fSuSiE enables fine-mapping of QTLs from genome-scale molecular profiles

Molecular quantitative trait locus (QTL) studies seek to identify the causal variants affecting molecular traits like DNA methylation and histone modifications. However, existing fine-mapping tools are not well suited to molecular traits, and so molecular QTL analyses typically proceed by considering each variant and each molecular measurement independently, ignoring the LD among variants and the spatial correlation in effects between nearby sites. This severely limits accuracy in identifying causal variants and quantifying their molecular trait effects. Here, we introduce fSuSiE (``functional Sum of Single Effects''), a fine-mapping method that addresses these challenges by explicitly modeling the spatial structure of genetic effects on molecular traits. fSuSiE integrates wavelet-based functional regression with the computationally efficient ``Sum of Single Effects'' framework to simultaneously finemap causal variants and identify the molecular traits they affect. In simulations, fSuSiE identified causal variants and affected CpGs more accurately than methods that ignore spatial structure. In applications to DNA methylation and histone acetylation (H3K9ac) data from the ROSMAP study of the dorsolateral prefrontal cortex, fSuSiE achieved dramatically higher resolution than existing methods (e.g., identifying 6,355 single-variant methylation credible sets compared to only 328 from an existing approach). Applied to Alzheimer's disease (AD) risk loci, fSuSiE identified potential causal variants colocalizing with AD GWAS signals for established genes, including CASS4 and CR1/CR2, suggesting specific potential regulatory mechanisms underlying these AD risk loci.