Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models

Lee, Chaeyoung

doi:10.3389/fgene.2018.00341

Cited by 23 publications

(22 citation statements)

References 75 publications

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“…The erQTLs for RPs at mRNA transcription, ribosome occupancy, and protein abundance were identified using a mixed linear model. The analytical model for genome-wide erQTL included polygenic effects with a genomic similarity matrix (GSM) to avoid population stratification (Lee, 2018) as follows:…”

Section: Discussionmentioning

confidence: 99%

Regulatory Nucleotide Sequence Signals for Expression of the Genes Encoding Ribosomal Proteins

Ryu

Lee

2020

Front. Genet.

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Ribosomal proteins (RPs) are essential components that translate genetic information from mRNA templates into proteins. Their expressional dysregulation adversely affects the survival and growth of human cells. Nevertheless, little is known about the nucleotide sequences regulating the expression of RPs. Genome-wide associations for expression level of 70 RP genes were conducted across expression stages. Eighteen expression regulatory quantitative trait loci (erQTLs) were identified for protein abundances of 21 RPs (P < 1 × 10 −5), but not for their mRNA expression and ribosome occupancy (P > 1 × 10 −5). These erQTLs for protein abundance (pQTLs) were all transacting. Three of the pQTLs were associated with the expression of long noncoding RNAs (lncRNAs). Target genes of these lncRNAs may produce ribosomal components or may control the metabolic cues for ribosome synthesis. mRNAs of the RP genes extensively interact with miRNAs. The protein-specific erQTLs may become engendered by intensive miRNA controls at the translational stage, which in turn can produce RPs efficient in handling instantaneous cell requirements. This study suggests that the expression levels of RPs may be greatly influenced by transacting regulation, presumably via interference of miRNAs and target genes of lncRNAs. Further studies are warranted to examine the molecular functions of pQTLs presented in this study and to understand the underlying regulatory mechanisms of gene expression of RPs.

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Section: Discussionmentioning

confidence: 99%

Regulatory Nucleotide Sequence Signals for Expression of the Genes Encoding Ribosomal Proteins

Ryu

Lee

2020

Front. Genet.

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“…(Supplemental Figure 8). Such spurious associations from population stratification have been described 4 in many previous studies of eQTL analysis [47][48][49][50].…”

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confidence: 86%

An approach for normalization and quality control for NanoString RNA expression data

Bhattacharya

Hamilton

Furberg

et al. 2020

Preprint

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ABSTRACTThe NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility, and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization, and iterative data visualization and biological validation. The approach was evaluated using a large cohort (N = 1,649) from the Carolina Breast Cancer Study, two cohorts of moderate sample size (N = 359 and 130), and a small published dataset (N = 12). The iterative process developed here eliminates technical variation (e.g. from different study phases or sites) more reliably than the three other methods, including NanoString’s commercial package, without diminishing biological variation, especially in long-term longitudinal multi-phase or multi-site cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that systematic quality control, normalization, and visualization of NanoString nCounter data is an imperative component of study design that influences results in downstream analyses.

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“…This may be reason enough to employ mixed models, even if these models possess no other strengths. Details concerning the strengths of using mixed models were discussed by Lee (2018).…”

Section: Introductionmentioning

confidence: 99%

“…A variety of methods to estimate variance components exist (Searle et al, 2009). Restricted maximum likelihood (REML) estimation is considered a standard method regardless of its computing algorithms in the frequentist mixed model framework (Lee, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the purpose of this review is to encourage those interested in developing relevant methods and algorithms for Bayesian inference. General concepts and considerations for genome-wide eQTL analysis using mixed models were discussed in the previous review (Lee, 2018). The current review highlights the Bayesian approach as a sequel to the frequentist approach for mixed model-based genome-wide eQTL analysis.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Inference for Mixed Model-Based Genome-Wide Analysis of Expression Quantitative Trait Loci by Gibbs Sampling

Lee

2019

Front. Genet.

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The importance of expression quantitative trait locus (eQTL) has been emphasized in understanding the genetic basis of cellular activities and complex phenotypes. Mixed models can be employed to effectively identify eQTLs by explaining polygenic effects. In these mixed models, the polygenic effects are considered as random variables, and their variability is explained by the polygenic variance component. The polygenic and residual variance components are first estimated, and then eQTL effects are estimated depending on the variance component estimates within the frequentist mixed model framework. The Bayesian approach to the mixed model-based genome-wide eQTL analysis can also be applied to estimate the parameters that exhibit various benefits. Bayesian inferences on unknown parameters are based on their marginal posterior distributions, and the marginalization of the joint posterior distribution is a challenging task. This problem can be solved by employing a numerical algorithm of integrals called Gibbs sampling as a Markov chain Monte Carlo. This article reviews the mixed model-based Bayesian eQTL analysis by Gibbs sampling. Theoretical and practical issues of Bayesian inference are discussed using a concise description of Bayesian modeling and the corresponding Gibbs sampling. The strengths of Bayesian inference are also discussed. Posterior probability distribution in the Bayesian inference reflects uncertainty in unknown parameters. This factor is useful in the context of eQTL analysis where a sample size is too small to apply the frequentist approach. Bayesian inference based on the posterior that reflects prior knowledge, will be increasingly preferred with the accumulation of eQTL data. Extensive use of the mixed model-based Bayesian eQTL analysis will accelerate understanding of eQTLs exhibiting various regulatory functions.

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Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models

Cited by 23 publications

References 75 publications

Regulatory Nucleotide Sequence Signals for Expression of the Genes Encoding Ribosomal Proteins

Regulatory Nucleotide Sequence Signals for Expression of the Genes Encoding Ribosomal Proteins

An approach for normalization and quality control for NanoString RNA expression data

Bayesian Inference for Mixed Model-Based Genome-Wide Analysis of Expression Quantitative Trait Loci by Gibbs Sampling

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