Monlong, JeanCalvo, Miquel (Calvo Llorca)Ferreira, Pedro G.Guigó Serra, Roderic2015-06-162015-06-162014Monlong J, Calvo M, Ferreira PG, Guigó R. Identification of genetic variants associated with alternative splicing using sQTLseekeR. Nature Communications. 2014;5:4698. DOI 10.1038/ncomms56982041-1723http://hdl.handle.net/10230/23832Identification of genetic variants affecting splicing in RNA sequencing population studies is still in its infancy. Splicing phenotype is more complex than gene expression and ought to be treated as a multivariate phenotype to be recapitulated completely. Here we represent the splicing pattern of a gene as the distribution of the relative abundances of a gene’s alternative transcript isoforms. We develop a statistical framework that uses a distance-based approach to compute the variability of splicing ratios across observations, and a non-parametric analogue to multivariate analysis of variance. We implement this approach in the R package sQTLseekeR and use it to analyze RNA-Seq data from the Geuvadis project in 465 individuals. We identify hundreds of single nucleotide polymorphisms (SNPs) as splicing QTLs (sQTLs), including some falling in genome-wide association study SNPs. By developing the appropriate metrics, we show that sQTLseekeR compares favorably with existing methods that rely on univariate approaches, predicting variants that behave as expected from mutations affecting splicing.application/pdfeng© Nature Publishing Group. http://dx.doi.org/10.1038/ncomms5698/nThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the materialRNABiologia molecularGenomesinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/ncomms5698Biological sciencesBioinformaticsGeneticsinfo:eu-repo/semantics/openAccess