Our method shares similarities to recent integrative approaches proposed in the context of GWAS [10]. Although conceptually both approaches integrate functional and association signal, the two methodologies are fundamentally distinct in their aims. Whereas [10] seeks to identify novel risk loci by leveraging functional information, we instead propose our method as way to refine signal at known GWAS loci. This fundamental distinction leads to different statistical models and optimization procedures allowing for superior accuracy for refining association signal through fine-mapping. In addition our method addresses a limitation of [10] by allowing for the possibility of multiple causal variants at a risk locus.
