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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Classical and Bayesian random-effects meta-analysis models with sample quality weights in gene expression studies.

Uma Siangphoe1, Kellie J Archer2, Nitai D Mukhopadhyay3

  • 1Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA. siangphoeu@vcu.edu.

BMC Bioinformatics
|January 11, 2019
PubMed
Summary
This summary is machine-generated.

Sample quality weights improve random-effects models for gene expression meta-analysis, enhancing precision and accuracy in detecting differentially expressed genes, especially in heterogeneous datasets like Alzheimer's research.

Keywords:
Alzheimer’s diseaseBayesian random-effects modelGene expressionMeta-analysisRandom-effects modelSample quality weightsStudy heterogeneity

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Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Genetics

Background:

  • Gene expression meta-analysis commonly uses random-effects (RE) models to address heterogeneity.
  • Sample quality variations can significantly influence heterogeneity and the reliability of identified differentially expressed (DE) genes.
  • Existing RE models may not adequately account for sample quality inconsistencies, potentially leading to inaccurate results.

Purpose of the Study:

  • To investigate the impact of sample quality weights on RE models for gene expression meta-analysis.
  • To compare the performance of weighted and unweighted DerSimonian and Laird (DSL), two-step DSL (DSLR2), and Bayesian random-effects (BRE) models.
  • To evaluate the utility of these methods in identifying DE genes, particularly in heterogeneous datasets like those from Alzheimer's disease research.

Main Methods:

  • Applied sample-quality weights (wP6) to adjust heterogeneity in DSL, DSLR2, and BRE models.
  • Utilized Gibbs and Metropolis-Hastings (MH) sampling algorithms for BRE models.
  • Compared weighted common-effect and weighted between-study variance approaches.
  • Evaluated model performance through simulations and application to Alzheimer's gene expression data.

Main Results:

  • Sample quality adjusting within-study variance (wP6) models effectively reduced DE genes compared to other weighting functions in classical RE models.
  • The Bayesian random-effects model with a uniform(0,1) prior was optimal for DE gene detection.
  • The DSLR2wP6 weighted model enhanced DE gene detection precision in heterogeneous data.
  • wP6-weighted data improved DE gene detection precision, while wP6-weighted between-study variance models increased overall accuracy.
  • Application to Alzheimer's data revealed decreased DE genes with DSLR2wP6 and wP6-weighted between-study variance models, identifying potential down-regulated genes.

Conclusions:

  • Sample quality weights enhance the precision and accuracy of classical RE and Bayesian RE models in gene expression meta-analysis.
  • Model performance is contingent upon data characteristics, sample quality levels, and parameter estimation adjustments.
  • Weighted models, particularly the wP6-weighted between-study variance approach, show promise for identifying biologically relevant genes in complex diseases like Alzheimer's.