or severe asthma risk remains largely unknown.MethodsIn the current study, we systematically integrated 3 independent expression quantitative trait loci (eQTL) data (N = 1977) and a large-scale GWAS summary data of moderate-to-severe asthma (N = 30,810) by using the Sherlock Bayesian analysis to identify whether expression-related variants contribute risk to severe asthma. Furthermore, we performed various bioinformatics analyses, including pathway enrichment analysis, PPI network enrichment analysis, in silico permutation analysis, DEG analysis and co-expression analysis, to prioritize important genes associated with severe asthma.ResultsIn the discovery stage, we identified 1129 significant genes associated with moderate-to-severe asthma by using the Sherlock Bayesian analysis. Two hundred twenty-eight genes were prominently replicated by using MAGMA gene-based analysis. These 228 replicated genes were enriched in 17 biological pathways including antigen processing and presentation (Corrected P = 4.30 × 10- 6), type I diabetes mellitus (Corrected P = 7.09 × 10- 5), and asthma (Corrected P = 1.72 × 10- 3). With the use of a series of bioinformatics analyses, we highlighted 11 important genes such as GNGT2, TLR6, and TTC19 as authentic risk genes associated with moderate-to-severe/severe asthma. With respect to GNGT2, there were 3 eSNPs of rs17637472 (PeQTL = 2.98 × 10- 8 and PGWAS = 3.40 × 10- 8), rs11265180 (PeQTL = 6.0 × 10- 6 and PGWAS = 1.99 × 10- 3), and rs1867087 (PeQTL = 1.0 × 10- 4 and PGWAS = 1.84 × 10- 5) identified. In addition, GNGT2 is significantly expressed in severe asthma compared with mild-moderate asthma (P = 0.045), and Gngt2 shows significantly distinct expression patterns between vehicle and various glucocorticoids (Anova P = 1.55 × 10- 6).ConclusionsOur current study provides multiple lines of evidence to support that these 11 identified genes as important candidates implicated in the pathogenesis of severe asthma.
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Project description:Mitogen-activated protein kinase (MAPK) cascades play a crucial role in plant growth and development, as well as their biotic and abiotic stress responses. As a nodal point of the MAPK cascade, the MKK gene family has not been systematically studied in cotton. Here, we identified 11 putative MKK genes in the Gossypium raimondii genome. Phylogenetic analysis showed that the MKKs were supported by architectures of conserved protein motifs. Expression patterns of MKKs under hormone treatments or abiotic stresses revealed their diverse functions in stress responses. Based on a yeast two hybrid, a total of 63 interactive pairs of MKKs and MAPKs were identified in cotton. Among these, 40 interactive pairs were newly identified compared to that reported previously in Arabidopsis. Integration analysis of the interaction network and expression patterns of MKK and MAPK family members revealed 13 potential MAPK signaling modules that are involved in the complicated cross-talk between hormones and abiotic stresses. Taken together, our data enhance the understanding of the evolution and function of MAPK cascades in cotton, and lay the foundation for the improvement of various defense responses that use MAPK signaling modules in the future.
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Project description:The study of disease-relevant gene modules is one of the main methods to discover disease pathway and potential drug targets. Recent studies have found that most disease proteins tend to form many separate connected components and scatter across the protein-protein interaction network. However, most of the research on discovering disease modules are biased toward well-studied seed genes, which tend to extend seed genes into a single connected subnetwork. In this paper, we propose N2V-HC, an algorithm framework aiming to unbiasedly discover the scattered disease modules based on deep representation learning of integrated multi-layer biological networks. Our method first predicts disease associated genes based on summary data of Genome-wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL) studies, and generates an integrated network on the basis of human interactome. The features of nodes in the network are then extracted by deep representation learning. Hierarchical clustering with dynamic tree cut methods are applied to discover the modules that are enriched with disease associated genes. The evaluation on real networks and simulated networks show that N2V-HC performs better than existing methods in network module discovery. Case studies on Parkinson's disease and Alzheimer's disease, show that N2V-HC can be used to discover biological meaningful modules related to the pathways underlying complex diseases.
| S-EPMC7218106 | biostudies-literature
Project description:Melatonin (MT; N-acetyI-5-methoxytryptamine) is an amine hormone involved in abiotic stress resistance. Previous studies have confirmed that melatonin can promote seed germination, mediate physiological regulation mechanisms, and stimulate crop growth under stress. However, the osmotic regulation mechanism by which exogenous melatonin mediates salt tolerance in cotton is still largely unknown. To investigate the effect of salt stress on melatonin concentration in germinating cotton seeds, we analyzed melatonin content over time during seed germination under different treatments. Melatonin content reached its minimum at day 6, while cotton germination rates peaked at day 6, indicating melatonin content and seed germination are correlated. Then we investigated the effects of 10-100 μM melatonin treatments on membrane lipid peroxides and osmotic adjustment substances during cotton seed germination under salt stress. Salt stress led to electrolyte leakage (EL) as well as accumulations of hydrogen peroxide (H2O2), malondialdehyde (MDA), organic osmotic substances (i.e., proline, soluble sugars), and inorganic osmotic substances (i.e., Na+, Cl-). Meanwhile, the contents of melatonin, soluble proteins, and K+ as well as the K+/Na+ balance decreased, indicating that salt stress inhibited melatonin synthesis and damaged cellular membranes, seriously affecting seed germination. However, melatonin pretreatment at different concentrations alleviated the adverse effects of salt stress on cotton seeds and reduced EL as well as the contents of H2O2, MDA, Na+, and Cl-. The exogenous application of melatonin also promoted melatonin, soluble sugar, soluble proteins, proline, and K+/Na+ contents under salt stress. These results demonstrate that supplemental melatonin can effectively ameliorate the repression of cotton seed germination by enhancing osmotic regulating substances and adjusting ion homeostasis under salt stress. Thus, melatonin may potentially be used to protect cotton seeds from salt stress, with the 20 μM melatonin treatment most effectively promoting cotton seed germination and improving salt stress tolerance.
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