Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Biological interpretation of GWAS data frequently involves assessing whether SNPs linked to a biological process, e.g., binding of a transcription factor (TF), show unsigned enrichment for disease signal. However, signed annotations quantifying whether each SNP allele promotes or hinders the biological process can enable stronger statements about disease mechanism. We introduce a method, signed LD profile regression, for detecting genome-wide directional effects of signed functional annotations on disease risk. We validate the method via simulations and application to molecular QTL in blood, recovering known transcriptional regulators. We apply the method to eQTL in 48 GTEx tissues, identifying 651 TF-tissue associations including 30 with robust evidence of tissue specificity. We apply the method to 46 diseases and complex traits (average N=290K), identifying 77 annotation-trait associations representing 12 independent TF-trait associations, and characterize the underlying transcriptional programs using gene-set enrichment analyses. Our results implicate new causal disease genes and new disease mechanisms.