scholarly journals Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhouzhou Dong ◽  
Yunlong Ma ◽  
Hua Zhou ◽  
Linhui Shi ◽  
Gongjie Ye ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
Severe Asthma ◽  
Antigen Processing ◽  
Association Studies ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Bioinformatics Analyses

Abstract Background Severe 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. Methods In 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. Results In 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). Conclusions Our current study provides multiple lines of evidence to support that these 11 identified genes as important candidates implicated in the pathogenesis of severe asthma.

scholarly journals SNP-based pathway enrichment analysis for genome-wide association studies

2011 ◽  
Vol 12 (1) ◽  
pp. 99 ◽  
Author(s):  
Lingjie Weng ◽  
Fabio Macciardi ◽  
Aravind Subramanian ◽  
Guia Guffanti ◽  
Steven G Potkin ◽  
...  
Keyword(s):  
Association Studies ◽  
Enrichment Analysis ◽  
Genome Wide Association ◽  
Pathway Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Pathway Enrichment ◽  
Genome Wide

scholarly journals Unravelling the shared genetic mechanisms underlying 18 autoimmune diseases using a systems approach

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Evgeniia Golovina ◽  
Tayaza Fadason ◽  
Justin M. O'Sullivan
Keyword(s):  
Autoimmune Diseases ◽  
Systems Approach ◽  
Association Studies ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interaction ◽  
Disease Specific ◽  
Shared Genetic

Autoimmune diseases (AiDs) are complex heterogeneous diseases characterized by hyperactive immune responses against self. Genome-wide association studies have identified thousands of single nucleotide polymorphisms (SNPs) associated with several AiDs. While these studies have identified a handful of pleiotropic loci that confer risk to multiple AiDs, they lack the power to detect shared genetic factors residing outside of these loci. Here, we integrated chromatin contact, expression quantitative trait loci and protein-protein interaction (PPI) data to identify genes that are regulated by both pleiotropic and non-pleiotropic SNPs. The PPI analysis revealed complex interactions between the shared and disease-specific genes. Furthermore, pathway enrichment analysis demonstrated that the shared genes co-occur with disease-specific genes within the same biological pathways. In conclusion, our results are consistent with the hypothesis that genetic risk loci associated with multiple AiDs converge on a core set of biological processes that potentially contribute to the emergence of polyautoimmunity.

scholarly journals Transcriptome-wide association study identifies new susceptibility genes and pathways for depression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoyan Li ◽  
◽  
Xi Su ◽  
Jiewei Liu ◽  
Huijuan Li ◽  
...  
Keyword(s):  
Association Study ◽  
Association Studies ◽  
Functional Characterization ◽  
Enrichment Analysis ◽  
Mechanistic Study ◽  
Pathway Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Depression Risk ◽  
Risk Variants

AbstractDepression is the most prevalent mental disorder with substantial morbidity and mortality. Although genome-wide association studies (GWASs) have identified multiple risk variants for depression, due to the complicated gene regulatory mechanisms and complexity of linkage disequilibrium (LD), the biological mechanisms by which the risk variants exert their effects on depression remain largely unknown. Here, we perform a transcriptome-wide association study (TWAS) of depression by integrating GWAS summary statistics from 807,553 individuals (246,363 depression cases and 561,190 controls) and summary-level gene-expression data (from the dorsolateral prefrontal cortex (DLPFC) of 1003 individuals). We identified 53 transcriptome-wide significant (TWS) risk genes for depression, of which 23 genes were not implicated in risk loci of the original GWAS. Seven out of 53 risk genes (B3GALTL, FADS1, TCTEX1D1, XPNPEP3, ZMAT2, ZNF501 and ZNF502) showed TWS associations with depression in two independent brain expression quantitative loci (eQTL) datasets, suggesting that these genes may represent promising candidates. We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Finally, pathway enrichment analysis revealed biologically pathways relevant to depression. Our study identified new depression risk genes whose expression dysregulation may play a role in depression. More importantly, we translated the GWAS associations into risk genes and relevant pathways. Further mechanistic study and functional characterization of the TWS depression risk genes will facilitate the diagnostics and therapeutics for depression.

scholarly journals Genetic Determinants of Poor Response to Treatment in Severe Asthma

