deviation of 0.13 within each genotype class. This level of noise is based on estimates from initial GTEx data. The effect size depends both on the minor allele frequency of the SNP (MAF) and the actual log expression change between genotype classes (denoted by Δ). Figure 1a shows the statistical power of a cis-eQTL analysis, and Figure 1b a trans-eQTL analysis, using an ANOVA statistical test as a function the number of subjects and the minor allele frequency (MAF), and assumes Δ=0.13 (equivalent to detecting a log-expression change similar to the standard deviation within a single genotype class). A final GTEx resource of 900 or more donors would realistically yield ~750 samples of any given tissue, since not all organs are available for collection from each donor. At an effective sample size of 750, we would have 80% power to detect cis-eQTLs with MAF as low as 2% and trans-eQTLs with MAF as low as 4%. The statistical power may be higher using methods that leverage the fact that multiple tissues are collected and analyzed for each donor. Since the underlying parameters are merely rough estimates, we repeated the power analysis with different values (10 to 20 SNPs, 20,000–100,000 transcripts)
