GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data
1 Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CHS 33-228, 650 Charles E. Young Drive South, Los Angeles, CA 90095, USA
2 Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
3 Department of Statistics, University of California, Los Angeles, 8125 Math Sciences Building, Los Angeles, CA 90095, USA
Citation and License
Genome Biology 2013, 14:R74 doi:10.1186/gb-2013-14-7-r74Published: 22 July 2013
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.