biostudies-literatureZhao KNHLBI NIH HHSNIGMS NIH HHSR74https://www.ebi.ac.uk/biostudies/studies/S-EPMC4054007biostudies-literatureUnknown14(7)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.Genome biologyGLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data.PMC4054007R01GM088342R01 GM088342T32HL007638Lu ZXPark JWZhou QXing YZhao KfalseGLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data.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.2013-01-01T00:00:00Z2013 Jul2026-05-02T12:00:37.155Z2026-04-29T03:08:31.216ZS-EPMC40540072387640110.1186/gb-2013-14-7-r74