WISARD: workbench for integrated superfast association studies for related datasets

Lee, Sung Young; Choi, Sungkyoung; Qiao, Dandi; Cho, Michael; Silverman, Edwin K.; Park, Taesung; Won, Sungho

doi:10.1186/s12920-018-0345-y

Cited by 11 publications

(10 citation statements)

References 50 publications

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“…In this work, we carried out the MFQLS test implemented in the Workbench for Integrated Superfast Association study with Related Data (WISARD) toolkit, a quasi-likelihood method of estimation extended from the Cochran-Armitage test, suited for familial cohorts considering kinship coefficients, and can be used to associate multiple phenotypes with covariates. 43,44 The analyses were made with the asthma phenotype using sex, age and smoking status (a never or an ever smoker) as covariates.…”

Section: Genetic Association and Definition Of Haplotype Blocksmentioning

confidence: 99%

GSDMAdrives the most replicated association with asthma in naïve CD4⁺T cells

Madore

Pain

Boucher-Lafleur

et al. 2019

Preprint

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Background The 17q12-21 locus is the most replicated association with asthma. However, no study had described the genetic mechanisms underlying this association considering all genes of the locus in immune cell samples isolated from asthmatic and non-asthmatic individuals. Objective This study takes benefit of samples from naïve CD4 + T cells and eosinophils isolated from the same 200 individuals to describe specific interactions between genetic variants, gene expression and DNA methylation levels for the 17q12-21 asthma locus. Methods and Results After isolation of naïve CD4 + T cells and eosinophils from blood samples, next generation sequencing was used to measure DNA methylation levels and gene expression counts. Genetic interactions were then evaluated considering genetic variants from imputed genotype data. In naïve CD4 + T cells but not eosinophils, 20 SNPs in the fourth and fifth haplotype blocks modulated both GSDMA expression and methylation levels,showing an opposite pattern of allele frequencies and expression counts in asthmatics compared to controls. Moreover, negative correlations have been measured between methylation levels of CpG sites located within the 1.5 kb region from the transcription start site of GSDMA and its expression counts. Conclusion Availability of sequencing data from two key cell types isolated from asthmatic and non-asthmatic individuals allowed identifying a new gene in naïve CD4 + T cells that drives the association with the 17q12-21 locus, leading to a better understanding of the genetic mechanisms taking place in it.

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Section: Genetic Association and Definition Of Haplotype Blocksmentioning

confidence: 99%

GSDMAdrives the most replicated association with asthma in naïve CD4⁺T cells

Madore

Pain

Boucher-Lafleur

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…In this study, we developed the software for HisCoM-G×E ( ). The basic framework of the HisCoM-G×E software is based on the “Workbench for Integrated Superfast Association study with Related Data” (WISARD) [ 31 ]. The HisCoM-G×E software was implemented in C++, and was developed for Linux.…”

Section: Discussionmentioning

confidence: 99%

HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions

Choi

Lee

Huh

et al. 2020

IJMS

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Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

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“…After estimation, we performed permutation testing by resampling the phenotypes, to test the significance of the parameters. Here, we use a tool called WISARD (Workbench for Integrated Superfast Association study with Related Data) [18], which was developed for fast and a comprehensive analysis of SNP-chip and next-generation sequencing data. WISARD can perform the standard pathway analysis with SNP data as input.…”

Section: Hiscom-pcamentioning

confidence: 99%

Hierarchical structural component model for pathway analysis of common variants

2020

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Background: Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a small portion of the overall estimated genetic heritability. To address this 'missing heritability' issue, gene-and pathway-based analysis, and biological mechanisms, have been used for many GWAS studies. However, many of these methods often neglect the correlation between genes and between pathways. Methods: We constructed a hierarchical component model that considers correlations both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summarizes the common variants of each gene, first at the gene-level, and then analyzes all pathways simultaneously by ridge-type penalization of both the gene and pathway effects on the phenotype. Statistical significance of the gene and pathway coefficients can be examined by permutation tests. Results: Using the simulation data set of Genetic Analysis Workshop 17 (GAW17), for both binary and continuous phenotypes, we showed that HisCoM-PCA well-controlled type I error, and had a higher empirical power compared to several other methods. In addition, we applied our method to a SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance. Conclusions: Our approach has the advantage of providing an intuitive biological interpretation for associations between common variants and phenotypes, via pathway information, potentially addressing the missing heritability conundrum.

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WISARD: workbench for integrated superfast association studies for related datasets

Cited by 11 publications

References 50 publications

GSDMAdrives the most replicated association with asthma in naïve CD4⁺T cells

GSDMAdrives the most replicated association with asthma in naïve CD4⁺T cells

HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions

Hierarchical structural component model for pathway analysis of common variants

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WISARD: workbench for integrated superfast association studies for related datasets

Cited by 11 publications

References 50 publications

GSDMAdrives the most replicated association with asthma in naïve CD4+T cells

GSDMAdrives the most replicated association with asthma in naïve CD4+T cells

HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions

Hierarchical structural component model for pathway analysis of common variants

Contact Info

Product

Resources

About

GSDMAdrives the most replicated association with asthma in naïve CD4⁺T cells

GSDMAdrives the most replicated association with asthma in naïve CD4⁺T cells