Genetic Analysis of Human Traits In-Vitro: Drug Response and Gene Expression in Lymphoblastoid Cell Lines
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PROVIDER: PRJNA106213 | ENA |
REPOSITORIES: ENA
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Project description:Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype-phenotype relationships in human cells. These cell lines have been used to search for genetic variants that are associated with drug response as well as with more basic cellular traits such as RNA levels. In setting out to extend such studies by searching for genetic variants contributing to drug response, we observed that phenotypes in LCLs were, in our lab and others, significantly affected by experimental confounders (i.e. in vitro growth rate, metabolic state, and relative levels of the Epstein-Barr virus used to transform the cells). As we did not find any SNPs associated with genome-wide significance to drug response, we evaluated whether incorporating RNA expression levels (and eQTLs) in the analysis could increase power to detect such effects. As previously shown, cis-acting eQTLs were detectable for a sizeable fraction of RNAs and baseline levels of many RNAs predicted response to several drugs. However, we found only limited evidence that SNPs influenced drug response through their effect on expression of RNA. Efforts to use LCLs to map genes underlying cellular traits will require great care to control experimental confounders, unbiased methods for integrating and interpreting such multi-dimensional data, and much larger sample sizes than have been applied to date. Keywords: baseline RNA expression
2008-05-29 | GSE11582 | GEO
Project description:Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype-phenotype relationships in human cells, including searches for QTLs influencing levels of individual mRNAs and responses to drugs and radiation. In the course of attempting to map genes for drug response using 269 LCLs from the International HapMap Project, we evaluated the extent to which biological noise and non-genetic confounders contribute to trait variability in LCLs. While drug responses could be technically well measured on a given day, we observed significant day-to-day variability and substantial correlation to non-genetic confounders, such as baseline growth rates and metabolic state in culture. After correcting for these confounders, we were unable to detect any QTLs with genome-wide significance for drug response. A much higher proportion of variance in mRNA levels may be attributed to non-genetic factors (intra-individual variance--i.e., biological noise, levels of the EBV virus used to transform the cells, ATP levels) than to detectable eQTLs. Finally, in an attempt to improve power, we focused analysis on those genes that had both detectable eQTLs and correlation to drug response; we were unable to detect evidence that eQTL SNPs are convincingly associated with drug response in the model. While LCLs are a promising model for pharmacogenetic experiments, biological noise and in vitro artifacts may reduce power and have the potential to create spurious association due to confounding.
| S-EPMC2583954 | biostudies-literature
Project description:Lymphoblastoid cell lines (LCLs) are a highly successful model for evaluating the genetic etiology of cancer drug response, but applications using this model have typically focused on single drugs. Combination therapy is quite common in modern chemotherapy treatment since drugs often work synergistically, and it is an important progression in the use of the LCL model to expand work for drug combinations. In the present work, we demonstrate that synergy occurs and can be quantified in LCLs across a range of clinically important drug combinations. Lymphoblastoid cell lines have been commonly employed in association mapping in cancer pharmacogenomics, but it is so far untested as to whether synergistic effects have a genetic etiology. Here we use cell lines from extended pedigrees to demonstrate that there is a substantial heritable component to synergistic drug response. Additionally, we perform linkage mapping in these pedigrees to identify putative regions linked to this important phenotype. This demonstration supports the premise of expanding the use of the LCL model to perform association mapping for combination therapies.
| S-EPMC6804467 | biostudies-literature
Project description:Genotyping FATO and SATO3 human lymphoblastoid cell lines
2010-08-27 | GSE23813 | GEO
Project description:The hidden complexity of Mendelian traits across yeast natural populations
| PRJEB11500 | ENA
Project description:RNA-seq of lymphoblastoid cell lines from KMT2D/MLL2 and KDM6A patients.
