Fig. 5: Phenome-wide Analysis of 1403 binary phenotypes from UK biobank data with 408,961 white British participants with European ancestry. | Nature Communications

Fig. 5: Phenome-wide Analysis of 1403 binary phenotypes from UK biobank data with 408,961 white British participants with European ancestry.

From: GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies

Fig. 5: Phenome-wide Analysis of 1403 binary phenotypes from UK biobank data with 408,961 white British participants with European ancestry.The alternative text for this image may have been generated using AI.

A, B Comparison between conventional GWAS and GhostKnockoff. C Summary of (A) and (B). For each phenotype, we calculated the ratio between the total number of identified loci/ the average number of proxy variants per shared locus by GhostKnockoff and by conventional GWAS (capped at 500 for better visualization). Panel (C) presents the average ratio (as in (A) and (B)) across 1403 phenotypes. The standard error is calculated as \(\frac{{{{{{\rm{standard}}}}}}\,{{{{{\rm{deviation}}}}}}\,{{{{{\rm{of}}}}}}\,{{{{{\rm{the}}}}}}\,{{{{{\rm{ratio}}}}}}}{\sqrt{{{{{{\rm{total}}}}}}\,{{{{{\rm{number}}}}}}\,{{{{{\rm{of}}}}}}\,{{{{{\rm{phenotypes}}}}}}-1}}\). D Distribution of the number of identified loci. We present boxplot (median and 25%/75% quantiles) for each disease category. E For loci identified by both conventional GWAS and the proposed method, we present median and 25%/75% quantiles of the number of identified variants per locus. For visualization purposes, we present the results for disease phenotypes with \(\ge 5\) loci identified by either conventional GWAS or the proposed multiple knockoff inference for panels (D, E). F Functional score of variants identified by GhostKnockoff compared to that of genome-wide background variants. Each data point in the boxplot corresponds to the average score of one disease category. The boxplot presents median and 25%/75% quantiles.

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