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. 2019 May 16;104(6):1097–1115. doi: 10.1016/j.ajhg.2019.04.009

Figure 7.

Figure 7

Sparsity and Polygenicity of Gene Expression in African Americans

We characterized the sparsity or polygenicity of gene expression traits by using a Bayesian sparse linear mixed model (BSLMM) analysis in the GTEx African American (AA) skeletal-muscle data. We estimated the proportion of variance in gene expression that can be explained by sparse effects (PGE) and the proportion of variance in gene expression that can be explained by sparse effects and random effects together (PVEg,BSLMM), the latter of which is most equivalent to our LMM-based PVEg,LMM. In the genes analyzed, estimated PVEg,LMM values that were significant at p value < 0.05 were defined as nominally significant estimates.

A. The comparison of PVEg,BSLMMˆ and the PGE estimate from the BSLMM. Genes with a large PVEg,BSLMMˆ and a large PGE estimate are likely to have highly sparse local genetic architecture.

B. The comparison of PVEg,LMMˆfrom GCTA and the PGE estimate from the BSLMM showing the independence of the two components.