2021-04-08 | GSE148479 | GEO
Project description:Role of AHR in lymphoblastoid cell lines
| PRJNA479759 | ENA
Project description:The degree to which the level of genetic variation for gene expression is shared across multiple tissues has important implications for research investigating the role of expression on the aetiology of complex human traits and diseases. In the last few years, a number of studies have been published reporting the extent of overlap in expression quantitative trait loci (eQTL) identified in multiple tissues or cell types. Whilst these studies provide important information on the regulatory control of genes across tissues, their limited power means they can typically only explain a small proportion of genetic variation for gene expression. Here, using expression data from monozygotic twins (MZ), we investigate the genetic control of gene expression in lymphoblastoid cell lines (LCL) and whole blood (WB). We estimate the genetic correlation which represents the combined effects of all causal loci across the whole genome and is a measure of the level of common genetic control of gene expression between the two RNA sources. Our results show that, when averaged across the genome, mean levels of genetic correlation for gene expression in LCL and WB samples are close to zero. We support our results with evidence from gene expression in an independent sample of LCL, T-cells and Fibroblasts. In addition, we provide evidence that housekeeping genes, which maintain basic cellular functions, are more likely to have high genetic correlations between the RNA sources than non-housekeeping gene, implying a relationship between the transcript function and the degree to which a gene has tissue specific genetic regulatory control.
2011-10-29 | GSE33321 | GEO
Project description:Variation in the expression level and activity of genes involved in drug disposition and action (“pharmacogenes”) can affect drug response and toxicity, especially when in tissues of pharmacological importance. Previous studies have relied primarily on microarrays to understand gene expression differences, or have focused on a single tissue or small number of samples. The goal of this study was to use RNA-seq to determine the expression levels and alternative splicing of 389 PGRN pharmacogenes across four tissues (liver, kidney, heart and adipose) and lymphoblastoid cell lines (LCLs), which are used widely in pharmacogenomics studies. Analysis of RNA-seq data from different individuals across the 5 tissues (N = 139 samples) revealed substantial variation in both expression levels and splicing across samples and tissue types. This in-depth exploration also revealed 183 splicing events in pharmacogenes that were previously not annotated. Overall, this study serves as a rich resource for the research community to inform biomarker and drug discovery and use.
2016-01-18 | GSE70503 | GEO
Project description:The degree to which the level of genetic variation for gene expression is shared across multiple tissues has important implications for research investigating the role of expression on the aetiology of complex human traits and diseases. In the last few years, a number of studies have been published reporting the extent of overlap in expression quantitative trait loci (eQTL) identified in multiple tissues or cell types. Whilst these studies provide important information on the regulatory control of genes across tissues, their limited power means they can typically only explain a small proportion of genetic variation for gene expression. Here, using expression data from monozygotic twins (MZ), we investigate the genetic control of gene expression in lymphoblastoid cell lines (LCL) and whole blood (WB). We estimate the genetic correlation which represents the combined effects of all causal loci across the whole genome and is a measure of the level of common genetic control of gene expression between the two RNA sources. Our results show that, when averaged across the genome, mean levels of genetic correlation for gene expression in LCL and WB samples are close to zero. We support our results with evidence from gene expression in an independent sample of LCL, T-cells and Fibroblasts. In addition, we provide evidence that housekeeping genes, which maintain basic cellular functions, are more likely to have high genetic correlations between the RNA sources than non-housekeeping gene, implying a relationship between the transcript function and the degree to which a gene has tissue specific genetic regulatory control. In this study, the global genetic control of expression levels for 9555 genes were examined from two samples of RNA sources, collected on pairs of monozygotic (MZ) twins. From each individual, gene expression profiles were generated whole blood (WB), collected using the PAXgeneTM tube system, and lymphoblastoid cell lines (LCL). Using expression data collected from MZ twins is a powerful means of estimating the genetic contribution of expression variation. In particular, the observed phenotypic covariance in gene expression in LCLs and WBs between MZ twins can be partitioned into variation due to within-person environmental effects and between-person genetic effects.
2011-10-28 | E-GEOD-33321 | biostudies-arrayexpress