2021 ◽  
Vol 22 (8) ◽  
pp. 4251
Author(s):  
Ricardo G. Figueiredo ◽  
Ryan S. Costa ◽  
Camila A. Figueiredo ◽  
Alvaro A. Cruz
Keyword(s):  
Severe Asthma ◽  
Inhaled Corticosteroids ◽  
Association Studies ◽  
Airway Remodeling ◽  
Expression Patterns ◽  
Poor Response ◽  
Response To Treatment ◽  
Genome Wide Association Studies ◽  
Genetic Determinants ◽  
Increased Risk

Severe asthma is a multifactorial disorder with marked phenotypic heterogeneity and complex interactions between genetics and environmental risk factors, which could, at least in part, explain why during standard pharmacologic treatment, many patients remain poorly controlled and at an increased risk of airway remodeling and disease progression. The concept of “precision medicine” to better suit individual unique needs is an emerging trend in the management of chronic respiratory diseases. Over the past few years, Genome-Wide Association Studies (GWAS) have revealed novel pharmacogenetic variants related to responses to inhaled corticosteroids and the clinical efficacy of bronchodilators. Optimal clinical response to treatment may vary between racial/ethnic groups or individuals due to genetic differences. It is also plausible to assume that epigenetic factors play a key role in the modulation of gene expression patterns and inflammatory cytokines. Remarkably, specific genetic variants related to treatment effectiveness may indicate promising pathways for novel therapies in severe asthma. In this review, we provide a concise update of genetic determinants of poor response to treatment in severe asthma and future directions in the field.

scholarly journals Unravelling the Shared Genetic Mechanisms Underlying 18 Autoimmune Diseases Using a Systems Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Evgeniia Golovina ◽  
Tayaza Fadason ◽  
Justin O’Sullivan
Keyword(s):  
Autoimmune Diseases ◽  
Systems Approach ◽  
Association Studies ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interaction ◽  
Disease Specific ◽  
Shared Genetic

Autoimmune diseases (AiDs) are complex heterogeneous diseases characterized by hyperactive immune responses against self. Genome-wide association studies have identified thousands of single nucleotide polymorphisms (SNPs) associated with several AiDs. While these studies have identified a handful of pleiotropic loci that confer risk to multiple AiDs, they lack the power to detect shared genetic factors residing outside of these loci. Here, we integrated chromatin contact, expression quantitative trait loci and protein-protein interaction (PPI) data to identify genes that are regulated by both pleiotropic and non-pleiotropic SNPs. The PPI analysis revealed complex interactions between the shared and disease-specific genes. Furthermore, pathway enrichment analysis demonstrated that the shared genes co-occur with disease-specific genes within the same biological pathways. In conclusion, our results are consistent with the hypothesis that genetic risk loci associated with multiple AiDs converge on a core set of biological processes that potentially contribute to the emergence of polyautoimmunity.

scholarly journals Accumulation of Minor Alleles of Common SNPs in Schizophrenia

2017 ◽  
Author(s):  
Pei He ◽  
Xiaoyun Lei ◽  
Dejian Yuan ◽  
Zuobin Zhu ◽  
Shi Huang
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Enrichment Analysis ◽  
Individual Risk ◽  
Pathway Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Risk Alleles ◽  
Minor Alleles ◽  
Public Datasets

Schizophrenia is a common neuropsychiatric disorder with a lifetime risk of 1%. A number of large scale genome wide association studies have identified numerous individual risk single nucleotide polymorphisms (SNPs) whose precise roles in schizophrenia remain unknown. Accumulation of many of these risk alleles has been found to be a more important risk factor. Consistently, recent studies showed a role for enrichment of minor alleles (MAs) in complex diseases. Here we studied the role of MAs in general in schizophrenia using public datasets. Relative to matched controls, schizophrenia cases showed higher minor allele content (MAC), especially for the sporadic cases. By linkage analysis, we identified 82 419 SNPs that could be used to predict 2.2% schizophrenia cases with 100% certainty. Pathway enrichment analysis of these SNPs identified 17 pathways, 15 of which are known to be linked with Schizophrenia with the remaining 2 associated with other mental disorders. These results suggest a role for a collective effect of MAs in schizophrenia and provide a method to genetically screen for schizophrenia.

