scholarly journals Genome-wide association study identifies candidate loci associated with chronic pain and postherpetic neuralgia

2021 ◽  
Vol 17 ◽  
pp. 174480692199992
Author(s):  
Daisuke Nishizawa ◽  
Masako Iseki ◽  
Hideko Arita ◽  
Kazuo Hanaoka ◽  
Choku Yajima ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Postherpetic Neuralgia ◽  
Genetic Models ◽  
Genome Wide Association ◽  
Gene Region ◽  
Genome Wide Association Studies ◽  
Additional Gene ◽  
Gene Set ◽  
Trend Model ◽  
Genome Wide

Background Human twin studies and other studies have indicated that chronic pain has heritability that ranges from 30% to 70%. We aimed to identify potential genetic variants that contribute to the susceptibility to chronic pain and efficacy of administered drugs. We conducted genome-wide association studies (GWASs) using whole-genome genotyping arrays with more than 700,000 markers in 191 chronic pain patients and a subgroup of 89 patients with postherpetic neuralgia (PHN) in addition to 282 healthy control subjects in several genetic models, followed by additional gene-based and gene-set analyses of the same phenotypes. We also performed a GWAS for the efficacy of drugs for the treatment of pain. Results Although none of the single-nucleotide polymorphisms (SNPs) were found to be genome-wide significantly associated with chronic pain ( p ≥ 1.858 × 10−7), the GWAS of PHN patients revealed that the rs4773840 SNP within the ABCC4 gene region was significantly associated with PHN in the trend model (nominal p = 1.638 × 10−7). In the additional gene-based analysis, one gene, PRKCQ, was significantly associated with chronic pain in the trend model (adjusted p = 0.03722). In the gene-set analysis, several gene sets were significantly associated with chronic pain and PHN. No SNPs were significantly associated with the efficacy of any of types of drugs in any of the genetic models. Conclusions These results suggest that the PRKCQ gene and rs4773840 SNP within the ABCC4 gene region may be related to the susceptibility to chronic pain conditions and PHN, respectively.

Identification of Critical Core Genes of Sarcoma Based on Centrality Analysis of Networks Nodes

2020 ◽  
Vol 10 (7) ◽  
pp. 1776-1784
Author(s):  
Shudong Wang ◽  
Jixiao Wang ◽  
Xinzeng Wang ◽  
Yuanyuan Zhang ◽  
Tao Yi
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Complex Diseases ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Core Gene ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Set ◽  
Genome Wide

Genome-wide association studies (GWAS) are powerful tools for identifying pathogenic genes of complex diseases and revealing genetic structure of diseases. However, due to gene-to-gene interactions, only a part of the hereditary factors can be revealed. The meta-analysis based on GWAS can integrate gene expression data at multiple levels and reveal the complex relationship between genes. Therefore, we used meta-analysis to integrate GWAS data of sarcoma to establish complex networks and discuss their significant genes. Firstly, we established gene interaction networks based on the data of different subtypes of sarcoma to analyze the node centralities of genes. Secondly, we calculated the significant score of each gene according to the Staged Significant Gene Network Algorithm (SSGNA). Then, we obtained the critical gene set HYC of sarcoma by ranking the scores, and then combined Gene Ontology enrichment analysis and protein network analysis to further screen it. Finally, the critical core gene set Hcore containing 47 genes was obtained and validated by GEPIA analysis. Our method has certain generalization performance to the study of complex diseases with prior knowledge and it is a useful supplement to genome-wide association studies.

scholarly journals Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Michal Marczyk ◽  
Agnieszka Macioszek ◽  
Joanna Tobiasz ◽  
Joanna Polanska ◽  
Joanna Zyla
Keyword(s):  
Association Studies ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Gene Set Analysis ◽  
Genome Wide Association Studies ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Genome Wide ◽  
The Impact

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar’s test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

scholarly journals Pathway Association Studies Reveal Gene Loci and Pathway Networks that Associated With Plasma Cystatin C Levels

2021 ◽  
Vol 12 ◽  
Author(s):  
Hongxiao Jiao ◽  
Miaomiao Zhang ◽  
Yuan Zhang ◽  
Yaogang Wang ◽  
Wei-Dong Li
Keyword(s):  
Cystatin C ◽  
Association Studies ◽  
Membrane Lipid ◽  
Genome Wide Association ◽  
Gene Region ◽  
Genome Wide Association Studies ◽  
False Discovery ◽  
Genome Wide ◽  
Endothelial Migration ◽  
Pathway Networks

As a marker for glomerular filtration, plasma cystatin C level is used to evaluate kidney function. To decipher genetic factors that control the plasma cystatin C level, we performed genome-wide association and pathway association studies using United Kingdom Biobank data. One hundred fifteen loci yielded p values less than 1 × 10−100, three genes (clusters) showed the most significant associations, including the CST8-CST9 cluster on chromosome 20, the SH2B3-ATXN2 gene region on chromosome 12, and the SHROOM3-CCDC158 gene region on chromosome 4. In pathway association studies, forty significant pathways had FDR (false discovery rate) and or FWER (family-wise error rate) ≤ 0.001: spermatogenesis, leukocyte trans-endothelial migration, cell adhesion, glycoprotein, membrane lipid, steroid metabolic process, and insulin signaling pathways were among the most significant pathways that associated with the plasma cystatin C levels. We also performed Genome-wide association studies for eGFR, top associated genes were largely overlapped with those for cystatin C.

scholarly journals META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0140179 ◽  
Author(s):  
Albert Rosenberger ◽  
Stefanie Friedrichs ◽  
Christopher I. Amos ◽  
Paul Brennan ◽  
Gordon Fehringer ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Set ◽  
Genome Wide

scholarly journals Robust Association Tests for the Replication of Genome-Wide Association Studies

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Jungnam Joo ◽  
Ju-Hyun Park ◽  
Bora Lee ◽  
Boram Park ◽  
Sohee Kim ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Genetic Model ◽  
Association Studies ◽  
Statistical Significance ◽  
Genetic Models ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Association Tests ◽  
Genome Wide ◽  
Wide Range

In genome-wide association study (GWAS), robust genetic association tests such as maximum of three CATTs (MAX3), each corresponding to recessive, additive, and dominant genetic models, the minimumpvalue of Pearson’s Chi-square test with 2 degrees of freedom, and CATT based on additive genetic model (MIN2), genetic model selection (GMS), and genetic model exclusion (GME) methods have been shown to provide better power performance under wide range of underlying genetic models. In this paper, we demonstrate how these robust tests can be applied to the replication study of GWAS and how the overall statistical significance can be evaluated using the combined test formed bypvalues of the discovery and replication studies.

scholarly journals Integration of a priori gene set information into genome-wide association studies

2009 ◽  
Vol 3 (Suppl 7) ◽  
pp. S95 ◽  
Author(s):  
Melanie Sohns ◽  
Albert Rosenberger ◽  
Heike Bickeböller
Keyword(s):  
Association Studies ◽  
A Priori ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Set ◽  
Genome Wide
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