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We developed tractable count-based models for detecting expression quantitative trait loci (eQTLs). An arcsin transformation of Laplace-smoothed RNA-seq data performed comparably to state-of-the-art methods, especially in small sample sizes.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Identifying genetic variants influencing gene expression (eQTLs) is crucial for understanding complex traits.
  • Detecting multiple eQTLs uses transformation-based or count-based models.
  • Count-based models are popular but mathematically intractable.

Purpose of the Study:

  • To develop tractable count-based models for eQTL detection.
  • To evaluate RNA-seq data transformation methods for eQTL analysis.
  • To compare the performance of transformation-based and count-based models.

Main Methods:

  • Developed sparse Bayesian models with latent variables for tractable count-based eQTL analysis.
  • Investigated arcsin, logit, and Laplace smoothing for RNA-seq data transformation.
  • Benchmarked models using data from the 1000 Genomes and gEUVADIS projects.

Main Results:

  • An arcsin transformation of Laplace-smoothed data matched state-of-the-art eQTL models, particularly in small samples.
  • An over-dispersed Poisson model proved comparable to the Negative Binomial model but easier to estimate.
  • Transformation-based models showed strong support over count-based models for eQTL mapping.

Conclusions:

  • Tractable count-based models and effective RNA-seq data transformations enhance eQTL detection.
  • The arcsin transformation offers a robust preprocessing step for transformation-based eQTL models.
  • This work supports transformation-based approaches for robust and efficient eQTL analysis.