A genome-wide linkage study of GAW15 gene expression data

The use of high-density DNA arrays to monitor gene expression at a genome-wide scale constitutes a fundamental advance in biology. In particular, the expression pattern of all genes in Saccharomyces cerevisiae can be interrogated using microarray analysis where cDNAs are hybridized to an array of each of the ∼6000 genes in the yeast genome. In this survey I review three recent experiments related to transcriptional regulation and discuss the great challenge for computational biologists trying to extract functional information from such large-scale gene expression data.

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Faculty Opinions recommendation of Meta-Analysis of Genome-Wide Association Studies and Network Analysis-Based Integration with Gene Expression Data Identify New Suggestive Loci and Unravel a Wnt-Centric Network Associated with Dupuytren's Disease.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726582766.793522025 ◽

2016 ◽

Author(s):

Rik Lories

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Gene Expression Data ◽

Association Studies ◽

Meta Analysis ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Expression Data ◽

Dupuytren's Disease ◽

Genome Wide

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Genome-wide identification of suitable zebrafish Danio rerio reference genes for normalization of gene expression data by RT-qPCR

Journal of Fish Biology ◽

10.1111/jfb.12915 ◽

2016 ◽

Vol 88 (6) ◽

pp. 2095-2110 ◽

Cited By ~ 34

Author(s):

H. Xu ◽

C. Li ◽

Q. Zeng ◽

I. Agrawal ◽

X. Zhu ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Danio Rerio ◽

Reference Genes ◽

Expression Data ◽

Genome Wide

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Identification of unique venous thromboembolism-susceptibility variants in African-Americans

Thrombosis and Haemostasis ◽

10.1160/th16-08-0652 ◽

2017 ◽

Vol 117 (04) ◽

pp. 758-768 ◽

Cited By ~ 16

Author(s):

Sebastian Armasu ◽

Bryan McCauley ◽

Iftikhar Kullo ◽

Hugues Sicotte ◽

Jyotishman Pathak ◽

...

Keyword(s):

Gene Expression ◽

Venous Thromboembolism ◽

African Americans ◽

Differential Expression ◽

White Women ◽

Genome Wide Association Study ◽

Expression Data ◽

Significant Differential Expression ◽

Genome Wide ◽

A Genome

SummaryTo identify novel single nucleotide polymorphisms (SNPs) associated with venous thromboembolism (VTE) in African-Americans (AAs), we performed a genome-wide association study (GWAS) of VTE in AAs using the Electronic Medical Records and Genomics (eMERGE) Network, comprised of seven sites each with DNA biobanks (total ~39,200 unique DNA samples) with genome-wide SNP data (imputed to 1000 Genomes Project cosmopolitan reference panel) and linked to electronic health records (EHRs). Using a validated EHR-driven phenotype extraction algorithm, we identified VTE cases and controls and tested for an association between each SNP and VTE using unconditional logistic regression, adjusted for age, sex, stroke, site-platform combination and sickle cell risk genotype. Among 393 AA VTE cases and 4,941 AA controls, three intragenic SNPs reached genome-wide significance: LEMD3 rs138916004 (OR=3.2; p=1.3E-08), LY86 rs3804476 (OR=1.8; p=2E-08) and LOC100130298 rs142143628 (OR=4.5; p=4.4E-08); all three SNPs validated using internal cross-validation, parametric bootstrap and meta-analysis methods. LEMD3 rs138916004 and LOC100130298 rs142143628 are only present in Africans (1000G data). LEMD3 showed a significant differential expression in both NCBI Gene Expression Omnibus (GEO) and the Mayo Clinic gene expression data, LOC100130298 showed a significant differential expression only in the GEO expression data, and LY86 showed a significant differential expression only in the Mayo expression data. LEMD3 encodes for an antagonist of TGF-β-induced cell proliferation arrest. LY86 encodes for MD-1 which down-regulates the pro-inflammatory response to lipopolysaccharide; LY86 variation was previously associated with VTE in white women; LOC100130298 is a non-coding RNA gene with unknown regulatory activity in gene expression and epigenetics.Supplementary Material to this article is available online at www.thrombosis-online.com.

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GENOME-WIDE PATHWAY ANALYSIS AND VISUALIZATION USING GENE EXPRESSION DATA

Biocomputing 2002 ◽

10.1142/9789812799623_0043 ◽

2001 ◽

Cited By ~ 2

Author(s):

M. P. KURHEKAR ◽

S. ADAK ◽

S. JHUNJHUNWALA ◽

K. RAGHUPATHY

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Pathway Analysis ◽

Expression Data ◽

Genome Wide

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DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation

BMC Medical Genomics ◽

10.1186/s12920-020-0657-6 ◽

2020 ◽

Vol 13 (1) ◽

Cited By ~ 2

Author(s):

Ieva Rauluseviciute ◽

Finn Drabløs ◽

Morten Beck Rye

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Dna Methylation ◽

Epigenetic Regulation ◽

Normal Tissue ◽

Expression Profiles ◽

Expression Data ◽

Dna Hypermethylation ◽

Genome Wide ◽

A Genome

Abstract Background Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. Results In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. Conclusion We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.

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