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

scholarly journals Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

2018 ◽  
Author(s):  
David M. Howard ◽  
Mark J. Adams ◽  
Toni-Kim Clarke ◽  
Jonathan D. Hafferty ◽  
Jude Gibson ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Drug Repositioning ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Synaptic Structure ◽  
Genome Wide

AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

scholarly journals 297 GWAS for complex models accounting for populations structure with GBLUP and ssGBLUP

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 32-32
Author(s):  
Juan P Steibel ◽  
Ignacio Aguilar
Keyword(s):  
Hypothesis Testing ◽  
Large Scale ◽  
Mixed Model ◽  
Prediction Models ◽  
Association Studies ◽  
Least Square ◽  
Type I ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Formal Hypothesis Testing

Abstract Genomic Best Linear Unbiased Prediction (GBLUP) is the method of choice for incorporating genomic information into the genetic evaluation of livestock species. Furthermore, single step GBLUP (ssGBLUP) is adopted by many breeders’ associations and private entities managing large scale breeding programs. While prediction of breeding values remains the primary use of genomic markers in animal breeding, a secondary interest focuses on performing genome-wide association studies (GWAS). The goal of GWAS is to uncover genomic regions that harbor variants that explain a large proportion of the phenotypic variance, and thus become candidates for discovering and studying causative variants. Several methods have been proposed and successfully applied for embedding GWAS into genomic prediction models. Most methods commonly avoid formal hypothesis testing and resort to estimation of SNP effects, relying on visual inspection of graphical outputs to determine candidate regions. However, with the advent of high throughput phenomics and transcriptomics, a more formal testing approach with automatic discovery thresholds is more appealing. In this work we present the methodological details of a method for performing formal hypothesis testing for GWAS in GBLUP models. First, we present the method and its equivalencies and differences with other GWAS methods. Moreover, we demonstrate through simulation analyses that the proposed method controls type I error rate at the nominal level. Second, we demonstrate two possible computational implementations based on mixed model equations for ssGBLUP and based on the generalized least square equations (GLS). We show that ssGBLUP can deal with datasets with extremely large number of animals and markers and with multiple traits. GLS implementations are well suited for dealing with smaller number of animals with tens of thousands of phenotypes. Third, we show several useful extensions, such as: testing multiple markers at once, testing pleiotropic effects and testing association of social genetic effects.

scholarly journals Identification of candidate genes controlling chilling tolerance of rice in the cold region at the booting stage by BSA-Seq and RNA-Seq

2020 ◽  
Vol 7 (11) ◽  
pp. 201081
Author(s):  
Zhenhua Guo ◽  
Lijun Cai ◽  
Zhiqiang Chen ◽  
Ruiying Wang ◽  
Lanming Zhang ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Bulked Segregant Analysis ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Chilling Tolerance ◽  
Pathway Enrichment Analysis ◽  
Potential Candidate ◽  
Rice Varieties ◽  
Booting Stage ◽  
F2 Population

Rice is sensitive to low temperatures, specifically at the booting stage. Chilling tolerance of rice is a quantitative trait loci that is governed by multiple genes, and thus, its precise identification through the conventional methods is an arduous task. In this study, we investigated the candidate genes related to chilling tolerance at the booting stage of rice. The F2 population was derived from Longjing25 (chilling-tolerant) and Longjing11 (chilling-sensitive) cross. Two bulked segregant analysis pools were constructed. A 0.82 Mb region containing 98 annotated genes on chromosomes 6 and 9 was recognized as the candidate region associated with chilling tolerance of rice at the booting stage. Transcriptomic analysis of Longjing25 and Longjing11 revealed 50 differentially expressed genes (DEGs) on the candidate intervals. KEGG pathway enrichment analysis of DEGs was performed. Nine pathways were found to be enriched, which contained 10 DEGs. A total of four genes had different expression patterns or levels between Longjing25 and Longjing11. Four out of the 10 DEGs were considered as potential candidate genes for chilling tolerance. This study will assist in the cloning of the candidate genes responsible for chilling tolerance and molecular breeding of rice for the development of chilling-tolerant rice varieties.

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