Causal splicing variants revealed by deep-learning integration of single-cell sQTL mapping under influenza infection

Molecular quantitative trait locus (molQTL) analyses, e.g. expression QTL (eQTL) and splicing QTL (sQTL), link genetic variants to intermediate molecular phenotypes, but pinpointing causal variants and their regulatory effects remains challenging. Here, we integrate sQTL analysis with deep-learning-based splicing effect annotation to identify causal genetic variants and elucidate their functional mechanisms affecting human phenotypes.

Using a single-cell GWAS method (scHi-HOST) on 96 lymphoblastoid cell lines (LCLs) with and without influenza A virus (IAV) infection, we discovered ~43,000 sQTL-SNP-junction pairs associated with 217 genes during IAV infection. Integrating sQTLs with AI splice prediction and statistical fine-mapping, we uncovered 57 likely causal variants that affect cis-acting molecular splicing components (5’ donor, 3’ acceptor). Among these, we experimentally validated a causal sQTL signal affecting poly (ADP-ribose) polymerase 2 (PARP2).