Multiple research projects have been focused on exploring the genetic and environmental risk factors related to ALS. The expression level changes in genes located in GWAS‐identified loci were assessed between partially resistant and susceptible genotypes through a RNA‐seq analysis of the stem tissue collected at various time points after . Given that GWAS-identified loci often fall within gene deserts or in regions with many equally plausible causative genes, it can be challenging to interpret GWAS signals biologically (Nica et al., 2010). de Bary, is a necrotrophic fungus capable of infecting a wide range of plants. J. However, the detection of gene-gene interaction is difficult due to combinatorial explosion. Integrating GWAS, linkage mapping and gene expression analyses reveals the genetic control of growth period traits in rapeseed ( Brassica napus L.) Integrating GWAS, linkage mapping and gene expression analyses reveals the genetic control of growth period traits in rapeseed ( Brassica napus L.) the gene expression. cis-genetic component of gene expression relevant to the trait (9). Integrating GWAS and Expression Data for Functional Characterization of Disease-Associated SNPs: An Application to Follicular Lymphoma . Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean White mould of soya bean, caused by Sclerotinia sclerotiorum (Lib.) integrating GWAS and eQTL summary statistics to test for pleotropic association between gene expression and MDD due to a shared and potentially causal variant at a locus. By integrating GWAS and gene co-expression network analysis, some promising candidate genes, such as ESKIMO1 ( ESK1, BnaC08g26920D ), CELLULOSE SYNTHASE 6 ( CESA6, BnaA09g06990D ), and FRAGILE FIBER 8 ( FRA8, BnaC04g39510D ), were prioritized for further research. Discussion. One family of methods called 6 transcriptome-wide association studies (TWAS) has been developed to integrate GWAS 7 and gene expression datasets to identify gene-trait associations [4]. Here, we adapt a multivariable MR method tailored to gene expression levels as exposure, termed TWMR (Transcriptome-Wide Mendelian Randomization), which integrates summary-level data from GWAS and. Wen, Z. et al. The integrative methods, including GWAS, RNA ‐seq and genomic selection ( GS ), applied in this study facilitated the identification of causal variants, enhanced our understanding of mechanisms of white mould resistance and provided valuable information regarding breeding for disease resistance through genomic selection in soya bean. The GWAS meta-analysis summary data of diabetes was input into SMR for single gene expression association analysis of fasting glucose and insulin resistance. Integrating GWAS and Expression Data for Functional Characterization of Disease-Associated SNPs: An Application to Follicular Lymphoma . Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean Zixiang Wen1, Ruijuan Tan1, Shichen Zhang1, Paul J. Collins1, Jiazheng Yuan1,2, Wenyan Du1, Cuihua Gu1, Shujun Ou3, Qijian Song4, Yong-Qiang Charles An5, John F. Boyse1, Martin I. Chilvers1 and Dechun Wang1,* 1Department of Plant, Soil and Microbial Sciences, Michigan State . Three gene expression datasets are analyzed to illustrate our method, although it can also be applied to other types of high-dimensional data. Here, we aim to leverage tissue-specific gene co-expression networks to infer trait relevant tissues through integrating GWAS and gene expression studies. Integrating GWAS and expression data for functional characterization of disease-associated SNPs: an application to follicular lymphoma. Integrating large-scale kidney function GWAS with gene expression datasets identified kidney and liver as the primary organs for kidney function traits. In stage 3, we performed a pathway analysis using the dysregulated MS gene list from seven human MS case-control expression datasets. Multiple research projects have been focused on exploring the genetic and environmental risk factors related to ALS. Through integrating MDD GWAS with relevant biological phenotypes, gene-expression analyses, and independent genetic analyses, a convergent functional genomics identify several potential risk genes of MDD at the MHC region (Li et al., 2019). Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets . In particular, TWAS 8 Several studies demonstrate that GWAS risk variants may have an association with gene expression or splicing. A novel analytical framework through a summary data-based MR (SMR) approach integrating cis-expression quantitative trait loci (cis-eQTL) or cis-DNA methylation QTL (cis-mQTL) and GWAS data have . Role of GWAS candidate gene, SCYL3, in TDP-43 expression and aggregationMarilyn Ngo, MPHUniversity of Pittsburgh ADRCWe have performed a GWAS study to look a. Integrating GWAS and Co-expression Network Data Identifies Bone Mineral Density Genes SPTBN1 and MARK3 and an Osteoblast Functional Module Author links open overlay panel Gina M. Calabrese 1 Larry D. Mesner 1 Joseph P. Stains 2 Steven M. Tommasini 3 Mark C. Horowitz 3 Clifford J. Rosen 4 Charles R. Farber 1 5 6 The methodology we applied, of integrating GWAS results with gene expression data, is simple, intuitive and relevant to additional disorders. Here, we extend TWAS from gene-based analysis to pathway-based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statistics (or GWAS individual-level data) to identify gene pathways associated with complex traits. Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets Qing Xiong1, Nicola Ancona 2, Elizabeth R. Hauser3, Sayan Mukherjee4,#,*, Terrence S. Furey1,#,* 1Department of Genetics, Department of Biology, Lineberger Comprehensive Cancer Center, and Carolina Center for Genomics and Society, The University of North Carolina at Chapel Hill, Chapel Hill, The protein encoded by this gene is a basic leucine zipper (bZIP) transcription factor that lacks a transactivation domain. Plant Biotechnol. Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk. Systematic integration of omics data (e.g . GWAS with whole-genome expression analysis. Role of GWAS candidate gene, SCYL3, in TDP-43 expression and aggregationMarilyn Ngo, MPHUniversity of Pittsburgh ADRCWe have performed a GWAS study to look a. By integrating genome-wide association studies (GWAS) and gene expression analyses, Meijón et al. By integration of genetic variants, transcriptome, and phenotypic the disease [15-17]. 1. 2015 Oct 1;24(10):1574-1584. However, the . Due to the increase in H. pylori antimicrobial resistance new methods to identify the molecular mechanisms of H. pylori-induced pathology are urgently needed. Integrating GWAS and Co-expression Network Data Identifies Causal Bone Mineral Density Genes SPTBN1and MARK3and an Osteoblast Functional Module Gina M. Calabrese,1Larry D. Mesner,1Joseph P. Stains,2Steven M. Tommasini,3Mark C. Horowitz,3Clifford J. Rosen,4and Charles R. Farber1,5 Gina M. Calabrese By integrating genome-wide association studies (GWAS) and gene expression analyses, Meijón et al. (2013) identified a new F-box gene KUK that was associated with root cell length in Arabidopsis [ 21 ]. GWAS have revealed genetic variants associated with an increased risk of developing breast cancer. Aben5,6, Hoda Anton-Culver7, Natalia Antonenkova8; Georgia Chenevix-Trench9, on behalf of the 2 The Emotion-Cognition Research Center, Shalvata Mental Health Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel To identify genes associated with depression, we performed a TWAS by integrating two gene-expression reference panels (i.e. However, the . TWAS analysis utilizes disease GWAS summary statistics combining with pre-computed gene expression weights to calculate the association of every gene with known diseases (Gusev et al., 2018). Kar SP, Tyrer JP, Li Q, Lawrenson K, Aben KKH, Anton-Culver H et al. eQtL AnALysis: gene expression As Qu ntit tive tr it In a traditional GWAS, the trait being investigated is as-sociated with a region in the genome. We integrated GWAS summary data of 30 complex traits with gene expression to identify 1,196 susceptibility genes (i.e., genes with at least one significant trait association), comprising 5,490 total associations (after Bonferroni correction; see Material and Methods).Of these associations, we observed 1,789 distinct gene-trait pairs, of which 783 were found in anthropometric traits, 423 in . The coronavirus disease 19 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has a rapidly increasing prevalence and has caused significant morbidity/mortality. We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. In stage 2, we performed a candidate pathway analysis of the large-scale MS-GWAS dataset. Rejection of the null hypothesis (i.e., P 1 Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel. The integrative methods, including GWAS, RNA‐seq and genomic selection, applied in this study facilitated the identification of causal variants, enhanced the understanding of mechanisms of white mould resistance and provided valuable information regarding breeding for disease resistance through genomic selection in soya bean. Analysis of differential gene expression has been proposed as a promising approach to aid the interpretation (Emilsson et al., 2008). The coronavirus disease 19 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has a rapidly increasing prevalence and has caused significant morbidity/mortality. Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean. We investigated the influence on gene expression of three established FL-associated loci—rs10484561, rs2647012, and rs6457327—by measuring their correlation with human-leukocyte-antigen (HLA) expression . In the present study, we performed the TWAS analysis of ALS through integrating the GWAS summary data and precomputed gene expression weights of CBRS, CBR, NBL, and YBL. There are other methods for detecting gene-trait associations using GWAS and gene expression data 36 . The heterogeneity in dependent instruments (HEIDI) test was done to explore the existence of linkage in the observed association. Further, the gene expression data used to support many GWAS are drawn from individuals distinct from those who were genotyped [16,18,19,21,23], rendering the ana-lysis of any effects of genotype on gene expression indir-ect and potentially biased due to differences in subjects who were genotyped versus subjects with mRNA data. A total of 29 TF genes were located within 1 Mb of the top risk-associated SNP at seven of the 12 known genome-wide significant serous EOC risk loci . . Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk Siddhartha P. Kar1, Jonathan P.Tyrer2, Qiyuan Li3, Kate Lawrenson4, Katja K.H. Although most GWAS do not concomi-tantly measure gene expression, the influence of genetic var-iation on gene expression allows the use of reference datasets (e.g., GTEx [18]) to predict gene expression given a set of genotypes and to subsequently identify new disease-associated genes [11, 13, 19]. Conclusions: Integration of GWAS statistics of kidney function traits and gene expression data identified relevant tissues and cell types, as a basis for further mechanistic studies to understand GWAS loci. Despite the availability of many vaccines that can offer widespread immunization, it is also important to reach effective treatment for COVID-19 patients. Summary White mould of soya bean, caused by Sclerotinia . By integrating genome-wide association studies (GWAS) and gene expression analyses, Meijón et al. TWAS of ON was performed using the FUSION software 3 through integrating the UK Biobank ON GWAS summary data and pre-computed gene expression reference weights of peripheral blood, whole blood, and muscle skeleton (Gusev and Ko, 2016). Helicobacter pylori is a gram-negative bacterium that colonizes the human gastric mucosa and can lead to gastric inflammation, ulcers, and stomach cancer. By integrating GWAS and gene co-expression network analysis, some promising candidate genes, such as ESKIMO1 (ESK1, BnaC08g26920D), CELLULOSE SYNTHASE 6 (CESA6, BnaA09g06990D) and FRAGILE FIBER 8 (FRA8, BnaC04g39510D), were prioritized for further research. Thus, the transcriptome . Integration of gene expression and GWAS results supports involvement of calcium signaling in Schizophrenia L.Hertzbergab P.Katselc P.Roussosc V.Haroutuniancd E.Domany a https://doi.org/10.1016/j.schres.2015.02.001 Get rights and content Abstract The number of Genome Wide Association Studies (GWAS) of schizophrenia is rapidly growing. The network-based analytic strategy we used to integrate serous EOC GWAS and gene expression datasets is outlined in Fig. 4,5 In recent years, there has been an increase in the number of publicly available brain-derived QTL data sets, including those released by the UK Brain Expression Consortium and the Genotype-Tissue Expression (GTEx) Consortium used in . . The HEIDI (heterogeneity in dependent instruments) test was introduced to test against the null hypothesis that there is a single causal variant . 2010b). Network-based integration of GWAS and gene expression identifies a HOX-centric network associated with serous ovarian cancer risk. For each gene, we assigned the cell type with the strongest expression, and discovered that BECs contained the most AD-related GWAS genes, followed by microglia or macrophages (Fig. CMC and BrainSeq2) and summary-level association data from the largest . TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. Results: We present a novel feature selection method incorporating variable interaction. SMR is capable of integrating GWAS results with eQTLs annotation information to evaluate the relationships between gene expression levels and complex traits [ 9 ]. The module associated with cellulose biosynthesis was highlighted. Then, the calculated tissue-related expression weights were integrated with summary-level GWAS results to impute the association statistics be-tween gene expression and the target disease. research suggested that a large portion of GWAS loci might influence complex traits 5 through regulating gene expression levels [2,3]. 