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

Biological interpretation of GWAS data frequently involves analyzing unsigned genomic annotations comprising SNPs involved in a biological process and assessing enrichment for disease signal. However, it is often possible to generate signed annotations quantifying whether each SNP allele promotes or hinders a biological process, e.g., binding of a transcription factor (TF). Directional effects of such annotations on disease risk enable stronger statements about causal mechanisms of disease than enrichments of corresponding unsigned annotations. Here we introduce a new method, signed LD profile regression, for detecting such directional effects using GWAS summary statistics, and we apply the method using 382 signed annotations reflecting predicted TF binding. We show via theory and simulations that our method is well-powered and is well-calibrated even when TF binding sites co-localize with other enriched regulatory elements, which can confound unsigned enrichment methods. We apply our method to 12 molecular traits and recover many known relationships including positive associations between gene expression and genome-wide binding of RNA polymerase II, NF-κB, and several ETS family members, as well as between known chromatin modifiers and their respective chromatin marks. Finally, we apply our method to 46 diseases and complex traits (average N = 289, 617) and identify 77 significant associations at per-trait FDR < 5%, representing 12 independent signals. Our results include a positive association between educational attainment and genome-wide binding of BCL11A, consistent with recent work linking BCL11A hemizygosity to intellectual disability; a negative association between lupus risk and genome-wide binding of CTCF, which has been shown to suppress myeloid differentiation; and a positive association between Crohn’s disease (CD) risk and genome-wide binding of IRF1, an immune regulator that lies inside a CD GWAS locus and has eQTLs that increase CD risk. Our method provides a new way to leverage functional data to draw inferences about causal mechanisms of disease. 1 . CC-BY-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/204685 doi: bioRxiv preprint first posted online Oct. 17, 2017;