scholarly journals Available Software for Meta-Analyses of Genome-Wide Expression Studies

2019 ◽  
Vol 20 (5) ◽  
pp. 325-331 ◽  
Author(s):  
Diego A. Forero
Keyword(s):  
Meta Analysis ◽  
Model Organisms ◽  
Online Programs ◽  
Advantages And Disadvantages ◽  
Genome Expression ◽  
Expression Studies ◽  
Genome Wide ◽  
Powerful Approach ◽  
Meta Analyses ◽  
Genome Wide Expression

Advances in transcriptomic methods have led to a large number of published Genome- Wide Expression Studies (GWES), in humans and model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, the main features of available software for carrying out meta-analysis of GWES have been reviewed and seven packages from the Bioconductor platform and five packages from the CRAN platform have been described. In addition, nine previously described programs and four online programs are reviewed. Finally, advantages and disadvantages of these available programs and proposed key points for future developments have been discussed.

scholarly journals Available software for meta-analyses of genome-wide expression studies

2019 ◽  
Author(s):  
Diego A Forero
Keyword(s):  
Meta Analysis ◽  
Model Organisms ◽  
Online Programs ◽  
Advantages And Disadvantages ◽  
Genome Expression ◽  
Expression Studies ◽  
Genome Wide ◽  
Powerful Approach ◽  
Meta Analyses ◽  
Genome Wide Expression

Advances in transcriptomic methods have led to a large number of published genome-wide expression studies (GWES), in humans and in model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, I review the main features of available software for carrying out meta-analysis of GWES. I describe seven packages from the Bioconductor platform and 5 packages from the CRAN platform. In addition, nine previously described programs and two online programs are reviewed. Finally, I discuss advantages and disadvantages of these available programs and propose key points for future developments.

scholarly journals Available software for meta-analyses of genome-wide expression studies

2019 ◽  
Author(s):  
Diego A Forero
Keyword(s):  
Meta Analysis ◽  
Model Organisms ◽  
Online Programs ◽  
Advantages And Disadvantages ◽  
Genome Expression ◽  
Expression Studies ◽  
Genome Wide ◽  
Powerful Approach ◽  
Meta Analyses ◽  
Genome Wide Expression

Advances in transcriptomic methods have led to a large number of published genome-wide expression studies (GWES), in humans and in model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, I review the main features of available software for carrying out meta-analysis of GWES. I describe seven packages from the Bioconductor platform and 5 packages from the CRAN platform. In addition, nine previously described programs and two online programs are reviewed. Finally, I discuss advantages and disadvantages of these available programs and propose key points for future developments.

scholarly journals Common genes and pathways involved in the response to stressful stimuli by astrocytes: A meta-analysis of genome-wide expression studies

Genomics ◽  
2021 ◽  
Vol 113 (2) ◽  
pp. 669-680
Author(s):  
Yeimy González-Giraldo ◽  
Diego A. Forero ◽  
George E. Barreto ◽  
Andrés Aristizábal-Pachón
Keyword(s):  
Meta Analysis ◽  
Expression Studies ◽  
Genome Wide ◽  
Genome Wide Expression

Abstract P260: Large-scale Meta-analysis of Diverse Ancestry Genome-wide Association Studies to Identify Pharmacogenomic Interactions of Sulfonylureas and ECG Phenotypes

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
James S Floyd ◽  
Colleen Sitlani ◽  
Christy L Avery ◽  
Eric A Whitsel ◽  
Leslie Lange ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Qt Interval ◽  
Association Studies ◽  
Meta Analysis ◽  
Small Sample ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Meta Analyses

