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GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data

Keyan Zhao12, Zhi-xiang Lu12, Juw Won Park12, Qing Zhou3 and Yi Xing12*

Author Affiliations

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

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Genome Biology 2013, 14:R74  doi:10.1186/gb-2013-14-7-r74

Published: 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.

RNA-seq; alternative splicing; sQTL; exon; generalized linear mixed model