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GLiMMPS: Robust statistical model for regulatory variation of alternative splicing using RNA-Seq data

  • Author(s): Zhao, Keyan
  • Lu, Zhi-xiang
  • Park, Juw
  • Zhou, Qing
  • Xing, Yi
  • et al.
Abstract

Abstract To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validation rate of 100%. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms.

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