2015 Oct;24(10):1574-84. doi: 10.1158/1055-9965.EPI-14-1270. It is known to bind the US-2 DNA element in the promoter of the oxytocin receptor (OTR) gene and most likely heterodimerizes with other leucine zipper-containing proteins to enhance expression of the OTR gene during term . As we have demonstrated, it has the potential to improve the understanding of the biological meaning of GWAS results. Summary Although genome-wide association studies (GWAS) identify variants associated with traits of interest, they often fail in identifying causative genes underlying a given phenotype. Since the beginning of the pandemic, we focused on the collection and integration of SARS-CoV-2 databases, which contain information on the structure of the virus and on its ability to spread, mutate, and evolve; data are made available from several open-source databases . To do so, we perform gene-centric analysis and focus on a common set of m genes that are measured in both GWAS and gene expression studies. 6e . References 1. (2013) identified a new F-box gene KUK that was associated with root cell length in Arabidopsis [21]. Integrating GWAS and gene coexpression networks can help prioritize high-confidence candidate genes, as the expression profiles of trait-associated genes can be used to mine novel candidates. Candidate genes were identified integrating three approaches: (i) correlation of phenotypes and gene expression levels, (ii) association between SNP markers and gene expression levels (eQTL), and (iii) association between SNP markers and phenotypes (GWA). Commentary: Integration of gene expression and GWAS results supports involvement of calcium signaling in Schizophrenia. (2013) identified a new F-box gene KUK that was associated with root cell length in Arabidopsis [21]. In the present study, we performed the TWAS analysis of ALS through integrating the GWAS summary data and precomputed gene expression weights of CBRS, CBR, NBL, and YBL. First, we use the probabilistic Mendelian randomization (PMR-Egger . Integrating GWAS and Co-expression Network Data Identifies Bone Mineral Density Genes SPTBN1 and MARK3 and an Osteoblast Functional Module Author links open overlay panel Gina M. Calabrese 1 Larry D. Mesner 1 Joseph P. Stains 2 Steven M. Tommasini 3 Mark C. Horowitz 3 Clifford J. Rosen 4 Charles R. Farber 1 5 6 Here we utilized a computational biology approach, harnessing genome-wide . L. Hertzberg 1,2*, E. Domany 1. Therefore, a new approach integrating the genetics of gene expression and GWAS pathway analysis is appealing for its ability to improve the power of detecting the association (Zhong et al. Cancer Epidemiology Biomarkers and Prevention . Gene Set/Pathway Enrichment Analysis. The outbreak of the COVID-19 epidemic has focused enormous attention on the genetics of viral infection and related disease. In other words, TWAS can integrate the associations between GWAS and gene expression measurements to identify genes associated with traits. We applied the summary data-based Mendelian randomization (SMR) method integrating GWAS and gene expression quantitative trait loci (eQTL) data in 13 brain regions to identify genes that were pleiotropically associated with MDD. In the kidney, proximal tubule was the critical cell type for eGFR and urate, as well as for monogenic electrolyte or metabolic disease genes. We considered that the genes and interactions that were present in both the GWAS and RNA-seq networks had a higher probability of being actually involved in sperm quality . Despite the availability of many vaccines that can offer widespread immunization, it is also important to reach effective treatment for COVID-19 patients. Expression quantitative trait loci (eQTL) analysis in specific tissues is a valuable tool to identify potentially causal SNPs [7-10]. Integrating GWAS and Gene Expression Analysis Identifies Candidate Genes for Root Morphology Traits in Maize at the Seedling Stage Authors Houmiao Wang 1 2 , Jie Wei 3 , Pengcheng Li 4 5 , Yunyun Wang 6 , Zhenzhen Ge 7 , Jiayi Qian 8 , Yingying Fan 9 , Jinran Ni 10 , Yang Xu 11 12 , Zefeng Yang 13 14 15 , Chenwu Xu 16 17 18 Affiliations We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). The genes responsible for associations identified by genome-wide association studies (GWASs) are largely unknown. Humana Press, quantification of gene