Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories

Boedigheimer, Michael; Wolfinger, Russell D.; Bass, Michael; Bushel, Pierre R.; Chou, Jeff W.; Cooper, Matthew N.; Corton, J. Christopher; Fostel, Jennifer; Hester, Susan; Lee, Janice S.; Liu, Fenglong; Liu, Jie; Qian, Hui-Rong; Quackenbush, John; Pettit, Syril; Thompson, Karol L.

doi:10.1186/1471-2164-9-285

Cited by 82 publications

(80 citation statements)

References 27 publications

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“…The PVCA plot describes the relative weights of corresponding eigenvectors related to the eigenvalues that can be explained by factors in the experimental design and other covariates 37,38 . Here we considered eleven possible sources of variation: the two types of cell; age at cycle -baseline (age_bl) which can be the cognitive date, interview date, Fig.S4, S5).…”

Section: Principal Variance Component Analysis (Pvca)mentioning

confidence: 99%

Positional effects revealed in Illumina Methylation Array and the impact on analysis

Jiao

Zhang

Dai

et al. 2017

Preprint

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Positional effects revealed in Illumina Methylation Array and the impact on analysisWith the evolution of rapid epigenetic research, Illumina Infinium HumanMethylation BeadChips have been widely used to study DNA methylation. However, in evaluating the accuracy of this method, we found that the commonly used Illumina HumanMethylation BeadChips are substantially affected by positional effects; the DNA sample's location in a chip affects the measured methylation levels. We analyzed three HumanMethylation450 and three HumanMethylation27 datasets by using four methods to prove the existence of positional effects. Three datasets were analyzed further for technical replicate analysis or differential methylation CpG sites analysis. The pre-and postcorrection comparisons indicate that the positional effects could alter the measured methylation values and downstream analysis results. Nevertheless, ComBat, linear regression and functional normalization could all be used to minimize such artifact. We recommend performing ComBat to correct positional effects followed by the correction of batch effects in data preprocessing as this procedure slightly outperforms the others. In addition, randomizing the sample placement should be a critical laboratory practice for using such experimental platforms. Code for our method is freely available at:https://github.com/ChuanJ/posibatch. . DNA methylation has also been implicated in the development of cancer 4-6 and other diseases [7][8][9] . Furthermore, several studies indicated that the DNA methylation levels could vary by age 10 , sex 11, disease affected status [4][5][6][7][8][9] , circadian rhythms 12 , tissues types 13 and other factors.Many methods have been used to measure the methylation levels of cytosines, such as blotting, atomic force spectroscopy, genomic sequencing, bisulfite sequencing, peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/153858 doi: bioRxiv preprint first posted online . It has twelve sample sections in one array arranged in a six by two format (Fig.S1). While Methyl27 measures the methylation status of over 27,000 CpG sites in the genome using the Type I assay with twelve sample locations arranged by twelve rows (Fig.S1) peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/153858 doi: bioRxiv preprint first posted online In this article, we compared three methods for correcting the positional effects:ComBat, linear regression model and functional normalization. ComBat adjusts for known batches using an empirical Bayesian method even in small sample sizes, the linear regression model is a classical method to remove known confounders, and FN is an unsupervised method using control probes as surrogates for unwanted variation Few studies had properly addressed the positional effects, which could lead to p...

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Section: Principal Variance Component Analysis (Pvca)mentioning

confidence: 99%

Positional effects revealed in Illumina Methylation Array and the impact on analysis

Jiao

Zhang

Dai

et al. 2017

Preprint

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“…The use of gene expression profiling in both clinical and laboratory settings would be enhanced by better characterization of variance due to individual, environmental, and technical factors. The study factors that emerged as key sources of variability included gender, organ section, strain, and fasting state [67]. Butterbur extracts (Petasites hybridus) are recommended for the prevention of migraine, but pharmacovigilance reports may be suggestive for rare hepatobiliary toxicity.In a 28-day toxicity study at approximately 200-fold of therapeutic doses induced liver transaminases and bilirubin elevations were observed.…”

