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Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits
Fig 3
BEAVR is robust in realistic settings.
(A) Using SNP data from chromosome 22 (M = 9, 564 array SNPs, N = 337K individuals), we simulated 100 replicates where the genome-wide heritability was 
and p = 0.01. We divided the data into 6-Mb consecutive regions for a total of 6 regions and estimated the regional heritability using external software (HESS [12]). Using BEAVR and the estimated regional heritability, we estimated the regional polygenicity to be unbiased across all regions. (B) We ran 100 replicates where the genome-wide heritability is fixed 
, polygenicity pr = 0.01, sample size N = 500K, and then varied the number of SNPs in the region from M = 500, 1K, 5K SNPs. We used BEAVR to estimate the polygenicity in each region and found our results to be unbiased across all regions. (C) We set the genome-wide heritability to 
, regional polygenicity pr = 0.01, and sample size N = 500K. We find that the accuracy of our results is invariant to our choice of prior hyper-parameter (α).
doi: https://doi.org/10.1371/journal.pcbi.1009483.g003