transcripts may act as intermediate phenotypes between genetic loci and the clinical phenotypes. We investigated the influence on gene expression of three established FL-associated loci-rs10484561, rs2647012, and rs6457327-by measuring their correlation with human-leukocyte-antigen (HLA) expression . Finally, 761 causal genes were discovered for ALS. Siddhartha P Kar Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. Integrating GWAS, linkage mapping and gene expression analyses reveals the genetic control of growth period traits in rapeseed ( Brassica napus L.) Tengyue Wang , Lijuan Wei , Jia Wang , Ling Xie , Yang Yang Li , Shuyao Ran , Lanyang Ren , Kun Lu , Jiana Li , Michael P. Timko & Liezhao Liu Biotechnology for Biofuels This document highlights some of the potential benefits of incorporating whole-genome gene expression data into a clinical trait-based GWAS. We investigated the influence on gene expression of three established FL-associated loci—rs10484561, rs2647012, and rs6457327—by measuring their correlation with human-leukocyte-antigen (HLA) expression . demonstrate that, by integrating GWAS and co-expression data, it is possible to provide insight into the identity of causal GWAS genes and how they may influence a complex trait. eQTL, expression quantitative trait locus; GWA, genome-wide association; SNP, single . A novel integrative genomics approach that combines GWAS information with gene expression and other multi-omics data for the discovery of potential clinically actionable biomarkers and identification of gene regulatory networks and biological pathways enriched for genetic variants is needed. Using the GWAS and RNA-seq data, we built a gene interaction network. Authors Siddhartha P Kar 1 . Epub 2015 Jul 24. A systematic approach generated a comprehensive list of GWAS genes prioritized by cell type-specific expression. Methods: We used the summary data-based Mendelian randomization (SMR) to integrate GWAS with expression quantitative trait loci (eQTL) studies and methylation quantitative trait loci (mQTL) studies. To do this, we integrated our myasthenia gravis GWAS results from the overall and subgroup analysis with the gene expression profile in normal skeletal muscle, peripheral nerve, and whole blood obtained from public Genotype-Tissue Expression (GTEx) datasets (10). 16 (11), 1825-1835. They integrated the AFGen 2017 GWAS result, a whole blood methylome-wide association study, and a whole blood transcriptome-wide association study, and identified nearly 2000 AF genes. This study conducted a two-stage integrative analysis of the summary statistics from the genome-wide association study (GWAS, n = 552) of the lowest absolute neutrophil count (ANC) during clozapine treatment and the summary data of the expressed quantitative trait locus (eQTL). Integrating GWAS and Gene Expression Analysis Identifies Candidate Genes for Root Morphology Traits in Maize at the Seedling Stage by Houmiao Wang 1,2, Jie Wei 1, Pengcheng Li 1,2, Yunyun Wang 1, Zhenzhen Ge 1, Jiayi Qian 1, Yingying Fan 1, Jinran Ni 1, Yang Xu 1,2, Zefeng Yang 1,2,3 and Chenwu Xu 1,2,3,* 1 Finally, 761 causal genes were discovered for ALS. In this study, the gene expression weight panels for the periph-eral blood and skeletal muscle were downloaded from In their recent work, Wang et.al developed a strategy to integrate multiple omics data to identify AF-related genes ( 23 ). TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. In stage 1, we conducted multiple pathway analyses of two MS-GWAS datasets. Thus, integrating GWAS information using gene expression data from LAR and MES as the intermediate phenotype holdspromise for establishing the causal association between genetic susceptibility and the two subtypes of TNBC. By expression (e)GWAS, we identified three trans-expression QTL involving the genes IQCJ, ACTR2 and HARS. A brassinosteroid signaling kinase BSK3 that modulated root elongation under mild nitrogen deficiency also was identified by GWAS [ 22 ]. Hung JH. Briefly, the gene expression weights of a certain tissue were first calculated using the prediction models . Calabrese et al. Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk Cancer Epidemiol Biomarkers Prev. Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean . The hypothesis behind this study was that integration of GWAS data with protein-protein interactions and gene expression would facilitate a systems-based understanding of type 1 diabetes pathogenetic mechanisms ().We took a focused approach using only proteins from GWAS regions as input proteins for generating protein networks.