GWAB: a web server for the network-based boosting of human genome-wide association data

Shim, Jung Eun; Bang, Changbae; Yang, Sunmo; Lee, Tak H.; Hwang, Sohyun; Kim, Chan Yeong; Singh-Blom, U. Martin; Marcotte, Edward M.; Lee, Insuk

doi:10.1093/nar/gkx284

Cited by 28 publications

(39 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key challenge remains in prioritizing GWAS signals and improving weak signals obtained from low frequency causative variants (Lee and Lee, 2018). The availability of whole-genome assemblies and corresponding protein-coding gene annotations, alongside additional layers of information, such as expression profiles from RNA-Seq experiments, now facilitate the integration of multiple data layers to improve GWAS results (Shim et al , 2017; Lee and Lee, 2018; Schaefer et al , 2018). Our example of the CmAPRR2 gene suggests that adding functional annotation prediction to GWAS SNPs and treating predicted genes as integral functional units could potentially be used as an informative layer that can boost the signal of causative weak associations.…”

Section: Discussionmentioning

confidence: 99%

Multi-allelic APRR2 Gene is Associated with Fruit Pigment Accumulation in Melon and Watermelon

Oren

Tzuri

Vexler

et al. 2019

Preprint

View full text Add to dashboard Cite

23Color and pigment content are important aspects of fruit quality and consumer 24 acceptance of cucurbit crops. Here, we describe the independent mapping and cloning 25 of a common causative APRR2 gene regulating pigment accumulation in melon and 26 watermelon. We initially show that the APRR2 transcription factor is causative for the 27 qualitative difference between dark and light green rind in both crops. Further analyses 28 establish the link between sequence or expression level variations in the CmAPRR2 29 gene and pigments content in the rind and flesh of mature melon fruits. GWAS of young 30 fruit rind color in a panel composed of 177 diverse melon accessions did not result in 31 any significant association, leading to an earlier assumption that multiple genes are 32 involved in shaping the overall phenotypic variation at this trait. Through resequencing 33 of 25 representative accessions and allelism tests between light rind accessions, we 34 show that multiple independent SNPs in the CmAPRR2 gene are causative for the light 35 rind phenotype. The multi-haplotypic nature of this gene explain the lack of detection 36 power obtained through GBS-based GWAS and confirm the pivotal role of this gene in 37 shaping fruit color variation in melon. This study demonstrates the power of combining 38 bi-and multi-allelic designs with deep sequencing, to resolve lack of power due to high 39 haplotypic diversity and low allele frequencies. Due to its central role and broad effect 40 on pigment accumulation in fruits, the APRR2 gene is an attractive target for 41 carotenoids bio-fortification of cucurbit crops.42 43

show abstract

Section: Discussionmentioning

confidence: 99%

Multi-allelic APRR2 Gene is Associated with Fruit Pigment Accumulation in Melon and Watermelon

Oren

Tzuri

Vexler

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Holding the view that the genes associated with a disease tend to be functionally together, GWAB prioritizes candidate disease genes by integrating the GWAS data and human functional gene network (Shim et al, 2017). Unlike other network-based approach, GWAB has an advantage that it integrates complementary information from both populationbased approach and molecular profiling approach to identify disease-associated genes.…”

Section: Genome-wide Association Boostingmentioning

confidence: 99%

“…Therefore, it can make the best of information identified from functional experiments apart from the statistical association from GWAS. Shim et al (2017) used GWAS data from Schunkert et al (2011) as input and referenced genes from three disease gene databases (OMIM, DO, and CADgeneDB) as benchmark to predict for diseaseassociated genes. We therefore included Jung Eun Shim's results into our study.…”

Section: Genome-wide Association Boostingmentioning

confidence: 99%

“…Therefore, the complexity of LD structure and distal regulation impedes our ability to identify causal genes from GWAS results. To address this issue, many GWAS-based computational methods aiming to prioritize the most likely causal genes have been developed (Rossin et al, 2011;He et al, 2013;Greene et al, 2015;Pers et al, 2015;Tasan et al, 2015;Gusev et al, 2016;Zhu et al, 2016;Shim et al, 2017). For example, Sherlock, SMR, and TWAS prioritize causal genes by combining GWAS and eQTL data; DAPPLE integrates GWAS data with PPI network to identify potential causal genes; DEPICT identifies causal genes through integrating GWAS and predicts gene functions; and prix fixe, NetWAS, and GWAB predict causal genes using co-function network, tissue-specific network, and human functional gene network, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease

