Format

Send to

Choose Destination
Nat Commun. 2014 Aug 20;5:4698. doi: 10.1038/ncomms5698.

Identification of genetic variants associated with alternative splicing using sQTLseekeR.

Author information

1
1] Center for Genomic Regulation, Universitat Pompeu Fabra, C/ Dr Aiguader 88 08003, Barcelona, Catalonia, Spain [2] Department of Human Genetics, McGill University, 740 Dr Penfield Avenue, Montréal, Canada H3A 0G1.
2
Department of Statistics, Facultat de Biologia, Universitat de Barcelona, Av. Diagonal 643 08028, Barcelona, Catalonia, Spain.
3
1] Center for Genomic Regulation, Universitat Pompeu Fabra, C/ Dr Aiguader 88 08003, Barcelona, Catalonia, Spain [2] Department of Genetic Medicine and Development, University of Geneva Medical School 1211, Geneva, Switzerland [3] Institute for Genetics and Genomics in Geneva (G3), University of Geneva, 1 rue Michel-Servet 1211, Geneva, Switzerland [4] Swiss Institute of Bioinformatics, 1211, Geneva, Switzerland.
4
1] Center for Genomic Regulation, Universitat Pompeu Fabra, C/ Dr Aiguader 88 08003, Barcelona, Catalonia, Spain [2] Department of Experimental and Health Sciences, Universitat Pompeu Fabra 08003, Barcelona, Catalonia, Spain.

Abstract

Identification 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.

PMID:
25140736
PMCID:
PMC4143934
DOI:
10.1038/ncomms5698
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center