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Fig. 3 | Genome Biology

Fig. 3

From: PWAS: proteome-wide association study—linking genes and phenotypes by functional variation in proteins

Fig. 3

Simulation analysis. Results of a simulation analysis comparing between GWAS, SKAT, and PWAS. The statistical power of each method is shown as a function of cohort size (1000, 10,000, 50,000, 100,000, or all 332,709 filtered UKBB samples, shown in a log scale). Estimated values are shown as solid lines, with flanking 95% confidence intervals as semi-transparent area bands. Each iteration of the simulation considered a single protein-coding gene affecting a simulated continuous phenotype of the form y = βx + σ, where x is the effect of the gene on the phenotype (normalized to have mean 0 and standard deviation 1 across the UKBB population), β {0.01,0.05} is the gene’s effect size, and σ~N(0, 1) is a random Gaussian noise. The gene effect x was simulated according to the PWAS model, with either a dominant, recessive, or additive inheritance. A noise parameter ϵ  {0,0.25} was introduced to FIRM, the underlying machine learning model that estimates the damage of variants. Gene architectures, genotyping data, and the 173 included covariates were taken from the UKBB cohort

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