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 Identifying insomnia-related chemicals through integrative analysis of genome-wide association studies and chemical–genes interaction information

SLEEP ◽  
2020 ◽  
Vol 43 (9) ◽  
Author(s):  
Om Prakash Kafle ◽  
Shiqiang Cheng ◽  
Mei Ma ◽  
Ping Li ◽  
Bolun Cheng ◽  
...  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Modern Society ◽  
Environmental Chemicals ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Study Results

Abstract Study Objectives Insomnia is a common sleep disorder and constitutes a major issue in modern society. We provide new clues for revealing the association between environmental chemicals and insomnia. Methods Three genome-wide association studies (GWAS) summary datasets of insomnia (n = 113,006, n = 1,331,010, and n = 453,379, respectively) were driven from the UK Biobank, 23andMe, and deCODE. The chemical–gene interaction dataset was downloaded from the Comparative Toxicogenomics Database. First, we conducted a meta-analysis of the three datasets of insomnia using the METAL software. Using the result of meta-analysis, transcriptome-wide association studies were performed to calculate the expression association testing statistics of insomnia. Then chemical-related gene set enrichment analysis (GSEA) was used to explore the association between chemicals and insomnia. Results For GWAS meta-analysis dataset of insomnia, we identified 42 chemicals associated with insomnia in brain tissue (p < 0.05) by GSEA. We detected five important chemicals such as pinosylvin (p = 0.0128), bromobenzene (p = 0.0134), clonidine (p = 0.0372), gabapentin (p = 0.0372), and melatonin (p = 0.0404) which are directly associated with insomnia. Conclusion Our study results provide new clues for revealing the roles of environmental chemicals in the development of insomnia.

Genome-Wide Association Studies Identify Two Novel Loci Conferring Susceptibility to Diabetic Retinopathy in Japanese Patients with Type 2 Diabetes

2021 ◽  
Author(s):  
Minako Imamura ◽  
Atsushi Takahashi ◽  
Masatoshi Matsunami ◽  
Momoko Horikoshi ◽  
Minoru Iwata ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Association Studies ◽  
Meta Analysis ◽  
Japanese Patients ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Stage 1

Abstract Several reports have suggested that genetic susceptibility contributes to the development and progression of diabetic retinopathy. We aimed to identify genetic loci that confer susceptibility to diabetic retinopathy in Japanese patients with type 2 diabetes. We analysed 5 790 508 single nucleotide polymorphisms (SNPs) in 8880 Japanese patients with type 2 diabetes, 4839 retinopathy cases and 4041 controls, as well as 2217 independent Japanese patients with type 2 diabetes, 693 retinopathy cases, and 1524 controls. The results of these two genome-wide association studies (GWAS) were combined with an inverse variance meta-analysis (Stage-1), followed by de novo genotyping for the candidate SNP loci (p < 1.0 × 10−4) in an independent case–control study (Stage-2, 2260 cases and 723 controls). After combining the association data (Stage-1 and -2) using meta-analysis, the associations of two loci reached a genome-wide significance level: rs12630354 near STT3B on chromosome 3, p = 1.62 × 10−9, odds ratio (OR) = 1.17, 95% confidence interval (CI) 1.11–1.23, and rs140508424 within PALM2 on chromosome 9, p = 4.19 × 10−8, OR = 1.61, 95% CI 1.36–1.91. However, the association of these two loci were not replicated in Korean, European, or African American populations. Gene-based analysis using Stage-1 GWAS data identified a gene-level association of EHD3 with susceptibility to diabetic retinopathy (p = 2.17 × 10−6). In conclusion, we identified two novel SNP loci, STT3B and PALM2, and a novel gene, EHD3, that confers susceptibility to diabetic retinopathy; however, further replication studies are required to validate these associations.

scholarly journals A meta-analysis of genome-wide association studies for average daily gain and lean meat percentage in two Duroc pig populations

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shenping Zhou ◽  
Rongrong Ding ◽  
Fanming Meng ◽  
Xingwang Wang ◽  
Zhanwei Zhuang ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Growth And Development ◽  
Genetic Architecture ◽  
Association Studies ◽  
Meta Analysis ◽  
Average Daily Gain ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Daily Gain

Abstract Background Average daily gain (ADG) and lean meat percentage (LMP) are the main production performance indicators of pigs. Nevertheless, the genetic architecture of ADG and LMP is still elusive. Here, we conducted genome-wide association studies (GWAS) and meta-analysis for ADG and LMP in 3770 American and 2090 Canadian Duroc pigs. Results In the American Duroc pigs, one novel pleiotropic quantitative trait locus (QTL) on Sus scrofa chromosome 1 (SSC1) was identified to be associated with ADG and LMP, which spans 2.53 Mb (from 159.66 to 162.19 Mb). In the Canadian Duroc pigs, two novel QTLs on SSC1 were detected for LMP, which were situated in 3.86 Mb (from 157.99 to 161.85 Mb) and 555 kb (from 37.63 to 38.19 Mb) regions. The meta-analysis identified ten and 20 additional SNPs for ADG and LMP, respectively. Finally, four genes (PHLPP1, STC1, DYRK1B, and PIK3C2A) were detected to be associated with ADG and/or LMP. Further bioinformatics analysis showed that the candidate genes for ADG are mainly involved in bone growth and development, whereas the candidate genes for LMP mainly participated in adipose tissue and muscle tissue growth and development. Conclusions We performed GWAS and meta-analysis for ADG and LMP based on a large sample size consisting of two Duroc pig populations. One pleiotropic QTL that shared a 2.19 Mb haplotype block from 159.66 to 161.85 Mb on SSC1 was found to affect ADG and LMP in the two Duroc pig populations. Furthermore, the combination of single-population and meta-analysis of GWAS improved the efficiency of detecting additional SNPs for the analyzed traits. Our results provide new insights into the genetic architecture of ADG and LMP traits in pigs. Moreover, some significant SNPs associated with ADG and/or LMP in this study may be useful for marker-assisted selection in pig breeding.

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