Zheng

Chen

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

Background: Genome-wide association studies (GWASs) have identified more than 150 genetic loci that demonstrate robust association with coronary artery disease (CAD). In contrast to the success of GWAS, the translation from statistical signals to biological mechanism and exploration of causal genes for drug development remain difficult, owing to the complexity of gene regulatory and linkage disequilibrium patterns. We aim to prioritize the plausible causal genes for CAD at a genome-wide level.Methods: We integrated the latest GWAS summary statistics with other omics data from different layers and utilized eight different computational methods to predict CAD potential causal genes. The prioritized candidate genes were further characterized by pathway enrichment analysis, tissue-specific expression analysis, and pathway crosstalk analysis.Results: Our analysis identified 55 high-confidence causal genes for CAD, among which 15 genes (LPL, COL4A2, PLG, CDKN2B, COL4A1, FES, FLT1, FN1, IL6R, LPA, PCSK9, PSRC1, SMAD3, SWAP70, and VAMP8) ranked the highest priority because of consistent evidence from different data-driven approaches. GO analysis showed that these plausible causal genes were enriched in lipid metabolic and extracellular regions. Tissue-specific enrichment analysis revealed that these genes were significantly overexpressed in adipose and liver tissues. Further, KEGG and crosstalk analysis also revealed several key pathways involved in the pathogenesis of CAD. Conclusion:Our study delineated the landscape of CAD potential causal genes and highlighted several biological processes involved in CAD pathogenesis. Further studies and experimental validations of these genes may shed light on mechanistic insights into CAD development and provide potential drug targets for future therapeutics. HIGHLIGHTS-Conducted an integrative analysis to prioritize the CAD potential causal genes by using eight computational methods. -Identified 55 high-confidence causal genes for CAD, among which 15 genes ranked the highest priority. -Plausible causal genes were enriched in lipid metabolic and extracellular region.

show abstract

“…This has been motivated by the recognition that the genes associated with a disease tend to be functionally or physically coupled. Several different approaches have been developed: Chen et al [16] proposed a Markov random field (MRF) model to incorporate pathway topology into association analysis; GWAB [17] performs network propagation on a gene cofunctional network; NetWAS [18] trains a support vector machine classifier to prioritize genes with edges in a tissue-specific network as features; and REGENT [19] utilizes a hierarchical model to integrate the embedding of genes, which are related based on multiple networks, into GWAS data. However, the gene-level association scores in all these methods are directly derived from the nearby SNPs' GWAS p value, without considering the eQTL information.…”

mentioning

confidence: 99%

Inferring Gene-Disease Association by an Integrative Analysis of eQTL Genome-Wide Association Study and Protein-Protein Interaction Data

et al. 2018

View full text Add to dashboard Cite

Objectives: Genome-wide association studies (GWASs) have revealed many candidate SNPs, but the mechanisms by which these SNPs influence diseases are largely unknown. In order to decipher the underlying mechanisms, several methods have been developed to predict disease-associated genes based on the integration of GWAS and eQTL data (e.g., Sherlock and COLOC). A number of studies have also incorporated information from gene networks into GWAS analysis to reprioritize candidate genes. Methods: Motivated by these two different approaches, we have developed a statistical framework to integrate information from GWAS, eQTL, and protein-protein interaction (PPI) data to predict disease-associated genes. Our approach is based on a hidden Markov random field (HMRF) model, and we called the resulting computational algorithm GeP-HMRF (a GWAS-eQTL-PPI-based HMRF). Results: We compared the performance of GeP-HMRF with Sherlock, COLOC, and NetWAS methods on 9 GWAS datasets, using the disease-related genes in the MalaCards database as the standard, and found that GeP-HMRF significantly improves the prediction accuracy. We also applied GeP-HMRF to an age-related macular degeneration disease (AMD) dataset. Among the top 50 genes predicted by GeP-HMRF, 7 are reported by the MalaCards database to be AMD-related with an enrichment p value of 3.61 × 10^–119. Among the top 20 genes predicted by GeP-HMRF, CFHR1, CGHR3, HTRA1, and CFH are AMD-related in the MalaCards database, and another 9 genes are supported by the literature. Conclusions: We built a unified statistical model to predict disease-related genes by integrating GWAS, eQTL, and PPI data. Our approach outperforms Sherlock, COLOC, and NetWAS in simulation studies and 9 GWAS datasets. Our approach can be generalized to incorporate other molecular trait data beyond eQTL and other interaction data beyond PPI.

show abstract

GWAB: a web server for the network-based boosting of human genome-wide association data

Cited by 28 publications

References 42 publications

Multi-allelic APRR2 Gene is Associated with Fruit Pigment Accumulation in Melon and Watermelon

Multi-allelic APRR2 Gene is Associated with Fruit Pigment Accumulation in Melon and Watermelon

The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease

Inferring Gene-Disease Association by an Integrative Analysis of eQTL Genome-Wide Association Study and Protein-Protein Interaction Data

Contact Info

Product

Resources

About