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A systematic comparison of normalization methods for eQTL analysis.

Jiajun Yang1, Dongyang Wang1, Yanbo Yang1

  • 1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China.

Briefings in Bioinformatics
|May 20, 2021
PubMed
Summary
This summary is machine-generated.

Choosing the right gene expression normalization method is crucial for accurate expression quantitative trait loci (eQTL) identification from RNA-sequencing (RNA-Seq) data. The Trimmed Mean of M-values (TMM) method is recommended for optimal eQTL analysis.

Keywords:
RNA-Seq dataeQTLexpression quantitative trait locigene expressionnormalization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Expression quantitative trait loci (eQTL) analysis links genetic variants to gene expression, aiding disease-associated loci interpretation.
  • RNA-sequencing (RNA-Seq) is vital for genome-wide gene expression quantification in eQTL studies.
  • Normalization methods significantly impact RNA-Seq downstream analyses, necessitating a comparison for eQTL identification.

Purpose of the Study:

  • To systematically evaluate the impact of eight common normalization methods on eQTL identification using RNA-Seq data.
  • To compare the performance of single normalization methods and their pairwise combinations.
  • To identify the optimal normalization method for eQTL analysis.

Main Methods:

  • Utilized RNA-Seq and genotype data from four cancer types in The Cancer Genome Atlas (TCGA).
  • Assessed eight widely-used gene expression normalization methods (COUNT, MED, TMM, FPKM, RANK, etc.).
  • Evaluated method performance using accuracy metrics and Receiver Operating Characteristic (ROC) curves, including pairwise combinations.

Main Results:

  • Different normalization methods led to 20-30% variation in eQTL identification results.
  • COUNT, Median of Ratio (MED), and Trimmed Mean of M-values (TMM) yielded similar eQTL identification outcomes.
  • Fragments Per Kilobase Million (FPKM) and RANK showed more divergent results compared to other methods.
  • The TMM method demonstrated optimal performance for gene expression normalization in eQTL analysis.
  • Combined normalization methods identified more cis-eQTLs and improved ROC curve performance over single methods.

Conclusions:

  • Normalization method selection critically influences eQTL identification from RNA-Seq data.
  • The TMM method is recommended as the optimal choice for normalizing gene expression in eQTL studies.
  • Combining normalization methods offers enhanced eQTL identification and improved analytical performance.
  • This study provides practical recommendations for choosing normalization strategies in eQTL analysis.