| Institution: | 1. Department of Psychosocial Research and Epidemiology, Room H.8.040, Netherlands Cancer Institute, Amsterdam, The Netherlands;2. Department of Epidemiology and Data Science, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg;3. Biobank-based Integrative Omics Study Consortium, The Netherlands;4. Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands;5. Department for Health Evidence, section 6. Biostatistics, Radboud University Medical Center, Nijmegen, The Netherlands |
| Abstract: | Statistical methods to test for effects of single nucleotide polymorphisms (SNPs) on exon inclusion exist but often rely on testing of associations between multiple exon–SNP pairs, with sometimes subsequent summarization of results at the gene level. Such approaches require heavy multiple testing corrections and detect mostly events with large effect sizes. We propose here a test to find spliceQTL (splicing quantitative trait loci) effects that takes all exons and all SNPs into account simultaneously. For any chosen gene, this score-based test looks for an association between the set of exon expressions and the set of SNPs, via a random-effects model framework. It is efficient to compute and can be used if the number of SNPs is larger than the number of samples. In addition, the test is powerful in detecting effects that are relatively small for individual exon–SNP pairs but are observed for many pairs. Furthermore, test results are more often replicated across datasets than pairwise testing results. This makes our test more robust to exon–SNP pair-specific effects, which do not extend to multiple pairs within the same gene. We conclude that the test we propose here offers more power and better replicability in the search for spliceQTL effects. |
