title: ulfasQTL: an ultra-fast method of composite splicing QTL analysis.
abstract: Alternative splicing plays important roles in many regulatory processes and diseases in human. Many genetic variants contribute to phenotypic differences in gene expression and splicing that determine variations in human traits. Detecting genetic variants that affect splicing phenotypes is essential for understanding the functional impact of genetic variations on alternative splicing. For many situations, the key phenotype is the relative splicing ratios of alternative isoforms rather than the expression values of individual isoforms. Splicing quantitative trait loci (sQTL) analysis methods have been proposed for detecting associations of genetic variants with the vectors of isoform splicing ratios of genes. We call this task as composite sQTL analysis. Existing methods are computationally intensive and cannot scale up for whole genome analysis.
  
  
  
  We developed an ultra-fast method named ulfasQTL for this task based on a previous method sQTLseekeR. It transforms tests of splicing ratios of multiple genes to a matrix form for efficient computation, and therefore can be applied for sQTL analysis at whole-genome scales at the speed thousands times faster than the existing method. We tested ulfasQTL on the data from the GEUVADIS project and compared it with an existing method.
  
  
  
  ulfasQTL is a very efficient tool for composite splicing QTL analysis and can be applied on whole-genome analysis with acceptable time.
authors.affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Yang
authors.affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
authors.forename: Yue
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA.
authors.forename: Jun
authors.initials: J
authors.lastname: Li
authors.affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China. zhangxg@tsinghua.edu.cn.|School of Life Sciences, Tsinghua University, Beijing, 100084, China. zhangxg@tsinghua.edu.cn.|Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China. zhangxg@tsinghua.edu.cn.
authors.forename: Xuegong
authors.initials: X
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D017398:Alternative Splicing; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D014644:Genetic Variation; D006801:Humans; D040641:Quantitative Trait Loci; D012984:Software; D059467:Transcriptome
keywords: Alternative splicing; Genetic variants; Genome-wide; Ultra-fast method; sQTL