Introduction: Sulfonylureas are a commonly-used class of diabetes medication that can prolong the QT-interval, which is a leading cause of drug withdrawals from the market given the possible risk of life-threatening arrhythmias. Previously, we conducted a meta-analysis of genome-wide association studies of sulfonylurea-genetic interactions on QT interval among 9 European-ancestry (EA) cohorts using cross-sectional data, with null results. To improve our power to identify novel drug-gene interactions, we have included repeated measures of medication use and QT interval and expanded our study to include several additional cohorts, including African-American (AA) and Hispanic-ancestry (HA) cohorts with a high prevalence of sulfonylurea use. To identify potentially differential effects on cardiac depolarization and repolarization, we have also added two phenotypes - the JT and QRS intervals, which together comprise the QT interval. Hypothesis: The use of repeated measures and expansion of our meta-analysis to include diverse ancestry populations will allow us to identify novel pharmacogenomic interactions for sulfonylureas on the ECG phenotypes QT, JT, and QRS. Methods: Cohorts with unrelated individuals used generalized estimating equations to estimate interactions; cohorts with related individuals used mixed effect models clustered on family. For each ECG phenotype (QT, JT, QRS), we conducted ancestry-specific (EA, AA, HA) inverse variance weighted meta-analyses using standard errors based on the t-distribution to correct for small sample inflation in the test statistic. Ancestry-specific summary estimates were combined using MANTRA, an analytic method that accounts for differences in local linkage disequilibrium between ethnic groups. Results: Our study included 65,997 participants from 21 cohorts, including 4,020 (6%) sulfonylurea users, a substantial increase from the 26,986 participants and 846 sulfonylureas users in the previous meta-analysis. Preliminary ancestry-specific meta-analyses have identified genome-wide significant associations (P < 5х10–8) for each ECG phenotype, and analyses with MANTRA are in progress. Conclusions: In the setting of the largest collection of pharmacogenomic studies to date, we used repeated measurements and leveraged diverse ancestry populations to identify new pharmacogenomic loci for ECG traits associated with cardiovascular risk.

scholarly journals Meta-analysis of genome-wide expression patterns associated with behavioral maturation in honey bees

BMC Genomics ◽  
2008 ◽  
Vol 9 (1) ◽  
pp. 503 ◽  
Author(s):  
Heather A Adams ◽  
Bruce R Southey ◽  
Gene E Robinson ◽  
Sandra L Rodriguez-Zas
Keyword(s):  
Honey Bees ◽  
Meta Analysis ◽  
Expression Patterns ◽  
Genome Wide ◽  
Behavioral Maturation ◽  
Genome Wide Expression

scholarly journals Genome-Wide Meta-Analyses of FTND and TTFC Phenotypes

2019 ◽  
Vol 22 (6) ◽  
pp. 900-909 ◽  
Author(s):  
Jingchun Chen ◽  
Anu Loukola ◽  
Nathan A Gillespie ◽  
Roseann Peterson ◽  
Peilin Jia ◽  
...  
Keyword(s):  
Cigarette Smoking ◽  
Nicotine Dependence ◽  
Meta Analysis ◽  
Mapk Signaling ◽  
European Ancestry ◽  
Effective Prevention ◽  
Network Analyses ◽  
Chemokine Signaling ◽  
Genome Wide ◽  
Meta Analyses

Abstract Introduction FTND (Fagerstrӧm test for nicotine dependence) and TTFC (time to smoke first cigarette in the morning) are common measures of nicotine dependence (ND). However, genome-wide meta-analysis for these phenotypes has not been reported. Methods Genome-wide meta-analyses for FTND (N = 19,431) and TTFC (N = 18,567) phenotypes were conducted for adult smokers of European ancestry from 14 independent cohorts. Results We found that SORBS2 on 4q35 (p = 4.05 × 10−8), BG182718 on 11q22 (p = 1.02 × 10−8), and AA333164 on 14q21 (p = 4.11 × 10−9) were associated with TTFC phenotype. We attempted replication of leading candidates with independent samples (FTND, N = 7010 and TTFC, N = 10 061), however, due to limited power of the replication samples, the replication of these new loci did not reach significance. In gene-based analyses, COPB2 was found associated with FTND phenotype, and TFCP2L1, RELN, and INO80C were associated with TTFC phenotype. In pathway and network analyses, we found that the interconnected interactions among the endocytosis, regulation of actin cytoskeleton, axon guidance, MAPK signaling, and chemokine signaling pathways were involved in ND. Conclusions Our analyses identified several promising candidates for both FTND and TTFC phenotypes, and further verification of these candidates was necessary. Candidates supported by both FTND and TTFC (CHRNA4, THSD7B, RBFOX1, and ZNF804A) were associated with addiction to alcohol, cocaine, and heroin, and were associated with autism and schizophrenia. We also identified novel pathways involved in cigarette smoking. The pathway interactions highlighted the importance of receptor recycling and internalization in ND. Implications Understanding the genetic architecture of cigarette smoking and ND is critical to develop effective prevention and treatment. Our study identified novel candidates and biological pathways involved in FTND and TTFC phenotypes, and this will facilitate further investigation of these candidates and pathways.

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