View a video from Hyungkyung Kim explaining how to navigate the Genetic Loci Clustering interfaces:
Functional annotation and enrichment analysis methods
We calculated Bayes factors for all variants 500 kb upstream and downstream with r2 greater than 0.1, with the index variant (100% credible set) at each locus from effect size estimates and standard errors, using the approach of Wakefield (Wakefield 2007). We then calculated a posterior probability for each variant by dividing the Bayes factor by the sum of all Bayes factors in the credible set.
We obtained previously published 13-state ChromHMM (Ernst and Kellis 2012) chromatin state calls for 28 cell types, excluding cancer cell lines (Varshney et al. 2017). For each cell type, we extracted chromatin state annotations for enhancer (Active Enhancer 1, Active Enhancer 2, Weak Enhancer, Genic Enhancer) and promoter (Active Promoter) elements. We also compiled candidate cis-regulatory elements (cCREs) for 61 cell types from published single cell chromatin accessibility datasets (Zhang et al. 2021, Chiou et al. 2019).
We assessed enrichment of annotations within clusters by overlapping 100% credible set variants for signals in each cluster with cell type epigenomic annotations (chromatin states and cCREs). We calculated cell type probabilities for each cluster by summing the posterior probabilities of variants in cell type enhancers or promoters, divided by the number of signals in the cluster. We derived significance for cell type probabilities for each cluster using a permutation based test. We permuted signals and cell type labels within each cluster and then recalculated cell type probabilities, as above. We then used cell type probabilities derived from 10,000 permutations as a background distribution and performed a one-tailed test to ascertain significance for each cell type.