Learning biology using GWAS data frequently involves identifying genomic regions involved in a biological process and assessing for enrichment of GWAS signal in those regions. But in some cases, e.g., binding of a transcription factor (TF), improving models and growing data sets allow us to estimate in a signed way whether genetic variants promote or hinder a biological process. I'll present a new method, signed LD profile regression, for combining this type of information with GWAS data to draw relatively strong inferences about trait mechanism. I'll then describe how this method can be applied in conjunction with signed genomic annotations reflecting binding of ~100 TFs in various cell lines generated using a convolutional neural network, Basset. Finally, I'll discuss some results from applying our method to GWAS data about a range of traits including gene expression, epigenetic traits, and several diseases.