Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Genotype data from early passage UACC melanoma cell lines

ABSTRACT: eQTL is a powerful method to detect genotype-expression correlation. To be able to identify the genes whose expression levels are correlated with melanoma-associated common variants, eQTL analysis was performed in 59 early passage melanoma cell lines

ORGANISM(S): Homo sapiens

PROVIDER: GSE99193 | GEO | 2017/07/01

SECONDARY ACCESSION(S): PRJNA387590

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Expression data from early passage UACC melanoma cell lines

Project description:eQTL is a powerful method to detect genotype-expression correlation. To be able to identify the genes whose expression levels are correlated with melanoma-associated common variants, eQTL analysis was performed in 59 early passage melanoma cell lines

2017-07-01 | GSE78995 | GEO

Expression quantitative trait loci of primary melanocytes facilitate identification of melanoma susceptibility genes

Project description:eQTL is a powerful method to detect genotype-expression correlation. To be able to identify the genes whose expression levels are correlated with germline genetic variants including the ones associated with melanoma, eQTL analysis was performed in 106 primary melanocyte cultrures

2019-01-01 | GSE101771 | GEO

Multiomic profiling of early-passage melanoma cell lines.

Project description:This study utilises multiomic profiling of early-passage melanoma cell lines to explore the interactions between distinct modalities of molecular regulation influencing melanoma cellular phenotype.

| EGAS00001004536 | EGA

Genotype-Smoking Environment Interactions affecting eQTL in the Human Lung

Project description:Background: Cases where genotype-phenotype relationships depend on environmental factors have been quantified for many complex diseases. Such genotype-environment interactions (GEI or GxE) may also affect expression Quantitative Trait Loci (eQTL) present in tissues critical for the manifestation of disease. To assess this hypothesis, we performed an analysis of eQTL-GEI resulting from an individual's smoking environment in the lung small airway epithelium (SAE). While the SAE is challenging to sample, this is the cell population that shows the first signs of smoking related stress and gene expression in the SAE appears to play a role in mediating smoking effects on lung disease. Results: We used expression microarrays to assay the SAE transcriptome for a small sample of African-American individuals and we analyzed SNPs genotyped genome-wide to identify GEI affecting eQTL. While a genome-wide trans- analysis identified few instances of GEI after a multiple test correction, an analysis of cis-genotypes identified a small but significant number of GEI affecting lung SAE gene expression. We determined that significant cases of eQTL-GEI were not driven by outliers and we were also able to find corroborative evidence for a few of these eQTL-GEI in a small, independent sample of individuals of European ancestry. Conclusion: Given that the power of GEI tests is low compared to tests of genotype association and that the total sample size of our study was small, including only 61 African American individuals in our focal population, the identification of significant GEI in our study implies that there may be considerable genotype-specific effects on eQTL due to smoking environment. We discuss individual cases of GEI of interest for lung disease, such as SDC1 and ZAK, as well as the broader implications of our results for the analysis of eQTL and for genome-wide association analysis of complex diseases.

2024-06-05 | GSE23087 | GEO

Characterization of melanoma cells exposed to melanoma-specific CD8+ T cells

Project description:Human low passage melanoma cell lines were compared after 24h culture alone or in presence of melanoma-specific CD8+ T cell clones

2016-08-30 | GSE79991 | GEO

MeDIP for ten early passage melanoma cell cultures

Project description:Malignant melanoma is the most fatal skin cancer with a high degree of genetic and epigenetic aberrations. To investigate the role of DNA methylation on melanoma heterogeneity, we performed methylated DNA immunoprecipitation (MeDIP) microarray analysis of 10 primary melanoma cell cultures. methylated DNA immunoprecipitation (MeDIP) microarray analysis of 10 primary melanoma cell cultures

2014-05-28 | E-GEOD-57971 | biostudies-arrayexpress

MeDIP for ten early passage melanoma cell cultures

2014-05-28 | GSE57971 | GEO

African American hepatocyte genotype

Project description:African Americans (AAs) are disproportionately affected by metabolic diseases and adverse drug events, with limited publicly available genomic and transcriptomic data to advance the knowledge of the molecular underpinnings or genetic associations to these diseases or drug response phenotypes. To fill this gap, we obtained 60 primary hepatocyte cultures from AA liver donors for genome-wide mapping of expression quantitative trait loci (eQTL) using LAMatrix. We identified 277 eGenes and 19,770 eQTLs, of which 67 eGenes and 7,415 eQTLs are not observed in the Genotype-Tissue Expression Project (GTEx) liver eQTL analysis. Of the eGenes found in GTEx only 25 share the same lead eQTL. These AA-specific eQTLs are less correlated to GTEx eQTL in effect sizes and have larger Fst values compared to eQTLs found in both cohorts (overlapping eQTLs). We assessed the overlap between GWAS variants and their tagging variants with AA hepatocyte eQTLs and demonstrated that AA hepatocyte eQTLs can decrease the number of potential causal variants at GWAS loci. Additionally, we identified 75,002 exon QTLs of which 48.8% are not eQTLs in AA hepatocytes. Our analysis provides the first comprehensive characterization of AA hepatocyte eQTLs and highlights the unique discoveries that are made possible due to the increased genetic diversity within the African ancestry genome.

2020-03-27 | GSE147628 | GEO

plant genotype x rhizobium genotype interaction

Project description:plant genotype x rhizobium genotype interaction

| PRJNA140483 | ENA

Homo sapiens

Project description:Genotype data from early passage UACC melanoma cell lines

| PRJNA387590 | ENA