Section: Toxicogenomics and Clinical Mechanismsmentioning

confidence: 99%

Toxicogenomics and cancer pathogenesis

Gupta¹,

AK²,

Boraste³

et al. 2009

Int J Genet

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Abstract-Toxicogenomics is emerging field give idea of the application of large-scale differential gene expression data to toxicology, starting to influence drug discovery and development in the pharmaceutical industry. Its future potential in cancer pathogenesis, study of poisons and poisoning and has an ancient and venerable history. Toxicogenomics is the merging of toxicology with technologies that have been developed, together with bioinformatics, to identify and quantify global gene expression changes for gene therapy. Immunotoxic processes and the development of in vitro screening assays is therefore expected to be of value for mechanistic insight into immunotoxicity and hazard identification of existing and novel compounds. Successful application of toxicogenomic approaches, such as DNA microarrays, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. Toxicogenomics is considered a valuable tool for reducing pharmaceutical candidate attrition by facilitating earlier identification, prediction and understanding of toxicities. Pharmaco-epidemiological (toxicogenomics) data is now available for both antiepileptic drugs, evidence for human carcinogenicity. Therapeutic researches converge triad of rejuvenation, regeneration or replacement strategies that rely on self-healing processes, stem cell regeneration organ transplantation. Metastases studies usually display more genetic changes than the primary tumour. Helix-loop-helix application in translational and functional toxicogenomics transcription factors has been implicated in diverse cellular processes such as proliferation, apoptosis, differentiation, and migration.

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“…PVCA was used to estimate the extent of batch, sex, and age effects within the whole data set (Boedigheimer et al 2008;Li et al 2009). This method involves first decomposing the data set into a series of PCs and estimating the effects of each covariate on these components.…”

Section: Nongenetic Contributions To Pcsmentioning

confidence: 99%

Genetic and Nongenetic Variation Revealed for the Principal Components of Human Gene Expression

et al. 2013

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Principal components analysis has been employed in gene expression studies to correct for population substructure and batch and environmental effects. This method typically involves the removal of variation contained in as many as 50 principal components (PCs), which can constitute a large proportion of total variation present in the data. Each PC, however, can detect many sources of variation, including gene expression networks and genetic variation influencing transcript levels. We demonstrate that PCs generated from gene expression data can simultaneously contain both genetic and nongenetic factors. From heritability estimates we show that all PCs contain a considerable portion of genetic variation while nongenetic artifacts such as batch effects were associated to varying degrees with the first 60 PCs. These PCs demonstrate an enrichment of biological pathways, including core immune function and metabolic pathways. The use of PC correction in two independent data sets resulted in a reduction in the number of cis-and transexpression QTL detected. Comparisons of PC and linear model correction revealed that PC correction was not as efficient at removing known batch effects and had a higher penalty on genetic variation. Therefore, this study highlights the danger of eliminating biologically relevant data when employing PC correction in gene expression data. GENE expression profiling has become a very popular technique used to quantify regulatory changes in messenger (m)RNA expression associated with disease and environmental factors. Gene expression acts as an intermediate phenotype between genotypes and complex traits and is known to act as a modifier to disease susceptibility (Nica and Dermitzakis 2008;Li et al. 2012). Genetic variation underlying gene expression levels has been well established and reported within the literature, with the transcript levels for the majority of genes being heritable to some degree (Price et al. 2011;Grundberg et al. 2012;Powell et al. 2012b).Microarray technology can simultaneously capture the expression of thousands of transcripts within an individual.However, these arrays are sensitive to environmental or experimental perturbations, for example due to different laboratory technicians and reagents (Churchill 2002;Irizarry et al. 2005), microarray chip and chip position (Luo et al. 2010), temperature (Scherer 2009), and even ozone levels (Thomas et al. 2003). These effects can constitute a substantial proportion of variance within a data set (Leek et al. 2010).Normalization strategies have become standard in gene expression studies to correct for nonnormal distributions and inconsistencies between arrays ( Allison et al. 2006). However, normalization techniques do not control for batch effects caused by technical artifacts. These batch effects require additional correction techniques (Scherer 2009) and failure to do so has led to spurious associations (Spielman and Cheung 2007;Baggerly et al. 2008).Many different correction and normalization techniques are currently us...

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Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories

Cited by 82 publications

References 27 publications

Positional effects revealed in Illumina Methylation Array and the impact on analysis

Positional effects revealed in Illumina Methylation Array and the impact on analysis

Toxicogenomics and cancer pathogenesis

Genetic and Nongenetic Variation Revealed for the Principal Components of Human Gene Expression

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