A powerful global test for spliceQTL effects

Statistical methods to test for effects of 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 correction, and detect mostly events with large effect sizes. We propose here a test to find spliceQTL effects which takes all exons and all SNPs into account simultaneously. For any chosen gene, this score-based test looks for 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 to detect 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 partly our test is more robust to exon-SNP pair-specific effects, but 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.