A deep dive into statistical modeling of RNA splicing QTLs reveals variants that explain neurodegenerative disease.

Genome-wide association studies (GWASs) have identified thousands of putative disease-causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show that current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a pipeline for sQTL discovery. MAJIQTL includes two statistical methods: a weighted multiple-testing approach for sGene discovery and a model for sQTL effect-size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the variant rs528823 in Alzheimer disease. Using antisense oligonucleotides, we test this variant's effect by blocking the implicated YBX3 binding site, leading to exon skipping in MS4A3.